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81D740CEF3967C20721612B7866072EF240484E9 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOJava.html?context=cdpaas&locale=en | Decision Optimization Java models | Decision Optimization Java models
You can create and run Decision Optimization models in Java by using the Watson Machine Learning REST API.
You can build your Decision Optimization models in Java or you can use Java worker to package CPLEX, CPO, and OPL models.
For more information about these models, see the following reference manuals.
* [Java CPLEX reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/refjavacplex/html/overview-summary.html)
* [Java CPO reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/refjavacpoptimizer/html/overview-summary.html)
* [Java OPL reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/refjavaopl/html/overview-summary.html)
To package and deploy Java models in Watson Machine Learning, see [Deploying Java models for Decision Optimization](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployJava.html) and the boilerplate provided in the [Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md).
| # Decision Optimization Java models #
You can create and run Decision Optimization models in Java by using the Watson Machine Learning REST API\.
You can build your Decision Optimization models in Java or you can use Java worker to package CPLEX, CPO, and OPL models\.
For more information about these models, see the following reference manuals\.
<!-- <ul> -->
* [Java CPLEX reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/refjavacplex/html/overview-summary.html)
* [Java CPO reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/refjavacpoptimizer/html/overview-summary.html)
* [Java OPL reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/refjavaopl/html/overview-summary.html)
<!-- </ul> -->
To package and deploy Java models in Watson Machine Learning, see [Deploying Java models for Decision Optimization](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployJava.html) and the boilerplate provided in the [Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md)\.
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<h1 class="topictitle1" id="DOJava__title__1"><span class="keyword">Decision Optimization</span> <span class="keyword">Java models</span></h1>
<div class="body">
<p class="shortdesc">You can create and run <span class="keyword">Decision Optimization</span> models in Java by using the <span class="keyword">Watson Machine Learning</span> REST API.</p>
<p>You can build your <span class="keyword">Decision Optimization</span> models in Java or you can use <span class="keyword">Java worker</span> to package CPLEX, CPO, and OPL models.</p>
<div class="p">
For more information about these models, see the following reference manuals.
<ul>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/refjavacplex/html/overview-summary.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java CPLEX reference documentation</span></a></li>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/refjavacpoptimizer/html/overview-summary.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java CPO reference documentation</span></a></li>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/refjavaopl/html/overview-summary.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java OPL reference documentation</span></a></li>
</ul>
</div>
<p>To package and deploy <span class="keyword">Java models</span> in <span class="keyword">Watson Machine Learning</span>, see <a href="../WML_Deployment/DeployJava.html" title="You can deploy Decision Optimization Java models in Watson Machine Learning by using the Watson Machine Learning REST API.">Deploying Java models for Decision Optimization</a> and the boilerplate provided in the <a href="https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java worker GitHub</span></a>.</p>
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<strong>Parent topic:</strong> <a href="../DOWS-Cloud_home.html" title="IBM® Decision Optimization gives you access to IBM's industry-leading solution engines for mathematical programming and constraint programming. You can build Decision Optimization models either with notebooks or by using the powerful Decision Optimization experiment UI (Beta version). Here you can import, or create and edit models in Python, in OPL or with natural language expressions provided by the intelligent Modeling Assistant (Beta version). You can also deploy models with Watson Machine Learning.">Decision Optimization</a>
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<h2 class="linkheading">Related tasks</h2>
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<li><a data-hd-product="cloud wx" href="../WML_Deployment/ModelDeploymentTaskCloud.html" title="To deploy a Decision Optimization model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.">Model deployment</a></li>
<li><a href="../WML_Deployment/DeployModelRest.html" title="You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.">REST API example</a></li>
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6DBD14399B24F78CAFEC6225B77DAFAE357DDEE5 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DONotebooks.html?context=cdpaas&locale=en | Decision Optimization notebooks | Decision Optimization notebooks
You can create and run Decision Optimization models in Python notebooks by using DOcplex, a native Python API for Decision Optimization. Several Decision Optimization notebooks are already available for you to use.
The Decision Optimization environment currently supports Python 3.10. The following Python environments give you access to the Community Edition of the CPLEX engines. The Community Edition is limited to solving problems with up to 1000 constraints and 1000 variables, or with a search space of 1000 X 1000 for Constraint Programming problems.
* Runtime 23.1 on Python 3.10 S/XS/XXS
* Runtime 22.2 on Python 3.10 S/XS/XXS
To run larger problems, select a runtime that includes the full CPLEX commercial edition. The Decision Optimization environment ( DOcplex) is available in the following runtimes (full CPLEX commercial edition):
* NLP + DO runtime 23.1 on Python 3.10 with CPLEX 22.1.1.0
* DO + NLP runtime 22.2 on Python 3.10 with CPLEX 20.1.0.1
You can easily change environments (runtimes and Python version) inside a notebook by using the Environment tab (see [Changing the environment of a notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/notebook-environments.htmlchange-env)). Thus, you can formulate optimization models and test them with small data sets in one environment. Then, to solve models with bigger data sets, you can switch to a different environment, without having to rewrite or copy the notebook code.
Multiple examples of Decision Optimization notebooks are available in the Samples, including:
* The Sudoku example, a Constraint Programming example in which the objective is to solve a 9x9 Sudoku grid.
* The Pasta Production Problem example, a Linear Programming example in which the objective is to minimize the production cost for some pasta products and to ensure that the customers' demand for the products is satisfied.
These and more examples are also available in the jupyter folder of the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples)
All Decision Optimization notebooks use DOcplex.
| # Decision Optimization notebooks #
You can create and run Decision Optimization models in Python notebooks by using DOcplex, a native Python API for Decision Optimization\. Several Decision Optimization notebooks are already available for you to use\.
The Decision Optimization environment currently supports `Python 3.10`\. The following Python environments give you access to the Community Edition of the CPLEX engines\. The Community Edition is limited to solving problems with up to 1000 constraints and 1000 variables, or with a search space of 1000 X 1000 for Constraint Programming problems\.
<!-- <ul> -->
* `Runtime 23.1 on Python 3.10 S/XS/XXS`
* `Runtime 22.2 on Python 3.10 S/XS/XXS`
<!-- </ul> -->
To run larger problems, select a runtime that includes the full CPLEX commercial edition\. The Decision Optimization environment ( DOcplex) is available in the following runtimes (full CPLEX commercial edition):
<!-- <ul> -->
* `NLP + DO runtime 23.1 on Python 3.10` with `CPLEX 22.1.1.0`
* `DO + NLP runtime 22.2 on Python 3.10` with `CPLEX 20.1.0.1`
<!-- </ul> -->
You can easily change environments (runtimes and Python version) inside a notebook by using the Environment tab (see [Changing the environment of a notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/notebook-environments.html#change-env))\. Thus, you can formulate optimization models and test them with small data sets in one environment\. Then, to solve models with bigger data sets, you can switch to a different environment, without having to rewrite or copy the notebook code\.
Multiple examples of Decision Optimization notebooks are available in the Samples, including:
<!-- <ul> -->
* The Sudoku example, a Constraint Programming example in which the objective is to solve a 9x9 Sudoku grid\.
* The Pasta Production Problem example, a Linear Programming example in which the objective is to minimize the production cost for some pasta products and to ensure that the customers' demand for the products is satisfied\.
<!-- </ul> -->
These and more examples are also available in the **jupyter** folder of the **[DO\-samples](https://github.com/IBMDecisionOptimization/DO-Samples)**
All Decision Optimization notebooks use DOcplex\.
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<h1 class="topictitle1" id="DONotebooks__title__1"><span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span></h1>
<div class="body">
<p class="shortdesc">You can create and run <span class="keyword">Decision Optimization</span> models in Python <span class="keyword">notebooks</span> by using <span class="keyword">DOcplex</span>, a native Python API for <span class="keyword">Decision Optimization</span>. Several <span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span> are already available for you to use.</p>
<div class="p" data-hd-product="cloud wx">
The <span class="keyword">Decision Optimization</span> environment currently supports <code class="ph codeph">Python <span class="keyword">3.10</span></code>. The following Python environments give you access to the Community Edition of the CPLEX engines. The Community Edition is limited to solving problems with up to 1000 constraints and 1000 variables, or with a search space of 1000 X 1000 for Constraint Programming problems.
<ul data-hd-product="cloud wx">
<li><code class="ph codeph">Runtime <span class="keyword">23.1</span> on Python <span class="keyword">3.10</span> S/XS/XXS</code></li>
<li><code class="ph codeph">Runtime <span class="keyword">22.2</span> on Python <span class="keyword">3.10</span> S/XS/XXS</code></li>
</ul>
</div>
<div class="p" data-hd-product="cloud wx">
To run larger problems, select a runtime that includes the full CPLEX commercial edition. The <span class="keyword">Decision Optimization</span> environment (<span class="keyword">DOcplex</span>) is available in the following runtimes (full CPLEX commercial edition):
<ul id="DONotebooks__runtimescloud">
<li><code class="ph codeph">NLP + DO runtime <span class="keyword">23.1</span> on Python <span class="keyword">3.10</span></code> with <code class="ph codeph">CPLEX <span class="keyword">22.1.1.0</span></code></li>
<li><code class="ph codeph">DO + NLP runtime <span class="keyword">22.2</span> on Python <span class="keyword">3.10</span></code> with <code class="ph codeph">CPLEX <span class="keyword">20.1.0.1</span></code></li>
</ul>
</div>
<p data-hd-product="cloud wx">You can easily change environments (runtimes and Python version) inside a <span class="keyword">notebook</span> by using the <span class="ph uicontrol">Environment tab</span> (see <a href="../../wsj/analyze-data/notebook-environments.html#change-env">Changing the environment of a notebook</a>). Thus, you can formulate optimization models and test them with small data sets in one environment. Then, to solve models with bigger data sets, you can switch to a different environment, without having to rewrite or copy the <span class="keyword">notebook</span> code.</p>
<div class="p" data-hd-product="cloud wx">
Multiple examples of <span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span> are available in the <span class="ph uicontrol"><span class="keyword">Samples</span></span>, including:
<ul id="DONotebooks__ul_ild_lbc_mjb">
<li>The Sudoku example, a Constraint Programming example in which the objective is to solve a 9x9 Sudoku grid.</li>
<li>The Pasta Production Problem example, a Linear Programming example in which the objective is to minimize the production cost for some pasta products and to ensure that the customers' demand for the products is satisfied.</li>
</ul> These and more examples are also available in the <strong><span class="ph filepath">jupyter</span></strong> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>
</div>
<p>All <span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span> use <span class="keyword">DOcplex</span>.</p>
<section class="section" role="region" aria-labelledby="DONotebooks__section_docplex__title__1" id="DONotebooks__section_docplex">
<h2 class="sectiontitle" id="DONotebooks__section_docplex__title__1"><span class="keyword">DOcplex</span></h2>
<p>The <span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span> use <a href="https://ibmdecisionoptimization.github.io/docplex-doc/2.23.222/index.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">DOcplex</span></a>, a native Python API for modeling and solving <span class="keyword">Decision Optimization</span> problems. The API is available by default as part of the Python environment.</p>
<div class="p">
It is composed of two modules:
<ul id="DONotebooks__ul_gct_jd3_j3b">
<li>Mathematical Programming Modeling for Python that uses <code class="ph codeph">docplex.mp</code></li>
<li>Constraint Programming Modeling for Python that uses <code class="ph codeph">docplex.cp</code></li>
</ul> In your code you can specify the library you want to use as follows, for example for Mathematical Programming libraries:
<pre class="codeblock"><code>from docplex.mp.model import Model</code></pre>
</div>
<p>The API is licensed under the Apache License V2.0 and is <code class="ph codeph">numpy/pandas</code> friendly.</p>
<p>You can read the full <a href="https://ibmdecisionoptimization.github.io/docplex-doc/2.23.222/index.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DOcplex API documentation on rawgit</a>. You can find <span class="keyword">DOcplex</span> examples on the <a href="https://github.com/IBMDecisionOptimization/docplex-examples" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong><span class="keyword">Decision Optimization GitHub</span></strong></a>.</p>
</section>
<section class="section" role="region" aria-labelledby="DONotebooks__section_fvc_df3_j3b__title__1" id="DONotebooks__section_fvc_df3_j3b">
<h2 class="sectiontitle" id="DONotebooks__section_fvc_df3_j3b__title__1"><span class="keyword">Decision Optimization</span> client API</h2>
<p>In addition to <span class="keyword">DOcplex</span>, a <span class="keyword">Decision Optimization</span> client API is available for you to create scenarios and handle models that are made in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>. For example, see <a href="../DODS_Notebooks/multiScenario.html#task_fns_tts_n1b" title="This tutorial shows you how to generate multiple scenarios from a notebook using randomized data. Generating multiple scenarios lets you test a model by exposing it to a wide range of data.">Generating multiple scenarios</a>.</p>
<p>See the <a href="https://ibmdecisionoptimization.github.io/decision-optimization-client-doc/" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Decision Optimization</span> client API documentation</a>. You can also find the previous example in the <strong><span class="ph filepath">jupyter</span></strong> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>.</p>
</section>
<section class="section" role="region" aria-labelledby="DONotebooks__section_RunningNotebookCloud__title__1" data-hd-product="cloud wx" id="DONotebooks__section_RunningNotebookCloud">
<h2 class="sectiontitle" id="DONotebooks__section_RunningNotebookCloud__title__1">Running Decision Optimization <span class="keyword">notebooks</span></h2>
<p>Depending on whether you are interested in Constraint Programming or Linear Programming, choose one of the two <span class="keyword">notebooks</span> presented earlier and run it as follows.</p>
<div class="p">
If you already have a project in <span class="keyword" data-hd-product="wx">watsonx.ai</span>:
<ol id="DONotebooks__ol_d5j_rkk_yfb">
<li>From the <span class="keyword">Samples</span>, open the <span class="keyword">notebook</span> you want to work with.</li>
<li>If you have already created a project, click <span class="ph uicontrol">Add to project</span>.</li>
<li>Select an existing project in the drop-down list, and select a <strong>runtime</strong>, for example Runtime <span class="keyword">23.1</span> on Python <span class="keyword">3.10</span> XS (or for larger models that require the Commercial Edition of CPLEX engines, select DO + NLP Runtime <span class="keyword">23.1</span> on Python <span class="keyword">3.10</span> XS), and click <strong><span class="ph uicontrol">Create</span></strong>. The <span class="keyword">notebook</span> is added to your project.</li>
</ol>
</div>
<p>If you do not already have a project , click the Download button <img id="DONotebooks__image_fcp_rs5_bgb" src="images/Clouddownload.jpg" alt="Download button"> to download the example onto your computer.</p>
<ol id="DONotebooks__ol_mpp_2s5_bgb">
<li>Create a new project: select <span class="ph uicontrol">Projects</span> > <span class="ph uicontrol">View all Projects</span> from the menu and click the <span class="ph uicontrol">New Project</span> button.</li>
<li>Select <span class="ph uicontrol">Create an empty project</span> and in the window that opens enter a name and click <strong><span class="ph uicontrol">Create</span></strong>.</li>
<li><span class="ph">Select the <span class="ph" data-hd-product="wx"><span class="ph uicontrol"><span class="keyword">Assets</span></span></span> tab.</span></li>
<li><span class="ph" data-hd-product="wx">Select <span class="ph uicontrol"><span class="keyword">New asset > Work with data and models in Python or R notebooks</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></li>
<li>Choose <strong><span class="ph uicontrol">From file</span></strong>. Then click <strong><span class="ph uicontrol"><span class="keyword">Drag and drop files or upload</span></span></strong> and browse to the <span class="keyword">notebook</span> onto your computer.</li>
<li>Click <strong><span class="ph uicontrol">Create Notebook</span></strong>.<span class="ph">The <span class="keyword">notebook</span> is added to your project.</span></li>
</ol> Your <span class="keyword">notebook</span> automatically opens.
<p>To run your <span class="keyword">notebook</span>, click <strong><span class="ph uicontrol">Cell > Run All</span></strong>.</p>
<p>Example Python <span class="keyword">notebooks</span> are provided in the <span class="keyword">Decision Optimization GitHub</span>. To use these notebooks, see <a href="docExamples.html#Examples__section_xrg_fdj_cgb">Jupyter notebook samples</a>. These examples do not use the <span class="keyword">experiment UI</span>.</p>
<p>Also a Python <span class="keyword">notebook</span> that shows you how to generate multiple scenarios and that uses randomized data, is provided in the <strong><span class="ph filepath">jupyter</span></strong> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>. This approach can be useful to test a model made in the <strong><span class="keyword">experiment UI</span></strong> with different data sets. For more information, see <a href="../DODS_Notebooks/multiScenario.html#task_fns_tts_n1b" title="This tutorial shows you how to generate multiple scenarios from a notebook using randomized data. Generating multiple scenarios lets you test a model by exposing it to a wide range of data.">Generating multiple scenarios</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="DONotebooks__LearnMoreDO__title__1" id="DONotebooks__LearnMoreDO">
<h2 class="sectiontitle" id="DONotebooks__LearnMoreDO__title__1">Decision Optimization tutorials</h2>
<p>You can find more <span class="keyword">DOcplex</span> examples that introduce you to the <span class="keyword">DOcplex</span> Python API on the Decision Optimization GitHub:</p>
<dl>
<dt class="dlterm">
Linear Programming
</dt>
<dd class="dlentry">
You can read a detailed description of this <span class="keyword">notebook</span> in this <a href="https://ibmdecisionoptimization.github.io/tutorials/html/Linear_Programming.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Linear Programming (CPLEX Part 1) tutorial</a>. You can clone or download this <a href="https://github.com/IBMDecisionOptimization/tutorials/blob/master/jupyter/Linear_Programming.ipynb" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Decision Optimization</span> Linear Programming <span class="keyword">notebook</span> from Github</a>.
</dd>
<dt class="dlterm">
Beyond Linear Programming
</dt>
<dd class="dlentry">
You can read a detailed description of this <span class="keyword">notebook</span> in this <a href="https://ibmdecisionoptimization.github.io/tutorials/html/Beyond_Linear_Programming.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Linear Programming (CPLEX Part 2) tutorial</a>. You can clone or download this <a href="https://github.com/IBMDecisionOptimization/tutorials/blob/master/jupyter/Beyond_Linear_Programming.ipynb" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Decision Optimization</span> Beyond Linear Programming <span class="keyword">notebook</span> from Github</a>.
</dd>
<dt class="dlterm">
Getting started with Scheduling in CPLEX for Python
</dt>
<dd class="dlentry">
You can read a detailed description of this <span class="keyword">notebook</span> in this <a href="https://ibmdecisionoptimization.github.io/tutorials/html/Scheduling_Tutorial.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Scheduling in CPLEX for Python tutorial</a>. You can clone or download this <a href="https://github.com/IBMDecisionOptimization/tutorials/blob/master/jupyter/Scheduling_Tutorial.ipynb" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Getting started with Scheduling in CPLEX for Python <span class="keyword">notebook</span> from Github</a>.
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277C8CB678CAF766466EDE03C506EB0A822FD400 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html?context=cdpaas&locale=en | Supported data sources in Decision Optimization | Supported data sources in Decision Optimization
Decision Optimization supports the following relational and nonrelational data sources on . watsonx.ai.
* [IBM data sources](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html?context=cdpaas&locale=enDOConnections__ibm-data-src)
* [Third-party data sources](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html?context=cdpaas&locale=enDOConnections__third-party-data-src)
| # Supported data sources in Decision Optimization #
Decision Optimization supports the following relational and nonrelational data sources on \. watsonx\.ai\.
<!-- <ul> -->
* [IBM data sources](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html?context=cdpaas&locale=en#DOConnections__ibm-data-src)
* [Third\-party data sources](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html?context=cdpaas&locale=en#DOConnections__third-party-data-src)
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<h1 class="topictitle1" id="DOConnections__title__1">Supported data sources in <span class="keyword">Decision Optimization</span></h1>
<div class="body refbody">
<div class="abstract">
<p class="shortdesc"><span class="keyword">Decision Optimization</span> supports the following relational and nonrelational data sources on .<span class="ph" data-hd-product="wx"><span class="keyword">watsonx.ai</span>.</span></p>
<ul>
<li><a href="#DOConnections__ibm-data-src">IBM data sources</a></li>
<li><a href="#DOConnections__third-party-data-src">Third-party data sources</a></li>
</ul>
</div>
<section class="section" role="region" aria-labelledby="DOConnections__ibm-data-src__title__1" id="DOConnections__ibm-data-src">
<h2 class="sectiontitle" id="DOConnections__ibm-data-src__title__1">IBM data sources</h2>
<p>The following list shows you the IBM® data sources that you can connect to from <span class="keyword">Decision Optimization</span>.</p>
<ul>
<li><a href="../../wsj/manage-data/conn-datastax.html"><span class="keyword" translate="no">IBM Cloud® Databases for DataStax</span></a></li>
<li><a href="../../wsj/manage-data/conn-mongodb.html"><span class="keyword" translate="no">IBM Cloud Databases for MongoDB</span></a></li>
<li><a href="../../wsj/manage-data/conn-cos.html"><span class="keyword" translate="no">IBM Cloud Object Storage</span></a></li>
<li><a href="../../wsj/manage-data/conn-cos-infra.html"><span class="keyword" translate="no">IBM Cloud Object Storage</span> (infrastructure)</a></li>
<li><a href="../../wsj/manage-data/conn-cloudant.html">IBM <span class="keyword" translate="no">Cloudant®</span></a></li>
<li><a href="../../wsj/manage-data/conn-db2.html"><span class="keyword" translate="no">IBM Db2®</span></a></li>
<li><a href="../../wsj/manage-data/conn-db2-bigsql.html"><span class="keyword" translate="no">IBM Db2 Big SQL</span></a></li>
<li><a href="../../wsj/manage-data/conn-db2zos.html"><span class="keyword" translate="no">IBM Db2 for z/OS®</span></a></li>
<li><a href="../../wsj/manage-data/conn-db2-cloud.html"><span class="keyword" translate="no">IBM Db2 on Cloud</span></a></li>
<li><a href="../../wsj/manage-data/conn-db2-wh.html"><span class="keyword" translate="no">IBM Db2 Warehouse</span></a></li>
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<h2 class="sectiontitle" id="DOConnections__third-party-data-src__title__1">Third-party data sources</h2>
<p>The following list shows you the third-party data sources that you can connect to from <span class="keyword">Decision Optimization</span>.</p>
<ul>
<li><a href="../../wsj/manage-data/conn-azrds-mysql.html"><span class="keyword" translate="no">Amazon RDS for MySQL</span></a></li>
<li><a href="../../wsj/manage-data/conn-azrds-oracle.html"><span class="keyword" translate="no">Amazon RDS for Oracle</span></a></li>
<li><a href="../../wsj/manage-data/conn-azrds-postresql.html"><span class="keyword" translate="no">Amazon RDS for PostgreSQL</span></a></li>
<li><a href="../../wsj/manage-data/conn-amazon-s3.html"><span class="keyword" translate="no">Amazon S3</span></a></li>
<li><a href="../../wsj/manage-data/conn-cassandra.html"><span class="keyword" translate="no">Apache Cassandra</span></a></li>
<li><a href="../../wsj/manage-data/conn-cloud-storage.html"><span class="keyword" translate="no">Google Cloud Storage</span></a></li>
<li><a href="../../wsj/manage-data/conn-mariadb.html"><span class="keyword" translate="no">MariaDB</span></a></li>
<li><a href="../../wsj/manage-data/conn-azurefs.html"><span class="keyword" translate="no">Microsoft Azure File Storage</span> </a></li>
<li><a href="../../wsj/manage-data/conn-cosmosdb.html"><span class="keyword" translate="no">Microsoft Azure Cosmos DB</span> </a></li>
<li><a href="../../wsj/manage-data/conn-azuredls.html"><span class="keyword" translate="no">Microsoft Azure Data Lake Storage</span> </a></li>
<li><a href="../../wsj/manage-data/conn-azure-sql.html"><span class="keyword" translate="no">Microsoft Azure SQL Database</span></a></li>
<li><a href="../../wsj/manage-data/conn-sql-server.html"><span class="keyword" translate="no">Microsoft SQL Server</span></a></li>
<li><a href="../../wsj/manage-data/conn-mongo.html"><span class="keyword" translate="no">MongoDB</span></a></li>
<li><a href="../../wsj/manage-data/conn-postgresql.html"><span class="keyword" translate="no">PostgreSQL</span></a></li>
<li><a href="../../wsj/manage-data/conn-singlestore.html"><span class="keyword" translate="no">SingleStoreDB</span></a></li>
<li><a href="../../wsj/manage-data/conn-snowflake.html"><span class="keyword" translate="no">Snowflake</span></a></li>
<li><a href="../../wsj/manage-data/conn-teradata.html"><span class="keyword" translate="no">Teradata</span></a>
<p><em id="DOConnections__EM8"><span class="keyword" translate="no">Teradata</span> JDBC Driver 17.00.00.03 Copyright (C) 2023 by <span class="keyword" translate="no">Teradata</span> IBM provides embedded usage of the <span class="keyword" translate="no">Teradata</span> JDBC Driver under license from <span class="keyword" translate="no">Teradata</span> solely for use as part of the <span class="ph" data-hd-product="wx">IBM Watson®<span class="keyword" translate="no">IBM watsonx</span></span> service offering.</em></p></li>
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E990E009903E315FA6752E7E82C2634AF4A425B9 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOintro.html?context=cdpaas&locale=en | Ways to use Decision Optimization | Ways to use Decision Optimization
To build Decision Optimization models, you can create Python notebooks with DOcplex, a native Python API for Decision Optimization, or use the Decision Optimization experiment UI that has more benefits and features.
| # Ways to use Decision Optimization #
To build Decision Optimization models, you can create Python notebooks with DOcplex, a native Python API for Decision Optimization, or use the Decision Optimization experiment UI that has more benefits and features\.
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<h1 class="topictitle1" id="DOIntro__title__1"><span class="ph">Ways to use <span class="keyword">Decision Optimization</span></span></h1>
<div class="body">
<p class="shortdesc"><span class="ph">To build <span class="keyword">Decision Optimization</span> models, you can create Python <span class="keyword">notebooks</span> with <span class="keyword">DOcplex</span>, a native Python API for Decision Optimization, or use the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> that has more benefits and features.</span></p>
<section class="section" role="region" aria-labelledby="DOIntro__section_e5t_jhw_sjb__title__1" id="DOIntro__section_e5t_jhw_sjb">
<h2 class="sectiontitle" id="DOIntro__section_e5t_jhw_sjb__title__1">Different ways to use <span class="keyword">Decision Optimization</span></h2>
<p>Depending on your skills and expertise, you can use <span class="keyword">Decision Optimization</span>, in the following different ways.</p>
<ul>
<li>
<dl>
<dt class="dlterm">
Python <span class="keyword">notebooks</span>
</dt>
<dd class="dlentry">
You can create Python <span class="keyword">notebooks</span> with <span class="keyword">DOcplex</span>, a native Python API for <span class="keyword">Decision Optimization</span>. See <a href="https://ibmdecisionoptimization.github.io/docplex-doc/2.23.222/index.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">DOcplex</span></a>. You need Operational Research (OR) modeling expertise to create variables, objectives, and constraints to represent your problem.
<p>For more information about supported Python environments, see <a href="DONotebooks.html" title="You can create and run Decision Optimization models in Python notebooks by using DOcplex, a native Python API for Decision Optimization. Several Decision Optimization notebooks are already available for you to use."><span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span></a>.</p>
</dd>
</dl></li>
<li>
<dl>
<dt class="dlterm">
<span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>
</dt>
<dd class="dlentry">
The <span class="keyword">experiment UI</span> facilitates workflow and provides many other features. See <a href="#DOIntro__section_ekh_zdk_ycb"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> advantages</a>.
</dd>
<dd class="ddexpand">
It helps you to create and run (solve) scenarios with the following model types:
<dl>
<dt class="dlterm">
Python models
</dt>
<dd class="dlentry">
You can create these models with <span class="keyword">DOcplex</span>. See <a href="../DODS_Notebooks/solveIntro.html#SolvingPythonModel" title="You can solve Python DOcplex models in a Decision Optimization experiment."><span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span></a>
</dd>
<dt class="dlterm">
<span class="keyword">Modeling Assistant</span> models
</dt>
<dd class="dlentry">
The <span class="keyword">Modeling Assistant</span> helps you to formulate models in natural language, which requires little to no knowledge of OR, and does not require you to write Python code. See <a href="../DODS_Mdl_Assist/exhousebuildintro.html#topic_jzq_hbq_m1b" title="You can model and solve Decision Optimization problems using the Modeling Assistant (which enables you to formulate models in natural language). This requires little to no knowledge of Operational Research (OR) and does not require you to write Python code. The Modeling Assistant is only available in English and is not globalized."><span class="keyword">Modeling Assistant</span> models</a>.
</dd>
<dd class="ddexpand">
This feature is available for certain model types. See <a href="../DODS_Mdl_Assist/mdl_asst_domains.html#topic_jdecisionOptimDomains" title="There are different decision domains currently available in the Modeling Assistant and you can be guided to choose the right domain for your problem.">Selecting a Decision domain in the Modeling Assistant</a>.
</dd>
<dd class="ddexpand">
The <span class="keyword">Modeling Assistant</span> is <strong>only available in English</strong> and is not globalized.
</dd>
<dt class="dlterm">
OPL models
</dt>
<dd class="dlentry">
You can create, import, and edit OPL models. For more information, see <a href="OPLmodels.html#topic_oplmodels" title="You can build OPL models in the Decision Optimization experiment UI in watsonx.ai.">OPL models</a>.
</dd>
<dt class="dlterm">
CPLEX and CP Optimizer (CPO) models.
</dt>
<dd class="dlentry">
You can create, import, and edit (<span class="ph filepath">.lp</span> and <span class="ph filepath">.cpo</span> files), and import and edit <span class="ph filepath">.mps</span> files. You can then solve them and download the solution files.
</dd>
</dl>
</dd>
<dd class="ddexpand">
For more information, see <a href="buildingmodels.html#topic_buildingmodels" title="If you use the Decision Optimization experiment UI, you can take advantage of its many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with Watson Machine Learning."><span class="keyword">experiment UI</span></a>.
</dd>
</dl></li>
<li>
<dl>
<dt class="dlterm">
Java models
</dt>
<dd class="dlentry">
You can use the <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> REST API to deploy and run Java models. For more information, see <a href="DOJava.html" title="You can create and run Decision Optimization models in Java by using the Watson Machine Learning REST API.">Decision Optimization Java models</a>.
</dd>
</dl></li>
<li>
<dl>
<dt class="dlterm">
Batch deployment
</dt>
<dd class="dlentry">
For more information about deployment with <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>, see <a href="../wml_cpd_home.html#topic_deploying" title="When you have created and solved your Decision Optimization models, you can deploy them using Watson Machine Learning.">Decision Optimization</a>.
</dd>
</dl></li>
</ul>
<p id="DOIntro__quickstartvideo">For a step-by-step guide to build, solve and deploy a <span class="keyword">Decision Optimization</span> model, by using the user interface, see the <a href="../../wsj/getting-started/get-started-do.html">Quick start tutorial with video</a>.</p>
<figure class="fignone" data-hd-product="cloud wx" id="DOIntro__fig_ydq_nvj_snb">
<figcaption>
Figure 1. Modeling and solving with the Decision Optimization experiments
</figcaption><img id="DOIntro__image_zdq_nvj_snb" src="images/new_WaysUseDO-3.jpg" alt="Chart showing workflow and different ways to use the model builder">
</figure>
</section>
<section class="section" role="region" aria-labelledby="DOIntro__section_ekh_zdk_ycb__title__1" id="DOIntro__section_ekh_zdk_ycb">
<h2 class="sectiontitle" id="DOIntro__section_ekh_zdk_ycb__title__1"><span class="ph"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> advantages</span></h2>
<p id="DOIntro__comparisontable">The following table highlights how you can perform different functions both with and without the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>. Jupyter <span class="keyword">notebooks</span> in this table are <span class="keyword">notebooks</span> without the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>. As you can see, you have more advantages when you use the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>. See <a href="buildingmodels.html#topic_buildingmodels__ul_vr5_vpx_fdb">Model builder features</a>.</p>
<div class="tablenoborder">
<table summary="Table showing different steps of optimization and how these steps can be performed." id="DOIntro__table_hvv_zpk_ycb" class="defaultstyle">
<caption>
<span class="tablecap">Table 1. <span class="keyword">Decision Optimization</span> with the <span class="keyword">experiment UI</span></span>
</caption>
<colgroup>
<col style="width:16.830065359477125%">
<col style="width:17.320261437908496%">
<col style="width:16.33986928104575%">
<col style="width:16.33986928104575%">
<col style="width:16.33986928104575%">
<col style="width:16.830065359477125%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th class="firstcol" id="DOIntro__table_hvv_zpk_ycb__entry__1" rowspan="2">Task</th>
<th rowspan="2" id="DOIntro__table_hvv_zpk_ycb__entry__2">Jupyter <span class="keyword">notebook</span> (without the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>)</th>
<th colspan="4" id="DOIntro__table_hvv_zpk_ycb__entry__3" class="thcenter"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> (4 types of models)</th>
</tr>
<tr>
<th class="firstcol" id="DOIntro__table_hvv_zpk_ycb__entry__4">Python</th>
<th id="DOIntro__table_hvv_zpk_ycb__entry__5">OPL models</th>
<th id="DOIntro__table_hvv_zpk_ycb__entry__6">CPLEX and CPO models</th>
<th id="DOIntro__table_hvv_zpk_ycb__entry__7"><span class="keyword">Modeling Assistant</span></th>
</tr>
</thead>
<tbody>
<tr>
<th class="firstcol" id="DOIntro__table_hvv_zpk_ycb__entry__8" headers="DOIntro__table_hvv_zpk_ycb__entry__1 DOIntro__table_hvv_zpk_ycb__entry__4 ">Manage data</th>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__8 DOIntro__table_hvv_zpk_ycb__entry__2 DOIntro__table_hvv_zpk_ycb__entry__5 ">
<p>Import data from Projects.</p></td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__8 DOIntro__table_hvv_zpk_ycb__entry__3 DOIntro__table_hvv_zpk_ycb__entry__6 ">
<p>Import data from Projects and edit data in the Prepare data view. See <a href="modelbuilderUI.html#ModelBuilderInterface__section_preparedata">Prepare data view</a>.</p></td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__8 DOIntro__table_hvv_zpk_ycb__entry__3 DOIntro__table_hvv_zpk_ycb__entry__7 ">
<p>Import data from Projects and edit data in the Prepare data view. See <a href="modelbuilderUI.html#ModelBuilderInterface__section_preparedata">Prepare data view</a>.</p></td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__8 DOIntro__table_hvv_zpk_ycb__entry__3 "> </td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__8 DOIntro__table_hvv_zpk_ycb__entry__3 ">
<p>Relationships in your data are intelligently deduced.</p></td>
</tr>
<tr>
<th class="firstcol" id="DOIntro__table_hvv_zpk_ycb__entry__14" headers="DOIntro__table_hvv_zpk_ycb__entry__1 DOIntro__table_hvv_zpk_ycb__entry__4 ">Formulate and run optimization models</th>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__14 DOIntro__table_hvv_zpk_ycb__entry__2 DOIntro__table_hvv_zpk_ycb__entry__5 ">
<p>Create a model formulation from scratch in a Python <span class="keyword" translate="no">notebook</span>. using the DOcplex API.</p>
<p>With <span class="keyword">notebooks</span> individual cells can be run interactively, which facilitates debugging.</p></td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__14 DOIntro__table_hvv_zpk_ycb__entry__3 DOIntro__table_hvv_zpk_ycb__entry__6 ">
<p>Create a model formulation from scratch in Python.</p>
<p>Import and view a model formulation from a <span class="keyword">notebook</span> or file.</p>
<p>Edit the imported Python model directly.</p>
<p>Export your model as a <span class="keyword">notebook</span>. With <span class="keyword">notebooks</span> individual cells can be run interactively, which facilitates debugging.</p></td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__14 DOIntro__table_hvv_zpk_ycb__entry__3 DOIntro__table_hvv_zpk_ycb__entry__7 ">
<p>Create a model formulation from scratch in OPL.</p>
<p>Import and view a model formulation from an OPL file.</p>
<p>Edit the imported OPL model directly.</p></td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__14 DOIntro__table_hvv_zpk_ycb__entry__3 ">
<p>Create a model formulation from scratch in CPLEX or CPO.</p>
<p>Import a CPLEX or CPO model file (<span class="ph filepath">.lp</span>, <span class="ph filepath">.mps</span>, and <span class="ph filepath">.cpo</span> files).</p>
<p>Edit <span class="ph filepath">.lp</span>, <span class="ph filepath">.mps</span>, and <span class="ph filepath">.cpo</span> files.</p>
<p>Run model and download solution file.</p></td>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__14 DOIntro__table_hvv_zpk_ycb__entry__3 ">
<p>Create a model formulation from scratch by selecting from the proposed options expressed in natural language.</p>
<p>Import and view a Modeling Assistant model formulation from a scenario.</p>
<p>Edit the imported model directly.</p></td>
</tr>
<tr>
<th class="firstcol" id="DOIntro__table_hvv_zpk_ycb__entry__20" headers="DOIntro__table_hvv_zpk_ycb__entry__1 DOIntro__table_hvv_zpk_ycb__entry__4 ">Create and compare multiple scenarios</th>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__20 DOIntro__table_hvv_zpk_ycb__entry__2 DOIntro__table_hvv_zpk_ycb__entry__5 ">
<p>Write Python code to handle scenario management.</p></td>
<td colspan="4" headers="DOIntro__table_hvv_zpk_ycb__entry__20 DOIntro__table_hvv_zpk_ycb__entry__3 DOIntro__table_hvv_zpk_ycb__entry__6 DOIntro__table_hvv_zpk_ycb__entry__7 ">
<p>Create and manage scenarios to compare different instances of model, data, and solutions. See <a href="modelbuilderUI.html#ModelBuilderInterface__scenariopanel">Scenario pane</a> and <a href="modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a>.</p></td>
</tr>
<tr>
<th class="firstcol" id="DOIntro__table_hvv_zpk_ycb__entry__23" headers="DOIntro__table_hvv_zpk_ycb__entry__1 DOIntro__table_hvv_zpk_ycb__entry__4 ">Create and share reports</th>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__23 DOIntro__table_hvv_zpk_ycb__entry__2 DOIntro__table_hvv_zpk_ycb__entry__5 ">
<p>Create reports in your <span class="keyword">notebooks</span> by using Python data visualization tools.</p></td>
<td colspan="4" headers="DOIntro__table_hvv_zpk_ycb__entry__23 DOIntro__table_hvv_zpk_ycb__entry__3 DOIntro__table_hvv_zpk_ycb__entry__6 DOIntro__table_hvv_zpk_ycb__entry__7 ">
<p>Rapidly create reports in the <a href="Visualization.html#topic_visualization" title="With the Decision Optimization experiment Visualization view, you can configure the graphical representation of input data and solutions for one or several scenarios.">Visualization view</a> by using widgets, pages, and a JSON editor.</p>
<p>Download your report as a JSON file to share with your team.</p></td>
</tr>
<tr>
<th class="firstcol" id="DOIntro__table_hvv_zpk_ycb__entry__26" headers="DOIntro__table_hvv_zpk_ycb__entry__1 DOIntro__table_hvv_zpk_ycb__entry__4 ">Deploy a model</th>
<td headers="DOIntro__table_hvv_zpk_ycb__entry__26 DOIntro__table_hvv_zpk_ycb__entry__2 DOIntro__table_hvv_zpk_ycb__entry__5 ">
<p>Deploy <span class="keyword">notebooks</span> by using <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> REST API or Python client.</p></td>
<td colspan="4" headers="DOIntro__table_hvv_zpk_ycb__entry__26 DOIntro__table_hvv_zpk_ycb__entry__3 DOIntro__table_hvv_zpk_ycb__entry__6 DOIntro__table_hvv_zpk_ycb__entry__7 ">
<p>Select the scenario that you want to save ready for promotion to the deployment space. See <a href="../WML_Deployment/DeployModelUI-WML.html#task_deployUIWML" title="You can save a model for deployment in the Decision Optimization experiment UI and promote it to your Watson Machine Learning deployment space.">Deploying a Decision Optimization model by using the user interface</a>.</p>
<p>Deploy your <span class="keyword">Decision Optimization</span> prescriptive model and associated common data once, and then submit job requests to this deployment with only the related transactional data. You can deploy models by using the <span class="keyword">Watson Machine Learning REST API</span> or by using the <span class="keyword">Watson Machine Learning Python client</span>. See <a href="../WML_Deployment/DeployModelRest.html#task_deploymodelREST" title="You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API."><span class="keyword">Watson Machine Learning REST API</span></a> and <a href="../WML_Deployment/DeployPythonClient.html#topic_wmlpythonclient" title="You can deploy a Decision Optimization model, create and monitor jobs, and get solutions by using the Watson Machine Learning Python client."><span class="keyword">Watson Machine Learning Python client</span></a>.</p></td>
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8892A757ECB2C4A02806A7B262712FF2E30CE044 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.html?context=cdpaas&locale=en | OPL models | OPL models
You can build OPL models in the Decision Optimization experiment UI in watsonx.ai.
In this section:
* [Inputs and Outputs](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.html?context=cdpaas&locale=entopic_oplmodels__section_oplIO)
* [Engine settings](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.html?context=cdpaas&locale=entopic_oplmodels__engsettings)
To create an OPL model in the experiment UI, select in the model selection window. You can also import OPL models from a file or import a scenario .zip file that contains the OPL model and the data. If you import from a file or scenario .zip file, the data must be in .csv format. However, you can import other file formats that you have as project assets into the experiment UI. You can also import data sets including connected data into your project from the model builder in the [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_preparedata).
For more information about the OPL language and engine parameters, see:
* [OPL language reference manual](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/OPL_Studio/opllangref/topics/opl_langref_modeling_language.html)
* [OPL Keywords](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/OPL_Studio/opllang_quickref/topics/opl_keywords_top.html)
* [A list of CPLEX parameters](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/CPLEX/Parameters/topics/introListTopical.html)
* [A list of CPO parameters](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/CP_Optimizer/Parameters/topics/paramcpoptimizer.html)
| # OPL models #
You can build OPL models in the Decision Optimization experiment UI in watsonx\.ai\.
In this section:
<!-- <ul> -->
* [Inputs and Outputs](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.html?context=cdpaas&locale=en#topic_oplmodels__section_oplIO)
* [Engine settings](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.html?context=cdpaas&locale=en#topic_oplmodels__engsettings)
<!-- </ul> -->
To create an OPL model in the experiment UI, select in the model selection window\. You can also import OPL models from a file or import a scenario \.zip file that contains the OPL model and the data\. If you import from a file or scenario \.zip file, the data must be in \.csv format\. However, you can import other file formats that you have as project assets into the experiment UI\. You can also import data sets including connected data into your project from the model builder in the [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_preparedata)\.
For more information about the OPL language and engine parameters, see:
<!-- <ul> -->
* [OPL language reference manual](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/OPL_Studio/opllangref/topics/opl_langref_modeling_language.html)
* [OPL Keywords](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/OPL_Studio/opllang_quickref/topics/opl_keywords_top.html)
* [A list of CPLEX parameters](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/CPLEX/Parameters/topics/introListTopical.html)
* [A list of CPO parameters](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/CP_Optimizer/Parameters/topics/paramcpoptimizer.html)
<!-- </ul> -->
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<h1 class="topictitle1" id="topic_oplmodels__title__1"><span class="ph" data-hd-product="cloud wx">OPL models</span></h1>
<div class="body">
<p class="shortdesc">You can build OPL models in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> in <span class="keyword" data-hd-product="wx">watsonx.ai</span>.</p>
<div class="bodydiv">
<p>In this section:</p>
<ul>
<li><a href="#topic_oplmodels__section_oplIO">Inputs and Outputs</a></li>
<li><a href="#topic_oplmodels__engsettings">Engine settings</a></li>
</ul>
</div>
<p>To create an OPL model in the <span class="keyword">experiment UI</span>, select <span class="ph menucascade"><span class="ph uicontrol">Create</span><abbr title="and then"> > </abbr><span class="ph uicontrol">OPL</span></span> in the model selection window. You can also import OPL models from a file or import a scenario .zip file that contains the OPL model and the data. If you import from a file or scenario .zip file, the data must be in <span class="ph filepath">.csv</span> format. However, you can import other file formats that you have as project assets into the <span class="keyword">experiment UI</span>. You can also import data sets including connected data into your project from the model builder in the <a href="modelbuilderUI.html#ModelBuilderInterface__section_preparedata">Prepare data view</a>.</p>
<section class="section" role="region" aria-labelledby="topic_oplmodels__section_oplIO__title__1" id="topic_oplmodels__section_oplIO">
<h2 class="sectiontitle" id="topic_oplmodels__section_oplIO__title__1">Inputs and Outputs</h2>
<div class="p">
In an OPL model you must declare a <code class="ph codeph">tupleset</code>, for each table that you imported in the <span class="ph uicontrol"><span class="keyword">Prepare data</span></span> <span class="keyword">view</span> using the same names. The schema for each tupleset must have same number of columns as the table and use the same field names. For example, if you have an input table in your <span class="keyword">Prepare data</span> <span class="keyword">view</span> called <code class="ph codeph">Product</code> with the attributes <code class="ph codeph">name, demand, insideCost,</code> and <code class="ph codeph">outsideCost</code>, your OPL model must contain the following definition:
<pre class="codeblock language-shell"><code class="language-shell">tuple TProduct {
key string name;
float demand;
float insideCost;
float outsideCost;
};
{TProduct} Product = ...;</code></pre>
</div>
<div class="p">
Similarly if you want to display a table in the <span class="ph uicontrol">Explore solution</span> <span class="keyword">view</span>, you must define a <code class="ph codeph">tupleset</code> for this output table in your OPL model. For example, this code produces an output table with 3 columns in the solution.
<pre class="codeblock language-shell"><code class="language-shell">/// solution
tuple TPlannedProduction {
key string productId;
float insideProduction;
float outsideProduction;
}</code></pre>
</div>
<p>You can find this example OPL model for a pasta production problem in the <span class="ph filepath">Model_Builder</span> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>. You can download and extract all the samples. <span class="ph">Select the relevant product and version subfolder.</span></p>
</section>
<section class="section" role="region" aria-labelledby="topic_oplmodels__engsettings__title__1" id="topic_oplmodels__engsettings">
<h2 class="sectiontitle" id="topic_oplmodels__engsettings__title__1">Engine settings</h2>
<p>You can add an OPL parameter settings (.ops) file in your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>. An OPL settings file is where you store user-defined values of OPL options for mathematical programming, or constraint programming, and for the OPL language. It gives you access to the solver (engine) parameters so that you can modify them.</p>
<p>Click <span class="ph uicontrol">+</span> and select <span class="ph uicontrol">Add engine settings </span> in the <span class="ph uicontrol"><span class="keyword">Build model</span></span> <span class="keyword">view</span>. The <span class="keyword">Visual editor</span> opens and displays different categories of parameters with their default values, which you can customize for your model. You can also search for specific parameters by entering a name in the <span class="ph uicontrol">Find settings</span> search field.<img src="images/engsettingsfilter.png" alt="OPL Engine settings .ops file shown open in Visual Editor view with one customized parameter"></p>
<p>In this window, you can select different parameters or edit fields. If you modify the default parameters, a <span class="ph uicontrol">Customized Settings </span> pane that lists your changes.</p>
<p>You can toggle the <span class="ph uicontrol"><span class="keyword">Visual editor</span></span> switch to the off position to view your changes in an XML editor. The file, when displayed in the XML editor, only contains the parameters that you changed, and does not list all the default parameters. You can also edit the parameters in this XML editor and your changes will be displayed in the <span class="keyword">Visual editor</span> when you toggle the switch back to the on position. <img src="images/engsettingsXML.png" alt="XML editor showing modifications made to default engine setting parameters"></p>
<p>You can import an <span class="ph filepath">.ops</span> file to use for your engine settings, but you can only have one engine settings file for your model. Importing such a file can be useful if you have some non-default parameters that you have specified in IBM ILOG CPLEX Optimization Studio that you want to import into your experiment.</p>
</section>
<div class="p">
For more information about the OPL language and engine parameters, see:
<ul id="topic_oplmodels__ul_vrf_w4q_h3b">
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/OPL_Studio/opllangref/topics/opl_langref_modeling_language.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">OPL language reference manual</span></a></li>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/OPL_Studio/opllang_quickref/topics/opl_keywords_top.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">OPL Keywords</span></a></li>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/CPLEX/Parameters/topics/introListTopical.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">A list of CPLEX parameters</span></a></li>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/CP_Optimizer/Parameters/topics/paramcpoptimizer.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">A list of CPO parameters</span></a></li>
</ul>
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8E56F0EFD08FF4A97E439EA3B8DE2B7AF1A302C9 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en | Decision Optimization Visualization view | Visualization view
With the Decision Optimization experiment Visualization view, you can configure the graphical representation of input data and solutions for one or several scenarios.
Quick links:
* [Visualization view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section-dashboard)
* [Table search and filtering](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_tablefilter)
* [Visualization widgets syntax](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_widgetssyntax)
* [Visualization Editor](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__viseditor)
* [Visualization pages](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__vispages)
The Visualization view is common to all scenarios in a Decision Optimization experiment.
For example, the following image shows the default bar chart that appears in the solution tab for the example that is used in the tutorial [Solving and analyzing a model: the diet problem](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveModel.htmltask_mtg_n3q_m1b).
The Visualization view helps you compare different scenarios to validate models and business decisions.
For example, to show the two scenarios solved in this diet example tutorial, you can add another bar chart as follows:
1. Click the chart widget and configure it by clicking the pencil icon.
2. In the Chart widget editor, select Add scenario and choose scenario 1 (assuming that your current scenario is scenario 2) so that you have both scenario 1 and scenario 2 listed.
3. In the Table field, select the Solution data option and select solution from the drop-down list.
4. In the bar chart pane, select Descending for the Category order, Y-axis for the Bar type and click OK to close the Chart widget editor. A second bar chart is then displayed showing you the solution results for scenario 2.
5. Re-edit the chart and select @Scenario in the Split by field of the Bar chart pane. You then obtain both scenarios in the same bar chart:
.
You can select many different types of charts in the Chart widget editor.
Alternatively using the Vega Chart widget, you can similarly choose Solution data>solution to display the same data, select value and name in both the x and y fields in the Chart section of the Vega Chart widget editor. Then, in the Mark section, select @Scenario for the color field. This selection gives you the following bar chart with the two scenarios on the same y-axis, distinguished by different colors.
.
If you re-edit the chart and select @Scenario for the column facet, you obtain the two scenarios in separate charts side-by-side as follows:
You can use many different types of charts that are available in the Mark field of the Vega Chart widget editor.
You can also select the JSON tab in all the widget editors and configure your charts by using the JSON code. A more advanced example of JSON code is provided in the [Vega Chart widget specifications](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_hdc_5mm_33b) section.
The following widgets are available:
* [Notes widget](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_edc_5mm_33b)
Add simple text notes to the Visualization view.
* [Table widget](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_fdc_5mm_33b)
Present input data and solution in tables, with a search and filtering feature. See [Table search and filtering](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_tablefilter).
* [Charts widgets](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_alh_lfn_l2b)
Present input data and solution in charts.
* [Gantt chart widget](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=entopic_visualization__section_idc_5mm_33b)
Display the solution to a scheduling problem (or any other type of suitable problem) in a Gantt chart.
This widget is used automatically for scheduling problems that are modeled with the Modeling Assistant. You can edit this Gantt chart or create and configure new Gantt charts for any problem even for those models that don't use the Modeling Assistant.
| # Visualization view #
With the Decision Optimization experiment Visualization view, you can configure the graphical representation of input data and solutions for one or several scenarios\.
Quick links:
<!-- <ul> -->
* [Visualization view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section-dashboard)
* [Table search and filtering](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_tablefilter)
* [Visualization widgets syntax](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_widgetssyntax)
* [Visualization Editor](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__viseditor)
* [Visualization pages](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__vispages)
<!-- </ul> -->
The Visualization view is common to all scenarios in a Decision Optimization experiment\.
For example, the following image shows the default bar chart that appears in the solution tab for the example that is used in the tutorial [Solving and analyzing a model: the diet problem](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveModel.html#task_mtg_n3q_m1b)\.
The Visualization view helps you compare different scenarios to validate models and business decisions\.
For example, to show the two scenarios solved in this diet example tutorial, you can add another bar chart as follows:
<!-- <ol> -->
1. Click the chart widget and configure it by clicking the pencil icon\.
2. In the Chart widget editor, select Add scenario and choose scenario 1 (assuming that your current scenario is scenario 2) so that you have both scenario 1 and scenario 2 listed\.
3. In the Table field, select the Solution data option and select solution from the drop\-down list\.
4. In the bar chart pane, select Descending for the Category order, Y\-axis for the Bar type and click OK to close the Chart widget editor\. A second bar chart is then displayed showing you the solution results for scenario 2\.
5. Re\-edit the chart and select @Scenario in the Split by field of the Bar chart pane\. You then obtain both scenarios in the same bar chart:
<!-- </ol> -->
\.
You can select many different types of charts in the Chart widget editor\.
Alternatively using the Vega Chart widget, you can similarly choose Solution data>solution to display the same data, select value and name in both the x and y fields in the Chart section of the Vega Chart widget editor\. Then, in the Mark section, select @Scenario for the color field\. This selection gives you the following bar chart with the two scenarios on the same y\-axis, distinguished by different colors\.
\.
If you re\-edit the chart and select @Scenario for the column facet, you obtain the two scenarios in separate charts side\-by\-side as follows:
You can use many different types of charts that are available in the Mark field of the Vega Chart widget editor\.
You can also select the JSON tab in all the widget editors and configure your charts by using the JSON code\. A more advanced example of JSON code is provided in the [Vega Chart widget specifications](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_hdc_5mm_33b) section\.
The following widgets are available:
<!-- <ul> -->
* [**Notes widget**](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_edc_5mm_33b)
Add simple text notes to the Visualization view.
* [**Table widget**](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_fdc_5mm_33b)
Present input data and solution in tables, with a search and filtering feature. See [Table search and filtering](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_tablefilter).
* **[Charts widgets](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_alh_lfn_l2b)**
Present input data and solution in charts.
* [**Gantt chart widget**](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html?context=cdpaas&locale=en#topic_visualization__section_idc_5mm_33b)
Display the solution to a scheduling problem (or any other type of suitable problem) in a Gantt chart.
This widget is used automatically for scheduling problems that are modeled with the Modeling Assistant. You can edit this Gantt chart or create and configure new Gantt charts for any problem even for those models that don't use the Modeling Assistant.
<!-- </ul> -->
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<title>Decision Optimization Visualization view</title>
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<h1 class="topictitle1" id="topic_visualization__title__1"><span class="ph" data-hd-product="cloud wx"><span class="keyword">Visualization view</span></span></h1>
<div class="body">
<p class="shortdesc">With the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> <span class="keyword">Visualization view</span>, you can configure the graphical representation of input data and solutions for one or several scenarios.</p>
<div class="bodydiv">
<p>Quick links:</p>
<ul id="topic_visualization__ul_ggy_4qy_m3b">
<li><a href="#topic_visualization__section-dashboard">Visualization view</a></li>
<li><a href="#topic_visualization__section_tablefilter">Table search and filtering</a></li>
<li><a href="#topic_visualization__section_widgetssyntax">Visualization widgets syntax</a></li>
<li><a href="#topic_visualization__viseditor">Visualization Editor</a></li>
<li><a href="#topic_visualization__vispages">Visualization pages</a></li>
</ul>
</div>
<p>The <span class="keyword">Visualization view</span> is common to all scenarios in a Decision Optimization <span class="keyword">experiment</span>.</p>
<div class="section" id="topic_visualization__section-dashboard">
<p>For example, the following image shows the default bar chart that appears in the solution tab for the example that is used in the tutorial <a href="../DODS_Notebooks/solveModel.html#task_mtg_n3q_m1b" title="This example shows you how to create and solve a Python-based model by using a sample.">Solving and analyzing a model: the diet problem</a>.</p>
<p><img data-hd-product="cloud wx" id="topic_visualization__image_lgg_kdq_h3b" src="images/Cloudvisualization.jpg" alt="Visualization panel showing solution in table and bar chart"></p>
<p>The <span class="keyword">Visualization view</span> helps you compare different scenarios to validate models and business decisions.</p>
<div class="p">
For example, to show the two scenarios solved in this diet example tutorial, you can add another bar chart as follows:
<ol>
<li>Click the chart widget and configure it by clicking the pencil icon.</li>
<li>In the Chart widget editor, select <span class="ph uicontrol">Add scenario</span> and choose <span class="ph uicontrol">scenario 1</span> (assuming that your current scenario is scenario 2) so that you have both scenario 1 and scenario 2 listed.</li>
<li>In the Table field, select the <span class="ph uicontrol">Solution data</span> option and select <span class="ph uicontrol">solution</span> from the drop-down list.</li>
<li>In the bar chart pane, select <span class="ph uicontrol">Descending</span> for the <span class="ph uicontrol">Category order</span>, <span class="ph uicontrol">Y-axis</span> for the <span class="ph uicontrol">Bar type</span> and click <span class="ph uicontrol">OK</span> to close the Chart widget editor. A second bar chart is then displayed showing you the solution results for scenario 2.</li>
<li>Re-edit the chart and select <span class="ph uicontrol">@Scenario</span> in the <span class="ph uicontrol">Split by</span> field of the Bar chart pane. You then obtain both scenarios in the same bar chart:</li>
</ol>
</div>
<p><img id="topic_visualization__image_rh4_cby_smb" src="images/ChartVisu2Scen.png" alt="Chart with two scenarios displayed in one chart.">.</p>
<p>You can select many different types of charts in the Chart widget editor.</p>
<p>Alternatively using the Vega Chart widget, you can similarly choose <span class="ph uicontrol">Solution data>solution</span> to display the same data, select value and name in both the x and y fields in the Chart section of the Vega Chart widget editor. Then, in the Mark section, select @Scenario for the color field. This selection gives you the following bar chart with the two scenarios on the same y-axis, distinguished by different colors.</p>
<p><img id="topic_visualization__image_o51_pcy_smb" src="images/VegaChart2Scen.jpg" alt="Vega chart showing 2 scenarios">.</p>
<p>If you re-edit the chart and select @Scenario for the column facet, you obtain the two scenarios in separate charts side-by-side as follows:</p>
<p><img id="topic_visualization__image_lbg_ldy_smb" src="images/VegaChart2Scen2.jpg" alt="Vega charts showing 2 scenarios side by side."></p>
<p>You can use many different types of charts that are available in the <span class="ph uicontrol">Mark</span> field of the Vega Chart widget editor.</p>
<p>You can also select the JSON tab in all the widget editors and configure your charts by using the JSON code. A more advanced example of JSON code is provided in the <a href="#topic_visualization__section_hdc_5mm_33b">Vega Chart widget specifications</a> section.</p>
<p>The following widgets are available:</p>
<div class="p">
<ul id="topic_visualization__ul_h1k_4mk_m3b">
<li><a href="#topic_visualization__section_edc_5mm_33b"><strong>Notes widget</strong></a>
<p>Add simple text notes to the <span class="keyword">Visualization view</span>.</p></li>
<li>
<p><a href="#topic_visualization__section_fdc_5mm_33b"><strong>Table widget</strong></a></p>
<p>Present input data and solution in tables, with a search and filtering feature. See <a href="#topic_visualization__section_tablefilter">Table search and filtering</a>.</p></li>
<li><strong><a href="#topic_visualization__section_alh_lfn_l2b">Charts widgets</a></strong>
<p>Present input data and solution in charts.</p></li>
<li><a href="#topic_visualization__section_idc_5mm_33b"><strong>Gantt chart widget</strong></a>
<p>Display the solution to a scheduling problem (or any other type of suitable problem) in a Gantt chart.</p>
<p>This widget is used automatically for scheduling problems that are modeled with the <span class="keyword">Modeling Assistant</span>. You can edit this Gantt chart or create and configure new Gantt charts for any problem even for those models that don't use the <span class="keyword">Modeling Assistant</span>.</p></li>
</ul>
</div>
</div>
<section class="section" role="region" aria-labelledby="topic_visualization__viseditor__title__1" id="topic_visualization__viseditor">
<h2 class="sectiontitle" id="topic_visualization__viseditor__title__1"><span class="keyword">Visualization Editor</span></h2>
<p>You can edit the widgets in the <strong><span class="keyword">Visualization Editor</span></strong> by clicking the Configure widget (pencil) icon in a widget. You can then customize it either in the Editor or by editing the JSON code.</p>
<p>In the Editor, you can easily change the name of your widget and select the source of the data you want to display in your <span class="keyword">Visualization view</span>.</p>
<p>As you modify a widget in the <span class="keyword">Visualization Editor</span>, a preview is also displayed showing you your changes. You can then choose to save your changes by clicking OK, which closes the <span class="keyword">Visualization Editor</span>, or you can select Cancel to abandon your changes.</p>
<p>The JSON editor gives you more advanced editing possibilities. For more information about the JSON widget syntax, see the following section: <span class="ph"><a href="#topic_visualization__section_widgetssyntax">Visualization widgets syntax</a></span>.</p>
<p>You can download your <span class="keyword">Visualization view</span> as a JSON file, containing the definitions and the data, making it easier for you to share your findings with your collaborators.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__vispages__title__1" id="topic_visualization__vispages">
<h2 class="sectiontitle" id="topic_visualization__vispages__title__1"><span class="keyword">Visualization</span> pages</h2>
<p>You can create different pages for different scenarios or combine scenarios on the same page.</p>
<p>You can add pages by double-clicking the plus sign. You can then customize what is displayed on each page.</p>
<p>To edit a page, click the Edit (pencil) icon. In the <span class="keyword">Visualization Editor</span> you can edit the page name, reorder, and add pages. Clicking OK in the <span class="keyword">Visualization Editor</span> saves your updates and closes the editor. Or you can select Cancel to abandon your changes.</p>
<p>To delete a page, click the page tab and a delete button appears in the tab.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__section_tablefilter__title__1" id="topic_visualization__section_tablefilter">
<h2 class="sectiontitle" id="topic_visualization__section_tablefilter__title__1">Table search and filtering</h2>
<p>You can filter tables (in both <span class="keyword">Prepare data</span> <span class="keyword">view</span> and the <span class="keyword">Visualization view</span>) by clicking the search icon and entering a value to search on. You can also specify a column name, colon, and a value. For example, if you enter <code class="ph codeph">food:hot</code> in the <code class="ph codeph">diet_food_nutrients</code> table search field, the table is filtered to display only the rows that contain the food "hot". In this example, you obtain just one row that contains the food <code class="ph codeph">Hotdog</code>. You can also enter the prefix of a column name. For example, entering <code class="ph codeph">fo:hot</code> obtains the same result as <code class="ph codeph">food:hot</code>. The column name is optional, so you can also enter <code class="ph codeph">hot</code> in this case. If the column name is not specified, all columns are searched and the corresponding rows are obtained. For example, if you enter 0, in the <code class="ph codeph">diet_food_nutrients</code> table you obtain four rows that contain this value in one of the columns. You can also filter by using numeric values as follows:</p>
<div class="p">
<table summary="" id="topic_visualization__simpletable_i5q_1vd_n2b" class="defaultstyle">
<colgroup>
<col style="width:50%">
<col style="width:50%">
</colgroup>
<thead>
<tr>
<th style="vertical-align:bottom;text-align:left;" id="topic_visualization__simpletable_i5q_1vd_n2b__stentry__1">Enter in search field</th>
<th style="vertical-align:bottom;text-align:left;" id="topic_visualization__simpletable_i5q_1vd_n2b__stentry__2">Result: rows displayed containing column values</th>
</tr>
</thead>
<tbody>
<tr>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__1">column_name<strong>:12</strong></td>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__2">equal to 12</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__1">column_name<strong>:10..</strong></td>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__2">greater than or equal to 10</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__1">column_name<strong>:..10</strong></td>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__2">less than or equal to 10</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__1">column_name<strong>:15..25</strong></td>
<td style="vertical-align:top;" headers="topic_visualization__simpletable_i5q_1vd_n2b__stentry__2">between 15 and 25</td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__section_widgetssyntax__title__1" id="topic_visualization__section_widgetssyntax">
<h2 class="sectiontitle" id="topic_visualization__section_widgetssyntax__title__1"><span class="keyword">Visualization</span> widgets syntax</h2>
<p>The widget syntax can be useful for customizing widgets beyond the functionality that is provided by the JSON editor.</p>
<p>The basic widget syntax is as follows:</p>
<pre class="codeblock"><code>{
"name": "Widget Title",
"type": "WidgetType",
"props": {}
}</code></pre>
<div class="tablenoborder">
<table summary="" id="topic_visualization__table_gkd_y1g_4bb" class="defaultstyle">
<caption>
<span class="tablecap">Table 1. Basic widget syntax</span>
</caption>
<colgroup>
<col style="width:50%">
<col style="width:50%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="topic_visualization__table_gkd_y1g_4bb__entry__1"> </th>
<th id="topic_visualization__table_gkd_y1g_4bb__entry__2">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="topic_visualization__table_gkd_y1g_4bb__entry__1 ">
<p><code class="ph codeph">name</code></p></td>
<td headers="topic_visualization__table_gkd_y1g_4bb__entry__2 ">
<p>Defines the widget title, which is displayed in the widget header.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_gkd_y1g_4bb__entry__1 ">
<p><code class="ph codeph">type</code></p></td>
<td headers="topic_visualization__table_gkd_y1g_4bb__entry__2 ">
<p>Defines the widget type.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_gkd_y1g_4bb__entry__1 ">
<p><code class="ph codeph">props</code></p></td>
<td headers="topic_visualization__table_gkd_y1g_4bb__entry__2 ">
<p>Defines the properties of the widget. The properties vary depending on the <code class="ph codeph">type</code> of widget.</p></td>
</tr>
</tbody>
</table>
</div>
<p>The basic widget syntax for widgets that are connected to data, such as tables and charts, is as follows:</p>
<pre class="codeblock"><code>{
"name": "Table Cars",
"type": "Table",
"props": {
"container": "",
"data": "cars",
"spec": {},
"search": ""
}
}</code></pre>
<div class="tablenoborder">
<table summary="" id="topic_visualization__table_n5v_gbg_4bb" class="defaultstyle">
<caption>
<span class="tablecap">Table 2. Basic syntax for widgets connected to data</span>
</caption>
<colgroup>
<col style="width:33.33333333333333%">
<col style="width:66.66666666666666%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="topic_visualization__table_n5v_gbg_4bb__entry__1"> </th>
<th id="topic_visualization__table_n5v_gbg_4bb__entry__2">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__1 ">
<p><code class="ph codeph">data</code></p></td>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__2 ">
<p>You usually specify <code class="ph codeph">data</code>. <code class="ph codeph">data</code> refers to the table from which you want to extract data.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__1 ">
<p><code class="ph codeph">spec</code></p></td>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__2 ">
<p>You usually leave <code class="ph codeph">spec</code> empty. The <span class="keyword">Visualization</span> generates a default <code class="ph codeph">spec</code> as a starting point.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__1 "><code class="ph codeph">container</code></td>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__2 ">
<p>Optionally specify <code class="ph codeph">container</code>. If <code class="ph codeph">container</code> equals <code class="ph codeph">""</code> or <code class="ph codeph">"$current-scenario"</code>, it references the current scenario. The latter is useful when you have multiple scenarios.</p>
<p><code class="ph codeph">container</code> can reference another scenario in the same Decision Optimization <span class="keyword">experiment</span> by its name: <code class="ph codeph">"container":"Scenario 1"</code>. It can also reference a list of different scenarios: <code class="ph codeph">"container":["Scenario April","Scenario June"]</code>.</p>
<p>To aggregate all the scenarios contained in a Decision Optimization <span class="keyword">experiment</span>, use <code class="ph codeph">"container":"*"</code>. <code class="ph codeph">container</code> also supports the following syntax: <code class="ph codeph">"container":"/regex/"</code> where all the scenarios with names that contain <code class="ph codeph">regex</code> will be referenced. Add <code class="ph codeph">i</code> after the forward slash to ignore case differences, for example <code class="ph codeph">"container":"/april/i"</code> will reference all scenarios with names that contain <code class="ph codeph">april</code> or <code class="ph codeph">April</code>.</p>
<p>The rows of the listed scenarios are concatenated in a single table, with an extra column <code class="ph codeph">$scenario</code> containing the name of the scenario.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__1 ">
<p><code class="ph codeph">search</code></p></td>
<td headers="topic_visualization__table_n5v_gbg_4bb__entry__2 ">
<p>Saves the content of the search-text field.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__section_edc_5mm_33b__title__1" id="topic_visualization__section_edc_5mm_33b">
<h2 class="sectiontitle" id="topic_visualization__section_edc_5mm_33b__title__1">Notes widget specifications</h2>
<p>The Notes widget can be styled as a post-it note, as shown in the following code sample:</p>
<pre class="codeblock"><code>{
"name": "Notes",
"type": "Notes",
"props": {
"notes": "My post-it note",
"style": {
"background": "#ffe"
},
"headerStyle": {
"background": "#ffe"
}
}
}</code></pre>
<p>This style example is applicable to other widgets.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__section_fdc_5mm_33b__title__1" id="topic_visualization__section_fdc_5mm_33b">
<h2 class="sectiontitle" id="topic_visualization__section_fdc_5mm_33b__title__1">Table widget specifications</h2>
<p>Table widget specifications are composed of a list of columns that follow this syntax:</p>
<pre class="codeblock"><code>{
"name": "Table Cars",
"type": "Table",
"property": "Acceleration",
"label": "Acceleration",
"type": "Number",
"visible": true,
"width": 100,
"style": {}
}</code></pre>
<div class="tablenoborder">
<table summary="" id="topic_visualization__table_tzf_f1g_4bb" class="defaultstyle">
<caption>
<span class="tablecap">Table 3. Table widget specifications</span>
</caption>
<colgroup>
<col style="width:39.0625%">
<col style="width:60.9375%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="topic_visualization__table_tzf_f1g_4bb__entry__1"> </th>
<th id="topic_visualization__table_tzf_f1g_4bb__entry__2">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__1 ">
<p><code class="ph codeph">property</code></p></td>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__2 ">
<p>Key property to access data in the specified row.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__1 ">
<p><code class="ph codeph">type</code></p></td>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__2 ">
<p>Required to have a proper search and filter feature, as numbers are not searched like strings.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__1 ">
<p><code class="ph codeph">visible</code></p></td>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__2 ">
<p>Allows the display or hiding of any column without completely removing its definition.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__1 ">
<p><code class="ph codeph">label</code></p></td>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__2 ">
<p>Defines the column headings.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__1 ">
<p><code class="ph codeph">style</code></p></td>
<td headers="topic_visualization__table_tzf_f1g_4bb__entry__2 ">
<p>Allows tables to be styled by adding CSS properties in camel case. For example, if you want to specify the alignment of text in a table, use <code class="ph codeph">textAlign</code> rather than <code class="ph codeph">text-align</code>.</p></td>
</tr>
</tbody>
</table>
</div>
<p>You can customize the rendering of tables by using the following elements:</p>
<div class="tablenoborder">
<table summary="" id="topic_visualization__table_r4j_41g_4bb" class="defaultstyle">
<caption>
<span class="tablecap">Table 4. Table widget customization</span>
</caption>
<colgroup>
<col style="width:39.525691699604735%">
<col style="width:60.47430830039525%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="topic_visualization__table_r4j_41g_4bb__entry__1"> </th>
<th id="topic_visualization__table_r4j_41g_4bb__entry__2">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__1 ">
<p><code class="ph codeph">numbered</code></p></td>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__2 ">
<p>Displays row numbers.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__1 ">
<p><code class="ph codeph">compact</code></p></td>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__2 ">
<p>Reduces row height.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__1 ">
<p><code class="ph codeph">columnExpand</code></p></td>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__2 ">
<p>Set <code class="ph codeph">columnExpand</code> to <code class="ph codeph">true</code> to expand column widths to fit into the widget.</p></td>
</tr>
<tr>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__1 ">
<p><code class="ph codeph">columnShrink</code></p></td>
<td headers="topic_visualization__table_r4j_41g_4bb__entry__2 ">
<p>Set <code class="ph codeph">columnShrink</code> to <code class="ph codeph">true</code> to shrink the column widths to fit into the widget.</p></td>
</tr>
</tbody>
</table>
</div>
<p>Search and filtering feature are available in tables. To search content in a table, click the search icon <img id="topic_visualization__image_gdc_5mm_33b" src="images/SearchIcon.jpg" alt="search icon">. You can limit your search to a specific column by adding a prefix to your search as follows: <code class="ph codeph">"column heading":"search"</code>, for example <code class="ph codeph">name:chevrolet</code>. To search values that range from x to x, use <code class="ph codeph">10..20</code>. You can also search values greater than x, for example <code class="ph codeph">10..</code>, and values less than x, for example <code class="ph codeph">..20</code>. For more information about table filtering, see <a href="#topic_visualization__section_tablefilter">Table search and filtering</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__section_alh_lfn_l2b__title__1" id="topic_visualization__section_alh_lfn_l2b">
<h2 class="sectiontitle" id="topic_visualization__section_alh_lfn_l2b__title__1">Charts widgets</h2>
<p>You can use two types of chart widgets: <strong>Vega Charts</strong> and <strong>Charts</strong>. Different types of charts are available when you open these Chart widget editors.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__section_hdc_5mm_33b__title__1" id="topic_visualization__section_hdc_5mm_33b">
<h2 class="sectiontitle" id="topic_visualization__section_hdc_5mm_33b__title__1">Vega Chart widget specifications</h2>
<p>The Vega Chart widget uses Vega-Lite specifications to create different types of chart (bar charts, point charts, and so on.). If you leave <code class="ph codeph">spec</code> empty, a simple bar chart is automatically generated with the first string column in x-axis and the first number in y-axis.</p>
<p>Vega-Lite enables data filtering and transformation. For example, strings can be transformed into dates.</p>
<p>To learn more about Vega-Lite, see <a href="https://vega.github.io/vega-lite/" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Vega-Lite - A High-Level Visualization Grammar</a>.</p>
<p>The following is an advanced example of a grouped bar chart based on a population data table. This example shows how to filter data and how to compute a virtual column:</p>
<pre class="codeblock"><code>{
"name": "Population Grouped Bar Chart",
"type": "Chart",
"props": {
"data": "population",
"spec": {
"transform": [
{
"filter": "datum.year == 2000"
},
{
"calculate": "datum.sex == 2 ? 'Female' : 'Male'",
"as": "gender"
}
],
"mark": "bar",
"encoding": {
"column": {
"field": "age",
"type": "ordinal"
},
"y": {
"aggregate": "sum",
"field": "people",
"type": "quantitative",
"axis": {
"title": "population",
"grid": false
}
},
"x": {
"field": "gender",
"type": "nominal",
"scale": {
"rangeStep": 12
},
"axis": {
"title": ""
}
},
"color": {
"field": "gender",
"type": "nominal",
"scale": {
"range": [
"#EA98D2",
"#659CCA"
]
}
}
},
"config": {
"facet": {
"cell": {
"strokeWidth": 0
}
},
"axis": {
"domainWidth": 1
}
}
}
}
}
</code></pre>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__section_idc_5mm_33b__title__1" id="topic_visualization__section_idc_5mm_33b">
<h2 class="sectiontitle" id="topic_visualization__section_idc_5mm_33b__title__1">Gantt chart widget</h2>
<p>Gantt charts are automatically created to display the solution for scheduling problems created with the <span class="keyword">Modeling Assistant</span>. You can, however, edit, create and configure Gantt charts for any data where it is meaningful, using the Gantt widget. Use the JSON editor for this, by clicking the pencil icon and selecting the JSON pane.</p>
<div class="p">
The Gantt chart automatically generated for the <span class="keyword">Modeling Assistant</span> uses the following JSON code. The <code class="ph codeph">data</code> field value <code class="ph codeph">$cognitive-gantt</code> here dynamically enables the Gantt widget to connect to the <span class="keyword">Modeling Assistant</span> solution. This dynamic loading can also imply that it might take a moment for the Gantt chart to appear.
<pre class="codeblock"><code>{
"name": "",
"type": "Gantt",
"props": {
"container": "",
"data": "$cognitive-gantt",
"spec": {},
"search": ""
}
}</code></pre>
</div>
<div class="p">
To define your own Gantt chart, you must edit the JSON code and provide the names of 3 of your tables to define the <code class="ph codeph">resources</code>, <code class="ph codeph">activities</code> and <code class="ph codeph">reservations</code> data, as follows:
<pre class="codeblock"><code> "data": [
"<em>resourcesTableName</em>",
"<em>activitiesTableName</em>",
"<em>reservationsTableName</em>"
],</code></pre>
</div>
<div class="p">
Also provide a <code class="ph codeph">spec</code> section to define these tables, as follows. The <code class="ph codeph">parent</code> fields are optional but all other fields are mandatory.
<pre class="codeblock"><code> "resources": {
"data": "<em>resourcesTableName</em>",
"id": "<em>id</em>",
"parent": "<em>parent</em>",
"name": "<em>name</em>"
},
"activities": {
"data": "<em>activitiesTableName</em>",
"id": "<em>id</em>",
"name": "<em>name</em>",
"start": "<em>start</em>",
"end": "<em>end</em>",
"parent": "<em>parent</em>"
},
"reservations": {
"data": "<em>reservationsTableName</em>",
"activity": "<em>activity</em>",
"resource": "<em>resource</em>"
},</code></pre>
</div>
<p>Another mandatory field that you must also define in the specification is the <code class="ph codeph"><strong>dateFormat</strong></code> so that all the common date formats can get converted into real dates. Some common date formats are for example '<code class="ph codeph">yyyy-MM-dd</code>', '<code class="ph codeph">yyyy-MM-dd HH:mm:ss</code>', and so on. You can also use <code class="ph codeph">S</code> for milliseconds for Epoch time, for example, <code class="ph codeph">"dateFormat": "S"</code>. The <code class="ph codeph">dateFormat</code> must match the <code class="ph codeph">"start"</code> and <code class="ph codeph">"end"</code> fields of the <code class="ph codeph">"activity"</code> table.</p>
<p>The error message <span class="ph uicontrol">No time window defined</span> is displayed until you define the <code class="ph codeph">activity</code> table, with <code class="ph codeph">start</code> and <code class="ph codeph">end</code> fields that use the specified <code class="ph codeph">dateFormat</code>.</p>
<div class="p">
There are also some optional fields available:
<ul id="topic_visualization__ul_pyd_qmk_4mb">
<li>
<p><strong>resourceQuantity</strong> where you can configure the quantity column in the resources table to enable the Gantt chart to get the necessary information to populate a Load Resource Chart for you. You can set this column as follows: <code class="ph codeph"> "resourceQuantity": "quantity"</code></p></li>
<li>You can also set the <strong>type</strong> of Gantt chart that you need: <code class="ph codeph">ActivityChart</code> or <code class="ph codeph">ScheduleChart</code>. You can set the type as follows:<code class="ph codeph">"type": "ActivityChart",</code>. You can also omit this setting and the default is <code class="ph codeph">ScheduleChart</code>. If you choose to have an <code class="ph codeph">ActivityChart</code>, you must provide more information concerning the constraints table name and the mapping for this table:
<pre class="codeblock"><code> "constraints": {
"data": "<em>constraintsTableName</em>",
"from": "<em>from</em>",
"to": "<em>to</em>",
"type": "<em>type</em>"
},</code></pre>
<div class="p">
The <code class="ph codeph">from</code> and <code class="ph codeph">to</code> values are the column names in your constraints table that define the order of precedence of tasks. The type values here correspond to the Gantt-chart library values 0 to 3:
<pre class="codeblock"><code>START_TO_START: 0,
START_TO_END: 2,
END_TO_END: 3,
END_TO_START: 1,</code></pre> For example, if your <code class="ph codeph">to</code> task starts after the end of your <code class="ph codeph">from</code> task, select 1 as the type value.
</div></li>
</ul>
</div>
<div class="p">
<code class="ph codeph">ScheduleChart</code> example
<pre class="codeblock"><code>{
"name": "",
"type": "Gantt",
"props": {
"container": "",
"data": [
"resources",
"activities",
"reservations"
],
"spec": {
"resources": {
"data": "resources",
"id": "id",
"parent": "parent",
"name": "name"
},
"activities": {
"data": "activities",
"id": "id",
"name": "name",
"start": "start",
"end": "end",
"parent": "parent"
},
"reservations": {
"data": "reservations",
"activity": "activity",
"resource": "resource"
},
"dateFormat": "S",
"resourceQuantity": "quantity"
},
"search": ""
}
}</code></pre>
</div>
<div class="p">
<code class="ph codeph">ActivityChart</code> example
<pre class="codeblock"><code>{
"name": "",
"type": "Gantt",
"props": {
"container": "",
"data": [
"resources",
"activities",
"reservations",
"constraints"
],
"spec": {
"type": "ActivityChart",
"resources": {
"data": "resources",
"id": "id",
"parent": "parent",
"name": "name"
},
"activities": {
"data": "activities",
"id": "id",
"name": "name",
"start": "start",
"end": "end",
"parent": "parent"
},
"constraints": {
"data": "constraints",
"from": "from",
"to": "to",
"type": "type"
},
"reservations": {
"data": "reservations",
"activity": "activity",
"resource": "resource"
},
"dateFormat": "S",
"resourceQuantity": "quantity"
},
"search": ""
}
}</code></pre>
</div>
<p>The error message <span class="ph uicontrol">No time window defined</span> is displayed until you define the <code class="ph codeph">activity</code> table, with <code class="ph codeph">start</code> and <code class="ph codeph">end</code> fields that use the specified <code class="ph codeph">dateFormat</code>.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_visualization__title__15">
<h2 class="sectiontitle" id="topic_visualization__title__15">Learn more</h2>
<p>For other type of charts, see <a href="../../dataview/idh_idc_cg_help_main.html">Visualizing your data</a>.</p>
</section>
</div>
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33923FE20855D3EA3850294C0FB447EC3F1B7BDF | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/buildingmodels.html?context=cdpaas&locale=en | Decision Optimization experiments | Decision Optimization experiments
If you use the Decision Optimization experiment UI, you can take advantage of its many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with Watson Machine Learning.
The Decision Optimization experiment UI facilitates workflow. Here you can:
* Select and edit the data relevant for your optimization problem, see [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_preparedata)
* Create, import, edit and solve Python models in the Decision Optimization experiment UI, see [Decision Optimization notebook tutorial](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveModel.htmltask_mtg_n3q_m1b)
* Create, import, edit and solve models expressed in natural language with the Modeling Assistant, see [Modeling Assistant tutorial](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.htmlcogusercase)
* Create, import, edit and solve OPL models in the Decision Optimization experiment UI, see [OPL models](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.htmltopic_oplmodels)
* Generate a notebook from your model, work with it as a notebook then reload it as a model, see [Generating a notebook from a scenario](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__generateNB) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_overview)
* Visualize data and solutions, see [Explore solution view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__solution)
* Investigate and compare solutions for multiple scenarios, see [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__scenariopanel) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_overview)
* Easily create and share reports with tables, charts and notes using widgets provided in the [Visualization Editor](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.htmltopic_visualization)
* Save models that are ready for deployment in Watson Machine Learning, see [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__scenariopanel) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_overview)
See the [Decision Optimization experiment UI comparison table](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOintro.htmlDOIntro__comparisontable) for a list of features available with and without the Decision Optimization experiment UI.
See [Views and scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface) for a description of the user interface and scenario management.
| # Decision Optimization experiments #
If you use the Decision Optimization experiment UI, you can take advantage of its many features in this user\-friendly environment\. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with Watson Machine Learning\.
The Decision Optimization experiment UI facilitates workflow\. Here you can:
<!-- <ul> -->
* Select and edit the data relevant for your optimization problem, see [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_preparedata)
* Create, import, edit and solve Python models in the Decision Optimization experiment UI, see [Decision Optimization notebook tutorial](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveModel.html#task_mtg_n3q_m1b)
* Create, import, edit and solve models expressed in natural language with the Modeling Assistant, see [Modeling Assistant tutorial](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html#cogusercase)
* Create, import, edit and solve OPL models in the Decision Optimization experiment UI, see [OPL models](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.html#topic_oplmodels)
* Generate a notebook from your model, work with it as a notebook then reload it as a model, see [Generating a notebook from a scenario](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__generateNB) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview)
* Visualize data and solutions, see [Explore solution view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__solution)
* Investigate and compare solutions for multiple scenarios, see [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__scenariopanel) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview)
* Easily create and share reports with tables, charts and notes using widgets provided in the [Visualization Editor](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/Visualization.html#topic_visualization)
* Save models that are ready for deployment in Watson Machine Learning, see [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__scenariopanel) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview)
<!-- </ul> -->
See the [Decision Optimization experiment UI comparison table](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOintro.html#DOIntro__comparisontable) for a list of features available with and without the Decision Optimization experiment UI\.
See [Views and scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface) for a description of the user interface and scenario management\.
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<h1 class="topictitle1" id="topic_buildingmodels__title__1"><span class="keyword">Decision Optimization</span> experiments</h1>
<div class="body">
<p class="shortdesc">If you use the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, you can take advantage of its many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>.</p>
<p>The <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> facilitates workflow. Here you can:</p>
<ul id="topic_buildingmodels__ul_vr5_vpx_fdb">
<li>Select and edit the data relevant for your optimization problem, see <a href="modelbuilderUI.html#ModelBuilderInterface__section_preparedata">Prepare data view</a></li>
<li>Create, import, edit and solve Python models in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, see <a href="../DODS_Notebooks/solveModel.html#task_mtg_n3q_m1b" title="This example shows you how to create and solve a Python-based model by using a sample."><span class="keyword">Decision Optimization</span> <span class="keyword">notebook</span> tutorial</a></li>
<li>Create, import, edit and solve models expressed in natural language with the <span class="keyword">Modeling Assistant</span>, see <a href="../DODS_Mdl_Assist/exhousebuild.html#cogusercase" title="This tutorial shows you how to use the Modeling Assistant to define, formulate and run a model for a house construction scheduling problem. The completed model with data is also provided in the DO-samples, see Importing Model Builder samples."><span class="keyword">Modeling Assistant</span> tutorial</a></li>
<li>Create, import, edit and solve OPL models in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, see <a href="OPLmodels.html#topic_oplmodels" title="You can build OPL models in the Decision Optimization experiment UI in watsonx.ai.">OPL models</a></li>
<li>Generate a <span class="keyword">notebook</span> from your model, work with it as a <span class="keyword">notebook</span> then reload it as a model, see <a href="modelbuilderUI.html#ModelBuilderInterface__generateNB">Generating a notebook from a scenario</a> and <a href="modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a></li>
<li>Visualize data and solutions, see <a href="modelbuilderUI.html#ModelBuilderInterface__solution">Explore solution view</a></li>
<li>Investigate and compare solutions for multiple scenarios, see <a href="modelbuilderUI.html#ModelBuilderInterface__scenariopanel">Scenario pane</a> and <a href="modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a></li>
<li>Easily create and share reports with tables, charts and notes using widgets provided in the <a href="Visualization.html#topic_visualization" title="With the Decision Optimization experiment Visualization view, you can configure the graphical representation of input data and solutions for one or several scenarios."><span class="keyword">Visualization Editor</span></a></li>
<li>Save models that are ready for deployment in <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>, see <a href="modelbuilderUI.html#ModelBuilderInterface__scenariopanel">Scenario pane</a> and <a href="modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a></li>
</ul>
<p>See the <a href="DOintro.html#DOIntro__comparisontable"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> comparison table</a> for a list of features available with and without the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>.</p>
<p>See <a href="modelbuilderUI.html#ModelBuilderInterface" title="The Decision Optimization experiment UI has different views in which you can select data, create models, solve different scenarios, and visualize the results.">Views and scenarios</a> for a description of the user interface and scenario management.</p>
<section class="section" role="region" aria-labelledby="topic_buildingmodels__section_m3z_4pl_b3b__title__1" id="topic_buildingmodels__section_m3z_4pl_b3b">
<h2 class="sectiontitle" id="topic_buildingmodels__section_m3z_4pl_b3b__title__1">Learn more</h2>
<ul>
<li>
<p>For a step-by-step guide to build, solve and deploy a <span class="keyword">Decision Optimization</span> model, by using the user interface, see the <a href="../../wsj/getting-started/get-started-do.html">Quick start tutorial with video</a>.</p></li>
</ul>
</section>
</div>
<aside role="complementary" aria-labelledby="topic_buildingmodels__title__1">
<nav role="navigation">
<ul class="ullinks">
<li class="ulchildlink"><strong><a href="../DODS_Introduction/modelbuilderUI.html">Decision Optimization experiment views and scenarios</a></strong><br>
The <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> has different <span class="keyword">views</span> in which you can select data, create models, solve different scenarios, and visualize the results.</li>
<li class="ulchildlink"><strong><a href="../DODS_Introduction/configureEnvironments.html#task_hwswconfig">Configuring environments and adding Python extensions</a></strong><br>
You can change your default environment for Python and CPLEX in the <span class="keyword">experiment</span> <span class="keyword">Overview</span>.</li>
<li class="ulchildlink"><strong><a href="../DODS_Introduction/Visualization.html">Visualization view</a></strong><br>
With the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> <span class="keyword">Visualization view</span>, you can configure the graphical representation of input data and solutions for one or several scenarios.</li>
<li class="ulchildlink"><strong><a href="../DODS_Mdl_Assist/exhousebuildintro.html">Modeling Assistant models</a></strong><br>
You can model and solve <span class="keyword">Decision Optimization</span> problems using the <span class="keyword">Modeling Assistant</span> (which enables you to formulate models in natural language). This requires little to no knowledge of Operational Research (OR) and does not require you to write Python code. The <span class="keyword">Modeling Assistant</span> is <strong>only available in English </strong>and is not globalized.</li>
<li class="ulchildlink"><strong><a href="../DODS_Notebooks/solveIntro.html">Python DOcplex models</a></strong><br>
You can solve Python <span><span class="keyword">DOcplex</span></span> models in a <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>.</li>
<li class="ulchildlink"><strong><a href="../DODS_Introduction/OPLmodels.html">OPL models</a></strong><br>
You can build OPL models in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> in <span class="keyword" data-hd-product="wx">watsonx.ai</span>.</li>
<li class="ulchildlink"><strong><a href="../DODS_RunParameters/runparams.html">Run parameters and Environment</a></strong><br>
You can select various run parameters for the optimization solve in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>.</li>
</ul>
<div class="familylinks">
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<strong>Parent topic:</strong> <a href="../DOWS-Cloud_home.html" title="IBM® Decision Optimization gives you access to IBM's industry-leading solution engines for mathematical programming and constraint programming. You can build Decision Optimization models either with notebooks or by using the powerful Decision Optimization experiment UI (Beta version). Here you can import, or create and edit models in Python, in OPL or with natural language expressions provided by the intelligent Modeling Assistant (Beta version). You can also deploy models with Watson Machine Learning.">Decision Optimization</a>
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497007D0D0ABAC3202BBF912A15BFC389066EBDA | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/configureEnvironments.html?context=cdpaas&locale=en | Decision Optimization experiment Python and CPLEX runtime versions and Python extensions | Configuring environments and adding Python extensions
You can change your default environment for Python and CPLEX in the experiment Overview.
Procedure
To change the default environment for DOcplex and Modeling Assistant models:
1. Open the Overview, click  to open the Information pane, and select the Environments tab.
2. Expand the environment section according to your model type. For Python and Modeling Assistant models, expand Python environment. You can see the default Python environment (if one exists). To change the default environment for OPL, CPLEX, or CPO models, expand the appropriate environment section according to your model type and follow this same procedure.
3. Expand the name of your environment, and select a different Python environment.
4. Optional: To create a new environment:
1. Select New environment for Python. A new window opens for you to define your new environment. 
2. Enter a name, and select a CPLEX version, hardware specification, copies (number of nodes), Python version and (optionally) you can set Associate a Python extension to On to include any Python libraries that you want to add.
3. Click New Python extension.
4. Enter a name for your extension in the new Create a Python extension window that opens, and click Create.
5. In the new Configure Python extension window that opens, you can set YAML code to On and enter or edit the provided YAML code.For example, use the provided template to add the custom libraries:
Modify the following content to add a software customization to an environment.
To remove an existing customization, delete the entire content and click Apply.
Add conda channels on a new line after defaults, indented by two spaces and a hyphen.
channels:
- defaults
To add packages through conda or pip, remove the comment on the following line.
dependencies:
Add conda packages here, indented by two spaces and a hyphen.
Remove the comment on the following line and replace sample package name with your package name:
- a_conda_package=1.0
Add pip packages here, indented by four spaces and a hyphen.
Remove the comments on the following lines and replace sample package name with your package name.
- pip:
- a_pip_package==1.0
You can also click Browse to add any Python libraries.
For example, this image shows a dynamic programming Python library that is imported and YAML code set to On.
Click Done.
6. Click Create in the New environment window.
Your chosen (or newly created) environment appears as ticked in the Python environments drop-down list in the Environments tab. The tick indicates that this is the default Python environment for all scenarios in your experiment.
5. Select Manage experiment environments to see a detailed list of all existing environments for your experiment in the Environments tab.
You can use the options provided by clicking the three vertical dots next to an environment to Edit, Set as default, Update in a deployment space or Delete the environment. You can also create a New environment from the Manage experiment environments window, but creating a new environment from this window does not make it the default unless you explicitly set is as the default.
Updating your environment for Python or CPLEX versions: Python versions are regularly updated. If however you have explicitly specified an older Python version in your model, you must update this version specification or your models will not work. You can either create a new Python environment, as described earlier, or edit one from Manage experiment environments. This is also useful if you want to select a different version of CPLEX for your default environment.
6. Click the Python extensions tab.
Here you can view your Python extensions and see which environment it is used in. You can also create a New Python extension or use the options to Edit, Download, and Delete existing ones. If you edit a Python extension that is used by an experiment environment, the environment will be re-created.
You can also view your Python environments in your deployment space assets and any Python extensions you have added will appear in the software specification.
Selecting a different run environment for a particular scenario
You can choose different environments for individual scenarios on the Environment tab of the Run configuration pane.
Procedure
1. Open the Scenario pane and select your scenario in the Build model view.
2. Click the Configure run icon next to the Run button to open the Run configuration pane and select the Environment tab.
3. Choose Select run environment for this scenario, choose an environment from the drop-down menu, and click Run.
4. Open the Overview information pane. You can now see that your scenario has your chosen environment, while other scenarios are not affected by this modification.
| # Configuring environments and adding Python extensions #
You can change your default environment for Python and CPLEX in the experiment Overview\.
## Procedure ##
To change the default environment for DOcplex and Modeling Assistant models:
<!-- <ol> -->
1. Open the Overview, click  to open the Information pane, and select the Environments tab\.
2. Expand the environment section according to your model type\. For Python and Modeling Assistant models, expand Python environment\. You can see the default Python environment (if one exists)\. To change the default environment for OPL, CPLEX, or CPO models, expand the appropriate environment section according to your model type and follow this same procedure\.
3. Expand the name of your environment, and select a different Python environment\.
4. Optional: **To create a new environment**:
<!-- <ol> -->
1. Select New environment for Python. A new window opens for you to define your new environment. 
2. Enter a name, and select a CPLEX version, hardware specification, copies (number of nodes), Python version and (optionally) you can set Associate a Python extension to On to include any Python libraries that you want to add.
3. Click New Python extension.
4. Enter a name for your extension in the new Create a Python extension window that opens, and click Create.
5. In the new Configure Python extension window that opens, you can set YAML code to On and enter or edit the provided YAML code.For example, use the provided template to add the custom libraries:
# Modify the following content to add a software customization to an environment.
# To remove an existing customization, delete the entire content and click Apply.
# Add conda channels on a new line after defaults, indented by two spaces and a hyphen.
channels:
- defaults
# To add packages through conda or pip, remove the comment on the following line.
# dependencies:
# Add conda packages here, indented by two spaces and a hyphen.
# Remove the comment on the following line and replace sample package name with your package name:
# - a_conda_package=1.0
# Add pip packages here, indented by four spaces and a hyphen.
# Remove the comments on the following lines and replace sample package name with your package name.
# - pip:
# - a_pip_package==1.0
You can also click Browse to add any Python libraries.
For example, this image shows a dynamic programming Python library that is imported and YAML code set to On.
Click Done.
6. Click Create in the New environment window.
<!-- </ol> -->
Your chosen (or newly created) environment appears as ticked in the Python environments drop-down list in the Environments tab. The tick indicates that this is the default Python environment for all scenarios in your experiment.
5. Select Manage experiment environments to see a detailed list of all existing environments for your experiment in the Environments tab\.
You can use the options provided by clicking the three vertical dots next to an environment to Edit, Set as default, Update in a deployment space or Delete the environment. You can also create a New environment from the Manage experiment environments window, but creating a new environment from this window does not make it the default unless you explicitly set is as the default.
Updating your environment for Python or CPLEX versions: Python versions are regularly updated. If however you have explicitly specified an older Python version in your model, you must update this version specification or your models will not work. You can either create a new Python environment, as described earlier, or edit one from Manage experiment environments. This is also useful if you want to select a different version of CPLEX for your default environment.
6. Click the Python extensions tab\.
Here you can view your Python extensions and see which environment it is used in. You can also create a New Python extension or use the options to Edit, Download, and Delete existing ones. If you edit a Python extension that is used by an experiment environment, the environment will be re-created.
You can also view your Python environments in your deployment space assets and any Python extensions you have added will appear in the software specification.
<!-- </ol> -->
<!-- <article "class="topic task nested1" role="article" id="task_envscenario" "> -->
## Selecting a different run environment for a particular scenario ##
You can choose different environments for individual scenarios on the Environment tab of the Run configuration pane\.
### Procedure ###
<!-- <ol> -->
1. Open the Scenario pane and select your scenario in the Build model view\.
2. Click the Configure run icon next to the Run button to open the Run configuration pane and select the Environment tab\.
3. Choose Select run environment for this scenario, choose an environment from the drop\-down menu, and click Run\.
4. Open the Overview information pane\. You can now see that your scenario has your chosen environment, while other scenarios are not affected by this modification\.
<!-- </ol> -->
<!-- </article "class="topic task nested1" role="article" id="task_envscenario" "> -->
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<title>Decision Optimization experiment Python and CPLEX runtime versions and Python extensions</title>
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<h1 class="topictitle1" id="task_hwswconfig__title__1"><span class="ph" data-hd-product="cloud wx">Configuring environments and adding Python extensions</span></h1>
<div class="body taskbody">
<p class="shortdesc">You can change your default environment for Python and CPLEX in the <span class="keyword">experiment</span> <span class="keyword">Overview</span>.</p>
<section role="region" class="section prereq" aria-label="Configuring environments and adding Python
extensions: Before you begin">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_hwswconfig__prereq__1">Before you begin</h2>
</div>
<div class="div">
<dl>
<dt class="dlterm">
Required permissions
</dt>
<dd class="dlentry">
To view environments, you can have any role in a deployment space. To edit or create environments, you must have the <span class="ph uicontrol">Editor</span> or <span class="ph uicontrol">Admin</span> role in the space. For more information, see <a href="../../wsj/analyze-data/collaborator-permissions-wml.html"><strong>Deployment space collaborator roles and permissions</strong></a>.
</dd>
</dl>
</div>
</section>
<section class="section context" role="region" aria-label="Configuring environments and adding Python
extensions: About this task">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_hwswconfig__context__1">About this task</h2>
</div>
<p><span class="ph">This video provides a visual method to learn the concepts and tasks in this documentation.</span></p>
<p>After you load the example in your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>, you can follow the video.</p>
<p>Video disclaimer: Some minor steps and graphical steps in this video might differ from your platform. The user interface is also frequently improved.</p><iframe webkitallowfullscreen="" allowfullscreen src="https://video.ibm.com/embed/channel/23952663/video/wx-do-environments-extensions" width="606" height="341" title="This video demonstrates changing environments and adding Python extensions to your Decision
Optimization
experiment"></iframe>
<p>When you use the <span class="keyword">experiment UI</span>, the necessary environments are created for you automatically. However, you can, configure the environment to be used for your solve, by changing the default environment. This environment will then be applied to all scenarios in your <span class="keyword">experiment</span>. The environment will depend on your model type: Python, OPL, CPLEX, CPO, or Modeling Assistant.</p>
<p>Python is used to run Decision Optimization models formulated in <span class="keyword">DOcplex</span> in <span class="keyword">Decision Optimization</span> experiments. <span class="keyword">Modeling Assistant</span> models also use Python because <span class="keyword">DOcplex</span> code is generated when models are run or deployed. Models formulated in OPL or in specific file formats for CPLEX or CP Optimizer, such as LP or CPO formats, do not use Python environments.</p>
<p><span class="ph">The <span class="keyword">Decision Optimization</span> environment currently supports Python <span class="keyword">3.10</span>. The default version is Python <span class="keyword">3.10</span>.</span></p>
<p>The following procedure shows you how to change the default environment for <span class="keyword">DOcplex</span> and <span class="keyword">Modeling Assistant</span> models. This can be useful for checking if your model works with the latest version of CPLEX, or for testing your model with larger data sets that require more hardware. Or perhaps you need to update the Python version or want to include some particular Python libraries using <span class="ph uicontrol">Python extensions</span>.</p>
<p>To select a different run environment for a particular scenario, see <a href="configureEnvironments.html#task_envscenario" title="You can choose different environments for individual scenarios on the Environment tab of the Run configuration pane.">Selecting a different run environment for a particular scenario</a>.</p>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_hwswconfig__steps__1">Procedure</h2>
</div>
<p class="li stepsection">To change the default environment for <span class="keyword">DOcplex</span> and <span class="keyword">Modeling Assistant</span> models:</p>
<ol class="steps">
<li class="step stepexpand" id="task_hwswconfig__OpenOverview"><span class="cmd">Open the <span class="ph uicontrol"><span class="keyword">Overview</span></span>, click <img src="images/infoicon.jpg" alt="information icon"> to open the <span class="ph uicontrol">Information</span> pane, and select the <span class="ph uicontrol">Environments</span> tab. </span>
<div class="itemgroup info">
<p><img id="task_hwswconfig__image_qqc_wl3_psb" src="images/overviewinfoenvirons.png" alt="Environment tab of information pane"></p>
</div></li>
<li class="step stepexpand"><span class="cmd">Expand the environment section according to your model type. For Python and Modeling Assistant models, expand <span class="ph uicontrol">Python environment</span>. You can see the default Python environment (if one exists). To change the default environment for OPL, CPLEX, or CPO models, expand the appropriate environment section according to your model type and follow this same procedure.</span></li>
<li class="step stepexpand"><span class="cmd">Expand the name of your environment, and select a different Python environment.</span></li>
<li class="step stepexpand"><span class="cmd">Optional: <strong>To create a new environment</strong>:</span>
<ol type="a" class="ol substeps">
<li class="li substep substepexpand"><span class="cmd">Select <span class="ph uicontrol">New environment for Python</span>. </span>
<div class="itemgroup stepresult">
A new window opens for you to define your new environment. <img id="task_hwswconfig__image_nqt_nm3_psb" src="images/overviewinfonewenv1.png" alt="New environment window showing empty fields">
</div></li>
<li class="li substep substepexpand"><span class="cmd">Enter a <span class="ph uicontrol">name</span>, and select a <span class="ph uicontrol">CPLEX version</span>, <span class="ph uicontrol">hardware specification</span>, <span class="ph uicontrol">copies</span> (number of nodes), <span class="ph uicontrol">Python version</span> and (optionally) you can set <span class="ph uicontrol">Associate a Python extension</span> to <span class="ph uicontrol">On</span> to include any <span class="ph uicontrol">Python libraries</span> that you want to add. </span></li>
<li class="li substep substepexpand"><span class="cmd">Click <span class="ph uicontrol">New Python extension</span>.</span></li>
<li class="li substep substepexpand"><span class="cmd">Enter a name for your extension in the new <span class="ph uicontrol">Create a Python extension</span> window that opens, and click <span class="ph uicontrol">Create</span>.</span></li>
<li class="li substep substepexpand"><span class="cmd">In the new Configure Python extension window that opens, you can set <span class="ph uicontrol">YAML code</span> to<span class="ph uicontrol"> On</span> and enter or edit the provided YAML code.</span>
<div class="itemgroup stepxmp">
For example, use the provided template to add the custom libraries:
<pre class="codeblock language-shell"><code class="language-shell"># Modify the following content to add a software customization to an environment.
# To remove an existing customization, delete the entire content and click Apply.
# Add conda channels on a new line after defaults, indented by two spaces and a hyphen.
channels:
- defaults
# To add packages through conda or pip, remove the comment on the following line.
# dependencies:
# Add conda packages here, indented by two spaces and a hyphen.
# Remove the comment on the following line and replace sample package name with your package name:
# - a_conda_package=1.0
# Add pip packages here, indented by four spaces and a hyphen.
# Remove the comments on the following lines and replace sample package name with your package name.
# - pip:
# - a_pip_package==1.0</code></pre>
<p>You can also click <span class="ph uicontrol">Browse</span> to add any Python libraries.</p>
<p>For example, this image shows a dynamic programming Python library that is imported and <span class="ph uicontrol">YAML code </span>set to <span class="ph uicontrol">On</span>.<img src="images/PythonExtension.png" alt="Configure Python extension window showing YAML code and a Dynamic Programming library included"></p>
<p>Click <span class="ph uicontrol">Done</span>.</p>
</div></li>
<li class="li substep substepexpand"><span class="cmd">Click <span class="ph uicontrol">Create</span> in the <span class="keyword wintitle">New environment </span>window.</span></li>
</ol>
<div class="itemgroup stepresult">
Your chosen (or newly created) environment appears as ticked in the <span class="ph uicontrol">Python environments</span> drop-down list in the <span class="ph uicontrol">Environments</span> tab. The tick indicates that this is the default Python environment for all scenarios in your <span class="keyword">experiment</span>.
</div></li>
<li class="step stepexpand"><span class="cmd">Select <span class="ph uicontrol">Manage experiment environments</span> to see a detailed list of all existing environments for your <span class="keyword">experiment</span> in the <span class="ph uicontrol">Environments</span> tab.</span>
<div class="itemgroup info">
<img src="images/manageenvextn.png" alt="Manage experiment environment with two environments and drop-down menu.">
<p>You can use the options provided by clicking the three vertical dots next to an environment to <span class="ph uicontrol">Edit</span>, <span class="ph uicontrol">Set as default</span>, <span class="ph uicontrol">Update in a deployment space</span> or <span class="ph uicontrol">Delete</span> the environment. You can also create a <span class="ph uicontrol">New environment</span> from the <span class="ph uicontrol">Manage experiment environments</span> window, but creating a new environment from this window does not make it the default unless you explicitly set is as the default.</p>
</div>
<div class="itemgroup info">
<div class="note note">
<span class="notetitle">Updating your environment for Python or CPLEX versions:</span> Python versions are regularly updated. If however you have explicitly specified an older Python version in your model, you must update this version specification or your models will not work. You can either create a new Python environment, as described earlier, or edit one from Manage experiment environments. This is also useful if you want to select a different version of CPLEX for your default environment.
</div>
</div></li>
<li class="step stepexpand"><span class="cmd">Click the <span class="ph uicontrol">Python extensions</span> tab.</span>
<div class="itemgroup info">
<p><img src="images/manageenvpyextn.png" alt="Python extensions tab showing created extension"></p>
<p>Here you can view your Python extensions and see which environment it is used in. You can also create a <span class="ph uicontrol">New Python extension</span> or use the options to <span class="ph uicontrol">Edit</span>, <span class="ph uicontrol">Download</span>, and <span class="ph uicontrol">Delete</span> existing ones. If you edit a Python extension that is used by an experiment environment, the environment will be re-created.</p>
<p>You can also view your Python environments in your deployment space assets and any Python extensions you have added will appear in the software specification.</p>
</div></li>
</ol>
</div>
<article class="topic task nested1" role="article" aria-labelledby="task_envscenario__title__1" lang="en-us" id="task_envscenario">
<h2 class="topictitle2" id="task_envscenario__title__1"><span class="ph" data-hd-product="cloud wx">Selecting a different run environment for a particular scenario</span></h2>
<div class="body taskbody">
<p class="shortdesc">You can choose different environments for individual scenarios on the Environment tab of the Run configuration pane.</p>
<section class="section context" role="region" aria-label="Selecting a different run environment for a
particular scenario: About this task">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_envscenario__context__1">About this task</h3>
</div>
<p>This task is useful if you don't want certain scenarios to use the default environment (this is when more than one Python version is supported, currently Python <span class="keyword">3.10</span> is available). See <a href="modelbuilderUI.html#ModelBuilderInterface__envtabConfigRun">Run environment tab</a> for more details.</p>
<p>To select a different run environment for a particular scenario, without changing the default for all the other scenarios:</p>
</section>
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_envscenario__steps__1">Procedure</h3>
</div>
<ol class="steps">
<li class="step"><span class="cmd">Open the <span class="ph uicontrol"><span class="keyword">Scenario</span></span> pane and select your scenario in the <span class="ph uicontrol"><span class="keyword">Build model</span></span> <span class="keyword">view</span>.</span></li>
<li class="step"><span class="cmd">Click the <span class="ph uicontrol">Configure run</span> icon next to the <span class="ph uicontrol">Run</span> button to open the Run configuration pane and select the <span class="ph uicontrol">Environment</span> tab.</span></li>
<li class="step"><span class="cmd">Choose <span class="ph uicontrol">Select run environment for this scenario</span>, choose an environment from the drop-down menu, and click <span class="ph uicontrol">Run</span>.</span></li>
<li class="step"><span class="cmd">Open the <span class="ph uicontrol"><span class="keyword">Overview</span></span> information pane. You can now see that your scenario has your chosen environment, while other scenarios are not affected by this modification.</span></li>
</ol>
</div>
<aside role="complementary" aria-labelledby="task_envscenario__title__1">
<nav role="navigation">
<div class="linklist relinfo" lang="en-us">
<h2 class="linkheading">Related information</h2>
<ul>
<li><a href="modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview tab</a></li>
<li><a href="modelbuilderUI.html#ModelBuilderInterface__envtabConfigRun">Run environment tab</a></li>
<li><a href="buildingmodels.html" title="If you use the Decision Optimization experiment UI, you can take advantage of its many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with Watson Machine Learning."><strong>Parent topic</strong>: Decision Optimization experiments</a></li>
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5788D38721AEAE446CFAD7D9288B6BAB33FA1EF9 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=en | Decision Optimization sample models and notebooks | Sample models and notebooks for Decision Optimization
Several examples are presented in this documentation as tutorials. You can also use many other examples that are provided in the Decision Optimization GitHub, and in the Samples.
Quick links:
* [Examples used in this documentation](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=enExamples__docexamples)
* [Decision Optimization experiment samples (Modeling Assistant, Python, OPL)](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=enExamples__section_modelbuildersamples)
* [Jupyter notebook samples](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=enExamples__section_xrg_fdj_cgb)
* [Python notebooks in the Samples](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=enExamples__section_pythoncommunity)
| # Sample models and notebooks for Decision Optimization #
Several examples are presented in this documentation as tutorials\. You can also use many other examples that are provided in the Decision Optimization GitHub, and in the Samples\.
Quick links:
<!-- <ul> -->
* [Examples used in this documentation](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=en#Examples__docexamples)
* [Decision Optimization experiment samples (Modeling Assistant, Python, OPL)](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=en#Examples__section_modelbuildersamples)
* [Jupyter notebook samples](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=en#Examples__section_xrg_fdj_cgb)
* [Python notebooks in the Samples](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html?context=cdpaas&locale=en#Examples__section_pythoncommunity)
<!-- </ul> -->
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<h1 class="topictitle1" id="Examples__title__1">Sample models and notebooks for <span class="keyword">Decision Optimization</span></h1>
<div class="body">
<p class="shortdesc">Several examples are presented in this documentation as tutorials. You can also use many other examples that are provided in the <span class="keyword">Decision Optimization GitHub</span>, and in the <span class="keyword">Samples</span>.</p>
<div class="bodydiv">
<p>Quick links:</p>
<ul id="Examples__ul_k4l_kfp_cgb">
<li><a href="#Examples__docexamples">Examples used in this documentation</a></li>
<li><a href="#Examples__section_modelbuildersamples">Decision Optimization experiment samples (Modeling Assistant, Python, OPL)</a></li>
<li><a href="#Examples__section_xrg_fdj_cgb">Jupyter notebook samples</a></li>
<li data-hd-product="cloud wx"><a href="#Examples__section_pythoncommunity">Python notebooks in the Samples</a></li>
</ul>
</div>
<section class="section" role="region" aria-labelledby="Examples__title__2">
<h2 class="sectiontitle" id="Examples__title__2"><span class="keyword">Decision Optimization GitHub</span> DO-samples</h2>
<p>See <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Decision Optimization GitHub</span></a> for a repository of samples for use with IBM <span class="keyword" data-hd-product="wx">watsonx.ai</span>. For <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> samples, see the following section <a href="#Examples__section_modelbuildersamples"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> samples</a>. This repository also contains Jupyter <span class="keyword">notebook</span> samples that can be imported into <span class="keyword" data-hd-product="wx">watsonx.ai</span>. See <a href="#Examples__section_xrg_fdj_cgb">Jupyter <span class="keyword">notebooks</span></a>.</p>
</section>
<section class="section" role="region" aria-labelledby="Examples__title__3">
<h2 class="sectiontitle" id="Examples__title__3">Java example</h2>
<p>See the Java model example provided in the <span class="keyword">Decision Optimization</span> <span class="keyword">Java™ worker</span> boilerplate in the <a href="https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java worker GitHub</span></a>.</p>
</section>
<section class="section" role="region" aria-labelledby="Examples__docexamples__title__1" id="Examples__docexamples">
<h2 class="sectiontitle" id="Examples__docexamples__title__1">Examples described in this documentation</h2>
<p>The following table lists example models that are described in this documentation, and that show you how to use <span class="keyword">Decision Optimization</span>.</p>
<div class="tablenoborder">
<table summary="Table explaining the examples" id="Examples__table_kzz_yf3_bxb" class="defaultstyle">
<caption>
<span class="tablecap">Table 1. <span class="keyword">Decision Optimization</span> documentation examples</span>
</caption>
<colgroup>
<col style="width:25%">
<col style="width:25%">
<col style="width:25%">
<col style="width:25%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="Examples__table_kzz_yf3_bxb__entry__1"> </th>
<th id="Examples__table_kzz_yf3_bxb__entry__2">Examples</th>
<th id="Examples__table_kzz_yf3_bxb__entry__3">
<p>Learn how to ...</p></th>
<th id="Examples__table_kzz_yf3_bxb__entry__4">
<p>See</p></th>
</tr>
</thead>
<tbody>
<tr>
<td headers="Examples__table_kzz_yf3_bxb__entry__1 ">
<p>Create scheduling models by using the <span class="keyword">Modeling Assistant</span>.</p></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__2 ">
<p>House Construction example</p></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__3 ">
<div class="p">
<ul>
<li>Create, edit, and solve a planning and scheduling model with the <span class="keyword">Modeling Assistant</span>.</li>
<li>Create and examine different scenarios.</li>
</ul>
</div></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__4 ">
<p><a href="../DODS_Mdl_Assist/exhousebuildintro.html#topic_jzq_hbq_m1b" title="You can model and solve Decision Optimization problems using the Modeling Assistant (which enables you to formulate models in natural language). This requires little to no knowledge of Operational Research (OR) and does not require you to write Python code. The Modeling Assistant is only available in English and is not globalized.">Solving a model using the <span class="keyword">Modeling Assistant</span></a></p></td>
</tr>
<tr>
<td rowspan="2" headers="Examples__table_kzz_yf3_bxb__entry__1 ">
<p>Create Python optimization models by using the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>.</p></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__2 ">
<p>Diet example</p></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__3 ">
<div class="p">
<ul>
<li>Create and solve a Python model that is generated from an existing scenario.</li>
<li>Create and examine a new scenario.</li>
</ul>
</div></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__4 ">
<p><a href="../DODS_Notebooks/solveIntro.html#SolvingPythonModel" title="You can solve Python DOcplex models in a Decision Optimization experiment.">Solving a Python DOcplex model</a></p></td>
</tr>
<tr>
<td headers="Examples__table_kzz_yf3_bxb__entry__2 ">
<p>Multiple scenario example</p></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__3 ">
<div class="p">
<ul>
<li>Create a Python model from a Python <span class="keyword" translate="no">notebook</span> imported into <span class="keyword">Decision Optimization</span> and solve it.</li>
<li>Generate multiple scenarios from a Python <span class="keyword" translate="no">notebook</span> by using randomized data.</li>
<li>Export tables from scenario.</li>
</ul>
</div></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__4 ">
<p><a href="../DODS_Notebooks/multiIntro.html#topic_u1f_t2s_n1b" title="You can generate multiple scenarios to test your model against a wide range of data and understand how robust the model is.">Working with multiple scenarios</a></p></td>
</tr>
<tr>
<td headers="Examples__table_kzz_yf3_bxb__entry__1 ">
<p>Create or import DOcplex Python <span class="keyword">notebooks</span>.</p></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__2 ">
<p>Decision Optimization <span class="keyword">notebook</span> examples</p></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__3 ">
<div class="p">
<ul>
<li>Download a <span class="keyword">notebook</span> and add it to a project.</li>
<li>Run a <span class="keyword">notebook</span>.</li>
</ul>
</div></td>
<td headers="Examples__table_kzz_yf3_bxb__entry__4 ">
<p><a href="DONotebooks.html#DONotebooks" title="You can create and run Decision Optimization models in Python notebooks by using DOcplex, a native Python API for Decision Optimization. Several Decision Optimization notebooks are already available for you to use."> Running Decision Optimization <span class="keyword" translate="no">notebooks</span></a></p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="section" role="region" aria-labelledby="Examples__section_modelbuildersamples__title__1" id="Examples__section_modelbuildersamples">
<h2 class="sectiontitle" id="Examples__section_modelbuildersamples__title__1"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> samples (<span class="keyword">Modeling Assistant</span>, Python, OPL)</h2>
<p>For a step-by-step guide to build, solve and deploy a <span class="keyword">Decision Optimization</span> model, by using the user interface, see the <a href="../../wsj/getting-started/get-started-do.html">Quick start tutorial with video</a>.</p>
<p>The following table lists the <span class="keyword">Decision Optimization</span> samples that are provided in <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong> in the <span class="keyword">Decision Optimization GitHub</span>. All these assets use the <span class="keyword">Decision Optimization</span> <strong><span class="keyword">experiment UI</span></strong> and contain data.</p>
<div class="note" data-hd-product="cloud wx">
<span class="notetitle">Note:</span>
<p data-hd-product="cloud wx">To run models, you must associate a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> instance with your <span class="ph uicontrol">Project </span> and associate a deployment space with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>. You must also have the <strong>Editor</strong> or <strong>Admin</strong> <a href="../../wsj/analyze-data/collaborator-permissions-wml.html">role in the deployment space</a>.</p>
</div>
<div class="p">
To use these samples:
<ol id="Examples__ol_klq_xp1_cgb">
<li>Download and extract all the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a> on to your computer. You can also download just the one sample, but in this case, do not extract it.</li>
<li><span class="ph" id="Examples__CreateProject">Open your project or create an empty project.</span></li>
<li data-hd-product="cloud wx"><span class="ph" id="Examples__AddMLToProject">On the <span class="ph uicontrol">Manage</span> tab of your project, select the <span class="ph uicontrol">Services and integrations</span> section and click <span class="ph uicontrol">Associate service</span>. Then select an existing <span class="keyword">Machine Learning</span> service instance (or create a new one ) and click <span class="ph uicontrol">Associate</span>. When the service is associated, a success message is displayed, and you can then close the <span class="keyword wintitle">Associate service</span> window. </span></li>
<li data-hd-product="cloud icpd wx"><span class="ph" id="Examples__AddToProject">Select the <span class="ph" data-hd-product="wx"><span class="ph uicontrol"><span class="keyword">Assets</span></span></span> tab.</span></li>
<li data-hd-product="wx"><span class="ph" id="Examples__wx_create_do">Select <span class="ph uicontrol"><span class="keyword">New asset > Solve optimization problems</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></li>
<li><span class="ph" id="Examples__FromFile">Click <span class="ph uicontrol">Local file</span> in the <span class="ph" data-hd-product="wx"><span class="keyword">Solve optimization problems</span></span> window that opens.</span></li>
<li>Browse to the <span class="ph filepath">Model_Builder</span> folder in your downloaded <span class="keyword">DO-samples</span>. <span class="ph">Select the relevant product and version subfolder.</span> Choose your sample <span class="ph filepath">.zip</span> file and click <span class="ph uicontrol">Open</span>. Alternatively drag the sample into the window.</li>
<li data-hd-product="cloud wx"><span class="ph" id="Examples__ChooseSpaceAndMLifnotpreviously">If you haven't already associated a <span class="keyword">Machine Learning</span> service with your project, you must first select <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span> to select or create one before you choose a deployment space for your <span class="keyword">experiment</span>.</span></li>
<li><span class="ph" id="Examples__choosedeploysp">Click <span class="ph uicontrol">New deployment space</span>, enter a name, and click <span class="ph uicontrol">Create</span> (or select an existing space from the drop-down menu).</span></li>
<li>Click <strong>Create</strong>.
<p>A <span class="keyword">Decision Optimization</span> model is created with the same name as the sample.</p></li>
</ol>
</div>
<div class="tablenoborder">
<table summary="Table explaining the models provided in the Decision Optimization GitHub." id="Examples__table_apn_slr_ycb" class="defaultstyle">
<caption>
<span class="tablecap">Table 2. <span class="keyword">Decision Optimization</span> Models</span>
</caption>
<colgroup>
<col style="width:41.51785714285714%">
<col style="width:36.160714285714285%">
<col style="width:22.32142857142857%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="Examples__table_apn_slr_ycb__entry__1">Models for <span class="keyword">Decision Optimization</span></th>
<th id="Examples__table_apn_slr_ycb__entry__2">Problem type</th>
<th id="Examples__table_apn_slr_ycb__entry__3">Model type</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">BridgeScheduling</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Scheduling</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 "><span class="keyword">Modeling Assistant</span></td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">Diet</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Blending</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">Python</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">DietLP</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Blending</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">LP (CPLEX)</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">EnvironmentAndExtension</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Using an environment with an extension that contains a library file and YAML code.</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">Python</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">HouseConstructionScheduling</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Scheduling with assignment</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 "><span class="keyword">Modeling Assistant</span></td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">IntermediateSolutions</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Enabling intermediate solutions for CPLEX and CPO models</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">Python</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">MarketingCampaignAssignment</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Resource Assignment (<em>Scenarios 1 - 4</em>)
<p>Selection and Allocation (<em>Scenario 4 - Selection</em>)</p></td>
<td headers="Examples__table_apn_slr_ycb__entry__3 "><span class="keyword">Modeling Assistant</span></td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">Multifiles</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Using a model with multiple files.</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">Python and LP</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">PastaProduction</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Production</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">OPL</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">PortfolioAllocation</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Selection & Allocation</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 "><span class="keyword">Modeling Assistant</span></td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">PythonEngineSettings</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Geometrical puzzle with customized engine settings</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">Python</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">ShiftAssignment</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Resource Assignment with custom decisions and a custom constraint</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 "><span class="keyword">Modeling Assistant</span></td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">StaffPlanning</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Multi-Scenario Planning
<p>(to be used with <span class="ph filepath">CopyAndSolveScenarios.ipynb</span>)</p></td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">Python</td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">SupplyDemandPlanning</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Supply & Demand Planning</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 "><span class="keyword">Modeling Assistant</span></td>
</tr>
<tr>
<td headers="Examples__table_apn_slr_ycb__entry__1 ">TalentCPO</td>
<td headers="Examples__table_apn_slr_ycb__entry__2 ">Movie scheduling</td>
<td headers="Examples__table_apn_slr_ycb__entry__3 ">CPO (CP Optimizer)</td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="section" role="region" aria-labelledby="Examples__section_xrg_fdj_cgb__title__1" id="Examples__section_xrg_fdj_cgb">
<h2 class="sectiontitle" id="Examples__section_xrg_fdj_cgb__title__1">Jupyter <span class="keyword">notebook</span> samples</h2>
<div class="p">
Jupyter <span class="keyword">notebooks</span> are also provided in the <span class="keyword">Decision Optimization GitHub</span> that do not use the <span class="keyword">experiment UI</span>. To use these Python <span class="keyword">notebook</span> samples :
<ol>
<li>Download and extract all the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a> on to your computer. You can also download just one sample.</li>
<li>Open your project or create an empty project.</li>
<li data-hd-product="wx"><span class="ph" id="Examples__AddtoProjectwx">Select the <span class="ph uicontrol"><span class="keyword">Assets</span></span> tab.</span></li>
<li data-hd-product="wx"><span class="ph" id="Examples__wx_selectNB">Select <span class="ph uicontrol"><span class="keyword">New asset > Work with data and models in Python or R notebooks</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></li>
<li>Select the <strong><span class="ph uicontrol">From file</span></strong> tab in the new window that opens.</li>
<li>Name your <span class="keyword">notebook</span>, click <span class="ph" id="Examples__browsefile"><span class="ph uicontrol"><span class="keyword">Drag and drop files or upload</span></span> and browse</span> to the <span class="keyword">notebook</span> in the <span class="ph filepath">jupyter</span> folder. <span class="ph" id="Examples__relevantfolder">Select the relevant product and version subfolder in your downloaded <span class="keyword">DO-samples</span>. </span></li>
<li>Click <strong><span class="ph uicontrol">Create</span></strong>. <span class="ph" id="Examples__sourcecodenotebk">The <span class="keyword">notebook</span> is added to your project.</span></li>
</ol>
</div>
</section>
<section class="section" role="region" aria-labelledby="Examples__section_pythoncommunity__title__1" data-hd-product="cloud wx" id="Examples__section_pythoncommunity">
<h2 class="sectiontitle" id="Examples__section_pythoncommunity__title__1">Python <span class="keyword">notebooks</span> in the <span class="keyword">Samples</span></h2>
<p><span class="keyword">Decision Optimization</span> Python <span class="keyword">notebooks</span> are available from the <span class="ph uicontrol"><span class="keyword">Samples</span></span>. To use these <span class="keyword">notebooks</span> in an existing project, open a <span class="keyword">notebook</span> in the <span class="ph uicontrol"><span class="keyword">Samples</span></span>, click <strong><span class="ph uicontrol">Add to project</span></strong>, select your Project, and click <strong><span class="ph uicontrol">Create</span></strong>.</p>
</section>
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167D5677958594BA275E34B8748F7E8091782560 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en | Decision Optimization experiment UI views and scenarios | Decision Optimization experiment views and scenarios
The Decision Optimization experiment UI has different views in which you can select data, create models, solve different scenarios, and visualize the results.
Quick links to sections:
* [ Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__section_overview)
* [Hardware and software configuration](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__section_environment)
* [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__section_preparedata)
* [Build model view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__ModelView)
* [Multiple model files](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__section_g21_p5n_plb)
* [Run models](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__runmodel)
* [Run configuration](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__section_runconfig)
* [Run environment tab](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__envtabConfigRun)
* [Explore solution view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__solution)
* [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__scenariopanel)
* [Generating notebooks from scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__generateNB)
* [Importing scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__p_Importingscenarios)
* [Exporting scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=enModelBuilderInterface__p_Exportingscenarios)
Note: To create and run Optimization models, you must have both a Machine Learning service added to your project and a deployment space that is associated with your experiment:
1. Add a [Machine Learning service](https://cloud.ibm.com/catalog/services/machine-learning) to your project. You can either add this service at the project level (see [Creating a Watson Machine Learning Service instance](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-service-instance.html)), or you can add it when you first create a new Decision Optimization experiment: click Add a Machine Learning service, select, or create a New service, click Associate, then close the window.
2. Associate a [deployment space](https://dataplatform.cloud.ibm.com/ml-runtime/spaces) with your Decision Optimization experiment (see [Deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-spaces_local.htmlcreate)). A deployment space can be created or selected when you first create a new Decision Optimization experiment: click Create a deployment space, enter a name for your deployment space, and click Create. For existing models, you can also create, or select a space in the [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_overview) information pane.
When you add a Decision Optimization experiment as an asset in your project, you open the Decision Optimization experiment UI.
With the Decision Optimization experiment UI, you can create and solve prescriptive optimization models that focus on the specific business problem that you want to solve. To edit and solve models, you must have Admin or Editor roles in the project. Viewers of shared projects can only see experiments, but cannot modify or run them.
You can create a Decision Optimization model from scratch by entering a name or by choosing a .zip file, and then selecting Create. Scenario 1 opens.
With the Decision Optimization experiment UI, you can create several scenarios, with different data sets and optimization models. Thus, you, can create and compare different scenarios and see what impact changes can have on a problem.
For a step-by-step guide to build, solve and deploy a Decision Optimization model, by using the user interface, see the [Quick start tutorial with video](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-do.html).
For each of the following views, you can organize your screen as full-screen or as a split-screen. To do so, hover over one of the view tabs ( Prepare data, Build model, Explore solution) for a second or two. A menu then appears where you can select Full Screen, Left or Right. For example, if you choose Left for the Prepare data view, and then choose Right for the Explore solution view, you can see both these views on the same screen.
| # Decision Optimization experiment views and scenarios #
The Decision Optimization experiment UI has different views in which you can select data, create models, solve different scenarios, and visualize the results\.
Quick links to sections:
<!-- <ul> -->
* [ Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__section_overview)
* [Hardware and software configuration](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__section_environment)
* [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__section_preparedata)
* [Build model view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__ModelView)
* [Multiple model files](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__section_g21_p5n_plb)
* [Run models](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__runmodel)
* [Run configuration](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__section_runconfig)
* [Run environment tab](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__envtabConfigRun)
* [Explore solution view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__solution)
* [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__scenariopanel)
* [Generating notebooks from scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__generateNB)
* [Importing scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__p_Importingscenarios)
* [Exporting scenarios](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html?context=cdpaas&locale=en#ModelBuilderInterface__p_Exportingscenarios)
<!-- </ul> -->
Note: To create and run Optimization models, you must have both a Machine Learning service added to your project and a deployment space that is associated with your experiment:
<!-- <ol> -->
1. Add a [**Machine Learning** service](https://cloud.ibm.com/catalog/services/machine-learning) to your project\. You can either add this service at the project level (see [Creating a Watson Machine Learning Service instance](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-service-instance.html)), or you can add it when you first create a new Decision Optimization experiment: click Add a Machine Learning service, select, or create a New service, click Associate, then close the window\.
2. Associate a [**deployment space**](https://dataplatform.cloud.ibm.com/ml-runtime/spaces) with your Decision Optimization experiment (see [Deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-spaces_local.html#create))\. A deployment space can be created or selected when you first create a new Decision Optimization experiment: click Create a deployment space, enter a name for your deployment space, and click Create\. For existing models, you can also create, or select a space in the [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview) information pane\.
<!-- </ol> -->
When you add a **Decision Optimization experiment** as an asset in your project, you open the **Decision Optimization experiment UI**\.
With the Decision Optimization experiment UI, you can create and solve prescriptive optimization models that focus on the specific business problem that you want to solve\. To edit and solve models, you must have Admin or Editor roles in the project\. Viewers of shared projects can only see experiments, but cannot modify or run them\.
You can create a Decision Optimization model from scratch by entering a name or by choosing a `.zip` file, and then selecting Create\. Scenario 1 opens\.
With the Decision Optimization experiment UI, you can create several scenarios, with different data sets and optimization models\. Thus, you, can create and compare different scenarios and see what impact changes can have on a problem\.
For a step\-by\-step guide to build, solve and deploy a Decision Optimization model, by using the user interface, see the [Quick start tutorial with video](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-do.html)\.
For each of the following views, you can organize your screen as full\-screen or as a **split\-screen**\. To do so, hover over one of the view tabs ( Prepare data, Build model, Explore solution) for a second or two\. A menu then appears where you can select Full Screen, Left or Right\. For example, if you choose Left for the Prepare data view, and then choose Right for the Explore solution view, you can see both these views on the same screen\.
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<title>Decision Optimization experiment UI views and scenarios</title>
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<h1 class="topictitle1" id="ModelBuilderInterface__title__1"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> <span class="keyword">views</span> and scenarios</h1>
<div class="body">
<p class="shortdesc">The <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> has different <span class="keyword">views</span> in which you can select data, create models, solve different scenarios, and visualize the results.</p>
<div class="bodydiv">
<p>Quick links to sections:</p>
<ul>
<li><a href="#ModelBuilderInterface__section_overview"> Overview</a></li>
<li><a href="#ModelBuilderInterface__section_environment">Hardware and software configuration</a></li>
<li><a href="#ModelBuilderInterface__section_preparedata">Prepare data view</a></li>
<li><a href="#ModelBuilderInterface__ModelView">Build model view</a></li>
<li><a href="#ModelBuilderInterface__section_g21_p5n_plb">Multiple model files</a></li>
<li><a href="#ModelBuilderInterface__runmodel">Run models</a></li>
<li><a href="#ModelBuilderInterface__section_runconfig">Run configuration</a></li>
<li><a href="#ModelBuilderInterface__envtabConfigRun">Run environment tab</a></li>
<li><a href="#ModelBuilderInterface__solution">Explore solution view</a></li>
<li><a href="#ModelBuilderInterface__scenariopanel">Scenario pane</a></li>
<li><a href="#ModelBuilderInterface__generateNB">Generating <span class="keyword">notebooks</span> from scenarios</a></li>
<li><a href="#ModelBuilderInterface__p_Importingscenarios">Importing scenarios</a></li>
<li><a href="#ModelBuilderInterface__p_Exportingscenarios">Exporting scenarios</a></li>
</ul>
</div>
<div class="note" data-hd-product="cloud wx" id="ModelBuilderInterface__MLandSpaceNeeded">
<span class="notetitle">Note:</span> To create and run Optimization models, you must have both a <span class="keyword">Machine Learning</span> service added to your project and a deployment space that is associated with your <span class="keyword">experiment</span>:
<ol id="ModelBuilderInterface__ol_cg3_mpy_qmb">
<li>Add a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong><span class="keyword">Machine Learning</span></strong> service</a> to your project. You can either add this service at the project level (see <a href="../../wsj/analyze-data/ml-service-instance.html">Creating a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> Service instance</a>), or you can add it when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span>, select, or create a <span class="ph uicontrol">New service</span>, click <span class="ph uicontrol">Associate</span>, then close the window.</li>
<li>Associate a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong>deployment space</strong></a> with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> (see <a href="../../wsj/analyze-data/ml-spaces_local.html#create">Deployment spaces</a>). A deployment space can be created or selected when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Create a deployment space</span>, enter a name for your deployment space, and click <span class="ph uicontrol">Create</span>. For existing models, you can also create, or select a space in the <a href="modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> information pane.</li>
</ol>
</div>
<p>When you add a <strong><span class="ph uicontrol"><span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span></span></strong> as an asset in your project, you open the <strong><span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span></strong>.</p>
<p>With the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, you can create and solve prescriptive optimization models that focus on the specific business problem that you want to solve. <span class="ph" data-hd-product="cloud wx">To edit and solve models, you must have Admin or Editor roles in the project. Viewers of shared projects can only see <span class="keyword">experiments</span>, but cannot modify or run them</span>.</p>
<p>You can create a <span class="keyword">Decision Optimization</span> model from scratch by entering a name or by choosing a <code class="ph codeph">.zip</code> file, and then selecting <span class="ph uicontrol">Create</span>. Scenario 1 opens.</p>
<p>With the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, you can create several scenarios, with different data sets and optimization models. Thus, you, can create and compare different scenarios and see what impact changes can have on a problem.</p>
<p>For a step-by-step guide to build, solve and deploy a <span class="keyword">Decision Optimization</span> model, by using the user interface, see the <a href="../../wsj/getting-started/get-started-do.html">Quick start tutorial with video</a>.</p>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__section_overview__title__1" id="ModelBuilderInterface__section_overview">
<h2 class="sectiontitle" id="ModelBuilderInterface__section_overview__title__1"><span class="keyword">Overview</span></h2>
<p>The overview tab provides a summary of information about all your scenarios. (For more information about scenarios, see <a href="#ModelBuilderInterface__scenariopanel">Scenario pane</a>). This summary is useful when you have several scenarios, as it gives you model, data an impact changed solution information for all your scenarios at a glance. It also shows whether your scenario uses the default environment set for that type of model or if it uses a different environment for that particular scenario. For more information, see <a href="configureEnvironments.html#task_envscenario" title="You can choose different environments for individual scenarios on the Environment tab of the Run configuration pane.">Selecting a different run environment for a particular scenario</a>.</p>
<div class="p">
From this <span class="keyword">view</span>, you can create a scenario from scratch or from a file, or you can select a scenario and click the three dots to perform the following actions:
<ul id="ModelBuilderInterface__ul_xyv_jdl_fmb">
<li>Create a scenario.</li>
<li>Duplicate a scenario.</li>
<li>Rename a scenario.</li>
<li>Run a scenario.</li>
<li>Export the scenario as a <code class="ph codeph">.zip</code> file.</li>
<li>Generate a Python <span class="keyword">notebook</span> from a scenario.</li>
<li>Save the scenario as a model for deployment. (The data types set in the <span class="keyword">Prepare data</span> <span class="keyword">view</span> and any run configuration parameters that you might have set for that scenario are also saved in the deployment.)</li>
<li>Delete a scenario.</li>
</ul>
</div>
<p>In this <span class="keyword">view</span> when you click the information icon <img id="ModelBuilderInterface__image_whm_zr5_kmb" src="images/infoicon.jpg" alt="Information pane icon">, the information pane opens showing you details about your <span class="keyword">experiment</span> and the name of your associated deployment space. Here you can create a <span class="ph uicontrol"><span class="keyword">Machine Learning</span> service</span> and even add this service to your project if you haven't already done so. You can also create or choose a <span class="ph uicontrol">deployment space</span> for your <span class="keyword">experiment</span> so that you can use a different space for a particular solve. The creation date and name of the <span class="keyword">experiment</span> creator is also provided here. This information is useful if you are sharing an <span class="keyword">experiment</span> created by another collaborator.</p>
<p><img data-hd-product="cloud wx" id="ModelBuilderInterface__image_vh4_4mj_5pb" src="images/CloudOverviewInfo2.png" alt="Overview pane showing 2 scenarios and info pane open"></p>
<p id="ModelBuilderInterface__environtab">The information pane <img id="ModelBuilderInterface__image_krp_fvf_rsb" src="images/infoicon.jpg" alt="Information pane icon"> also has an <span class="ph uicontrol">Environment</span> tab. Here you can see the default run environment that is used for the solve when you click <span class="ph uicontrol">Run</span> in the <span class="keyword">Build model</span> <span class="keyword">view</span>. The environment depends on your model type. <span class="keyword">Modeling Assistant</span> models require Python environments. See <a href="#ModelBuilderInterface__section_environment">Hardware and software configuration</a>.</p>
<p>You can <strong>run or delete multiple scenarios</strong> from this <span class="keyword">Overview</span> by selecting them and clicking <span class="ph uicontrol">Run</span> or <span class="ph uicontrol">Delete</span>. These buttons are only visible when a selection is made. If one or more scenarios in your selection cannot be run (for example because no environment has been created) the <span class="ph uicontrol">Run</span> button is unavailable. However, a tooltip provides you with information about why the scenario cannot be run. You can also stop a run from the <span class="keyword">Overview</span> pane by clicking the stop button that appears while the scenario is running.</p>
<p>You can also configure this <span class="keyword">Overview</span> pane by clicking the <strong><span class="ph uicontrol">Settings</span></strong> icon <img id="ModelBuilderInterface__image_unk_f5j_qmb" src="images/overviewsettings.jpg" alt="Overview settings icon">. This action opens a pane where you can select the columns that you want to display in your <span class="keyword">Overview</span> pane. You can add engine settings as a column for OPL models, and in this case the value <span class="ph uicontrol">yes</span> will appear in the table. If you click this value, the <a href="OPLmodels.html#topic_oplmodels__engsettings">engine settings</a> are displayed.</p>
</section>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__section_environment__title__1" id="ModelBuilderInterface__section_environment">
<h2 class="sectiontitle" id="ModelBuilderInterface__section_environment__title__1">Hardware and software configuration</h2>
<p>When you use the <span class="keyword">experiment UI</span>, the necessary environments are created for you automatically. However, you can configure the environment to be used for your solve, by changing the default environment. This environment will then be applied to all scenarios in your <span class="keyword">experiment</span>. The environment depends on your model type: Python, OPL, CPLEX, CPO, or Modeling Assistant (which uses Python environments). For example, to change the default Python environment for <span class="keyword">DOcplex</span> and <span class="keyword">Modeling Assistant</span> models see <a href="configureEnvironments.html#task_hwswconfig" title="You can change your default environment for Python and CPLEX in the experiment Overview.">Configuring environments and adding Python extensions</a>. It also shows you how to select a <strong>different run environment for a particular scenario</strong>, without changing the default for all the other scenarios.</p>
<p><span class="ph">The <span class="keyword">Decision Optimization</span> environment currently supports Python <span class="keyword">3.10</span>. The default version is Python <span class="keyword">3.10</span>.</span></p>
</section>
<p>For each of the following views, you can organize your screen as full-screen or as a <strong>split-screen</strong>. To do so, hover over one of the <span class="keyword">view</span> tabs (<span class="keyword">Prepare data</span>, <span class="keyword">Build model</span>, <span class="keyword">Explore solution</span>) for a second or two. A menu then appears where you can select <span class="ph uicontrol">Full Screen</span>, <span class="ph uicontrol">Left</span> or <span class="ph uicontrol">Right</span>. For example, if you choose <span class="ph uicontrol">Left</span> for the <span class="keyword">Prepare data</span> <span class="keyword">view</span>, and then choose <span class="ph uicontrol">Right</span> for the <span class="keyword">Explore solution</span> <span class="keyword">view</span>, you can see both these views on the same screen.</p>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__section_preparedata__title__1" id="ModelBuilderInterface__section_preparedata">
<h2 class="sectiontitle" id="ModelBuilderInterface__section_preparedata__title__1"><span class="keyword">Prepare data</span> <span class="keyword">view</span></h2>
<p>When you create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> in your project, the <strong><span class="keyword">Prepare data</span></strong> <span class="keyword">view</span> opens. In this <span class="keyword">view</span> you can browse and import data sets, including connected data, that you already have in your <strong><span class="ph uicontrol">Project</span></strong>. You can also choose to add data that you want to add to your project. Click <span class="ph uicontrol">add data</span> and then <span class="ph uicontrol">Browse</span> in the data pane that opens. Browse and select your files and click <span class="ph uicontrol">open</span> to add them. When you add a data set in this way, it appears listed in the <strong><span class="ph uicontrol"><span class="keyword">Prepare data</span></span></strong> <span class="keyword">view</span> and also in the <span class="ph uicontrol">Data assets</span> listed in your project.</p>
<p>Select the files that you want to import to your <span class="keyword">Scenario</span> and click <span class="ph uicontrol">Import</span>. You can import files in most formats, including <code class="ph codeph">.csv</code>, <code class="ph codeph">.xls</code>, <code class="ph codeph">.json</code> files, and <a href="../../wsj/manage-data/connected-data.html">connected data</a>. If you are using Excel files with multiple sheets, only the first sheet will be imported. However, you can export each sheet as a <code class="ph codeph">.csv</code> file to import your data into your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>.</p>
<div class="note">
<span class="notetitle">Note:</span> If your <code class="ph codeph">.cvs</code> file contains any malicious payload (formulas for example) in an input field, these items might be executed.
</div>
<p>Subsequently, if you modify, replace or delete a data set in your <span class="ph uicontrol">Project</span>, these actions will have no impact on your scenario, unless you choose to <span class="ph uicontrol">import</span> it into your scenario. Similarly, if you re-upload a new version of a table using the add data button in the <span class="ph uicontrol"><span class="keyword">Prepare data</span></span> <span class="keyword">view</span>, your scenario is not affected, unless you choose to <span class="ph uicontrol">import</span> it into your scenario.</p>
<p><img data-hd-product="cloud wx" id="ModelBuilderInterface__image_e13_hjp_h3b" src="images/Cloudpreparedata3.png" alt="Prepare data view showing diet data"></p>
<div class="p">
When you have imported your data files in to your<span class="ph uicontrol"> scenario</span>, the <span class="ph uicontrol"><span class="keyword">Prepare data</span></span> <span class="keyword">view</span> opens automatically. In this <span class="keyword">view</span> you can:
<ul id="ModelBuilderInterface__ul_onj_q2k_h3b">
<li>Rename or delete a table.</li>
<li>Edit the data directly in a table. You can scroll the table to see more rows (or Open the table in full mode to see the whole table and edit it in a new window).</li>
<li>Rename column names.</li>
<li>Resize columns.</li>
<li>Change the data type (number or string) of a column. (These types are used when you save your scenario as a model for deployment.)</li>
<li>Add or remove rows.</li>
<li>Search and filter table values. See <a href="Visualization.html#topic_visualization__section_tablefilter">Table search and filtering</a>.</li>
<li>Sort tables.</li>
<li>Export tables to project.</li>
<li>Run the model.</li>
</ul>
</div>
<p>If you re-import a file at any time, you can choose to import it with a new name. This renaming can be useful if you want to use different versions of the same data table. You can also choose to update and overwrite the current table in your Scenario. If you choose to re-import and update a table, a notification message will appear to remind you of which tables have been overwritten.</p>
<p>Changes that you make in the <span class="ph uicontrol"><span class="keyword">Prepare data</span></span> <span class="keyword">view</span> will be saved in your scenario, but not in the project data assets, unless you export the table to your project. Similarly, if you modify the project data assets, unless you import these changes into your scenario, they will not appear in the <span class="ph uicontrol"><span class="keyword">Prepare data</span></span> <span class="keyword">view</span>.</p>
<p id="ModelBuilderInterface__export_tables">To export a table to your project: click the three dots and select <span class="ph uicontrol">Export to project</span>. A new window opens where you can enter a file name and choose to create a new project data asset or overwrite an existing one. If you choose to overwrite a connected data file, the table in the connection will be updated as well. <img src="images/export-table.png" alt="Export table to your project window"></p>
<p>For an example that includes exporting tables see the <span class="ph filepath">CopyAndSolveScenarios</span> <span class="keyword">notebook</span> in the <span class="ph filepath">Jupyter</span> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong> in the <span class="keyword">Decision Optimization GitHub</span>.</p>
<p>You can access your imported data from your Python DOcplex model by using the syntax <code class="ph codeph">inputs['tablename']</code>. See <a href="../DODS_Notebooks/preparedataIO.html#topic_prepareIO" title="You can access the input and output data you defined in the experiment UI by using the following dictionaries.">Input and output data</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__ModelView__title__1" id="ModelBuilderInterface__ModelView">
<h2 class="sectiontitle" id="ModelBuilderInterface__ModelView__title__1"><span class="ph uicontrol"><span class="keyword">Build model</span></span> <span class="keyword">view</span></h2>
<p>When you click <strong><span class="ph uicontrol"><span class="keyword">Build model</span></span></strong> in the sidebar for the first time, a window appears where you can choose how you want to formulate your model. You can choose to use the assisted mode with the <span class="keyword">Modeling Assistant</span>, or create or import a model in Python, OPL, LP (CPLEX), or CPO code.<img data-hd-product="cloud wx icpd" id="ModelBuilderInterface__image_zzm_nkp_h3b" src="images/newrunmodel3.png" alt="Build model view showing Python diet model"></p>
<p>In this <span class="keyword">view</span>, you can formulate, or import, optimization models and run them.</p>
<p>You have several options to create a model:</p>
<div class="p">
<ul id="ModelBuilderInterface__ul_z22_ygw_h2b">
<li>Create and edit a <strong>Python</strong> or <strong>OPL</strong> model in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>. See <a href="OPLmodels.html" title="You can build OPL models in the Decision Optimization experiment UI in watsonx.ai.">OPL models</a>.</li>
<li>Use the <strong><span class="keyword">Modeling Assistant</span></strong> to formulate models in natural language. See <a href="../DODS_Mdl_Assist/exhousebuild.html#cogusercase" title="This tutorial shows you how to use the Modeling Assistant to define, formulate and run a model for a house construction scheduling problem. The completed model with data is also provided in the DO-samples, see Importing Model Builder samples.">Formulating and running a model: house construction scheduling </a> for a tutorial on formulating models with the <span class="keyword">Modeling Assistant</span>.</li>
<li>Import and edit a Python optimization model from an <strong>existing <span class="keyword" translate="no">notebook</span></strong>. Use this option to import a <span class="keyword">notebook</span> from your project. If your <span class="keyword">notebook</span> is running on a Jupyter customized environment , when you import the <span class="keyword" translate="no">notebook</span> into the <span class="keyword">experiment UI</span>, you also import this environment definition. Thus, you can <strong>use additional Python libraries when you run models from the <span class="keyword">experiment UI</span></strong>. This custom software definition will also be used when you deploy your model in <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> (both when you save your model for deployment and when you promote it to your deployment space).</li>
<li>Import and edit a Python optimization model from an <strong>external file</strong>. Use this option to import a Python file from your local computer.</li>
<li>Import and edit an OPL model from a file.</li>
<li>Import and edit a CPLEX model from a file.</li>
<li>Import and <span class="ph filepath">scenario.zip</span> file (that contains both model and data). This file can be a new scenario or one that you have previously exported from the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> and edited locally.</li>
<li>Generate a Python model from your current scenario (Python and Modeling Assistant models only). This creates a Python <span class="keyword">notebook</span> optimization model in your project.</li>
</ul>
</div>
<p>When you edit your model formulation in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> your content is saved automatically, and the <span class="ph uicontrol">Last saved time</span> is displayed.</p>
<p>When you have created a model, the <span class="ph uicontrol">Replace</span> arrow <img id="ModelBuilderInterface__image_j2w_vny_vjb" height="20" src="images/replace.svg" alt="Replace icon (arrow)"> appears. If you click this Replace arrow, you return to the Model wizard. Note that if you create a new model, the previous one is deleted.</p>
<p>When you have finished editing your model, you can solve it by clicking the <span class="ph uicontrol">Run</span> button in this <span class="keyword">view</span>.</p>
</section>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__section_g21_p5n_plb__title__1" id="ModelBuilderInterface__section_g21_p5n_plb">
<h2 class="sectiontitle" id="ModelBuilderInterface__section_g21_p5n_plb__title__1">Multiple model files</h2>
<p id="ModelBuilderInterface__multifile">You can create a Python or OPL models using multiple model files, by clicking the + tab next to <span class="ph uicontrol">MODEL</span>, and selecting <span class="ph uicontrol">Add new empty</span> or <span class="ph uicontrol">Upload Files</span> (to add any type of file). The <span class="ph uicontrol">MODEL</span> tab must always contain your main model. If you try to upload another file with the same name, for example <span class="ph filepath">model.py</span>, you are prompted to upload it with new name or replace your main model. You can also replace a model by clicking the Import <img id="ModelBuilderInterface__image_idz_kjl_p3b" src="images/ImportIcon.jpg" alt="Import icon"> icon. See the <span class="ph filepath">Multifile</span> example in the <strong><span class="ph filepath">Model_Builder</span></strong> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>.</p>
</section>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__runmodel__title__1" id="ModelBuilderInterface__runmodel">
<h2 class="sectiontitle" id="ModelBuilderInterface__runmodel__title__1">Run models</h2>
<p data-hd-product="cloud wx" id="ModelBuilderInterface__p_wmlinstance">To run models, you must associate a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> instance with your <span class="ph uicontrol">Project </span> and associate a deployment space with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>. You must also have the <strong>Editor</strong> or <strong>Admin</strong> <a href="../../wsj/analyze-data/collaborator-permissions-wml.html">role in the deployment space</a>.</p>
<p>When you run a model from the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, the <code class="ph codeph">do_<span class="keyword">22.1</span></code> runtime is used by default.</p>
<p>You can view and change this CPLEX runtime and your Python environment in the experiment <a href="#ModelBuilderInterface__section_overview">Overview</a> by opening the <span class="ph uicontrol">Environment</span> tab of the <span class="ph uicontrol">Information</span> pane, and selecting one of the available environments for your type of model (Python, OPL, CPLEX, CPO). Python is used to run <span class="keyword">Decision Optimization</span> models that are formulated in <span class="keyword">DOcplex</span> in both <span class="keyword">Decision Optimization</span> experiments and Jupyter <span class="keyword">notebooks</span>. Modeling Assistant models also use Python because <span class="keyword">DOcplex</span> code is generated when models are deployed.</p>
<p>You can also set and modify certain optimization parameters by clicking the <span class="ph uicontrol">Configure run</span> icon next to the <span class="ph uicontrol">Run</span> button. These parameters will be then applied each time that you click <strong><span class="ph uicontrol">Run</span></strong>. For more information, see <a href="#ModelBuilderInterface__section_runconfig">Run configuration</a>.</p>
<p>During the run, a graphical display shows the feasible solutions that are obtained until the optimal solution is found. If you have set the <span class="ph uicontrol">intermediate solution delivery</span> parameter in the run configuration to a certain frequency, a sample of intermediate solutions are displayed with that frequency. To see these intermediate solutions, you must click <span class="ph uicontrol">New data available</span>. A maximum of 3 intermediate solutions are displayed at a time. You can use the tabs to see <span class="ph uicontrol">Engine statistics,</span>, <span class="ph uicontrol">KPIs</span>, the <span class="ph uicontrol">Log</span> file, and you can see the solution tables of the last sampled solution in the <span class="ph uicontrol">Solution assets</span> tab. To obtain intermediate solutions for Python DOcplex models, you must implement a specific callback in your model. See the <code class="ph codeph">IntermediateSolutions</code> sample in the <span class="ph filepath">Model_Builder</span> folder of the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a> in the <span class="keyword">Decision Optimization GitHub</span>. <span class="ph">Select the relevant product and version subfolder.</span></p><img src="images/rundisplay.png" alt="Graphical display showing run statistics with intermediate solutions.">
</section>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__section_runconfig__title__1" id="ModelBuilderInterface__section_runconfig">
<h2 class="sectiontitle" id="ModelBuilderInterface__section_runconfig__title__1">Run configuration</h2>
<p id="ModelBuilderInterface__runconfig1">When you click the <span class="ph uicontrol">Configure run</span> icon <img id="ModelBuilderInterface__image_rk1_cp3_psb" src="images/configurerunicon.jpg" alt="Configure run icon"> next to the <span class="ph uicontrol">Run</span> button in the <span class="keyword">Build model</span> <span class="keyword">view</span>, a window opens showing you the currently set parameter values.</p><img id="ModelBuilderInterface__image_udd_nrv_2jb" src="images/runconfig.png" alt="Run configuration pane for scenario 1">
<p><span class="ph" id="ModelBuilderInterface__runconfig2">Here you can select and edit different run configuration parameters.</span> For more information, see <a href="../DODS_RunParameters/runparams.html#RunConfig" title="You can select various run parameters for the optimization solve in the Decision Optimization experiment UI.">Run parameters</a>.</p>
<p id="ModelBuilderInterface__runconfig3">After you set the run configuration parameters, they will be used with those values for all subsequent runs for that scenario.</p>
<p id="ModelBuilderInterface__runconfig4">You can remove set parameters by hovering over the parameter and clicking the <span class="ph uicontrol">Remove</span> icon.</p>
<p></p>
<p id="ModelBuilderInterface__envtabConfigRun">The <span class="ph uicontrol">Environment</span> tab in this pane shows you the default run environment that is being used for your <span class="keyword">experiment</span>. <img id="ModelBuilderInterface__image_wpr_lh3_psb" src="images/runconfigEnv.png" alt="Environment tab of Run Configuration pane for scenario 1"></p>
<p>When you solve a model by clicking <span class="ph uicontrol">Run</span>, this default environment is used or, if it doesn’t exist, it is created automatically. The type of environment that is used depends on your model type (Python, OPL, CPLEX, CPO, Modeling Assistant). For more information, see <a href="#ModelBuilderInterface__environtab">Environment tab in Overview information pane</a>. You can also <a href="configureEnvironments.html#task_hwswconfig" title="You can change your default environment for Python and CPLEX in the experiment Overview.">configure your environments</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__solution__title__1" id="ModelBuilderInterface__solution">
<h2 class="sectiontitle" id="ModelBuilderInterface__solution__title__1"><span class="ph uicontrol"><span class="keyword">Explore solution</span></span> <span class="keyword">view</span></h2>
<p><img data-hd-product="cloud wx icpd" id="ModelBuilderInterface__image_ab2_5gj_5pb" src="images/Cloudexploresolution2.png" alt="Explore solution view showing results for solved diet model"></p>
<p>When your run completes successfully, the solution is displayed in one or several tables, or as a file for CPLEX and CPO models, in the <strong><span class="ph uicontrol"><span class="keyword">Explore solution</span></span></strong> <span class="keyword">view</span>.</p>
<p>The <span class="ph uicontrol">Results</span> section contains several tabs. The first tab shows the <span class="ph uicontrol">Objectives </span>and <span class="ph uicontrol">KPIs</span>. The <span class="ph uicontrol">Solutions tables</span> tab shows the resulting (best) values for the decision variables. These solution tables are automatically displayed in alphabetical order. Note that these solution tables are not editable but can be filtered. See <a href="Visualization.html#topic_visualization__section_tablefilter">Table search and filtering</a>. You can download both the objectives and solution tables. For CPLEX and CPO models, the solutions are not provided in tables, but in files that you can download.</p>
<p>You can define output tables to appear in this <span class="keyword">view</span> in a Python <span class="keyword">DOcplex</span> model that uses the syntax <code class="ph codeph">outputs['tablename'],</code> see <a href="../DODS_Notebooks/preparedataIO.html#topic_prepareIO" title="You can access the input and output data you defined in the experiment UI by using the following dictionaries.">Input and output data</a>.</p>
<p>The <span class="ph uicontrol">Relaxations</span> and <span class="ph uicontrol">Conflicts</span> tabs show if there have been any conflicting constraints or bounds in the model. Also, if these options were chosen, these tabs show which constraints or bounds were relaxed in order to solve the model.</p>
<p>The <span class="ph uicontrol">Engine statistics</span> tab shows you information about the run status (processed, stopped, or failed), graphical information about the solution, and model statistics. You can zoom-in on the graph by moving the end points of the horizontal zoom bar, or by selecting an area in the graph. To restore the original graph after zooming in, you can fully expand the zoom bar or refresh the page.</p>
<p><img id="ModelBuilderInterface__image_mcn_2yj_snb" src="images/enginestats.jpg" alt="Engine statistics tab showing solution for diet model."></p>
<p>The <span class="ph uicontrol">Log</span> tab displays the log file from the CPLEX or CP Optimizer engines, which you can also download.</p>
<p><img id="ModelBuilderInterface__image_mxp_dzj_snb" src="images/solnlog.jpg" alt="Engine log tab showing log for diet model"></p>
<p id="ModelBuilderInterface__p_weightscales">For multi-objective models formulated with the <span class="keyword">Modeling Assistant</span>, the solution table also displays the <strong>sliders, weights</strong>, and<strong> scale factors</strong> that were set in the model. The combined objective is the sum of all the objective values (positive additions for minimize objectives and negative for maximize objectives) multiplied by the scale factor (1 by default) and the weight factor. The weight factor is 2 to the power of the slider weight minus 1. For example, a slider weight of 5, the weight factor is 2<sup>5-1</sup>= 2<sup>4</sup>= 16. The <span class="ph uicontrol">scaled weighted value</span> is thus the objective function value multiplied by this weight factor.</p>
<p data-hd-product="cloud wx">To run models, you must associate a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> instance with your <span class="ph uicontrol">Project </span> and associate a deployment space with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>. You must also have the <strong>Editor</strong> or <strong>Admin</strong> <a href="../../wsj/analyze-data/collaborator-permissions-wml.html">role in the deployment space</a>.</p>
<p id="ModelBuilderInterface__export_files">You can export files from this <span class="keyword">view</span>. See <a href="#ModelBuilderInterface__export_tables">Exporting data</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="ModelBuilderInterface__scenariopanel__title__1" id="ModelBuilderInterface__scenariopanel">
<h2 class="sectiontitle" id="ModelBuilderInterface__scenariopanel__title__1"><span class="keyword">Scenario</span> pane</h2>
<p>When you create a new Decision Optimization <span class="keyword">experiment</span>, a scenario is automatically created along with the model. A scenario contains data sets, a model, and a solution.</p>
<div class="p">
You can use scenarios to:
<ul>
<li>Make sure a specific model works with a variety of data.</li>
<li>See how different data sets impact the solution to your problem.</li>
<li>See how a model formulation impacts the solution to your problem.</li>
<li>Save the scenario as a model for deployment (any run configuration parameters that you might have set for that scenario are also saved in the deployment). See <a href="../WML_Deployment/DeployModelUI-WML.html#task_deployUIWML" title="You can save a model for deployment in the Decision Optimization experiment UI and promote it to your Watson Machine Learning deployment space.">Deploying a Decision Optimization model by using the user interface</a> for more details.</li>
</ul>
</div>
<p><img data-hd-product="cloud wx" id="ModelBuilderInterface__image_uvv_ymz_yfb" src="images/CloudScenarioPanel.jpg" alt="Scenario pane showing 2 scenarios with scenario 2 information expanded."></p>
<p>From the <span class="keyword">Scenario</span> pane you can easily manage different scenarios of a Decision Optimization <span class="keyword">experiment</span>.</p>
<p>To open the Scenario pane, click the <strong><span class="keyword">Open scenario pane</span></strong> button <img data-hd-product="cloud wx" id="ModelBuilderInterface__image_ejr_bcq_h3b" src="images/CPDscenariomanage.jpg" alt="Open scenario pane button">.</p>
<div class="p">
In this pane, you can:
<ul>
<li>Create new scenarios (<strong>create</strong> a new scenario from scratch, <strong>duplicate</strong> your current scenario, or <strong>import</strong> a new scenario from a file).</li>
<li>Select the scenario that you want to work in.</li>
<li>See existing scenarios and their details (input data, model, solution). Each one can be expanded or collapsed by clicking the arrow next to the scenario.</li>
<li>Manage existing scenarios (duplicate, rename, delete).</li>
<li>Generate a Python <span class="keyword">notebook</span> from a scenario.</li>
<li>Save the scenario as a model for deployment (The data types set in the <span class="keyword">Prepare data</span> <span class="keyword">view</span> and any run configuration parameters that you might have set for that scenario are also saved in the deployment). See <a href="../WML_Deployment/DeployModelUI-WML.html#task_deployUIWML" title="You can save a model for deployment in the Decision Optimization experiment UI and promote it to your Watson Machine Learning deployment space.">Deploying a Decision Optimization model by using the user interface</a> for more details.</li>
<li>Export the scenario as a <code class="ph codeph">.zip</code> file.</li>
</ul>
</div>
<p id="ModelBuilderInterface__generateNB">If you click <strong><span class="ph uicontrol">Generate a <span class="keyword" translate="no">notebook</span></span> from a scenario,</strong> the <span class="keyword">notebook</span> is saved as an asset in your project. If you have used <strong>multiple files</strong> in the <span class="keyword">Build model</span> <span class="keyword">view</span>, these files are automatically referenced in the generated <span class="keyword">notebook</span> so that you can read them from the <span class="keyword">notebook</span>. The Python version for your generated <span class="keyword">notebook</span> depends on the environment that you have configured for your scenario, see <a href="configureEnvironments.html#task_hwswconfig" title="You can change your default environment for Python and CPLEX in the experiment Overview.">Configuring environments</a>. If the environment was automatically created for your scenario, the <span class="keyword">notebook</span> uses the default Python version <span class="keyword">3.10</span>.</p>
<p id="ModelBuilderInterface__p_Exportingscenarios">If you click <span class="ph uicontrol">Export as zip file</span>, a <span class="ph filepath">scenario.json</span> file that describes the exported model is also included in the archive. If you make changes locally to this scenario (for example you add a table to your model), you can then edit this <code class="ph codeph">json</code> file to include these changes. You can then re-import your scenario and these changes appear in your scenario.</p>
<p id="ModelBuilderInterface__p_Importingscenarios"><strong>New scenarios</strong> can be <strong>imported</strong> by choosing <span class="ph uicontrol">From file </span> in the <span class="ph uicontrol">Create Scenario</span> menu and then selecting the <code class="ph codeph">.zip</code> file that contains your new scenario.</p>
<p>You can also use this method to create a new scenario from a debug <code class="ph codeph">.zip</code> file that you have generated (see <a href="../DODS_RunParameters/runparams.html" title="You can select various run parameters for the optimization solve in the Decision Optimization experiment UI.">Custom parameters</a>) and downloaded. The debug <code class="ph codeph">.zip</code> file provides you with a scenario that contains data, model, solution, and the run configuration parameters.</p>
<p>You can <span class="ph uicontrol">switch scenarios</span> while running a model and see in the scenario pane which scenarios are running or are queued.</p>
<p>Clicking the arrow next to a scenario in this pane also reveals summary information about the data, model, and solution.</p>
<p>Your scenario uses the <span class="ph uicontrol">default run environment</span> that was created for that model type. You can view this default on the <span class="ph uicontrol">Overview</span> Information pane <img id="ModelBuilderInterface__image_ly3_hvf_rsb" src="images/infoicon.jpg" alt="Information pane icon"> <span class="ph uicontrol">Environment </span>tab. For more information, see <a href="#ModelBuilderInterface__section_environment">Hardware and software configuration</a> and <a href="configureEnvironments.html#task_hwswconfig" title="You can change your default environment for Python and CPLEX in the experiment Overview.">Configuring environments</a>. To change the run environment for a particular scenario see <a href="configureEnvironments.html#task_envscenario" title="You can choose different environments for individual scenarios on the Environment tab of the Run configuration pane.">Selecting a different run environment for a particular scenario</a>.</p>
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1C20BD9F24D670DD18B6BC28E020FBB23C742682 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/CustomRules.html?context=cdpaas&locale=en | Creating advanced custom constraints with Python in the Decision Optimization Modeling Assistant | Creating advanced custom constraints with Python
This Decision Optimization Modeling Assistant example shows you how to create advanced custom constraints that use Python.
Procedure
To create a new advanced custom constraint:
1. In the Build model view of your open Modeling Assistant model, look at the Suggestions pane. If you have Display by category selected, expand the Others section to locate New custom constraint, and click it to add it to your model. Alternatively, without categories displayed, you can enter, for example, custom in the search field to find the same suggestion and click it to add it to your model.A new custom constraint is added to your model.
2. Click Enter your constraint. Use [brackets] for data, concepts, variables, or parameters and enter the constraint you want to specify. For example, type No [employees] has [onCallDuties] for more than [2] consecutive days and press enter.The specification is displayed with default parameters (parameter1, parameter2, parameter3) for you to customize. These parameters will be passed to the Python function that implements this custom rule.
3. Edit the default parameters in the specification to give them more meaningful names. For example, change the parameters to employees, on_call_duties, and limit and click enter.
4. Click function name and enter a name for the function. For example, type limitConsecutiveAssignments and click enter.Your function name is added and an Edit Python button appears.
5. Click the Edit Python button.A new window opens showing you Python code that you can edit to implement your custom rule. You can see your customized parameters in the code as follows:
Notice that the code is documented with corresponding data frames and table column names as you have defined in the custom rule. The limit is not documented as this is a numerical value.
6. Optional: You can edit the Python code directly in this window, but you might find it useful to edit and debug your code in a notebook before using it here. In this case, close this window for now and in the Scenario pane, expand the three vertical dots and select Generate a notebook for this scenario that contains the custom rule. Enter a name for this notebook.The notebook is created in your project assets ready for you to edit and debug. Once you have edited, run and debugged it you can copy the code for your custom function back into this Edit Python window in the Modeling Assistant.
7. Edit the Python code in the Modeling Assistant custom rule Edit Python window. For example, you can define the rule for consecutive days in Python as follows:
def limitConsecutiveAssignments(self, mdl, employees, on_call_duties, limit):
global helper_add_labeled_cplex_constraint, helper_get_index_names_for_type, helper_get_column_name_for_property
print('Adding constraints for the custom rule')
for employee, duties in employees.associated(on_call_duties):
duties_day_idx = duties.join(Day) Retrieve Day index from Day label
for d in Day['index']:
end = d + limit + 1 One must enforce that there are no occurence of (limit + 1) working consecutive days
duties_in_win = duties_day_idx[((duties_day_idx'index'] >= d) & (duties_day_idx'index'] <= end)) | (duties_day_idx'index'] <= end - 7)]
mdl.add_constraint(mdl.sum(duties_in_win.onCallDutyVar) <= limit)
8. Click the Run button to run your model with your custom constraint.When the run is completed you can see the results in the Explore solution view.
| # Creating advanced custom constraints with Python #
This Decision Optimization Modeling Assistant example shows you how to create advanced custom constraints that use Python\.
## Procedure ##
To create a new advanced custom constraint:
<!-- <ol> -->
1. In the Build model view of your open Modeling Assistant model, look at the Suggestions pane\. If you have Display by category selected, expand the Others section to locate New custom constraint, and click it to add it to your model\. Alternatively, without categories displayed, you can enter, for example, custom in the search field to find the same suggestion and click it to add it to your model\.A new custom constraint is added to your model\.
2. Click Enter your constraint\. Use \[brackets\] for data, concepts, variables, or parameters and enter the constraint you want to specify\. For example, type No \[employees\] has \[onCallDuties\] for more than \[2\] consecutive days and press enter\.The specification is displayed with default parameters (`parameter1, parameter2, parameter3`) for you to customize\. These parameters will be passed to the Python function that implements this custom rule\.
3. Edit the default parameters in the specification to give them more meaningful names\. For example, change the parameters to `employees, on_call_duties`, and `limit` and click enter\.
4. Click function name and enter a name for the function\. For example, type limitConsecutiveAssignments and click enter\.Your function name is added and an Edit Python button appears\.
5. Click the Edit Python button\.A new window opens showing you Python code that you can edit to implement your custom rule\. You can see your customized parameters in the code as follows:
Notice that the code is documented with corresponding data frames and table column names as you have defined in the custom rule. The limit is not documented as this is a numerical value.
6. Optional: You can edit the Python code directly in this window, but you might find it useful to edit and debug your code in a notebook before using it here\. In this case, close this window for now and in the Scenario pane, expand the three vertical dots and select Generate a notebook for this scenario that contains the custom rule\. Enter a name for this notebook\.The notebook is created in your project assets ready for you to edit and debug\. Once you have edited, run and debugged it you can copy the code for your custom function back into this Edit Python window in the Modeling Assistant\.
7. Edit the Python code in the Modeling Assistant custom rule Edit Python window\. For example, you can define the rule for consecutive days in Python as follows:
def limitConsecutiveAssignments(self, mdl, employees, on_call_duties, limit):
global helper_add_labeled_cplex_constraint, helper_get_index_names_for_type, helper_get_column_name_for_property
print('Adding constraints for the custom rule')
for employee, duties in employees.associated(on_call_duties):
duties_day_idx = duties.join(Day) # Retrieve Day index from Day label
for d in Day['index']:
end = d + limit + 1 # One must enforce that there are no occurence of (limit + 1) working consecutive days
duties_in_win = duties_day_idx[((duties_day_idx'index'] >= d) & (duties_day_idx'index'] <= end)) | (duties_day_idx'index'] <= end - 7)]
mdl.add_constraint(mdl.sum(duties_in_win.onCallDutyVar) <= limit)
8. Click the Run button to run your model with your custom constraint\.When the run is completed you can see the results in the **Explore solution** view\.
<!-- </ol> -->
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<h1 class="topictitle1" id="CustomRules__title__1"><span class="ph" data-hd-product="cloud wx">Creating advanced custom constraints with Python</span></h1>
<div class="body taskbody">
<p class="shortdesc">This <span class="keyword">Decision Optimization</span> <span class="keyword">Modeling Assistant</span> example shows you how to create advanced custom constraints that use Python.</p>
<section role="region" class="section prereq" id="CustomRules__prereq_frj_zjf_mrb" aria-labelledby="taskCustomRules__prereq_frj_zjf_mrb">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="taskCustomRules__prereq_frj_zjf_mrb">Before you begin</h2>
</div>Open any <span class="keyword">Decision Optimization</span> model in the <span class="keyword">Decision Optimization</span> <span class="keyword">Modeling Assistant</span>. This example uses the <code class="ph codeph">Shift Assignment</code> sample, that is available in the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a>, and uses the <code class="ph codeph">AssignmentWithOnCallDuties</code> scenario. The <code class="ph codeph">AssignmentWithCustomRule</code> scenario in this same sample shows you the completed model with this custom constraint already added.
</section>
<section class="section context" role="region" aria-labelledby="taskCustomRules__context__1">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="taskCustomRules__context__1">About this task</h2>
</div>
<p>The <span class="keyword">Modeling Assistant</span> provides you with many constraint suggestions for your problem domain which can be customized. You might, however, want to express constraints beyond those that are predefined for the given domains. You can achieve this by using more advanced custom constraints that use Python <a href="https://ibmdecisionoptimization.github.io/docplex-doc/2.23.222/index.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DOcplex</a>. This example illustrates how you can create these.</p>
<p><span class="ph">This video provides a visual method to learn the concepts and tasks in this documentation.</span> After you load the example in your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> you can follow the video.</p>
<p>Video disclaimer: Some minor steps and graphical steps in this video might differ from your platform. The user interface is also frequently improved.</p><iframe webkitallowfullscreen="" allowfullscreen src="https://video.ibm.com/embed/channel/23952663/video/wx-do-custom-constraints" width="606" height="341" title="This video demonstrates creating custom rules with Python DOcplex in the Modeling
Assistant"></iframe>
<p>Read more in this <a href="https://community.ibm.com/community/user/datascience/blogs/tymoteusz-gedliczka/2022/06/21/modeling-assistant-with-custom-constraints?CommunityKey=ab7de0fd-6f43-47a9-8261-33578a231bb7" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Decision Optimization blog</a> on custom constraints with Python found on the IBM Data Science community page.</p>
<div class="p">
<div class="note" data-hd-product="cloud wx">
<span class="notetitle">Note:</span> To create and run Optimization models, you must have both a <span class="keyword">Machine Learning</span> service added to your project and a deployment space that is associated with your <span class="keyword">experiment</span>:
<ol id="CustomRules__d23e180">
<li>Add a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong><span class="keyword">Machine Learning</span></strong> service</a> to your project. You can either add this service at the project level (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-service-instance.html">Creating a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> Service instance</a>), or you can add it when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span>, select, or create a <span class="ph uicontrol">New service</span>, click <span class="ph uicontrol">Associate</span>, then close the window.</li>
<li>Associate a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong>deployment space</strong></a> with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-spaces_local.html#create">Deployment spaces</a>). A deployment space can be created or selected when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Create a deployment space</span>, enter a name for your deployment space, and click <span class="ph uicontrol">Create</span>. For existing models, you can also create, or select a space in the <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> information pane.</li>
</ol>
</div>
</div>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="taskCustomRules__steps__1">Procedure</h2>
</div>
<p class="li stepsection">To create a new advanced custom constraint:</p>
<ol class="steps">
<li class="step stepexpand"><span class="cmd">In the <span class="ph uicontrol"><span class="keyword">Build model</span></span> <span class="keyword">view</span> of your open <span class="keyword">Modeling Assistant</span> model, look at the <span class="ph uicontrol">Suggestions</span> pane. If you have <span class="ph uicontrol">Display by category</span> selected, expand the<span class="ph uicontrol"> Others</span> section to locate <span class="ph uicontrol">New custom constraint</span>, and click it to add it to your model. Alternatively, without categories displayed, you can enter, for example, <kbd class="ph userinput">custom</kbd> in the search field to find the same suggestion and click it to add it to your model.</span>
<div class="itemgroup stepresult">
A new custom constraint is added to your model.
<p><img id="CustomRules__image_fvn_vwf_mrb" src="images/newcustomconstraint.jpg" alt="New custom constraint in model, with elements highlighted to be completed by user."></p>
</div></li>
<li class="step stepexpand"><span class="cmd">Click <span class="ph uicontrol">Enter your constraint. Use [brackets] for data, concepts, variables, or parameters </span> and enter the constraint you want to specify. For example, type <kbd class="ph userinput">No [employees] has [onCallDuties] for more than [2] consecutive days</kbd> and press enter.</span>
<div class="itemgroup stepresult">
The specification is displayed with default parameters (<code class="ph codeph">parameter1, parameter2, parameter3</code>) for you to customize. These parameters will be passed to the Python function that implements this custom rule.
<p><img id="CustomRules__image_nyq_vvl_mrb" src="images/customconstraintFillParameters.jpg" alt="Custom constraint expanded to show default parameters and function name."></p>
</div></li>
<li class="step stepexpand"><span class="cmd">Edit the default parameters in the specification to give them more meaningful names. For example, change the parameters to <code class="ph codeph">employees, on_call_duties</code>, and <code class="ph codeph">limit</code> and click enter.</span></li>
<li class="step stepexpand"><span class="cmd">Click function name and enter a name for the function. For example, type <kbd class="ph userinput">limitConsecutiveAssignments</kbd> and click enter.</span>
<div class="itemgroup stepresult">
Your function name is added and an <span class="ph uicontrol">Edit Python</span> button appears.
<p><img id="CustomRules__image_ax3_2vl_mrb" src="images/customconstraintParameters.jpg" alt="Custom rule showing customized parameters and Edit Python button."></p>
</div></li>
<li class="step stepexpand"><span class="cmd">Click the <span class="ph uicontrol">Edit Python</span> button.</span>
<div class="itemgroup stepresult">
A new window opens showing you Python code that you can edit to implement your custom rule. You can see your customized parameters in the code as follows:
<p><img src="images/CustomRulePythoncode.jpg" alt="Python code showing block to be customized"></p> Notice that the code is documented with corresponding data frames and table column names as you have defined in the custom rule. The limit is not documented as this is a numerical value.
</div></li>
<li class="step stepexpand"><span class="cmd">Optional: You can edit the Python code directly in this window, but you might find it useful to edit and debug your code in a notebook before using it here. In this case, close this window for now and in the <span class="keyword">Scenario</span> pane, expand the three vertical dots and select <span class="ph uicontrol">Generate a notebook</span> for this scenario that contains the custom rule. Enter a name for this notebook.</span>
<div class="itemgroup stepresult">
The notebook is created in your project assets ready for you to edit and debug. Once you have edited, run and debugged it you can copy the code for your custom function back into this <span class="ph uicontrol">Edit Python</span> window in the Modeling Assistant.
</div></li>
<li class="step stepexpand"><span class="cmd">Edit the Python code in the <span class="keyword">Modeling Assistant</span> custom rule <span class="ph uicontrol">Edit Python</span> window. </span>
<div class="itemgroup stepresult">
For example, you can define the rule for consecutive days in Python as follows:
<pre class="codeblock language-shell"><code class="language-shell"> def limitConsecutiveAssignments(self, mdl, employees, on_call_duties, limit):
global helper_add_labeled_cplex_constraint, helper_get_index_names_for_type, helper_get_column_name_for_property
print('Adding constraints for the custom rule')
for employee, duties in employees.associated(on_call_duties):
duties_day_idx = duties.join(Day) # Retrieve Day index from Day label
for d in Day['index']:
end = d + limit + 1 # One must enforce that there are no occurence of (limit + 1) working consecutive days
duties_in_win = duties_day_idx[((duties_day_idx['index'] >= d) & (duties_day_idx['index'] <= end)) | (duties_day_idx['index'] <= end - 7)]
mdl.add_constraint(mdl.sum(duties_in_win.onCallDutyVar) <= limit)</code></pre>
</div></li>
<li class="step stepexpand"><span class="cmd">Click the <span class="ph uicontrol">Run</span> button to run your model with your custom constraint.</span>
<div class="itemgroup stepresult">
When the run is completed you can see the results in the <strong><span class="ph uicontrol"><span class="keyword">Explore solution</span></span></strong> <span class="keyword">view</span>.
</div></li>
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C07CD6DF8C92EDD0F2638573BFDCE7BF18AA2EB0 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/advancedMA.html?context=cdpaas&locale=en | Creating constraints and custom decisions with the Decision Optimization Modeling Assistant | Adding multi-concept constraints and custom decisions: shift assignment
This Decision Optimization Modeling Assistant example shows you how to use multi-concept iterations, the associated keyword in constraints, how to define your own custom decisions, and define logical constraints. For illustration, a resource assignment problem, ShiftAssignment, is used and its completed model with data is provided in the DO-samples.
Procedure
To download and open the sample:
1. Download the ShiftAssignment.zip file from the Model_Builder subfolder in the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples). Select the relevant product and version subfolder.
2. Open your project or create an empty project.
3. On the Manage tab of your project, select the Services and integrations section and click Associate service. Then select an existing Machine Learning service instance (or create a new one ) and click Associate. When the service is associated, a success message is displayed, and you can then close the Associate service window.
4. Select the Assets tab.
5. Select New asset > Solve optimization problems in the Work with models section.
6. Click Local file in the Solve optimization problems window that opens.
7. Browse locally to find and choose the ShiftAssignment.zip archive that you downloaded. Click Open. Alternatively use drag and drop.
8. Associate a Machine Learning service instance with your project and reload the page.
9. If you haven't already associated a Machine Learning service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment.
10. Click Create.A Decision Optimization model is created with the same name as the sample.
11. Open the scenario pane and select the AssignmentWithOnCallDuties scenario.
Using multi-concept iteration
Procedure
To use multi-concept iteration, follow these steps.
1. Click Build model in the sidebar to view your model formulation.The model formulation shows the intent as being to assign employees to shifts, with its objectives and constraints.
2. Expand the constraint For each Employee-Day combination , number of associated Employee-Shift assignments is less than or equal to 1.
Defining custom decisions
Procedure
To define custom decisions, follow these steps.
1. Click Build model to see the model formulation of the AssignmentWithOnCallDuties Scenario.
The custom decision OnCallDuties is used in the second objective. This objective ensures that the number of on-call duties are balanced over Employees.
The constraint  ensures that the on-call duty requirements that are listed in the Day table are satisfied.
The following steps show you how this custom decision OnCallDuties was defined.
2. Open the Settings pane and notice that the Visualize and edit decisions is set to true (or set it to true if it is set to the default false).
This setting adds a Decisions tab to your Add to model window.
Here you can see OnCallDuty is specified as an assignment decision (to assign employees to on-call duties). Its two dimensions are defined with reference to the data tables Day and Employee. This means that your model will also assign on-call duties to employees. The Employee-Shift assignment decision is specified from the original intent.
3. Optional: Enter your own text to describe the OnCallDuty in the [to be documented] field.
4. Optional: To create your own decision in the Decisions tab, click the enter name, type in a name and click enter. A new decision (intent) is created with that name with some highlighted fields to be completed by using the drop-down menus. If you, for example, select assignment as the decision type, two dimensions are created. As assignment involves assigning at least one thing to another, at least two dimensions must be defined. Use select a table fields to define the dimensions.
Using logical constraints
Procedure
To use logical constraints:
1. Look at the constraint This constraint ensures that, for each employee and day combination, when no associated assignments exist (for example, the employee is on vacation on that day), that no on-call duties are assigned to that employee on that day. Note the use of the if...then keywords to define this logical constraint.
2. Optional: Add other logical constraints to your model by searching in the suggestions.
| # Adding multi\-concept constraints and custom decisions: shift assignment #
This Decision Optimization Modeling Assistant example shows you how to use multi\-concept iterations, the `associated` keyword in constraints, how to define your own custom decisions, and define logical constraints\. For illustration, a resource assignment problem, `ShiftAssignment`, is used and its completed model with data is provided in the **DO\-samples**\.
## Procedure ##
To download and open the sample:
<!-- <ol> -->
1. Download the ShiftAssignment\.zip file from the Model\_Builder subfolder in the **[DO\-samples](https://github.com/IBMDecisionOptimization/DO-Samples)**\. Select the relevant product and version subfolder\.
2. Open your project or create an empty project\.
3. On the Manage tab of your project, select the Services and integrations section and click Associate service\. Then select an existing Machine Learning service instance (or create a new one ) and click Associate\. When the service is associated, a success message is displayed, and you can then close the Associate service window\.
4. Select the Assets tab\.
5. Select New asset > Solve optimization problems in the Work with models section\.
6. Click Local file in the Solve optimization problems window that opens\.
7. Browse locally to find and choose the ShiftAssignment\.zip archive that you downloaded\. Click Open\. Alternatively use drag and drop\.
8. Associate a **Machine Learning service instance** with your project and reload the page\.
9. If you haven't already associated a Machine Learning service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment\.
10. Click **Create**\.A Decision Optimization model is created with the same name as the sample\.
11. Open the scenario pane and select the `AssignmentWithOnCallDuties` scenario\.
<!-- </ol> -->
<!-- <article "class="topic task nested1" role="article" id="task_multiconceptiterations" "> -->
## Using multi\-concept iteration ##
### Procedure ###
To use multi\-concept iteration, follow these steps\.
<!-- <ol> -->
1. Click Build model in the sidebar to view your model formulation\.The model formulation shows the intent as being to assign employees to shifts, with its objectives and constraints\.
2. Expand the constraint `For each Employee-Day combination , number of associated Employee-Shift assignments is less than or equal to 1`\.
<!-- </ol> -->
<!-- </article "class="topic task nested1" role="article" id="task_multiconceptiterations" "> -->
<!-- <article "class="topic task nested1" role="article" id="task_customdecision" "> -->
## Defining custom decisions ##
### Procedure ###
To define custom decisions, follow these steps\.
<!-- <ol> -->
1. Click Build model to see the model formulation of the `AssignmentWithOnCallDuties` Scenario\.
The custom decision `OnCallDuties` is used in the second objective. This objective ensures that the number of on-call duties are balanced over Employees.
The constraint  ensures that the on-call duty requirements that are listed in the Day table are satisfied.
The following steps show you how this custom decision `OnCallDuties` was defined.
2. Open the Settings pane and notice that the Visualize and edit decisions is set to `true` (or set it to true if it is set to the default false)\.
This setting adds a Decisions tab to your Add to model window.
Here you can see `OnCallDuty` is specified as an assignment decision (to assign employees to on-call duties). Its two dimensions are defined with reference to the data tables `Day` and `Employee`. This means that your model will also assign on-call duties to employees. The Employee-Shift assignment decision is specified from the original intent.
3. Optional: Enter your own text to describe the `OnCallDuty` in the \[to be documented\] field\.
4. Optional: To create your own decision in the Decisions tab, click the enter name, type in a name and click enter\. A new decision (intent) is created with that name with some highlighted fields to be completed by using the drop\-down menus\. If you, for example, select assignment as the decision type, two dimensions are created\. As assignment involves assigning at least one thing to another, at least two dimensions must be defined\. Use select a table fields to define the dimensions\.
<!-- </ol> -->
<!-- </article "class="topic task nested1" role="article" id="task_customdecision" "> -->
<!-- <article "class="topic task nested1" role="article" id="task_impliedconstraints" "> -->
## Using logical constraints ##
### Procedure ###
To use logical constraints:
<!-- <ol> -->
1. Look at the constraint This constraint ensures that, for each employee and day combination, when no associated assignments exist (for example, the employee is on vacation on that day), that no on\-call duties are assigned to that employee on that day\. Note the use of the `if...then` keywords to define this logical constraint\.
2. Optional: Add other logical constraints to your model by searching in the suggestions\.
<!-- </ol> -->
<!-- </article "class="topic task nested1" role="article" id="task_impliedconstraints" "> -->
<!-- </article "role="article" "> -->
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<h1 class="topictitle1" id="task_advMA__title__1"><span class="ph" data-hd-product="cloud wx">Adding multi-concept constraints and custom decisions: shift assignment</span></h1>
<div class="body taskbody">
<p class="shortdesc">This <span class="keyword">Decision Optimization</span> <span class="keyword">Modeling Assistant</span> example shows you how to use multi-concept iterations, the <code class="ph codeph">associated</code> keyword in constraints, how to define your own custom decisions, and define logical constraints. For illustration, a resource assignment problem, <code class="ph codeph">ShiftAssignment</code>, is used and its completed model with data is provided in the <strong><span class="keyword">DO-samples</span></strong>.</p>
<section class="section context" role="region" aria-label="Adding multi-concept constraints and custom
decisions: shift assignment: About this task">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_advMA__context__1">About this task</h2>
</div>
<p>This example is about assigning employees to different shifts, and each day requires that a required number of employees must be on-call. The files that are used in this sample are available in the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a>.</p>
<p><span class="ph" id="task_advMA__Thisvideo">This video provides a visual method to learn the concepts and tasks in this documentation.</span> After you load the example in your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>, you can follow the video.</p>
<p id="task_advMA__videodisclaimer">Video disclaimer: Some minor steps and graphical steps in this video might differ from your platform. The user interface is also frequently improved.</p><iframe webkitallowfullscreen="" allowfullscreen src="https://video.ibm.com/embed/channel/23952663/video/wx-do-modeling-assistant" width="606" height="341" title="This video provides an overview of the shift assignment example to illustrate multi-concept
constraints, custom decisions, and logical constraints."></iframe>
<div class="note" data-hd-product="cloud wx">
<span class="notetitle">Note:</span> To create and run Optimization models, you must have both a <span class="keyword">Machine Learning</span> service added to your project and a deployment space that is associated with your <span class="keyword">experiment</span>:
<ol id="task_advMA__d23e180">
<li>Add a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong><span class="keyword">Machine Learning</span></strong> service</a> to your project. You can either add this service at the project level (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-service-instance.html">Creating a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> Service instance</a>), or you can add it when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span>, select, or create a <span class="ph uicontrol">New service</span>, click <span class="ph uicontrol">Associate</span>, then close the window.</li>
<li>Associate a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong>deployment space</strong></a> with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-spaces_local.html#create">Deployment spaces</a>). A deployment space can be created or selected when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Create a deployment space</span>, enter a name for your deployment space, and click <span class="ph uicontrol">Create</span>. For existing models, you can also create, or select a space in the <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> information pane.</li>
</ol>
</div>
<div class="p">
Quick links:
<ul id="task_advMA__ul_qwt_vqn_4pb">
<li><a href="#task_advMA__data">Download and open the Shift Assignment sample</a></li>
<li><a href="#task_multiconceptiterations">Using multi-concept iteration</a></li>
<li><a href="#task_customdecision">Defining custom decisions</a></li>
<li><a href="#task_impliedconstraints">Using logical constraints</a></li>
</ul>
</div>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_advMA__data">Procedure</h2>
</div>
<p class="li stepsection">To download and open the sample:</p>
<ol class="steps" id="task_advMA__data">
<li class="step"><span class="cmd">Download the <span class="ph filepath">ShiftAssignment.zip</span> file from the <span class="ph filepath">Model_Builder</span> subfolder in the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>. <span class="ph">Select the relevant product and version subfolder.</span></span></li>
<li class="step"><span class="cmd"><span class="ph">Open your project or create an empty project.</span></span></li>
<li class="step" data-hd-product="cloud wx"><span class="cmd"><span class="ph">On the <span class="ph uicontrol">Manage</span> tab of your project, select the <span class="ph uicontrol">Services and integrations</span> section and click <span class="ph uicontrol">Associate service</span>. Then select an existing <span class="keyword">Machine Learning</span> service instance (or create a new one ) and click <span class="ph uicontrol">Associate</span>. When the service is associated, a success message is displayed, and you can then close the <span class="keyword wintitle">Associate service</span> window. </span></span></li>
<li class="step"><span class="cmd"><span class="ph">Select the <span class="ph" data-hd-product="wx"><span class="ph uicontrol"><span class="keyword">Assets</span></span></span> tab.</span></span></li>
<li class="step" data-hd-product="wx"><span class="cmd"><span class="ph">Select <span class="ph uicontrol"><span class="keyword">New asset > Solve optimization problems</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></span></li>
<li class="step"><span class="cmd"><span class="ph">Click <span class="ph uicontrol">Local file</span> in the <span class="ph" data-hd-product="wx"><span class="keyword">Solve optimization problems</span></span> window that opens.</span></span></li>
<li class="step"><span class="cmd">Browse locally to find and choose the <span class="ph filepath">ShiftAssignment.zip</span> archive that you downloaded. Click <span class="ph uicontrol">Open</span>. Alternatively use drag and drop.</span></li>
<li class="step" data-hd-product="cloud wx"><span class="cmd">Associate a <strong><span class="ph uicontrol">Machine Learning service instance</span></strong> with your project and reload the page.</span></li>
<li class="step" data-hd-product="cloud wx"><span class="cmd"><span class="ph">If you haven't already associated a <span class="keyword">Machine Learning</span> service with your project, you must first select <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span> to select or create one before you choose a deployment space for your <span class="keyword">experiment</span>.</span></span></li>
<li class="step"><span class="cmd">Click <strong><span class="ph uicontrol">Create</span></strong>.</span>
<div class="itemgroup stepresult">
A <span class="keyword">Decision Optimization</span> model is created with the same name as the sample.
</div></li>
<li class="step"><span class="cmd">Open the scenario pane and select the <code class="ph codeph">AssignmentWithOnCallDuties</code> scenario.</span></li>
</ol>
<section class="section result" role="region" id="task_advMA__result_abv_qj5_4pb" aria-label="Adding multi-concept constraints and custom
decisions: shift assignment: Results">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_advMA__result_abv_qj5_4pb">Results</h2>
</div>
<p>In the <span class="ph uicontrol"><span class="keyword">Prepare data</span></span> <span class="keyword">view</span> of the <code class="ph codeph">AssignmentWithOnCallDuties</code> <span class="keyword">Scenario</span>, you can see the data assets imported. These tables represent the shifts, the employees that need to be assigned to these shifts and days with their required on-call duties.</p>
<p><img data-hd-product="cloud wx" id="task_advMA__image_axz_nh4_5pb" src="images/CloudPrepareShift.jpg" alt="Prepare data view showing Staff Assignment data"></p>
</section>
</div>
<aside role="complementary" aria-labelledby="task_advMA__title__1">
<nav role="navigation">
<div class="familylinks">
<div class="parentlink">
<strong>Parent topic:</strong> <a href="../DODS_Mdl_Assist/exhousebuildintro.html" title="You can model and solve Decision Optimization problems using the Modeling Assistant (which enables you to formulate models in natural language). This requires little to no knowledge of Operational Research (OR) and does not require you to write Python code. The Modeling Assistant is only available in English and is not globalized.">Modeling Assistant models</a>
</div>
</div>
</nav>
</aside>
<article class="topic task nested1" role="article" aria-labelledby="task_multiconceptiterations__title__1" id="task_multiconceptiterations">
<h2 class="topictitle2" id="task_multiconceptiterations__title__1">Using multi-concept iteration</h2>
<div class="body taskbody">
<section class="section context" role="region" id="task_multiconceptiterations__context_fd4_nqz_4pb" aria-label="Using multi-concept iteration: About this task">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_multiconceptiterations__context_fd4_nqz_4pb">About this task</h3>
</div>
<p>Suppose that you want your model formulation to express the rule that each employee can’t work more than one shift per day. For example, an employee cannot be assigned two shifts on the same day. You might use, as shown in this model formulation, a constraint for each day of the week.</p>
<p><img id="task_multiconceptiterations__image_ecm_wsz_4pb" src="images/StaffAssignLegacyModel.jpg" alt="Several constraints, one for each day, to formulate only one shift per employee per day"></p>
<p>But listing constraints for each day of the week is cumbersome: if your model were based on days in the year, you must then list hundreds of these type of constraints. The following procedure shows you how to use multi-concept iteration and the <code class="ph codeph">associated</code> keyword to express iteration over more than one concept. Thus, you can express such a rule with just one phrase.</p>
</section>
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_multiconceptiterations__steps_jwg_pqn_4pb">Procedure</h3>
</div>
<p class="li stepsection">To use multi-concept iteration, follow these steps.</p>
<ol class="steps" id="task_multiconceptiterations__steps_jwg_pqn_4pb">
<li class="step"><span class="cmd">Click <span class="ph uicontrol"><span class="keyword">Build model</span></span> in the sidebar to view your model formulation.</span>
<div class="itemgroup stepresult">
The model formulation shows the intent as being to assign employees to shifts, with its objectives and constraints.
</div></li>
<li class="step"><span class="cmd">Expand the constraint <code class="ph codeph">For each Employee-Day combination , number of associated Employee-Shift assignments is less than or equal to 1</code>.</span></li>
</ol>
<section class="section result" role="region" id="task_multiconceptiterations__result_kgt_2j5_4pb" aria-label="Using multi-concept iteration: Results">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_multiconceptiterations__result_kgt_2j5_4pb">Results</h3>
</div><img id="task_multiconceptiterations__image_cpj_y5z_4pb" src="images/multiconcept.jpg" alt="Employee-day combination constraint expanded">
<p>This constraint combines employees and days with the keyword <code class="ph codeph">associated</code> so that the expression iterates over both employee and day. You can see that the employee in the employee-day combination is mapped to the employee in the employee-shift assignment. Also the day in the employee-day combination is mapped to the day property of the assigned shift. Thus, the combinations are correctly and automatically handled for you.</p>
<p>With this multi-concept iteration, you can specify new groups of rules that combine different concepts.</p>
</section>
</div>
</article>
<article class="topic task nested1" role="article" aria-labelledby="task_customdecision__title__1" id="task_customdecision">
<h2 class="topictitle2" id="task_customdecision__title__1">Defining custom decisions</h2>
<div class="body taskbody">
<section class="section context" role="region" id="task_customdecision__context_f3h_jvz_4pb" aria-label="Defining custom decisions: About this task">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_customdecision__context_f3h_jvz_4pb">About this task</h3>
</div>
<p>Suppose that you also want to assign on-call duties. You might create another model with the intent to assign employees to on-call duties, but then you would not be able to state dependency rules between the two models. By adding custom decisions to your existing model, as demonstrated in this example, you can define dependencies between shift assignment and on-call duties. Here the custom decision is called <code class="ph codeph">OnCallDuties</code>.</p>
</section>
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_customdecision__steps_s1h_ddn_4pb">Procedure</h3>
</div>
<p class="li stepsection">To define custom decisions, follow these steps.</p>
<ol class="steps" id="task_customdecision__steps_s1h_ddn_4pb">
<li class="step stepexpand"><span class="cmd">Click <span class="ph uicontrol"><span class="keyword">Build model</span></span> to see the model formulation of the <code class="ph codeph">AssignmentWithOnCallDuties</code> <span class="keyword">Scenario</span>.</span>
<div class="itemgroup stepresult">
<img data-hd-product="cloud wx" id="task_customdecision__image_mjm_pk4_5pb" src="images/CloudStaffAssignRunModel.png" alt="Build model view showing Shift Assignment formulation">
<p>The custom decision <code class="ph codeph">OnCallDuties</code> is used in the second objective. This objective ensures that the number of on-call duties are balanced over Employees.</p>
<p>The constraint <img id="task_customdecision__image_gyk_321_ppb" src="images/StaffAssignOncallDuty.jpg" alt="On call duty constraint"> ensures that the on-call duty requirements that are listed in the Day table are satisfied.</p>
<p>The following steps show you how this custom decision <code class="ph codeph">OnCallDuties</code> was defined.</p>
</div></li>
<li class="step stepexpand"><span class="cmd">Open the <span class="ph uicontrol">Settings</span> pane and notice that the <span class="ph uicontrol">Visualize and edit decisions</span> is set to <code class="ph codeph">true</code> (or set it to true if it is set to the default false).</span>
<div class="itemgroup stepresult">
<p>This setting adds a <span class="ph uicontrol">Decisions</span> tab to your <span class="ph uicontrol">Add to model</span> window.</p>
<p><img id="task_customdecision__image_d11_3f1_ppb" src="images/DecisionsTab.jpg" alt="Decisions tab of the Add to Model pane showing two intents"></p>
<p>Here you can see <code class="ph codeph">OnCallDuty</code> is specified as an assignment decision (to assign employees to on-call duties). Its two dimensions are defined with reference to the data tables <code class="ph codeph">Day</code> and <code class="ph codeph">Employee</code>. This means that your model will also assign on-call duties to employees. The Employee-Shift assignment decision is specified from the original intent.</p>
</div></li>
<li class="step stepexpand">Optional: <span class="cmd">Enter your own text to describe the <code class="ph codeph">OnCallDuty</code> in the <span class="ph uicontrol">[to be documented]</span> field.</span></li>
<li class="step stepexpand">Optional: <span class="cmd">To create your own decision in the <span class="ph uicontrol">Decisions</span> tab, click the <span class="ph uicontrol">enter name</span>, type in a name and click enter. </span>
<div class="itemgroup stepresult">
A new decision (intent) is created with that name with some highlighted fields to be completed by using the drop-down menus. If you, for example, select <span class="ph uicontrol">assignment </span> as the <span class="ph uicontrol">decision type</span>, two dimensions are created. As assignment involves assigning at least one thing to another, at least two dimensions must be defined. Use <span class="ph uicontrol">select a table</span> fields to define the dimensions.
</div></li>
</ol>
<section class="section result" role="region" id="task_customdecision__result_evg_cjd_tpb" aria-label="Defining custom decisions: Results">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_customdecision__result_evg_cjd_tpb">Results</h3>
</div>
<p>You are no longer restricted to using only decisions deduced from your intent. You can now define your own custom decisions by using the advanced settings and decision tabs, where you can select your decision type and its dimensions (data table or column). You can then configure new rules and objectives that use your newly defined decision.</p>
</section>
</div>
</article>
<article class="topic task nested1" role="article" aria-labelledby="task_impliedconstraints__title__1" id="task_impliedconstraints">
<h2 class="topictitle2" id="task_impliedconstraints__title__1">Using logical constraints</h2>
<div class="body taskbody">
<section class="section context" role="region" id="task_impliedconstraints__context_lld_fmb_ppb" aria-label="Using logical constraints: About this task">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_impliedconstraints__context_lld_fmb_ppb">About this task</h3>
</div>
<p>Suppose that you want to ensure that the assigned on-call duties do not occur when an employee is on vacation. You can achieve this by using logical constraints as follows.</p>
</section>
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_impliedconstraints__steps_uw2_xgn_4pb">Procedure</h3>
</div>
<p class="li stepsection">To use logical constraints:</p>
<ol class="steps" id="task_impliedconstraints__steps_uw2_xgn_4pb">
<li class="step"><span class="cmd">Look at the constraint <img id="task_impliedconstraints__image_zp2_mg4_5pb" src="images/impliedconstraint.jpg" alt="Logical constraint suggestion"> </span>
<div class="itemgroup info">
This constraint ensures that, for each employee and day combination, when no associated assignments exist (for example, the employee is on vacation on that day), that no on-call duties are assigned to that employee on that day. Note the use of the <code class="ph codeph">if...then</code> keywords to define this logical constraint.
</div></li>
<li class="step">Optional: <span class="cmd">Add other logical constraints to your model by searching in the suggestions.</span></li>
</ol>
<section class="section result" role="region" id="task_impliedconstraints__result_psl_jkb_ppb" aria-label="Using logical constraints: Results">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_impliedconstraints__result_psl_jkb_ppb">Results</h3>
</div>
<p>This constraint links the assignment of employees to shifts with on-call duties. With separate models, one for the original shift assignment and another for the on-call duties you can't achieve this linking.</p>
<p>By using logical constraints, together with the <code class="ph codeph">associated</code> keyword, you can specify that if one constraint applies, then another constraint also applies. The necessary logical connection between the concepts that you are referring to, are made automatically, without you having to use more complicated join expressions.</p>
</section>
</div>
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0EFC1AA12637C84918CEF9FA5DE5DA424822330C | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html?context=cdpaas&locale=en | Decision Optimization Modeling Assistant scheduling tutorial | Formulating and running a model: house construction scheduling
This tutorial shows you how to use the Modeling Assistant to define, formulate and run a model for a house construction scheduling problem. The completed model with data is also provided in the DO-samples, see [Importing Model Builder samples](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.htmlExamples__section_modelbuildersamples).
In this section:
* [Modeling Assistant House construction scheduling tutorial](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html?context=cdpaas&locale=encogusercase__section_The_problem)
* [More about the model view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html?context=cdpaas&locale=encogusercase__section_tbl_kdj_t1b)
* [Generating a Python notebook from your scenario](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html?context=cdpaas&locale=encogusercase__section_j2m_xnh_4bb)
| # Formulating and running a model: house construction scheduling #
This tutorial shows you how to use the Modeling Assistant to define, formulate and run a model for a house construction scheduling problem\. The completed model with data is also provided in the **DO\-samples**, see [Importing Model Builder samples](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/docExamples.html#Examples__section_modelbuildersamples)\.
In this section:
<!-- <ul> -->
* [Modeling Assistant House construction scheduling tutorial](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html?context=cdpaas&locale=en#cogusercase__section_The_problem)
* [More about the model view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html?context=cdpaas&locale=en#cogusercase__section_tbl_kdj_t1b)
* [Generating a Python notebook from your scenario](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html?context=cdpaas&locale=en#cogusercase__section_j2m_xnh_4bb)
<!-- </ul> -->
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<h1 class="topictitle1" id="cogusercase__title__1"><span class="ph" data-hd-product="cloud wx">Formulating and running a model: house construction scheduling</span></h1>
<div class="body">
<p class="shortdesc">This tutorial shows you how to use the <span class="keyword">Modeling Assistant</span> to define, formulate and run a model for a house construction scheduling problem. The completed model with data is also provided in the <strong><span class="keyword">DO-samples</span></strong>, see <a href="../DODS_Introduction/docExamples.html#Examples__section_modelbuildersamples">Importing Model Builder samples</a>.</p>
<div class="p">
In this section:
<ul id="cogusercase__ul_bwg_zs1_ppb">
<li><a href="#cogusercase__section_The_problem"><span class="keyword">Modeling Assistant</span> House construction scheduling tutorial</a></li>
<li><a href="#cogusercase__section_tbl_kdj_t1b">More about the model view</a></li>
<li><a href="#cogusercase__section_j2m_xnh_4bb">Generating a Python notebook from your scenario</a></li>
</ul>
</div>
<section class="section" role="region" aria-labelledby="cogusercase__section_The_problem__title__1" id="cogusercase__section_The_problem">
<h2 class="sectiontitle" id="cogusercase__section_The_problem__title__1">The problem</h2>
<p>You need to plan and schedule activities and subcontractors for a house construction project. Your schedule must start on a particular date. All the activities (masonry, carpentry, plumbing and so on) must be scheduled and there is a specified order of activities that must be respected (for example windows cannot be put in until the roof is completed). Each subcontractor can perform some of the necessary activities and with differing level of skills. Your schedule must determine the best (earliest) end time for the construction project ensuring that all activities have been scheduled and decide which subcontractor to assign to each activity. In addition, you would like to know how to optimize the skill level of your subcontractors on this project.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_Your_data__title__1" id="cogusercase__section_Your_data">
<h2 class="sectiontitle" id="cogusercase__section_Your_data__title__1">Your data</h2>
<p>You have data for this project as shown in the following spreadsheet. For each activity you have the duration that is needed to complete it, the activities that must precede it and the possible subcontractors who are available and qualified to perform that activity.</p>
<div class="image">
<img id="cogusercase__image_housebuildingdata" src="images/houseactivitiesdata.jpg" alt="House building spreadsheet of data with columns titles Activity, Duration in days, Preceding activities, Possible Subcontractors">
</div>
<p>For illustration purposes, there are just 10 activities and 3 subcontractors shown. With <span class="keyword">Decision Optimization</span> it is easy to change your data and solve the same problem with larger data sets.</p>
<p>For each activity you also have data concerning the level of expertise that each subcontractor has for that activity. The higher the number, the more expertise the subcontractor has. If a subcontractor has a zero skill level, he must not be assigned to the task. The following table shows part of this spreadsheet.</p>
<div class="p">
<div class="image">
<img id="cogusercase__image_subcontractorskill" src="images/subcontractorskills.jpg" alt="Activity spreadsheet showing some of the rows, and all the columns Activity, Subcontractor and Skill Level">
</div>
</div>
<p>You also have a table containing the names of the Subcontractors (Joe, Jack and so on) available for this project.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_wns_451_smb__title__1" id="cogusercase__section_wns_451_smb">
<h2 class="sectiontitle" id="cogusercase__section_wns_451_smb__title__1">Obtain data files for this example</h2>
<p>The data files used in this example are available in the <strong><span class="keyword">DO-samples</span></strong>. Normally, you would have your files already stored in a project as a data asset or locally on your machine, For illustration purposes however, so that you can build the model yourself, in this example you will first download the data files onto your machine and then import them into the project that you have just created. The completed model formulation with imported data is also provided as a sample, see <strong>HouseConstructionScheduling</strong> in <span class="keyword">DO-samples</span>.</p>
<ol id="cogusercase__ol_xns_451_smb">
<li>Download and extract all the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a> on to your machine.</li>
<li>Open your project.</li>
<li>Click <img id="cogusercase__image_xxv_s2y_gsb" src="images/datapaneicon.jpg" alt="Data pane icon"> to open the data pane.</li>
<li>Select <span class="ph uicontrol">Drop data files here or browse for files to upload</span>.</li>
<li>Browse to locate the <code class="ph codeph">house_activity.csv</code>, <code class="ph codeph">house_expertise.csv</code>, and <code class="ph codeph">house_subcontractor.csv</code> in the <strong><span class="ph filepath">datasets</span></strong> folder selecting the relevant product and version subfolder in your downloaded <span class="keyword">DO-samples</span>.</li>
<li>Click <strong><span class="ph uicontrol">Open</span></strong>. The files are uploaded as data assets in your project.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_create_project__title__1" id="cogusercase__section_create_project">
<h2 class="sectiontitle" id="cogusercase__section_create_project__title__1">Create a <span class="keyword">Scenario</span></h2>
<div class="note" data-hd-product="cloud wx">
<span class="notetitle">Note:</span> To create and run Optimization models, you must have both a <span class="keyword">Machine Learning</span> service added to your project and a deployment space that is associated with your <span class="keyword">experiment</span>:
<ol id="cogusercase__d23e180">
<li>Add a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong><span class="keyword">Machine Learning</span></strong> service</a> to your project. You can either add this service at the project level (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-service-instance.html">Creating a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> Service instance</a>), or you can add it when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span>, select, or create a <span class="ph uicontrol">New service</span>, click <span class="ph uicontrol">Associate</span>, then close the window.</li>
<li>Associate a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong>deployment space</strong></a> with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-spaces_local.html#create">Deployment spaces</a>). A deployment space can be created or selected when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Create a deployment space</span>, enter a name for your deployment space, and click <span class="ph uicontrol">Create</span>. For existing models, you can also create, or select a space in the <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> information pane.</li>
</ol>
</div>
<p data-hd-product="cloud wx">To create a <span class="keyword">Scenario</span>:</p>
<ol id="cogusercase__ol_frx_xyl_n1b">
<li><span class="ph">Open your project or create an empty project.</span></li>
<li data-hd-product="cloud wx"><span class="ph">On the <span class="ph uicontrol">Manage</span> tab of your project, select the <span class="ph uicontrol">Services and integrations</span> section and click <span class="ph uicontrol">Associate service</span>. Then select an existing <span class="keyword">Machine Learning</span> service instance (or create a new one ) and click <span class="ph uicontrol">Associate</span>. When the service is associated, a success message is displayed, and you can then close the <span class="keyword wintitle">Associate service</span> window. </span></li>
<li><span class="ph">Select the <span class="ph" data-hd-product="wx"><span class="ph uicontrol"><span class="keyword">Assets</span></span></span> tab.</span></li>
<li data-hd-product="wx"><span class="ph">Select <span class="ph uicontrol"><span class="keyword">New asset > Solve optimization problems</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></li>
<li>In the <strong><span class="ph uicontrol">New <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span></span></strong> window that opens, enter a name.</li>
<li data-hd-product="cloud wx"><span class="ph">If you haven't already associated a <span class="keyword">Machine Learning</span> service with your project, you must first select <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span> to select or create one before you choose a deployment space for your <span class="keyword">experiment</span>.</span></li>
<li><span class="ph">Click <span class="ph uicontrol">New deployment space</span>, enter a name, and click <span class="ph uicontrol">Create</span> (or select an existing space from the drop-down menu).</span></li>
<li>Click <strong><span class="ph uicontrol">Create</span></strong>. A Scenario 1 is created along with the model, and you work in Scenario 1.</li>
</ol>
<p>Your <span class="keyword">Scenario</span> specifies the combination of data and the optimization model formulation that you want to solve. You can create different scenarios with different variants of data and model formulations.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_cloudpreparedata__title__1" id="cogusercase__section_cloudpreparedata">
<h2 class="sectiontitle" id="cogusercase__section_cloudpreparedata__title__1">Prepare data</h2>
<p>The <span class="keyword">experiment UI</span> opens displaying the <span class="keyword">Prepare data</span> <span class="keyword">view</span>. The data files that you have in your project are displayed in the data pane. (If necessary, click <img id="cogusercase__image_hqq_sx1_smb" src="images/datapaneicon.jpg" alt="Data pane icon"> to open the data pane.) Select the three house sample files and click <strong><span class="ph uicontrol">Import</span></strong>.</p>
<p>The data files you imported are now displayed as tables in the <strong><span class="ph uicontrol">Prepare data</span></strong> <span class="keyword">view</span>. The following image shows the data files <code class="ph codeph">house_activity.csv</code>, <code class="ph codeph">house_expertise.csv</code>, and <code class="ph codeph">house_subcontractor.csv</code> imported in a <span class="keyword">Scenario</span>.</p>
<p><img data-hd-product="cloud wx" id="cogusercase__image_n5m_dgl_yfb" src="images/CloudPrepareDataHouse.jpg" alt="Prepare data view showing three tables: Activity, Subcontractor and Expertise"></p>
<p>You can view all the data by scrolling in a table. You can also view all the data by clicking the <strong><span class="ph uicontrol">Open the table in full mode</span></strong> icon of a particular data table. You can edit data values directly in the table as well as in full mode.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_createmodel__title__1" id="cogusercase__section_createmodel">
<h2 class="sectiontitle" id="cogusercase__section_createmodel__title__1">Choose the <span class="ph uicontrol"><span class="keyword">Modeling Assistant</span></span></h2>
<ol id="cogusercase__ol_i5b_rcm_n1b">
<li>Click <strong><span class="ph uicontrol"><span class="keyword">Build model</span></span></strong> in the sidebar and a pop-up window appears asking you how you want to formulate your model (whether you want to use the assisted mode with the <span class="keyword">Modeling Assistant</span> or create or import a model in Python, OPL, LP (CPLEX) or CPO code.</li>
<li>Select <strong><span class="ph uicontrol">Modeling Assistant</span></strong>.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_intent__title__1" id="cogusercase__section_intent">
<h2 class="sectiontitle" id="cogusercase__section_intent__title__1">Define your <span class="keyword">decision domain</span></h2>
<p>In the <strong>Model <span class="keyword">view</span></strong>: select the <span class="keyword">decision domain</span> for your problem. In this case select <strong><span class="ph uicontrol">Scheduling</span></strong>. The <span class="keyword">decision domains</span> currently defined are <strong>Scheduling</strong>, <strong>Resource Assignment</strong>, <strong>Selection and Allocation</strong> and <strong>Supply and Demand</strong> <span class="keyword">domains</span>.</p>
<ol id="cogusercase__ol_j5d_c2m_n1b">
<li>After selecting your <span class="keyword">domain</span>, a pop-up window appears for you to map your data to the scheduling concepts <strong>Tasks</strong> and <strong>Resources</strong>. Tasks are whatever you want to plan and schedule over time. You must define at least one task to be scheduled. In this example, your tasks are construction activities such as masonry. Resources can be human, machine, equipment or anything you want to use for the tasks. In this case your resources are your subcontractors.</li>
<li data-hd-product="cloud wx">Under TASKS, click <strong><span class="ph uicontrol">Choose a task </span></strong> and choose <code class="ph codeph">house_activity</code> from the drop-down list. Then under RESOURCES click <strong>Choose a resource </strong> and choose <code class="ph codeph">house_subcontractor</code>. The names of possible tasks and resources for you to choose from are taken from your imported data. For this example, you only need to map activities and subcontractors, but you could add other tasks and resource mappings if your model required it. You can remove any mapping by hovering over it and selecting the delete icon.</li>
<li>Click <strong><span class="ph uicontrol">Continue</span></strong>.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_lr5_l2m_n1b__title__1" id="cogusercase__section_lr5_l2m_n1b">
<h2 class="sectiontitle" id="cogusercase__section_lr5_l2m_n1b__title__1">How tasks will use resources</h2>
<p>In the window, <strong>for each task</strong> to be scheduled, you have three options :</p>
<div class="p">
<ul id="cogusercase__ul_fcm_5ft_s1b">
<li><strong>Use resources with assignment</strong>: You can select all the options and choose to have your activities assigned to specific subcontractors. This means that you want to obtain a schedule for your house construction activities with the best sequence of house construction activities, taking into account the start times, durations and precedence order, and so on, and with named subcontractors assigned to the activities. This option is selected by default.</li>
<li><strong>Use resources without assignment: </strong>You can use resources, and clear the <strong>While assigning... </strong> check box to choose not to assign specific contractors to your activities. This means that you want to obtain a schedule for your house construction activities with the best sequence of house construction activities, taking into account the start times, durations and precedence order, and so on. You still want the numbers and types of subcontractors you have available to be considered in the obtained schedule (for example 3 plumbers, 2 carpenters,... ), but they don't have to be assigned to specific people (for example Joe, Jack, Jim).
<p>When you use resources, with or without assignment, you can also decide to add further time-based capacity constraints to your model. For example you can specify limits on the number of subcontractors that can be used in parallel at any given time, or individual or total subcontractor availability over a time period.</p>
<p>For an example of scheduling without assignment, see <strong>BridgeScheduling</strong> in <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>.</p></li>
<li><strong>Continue without resources</strong> You can clear all the options and just click <strong><span class="ph uicontrol">Continue</span></strong> to schedule the tasks <em>ignoring all resource limits</em>. This means that you want to obtain a schedule with the best sequence of house construction activities, taking into account the start times, durations, precedence orders, and so on, but without considering your subcontractors.</li>
</ul>
</div>
<p>For this example:</p>
<ol id="cogusercase__ol_qy4_sfm_n1b">
<li>Choose the default setting with all options (<strong>Use the resources...</strong> <strong>While assigning...</strong>) selected and click <strong>Continue.</strong>
<p>The problem that you want to solve is now formulated in a concise statement.</p></li>
<li>Click <strong><span class="ph uicontrol">Finish</span></strong>.
<p>You return to the Model <span class="keyword">view</span>, and a guided tour opens that you can either follow or close. You can edit your problem definition again at any time, by clicking the Edit intent (pencil) icon and redefining your mappings and scheduling options.</p></li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_objsandconstraints__title__1" id="cogusercase__section_objsandconstraints">
<h2 class="sectiontitle" id="cogusercase__section_objsandconstraints__title__1">Your model formulation</h2>
<p>Now that you have specified the problem that you want to solve, the <span class="keyword">Modeling Assistant</span> provides you with a partially completed formulation in this model <span class="keyword">view</span>. The <span class="ph uicontrol">Objectives</span> and <span class="ph uicontrol">Constraints</span> pane contains the model that you will run. The <span class="ph uicontrol">Add to model</span> pane, contains more suggestions that you can include in your model formulation. If you have re-sized your window, it is possible that the Add to model pane appears underneath the Objectives and Constraints pane.</p>
<p>The model consists of an objective to be attained (maximized or minimized) and some constraints that must be satisfied. For scheduling problems like this, your objective is to work out the best schedule. The best, in this case, is one in which the time to complete all the activities is minimized. (You want to complete the house construction as quickly as possible as this will reduce costs.) This objective as well as some standard scheduling constraints have been automatically added to your model. You can also use the Objectives search field to search the objectives and constraints.</p>
<p><img data-hd-product="cloud wx" id="cogusercase__image_r1y_rhl_yfb" src="images/CloudRunModelHouse2.png" alt="Model view showing the Objective function and constraints pane, and the possible suggestions pane"></p>
<div class="p">
These scheduling constraints ensure that:
<ul id="cogusercase__constraints">
<li>the scheduling will be performed from the start time that you define for your construction project</li>
<li>each subcontractor can only be assigned to one task at a time.</li>
<li>each activity has one subcontractor assigned to it</li>
<li>all activities are present in the schedule, in other words, no activity can be omitted from the schedule</li>
<li>the duration time for each activity is respected</li>
</ul>
</div>
<p>It is possible that your constraints are displayed in a different order. There is also a constraint that is automatically added to all scheduling problems with assignment. This enables you to accept or refuse to assign subcontractors who have unavailable periods during the activities that are scheduled. In this example, unavailable periods are not considered so leave this constraint as it appears by default.</p>
<p></p>
<p>Some constraints have more details that can be displayed or hidden by clicking the arrows on each line. A bar next to the constraint indicates that there is a value or definition that you must add. You can add items by clicking the term shown underlined and typing in or selecting from a drop-down list, and you must complete the model before running it, but before doing this, first save a copy by duplicating the scenario as explained later in this section.</p>
<p><span class="ph">In the model <span class="keyword">view</span> of your scenario, if you click the Replace arrow <img id="cogusercase__image_jrz_q4y_vjb" height="20" src="../DODS_Introduction/images/replace.svg" alt="Replace icon (arrow)"> next to Modeling Assistant, you will return to the screen where you choose whether you want to create your model in Python or OPL, with the <span class="keyword">Modeling Assistant</span> or in a Python <span class="keyword" translate="no">notebook</span> or import an existing model.</span> If you choose to replace your model at this stage, you will overwrite your current model and lose your changes. If you want to keep a copy of your current work in progress, create a new scenario before changing the model.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_duplicatescenario__title__1" id="cogusercase__section_duplicatescenario">
<h2 class="sectiontitle" id="cogusercase__section_duplicatescenario__title__1">Duplicate the scenario</h2>
<p>To keep a copy of this model, make a copy of this scenario:</p>
<ol id="cogusercase__ol_k54_1k5_s1b">
<li>If the scenario pane is not open, click the <span class="ph uicontrol">Scenarios</span> icon.</li>
<li>Click the three dots next to Scenario 1 and select <strong><span class="ph uicontrol">Duplicate</span></strong>.</li>
<li>Enter a name for the new scenario, <kbd class="ph userinput">Scenario 2</kbd>, for example, and click <strong><span class="ph uicontrol">Create</span></strong>. You continue working in Scenario 2.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_uh1_12j_t1b__title__1" id="cogusercase__section_uh1_12j_t1b">
<h2 class="sectiontitle" id="cogusercase__section_uh1_12j_t1b__title__1">Complete your model</h2>
<div class="p">
Complete the constraints that are highlighted. Define a duration for each activity and a Schedule start in your constraints :
<ol id="cogusercase__ol_xcn_qxp_t1b">
<li>If necessary, expand the duration constraint by clicking the arrow on this line to display the full definition. Select <span class="ph"><strong><span class="ph uicontrol" translate="no">definition</span></strong></span> that is shown highlighted, and choose the column name <code class="ph codeph">Duration in days</code>. The default duration unit <strong>expressed in default duration unit</strong> is added to the end of the constraint. You might modify this by clicking <strong>default duration unit</strong> and selecting <strong>days</strong>, but the default unit is days. You can also modify the default duration unit and customize how dates and times are defined, in the <strong>Settings</strong> panel. Once you have completed the duration constraint, the row is no longer highlighted.
<p><img id="cogusercase__image_dcc_rpq_yfb" src="images/CloudCompletedDurationConstraint.jpg" alt="Completed duration constraint"></p></li>
<li>In the Schedule start constraint, click the date displayed. Then enter a date (or a date and time) and select this from the drop-down menu to replace the currently displayed date. If you enter a date without a time, the default time is taken to be 00:00.</li>
</ol>
</div>
<p>The constraints are no longer highlighted once you have entered values. The model, however, isn't quite complete. You might want to make sure that your schedule takes into account the order of precedence of tasks so that each activity can only start after those that must precede it. You will add this constraint later.</p>
<p>If your model had more objectives and constraints, you could browse or filter them by using the <strong><span class="ph uicontrol">Find in my objectives and constraints</span></strong> search field.</p>
<p>You can choose to <strong><span class="ph uicontrol">Disable</span></strong> or <strong><span class="ph uicontrol">Remove</span></strong> any one of the objectives or constraints in your model by clicking the 3 vertical dots next to the statement. This menu also enables you to reorganize the order of your statements by moving them up and down and you can also duplicate a statement.</p>
<p>When you have completed your model, or when there are no objectives or constraints still highlighted, you can run it to find a solution that will decide the best optimal schedule based on your model objectives and constraints.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_runmodel__title__1" id="cogusercase__section_runmodel">
<h2 class="sectiontitle" id="cogusercase__section_runmodel__title__1">Run your model</h2>
<p>You can change the solve time limit for your model in the <span class="ph uicontrol"><span class="keyword">Build model</span></span> <span class="keyword">view</span> by clicking the <strong><span class="ph uicontrol">Settings</span></strong> tab next to the suggestions. For this example, use the default limit. Other parameters can also be set using run configuration parameters (see <a href="../DODS_RunParameters/runparams.html#RunConfig" title="You can select various run parameters for the optimization solve in the Decision Optimization experiment UI.">Run parameters</a> for more information).</p>
<p>In Scenario 2, click the <strong>Run</strong> button in the <span class="ph uicontrol"><span class="keyword">Build model</span></span> <span class="keyword">view</span>. A pop-up window appears to show you the progress of this run and while this is showing, you cannot edit the model. When an initial objective value has been found, a <strong>Combined Objective</strong> is displayed in a graph in this run status pop-up window. If you want to end this run before the optimal solution is obtained, you can quit by clicking <strong><span class="ph uicontrol">Stop</span></strong>. When the optimal solution has been found the pop-up window closes.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_solution__title__1" id="cogusercase__section_solution">
<h2 class="sectiontitle" id="cogusercase__section_solution__title__1">Your solution</h2>
<p><span class="ph" id="cogusercase__solutiondesc">When the run is completed, you can see the results in the <strong><span class="ph uicontrol"><span class="keyword">Explore solution</span></span></strong> <span class="keyword">view</span>. You can also click <span class="ph uicontrol">Engine statistics</span> or <span class="ph uicontrol">Log</span> to see the solution chart and inspect the solver engine log files. The first tab in the <strong><span class="ph uicontrol"><span class="keyword">Explore solution</span></span></strong> <span class="keyword">view</span> shows the objective (or objectives if you have several) with its values and weights. The <span class="ph uicontrol">Solution tables</span> tab provides you with</span> the best schedule with the assignment of activities to subcontractors.</p>
<p id="cogusercase__downloadsoln">You can also download the solution tables as <code class="ph codeph">csv</code> files.</p>
<p id="cogusercase__conflictstab">If your model had any conflicting constraints, these would be shown in the <span class="ph uicontrol">Conflicts</span> tab with the <span class="ph uicontrol">Relaxations</span> necessary to solve the model.</p>
<p>In the <strong><span class="keyword">Visualization</span></strong> <span class="keyword">view</span>, click <strong>Gantt</strong> to display the solution as a Gantt chart.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_hpb_scq_t1b__title__1" id="cogusercase__section_hpb_scq_t1b">
<h2 class="sectiontitle" id="cogusercase__section_hpb_scq_t1b__title__1">Create a new scenario - different model, same data</h2>
<p>Although you have solved the model and now have the optimal schedule for your activities with subcontractors assigned, you haven't as yet considered the precedence of activities nor the skill level data of your subcontractors in making the assignments. Scenarios enable you to analyze and compare different models and data.</p>
<p>First, <strong>examine a new scenario with an additional constraint</strong>:</p>
<p>In the model <span class="keyword">view</span>, other objectives and constraints are offered in the <strong>Suggestions</strong> pane. You can add these to your model by clicking them. To see other suggestions that are not listed, start typing in the search field and press enter or the refresh button. You can then browse and add from the displayed propositions.</p>
<p>To add the precedence constraint to your model to ensure that there are no time lags between activities:</p>
<ol id="cogusercase__ol_it5_c1q_t1b">
<li>Duplicate Scenario 2 and call it Scenario 3. Then close the scenario pane.</li>
<li>In the Suggestions pane in the model <span class="keyword">view</span>, type in natural language <kbd class="ph userinput">activity after preceding activities</kbd>, for example, in the search field and click enter.</li>
<li>From the new list of suggestions, click <strong>Each house_activity starts after the end of preceding activities</strong> to add it to your constraints.
<p>The new precedence constraint appears in your model formulation.</p></li>
<li>Rerun the model (scenario 3) and look at the new solution. You can compare this to the solution you obtained in scenario 2, when you solved the model without this constraint. To compare solutions, open the <span class="keyword">Open scenario pane</span> pane and click each scenario. You can also click Gantt in the <strong><span class="keyword">Visualization</span></strong> <span class="keyword">view</span> and compare solutions displayed as Gantt charts for each scenario.</li>
</ol> Next, <strong>examine a new scenario with an additional objective and more constraints</strong>:
<ol id="cogusercase__ol_ipb_scq_t1b">
<li>Duplicate Scenario 3 and call it Scenario 4. Then close the scenario pane.
<p>To maximize the subcontractors' skill levels in their assignment to activities:</p></li>
<li>In the model <span class="keyword">view</span>, type <kbd class="ph userinput">overall quality</kbd> in the suggestions search field to find and add the following objective to your model :
<p><strong>Maximize overall quality of house_subcontractor-house_activity assignments according to table of assignment values</strong>.</p>
<p>Click the underlined <strong><table of assignment value></strong> and type or select <kbd class="ph userinput">house_expertise</kbd>.</p>
<p>Your new objective is now <strong>Maximize overall quality of house_subcontractor-house_activity assignments according to <kbd class="ph userinput">house_expertise</kbd>.</strong> Expand the objective and select <kbd class="ph userinput">Activity</kbd> for the task, <kbd class="ph userinput">Subcontractor</kbd> for the resource, and <kbd class="ph userinput">Skill level</kbd> for the value, (table columns) to complete the definition.</p>
<p>You now have two objectives. You can decide whether the objectives are to be considered equally or with <strong>different weightings</strong>. You can increase and decrease the weights on each objective by using the adjacent slider. Leave the two sliders at 5 so that your two objectives are equally weighted. You can also add scale factors for the objectives. For this example, leave the scale factors as 1. For more information see <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__p_weightscales">Weights and scale factors displayed in the solution</a>.</p></li>
<li>To ensure that subcontractors only undertake tasks that they are permitted to do, add a new constraint. Type in the suggestions, for example, <kbd class="ph userinput">subcontractor must be one of possible subcontractors</kbd>. You can also set <strong>Display by category</strong> to <strong>on</strong> (a tick is displayed on the switch) and select the filter <strong>Assignment</strong> to see suggestions related to assignment.</li>
<li>From the filtered suggestions, find and add the following constraint: <strong>For each house_subcontractor-house_activity assignment, assigned house_subcontractors must be one of Possible Subcontractors of house_activity</strong>.</li>
<li>Run the model</li>
</ol>
<p>The new solution now shows a new assignment of subcontractors.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_tbl_kdj_t1b__title__1" id="cogusercase__section_tbl_kdj_t1b">
<h2 class="sectiontitle" id="cogusercase__section_tbl_kdj_t1b__title__1">More about the model <span class="keyword">view</span></h2>
<p>The <strong>Suggestions</strong> filter</p>
<p>You can also filter the suggestions to find objectives and constraints. Set <strong><span class="ph uicontrol">Display by category</span></strong> to <strong>on</strong> (a tick is displayed on the switch) which opens a pane for you to select various categories of interest and apply filters to the list of suggestions. The filters enable you to see fewer suggestions. If you click the question mark icon next to the search field, you can see all possible expressions for the scheduling domain including those that are disabled. Hovering over the information icon for each expression provides you with a description. For disabled terms hovering over the expression itself also gives you an explanation for why it is disabled for this model.</p>
<p>The <strong>Settings</strong> tab</p>
<p>The Settings tab in the model <span class="keyword">view</span> lists different scheduling and optimization parameters that can be edited. In this example the default duration unit, the optimization run time and the date/time format are shown. You can specify here a customized date/time format to suit your data.</p>
<p id="cogusercase__dataschema">The <strong>Data Schema</strong> tab</p>
<p>The Data Schema tab <span class="keyword">view</span> lists, table by table, all information that the <span class="keyword">Modeling Assistant</span> has imported and deduced from the input data that is necessary for the scheduling problem to be solved. You can edit certain entries in the schema which will update your model and prompt you to accept the implied model changes or cancel your edits. This can be useful for expert users for data debugging purposes. For example if a column containing an ID has been deduced as numerical, it might be useful to change this to nominal so that it can be used as a primary key.</p>
<p>The <strong>Decisions</strong> tab</p>
<p>You can make this tab visible by setting the <span class="ph uicontrol"> Visualize and edit decisions</span> in the <span class="ph uicontrol">Settings</span> tab to <code class="ph codeph">true</code>. The Decisions tab shows you the decision (or intent) that is defined in your model. You can also add custom decisions here. For more information see <a href="advancedMA.html#task_customdecision">Defining custom decisions</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_j2m_xnh_4bb__title__1" id="cogusercase__section_j2m_xnh_4bb">
<h2 class="sectiontitle" id="cogusercase__section_j2m_xnh_4bb__title__1">Generating a Python <span class="keyword">notebook</span> from your scenario</h2>
<div class="p">
If you want to generate a Python <span class="keyword" translate="no">notebook</span> from your model created with the <span class="keyword">Modeling Assistant</span>:
<ol id="cogusercase__generatingPython">
<li>If the scenario pane is not open, click the Scenarios icon.</li>
<li>Click the three dots next to one of your scenarios and select <strong><span class="ph uicontrol">Generate notebook</span></strong>.</li>
<li>Enter a name for your <span class="keyword">notebook</span> and click <strong><span class="ph uicontrol">Generate</span></strong>.</li>
</ol> A Python <span class="keyword">notebook</span> for this model is created in your Project.
</div>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_overview__title__1" id="cogusercase__section_overview">
<h2 class="sectiontitle" id="cogusercase__section_overview__title__1"><span class="keyword">Overview</span> pane</h2>
<p id="cogusercase__P_overview">You can view summary information for all your scenarios at a glance in the <span class="ph uicontrol"><span class="keyword">Overview</span></span> pane. By selecting a scenario and clicking the three dots, you can perform actions such as duplicate, rename, generate a Python <span class="keyword">notebook</span>, export the scenario, or save it for deployment, for any selected scenario in this <span class="keyword">view</span>. See <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> for more information on how to configure this pane.</p>
</section>
<section class="section" role="region" aria-labelledby="cogusercase__section_dashboard__title__1" id="cogusercase__section_dashboard">
<h2 class="sectiontitle" id="cogusercase__section_dashboard__title__1"><span class="keyword">Visualization view</span></h2>
<p>In the <span class="keyword">Visualization view</span> you can customize what you want to see displayed from any scenario. You can view your input data, your solution and add notes. For example, for this house tutorial, you can see a Gantt chart for the optimal solution schedule.</p>
<p>You can use table widgets and chart widgets to customize the layout of these views. You can add headers, change background colors and other properties of your notes, tables or charts. You can choose different types of charts such as line charts, bar charts, and so on. You can define how data is aggregated in these charts and use the <code class="ph codeph">calculate</code> property to define how to represent certain data values in your charts.</p>
<p>If you select a table or chart widget, a default instance is displayed using some of your input data. To change the content and format of this object, click the pencil icon and edit the widget with either the graphical editor or by editing the <code class="ph codeph">json</code> file.</p>
<p>You can use this <span class="keyword">view</span> to visually compare scenarios.</p>
<p>See <a href="../DODS_Introduction/Visualization.html#topic_visualization" title="With the Decision Optimization experiment Visualization view, you can configure the graphical representation of input data and solutions for one or several scenarios.">Visualization view</a> for more information on this view's components.</p>
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312E91752782553D39C335D0DAAF189025739BB4 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuildintro.html?context=cdpaas&locale=en | Decision Optimization Modeling Assistant models | Modeling Assistant models
You can model and solve Decision Optimization problems using the Modeling Assistant (which enables you to formulate models in natural language). This requires little to no knowledge of Operational Research (OR) and does not require you to write Python code. The Modeling Assistant is only available in English and is not globalized.
The basic workflow to create a model with the Modeling Assistant and examine it under different scenarios is as follows:
1. Create a project.
2. Add a Decision Optimization experiment (a scenario is created by default in the experiment UI).
3. Add and import your data into the scenario.
4. Create a natural language model in the scenario, by first selecting your decision domain and then using the Modeling Assistant to guide you.
5. Run the model to solve it and explore the solution.
6. Create visualizations of solution and data.
7. Copy the scenario and edit the model and/or the data.
8. Solve the new scenario to see the impact of these changes.
This is demonstrated with a simple [planning and scheduling example ](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.htmlcogusercase).
For more information about deployment see .
| # Modeling Assistant models #
You can model and solve Decision Optimization problems using the Modeling Assistant (which enables you to formulate models in natural language)\. This requires little to no knowledge of Operational Research (OR) and does not require you to write Python code\. The Modeling Assistant is **only available in English** and is not globalized\.
The basic workflow to create a model with the Modeling Assistant and examine it under different scenarios is as follows:
<!-- <ol> -->
1. Create a project\.
2. Add a Decision Optimization experiment (a scenario is created by default in the experiment UI)\.
3. Add and import your data into the scenario\.
4. Create a natural language model in the scenario, by first selecting your decision domain and then using the Modeling Assistant to guide you\.
5. Run the model to solve it and explore the solution\.
6. Create visualizations of solution and data\.
7. Copy the scenario and edit the model and/or the data\.
8. Solve the new scenario to see the impact of these changes\.
<!-- </ol> -->
This is demonstrated with a simple [planning and scheduling example ](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/exhousebuild.html#cogusercase)\.
For more information about deployment see \.
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<h1 class="topictitle1" id="topic_jzq_hbq_m1b__title__1"><span class="ph" data-hd-product="cloud wx"><span class="keyword">Modeling Assistant</span> models</span></h1>
<div class="body">
<p class="shortdesc">You can model and solve <span class="keyword">Decision Optimization</span> problems using the <span class="keyword">Modeling Assistant</span> (which enables you to formulate models in natural language). This requires little to no knowledge of Operational Research (OR) and does not require you to write Python code. The <span class="keyword">Modeling Assistant</span> is <strong>only available in English </strong>and is not globalized.</p>
<p>The basic workflow to create a model with the <span class="keyword">Modeling Assistant</span> and examine it under different scenarios is as follows:</p>
<ol>
<li>Create a project.</li>
<li>Add a Decision Optimization <span class="keyword">experiment</span> (a scenario is created by default in the <span class="keyword">experiment UI</span>).</li>
<li>Add and import your data into the scenario.</li>
<li>Create a natural language model in the scenario, by first selecting your <span class="keyword">decision domain</span> and then using the <span class="keyword">Modeling Assistant</span> to guide you.</li>
<li>Run the model to solve it and explore the solution.</li>
<li>Create visualizations of solution and data.</li>
<li>Copy the scenario and edit the model and/or the data.</li>
<li>Solve the new scenario to see the impact of these changes.</li>
</ol>
<div class="imageleft">
<img data-hd-product="cloud wx" id="topic_jzq_hbq_m1b__image_tgf_lxq_tjb" src="images/new_overviewcognitive-3.jpg" alt="Workflow showing the previously mentioned steps">
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<p>This is demonstrated with a simple <a href="exhousebuild.html#cogusercase" title="This tutorial shows you how to use the Modeling Assistant to define, formulate and run a model for a house construction scheduling problem. The completed model with data is also provided in the DO-samples, see Importing Model Builder samples.">planning and scheduling example </a>.</p>
<p>For more information about deployment see .</p>
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<li class="ulchildlink"><strong><a href="../DODS_Mdl_Assist/mdl_asst_domains.html">Selecting a Decision domain in the Modeling Assistant</a></strong><br>
There are different <span class="keyword">decision domains</span> currently available in the <span class="keyword">Modeling Assistant</span> and you can be guided to choose the right <span class="keyword">domain</span> for your problem.</li>
<li class="ulchildlink"><strong><a href="../DODS_Mdl_Assist/exhousebuild.html">Formulating and running a model: house construction scheduling</a></strong><br>
This tutorial shows you how to use the <span class="keyword">Modeling Assistant</span> to define, formulate and run a model for a house construction scheduling problem. The completed model with data is also provided in the <strong><span class="keyword">DO-samples</span></strong>, see <a href="../DODS_Introduction/docExamples.html#Examples__section_modelbuildersamples">Importing Model Builder samples</a>.</li>
<li class="ulchildlink"><strong><a href="../DODS_Mdl_Assist/advancedMA.html">Adding multi-concept constraints and custom decisions: shift assignment</a></strong><br>
This <span class="keyword">Decision Optimization</span> <span class="keyword">Modeling Assistant</span> example shows you how to use multi-concept iterations, the <code class="ph codeph">associated</code> keyword in constraints, how to define your own custom decisions, and define logical constraints. For illustration, a resource assignment problem, <code class="ph codeph">ShiftAssignment</code>, is used and its completed model with data is provided in the <strong><span class="keyword">DO-samples</span></strong>.</li>
<li class="ulchildlink"><strong><a href="../DODS_Mdl_Assist/CustomRules.html">Creating advanced custom constraints with Python</a></strong><br>
This <span class="keyword">Decision Optimization</span> <span class="keyword">Modeling Assistant</span> example shows you how to create advanced custom constraints that use Python.</li>
</ul>
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2746F2E53D41F5810D92D843AF8C0AB2B36A0D47 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Mdl_Assist/mdl_asst_domains.html?context=cdpaas&locale=en | Selecting a Decision domain in the Modeling Assistant | Selecting a Decision domain in the Modeling Assistant
There are different decision domains currently available in the Modeling Assistant and you can be guided to choose the right domain for your problem.
Once you have added and imported your data into your model, the Modeling Assistant helps you to formulate your optimization model by offering you suggestions in natural language that you can edit. In order to make intelligent suggestions using your data, and to ensure that the proposed model formulation is well suited to your problem, you are asked to start by selecting a decision domain for your model.
If you need a decision domain that is not currently supported by the Modeling Assistant, you can still formulate your model as a Python notebook or as an OPL model in the experiment UI editor.
| # Selecting a Decision domain in the Modeling Assistant #
There are different decision domains currently available in the Modeling Assistant and you can be guided to choose the right domain for your problem\.
Once you have added and imported your data into your model, the Modeling Assistant helps you to formulate your optimization model by offering you suggestions in natural language that you can edit\. In order to make intelligent suggestions using your data, and to ensure that the proposed model formulation is well suited to your problem, you are asked to start by selecting a decision domain for your model\.
If you need a decision domain that is not currently supported by the Modeling Assistant, you can still formulate your model as a Python notebook or as an OPL model in the experiment UI editor\.
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<h1 class="topictitle1" id="topic_jdecisionOptimDomains__title__1"><span class="ph" data-hd-product="cloud wx">Selecting a Decision domain in the <span class="keyword">Modeling Assistant</span></span></h1>
<div class="body">
<p class="shortdesc">There are different <span class="keyword">decision domains</span> currently available in the <span class="keyword">Modeling Assistant</span> and you can be guided to choose the right <span class="keyword">domain</span> for your problem.</p>
<p>Once you have added and imported your data into your model, the <span class="keyword">Modeling Assistant</span> helps you to formulate your optimization model by offering you suggestions in natural language that you can edit. In order to make intelligent suggestions using your data, and to ensure that the proposed model formulation is well suited to your problem, you are asked to start by selecting a <span class="keyword">decision domain</span> for your model.</p>
<section class="section" role="region" aria-labelledby="topic_jdecisionOptimDomains__section_py5_jtp_h2b__title__1" id="topic_jdecisionOptimDomains__section_py5_jtp_h2b">
<h2 class="sectiontitle" id="topic_jdecisionOptimDomains__section_py5_jtp_h2b__title__1"><span class="keyword">Decision Optimization</span> <span class="keyword">domains</span></h2>
<p>The different <span class="keyword">decision domains</span> currently available (<strong>Scheduling</strong>, <strong>Resource Assignment</strong>, <strong>Selection & Allocation</strong>, and <strong>Supply & Demand Planning</strong>) are presented as cards. If you hover over each card, you can read a brief description of each <span class="keyword">domain</span>. If you know which option to choose you can select a <span class="keyword">decision domain</span> and start formulating your model. Further information about each <span class="keyword">domain</span> is provided later in this section.</p>
<p>If you are not sure of which <span class="keyword">decision domain</span> to choose, you can select "<strong>Use this question and answer guide</strong>" and the <span class="keyword">Modeling Assistant</span> will ask you a few questions and, based on your reply, it will quickly tell you which option you need.</p>
<p>The following diagram summarizes these questions and choices. The actual phrasing of these questions might vary and examples are also provided with each question, in the <span class="keyword">Modeling Assistant</span> interface.</p>
<p><img id="topic_jdecisionOptimDomains__image_jpy_yw3_h2b" height="500" src="images/new_modeltypes2.svg" alt="Flowchart showing question and yes-no answers leading to the choice of optimization model types"></p>
</section>
<p>If you need a <span class="keyword">decision domain</span> that is not currently supported by the <span class="keyword">Modeling Assistant</span>, you can still formulate your model as a Python <span class="keyword">notebook</span> or as an OPL model in the <span class="keyword">experiment UI</span> editor.</p>
<section class="section" role="region" aria-labelledby="topic_jdecisionOptimDomains__section_uhv_px3_h2b__title__1" id="topic_jdecisionOptimDomains__section_uhv_px3_h2b">
<h2 class="sectiontitle" id="topic_jdecisionOptimDomains__section_uhv_px3_h2b__title__1">Scheduling</h2>
<p>Scheduling problems are about <strong>ordering</strong> things. You can use the Scheduling <span class="keyword">domain</span> when you have tasks or activities that you need to schedule, to be done<strong> in a given order </strong>with <strong>specific start and end times</strong>, and rules (or precedence constraints) concerning what items can be performed before or after others. Your objective might be, for example, to minimize the total time taken to carry out all the tasks, or to minimize costs and to use resources efficiently. You also have the option to assign resources so that the solution will also tell you which particular resource to assign each task to. Scheduling with assignment is a very special case of scheduling. Any resource that you might assign to a task has a capacity of one. A capacity of one means that it can only be used for one task at the same time. If two tasks need the same individual resource then these two tasks cannot overlap and must be put in order: one of the two tasks must be performed before the other. The <span class="ph filepath">HouseConstructionScheduling</span> example provided in the <span class="keyword">DO-samples</span> and described later in this section is a example of a Scheduling problem with assignment.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_jdecisionOptimDomains__section_s2k_qx3_h2b__title__1" id="topic_jdecisionOptimDomains__section_s2k_qx3_h2b">
<h2 class="sectiontitle" id="topic_jdecisionOptimDomains__section_s2k_qx3_h2b__title__1">Resource Assignment</h2>
<p>Resource Assignment problems are about <strong>matching</strong> things. You can use the Resource Assignment <span class="keyword">domain</span> when you want to assign (or match) resources (workforce, equipment, budget,...) to targets (jobs, events, places), given their respective constraints. Your objective might be, for example, to minimize cost, or to maximize revenue from this assignment. The solution will provide you with a set of assignments (resource - target pairs). You can also choose to let the solution determine the quantities of resources needed for the assignments. The <span class="ph filepath">MarketingCampaignAssignment</span> model provided in the <span class="keyword">DO-samples</span> is an example of the Resource Assignment <span class="keyword">domain</span>.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_jdecisionOptimDomains__section_r1x_qx3_h2b__title__1" id="topic_jdecisionOptimDomains__section_r1x_qx3_h2b">
<h2 class="sectiontitle" id="topic_jdecisionOptimDomains__section_r1x_qx3_h2b__title__1">Selection and Allocation</h2>
<p>Selection problems are about <strong>choosing</strong> from a list of possibilities. You can use the Selection and Allocation <span class="keyword">domain</span> when you have combined all the possible choices you want to consider in <strong>one single table</strong>. This table might, for example, contain a pre-selection of choices you have already made based on predictive analysis. There might be still, however, a large number of possibilities to select from. You want Decision Optimization to help you decide the best (optimal) selection of these items (or combinations) so that you can attain your objectives and respect your constraints. Decision Optimization can also tell you the optimal quantities to allocate to each choice if this is appropriate. A typical example of a Selection and Allocation model is the sample <span class="ph filepath">PortfolioAllocation</span> where you have several financial stock investments to select from. Also, the <span class="ph filepath">MarketingCampaignAssignment</span> sample contains the scenario <em>Scenario 4 - Selection</em> which shows you how to formulate this Marketing Campaign problem as a Selection and Allocation model. In this case you have different marketing campaigns to select from all listed in the same data table.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_jdecisionOptimDomains__section_jbv_sx3_h2b__title__1" id="topic_jdecisionOptimDomains__section_jbv_sx3_h2b">
<h2 class="sectiontitle" id="topic_jdecisionOptimDomains__section_jbv_sx3_h2b__title__1">Planning</h2>
<p>Planning problems are about <strong>quantifying</strong> things. You can use the Planning <span class="keyword">domain</span> when you want to decide what quantities or levels to have (for example inventory, production, materials, service) over periods of time (for example weeks, months, quarters). A typical example of a Planning model is a Production Planning problem where you need to know how much raw materials to have in stock in each quarter to be able to produce the optimal number of products to satisfy your demand.</p>
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F37BD72C28F0DAC8D9478ECEABA4F077ABCDE0C9 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/createScenario.html?context=cdpaas&locale=en | Decision Optimization notebook tutorial create new scenario | Create new scenario
To solve with different versions of your model or data you can create new scenarios in the Decision Optimization experiment UI.
Procedure
To create a new scenario:
1. Click the Open scenario pane icon  to open the Scenario panel.
2. Use the Create Scenario drop-down menu to create a new scenario from the current one.
3. Add a name for the duplicate scenario and click Create.
4. Working in your new scenario, in the Prepare data view, open the diet_food data table in full mode.
5. Locate the entry for Hotdog at row 9, and set the qmax value to 0 to exclude hot dog from possible solutions.
6. Switch to the Build model view and run the model again.
7. You can see the impact of your changes on the solution by switching from one scenario to the other.
| # Create new scenario #
To solve with different versions of your model or data you can create new scenarios in the Decision Optimization experiment UI\.
## Procedure ##
To create a new scenario:
<!-- <ol> -->
1. Click the **Open scenario pane** icon  to open the **Scenario** panel\.
2. Use the Create Scenario drop\-down menu to create a new scenario from the current one\.
3. Add a name for the duplicate scenario and click **Create**\.
4. Working in your new scenario, in the Prepare data view, open the `diet_food` data table in full mode\.
5. Locate the entry for *Hotdog* at row 9, and set the `qmax` value to 0 to exclude hot dog from possible solutions\.
6. Switch to the **Build model** view and run the model again\.
7. You can see the impact of your changes on the solution by switching from one scenario to the other\.
<!-- </ol> -->
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<div class="body taskbody">
<p class="shortdesc">To solve with different versions of your model or data you can create new scenarios in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>.</p>
<section class="section context" role="region" aria-labelledby="tasktask_c1s_2mq_m1b__context__1">
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<h2 class="sectiontitle tasklabel" id="tasktask_c1s_2mq_m1b__context__1">About this task</h2>
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<p>You can change your input data to examine the effects on the model. As mentioned, this could take the form of excluding hot dog from the solution to generate a vegetarian variant of the diet.</p>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_c1s_2mq_m1b__steps__1">Procedure</h2>
</div>
<p class="li stepsection">To create a new scenario:</p>
<ol class="steps">
<li class="step"><span class="cmd">Click the <strong><span class="keyword">Open scenario pane</span></strong> icon <img data-hd-product="cloud wx" id="task_c1s_2mq_m1b__image_g4g_bgm_33b" src="../DODS_Introduction/images/CPDscenariomanage.jpg" alt="Open scenario pane button"> to open the <strong>Scenario</strong> panel. </span></li>
<li class="step"><span class="cmd">Use the <span class="ph uicontrol">Create Scenario</span> drop-down menu to create a new scenario from the current one.</span></li>
<li class="step"><span class="cmd">Add a name for the duplicate scenario and click <strong><span class="ph uicontrol">Create</span></strong>.</span></li>
<li class="step"><span class="cmd">Working in your new scenario, in the <span class="keyword">Prepare data</span> <span class="keyword">view</span>, open the <code class="ph codeph">diet_food</code> data table in full mode.</span></li>
<li class="step"><span class="cmd">Locate the entry for <em>Hotdog</em> at row 9, and set the <code class="ph codeph">qmax</code> value to 0 to exclude hot dog from possible solutions.</span></li>
<li class="step"><span class="cmd">Switch to the <strong><span class="ph uicontrol"><span class="keyword">Build model</span></span></strong> <span class="keyword">view</span> and run the model again.</span></li>
<li class="step"><span class="cmd">You can see the impact of your changes on the solution by switching from one scenario to the other.</span></li>
</ol>
<section class="section result" role="region" aria-labelledby="tasktask_c1s_2mq_m1b__result__1">
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<h2 class="sectiontitle tasklabel" id="tasktask_c1s_2mq_m1b__result__1">Results</h2>
</div>The vegetarian variant of the diet excludes hot dog from the solution but adds raisin bran and adjusts quantities of other foods.
<p>In the <strong><span class="keyword">Visualization view</span></strong>, the solution is displayed for the current scenario in the <strong><span class="ph uicontrol"><span class="keyword">Explore solution</span></span></strong> page. To add another chart to display the previous scenario, click the Chart icon and then edit it. In the chart widget editor that opens, give this chart a name and select <strong>Scenario 1</strong> from the <strong><span class="ph uicontrol">Add scenario</span></strong> drop-down menu in the <strong><span class="ph uicontrol">Scenarios</span></strong> section and remove <strong><span class="ph uicontrol">Current Scenario</span></strong>. Select <strong>solution</strong> in the <strong><span class="ph uicontrol">Table</span></strong> section. Close the Chart Widget Editor to save your updates. You now have two bar charts displaying the two scenarios in your <span class="keyword">Visualization view</span>.</p>
<p>You can change the type of chart and its display attributes. For example, to see the raisin bran in the bar chart, change the width to 1000 in the JSON tab of the Chart widget editor.</p>
<p>You can view summary information for all your scenarios at a glance in the <span class="ph uicontrol"><span class="keyword">Overview</span></span> pane. By selecting a scenario and clicking the three dots, you can perform actions such as duplicate, rename, generate a Python <span class="keyword">notebook</span>, export the scenario, or save it for deployment, for any selected scenario in this <span class="keyword">view</span>. See <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> for more information on how to configure this pane.</p>
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<strong>Parent topic:</strong> <a href="../DODS_Notebooks/solveModel.html" title="This example shows you how to create and solve a Python-based model by using a sample.">Solving and analyzing a model: the diet problem</a>
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056E37762231E9E32F0F443987C32ACF7BF1AED4 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/multiIntro.html?context=cdpaas&locale=en | Decision Optimization notebook multiple scenarios | Working with multiple scenarios
You can generate multiple scenarios to test your model against a wide range of data and understand how robust the model is.
This example steps you through the process to generate multiple scenarios with a model. This makes it possible to test the performance of the model against multiple randomly generated data sets. It's important in practice to check the robustness of a model against a wide range of data. This helps ensure that the model performs well in potentially stochastic real-world conditions.
The example is the StaffPlanning model in the DO-samples.
The example is structured as follows:
* The model StaffPlanning contains a default scenario based on two default data sets, along with five additional scenarios based on randomized data sets.
* The Python notebookCopyAndSolveScenarios contains the random generator to create the new scenarios in the StaffPlanning model.
For general information about scenario management and configuration, see [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__scenariopanel) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_overview).
For information about writing methods and classes for scenarios, see the [ Decision Optimization Client Python API documentation](https://ibmdecisionoptimization.github.io/decision-optimization-client-doc/).
| # Working with multiple scenarios #
You can generate multiple scenarios to test your model against a wide range of data and understand how robust the model is\.
This example steps you through the process to generate multiple scenarios with a model\. This makes it possible to test the performance of the model against multiple randomly generated data sets\. It's important in practice to check the robustness of a model against a wide range of data\. This helps ensure that the model performs well in potentially stochastic real\-world conditions\.
The example is the `StaffPlanning` model in the **DO\-samples**\.
The example is structured as follows:
<!-- <ul> -->
* The model `StaffPlanning` contains a default scenario based on two default data sets, along with five additional scenarios based on randomized data sets\.
* The Python notebook`CopyAndSolveScenarios` contains the random generator to create the new scenarios in the `StaffPlanning` model\.
<!-- </ul> -->
For general information about scenario management and configuration, see [Scenario pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__scenariopanel) and [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview)\.
For information about writing methods and classes for scenarios, see the [ Decision Optimization Client Python API documentation](https://ibmdecisionoptimization.github.io/decision-optimization-client-doc/)\.
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<h1 class="topictitle1" id="topic_u1f_t2s_n1b__title__1"><span class="ph" data-hd-product="cloud wx">Working with multiple scenarios</span></h1>
<div class="body">
<p class="shortdesc">You can generate multiple scenarios to test your model against a wide range of data and understand how robust the model is.</p>
<p>This example steps you through the process to generate multiple scenarios with a model. This makes it possible to test the performance of the model against multiple randomly generated data sets. It's important in practice to check the robustness of a model against a wide range of data. This helps ensure that the model performs well in potentially stochastic real-world conditions.</p>
<p>The example is the <code class="ph codeph">StaffPlanning</code> model in the <strong><span class="keyword">DO-samples</span></strong>.</p>
<p>The example is structured as follows:</p>
<ul>
<li>The model <code class="ph codeph">StaffPlanning</code> contains a default scenario based on two default data sets, along with five additional scenarios based on randomized data sets.</li>
<li>The Python <span class="keyword">notebook</span> <code class="ph codeph">CopyAndSolveScenarios</code> contains the random generator to create the new scenarios in the <code class="ph codeph">StaffPlanning</code> model.</li>
</ul>
<p>For general information about scenario management and configuration, see <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__scenariopanel">Scenario pane</a> and <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a>.</p>
<p>For information about writing methods and classes for scenarios, see the <a href="https://ibmdecisionoptimization.github.io/decision-optimization-client-doc/" rel="noopener" target="_blank" title="(Opens in a new tab or window)"> Decision Optimization Client Python API documentation</a>.</p>
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<li class="ulchildlink"><strong><a href="../DODS_Notebooks/multiScenario.html">Generating multiple scenarios</a></strong><br>
This tutorial shows you how to generate multiple scenarios from a <span class="keyword" translate="no">notebook</span> using randomized data. Generating multiple scenarios lets you test a model by exposing it to a wide range of data.</li>
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3BEB81A5A5953CD570FA673B2496F8AF98725438 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/multiScenario.html?context=cdpaas&locale=en | Decision Optimization notebook generating multiple scenarios | Generating multiple scenarios
This tutorial shows you how to generate multiple scenarios from a notebook using randomized data. Generating multiple scenarios lets you test a model by exposing it to a wide range of data.
Procedure
To create and solve a scenario using a sample:
1. Download and extract all the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples) on to your machine. You can also download just the StaffPlanning.zip file from the Model_Builder subfolder for your product and version, but in this case do not extract it.
2. Open your project or create an empty project.
3. On the Manage tab of your project, select the Services and integrations section and click Associate service. Then select an existing Machine Learning service instance (or create a new one ) and click Associate. When the service is associated, a success message is displayed, and you can then close the Associate service window.
4. Select the Assets tab.
5. Select New asset > Solve optimization problems in the Work with models section.
6. Click Local file in the Solve optimization problems window that opens.
7. Browse to choose the StaffPlanning.zip file in the Model_Builder folder. Select the relevant product and version subfolder in your downloaded DO-samples.
8. If you haven't already associated a Machine Learning service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment.
9. Click Create.A Decision Optimization model is created with the same name as the sample.
10. Working in Scenario 1 of the StaffPlanning model, you can see that the solution contains tables to identify which resources work which days to meet expected demand. If there is no solution displayed, or to rerun the model, click Build model in the sidebar, then click Run to solve the model.
| # Generating multiple scenarios #
This tutorial shows you how to generate multiple scenarios from a notebook using randomized data\. Generating multiple scenarios lets you test a model by exposing it to a wide range of data\.
## Procedure ##
To create and solve a scenario using a sample:
<!-- <ol> -->
1. Download and extract all the **[DO\-samples](https://github.com/IBMDecisionOptimization/DO-Samples)** on to your machine\. You can also download just the StaffPlanning\.zip file from the Model\_Builder subfolder for your product and version, but in this case do not extract it\.
2. Open your project or create an empty project\.
3. On the Manage tab of your project, select the Services and integrations section and click Associate service\. Then select an existing Machine Learning service instance (or create a new one ) and click Associate\. When the service is associated, a success message is displayed, and you can then close the Associate service window\.
4. Select the Assets tab\.
5. Select New asset > Solve optimization problems in the Work with models section\.
6. Click Local file in the Solve optimization problems window that opens\.
7. Browse to choose the StaffPlanning\.zip file in the **Model\_Builder** folder\. Select the relevant product and version subfolder in your downloaded DO\-samples\.
8. If you haven't already associated a Machine Learning service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment\.
9. Click **Create**\.A Decision Optimization model is created with the same name as the sample\.
10. Working in Scenario 1 of the `StaffPlanning` model, you can see that the solution contains tables to identify which resources work which days to meet expected demand\. If there is no solution displayed, or to rerun the model, click **Build model** in the sidebar, then click **Run** to solve the model\.
<!-- </ol> -->
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<h1 class="topictitle1" id="task_fns_tts_n1b__title__1"><span class="ph" data-hd-product="cloud wx">Generating multiple scenarios</span></h1>
<div class="body taskbody">
<p class="shortdesc">This tutorial shows you how to generate multiple scenarios from a <span class="keyword" translate="no">notebook</span> using randomized data. Generating multiple scenarios lets you test a model by exposing it to a wide range of data.</p>
<section class="section context" role="region" aria-labelledby="tasktask_fns_tts_n1b__context__1">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_fns_tts_n1b__context__1">About this task</h2>
</div>
<p>The files used in this example are in the <strong><span class="keyword">DO-samples</span></strong> project. The model concerned is <code class="ph codeph">StaffPlanning</code> and the <span class="keyword">notebook</span> is <code class="ph codeph">CopyAndSolveScenarios</code>.</p>
<div class="note" data-hd-product="cloud wx">
<span class="notetitle">Note:</span> To create and run Optimization models, you must have both a <span class="keyword">Machine Learning</span> service added to your project and a deployment space that is associated with your <span class="keyword">experiment</span>:
<ol id="task_fns_tts_n1b__d49e189">
<li>Add a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong><span class="keyword">Machine Learning</span></strong> service</a> to your project. You can either add this service at the project level (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-service-instance.html">Creating a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> Service instance</a>), or you can add it when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span>, select, or create a <span class="ph uicontrol">New service</span>, click <span class="ph uicontrol">Associate</span>, then close the window.</li>
<li>Associate a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong>deployment space</strong></a> with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-spaces_local.html#create">Deployment spaces</a>). A deployment space can be created or selected when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Create a deployment space</span>, enter a name for your deployment space, and click <span class="ph uicontrol">Create</span>. For existing models, you can also create, or select a space in the <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> information pane.</li>
</ol>
</div>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_fns_tts_n1b__steps__1">Procedure</h2>
</div>
<p class="li stepsection">To create and solve a scenario using a sample:</p>
<ol class="steps">
<li class="step"><span class="cmd">Download and extract all the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong> on to your machine. You can also download just the <span class="ph filepath">StaffPlanning.zip</span> file from the <span class="ph filepath">Model_Builder</span> subfolder for your product and version, but in this case do not extract it.</span></li>
<li class="step"><span class="cmd"><span class="ph">Open your project or create an empty project.</span></span></li>
<li class="step" data-hd-product="cloud wx"><span class="cmd"><span class="ph">On the <span class="ph uicontrol">Manage</span> tab of your project, select the <span class="ph uicontrol">Services and integrations</span> section and click <span class="ph uicontrol">Associate service</span>. Then select an existing <span class="keyword">Machine Learning</span> service instance (or create a new one ) and click <span class="ph uicontrol">Associate</span>. When the service is associated, a success message is displayed, and you can then close the <span class="keyword wintitle">Associate service</span> window. </span></span></li>
<li class="step"><span class="cmd"><span class="ph">Select the <span class="ph" data-hd-product="wx"><span class="ph uicontrol"><span class="keyword">Assets</span></span></span> tab.</span></span></li>
<li class="step" data-hd-product="wx"><span class="cmd"><span class="ph">Select <span class="ph uicontrol"><span class="keyword">New asset > Solve optimization problems</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></span></li>
<li class="step"><span class="cmd"><span class="ph">Click <span class="ph uicontrol">Local file</span> in the <span class="ph" data-hd-product="wx"><span class="keyword">Solve optimization problems</span></span> window that opens.</span></span></li>
<li class="step" data-hd-product="cloud wx"><span class="cmd">Browse to choose the <span class="ph filepath">StaffPlanning.zip</span> file in the <strong><span class="ph filepath">Model_Builder</span></strong> folder. <span class="ph">Select the relevant product and version subfolder in your downloaded <span class="keyword">DO-samples</span>. </span></span></li>
<li class="step" data-hd-product="cloud wx"><span class="cmd"><span class="ph">If you haven't already associated a <span class="keyword">Machine Learning</span> service with your project, you must first select <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span> to select or create one before you choose a deployment space for your <span class="keyword">experiment</span>.</span></span></li>
<li class="step"><span class="cmd">Click <strong><span class="ph uicontrol">Create</span></strong>.</span>
<div class="itemgroup stepresult">
A <span class="keyword">Decision Optimization</span> model is created with the same name as the sample.
</div></li>
<li class="step"><span class="cmd">Working in Scenario 1 of the <code class="ph codeph">StaffPlanning</code> model, you can see that the solution contains tables to identify which resources work which days to meet expected demand. </span>
<div class="itemgroup info">
If there is no solution displayed, or to rerun the model, click <strong><span class="ph uicontrol"><span class="keyword">Build model</span></span></strong> in the sidebar, then click <strong><span class="ph uicontrol">Run</span></strong> to solve the model.
</div></li>
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DECCA51BACC7BE33F484D36177B24C4BD0FE4CFD | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/preparedataIO.html?context=cdpaas&locale=en | Decision Optimization input and output data | Input and output data
You can access the input and output data you defined in the experiment UI by using the following dictionaries.
The data that you imported in the Prepare data view in the experiment UI is accessible from the input dictionary. You must define each table by using the syntax inputs['tablename']. For example, here food is an entity that is defined from the table called diet_food:
food = inputs['diet_food']
Similarly, to show tables in the Explore solution view of the experiment UI you must specify them using the syntax outputs['tablename']. For example,
outputs['solution'] = solution_df
defines an output table that is called solution. The entity solution_df in the Python model defines this table.
You can find this Diet example in the Model_Builder folder of the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples). To import and run (solve) it in the experiment UI, see [Solving and analyzing a model: the diet problem](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveModel.htmltask_mtg_n3q_m1b).
| # Input and output data #
You can access the input and output data you defined in the experiment UI by using the following dictionaries\.
The data that you imported in the **Prepare data view** in the experiment UI is accessible from the input dictionary\. You must define each table by using the syntax `inputs['tablename']`\. For example, here food is an entity that is defined from the table called `diet_food`:
food = inputs['diet_food']
Similarly, to show tables in the Explore solution view of the experiment UI you must specify them using the syntax `outputs['tablename']`\. For example,
outputs['solution'] = solution_df
defines an output table that is called `solution`\. The entity `solution_df` in the Python model defines this table\.
You can find this Diet example in the Model\_Builder folder of the [DO\-samples](https://github.com/IBMDecisionOptimization/DO-Samples)\. To import and run (solve) it in the experiment UI, see [Solving and analyzing a model: the diet problem](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveModel.html#task_mtg_n3q_m1b)\.
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<h1 class="topictitle1" id="topic_prepareIO__title__1"><span class="ph" data-hd-product="cloud wx">Input and output data</span></h1>
<div class="body">
<p class="shortdesc">You can access the input and output data you defined in the <span class="keyword">experiment UI</span> by using the following dictionaries.</p>
<div class="p">
The data that you imported in the <strong><span class="keyword">Prepare data</span> <span class="keyword">view</span></strong> in the <span class="keyword">experiment UI</span> is accessible from the input dictionary. You must define each table by using the syntax <code class="ph codeph">inputs['tablename']</code>. For example, here food is an entity that is defined from the table called <code class="ph codeph">diet_food</code>:
<pre class="codeblock"><code>food = inputs['diet_food']</code></pre>
</div>
<div class="p">
Similarly, to show tables in the <span class="ph uicontrol">Explore solution </span><span class="keyword">view</span> of the <span class="keyword">experiment UI</span> you must specify them using the syntax <code class="ph codeph">outputs['tablename']</code>. For example,
<pre class="codeblock"><code>outputs['solution'] = solution_df</code></pre> defines an output table that is called <code class="ph codeph">solution</code>. The entity <code class="ph codeph">solution_df</code> in the Python model defines this table.
</div>
<p>You can find this Diet example in the <span class="ph filepath">Model_Builder</span> folder of the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a>. To import and run (solve) it in the <span class="keyword">experiment UI</span>, see <a href="solveModel.html#task_mtg_n3q_m1b" title="This example shows you how to create and solve a Python-based model by using a sample.">Solving and analyzing a model: the diet problem</a>.</p>
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726175290D457B10A02C27F08ECA1F6546E64680 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveIntro.html?context=cdpaas&locale=en | Python DOcplex models | Python DOcplex models
You can solve Python DOcplex models in a Decision Optimization experiment.
The Decision Optimization environment currently supports Python 3.10. The default version is Python 3.10. You can modify this default version on the Environment tab of the [Run configuration pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_runconfig) or from the [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_overview) information pane.
The basic workflow to create a Python DOcplex model in Decision Optimization, and examine it under different scenarios, is as follows:
1. Create a project.
2. Add data to the project.
3. Add a Decision Optimization experiment (a scenario is created by default in the experiment UI).
4. Select and import your data into the scenario.
5. Create or import your Python model.
6. Run the model to solve it and explore the solution.
7. Copy the scenario and edit the data in the context of the new scenario.
8. Solve the new scenario to see the impact of the changes to data.
| # Python DOcplex models #
You can solve Python DOcplex models in a Decision Optimization experiment\.
The Decision Optimization environment currently supports Python 3\.10\. The default version is Python 3\.10\. You can modify this default version on the Environment tab of the [Run configuration pane](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_runconfig) or from the [Overview](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview) information pane\.
The basic workflow to create a Python DOcplex model in Decision Optimization, and examine it under different scenarios, is as follows:
<!-- <ol> -->
1. Create a project\.
2. Add data to the project\.
3. Add a Decision Optimization experiment (a scenario is created by default in the experiment UI)\.
4. Select and import your data into the scenario\.
5. Create or import your Python model\.
6. Run the model to solve it and explore the solution\.
7. Copy the scenario and edit the data in the context of the new scenario\.
8. Solve the new scenario to see the impact of the changes to data\.
<!-- </ol> -->
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<div class="body">
<p class="shortdesc">You can solve Python <span><span class="keyword">DOcplex</span></span> models in a <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>.</p>
<p><span class="ph" id="SolvingPythonModel__defPythonExpt">The <span class="keyword">Decision Optimization</span> environment currently supports Python <span class="keyword">3.10</span>. The default version is Python <span class="keyword">3.10</span>.</span> You can modify this default version on the Environment tab of the <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_runconfig">Run configuration pane</a> or from the <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> information pane.</p>
<p>The basic workflow to create a Python DOcplex model in <span class="keyword">Decision Optimization</span>, and examine it under different scenarios, is as follows:</p>
<ol>
<li>Create a project.</li>
<li>Add data to the project.</li>
<li>Add a <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> (a scenario is created by default in the <span class="keyword">experiment UI</span>).</li>
<li>Select and import your data into the scenario.</li>
<li>Create or import your Python model.</li>
<li>Run the model to solve it and explore the solution.</li>
<li>Copy the scenario and edit the data in the context of the new scenario.</li>
<li>Solve the new scenario to see the impact of the changes to data.</li>
</ol>
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<img data-hd-product="cloud wx" id="SolvingPythonModel__image_jjr_rxq_tjb" src="../DODS_Mdl_Assist/images/new_overviewcognitive-3.jpg" alt="Workflow showing previously mentioned steps">
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You can access the input and output data you defined in the <span class="keyword">experiment UI</span> by using the following dictionaries.</li>
<li class="ulchildlink"><strong><a href="../DODS_Notebooks/solveModel.html">Solving and analyzing a model: the diet problem</a></strong><br>
This example shows you how to create and solve a Python-based model by using a sample.</li>
<li class="ulchildlink"><strong><a href="../DODS_Notebooks/multiIntro.html">Working with multiple scenarios</a></strong><br>
You can generate multiple scenarios to test your model against a wide range of data and understand how robust the model is.</li>
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2E1F6D5703CE75AF284903C20E5DBDFA1AE706B4 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/solveModel.html?context=cdpaas&locale=en | Decision Optimization notebook tutorial | Solving and analyzing a model: the diet problem
This example shows you how to create and solve a Python-based model by using a sample.
Procedure
To create and solve a Python-based model by using a sample:
1. Download and extract all the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples) on to your computer. You can also download just the diet.zip file from the Model_Builder subfolder for your product and version, but in this case, do not extract it.
2. Open your project or create an empty project.
3. On the Manage tab of your project, select the Services and integrations section and click Associate service. Then select an existing Machine Learning service instance (or create a new one ) and click Associate. When the service is associated, a success message is displayed, and you can then close the Associate service window.
4. Select the Assets tab.
5. Select New asset > Solve optimization problems in the Work with models section.
6. Click Local file in the Solve optimization problems window that opens.
7. Browse to find the Model_Builder folder in your downloaded DO-samples. Select the relevant product and version subfolder. Choose the Diet.zip file and click Open. Alternatively use drag and drop.
8. If you haven't already associated a Machine Learning service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment.
9. Click New deployment space, enter a name, and click Create (or select an existing space from the drop-down menu).
10. Click Create.A Decision Optimization model is created with the same name as the sample.
11. In the Prepare data view, you can see the data assets imported.These tables represent the min and max values for nutrients in the diet (diet_nutrients), the nutrients in different foods (diet_food_nutrients), and the price and quantity of specific foods (diet_food).
12. Click Build model in the sidebar to view your model.The Python model minimizes the cost of the food in the diet while satisfying minimum nutrient and calorie requirements.
Note also how the inputs (tables in the Prepare data view) and the outputs (in this case the solution table to be displayed in the Explore solution view) are specified in this model.
13. Run the model by clicking the Run button in the Build model view.
| # Solving and analyzing a model: the diet problem #
This example shows you how to create and solve a Python\-based model by using a sample\.
## Procedure ##
To create and solve a Python\-based model by using a sample:
<!-- <ol> -->
1. Download and extract all the [DO\-samples](https://github.com/IBMDecisionOptimization/DO-Samples) on to your computer\. You can also download just the diet\.zip file from the Model\_Builder subfolder for your product and version, but in this case, do not extract it\.
2. Open your project or create an empty project\.
3. On the Manage tab of your project, select the Services and integrations section and click Associate service\. Then select an existing Machine Learning service instance (or create a new one ) and click Associate\. When the service is associated, a success message is displayed, and you can then close the Associate service window\.
4. Select the Assets tab\.
5. Select New asset > Solve optimization problems in the Work with models section\.
6. Click Local file in the Solve optimization problems window that opens\.
7. Browse to find the Model\_Builder folder in your downloaded DO\-samples\. Select the relevant product and version subfolder\. Choose the Diet\.zip file and click Open\. Alternatively use drag and drop\.
8. If you haven't already associated a Machine Learning service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment\.
9. Click New deployment space, enter a name, and click Create (or select an existing space from the drop\-down menu)\.
10. Click **Create**\.A Decision Optimization model is created with the same name as the sample\.
11. In the Prepare data view, you can see the data assets imported\.These tables represent the min and max values for nutrients in the diet (`diet_nutrients`), the nutrients in different foods (`diet_food_nutrients`), and the price and quantity of specific foods (`diet_food`)\.
12. Click Build model in the sidebar to view your model\.The Python model minimizes the cost of the food in the diet while satisfying minimum nutrient and calorie requirements\.
Note also how the **inputs** (tables in the Prepare data view) and the **outputs** (in this case the solution table to be displayed in the Explore solution view) are specified in this model.
13. Run the model by clicking the **Run** button in the Build model view\.
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<div class="body taskbody">
<p class="shortdesc">This example shows you how to create and solve a Python-based model by using a sample.</p>
<section class="section context" role="region" aria-labelledby="tasktask_mtg_n3q_m1b__context__1">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_mtg_n3q_m1b__context__1">About this task</h2>
</div>
<p>This well-known optimization problem identifies the best mix of foodstuffs to meet dietary requirements while minimizing costs. The data inputs are the nutritional profile and price of different foods and the min and max values for nutrients in a diet. The model is expressed as the minimization of a linear program. The files that are used in this sample are available in the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>.</p>
<div class="note" data-hd-product="cloud wx">
<span class="notetitle">Note:</span> To create and run Optimization models, you must have both a <span class="keyword">Machine Learning</span> service added to your project and a deployment space that is associated with your <span class="keyword">experiment</span>:
<ol id="task_mtg_n3q_m1b__d49e189">
<li>Add a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong><span class="keyword">Machine Learning</span></strong> service</a> to your project. You can either add this service at the project level (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-service-instance.html">Creating a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> Service instance</a>), or you can add it when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span>, select, or create a <span class="ph uicontrol">New service</span>, click <span class="ph uicontrol">Associate</span>, then close the window.</li>
<li>Associate a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><strong>deployment space</strong></a> with your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> (see <a href="../DODS_Introduction/../../wsj/analyze-data/ml-spaces_local.html#create">Deployment spaces</a>). A deployment space can be created or selected when you first create a new <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>: click <span class="ph uicontrol">Create a deployment space</span>, enter a name for your deployment space, and click <span class="ph uicontrol">Create</span>. For existing models, you can also create, or select a space in the <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> information pane.</li>
</ol>
</div>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_mtg_n3q_m1b__steps__1">Procedure</h2>
</div>
<p class="li stepsection">To create and solve a Python-based model by using a sample:</p>
<ol class="steps">
<li class="step stepexpand"><span class="cmd">Download and extract all the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a> on to your computer. You can also download just the <span class="ph filepath">diet.zip</span> file from the <span class="ph filepath">Model_Builder</span> subfolder for your product and version, but in this case, do not extract it.</span></li>
<li class="step stepexpand"><span class="cmd"><span class="ph">Open your project or create an empty project.</span></span></li>
<li class="step stepexpand" data-hd-product="cloud wx"><span class="cmd"><span class="ph">On the <span class="ph uicontrol">Manage</span> tab of your project, select the <span class="ph uicontrol">Services and integrations</span> section and click <span class="ph uicontrol">Associate service</span>. Then select an existing <span class="keyword">Machine Learning</span> service instance (or create a new one ) and click <span class="ph uicontrol">Associate</span>. When the service is associated, a success message is displayed, and you can then close the <span class="keyword wintitle">Associate service</span> window. </span></span></li>
<li class="step stepexpand"><span class="cmd"><span class="ph">Select the <span class="ph" data-hd-product="wx"><span class="ph uicontrol"><span class="keyword">Assets</span></span></span> tab.</span></span></li>
<li class="step stepexpand" data-hd-product="wx"><span class="cmd"><span class="ph">Select <span class="ph uicontrol"><span class="keyword">New asset > Solve optimization problems</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></span></li>
<li class="step stepexpand"><span class="cmd"><span class="ph">Click <span class="ph uicontrol">Local file</span> in the <span class="ph" data-hd-product="wx"><span class="keyword">Solve optimization problems</span></span> window that opens.</span></span></li>
<li class="step stepexpand"><span class="cmd">Browse to find the <span class="ph filepath">Model_Builder</span> folder in your downloaded <span class="keyword">DO-samples</span>. <span class="ph" id="task_mtg_n3q_m1b__relevantfolder">Select the relevant product and version subfolder.</span> Choose the <span class="ph filepath">Diet.zip</span> file and click <span class="ph uicontrol">Open</span>. Alternatively use drag and drop.</span></li>
<li class="step stepexpand" data-hd-product="cloud wx"><span class="cmd"><span class="ph">If you haven't already associated a <span class="keyword">Machine Learning</span> service with your project, you must first select <span class="ph uicontrol">Add a <span class="keyword">Machine Learning</span> service</span> to select or create one before you choose a deployment space for your <span class="keyword">experiment</span>.</span></span></li>
<li class="step stepexpand"><span class="cmd"><span class="ph">Click <span class="ph uicontrol">New deployment space</span>, enter a name, and click <span class="ph uicontrol">Create</span> (or select an existing space from the drop-down menu).</span></span></li>
<li class="step stepexpand"><span class="cmd">Click <strong><span class="ph uicontrol">Create</span></strong>.</span>
<div class="itemgroup stepresult">
A <span class="keyword">Decision Optimization</span> model is created with the same name as the sample.
</div></li>
<li class="step stepexpand"><span class="cmd">In the <span class="ph uicontrol"><span class="keyword">Prepare data</span></span> <span class="keyword">view</span>, you can see the data assets imported.</span>
<div class="itemgroup info">
These tables represent the min and max values for nutrients in the diet (<code class="ph codeph">diet_nutrients</code>), the nutrients in different foods (<code class="ph codeph">diet_food_nutrients</code>), and the price and quantity of specific foods (<code class="ph codeph">diet_food</code>).
<p><img data-hd-product="cloud wx" id="task_mtg_n3q_m1b__image_fz1_khj_5pb" src="../DODS_Introduction/images/Cloudpreparedata2.png" alt="Tables of input data in Prepare data view"></p>
</div></li>
<li class="step stepexpand"><span class="cmd">Click <span class="ph uicontrol"><span class="keyword">Build model</span></span> in the sidebar to view your model.</span>
<div class="itemgroup info">
The Python model minimizes the cost of the food in the diet while satisfying minimum nutrient and calorie requirements.
<p><img data-hd-product="cloud wx" id="task_mtg_n3q_m1b__image_ihr_mhj_5pb" src="../DODS_Introduction/images/newrunmodel3.png" alt="Python model for diet problem displayed in Run model view"></p>
<p>Note also how the <strong><span class="ph uicontrol">inputs</span></strong> (tables in the <span class="keyword">Prepare data</span> <span class="keyword">view</span>) and the <strong><span class="ph uicontrol">outputs</span></strong> (in this case the solution table to be displayed in the Explore solution <span class="keyword">view</span>) are specified in this model.</p>
</div></li>
<li class="step stepexpand"><span class="cmd">Run the model by clicking the <strong><span class="ph uicontrol">Run</span></strong> button in the <span class="ph uicontrol"><span class="keyword">Build model</span></span> <span class="keyword">view</span>.</span></li>
</ol>
<section class="section result" role="region" aria-labelledby="tasktask_mtg_n3q_m1b__result__1">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_mtg_n3q_m1b__result__1">Results</h2>
</div>
<p><span class="ph">When the run is completed, you can see the results in the <strong><span class="ph uicontrol"><span class="keyword">Explore solution</span></span></strong> <span class="keyword">view</span>. You can also click <span class="ph uicontrol">Engine statistics</span> or <span class="ph uicontrol">Log</span> to see the solution chart and inspect the solver engine log files. The first tab in the <strong><span class="ph uicontrol"><span class="keyword">Explore solution</span></span></strong> <span class="keyword">view</span> shows the objective (or objectives if you have several) with its values and weights. The <span class="ph uicontrol">Solution tables</span> tab provides you with</span> a list of foods and their quantities, along with the nutrients that they provide.</p>
<p>You can also download the solution tables as <code class="ph codeph">csv</code> files.</p>
<p>If your model had any conflicting constraints, these would be shown in the <span class="ph uicontrol">Conflicts</span> tab with the <span class="ph uicontrol">Relaxations</span> necessary to solve the model.</p>
<p>In the <strong><span class="keyword">Visualization view</span></strong>, the solution is displayed as a table and a chart in the <strong><span class="ph uicontrol">Solution</span></strong> page. You can add notes, different types of tables and charts to show input data, solution data or KPIs by selecting and editing the widgets. You can also create different pages in the <span class="ph"><span class="keyword">Visualization view</span></span>. For example, an <strong>Input</strong> page is also provided in this sample. See <a href="../DODS_Introduction/Visualization.html#topic_visualization" title="With the Decision Optimization experiment Visualization view, you can configure the graphical representation of input data and solutions for one or several scenarios.">Visualization view</a>.</p>
<p>You're ready to start running comparisons between different scenarios. For example, the basic solution contains a quantity of hot dog. You might want to check an alternate solution for someone who prefers a vegetarian diet.</p>
</section>
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To solve with different versions of your model or data you can create new scenarios in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>.</li>
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D51AD51E5407BF4EFAE5C97FE7E031DB56CF8733 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=en | Decision Optimization run parameters | Run parameters and Environment
You can select various run parameters for the optimization solve in the Decision Optimization experiment UI.
Quick links to sections:
* [CPLEX runtime version](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=enRunConfig__cplexruntime)
* [Python version](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=enRunConfig__pyversion)
* [Run configuration parameters](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=enRunConfig__section_runconfig)
* [Environment for scenario](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=enRunConfig__section_runparamenv)
| # Run parameters and Environment #
You can select various run parameters for the optimization solve in the Decision Optimization experiment UI\.
Quick links to sections:
<!-- <ul> -->
* [CPLEX runtime version](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=en#RunConfig__cplexruntime)
* [Python version](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=en#RunConfig__pyversion)
* [Run configuration parameters](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=en#RunConfig__section_runconfig)
* [Environment for scenario](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_RunParameters/runparams.html?context=cdpaas&locale=en#RunConfig__section_runparamenv)
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<h1 class="topictitle1" id="RunConfig__title__1"><span class="ph" data-hd-product="cloud wx">Run parameters and Environment</span></h1>
<div class="body">
<p class="shortdesc">You can select various run parameters for the optimization solve in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>.</p>
<div class="p">
Quick links to sections:
<ul>
<li><a href="#RunConfig__cplexruntime">CPLEX runtime version</a></li>
<li><a href="#RunConfig__pyversion">Python version</a></li>
<li><a href="#RunConfig__section_runconfig">Run configuration parameters</a></li>
<li><a href="#RunConfig__section_runparamenv">Environment for scenario</a></li>
</ul>
</div>
<section class="section" role="region" aria-labelledby="RunConfig__cplexruntime__title__1" id="RunConfig__cplexruntime">
<h2 class="sectiontitle" id="RunConfig__cplexruntime__title__1">CPLEX runtime version</h2>
<p>As CPLEX engine performance improves with each new version, older versions are deprecated and removed over time. Runtimes, based on these engines, are used in building and deploying Decision Optimization models. Currently, the do_<span class="keyword">22.1</span> runtime, based on CPLEX <span class="keyword">22.1</span> is used automatically when creating and running scenarios. The do_<span class="keyword">20.1</span> runtime based on CPLEX <span class="keyword">20.1</span> is also available. You can view and change your CPLEX runtime in the experiment Overview by opening the Environment tab of the Information pane and selecting one of the available environments for your type of model (Python, OPL, CPLEX, CPO). See <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> for more details.</p>
</section>
<section class="section" role="region" aria-labelledby="RunConfig__pyversion__title__1" id="RunConfig__pyversion">
<h2 class="sectiontitle" id="RunConfig__pyversion__title__1">Python version</h2>
<p>You can view and change the Python environment in the experiment <span class="keyword">Overview</span> on the Environment tab of the Information pane. For more information, see <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_overview">Overview</a> and <a href="../DODS_Introduction/configureEnvironments.html#task_hwswconfig" title="You can change your default environment for Python and CPLEX in the experiment Overview.">Configuring environments and adding Python extensions</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="RunConfig__section_runconfig__title__1" id="RunConfig__section_runconfig">
<h2 class="sectiontitle" id="RunConfig__section_runconfig__title__1">Run configuration parameters</h2>
<p>When you click the <span class="ph uicontrol">Configure run</span> icon <img id="RunConfig__d35e1137" src="../DODS_Introduction/images/configurerunicon.jpg" alt="Configure run icon"> next to the <span class="ph uicontrol">Run</span> button in the <span class="keyword">Build model</span> <span class="keyword">view</span>, a window opens showing you the currently set parameter values.</p><span class="ph">Here you can select and edit different run configuration parameters.</span>
<p>You can click <span class="ph uicontrol">Add parameter</span> and then choose from the following parameters from the <span class="ph uicontrol">Select Parameters</span> drop-down menu.</p>
<div class="p">
<table summary="" id="RunConfig__simpletable_kw4_n1y_h2b" class="defaultstyle">
<colgroup>
<col style="width:28.770301624129928%">
<col style="width:23.201856148491874%">
<col style="width:48.02784222737818%">
</colgroup>
<thead>
<tr>
<th style="vertical-align:bottom;text-align:left;" id="RunConfig__simpletable_kw4_n1y_h2b__stentry__1">Name</th>
<th style="vertical-align:bottom;text-align:left;" id="RunConfig__simpletable_kw4_n1y_h2b__stentry__2">Type</th>
<th style="vertical-align:bottom;text-align:left;" id="RunConfig__simpletable_kw4_n1y_h2b__stentry__3">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">Runtime limit</code></td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__2">Number</td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__3">You can use this parameter to set a time limit in seconds.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">Log detail level</code></td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__2">Enum
<ul id="RunConfig__ul_y5k_cd3_j2b">
<li><code class="ph codeph">OFF</code></li>
<li><code class="ph codeph">INFO</code></li>
<li><code class="ph codeph">FINE</code></li>
</ul></td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__3">You can use this parameter to define the level of detail provided by the engine log. The <strong>default </strong> value is <code class="ph codeph">INFO</code>.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">Job memory</code></td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__2">Number</td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__3">You can use this parameter to set a job memory limit in MB.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">Intermediate solution delivery</code></td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__2">Enum
<ul>
<li><code class="ph codeph">NO</code></li>
<li><code class="ph codeph">Every minute</code></li>
<li><code class="ph codeph">Every 2 minutes</code></li>
<li><code class="ph codeph">Every 5 minutes</code></li>
<li><code class="ph codeph">Every 10 minutes</code></li>
<li><code class="ph codeph">Every 15 minutes</code></li>
</ul></td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_kw4_n1y_h2b__stentry__3">You can use this parameter to obtain a sample of intermediate solutions while the solve is running. The <span class="ph uicontrol">default</span> value is <code class="ph codeph">NO</code>, which means that no intermediate solutions are displayed. To see intermediate solutions, set this parameter to a frequency. When set, you can see intermediate solutions by clicking <span class="ph uicontrol">New data available</span> in the graphical display that is shown during the run. The following values, which are sampled with the frequency you set, are then displayed.
<ul>
<li>Statistics for a maximum of 3 intermediate solutions.</li>
<li>KPIs for a maximum of 3 intermediate solutions.</li>
<li>The solution table that shows only values from the last sampling.</li>
</ul></td>
</tr>
</tbody>
</table> See the <code class="ph codeph">IntermediateSolutions</code> sample in the <span class="ph filepath">Model_Builder</span> folder of the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a> in the <span class="keyword">Decision Optimization GitHub</span>. <span class="ph">Select the relevant product and version subfolder.</span>
</div>
<p>If you choose <span class="ph uicontrol">Custom parameter</span> from the <span class="ph uicontrol">Select Parameters</span> drop-down menu, you can add the following advanced parameters.</p>
<div class="p">
<table summary="" id="RunConfig__simpletable_h1f_41y_h2b" class="defaultstyle">
<colgroup>
<col style="width:30.782029950083196%">
<col style="width:69.21797004991681%">
</colgroup>
<thead>
<tr>
<th style="vertical-align:bottom;text-align:left;" id="RunConfig__simpletable_h1f_41y_h2b__stentry__1">Name</th>
<th style="vertical-align:bottom;text-align:left;" id="RunConfig__simpletable_h1f_41y_h2b__stentry__2">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td style="vertical-align:top;" headers="RunConfig__simpletable_h1f_41y_h2b__stentry__1">Modeling Assistant only
<p>For CPLEX <code class="ph codeph">ma.cplex.parameters.</code><var class="keyword varname"><Python cplex parameter name></var></p>
<p>For CPO <code class="ph codeph">ma.cpo.parameters.</code><var class="keyword varname"><Python cpo parameter name></var></p></td>
<td style="vertical-align:top;" headers="RunConfig__simpletable_h1f_41y_h2b__stentry__2">Python names for CPLEX and CPO parameters can be entered with the prefixes <code class="ph codeph">ma.cplex.parameters.</code> or <code class="ph codeph">ma.cpo.parameters.</code>
<p>For example,</p>
<p><code class="ph codeph">ma.cplex.parameters.mip.tolerances.absmipgap</code></p>
<p><code class="ph codeph">ma.cpo.SearchType</code></p>
<div class="p">
For a list of parameters see:
<ul id="RunConfig__ul_bjj_5dv_l2b">
<li><a href="https://www.ibm.com/support/knowledgecenter/SSSA5P_12.10.0/ilog.odms.cplex.help/CPLEX/Parameters/topics/introListAlpha.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">CPLEX parameters</a></li>
<li><a href="https://www.ibm.com/support/knowledgecenter/SSSA5P_12.10.0/ilog.odms.cpo.help/CP_Optimizer/Parameters/topics/paramcpoptimizer.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">CPO parameters</a> (Python names do not need <span class="ph filepath">IloCP</span> prefixes) and <a href="http://ibmdecisionoptimization.github.io/docplex-doc/cp/docplex.cp.parameters.py.html#summary-of-parameters" rel="noopener" target="_blank" title="(Opens in a new tab or window)"> a summary of DOcplex.cp parameters</a>.</li>
</ul>
</div></td>
</tr>
</tbody>
</table>
</div>
<p>After you set the run configuration parameters, they will be used with those values for all subsequent runs for that scenario.</p>
<p>You can remove set parameters by hovering over the parameter and clicking the <span class="ph uicontrol">Remove</span> icon.</p>
</section>
<section class="section" role="region" aria-labelledby="RunConfig__section_runparamenv__title__1" id="RunConfig__section_runparamenv">
<h2 class="sectiontitle" id="RunConfig__section_runparamenv__title__1">Environment for scenario</h2>
<p>The <span class="ph uicontrol">Environment</span> tab in this pane shows you the default run environment that is being used for your <span class="keyword">experiment</span>. <img id="RunConfig__d35e1178" src="../DODS_Introduction/images/runconfigEnv.png" alt="Environment tab of Run Configuration pane for scenario 1"></p>
<p><span class="ph">The <span class="keyword">Decision Optimization</span> environment currently supports Python <span class="keyword">3.10</span>. The default version is Python <span class="keyword">3.10</span>.</span></p>
<p>See the <span class="ph filepath">EnvironmentAndExtension</span> example in the <span class="ph filepath">Model_Builder</span> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong> in the <span class="keyword">Decision Optimization GitHub</span>. This example uses an environment with an extension that contains a library file and YAML code.</p>
<p>You can also select a <span class="ph uicontrol">different run environment for a particular scenario</span>, without changing the default for all the other scenarios. See <a href="../DODS_Introduction/configureEnvironments.html#task_envscenario" title="You can choose different environments for individual scenarios on the Environment tab of the Run configuration pane.">Selecting a different run environment for a particular scenario</a> for more details.</p>
<p>See also <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__environtab">Environment tab in Overview information pane</a> and <a href="../DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_environment">Hardware and software configuration</a>.</p>
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C6EE4CACFC1E29BAFBB8ED5D98521EA68388D0CB | https://dataplatform.cloud.ibm.com/docs/content/DO/DOWS-Cloud_home.html?context=cdpaas&locale=en | Decision Optimization | Decision Optimization
IBM® Decision Optimization gives you access to IBM's industry-leading solution engines for mathematical programming and constraint programming. You can build Decision Optimization models either with notebooks or by using the powerful Decision Optimization experiment UI (Beta version). Here you can import, or create and edit models in Python, in OPL or with natural language expressions provided by the intelligent Modeling Assistant (Beta version). You can also deploy models with Watson Machine Learning.
Data format
: Tabular: .csv, .xls, .json files. See [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.htmlModelBuilderInterface__section_preparedata)
Data from [Connected data assets](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/connected-data.html)
For deployment see [Model input and output data file formats](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIOFileFormats.html)
Data size
: Any
| # Decision Optimization #
IBM® Decision Optimization gives you access to IBM's industry\-leading solution engines for mathematical programming and constraint programming\. You can build Decision Optimization models either with notebooks or by using the powerful Decision Optimization experiment UI (Beta version)\. Here you can import, or create and edit models in Python, in OPL or with natural language expressions provided by the intelligent Modeling Assistant (Beta version)\. You can also deploy models with Watson Machine Learning\.
Data format
: Tabular: `.csv`, `.xls`, `.json` files\. See [Prepare data view](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_preparedata)
Data from [Connected data assets](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/connected-data.html)
For deployment see [Model input and output data file formats](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIOFileFormats.html)
Data size
: Any
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<h1 class="topictitle1" id="topic_cloudHome__title__1"><span class="ph"><span class="keyword">Decision Optimization</span></span></h1>
<div class="body">
<p class="shortdesc">IBM® <span class="keyword">Decision Optimization</span> gives you access to IBM's industry-leading solution engines for mathematical programming and constraint programming. You can build <span class="keyword">Decision Optimization</span> models either with <span class="keyword">notebooks</span> or by using the powerful <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> (Beta version). Here you can import, or create and edit models in Python, in OPL or with natural language expressions provided by the intelligent <span class="keyword">Modeling Assistant</span> (Beta version). You can also deploy models with <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>.</p>
<dl>
<dt class="dlterm">
Data format
</dt>
<dd class="dlentry">
Tabular: <code class="ph codeph">.csv</code>, <code class="ph codeph">.xls</code>, <code class="ph codeph">.json</code> files. See <a href="DODS_Introduction/modelbuilderUI.html#ModelBuilderInterface__section_preparedata">Prepare data view</a>
<p>Data from <a href="../wsj/manage-data/connected-data.html">Connected data assets</a></p>
<p>For deployment see <a href="WML_Deployment/ModelIOFileFormats.html" title="With your Decision Optimization model, you can use the following input and output data identifiers and extension combinations.">Model input and output data file formats</a></p>
</dd>
<dt class="dlterm">
Data size
</dt>
<dd class="dlentry">
Any
</dd>
</dl>
<section class="section" role="region" aria-labelledby="topic_cloudHome__section_ths_dgb_jjb__title__1" id="topic_cloudHome__section_ths_dgb_jjb">
<h2 class="sectiontitle" id="topic_cloudHome__section_ths_dgb_jjb__title__1">Accessing <span class="keyword">Decision Optimization</span></h2>
<p>To create a <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span>, follow these steps.</p>
<ol>
<li>Open your project or create an empty project.</li>
<li data-hd-product="wx">Select the <span class="ph uicontrol"><span class="keyword">Assets</span></span> tab.</li>
<li data-hd-product="wx"><span class="ph">Select <span class="ph uicontrol"><span class="keyword">New asset > Solve optimization problems</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></li>
<li>If you haven't already associated a <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> service instance with your project, click <span class="ph uicontrol">Add a Machine Learning service</span>. Select a service and click <span class="ph uicontrol">Associate</span>.</li>
<li>Click <span class="ph uicontrol">New deployment space</span>, enter a name and click <span class="ph uicontrol">Create</span> (or select an existing space).</li>
<li>Enter a <strong>Name</strong> for your <span class="keyword">Decision Optimization</span> <span class="keyword">experiment</span> and click <span class="ph uicontrol">Create</span>.</li>
</ol>
<p>The <span class="keyword">Decision Optimization</span> <a href="DODS_Introduction/buildingmodels.html#topic_buildingmodels" title="If you use the Decision Optimization experiment UI, you can take advantage of its many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with Watson Machine Learning."><span class="keyword">experiment UI</span></a> (Beta version) opens where you can create and edit models formulated with the <span class="ph uicontrol">Modeling Assistant</span>, or in <span class="ph uicontrol">Python DOcplex</span>, or in <span class="ph uicontrol">OPL</span>.</p>
<p>Alternatively, to open and run <span class="keyword">Decision Optimization</span> <a href="DODS_Introduction/DONotebooks.html#DONotebooks" title="You can create and run Decision Optimization models in Python notebooks by using DOcplex, a native Python API for Decision Optimization. Several Decision Optimization notebooks are already available for you to use."><span class="keyword">notebooks</span></a> (without the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>), follow these steps.</p>
<ol data-hd-product="cloud wx">
<li><span class="ph">Select the <span class="ph" data-hd-product="wx"><span class="ph uicontrol"><span class="keyword">Assets</span></span></span> tab.</span></li>
<li data-hd-product="wx"><span class="ph">Select <span class="ph uicontrol"><span class="keyword">New asset > Work with data and models in Python or R notebooks</span></span> in the <span class="ph uicontrol"><span class="keyword">Work with models</span></span> section.</span></li>
</ol>
<p>For a step-by-step guide to build, solve and deploy a <span class="keyword">Decision Optimization</span> model, by using the user interface, see the <a href="../wsj/getting-started/get-started-do.html">Quick start tutorial with video</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_cloudHome__section_whatisdo__title__1" id="topic_cloudHome__section_whatisdo">
<h2 class="sectiontitle" id="topic_cloudHome__section_whatisdo__title__1">What is <span class="keyword">Decision Optimization</span>?</h2>
<p>People frequently use the term <em>optimization</em> to mean <strong>making something better.</strong> Although optimization often makes things better, it means a lot more than that: <strong>optimization means finding the most appropriate solution to a precisely defined situation.</strong> It is a sophisticated analytics technology, also called <strong>Prescriptive Analytics</strong>, which can explore a huge range of possible scenarios before suggesting the best way to respond to a present or future situation.</p>
<p><img id="topic_cloudHome__image_decisionoptim" src="DODS_Introduction/images/DecisionOptimization.jpg" alt="Decision optimization"></p>
<div class="p">
<ol id="topic_cloudHome__ol_aboutDO">
<li>The situation is generally a <strong>business problem</strong>, such as planning, scheduling, pricing, inventory, or resource management.</li>
<li>Whatever the problem is, resolving it starts with the <strong>optimization model</strong>, which is the mathematical formulation of the problem that can be interpreted and solved by an <strong>optimization engine</strong>. The optimization model specifies the relationships among the objectives, constraints, limitations, and choices that are involved in the decisions. But it is the input data that makes these relationships concrete. An optimization model for production planning, for example, can have the same form whether you are producing three products or a thousand. The optimization model plus the input data creates an instance of an optimization problem.</li>
<li><strong>Optimization engines</strong> (or solvers) apply mathematical algorithms to find a solution, a set of decisions that achieves the best values for the objectives and respects the constraints and limitations imposed. The optimization engine implements specialized algorithms that have been developed and tuned to efficiently solve a large variety of different problems. Decision Optimization uses the IBM CPLEX® and CP Optimizer engines that have been proved powerful in solving real-world applications.</li>
<li>The <strong>solution</strong> that emerges from the solver details the recommended values for all of the decisions that are represented in the model. Equally important are the metric values that represent the targets. These values measure the quality of the solution in terms of the business goals.</li>
<li>All of this can be made available to business users via a complementary business application. Usually, the objective and solution values are summarized in tabular or graphical views that provide understanding and insight.</li>
</ol>
</div>
<p>For training in using <span class="keyword">Decision Optimization</span> in <span class="keyword" data-hd-product="wx">watsonx.ai</span>, see <a href="https://cognitiveclass.ai/courses/mathematical-optimization-for-business-problems" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Mathematical Optimization for Business Problems Training</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_cloudHome__section_z2b_12g_5jb__title__1" id="topic_cloudHome__section_z2b_12g_5jb">
<h2 class="sectiontitle" id="topic_cloudHome__section_z2b_12g_5jb__title__1">Learn more</h2>
</section>
</div>
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<ul class="ullinks">
<li class="ulchildlink"><strong><a href="DODS_Introduction/DOintro.html">Ways to use Decision Optimization</a></strong><br><span class="ph">To build <span class="keyword">Decision Optimization</span> models, you can create Python <span class="keyword">notebooks</span> with <span class="keyword">DOcplex</span>, a native Python API for Decision Optimization, or use the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> that has more benefits and features.</span></li>
<li class="ulchildlink"><strong><a href="DODS_Introduction/DOconnections.html">Supported data sources in Decision Optimization</a></strong><br><span class="keyword">Decision Optimization</span> supports the following relational and nonrelational data sources on .<span class="ph" data-hd-product="wx"><span class="keyword">watsonx.ai</span>.</span></li>
<li class="ulchildlink"><strong><a href="DODS_Introduction/docExamples.html">Sample models and notebooks for Decision Optimization</a></strong><br>
Several examples are presented in this documentation as tutorials. You can also use many other examples that are provided in the <span class="keyword">Decision Optimization GitHub</span>, and in the <span class="keyword">Samples</span>.</li>
<li class="ulchildlink"><strong><a href="DODS_Introduction/DONotebooks.html">Decision Optimization notebooks</a></strong><br>
You can create and run <span class="keyword">Decision Optimization</span> models in Python <span class="keyword">notebooks</span> by using <span class="keyword">DOcplex</span>, a native Python API for <span class="keyword">Decision Optimization</span>. Several <span class="keyword">Decision Optimization</span> <span class="keyword">notebooks</span> are already available for you to use.</li>
<li class="ulchildlink"><strong><a href="DODS_Introduction/buildingmodels.html">Decision Optimization experiments</a></strong><br>
If you use the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, you can take advantage of its many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>.</li>
<li class="ulchildlink"><strong><a href="DODS_Introduction/DOJava.html">Decision Optimization Java models</a></strong><br>
You can create and run <span class="keyword">Decision Optimization</span> models in Java™ by using the <span class="keyword">Watson Machine Learning</span> REST API.</li>
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E45F37BDDB38D6656992642FBEA2707FE34E942A | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/CPLEXSolveWML.html?context=cdpaas&locale=en | Delegating CPLEX solve to Watson Machine Learning | Delegating the Decision Optimization solve to run on Watson Machine Learning from Java or .NET CPLEX or CPO models
You can delegate the Decision Optimization solve to run on Watson Machine Learning from your Java or .NET (CPLEX or CPO) models.
Delegating the solve is only useful if you are building and generating your models locally. You cannot deploy models and run jobs Watson Machine Learning with this method. For full use of Java models on Watson Machine Learning use the Java™ worker Important: To deploy and test models on Watson Machine Learning, use the Java worker. For more information about deploying Java models, see the [Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md).For the library and documentation for:
* Java CPLEX or CPO models. See [Decision Optimization GitHub DOforWMLwithJava](https://github.com/IBMDecisionOptimization/DOforWMLwithJava).
* .NET CPLEX or CPO models. See [Decision Optimization GitHub DOforWMLWith.NET](https://github.com/IBMDecisionOptimization/DOForWMLWith.NET).
| # Delegating the Decision Optimization solve to run on Watson Machine Learning from Java or \.NET CPLEX or CPO models #
You can delegate the Decision Optimization solve to run on Watson Machine Learning from your Java or \.NET (CPLEX or CPO) models\.
Delegating the solve is only useful if you are building and generating your models locally\. You cannot deploy models and run jobs Watson Machine Learning with this method\. For full use of Java models on Watson Machine Learning use the Java™ worker Important: To deploy and test models on Watson Machine Learning, use the Java worker\. For more information about deploying Java models, see the [Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md)\.For the library and documentation for:
<!-- <ul> -->
* Java CPLEX or CPO models\. See [Decision Optimization GitHub DOforWMLwithJava](https://github.com/IBMDecisionOptimization/DOforWMLwithJava)\.
* \.NET CPLEX or CPO models\. See [Decision Optimization GitHub DOforWMLWith\.NET](https://github.com/IBMDecisionOptimization/DOForWMLWith.NET)\.
<!-- </ul> -->
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<h1 class="topictitle1" id="topic_cplexsolvewml__title__1">Delegating the <span class="keyword">Decision Optimization</span> solve to run on <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> from Java or .NET CPLEX or CPO models</h1>
<div class="body">
<p class="shortdesc">You can delegate the <span class="keyword">Decision Optimization</span> solve to run on <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> from your Java or .NET (CPLEX or CPO) models.</p>
<div class="p">
Delegating the solve is only useful if you are building and generating your models locally. You cannot deploy models and run jobs <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> with this method. For full use of Java models on <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> use the <span class="keyword">Java™ worker</span>
<div class="note important">
<span class="importanttitle">Important:</span> To deploy and test models on <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>, use the <span class="keyword">Java worker</span>. For more information about deploying Java models, see the <a href="https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java worker GitHub</span></a>.
</div>
</div>
<div class="p">
For the library and documentation for:
<ul id="topic_cplexsolvewml__ul_xzz_bpc_l4b">
<li>Java CPLEX or CPO models. See <a href="https://github.com/IBMDecisionOptimization/DOforWMLwithJava" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Decision Optimization GitHub</span> DOforWMLwithJava</a>.</li>
<li>.NET CPLEX or CPO models. See <a href="https://github.com/IBMDecisionOptimization/DOForWMLWith.NET" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Decision Optimization GitHub</span> DOforWMLWith.NET</a>.</li>
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5BC48AB9A35E2E8BAEA5204C4406835154E2B836 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployIntro.html?context=cdpaas&locale=en | Decision Optimization deployment steps | Deployment steps
With IBM Watson Machine Learning you can deploy your Decision Optimization prescriptive model and associated common data once and then submit job requests to this deployment with only the related transactional data. This deployment can be achieved by using the Watson Machine Learning REST API or by using the Watson Machine Learning Python client.
See [REST API example](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelRest.htmltask_deploymodelREST) for a full code example. See [Python client examples](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployPythonClient.htmltopic_wmlpythonclient) for a link to a Python notebook available from the Samples.
| # Deployment steps #
With IBM Watson Machine Learning you can deploy your Decision Optimization prescriptive model and associated common data once and then submit job requests to this deployment with only the related transactional data\. This deployment can be achieved by using the Watson Machine Learning REST API or by using the Watson Machine Learning Python client\.
See [REST API example](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelRest.html#task_deploymodelREST) for a full code example\. See [Python client examples](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployPythonClient.html#topic_wmlpythonclient) for a link to a Python notebook available from the Samples\.
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<h1 class="topictitle1" id="topic_wmldeployintro__title__1"><span class="ph" data-hd-product="cloud wx">Deployment steps</span></h1>
<div class="body">
<p class="shortdesc">With IBM <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> you can deploy your <span class="keyword">Decision Optimization</span> prescriptive model and associated common data once and then submit job requests to this deployment with only the related transactional data. This deployment can be achieved by using the <span class="keyword">Watson Machine Learning REST API</span> or by using the <span class="keyword">Watson Machine Learning Python client</span>.</p>
<p>See <a href="DeployModelRest.html#task_deploymodelREST" title="You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.">REST API example</a> for a full code example. See <a href="DeployPythonClient.html#topic_wmlpythonclient" title="You can deploy a Decision Optimization model, create and monitor jobs, and get solutions by using the Watson Machine Learning Python client.">Python client examples</a> for a link to a Python <span class="keyword">notebook</span> available from the <span class="keyword">Samples</span>.</p>
<section class="section" role="region" aria-labelledby="topic_wmldeployintro__section_r1z_nrg_ghb__title__1" id="topic_wmldeployintro__section_r1z_nrg_ghb">
<h2 class="sectiontitle" id="topic_wmldeployintro__section_r1z_nrg_ghb__title__1">Overview</h2>
<p>The <strong>steps to deploy and submit jobs</strong> for a <span class="keyword">Decision Optimization</span> model are as follows. These steps are detailed in later sections.</p>
<div class="p">
<ol id="topic_wmldeployintro__ul_bzy_wgh_chb">
<li data-hd-product="cloud wx">Create a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Machine Learning</span> service</a>.</li>
<li data-hd-product="cloud wx">Create a deployment space by using the <a href="https://dataplatform.cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">https://dataplatform.cloud.ibm.com</a> user interface or with the REST API.</li>
<li>Deploy your model with common data. <span class="ph">This deployment can be done from the user interface (see <a href="DeployModelUI-WML.html#task_deployUIWML" title="You can save a model for deployment in the Decision Optimization experiment UI and promote it to your Watson Machine Learning deployment space.">Deploying from the user interface</a>) or by following the steps that are described in <a data-hd-product="cloud wx" href="ModelDeploymentTaskCloud.html" title="To deploy a Decision Optimization model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.">Model deployment</a>.</span> See also this <a href="DeployModelRest.html#task_deploymodelREST__uploadmodelstep">REST API example</a>.</li>
<li>Deploy your model with common data. <span class="ph" data-hd-product="cloud wx">This is described in <a href="ModelDeploymentTaskCloud.html#task_modeldeployCloud" title="To deploy a Decision Optimization model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.">Model deployment</a>.</span> See also this <a href="DeployModelRest.html#task_deploymodelREST__uploadmodelstep">REST API example</a>.</li>
<li>Create and monitor jobs to this deployed model.</li>
</ol>
</div>
<p></p>
<p><img data-hd-product="cloud wx" id="topic_wmldeployintro__image_e4z_nk3_lkb" src="images/new_deploylifecycleCloud.jpg" alt="Decision Optimization model lifecycle flowchart showing deployment and use steps"></p>
<p>The T-shirt size refers to predefined deployment configurations: small, medium, large, and extra large.</p>
<div class="p">
<div class="tablenoborder">
<table summary="" id="topic_wmldeployintro__table_i5m_hnv_f5b" class="defaultstyle">
<caption>
<span class="tablecap">Table 1. T-shirt sizes for Decision Optimization</span>
</caption>
<colgroup>
<col style="width:37.65060240963855%">
<col style="width:15.06024096385542%">
<col style="width:47.28915662650602%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="topic_wmldeployintro__table_i5m_hnv_f5b__entry__1">Definition</th>
<th id="topic_wmldeployintro__table_i5m_hnv_f5b__entry__2">Name</th>
<th id="topic_wmldeployintro__table_i5m_hnv_f5b__entry__3">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__1 ">2 vCPU and 8 GB</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__2 ">S</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__3 ">Small</td>
</tr>
<tr>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__1 ">4 vCPU and 16 GB</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__2 ">M</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__3 ">Medium</td>
</tr>
<tr>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__1 ">8 vCPU and 32 GB</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__2 ">L</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__3 ">Large</td>
</tr>
<tr>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__1 ">16 vCPU and 64 GB</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__2 ">XL</td>
<td headers="topic_wmldeployintro__table_i5m_hnv_f5b__entry__3 ">Extra Large</td>
</tr>
</tbody>
</table>
</div> See also <a href="Paralleljobs.html#topic_paralleljobs__34c6">Running jobs</a>.
</div>
</section>
<section class="section" role="region" aria-labelledby="topic_wmldeployintro__section_hyl_z3y_m3b__title__1" id="topic_wmldeployintro__section_hyl_z3y_m3b">
<h2 class="sectiontitle" id="topic_wmldeployintro__section_hyl_z3y_m3b__title__1">Learn more</h2>
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<ul class="ullinks">
<li class="ulchildlink"><strong><a href="../WML_Deployment/ModelDeploymentTaskCloud.html">Model deployment</a></strong><br>
To deploy a <span class="keyword">Decision Optimization</span> model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.</li>
<li class="ulchildlink"><strong><a href="../WML_Deployment/ModelExecution.html">Model execution</a></strong><br>
Once your model is deployed, you can submit <span class="keyword">Decision Optimization</span> jobs to this deployment.</li>
<li class="ulchildlink"><strong><a href="../WML_Deployment/ModelIOFileFormats.html">Model input and output data file formats</a></strong><br>
With your <span class="keyword">Decision Optimization</span> model, you can use the following input and output data identifiers and extension combinations.</li>
<li class="ulchildlink"><strong><a href="../WML_Deployment/ModelIODataDefn.html">Model input and output data adaptation</a></strong><br>
When submitting your job you can include your data inline or reference your data in your request. This data will be mapped to a file named with data identifier and used by the model. The data identifier extension will define the format of the file used.</li>
<li class="ulchildlink"><strong><a href="../WML_Deployment/OutputDataDefn.html">Output data definition</a></strong><br>
When submitting your job you can define what output data you want and how you collect it (as either inline or referenced data).</li>
<li class="ulchildlink"><strong><a href="../WML_Deployment/DeploySolveParams.html">Solve parameters</a></strong><br>
To control solve behavior, you can specify <span class="keyword">Decision Optimization</span> solve parameters in your request as named value pairs.</li>
</ul>
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134EB5D79038B55A3A6AC019016A21EC2B6A1917 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployJava.html?context=cdpaas&locale=en | Deploying Java models | Deploying Java models for Decision Optimization
You can deploy Decision Optimization Java models in Watson Machine Learning by using the Watson Machine Learning REST API.
With the Java worker API, you can create optimization models with OPL, CPLEX, and CP Optimizer Java APIs. Therefore, you can easily create your models locally, package them and deploy them on Watson Machine Learning by using the boilerplate that is provided in the public [Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md).
The Decision Optimization[Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md) contains a boilerplate with everything that you need to run, deploy, and verify your Java models in Watson Machine Learning, including an example. You can use the code in this repository to package your Decision Optimization Java model in a .jar file that can be used as a Watson Machine Learning model. For more information about Java worker parameters, see the [Java documentation](https://github.com/IBMDecisionOptimization/do-maven-repo/blob/master/com/ibm/analytics/optim/api_java_client/1.0.0/api_java_client-1.0.0-javadoc.jar).
You can build your Decision Optimization models in Java or you can use Java worker to package CPLEX, CPO, and OPL models.
For more information about these models, see the following reference manuals.
* [Java CPLEX reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/refjavacplex/html/overview-summary.html)
* [Java CPO reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/refjavacpoptimizer/html/overview-summary.html)
* [Java OPL reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/refjavaopl/html/overview-summary.html)
| # Deploying Java models for Decision Optimization #
You can deploy Decision Optimization Java models in Watson Machine Learning by using the Watson Machine Learning REST API\.
With the Java worker API, you can create optimization models with OPL, CPLEX, and CP Optimizer Java APIs\. Therefore, you can easily create your models locally, package them and deploy them on Watson Machine Learning by using the boilerplate that is provided in the public [Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md)\.
The Decision Optimization[Java worker GitHub](https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md) contains a boilerplate with everything that you need to run, deploy, and verify your Java models in Watson Machine Learning, including an example\. You can use the code in this repository to package your Decision Optimization Java model in a `.jar` file that can be used as a Watson Machine Learning model\. For more information about Java worker parameters, see the [Java documentation](https://github.com/IBMDecisionOptimization/do-maven-repo/blob/master/com/ibm/analytics/optim/api_java_client/1.0.0/api_java_client-1.0.0-javadoc.jar)\.
You can build your Decision Optimization models in Java or you can use Java worker to package CPLEX, CPO, and OPL models\.
For more information about these models, see the following reference manuals\.
<!-- <ul> -->
* [Java CPLEX reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/refjavacplex/html/overview-summary.html)
* [Java CPO reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/refjavacpoptimizer/html/overview-summary.html)
* [Java OPL reference documentation](https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/refjavaopl/html/overview-summary.html)
<!-- </ul> -->
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<h1 class="topictitle1" id="deployJava__title__1">Deploying <span class="keyword">Java models</span> for <span class="keyword">Decision Optimization</span></h1>
<div class="body">
<p class="shortdesc">You can deploy <span class="keyword">Decision Optimization</span> <span class="keyword">Java models</span> in <span class="keyword">Watson Machine Learning</span> by using the <span class="keyword">Watson Machine Learning</span> REST API.</p>
<p>With the <span class="keyword">Java worker</span> API, you can create optimization models with OPL, CPLEX, and CP Optimizer Java APIs. Therefore, you can easily create your models locally, package them and deploy them on Watson Machine Learning by using the boilerplate that is provided in the public <a href="https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java worker GitHub</span></a>.</p>
<p id="deployJava__javaintro">The <span class="keyword">Decision Optimization</span> <a href="https://github.com/IBMDecisionOptimization/cplex-java-worker/blob/master/README.md" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java worker GitHub</span></a> contains a boilerplate with everything that you need to run, deploy, and verify your Java models in <span class="keyword">Watson Machine Learning</span>, including an example. You can use the code in this repository to package your <span class="keyword">Decision Optimization</span> Java model in a <code class="ph codeph">.jar</code> file that can be used as a <span class="keyword">Watson Machine Learning</span> model. For more information about <span class="keyword">Java worker</span> parameters, see the <a href="https://github.com/IBMDecisionOptimization/do-maven-repo/blob/master/com/ibm/analytics/optim/api_java_client/1.0.0/api_java_client-1.0.0-javadoc.jar" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Java documentation</a>.</p>
<p id="deployJava__javabuildintro">You can build your <span class="keyword">Decision Optimization</span> models in Java or you can use <span class="keyword">Java worker</span> to package CPLEX, CPO, and OPL models.</p>
<div class="p" id="deployJava__javarefmans">
For more information about these models, see the following reference manuals.
<ul>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cplex.help/refjavacplex/html/overview-summary.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java CPLEX reference documentation</span></a></li>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.cpo.help/refjavacpoptimizer/html/overview-summary.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java CPO reference documentation</span></a></li>
<li><a href="https://www.ibm.com/docs/en/SSSA5P_22.1.1/ilog.odms.ide.help/refjavaopl/html/overview-summary.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Java OPL reference documentation</span></a></li>
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B92F42609B54B82BFE38A69B781052E876258C2C | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelRest.html?context=cdpaas&locale=en | Decision Optimization REST API deployment | REST API example
You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.
Procedure
1. Generate an IAM token using your [IBM Cloud API key](https://cloud.ibm.com/iam/apikeys) as follows.
curl "https://iam.bluemix.net/identity/token"
-d "apikey=YOUR_API_KEY_HERE&grant_type=urn%3Aibm%3Aparams%3Aoauth%3Agrant-type%3Aapikey"
-H "Content-Type: application/x-www-form-urlencoded"
-H "Authorization: Basic Yng6Yng="
Output example:
{
"access_token": " obtained IAM token ",
"refresh_token": "",
"token_type": "Bearer",
"expires_in": 3600,
"expiration": 1554117649,
"scope": "ibm openid"
}
Use the obtained token (access_token value) prepended by the word Bearer in the Authorization header, and the Machine Learning service GUID in the ML-Instance-ID header, in all API calls.
2. Optional: If you have not obtained your SPACE-ID from the user interface as described previously, you can create a space using the REST API as follows. Use the previously obtained token prepended by the word bearer in the Authorization header in all API calls.
curl --location --request POST
"https://api.dataplatform.cloud.ibm.com/v2/spaces"
-H "Authorization: Bearer TOKEN-HERE"
-H "ML-Instance-ID: MACHINE-LEARNING-SERVICE-GUID-HERE"
-H "Content-Type: application/json"
--data-raw "{
"name": "SPACE-NAME-HERE",
"description": "optional description here",
"storage": {
"resource_crn": "COS-CRN-ID-HERE"
},
"compute": [{
"name": "MACHINE-LEARNING-SERVICE-NAME-HERE",
"crn": "MACHINE-LEARNING-SERVICE-CRN-ID-HERE"
}]
}"
For Windows users, put the --data-raw command on one line and replace all " with " inside this command as follows:
curl --location --request POST ^
"https://api.dataplatform.cloud.ibm.com/v2/spaces" ^
-H "Authorization: Bearer TOKEN-HERE" ^
-H "ML-Instance-ID: MACHINE-LEARNING-SERVICE-GUID-HERE" ^
-H "Content-Type: application/json" ^
--data-raw "{"name": "SPACE-NAME-HERE","description": "optional description here","storage": {"resource_crn": "COS-CRN-ID-HERE" },"compute": [{"name": "MACHINE-LEARNING-SERVICE-NAME-HERE","crn": "MACHINE-LEARNING-SERVICE-CRN-ID-HERE" }]}"
Alternatively put the data in a separate file.A SPACE-ID is returned in id field of the metadata section.
Output example:
{
"entity": {
"compute": [
{
"crn": "MACHINE-LEARNING-SERVICE-CRN",
"guid": "MACHINE-LEARNING-SERVICE-GUID",
"name": "MACHINE-LEARNING-SERVICE-NAME",
"type": "machine_learning"
}
],
"description": "string",
"members": [
{
"id": "XXXXXXX",
"role": "admin",
"state": "active",
"type": "user"
}
],
"name": "name",
"scope": {
"bss_account_id": "account_id"
},
"status": {
"state": "active"
}
},
"metadata": {
"created_at": "2020-07-17T08:36:57.611Z",
"creator_id": "XXXXXXX",
"id": "SPACE-ID",
"url": "/v2/spaces/SPACE-ID"
}
}
You must wait until your deployment space status is "active" before continuing. You can poll to check for this as follows.
curl --location --request GET "https://api.dataplatform.cloud.ibm.com/v2/spaces/SPACE-ID-HERE"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
3. Create a new Decision Optimization model
All API requests require a version parameter that takes a date in the format version=YYYY-MM-DD. This code example posts a model that uses the file create_model.json. The URL will vary according to the chosen region/location for your machine learning service. See [Endpoint URLs](https://cloud.ibm.com/apidocs/machine-learningendpoint-url).
curl --location --request POST
"https://us-south.ml.cloud.ibm.com/ml/v4/models?version=2020-08-01"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
-d @create_model.json
The create_model.json file contains the following code:
{
"name": "ModelName",
"description": "ModelDescription",
"type": "do-docplex_22.1",
"software_spec": {
"name": "do_22.1"
},
"custom": {
"decision_optimization": {
"oaas.docplex.python": "3.10"
}
},
"space_id": "SPACE-ID-HERE"
}
The Python version is stated explicitly here in a custom block. This is optional. Without it your model will use the default version which is currently Python 3.10. As the default version will evolve over time, stating the Python version explicitly enables you to easily change it later or to keep using an older supported version when the default version is updated. Currently supported versions are 3.10.
If you want to be able to run jobs for this model from the user interface, instead of only using the REST API , you must define the schema for the input and output data. If you do not define the schema when you create the model, you can only run jobs using the REST API and not from the user interface.
You can also use the schema specified for input and output in your optimization model:
{
"name": "Diet-Model-schema",
"description": "Diet",
"type": "do-docplex_22.1",
"schemas": {
"input": [
{
"id": "diet_food_nutrients",
"fields":
{ "name": "Food", "type": "string" },
{ "name": "Calories", "type": "double" },
{ "name": "Calcium", "type": "double" },
{ "name": "Iron", "type": "double" },
{ "name": "Vit_A", "type": "double" },
{ "name": "Dietary_Fiber", "type": "double" },
{ "name": "Carbohydrates", "type": "double" },
{ "name": "Protein", "type": "double" }
]
},
{
"id": "diet_food",
"fields":
{ "name": "name", "type": "string" },
{ "name": "unit_cost", "type": "double" },
{ "name": "qmin", "type": "double" },
{ "name": "qmax", "type": "double" }
]
},
{
"id": "diet_nutrients",
"fields":
{ "name": "name", "type": "string" },
{ "name": "qmin", "type": "double" },
{ "name": "qmax", "type": "double" }
]
}
],
"output": [
{
"id": "solution",
"fields":
{ "name": "name", "type": "string" },
{ "name": "value", "type": "double" }
]
}
]
},
"software_spec": {
"name": "do_22.1"
},
"space_id": "SPACE-ID-HERE"
}
When you post a model you provide information about its model type and the software specification to be used.Model types can be, for example:
* do-opl_22.1 for OPL models
* do-cplex_22.1 for CPLEX models
* do-cpo_22.1 for CP models
* do-docplex_22.1 for Python models
Version 20.1 can also be used for these model types.
For the software specification, you can use the default specifications using their names do_22.1 or do_20.1. See also [Extend software specification notebook](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployPythonClient.htmltopic_wmlpythonclient__extendWML) which shows you how to extend the Decision Optimization software specification (runtimes with additional Python libraries for docplex models).
A MODEL-ID is returned in id field in the metadata.
Output example:
{
"entity": {
"software_spec": {
"id": "SOFTWARE-SPEC-ID"
},
"type": "do-docplex_20.1"
},
"metadata": {
"created_at": "2020-07-17T08:37:22.992Z",
"description": "ModelDescription",
"id": "MODEL-ID",
"modified_at": "2020-07-17T08:37:22.992Z",
"name": "ModelName",
"owner": "",
"space_id": "SPACE-ID"
}
}
4. Upload a Decision Optimization model formulation ready for deployment.First compress your model into a (tar.gz, .zip or .jar) file and upload it to be deployed by the Watson Machine Learning service.This code example uploads a model called diet.zip that contains a Python model and no common data:
curl --location --request PUT
"https://us-south.ml.cloud.ibm.com/ml/v4/models/MODEL-ID-HERE/content?version=2020-08-01&space_id=SPACE-ID-HERE&content_format=native"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/gzip"
--data-binary "@diet.zip"
You can download this example and other models from the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples). Select the relevant product and version subfolder.
5. Deploy your modelCreate a reference to your model. Use the SPACE-ID, the MODEL-ID obtained when you created your model ready for deployment and the hardware specification. For example:
curl --location --request POST "https://us-south.ml.cloud.ibm.com/ml/v4/deployments?version=2020-08-01"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
-d @deploy_model.json
The deploy_model.json file contains the following code:
{
"name": "Test-Diet-deploy",
"space_id": "SPACE-ID-HERE",
"asset": {
"id": "MODEL-ID-HERE"
},
"hardware_spec": {
"name": "S"
},
"batch": {}
}
The DEPLOYMENT-ID is returned in id field in the metadata. Output example:
{
"entity": {
"asset": {
"id": "MODEL-ID"
},
"custom": {},
"description": "",
"hardware_spec": {
"id": "HARDWARE-SPEC-ID",
"name": "S",
"num_nodes": 1
},
"name": "Test-Diet-deploy",
"space_id": "SPACE-ID",
"status": {
"state": "ready"
}
},
"metadata": {
"created_at": "2020-07-17T09:10:50.661Z",
"description": "",
"id": "DEPLOYMENT-ID",
"modified_at": "2020-07-17T09:10:50.661Z",
"name": "test-Diet-deploy",
"owner": "",
"space_id": "SPACE-ID"
}
}
6. Once deployed, you can monitor your model's deployment state. Use the DEPLOYMENT-ID.For example:
curl --location --request GET "https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
Output example:
7. You can then Submit jobs for your deployed model defining the input data and the output (results of the optimization solve) and the log file.For example, the following shows the contents of a file called myjob.json. It contains (inline) input data, some solve parameters, and specifies that the output will be a .csv file. For examples of other types of input data references, see [Model input and output data adaptation](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIODataDefn.htmltopic_modelIOAdapt).
{
"name":"test-job-diet",
"space_id": "SPACE-ID-HERE",
"deployment": {
"id": "DEPLOYMENT-ID-HERE"
},
"decision_optimization" : {
"solve_parameters" : {
"oaas.logAttachmentName":"log.txt",
"oaas.logTailEnabled":"true"
},
"input_data": [
{
"id":"diet_food.csv",
"fields" : "name","unit_cost","qmin","qmax"],
"values" :
"Roasted Chicken", 0.84, 0, 10],
"Spaghetti W/ Sauce", 0.78, 0, 10],
"Tomato,Red,Ripe,Raw", 0.27, 0, 10],
"Apple,Raw,W/Skin", 0.24, 0, 10],
"Grapes", 0.32, 0, 10],
"Chocolate Chip Cookies", 0.03, 0, 10],
"Lowfat Milk", 0.23, 0, 10],
"Raisin Brn", 0.34, 0, 10],
"Hotdog", 0.31, 0, 10]
]
},
{
"id":"diet_food_nutrients.csv",
"fields" : "Food","Calories","Calcium","Iron","Vit_A","Dietary_Fiber","Carbohydrates","Protein"],
"values" :
"Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2],
"Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2],
"Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1],
"Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3],
"Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2],
"Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9],
"Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1],
"Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4],
"Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]
]
},
{
"id":"diet_nutrients.csv",
"fields" : "name","qmin","qmax"],
"values" :
"Calories", 2000, 2500],
"Calcium", 800, 1600],
"Iron", 10, 30],
"Vit_A", 5000, 50000],
"Dietary_Fiber", 25, 100],
"Carbohydrates", 0, 300],
"Protein", 50, 100]
]
}
],
"output_data": [
{
"id":"..csv"
}
]
}
}
This code example posts a job that uses this file myjob.json.
curl --location --request POST "https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs?version=2020-08-01&space_id=SPACE-ID-HERE"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
-H "cache-control: no-cache"
-d @myjob.json
A JOB-ID is returned. Output example: (the job is queued)
{
"entity": {
"decision_optimization": {
"input_data": [{
"id": "diet_food.csv",
"fields": "name", "unit_cost", "qmin", "qmax"],
"values": "Roasted Chicken", 0.84, 0, 10], "Spaghetti W/ Sauce", 0.78, 0, 10], "Tomato,Red,Ripe,Raw", 0.27, 0, 10], "Apple,Raw,W/Skin", 0.24, 0, 10], "Grapes", 0.32, 0, 10], "Chocolate Chip Cookies", 0.03, 0, 10], "Lowfat Milk", 0.23, 0, 10], "Raisin Brn", 0.34, 0, 10], "Hotdog", 0.31, 0, 10]]
}, {
"id": "diet_food_nutrients.csv",
"fields": "Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
"values": "Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], "Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], "Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], "Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], "Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], "Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], "Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], "Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], "Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
}, {
"id": "diet_nutrients.csv",
"fields": "name", "qmin", "qmax"],
"values": "Calories", 2000, 2500], "Calcium", 800, 1600], "Iron", 10, 30], "Vit_A", 5000, 50000], "Dietary_Fiber", 25, 100], "Carbohydrates", 0, 300], "Protein", 50, 100]]
}],
"output_data": [
{
"id": "..csv"
}
],
"solve_parameters": {
"oaas.logAttachmentName": "log.txt",
"oaas.logTailEnabled": "true"
},
"status": {
"state": "queued"
}
},
"deployment": {
"id": "DEPLOYMENT-ID"
},
"platform_job": {
"job_id": "",
"run_id": ""
}
},
"metadata": {
"created_at": "2020-07-17T10:42:42.783Z",
"id": "JOB-ID",
"name": "test-job-diet",
"space_id": "SPACE-ID"
}
}
8. You can also monitor job states. Use the JOB-IDFor example:
curl --location --request GET
"https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs/JOB-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
Output example: (job has completed)
{
"entity": {
"decision_optimization": {
"input_data": [{
"id": "diet_food.csv",
"fields": "name", "unit_cost", "qmin", "qmax"],
"values": "Roasted Chicken", 0.84, 0, 10], "Spaghetti W/ Sauce", 0.78, 0, 10], "Tomato,Red,Ripe,Raw", 0.27, 0, 10], "Apple,Raw,W/Skin", 0.24, 0, 10], "Grapes", 0.32, 0, 10], "Chocolate Chip Cookies", 0.03, 0, 10], "Lowfat Milk", 0.23, 0, 10], "Raisin Brn", 0.34, 0, 10], "Hotdog", 0.31, 0, 10]]
}, {
"id": "diet_food_nutrients.csv",
"fields": "Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
"values": "Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], "Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], "Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], "Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], "Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], "Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], "Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], "Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], "Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
}, {
"id": "diet_nutrients.csv",
"fields": "name", "qmin", "qmax"],
"values": "Calories", 2000, 2500], "Calcium", 800, 1600], "Iron", 10, 30], "Vit_A", 5000, 50000], "Dietary_Fiber", 25, 100], "Carbohydrates", 0, 300], "Protein", 50, 100]]
}],
"output_data": [{
"fields": "Name", "Value"],
"id": "kpis.csv",
"values": "Total Calories", 2000], "Total Calcium", 800.0000000000001], "Total Iron", 11.278317739831891], "Total Vit_A", 8518.432542485823], "Total Dietary_Fiber", 25], "Total Carbohydrates", 256.80576358904455], "Total Protein", 51.17372234135308], "Minimal cost", 2.690409171696264]]
}, {
"fields": "name", "value"],
"id": "solution.csv",
"values": "Spaghetti W/ Sauce", 2.1551724137931036], "Chocolate Chip Cookies", 10], "Lowfat Milk", 1.8311671008899097], "Hotdog", 0.9296975991385925]]
}],
"output_data_references": [],
"solve_parameters": {
"oaas.logAttachmentName": "log.txt",
"oaas.logTailEnabled": "true"
},
"solve_state": {
"details": {
"KPI.Minimal cost": "2.690409171696264",
"KPI.Total Calcium": "800.0000000000001",
"KPI.Total Calories": "2000.0",
"KPI.Total Carbohydrates": "256.80576358904455",
"KPI.Total Dietary_Fiber": "25.0",
"KPI.Total Iron": "11.278317739831891",
"KPI.Total Protein": "51.17372234135308",
"KPI.Total Vit_A": "8518.432542485823",
"MODEL_DETAIL_BOOLEAN_VARS": "0",
"MODEL_DETAIL_CONSTRAINTS": "7",
"MODEL_DETAIL_CONTINUOUS_VARS": "9",
"MODEL_DETAIL_INTEGER_VARS": "0",
"MODEL_DETAIL_KPIS": "["Total Calories", "Total Calcium", "Total Iron", "Total Vit_A", "Total Dietary_Fiber", "Total Carbohydrates", "Total Protein", "Minimal cost"]",
"MODEL_DETAIL_NONZEROS": "57",
"MODEL_DETAIL_TYPE": "LP",
"PROGRESS_CURRENT_OBJECTIVE": "2.6904091716962637"
},
"latest_engine_activity": [
"2020-07-21T16:37:36Z, INFO] Model: diet",
"2020-07-21T16:37:36Z, INFO] - number of variables: 9",
"2020-07-21T16:37:36Z, INFO] - binary=0, integer=0, continuous=9",
"2020-07-21T16:37:36Z, INFO] - number of constraints: 7",
"2020-07-21T16:37:36Z, INFO] - linear=7",
"2020-07-21T16:37:36Z, INFO] - parameters: defaults",
"2020-07-21T16:37:36Z, INFO] - problem type is: LP",
"2020-07-21T16:37:36Z, INFO] Warning: Model: "diet" is not a MIP problem, progress listeners are disabled",
"2020-07-21T16:37:36Z, INFO] objective: 2.690",
"2020-07-21T16:37:36Z, INFO] "Spaghetti W/ Sauce"=2.155",
"2020-07-21T16:37:36Z, INFO] "Chocolate Chip Cookies"=10.000",
"2020-07-21T16:37:36Z, INFO] "Lowfat Milk"=1.831",
"2020-07-21T16:37:36Z, INFO] "Hotdog"=0.930",
"2020-07-21T16:37:36Z, INFO] solution.csv"
],
"solve_status": "optimal_solution"
},
"status": {
"completed_at": "2020-07-21T16:37:36.989Z",
"running_at": "2020-07-21T16:37:35.622Z",
"state": "completed"
}
},
"deployment": {
"id": "DEPLOYMENT-ID"
}
},
"metadata": {
"created_at": "2020-07-21T16:37:09.130Z",
"id": "JOB-ID",
"modified_at": "2020-07-21T16:37:37.268Z",
"name": "test-job-diet",
"space_id": "SPACE-ID"
}
}
9. Optional: You can delete jobs as follows:
curl --location --request DELETE "https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs/JOB-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE&hard_delete=true"
-H "Authorization: bearer TOKEN-HERE"
If you delete a job using the API, it will still be displayed in the user interface.
10. Optional: You can delete deployments as follows:If you delete a deployment that contains jobs using the API, the jobs will still be displayed in the deployment space in the user interface.
| # REST API example #
You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API\.
## Procedure ##
<!-- <ol> -->
1. **Generate an IAM token** using your [IBM Cloud API key](https://cloud.ibm.com/iam/apikeys) as follows\.
curl "https://iam.bluemix.net/identity/token" \
-d "apikey=YOUR_API_KEY_HERE&grant_type=urn%3Aibm%3Aparams%3Aoauth%3Agrant-type%3Aapikey" \
-H "Content-Type: application/x-www-form-urlencoded" \
-H "Authorization: Basic Yng6Yng="
Output example:
{
"access_token": "****** obtained IAM token ******************************",
"refresh_token": "**************************************",
"token_type": "Bearer",
"expires_in": 3600,
"expiration": 1554117649,
"scope": "ibm openid"
}
Use the obtained token (access\_token value) prepended by the word `Bearer` in the `Authorization` header, and the `Machine Learning service GUID` in the `ML-Instance-ID` header, in all API calls.
2. **Optional:** If you have not obtained your **SPACE\-ID** from the user interface as described previously, you can create a space using the REST API as follows\. Use the previously obtained token prepended by the word `bearer` in the `Authorization` header in all API calls\.
curl --location --request POST \
"https://api.dataplatform.cloud.ibm.com/v2/spaces" \
-H "Authorization: Bearer TOKEN-HERE" \
-H "ML-Instance-ID: MACHINE-LEARNING-SERVICE-GUID-HERE" \
-H "Content-Type: application/json" \
--data-raw "{
"name": "SPACE-NAME-HERE",
"description": "optional description here",
"storage": {
"resource_crn": "COS-CRN-ID-HERE"
},
"compute": [{
"name": "MACHINE-LEARNING-SERVICE-NAME-HERE",
"crn": "MACHINE-LEARNING-SERVICE-CRN-ID-HERE"
}]
}"
For **Windows** users, put the `--data-raw` command on one line and replace all `"` with `\"` inside this command as follows:
curl --location --request POST ^
"https://api.dataplatform.cloud.ibm.com/v2/spaces" ^
-H "Authorization: Bearer TOKEN-HERE" ^
-H "ML-Instance-ID: MACHINE-LEARNING-SERVICE-GUID-HERE" ^
-H "Content-Type: application/json" ^
--data-raw "{\"name\": "SPACE-NAME-HERE",\"description\": \"optional description here\",\"storage\": {\"resource_crn\": \"COS-CRN-ID-HERE\" },\"compute\": [{\"name\": "MACHINE-LEARNING-SERVICE-NAME-HERE\",\"crn\": \"MACHINE-LEARNING-SERVICE-CRN-ID-HERE\" }]}"
Alternatively put the data in a separate file.A **SPACE-ID** is returned in `id` field of the `metadata` section.
Output example:
{
"entity": {
"compute": [
{
"crn": "MACHINE-LEARNING-SERVICE-CRN",
"guid": "MACHINE-LEARNING-SERVICE-GUID",
"name": "MACHINE-LEARNING-SERVICE-NAME",
"type": "machine_learning"
}
],
"description": "string",
"members": [
{
"id": "XXXXXXX",
"role": "admin",
"state": "active",
"type": "user"
}
],
"name": "name",
"scope": {
"bss_account_id": "account_id"
},
"status": {
"state": "active"
}
},
"metadata": {
"created_at": "2020-07-17T08:36:57.611Z",
"creator_id": "XXXXXXX",
"id": "SPACE-ID",
"url": "/v2/spaces/SPACE-ID"
}
}
You must wait until your deployment space status is `"active"` before continuing. You can poll to check for this as follows.
curl --location --request GET "https://api.dataplatform.cloud.ibm.com/v2/spaces/SPACE-ID-HERE" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json"
3. Create a **new Decision Optimization model**
All API requests require a version parameter that takes a date in the format `version=YYYY-MM-DD`. This code example posts a model that uses the file `create_model.json`. The URL will vary according to the chosen region/location for your machine learning service. See [Endpoint URLs](https://cloud.ibm.com/apidocs/machine-learning#endpoint-url).
curl --location --request POST \
"https://us-south.ml.cloud.ibm.com/ml/v4/models?version=2020-08-01" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json" \
-d @create_model.json
The create\_model.json file contains the following code:
{
"name": "ModelName",
"description": "ModelDescription",
"type": "do-docplex_22.1",
"software_spec": {
"name": "do_22.1"
},
"custom": {
"decision_optimization": {
"oaas.docplex.python": "3.10"
}
},
"space_id": "SPACE-ID-HERE"
}
The *Python version* is stated explicitly here in a `custom` block. This is optional. Without it your model will use the default version which is currently Python 3.10. As the default version will evolve over time, stating the Python version explicitly enables you to easily change it later or to keep using an older supported version when the default version is updated. Currently supported versions are 3.10.
If you want to be able to run jobs for this model *from the user interface*, instead of only using the REST API , you must define the **schema** for the input and output data. If you do not define the schema when you create the model, you can only run jobs using the REST API and not from the user interface.
You can also use the schema specified for input and output in your optimization model:
{
"name": "Diet-Model-schema",
"description": "Diet",
"type": "do-docplex_22.1",
"schemas": {
"input": [
{
"id": "diet_food_nutrients",
"fields":
{ "name": "Food", "type": "string" },
{ "name": "Calories", "type": "double" },
{ "name": "Calcium", "type": "double" },
{ "name": "Iron", "type": "double" },
{ "name": "Vit_A", "type": "double" },
{ "name": "Dietary_Fiber", "type": "double" },
{ "name": "Carbohydrates", "type": "double" },
{ "name": "Protein", "type": "double" }
]
},
{
"id": "diet_food",
"fields":
{ "name": "name", "type": "string" },
{ "name": "unit_cost", "type": "double" },
{ "name": "qmin", "type": "double" },
{ "name": "qmax", "type": "double" }
]
},
{
"id": "diet_nutrients",
"fields":
{ "name": "name", "type": "string" },
{ "name": "qmin", "type": "double" },
{ "name": "qmax", "type": "double" }
]
}
],
"output": [
{
"id": "solution",
"fields":
{ "name": "name", "type": "string" },
{ "name": "value", "type": "double" }
]
}
]
},
"software_spec": {
"name": "do_22.1"
},
"space_id": "SPACE-ID-HERE"
}
When you post a model you provide information about its **model type** and the **software specification** to be used.**Model types** can be, for example:
<!-- <ul> -->
* `do-opl_22.1` for OPL models
* `do-cplex_22.1` for CPLEX models
* `do-cpo_22.1` for CP models
* `do-docplex_22.1` for Python models
<!-- </ul> -->
Version 20.1 can also be used for these model types.
For the **software specification**, you can use the default specifications using their names `do_22.1` or `do_20.1`. See also [Extend software specification notebook](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployPythonClient.html#topic_wmlpythonclient__extendWML) which shows you how to extend the Decision Optimization software specification (runtimes with additional Python libraries for docplex models).
A **MODEL-ID** is returned in `id` field in the `metadata`.
Output example:
{
"entity": {
"software_spec": {
"id": "SOFTWARE-SPEC-ID"
},
"type": "do-docplex_20.1"
},
"metadata": {
"created_at": "2020-07-17T08:37:22.992Z",
"description": "ModelDescription",
"id": "MODEL-ID",
"modified_at": "2020-07-17T08:37:22.992Z",
"name": "ModelName",
"owner": "***********",
"space_id": "SPACE-ID"
}
}
4. **Upload a Decision Optimization model formulation** ready for deployment\.First **compress your model** into a (`tar.gz, .zip or .jar`) file and upload it to be deployed by the Watson Machine Learning service\.This code example uploads a model called diet\.zip that contains a Python model and no common data:
curl --location --request PUT \
"https://us-south.ml.cloud.ibm.com/ml/v4/models/MODEL-ID-HERE/content?version=2020-08-01&space_id=SPACE-ID-HERE&content_format=native" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/gzip" \
--data-binary "@diet.zip"
You can download this example and other models from the **[DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples)**. Select the relevant product and version subfolder.
5. **Deploy your model**Create a reference to your model\. Use the **SPACE\-ID**, the **MODEL\-ID** obtained when you created your model ready for deployment and the **hardware specification**\. For example:
curl --location --request POST "https://us-south.ml.cloud.ibm.com/ml/v4/deployments?version=2020-08-01" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json" \
-d @deploy_model.json
`The deploy_model.json file contains the following code:`
{
"name": "Test-Diet-deploy",
"space_id": "SPACE-ID-HERE",
"asset": {
"id": "MODEL-ID-HERE"
},
"hardware_spec": {
"name": "S"
},
"batch": {}
}
The **DEPLOYMENT-ID** is returned in `id` field in the `metadata`. Output example:
{
"entity": {
"asset": {
"id": "MODEL-ID"
},
"custom": {},
"description": "",
"hardware_spec": {
"id": "HARDWARE-SPEC-ID",
"name": "S",
"num_nodes": 1
},
"name": "Test-Diet-deploy",
"space_id": "SPACE-ID",
"status": {
"state": "ready"
}
},
"metadata": {
"created_at": "2020-07-17T09:10:50.661Z",
"description": "",
"id": "DEPLOYMENT-ID",
"modified_at": "2020-07-17T09:10:50.661Z",
"name": "test-Diet-deploy",
"owner": "**************",
"space_id": "SPACE-ID"
}
}
6. Once deployed, you can **monitor your model's deployment state\.** Use the **DEPLOYMENT\-ID**\.For example:
curl --location --request GET "https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json"
Output example:
7. You can then **Submit jobs** for your deployed model defining the input data and the output (results of the optimization solve) and the log file\.For example, the following shows the contents of a file called `myjob.json`\. It contains (**inline**) input data, some solve parameters, and specifies that the output will be a \.csv file\. For examples of other types of input data references, see [Model input and output data adaptation](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIODataDefn.html#topic_modelIOAdapt)\.
{
"name":"test-job-diet",
"space_id": "SPACE-ID-HERE",
"deployment": {
"id": "DEPLOYMENT-ID-HERE"
},
"decision_optimization" : {
"solve_parameters" : {
"oaas.logAttachmentName":"log.txt",
"oaas.logTailEnabled":"true"
},
"input_data": [
{
"id":"diet_food.csv",
"fields" : "name","unit_cost","qmin","qmax"],
"values" :
"Roasted Chicken", 0.84, 0, 10],
"Spaghetti W/ Sauce", 0.78, 0, 10],
"Tomato,Red,Ripe,Raw", 0.27, 0, 10],
"Apple,Raw,W/Skin", 0.24, 0, 10],
"Grapes", 0.32, 0, 10],
"Chocolate Chip Cookies", 0.03, 0, 10],
"Lowfat Milk", 0.23, 0, 10],
"Raisin Brn", 0.34, 0, 10],
"Hotdog", 0.31, 0, 10]
]
},
{
"id":"diet_food_nutrients.csv",
"fields" : "Food","Calories","Calcium","Iron","Vit_A","Dietary_Fiber","Carbohydrates","Protein"],
"values" :
"Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2],
"Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2],
"Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1],
"Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3],
"Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2],
"Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9],
"Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1],
"Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4],
"Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]
]
},
{
"id":"diet_nutrients.csv",
"fields" : "name","qmin","qmax"],
"values" :
"Calories", 2000, 2500],
"Calcium", 800, 1600],
"Iron", 10, 30],
"Vit_A", 5000, 50000],
"Dietary_Fiber", 25, 100],
"Carbohydrates", 0, 300],
"Protein", 50, 100]
]
}
],
"output_data": [
{
"id":".*\.csv"
}
]
}
}
This code example posts a job that uses this file `myjob.json`.
curl --location --request POST "https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs?version=2020-08-01&space_id=SPACE-ID-HERE" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json" \
-H "cache-control: no-cache" \
-d @myjob.json
A **JOB-ID** is returned. Output example: (the job is queued)
{
"entity": {
"decision_optimization": {
"input_data": [{
"id": "diet_food.csv",
"fields": "name", "unit_cost", "qmin", "qmax"],
"values": "Roasted Chicken", 0.84, 0, 10], "Spaghetti W/ Sauce", 0.78, 0, 10], "Tomato,Red,Ripe,Raw", 0.27, 0, 10], "Apple,Raw,W/Skin", 0.24, 0, 10], "Grapes", 0.32, 0, 10], "Chocolate Chip Cookies", 0.03, 0, 10], "Lowfat Milk", 0.23, 0, 10], "Raisin Brn", 0.34, 0, 10], "Hotdog", 0.31, 0, 10]]
}, {
"id": "diet_food_nutrients.csv",
"fields": "Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
"values": "Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], "Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], "Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], "Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], "Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], "Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], "Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], "Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], "Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
}, {
"id": "diet_nutrients.csv",
"fields": "name", "qmin", "qmax"],
"values": "Calories", 2000, 2500], "Calcium", 800, 1600], "Iron", 10, 30], "Vit_A", 5000, 50000], "Dietary_Fiber", 25, 100], "Carbohydrates", 0, 300], "Protein", 50, 100]]
}],
"output_data": [
{
"id": ".*\.csv"
}
],
"solve_parameters": {
"oaas.logAttachmentName": "log.txt",
"oaas.logTailEnabled": "true"
},
"status": {
"state": "queued"
}
},
"deployment": {
"id": "DEPLOYMENT-ID"
},
"platform_job": {
"job_id": "",
"run_id": ""
}
},
"metadata": {
"created_at": "2020-07-17T10:42:42.783Z",
"id": "JOB-ID",
"name": "test-job-diet",
"space_id": "SPACE-ID"
}
}
8. You can also **monitor job states**\. Use the **JOB\-ID**For example:
curl --location --request GET \
"https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs/JOB-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json"
Output example: (job has completed)
{
"entity": {
"decision_optimization": {
"input_data": [{
"id": "diet_food.csv",
"fields": "name", "unit_cost", "qmin", "qmax"],
"values": "Roasted Chicken", 0.84, 0, 10], "Spaghetti W/ Sauce", 0.78, 0, 10], "Tomato,Red,Ripe,Raw", 0.27, 0, 10], "Apple,Raw,W/Skin", 0.24, 0, 10], "Grapes", 0.32, 0, 10], "Chocolate Chip Cookies", 0.03, 0, 10], "Lowfat Milk", 0.23, 0, 10], "Raisin Brn", 0.34, 0, 10], "Hotdog", 0.31, 0, 10]]
}, {
"id": "diet_food_nutrients.csv",
"fields": "Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
"values": "Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], "Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], "Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], "Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], "Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], "Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], "Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], "Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], "Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
}, {
"id": "diet_nutrients.csv",
"fields": "name", "qmin", "qmax"],
"values": "Calories", 2000, 2500], "Calcium", 800, 1600], "Iron", 10, 30], "Vit_A", 5000, 50000], "Dietary_Fiber", 25, 100], "Carbohydrates", 0, 300], "Protein", 50, 100]]
}],
"output_data": [{
"fields": "Name", "Value"],
"id": "kpis.csv",
"values": "Total Calories", 2000], "Total Calcium", 800.0000000000001], "Total Iron", 11.278317739831891], "Total Vit_A", 8518.432542485823], "Total Dietary_Fiber", 25], "Total Carbohydrates", 256.80576358904455], "Total Protein", 51.17372234135308], "Minimal cost", 2.690409171696264]]
}, {
"fields": "name", "value"],
"id": "solution.csv",
"values": "Spaghetti W/ Sauce", 2.1551724137931036], "Chocolate Chip Cookies", 10], "Lowfat Milk", 1.8311671008899097], "Hotdog", 0.9296975991385925]]
}],
"output_data_references": [],
"solve_parameters": {
"oaas.logAttachmentName": "log.txt",
"oaas.logTailEnabled": "true"
},
"solve_state": {
"details": {
"KPI.Minimal cost": "2.690409171696264",
"KPI.Total Calcium": "800.0000000000001",
"KPI.Total Calories": "2000.0",
"KPI.Total Carbohydrates": "256.80576358904455",
"KPI.Total Dietary_Fiber": "25.0",
"KPI.Total Iron": "11.278317739831891",
"KPI.Total Protein": "51.17372234135308",
"KPI.Total Vit_A": "8518.432542485823",
"MODEL_DETAIL_BOOLEAN_VARS": "0",
"MODEL_DETAIL_CONSTRAINTS": "7",
"MODEL_DETAIL_CONTINUOUS_VARS": "9",
"MODEL_DETAIL_INTEGER_VARS": "0",
"MODEL_DETAIL_KPIS": "[\"Total Calories\", \"Total Calcium\", \"Total Iron\", \"Total Vit_A\", \"Total Dietary_Fiber\", \"Total Carbohydrates\", \"Total Protein\", \"Minimal cost\"]",
"MODEL_DETAIL_NONZEROS": "57",
"MODEL_DETAIL_TYPE": "LP",
"PROGRESS_CURRENT_OBJECTIVE": "2.6904091716962637"
},
"latest_engine_activity": [
"2020-07-21T16:37:36Z, INFO] Model: diet",
"2020-07-21T16:37:36Z, INFO] - number of variables: 9",
"2020-07-21T16:37:36Z, INFO] - binary=0, integer=0, continuous=9",
"2020-07-21T16:37:36Z, INFO] - number of constraints: 7",
"2020-07-21T16:37:36Z, INFO] - linear=7",
"2020-07-21T16:37:36Z, INFO] - parameters: defaults",
"2020-07-21T16:37:36Z, INFO] - problem type is: LP",
"2020-07-21T16:37:36Z, INFO] Warning: Model: \"diet\" is not a MIP problem, progress listeners are disabled",
"2020-07-21T16:37:36Z, INFO] objective: 2.690",
"2020-07-21T16:37:36Z, INFO] \"Spaghetti W/ Sauce\"=2.155",
"2020-07-21T16:37:36Z, INFO] \"Chocolate Chip Cookies\"=10.000",
"2020-07-21T16:37:36Z, INFO] \"Lowfat Milk\"=1.831",
"2020-07-21T16:37:36Z, INFO] \"Hotdog\"=0.930",
"2020-07-21T16:37:36Z, INFO] solution.csv"
],
"solve_status": "optimal_solution"
},
"status": {
"completed_at": "2020-07-21T16:37:36.989Z",
"running_at": "2020-07-21T16:37:35.622Z",
"state": "completed"
}
},
"deployment": {
"id": "DEPLOYMENT-ID"
}
},
"metadata": {
"created_at": "2020-07-21T16:37:09.130Z",
"id": "JOB-ID",
"modified_at": "2020-07-21T16:37:37.268Z",
"name": "test-job-diet",
"space_id": "SPACE-ID"
}
}
9. Optional: You can **delete jobs** as follows:
curl --location --request DELETE "https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs/JOB-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE&hard_delete=true" \
-H "Authorization: bearer TOKEN-HERE"
If you delete a job using the API, it will still be displayed in the user interface.
10. Optional: You can **delete deployments** as follows:If you delete a deployment that contains jobs using the API, the jobs will still be displayed in the deployment space in the user interface\.
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<h1 class="topictitle1" id="task_deploymodelREST__title__1"><span class="ph" data-hd-product="cloud wx">REST API example</span></h1>
<div class="body taskbody">
<p class="shortdesc">You can deploy a <span class="keyword">Decision Optimization</span> model, create and monitor jobs and get solutions using the <span class="keyword">Watson Machine Learning REST API</span>.</p>
<section role="region" class="section prereq" data-hd-product="cloud wx" id="task_deploymodelREST__prereq_el2_nft_bhb" aria-labelledby="tasktask_deploymodelREST__prereq_el2_nft_bhb">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deploymodelREST__prereq_el2_nft_bhb">Before you begin</h2>
</div>You must have an <strong>IBM Cloud account</strong>. See <a href="https://www.ibm.com/cloud/" rel="noopener" target="_blank" title="(Opens in a new tab or window)">https://www.ibm.com/cloud/</a>.
<div class="p">
<ol id="task_deploymodelREST__ol_oqt_l2k_mmb">
<li>Log in to <a href="https://cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">IBM Cloud</a>.</li>
<li>Create your <a href="https://cloud.ibm.com/iam/apikeys" rel="noopener" target="_blank" title="(Opens in a new tab or window)">API key</a>. Copy or download it from the <span class="ph uicontrol">API key successfully created</span> open window (you cannot access it again once you close this pane).</li>
<li>Create or select a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Machine Learning</span> service</a>. Copy the <strong>service instance name</strong>, <strong>GUID</strong> and <strong>CRN</strong> from the information pane for your instance in the <span class="ph uicontrol">Resource List</span>><span class="ph uicontrol">Services</span> view on <a href="https://cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">IBM Cloud</a>. (Expand the list of services in the Resource List window. Click anywhere in the row next to your Machine Learning Service name, but not on the name itself. This opens the information pane in the same window.)</li>
<li>Create or select a <a href="https://cloud.ibm.com/catalog/services/cloud-object-storage" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Cloud Object Storage</a>. Copy the <strong>Cloud Object Storage instance name</strong> and <strong>CRN</strong> from the information pane from the information pane for your instance in the <span class="ph uicontrol">Resource List</span>><span class="ph uicontrol">Storage</span> view on <a href="https://cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">IBM Cloud</a>.</li>
</ol>
</div>
<div class="p" id="task_deploymodelREST__CreateSpace">
You must also create a <span class="ph uicontrol">deployment space </span> and obtain the <strong>Space ID</strong> using any one of these methods:
<ul id="task_deploymodelREST__ul_dyv_rft_mmb">
<li>From the <a href="https://dataplatform.cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">https://dataplatform.cloud.ibm.com</a> user interface, create a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)">deployment space</a>. Then view it and copy your Space ID from the settings tab. For more information see <a href="../../wsj/analyze-data/ml-spaces_local.html#create">Creating a deployment space</a>.</li>
<li>With the REST API. See <a href="#task_deploymodelREST__space-restAPI">Creating a deployment space using the REST API</a>.</li>
</ul>
</div>
</section>
<section class="section context" role="region" aria-labelledby="tasktask_deploymodelREST__context__1">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deploymodelREST__context__1">About this task</h2>
</div>The following steps show you how deploy a <span class="keyword">Decision Optimization</span> model using the <span class="keyword">Watson Machine Learning REST API</span>. The REST API example uses curl, a command line tool and library for transferring data with URL syntax. You can download curl and read more about it at <a href="http://curl.haxx.se/" rel="noopener" target="_blank" title="(Opens in a new tab or window)">http://curl.haxx.se</a>. For more information about the REST APIs relevant for <span class="keyword">Decision Optimization</span>, see the following sections:
<ul data-hd-product="cloud wx" id="task_deploymodelREST__ul_ah5_3lx_c3b">
<li><a href="https://cloud.ibm.com/apidocs/machine-learning#models-create" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Models</a></li>
<li><a href="https://cloud.ibm.com/apidocs/machine-learning#deployments-create" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Deployments</a></li>
<li><a href="https://cloud.ibm.com/apidocs/machine-learning#deployment-jobs-list" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Deployment jobs</a></li>
</ul>
<p>For <strong>Windows</strong> users, use ^ instead of \ for the multi-line separator and double quotation marks " throughout these code examples. Windows users also need to use indentation of at least one character space in the header lines.</p>
<p>For clarity, some code examples in this procedure have been placed in a <span class="ph filepath">json</span> file to make the commands more readable and easier to use.</p>
<p>Once you have created a deployment using the REST API, you can also view it and send jobs to it from the Deployment spaces page in the <a data-hd-product="cloud wx" href="https://dataplatform.cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">https://dataplatform.cloud.ibm.com</a> user interface.</p>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deploymodelREST__steps__1">Procedure</h2>
</div>
<ol class="steps">
<li class="step stepexpand" data-hd-product="cloud wx" id="task_deploymodelREST__gettoken"><span class="cmd"> <strong>Generate an IAM token</strong> using your <a href="https://cloud.ibm.com/iam/apikeys" rel="noopener" target="_blank" title="(Opens in a new tab or window)">IBM Cloud API key</a> as follows.</span>
<div class="itemgroup info">
<pre class="codeblock"><code>curl "<span class="keyword">https://iam.bluemix.net/identity/token</span>" \
-d "apikey=<em><strong><span class="ph hljs-callout">YOUR_API_KEY_HERE</span></strong></em>&grant_type=urn%3Aibm%3Aparams%3Aoauth%3Agrant-type%3Aapikey" \
-H "Content-Type: application/x-www-form-urlencoded" \
-H "Authorization: Basic Yng6Yng="</code></pre>
</div>
<div class="itemgroup stepresult">
Output example:
<div class="p">
<pre class="codeblock"><code>{
"access_token": "****** <em>obtained IAM token</em> ******************************",
"refresh_token": "**************************************",
"token_type": "Bearer",
"expires_in": 3600,
"expiration": 1554117649,
"scope": "ibm openid"
}</code></pre>
</div>
<p>Use the obtained token (access_token value) prepended by the word <code class="ph codeph">Bearer</code> in the <code class="ph codeph">Authorization</code> header, and the <code class="ph codeph"><span class="keyword">Machine Learning</span> service GUID</code> in the <code class="ph codeph">ML-Instance-ID</code> header, in all API calls.</p>
</div></li>
<li class="step stepexpand" data-hd-product="cloud icpd wx" id="task_deploymodelREST__space-restAPI"><span class="cmd"><strong>Optional:</strong> If you have not obtained your <strong>SPACE-ID</strong> from the user interface as described previously, you can create a space using the REST API as follows. Use the previously obtained token prepended by the word <code class="ph codeph" data-hd-product="cloud wx">bearer</code> in the <code class="ph codeph">Authorization</code> header in all API calls.</span>
<div class="itemgroup info">
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request POST \
<strong>"<span class="ph hljs-callout"><span class="keyword">https://api.dataplatform.cloud.ibm.com</span></span></strong>/v2/spaces" \
-H "Authorization: Bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "ML-Instance-ID: <em><strong><span class="ph hljs-callout">MACHINE-LEARNING-SERVICE-GUID-HERE</span></strong></em>" \
-H "Content-Type: application/json" \
--data-raw "{
"name": "<strong><em><span class="ph hljs-callout">SPACE-NAME-HERE</span></em></strong>",
"description": "optional description here",
"storage": {
"resource_crn": "<strong><em><span class="ph hljs-callout">COS-CRN-ID-HERE</span></em></strong>"
},
"compute": [{
"name": "<strong><em><span class="ph hljs-callout">MACHINE-LEARNING-SERVICE-NAME-HERE</span></em></strong>",
"crn": "<strong><em>MACHINE-LEARNING-SERVICE-CRN-ID-HERE</em></strong>"
}]
}"</code></pre>
<div class="p">
For <strong>Windows</strong> users, put the <code class="ph codeph">--data-raw</code> command on one line and replace all <code class="ph codeph">"</code> with <code class="ph codeph">\"</code> inside this command as follows:
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request POST ^
<strong>"<span class="ph hljs-callout"><span class="keyword">https://api.dataplatform.cloud.ibm.com</span></span></strong>/v2/spaces" ^
-H "Authorization: Bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" ^
-H "ML-Instance-ID: <em><strong><span class="ph hljs-callout">MACHINE-LEARNING-SERVICE-GUID-HERE</span></strong></em>" ^
-H "Content-Type: application/json" ^
--data-raw "{\"name\": "<strong><em><span class="ph hljs-callout">SPACE-NAME-HERE</span></em></strong>",\"description\": \"optional description here\",\"storage\": {\"resource_crn\": \"<strong><em><span class="ph hljs-callout">COS-CRN-ID-HERE</span></em></strong>\" },\"compute\": [{\"name\": "<strong><em><span class="ph hljs-callout">MACHINE-LEARNING-SERVICE-NAME-HERE</span></em></strong>\",\"crn\": \"<strong><em>MACHINE-LEARNING-SERVICE-CRN-ID-HERE</em></strong>\" }]}"</code></pre>Alternatively put the data in a separate file.
</div>
</div>
<div class="itemgroup stepresult">
A <strong>SPACE-ID</strong> is returned in <code class="ph codeph">id</code> field of the <code class="ph codeph">metadata</code> section.
<p>Output example:</p>
<pre class="codeblock" data-hd-product="cloud wx"><code>{
"entity": {
"compute": [
{
"crn": "MACHINE-LEARNING-SERVICE-CRN",
"guid": "MACHINE-LEARNING-SERVICE-GUID",
"name": "MACHINE-LEARNING-SERVICE-NAME",
"type": "machine_learning"
}
],
"description": "string",
"members": [
{
"id": "XXXXXXX",
"role": "admin",
"state": "active",
"type": "user"
}
],
"name": "name",
"scope": {
"bss_account_id": "account_id"
},
"status": {
"state": "active"
}
},
"metadata": {
"created_at": "2020-07-17T08:36:57.611Z",
"creator_id": "XXXXXXX",
"id": "<strong>SPACE-ID</strong>",
"url": "/v2/spaces/<strong>SPACE-ID</strong>"
}
}</code></pre>
<p>You must wait until your deployment space status is <code class="ph codeph">"active"</code> before continuing. You can poll to check for this as follows.</p>
<pre class="codeblock"><code>curl <span class="keyword">--location</span> --request GET "<span class="keyword">https://api.dataplatform.cloud.ibm.com</span>/v2/spaces/<strong><em>SPACE-ID-HERE</em></strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json"</code></pre>
</div></li>
<li class="step stepexpand" id="task_deploymodelREST__createmodel"><span class="cmd">Create a <strong>new <span class="keyword">Decision Optimization</span> model</strong></span>
<div class="itemgroup info">
<p>All API requests require a version parameter that takes a date in the format <code class="ph codeph">version=YYYY-MM-DD</code>. This code example posts a model that uses the file <code class="ph codeph">create_model.json</code>. The URL will vary according to the chosen region/location for your machine learning service. <span class="ph" data-hd-product="cloud wx">See <a href="https://cloud.ibm.com/apidocs/machine-learning#endpoint-url" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Endpoint URLs</a>.</span></p>
<pre class="codeblock"><code>curl <span class="keyword">--location</span> --request POST \
"<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/models?version=<strong>2020-08-01</strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json" \
-d @create_model.json</code></pre>The <span class="ph filepath">create_model.json</span> file contains the following code:
<pre class="codeblock"><code>{
"name": "ModelName",
"description": "ModelDescription",
"type": "<strong>do-docplex_<span class="keyword">22.1</span></strong>",
"software_spec": {
"name": "<strong>do_<span class="keyword">22.1</span></strong>"
},
"custom": {
"decision_optimization": {
"oaas.docplex.python": "<strong><span class="keyword">3.10</span></strong>"
}
},
"space_id": "<em><strong><span class="ph hljs-callout">SPACE-ID-HERE</span></strong></em>"
}</code></pre>
</div>
<div class="itemgroup info">
<p>The <em>Python version</em> is stated explicitly here in a <code class="ph codeph">custom</code> block. This is optional. Without it your model will use the default version which is currently Python <span class="keyword">3.10</span>. As the default version will evolve over time, stating the Python version explicitly enables you to easily change it later or to keep using an older supported version when the default version is updated. Currently supported versions are <span class="keyword">3.10</span>.</p>
</div>
<div class="itemgroup info">
<p>If you want to be able to run jobs for this model <em>from the user interface</em>, instead of only using the REST API , you must define the <strong>schema</strong> for the input and output data. If you do not define the schema when you create the model, you can only run jobs using the REST API and not from the user interface.</p>
<p>You can also use the schema specified for input and output in your optimization model:</p>
<pre class="codeblock"><code>{
"name": "Diet-Model-schema",
"description": "Diet",
"type": "do-docplex_<span class="keyword">22.1</span>",
"schemas": {
"input": [
{
"id": "diet_food_nutrients",
"fields": [
{ "name": "Food", "type": "string" },
{ "name": "Calories", "type": "double" },
{ "name": "Calcium", "type": "double" },
{ "name": "Iron", "type": "double" },
{ "name": "Vit_A", "type": "double" },
{ "name": "Dietary_Fiber", "type": "double" },
{ "name": "Carbohydrates", "type": "double" },
{ "name": "Protein", "type": "double" }
]
},
{
"id": "diet_food",
"fields": [
{ "name": "name", "type": "string" },
{ "name": "unit_cost", "type": "double" },
{ "name": "qmin", "type": "double" },
{ "name": "qmax", "type": "double" }
]
},
{
"id": "diet_nutrients",
"fields": [
{ "name": "name", "type": "string" },
{ "name": "qmin", "type": "double" },
{ "name": "qmax", "type": "double" }
]
}
],
"output": [
{
"id": "solution",
"fields": [
{ "name": "name", "type": "string" },
{ "name": "value", "type": "double" }
]
}
]
},
"software_spec": {
"name": "do_<span class="keyword">22.1</span>"
},
"space_id": "<em><strong><span class="ph hljs-callout">SPACE-ID-HERE</span></strong></em>"
}
</code></pre>
</div>
<div class="itemgroup info">
When you post a model you provide information about its <strong>model type</strong> and the <strong>software specification</strong> to be used.
<div class="p">
<strong>Model types</strong> can be, for example:
<ul id="task_deploymodelREST__ul_i5n_gs1_nmb">
<li><code class="ph codeph">do-opl_<span class="keyword">22.1</span></code> for OPL models</li>
<li><code class="ph codeph">do-cplex_<span class="keyword">22.1</span></code> for CPLEX models</li>
<li><code class="ph codeph">do-cpo_<span class="keyword">22.1</span></code> for CP models</li>
<li><code class="ph codeph">do-docplex_<span class="keyword">22.1</span></code> for Python models</li>
</ul>
</div>
<p>Version <span class="keyword">20.1</span> can also be used for these model types.</p>
<p>For the <strong>software specification</strong>, you can use the default specifications using their names <code class="ph codeph">do_<span class="keyword">22.1</span></code> or <code class="ph codeph">do_<span class="keyword">20.1</span></code>. See also <a href="DeployPythonClient.html#topic_wmlpythonclient__extendWML">Extend software specification notebook</a> which shows you how to extend the <span class="keyword">Decision Optimization</span> software specification (runtimes with additional Python libraries for docplex models).</p>
<p>A <strong>MODEL-ID</strong> is returned in <code class="ph codeph">id</code> field in the <code class="ph codeph">metadata</code>.</p>
</div>
<div class="itemgroup stepresult">
Output example:
<pre class="codeblock"><code>{
"entity": {
"software_spec": {
"id": "SOFTWARE-SPEC-ID"
},
"type": "do-docplex_20.1"
},
"metadata": {
"created_at": "2020-07-17T08:37:22.992Z",
"description": "ModelDescription",
"id": "<strong>MODEL-ID</strong>",
"modified_at": "2020-07-17T08:37:22.992Z",
"name": "ModelName",
"owner": "***********",
"space_id": "SPACE-ID"
}
}</code></pre>
</div></li>
<li class="step stepexpand" id="task_deploymodelREST__uploadmodelstep"><span class="cmd"> <strong>Upload a <span class="keyword">Decision Optimization</span> model formulation</strong> ready for deployment.</span>
<div class="itemgroup info">
First <strong>compress your model</strong> into a (<code class="ph codeph">tar.gz, .zip or .jar</code>) file and upload it to be deployed by the <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> service.
</div>
<div class="itemgroup info">
<div class="p">
This code example uploads a model called <span class="ph filepath">diet.zip</span> that contains a Python model and no common data:
<pre class="codeblock"><code>curl <span class="keyword">--location</span> --request PUT \
"<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/models/<strong><em><span class="ph hljs-callout">MODEL-ID-HERE</span></em></strong>/content?version=<strong>2020-08-01</strong>&space_id=<strong><em><span class="ph hljs-callout">SPACE-ID-HERE</span></em></strong>&content_format=native" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/gzip" \
--data-binary "@diet.zip"</code></pre>
</div>
</div>
<div class="itemgroup info">
You can download this example and other models from the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>. <span class="ph">Select the relevant product and version subfolder.</span>
</div></li>
<li class="step stepexpand" id="task_deploymodelREST__createdeploy"><span class="cmd"><strong>Deploy your model</strong></span>
<div class="itemgroup info">
Create a reference to your model. <span class="ph">Use the <strong>SPACE-ID</strong>, the <strong>MODEL-ID</strong> obtained when you created your model ready for deployment and the <strong>hardware specification</strong>.</span> For example:
<div class="p">
<pre class="codeblock"><code>curl <span class="keyword">--location</span> --request POST "<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/deployments?version=<strong>2020-08-01</strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json" \
-d @deploy_model.json</code></pre>
</div><code class="ph codeph">The <span class="ph filepath">deploy_model.json</span> file contains the following code:</code>
<pre class="codeblock"><code>{
"name": "Test-Diet-deploy",
"space_id": "<em><strong>SPACE-ID-HERE</strong></em>",
"asset": {
"id": "<em><strong>MODEL-ID-HERE</strong></em>"
},
"hardware_spec": {
"name": "<em><strong>S</strong></em>"
},
"batch": {}
}</code></pre>
</div>
<div class="itemgroup stepresult">
<span class="ph">The <strong>DEPLOYMENT-ID</strong> is returned in <code class="ph codeph">id</code> field in the <code class="ph codeph">metadata</code>.</span> Output example:
<pre class="codeblock"><code>{
"entity": {
"asset": {
"id": "MODEL-ID"
},
"custom": {},
"description": "",
"hardware_spec": {
"id": "HARDWARE-SPEC-ID",
"name": "S",
"num_nodes": 1
},
"name": "Test-Diet-deploy",
"space_id": "SPACE-ID",
"status": {
<strong>"state": "ready"</strong>
}
},
"metadata": {
"created_at": "2020-07-17T09:10:50.661Z",
"description": "",
"id": "<strong>DEPLOYMENT-ID</strong>",
"modified_at": "2020-07-17T09:10:50.661Z",
"name": "test-Diet-deploy",
"owner": "**************",
"space_id": "SPACE-ID"
}
}</code></pre>
</div></li>
<li class="step stepexpand"><span class="cmd">Once deployed, you can <strong>monitor your model's deployment state.</strong> Use the <strong>DEPLOYMENT-ID</strong>.</span>
<div class="itemgroup info">
For example:
<pre class="codeblock"><code>curl <span class="keyword">--location</span> --request GET "<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/deployments/<em><strong>DEPLOYMENT-ID-HERE</strong></em>?version=<strong>2020-08-01</strong>&space_id=<strong><em>SPACE-ID-HERE</em></strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json"</code></pre>
<p>Output example:</p>
</div></li>
<li class="step stepexpand"><span class="cmd">You can then <strong>Submit jobs</strong> for your deployed model defining the input data and the output (results of the optimization solve) and the log file.</span>
<div class="itemgroup info">
For example, the following shows the contents of a file called <code class="ph codeph">myjob.json</code>. It contains (<strong>inline</strong>) input data, some solve parameters, and specifies that the output will be a .csv file. For examples of other types of input data references, see <a href="ModelIODataDefn.html#topic_modelIOAdapt" title="When submitting your job you can include your data inline or reference your data in your request. This data will be mapped to a file named with data identifier and used by the model. The data identifier extension will define the format of the file used.">Model input and output data adaptation</a>.
<pre class="codeblock"><code>{
"name":"test-job-diet",
"space_id": "<strong><em>SPACE-ID-HERE</em></strong>",
"deployment": {
"id": "<em><strong>DEPLOYMENT-ID-HERE</strong></em>"
},
"decision_optimization" : {
"solve_parameters" : {
"oaas.logAttachmentName":"log.txt",
"oaas.logTailEnabled":"true"
},
"input_data": [
{
"id":"diet_food.csv",
"fields" : ["name","unit_cost","qmin","qmax"],
"values" : [
["Roasted Chicken", 0.84, 0, 10],
["Spaghetti W/ Sauce", 0.78, 0, 10],
["Tomato,Red,Ripe,Raw", 0.27, 0, 10],
["Apple,Raw,W/Skin", 0.24, 0, 10],
["Grapes", 0.32, 0, 10],
["Chocolate Chip Cookies", 0.03, 0, 10],
["Lowfat Milk", 0.23, 0, 10],
["Raisin Brn", 0.34, 0, 10],
["Hotdog", 0.31, 0, 10]
]
},
{
"id":"diet_food_nutrients.csv",
"fields" : ["Food","Calories","Calcium","Iron","Vit_A","Dietary_Fiber","Carbohydrates","Protein"],
"values" : [
["Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2],
["Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2],
["Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1],
["Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3],
["Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2],
["Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9],
["Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1],
["Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4],
["Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]
]
},
{
"id":"diet_nutrients.csv",
"fields" : ["name","qmin","qmax"],
"values" : [
["Calories", 2000, 2500],
["Calcium", 800, 1600],
["Iron", 10, 30],
["Vit_A", 5000, 50000],
["Dietary_Fiber", 25, 100],
["Carbohydrates", 0, 300],
["Protein", 50, 100]
]
}
],
"output_data": [
{
"id":".*\\.csv"
}
]
}
}</code></pre>This code example posts a job that uses this file <code class="ph codeph">myjob.json</code>.
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request POST "<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/deployment_jobs?version=<strong>2020-08-01</strong>&space_id=<strong>SPACE-ID-HERE</strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json" \
-H "cache-control: no-cache" \
-d @myjob.json
</code></pre>
</div>
<div class="itemgroup stepresult">
A <strong>JOB-ID</strong> is returned. Output example: (the job is queued)
<pre class="codeblock"><code>{
"entity": {
"decision_optimization": {
"input_data": [{
"id": "diet_food.csv",
"fields": ["name", "unit_cost", "qmin", "qmax"],
"values": [["Roasted Chicken", 0.84, 0, 10], ["Spaghetti W/ Sauce", 0.78, 0, 10], ["Tomato,Red,Ripe,Raw", 0.27, 0, 10], ["Apple,Raw,W/Skin", 0.24, 0, 10], ["Grapes", 0.32, 0, 10], ["Chocolate Chip Cookies", 0.03, 0, 10], ["Lowfat Milk", 0.23, 0, 10], ["Raisin Brn", 0.34, 0, 10], ["Hotdog", 0.31, 0, 10]]
}, {
"id": "diet_food_nutrients.csv",
"fields": ["Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
"values": [["Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], ["Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], ["Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], ["Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], ["Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], ["Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], ["Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], ["Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], ["Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
}, {
"id": "diet_nutrients.csv",
"fields": ["name", "qmin", "qmax"],
"values": [["Calories", 2000, 2500], ["Calcium", 800, 1600], ["Iron", 10, 30], ["Vit_A", 5000, 50000], ["Dietary_Fiber", 25, 100], ["Carbohydrates", 0, 300], ["Protein", 50, 100]]
}],
"output_data": [
{
"id": ".*\\.csv"
}
],
"solve_parameters": {
"oaas.logAttachmentName": "log.txt",
"oaas.logTailEnabled": "true"
},
"status": {
<strong>"state": "queued"</strong>
}
},
"deployment": {
"id": "DEPLOYMENT-ID"
},
"platform_job": {
"job_id": "",
"run_id": ""
}
},
"metadata": {
"created_at": "2020-07-17T10:42:42.783Z",
"id": "<strong>JOB-ID</strong>",
"name": "test-job-diet",
"space_id": "SPACE-ID"
}
}
</code></pre>
</div></li>
<li class="step stepexpand"><span class="cmd">You can also <strong>monitor job states</strong>. Use the <strong>JOB-ID</strong></span>
<div class="itemgroup info">
For example:
<pre class="codeblock"><code>curl <span class="keyword">--location</span> --request GET \
"<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/deployment_jobs/<strong><em>JOB-ID-HERE</em></strong>?version=<strong>2020-08-01</strong>&space_id=<strong><em>SPACE-ID-HERE</em></strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json"</code></pre>
</div>
<div class="itemgroup stepresult">
Output example: (job has completed)
<pre class="codeblock" data-hd-product="cloud wx"><code>{
"entity": {
"decision_optimization": {
"input_data": [{
"id": "diet_food.csv",
"fields": ["name", "unit_cost", "qmin", "qmax"],
"values": [["Roasted Chicken", 0.84, 0, 10], ["Spaghetti W/ Sauce", 0.78, 0, 10], ["Tomato,Red,Ripe,Raw", 0.27, 0, 10], ["Apple,Raw,W/Skin", 0.24, 0, 10], ["Grapes", 0.32, 0, 10], ["Chocolate Chip Cookies", 0.03, 0, 10], ["Lowfat Milk", 0.23, 0, 10], ["Raisin Brn", 0.34, 0, 10], ["Hotdog", 0.31, 0, 10]]
}, {
"id": "diet_food_nutrients.csv",
"fields": ["Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
"values": [["Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], ["Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], ["Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], ["Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], ["Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], ["Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], ["Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], ["Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], ["Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
}, {
"id": "diet_nutrients.csv",
"fields": ["name", "qmin", "qmax"],
"values": [["Calories", 2000, 2500], ["Calcium", 800, 1600], ["Iron", 10, 30], ["Vit_A", 5000, 50000], ["Dietary_Fiber", 25, 100], ["Carbohydrates", 0, 300], ["Protein", 50, 100]]
}],
"output_data": [{
"fields": ["Name", "Value"],
"id": "kpis.csv",
"values": [["Total Calories", 2000], ["Total Calcium", 800.0000000000001], ["Total Iron", 11.278317739831891], ["Total Vit_A", 8518.432542485823], ["Total Dietary_Fiber", 25], ["Total Carbohydrates", 256.80576358904455], ["Total Protein", 51.17372234135308], ["Minimal cost", 2.690409171696264]]
}, {
"fields": ["name", "value"],
"id": "solution.csv",
"values": [["Spaghetti W/ Sauce", 2.1551724137931036], ["Chocolate Chip Cookies", 10], ["Lowfat Milk", 1.8311671008899097], ["Hotdog", 0.9296975991385925]]
}],
"output_data_references": [],
"solve_parameters": {
"oaas.logAttachmentName": "log.txt",
"oaas.logTailEnabled": "true"
},
"solve_state": {
"details": {
"KPI.Minimal cost": "2.690409171696264",
"KPI.Total Calcium": "800.0000000000001",
"KPI.Total Calories": "2000.0",
"KPI.Total Carbohydrates": "256.80576358904455",
"KPI.Total Dietary_Fiber": "25.0",
"KPI.Total Iron": "11.278317739831891",
"KPI.Total Protein": "51.17372234135308",
"KPI.Total Vit_A": "8518.432542485823",
"MODEL_DETAIL_BOOLEAN_VARS": "0",
"MODEL_DETAIL_CONSTRAINTS": "7",
"MODEL_DETAIL_CONTINUOUS_VARS": "9",
"MODEL_DETAIL_INTEGER_VARS": "0",
"MODEL_DETAIL_KPIS": "[\"Total Calories\", \"Total Calcium\", \"Total Iron\", \"Total Vit_A\", \"Total Dietary_Fiber\", \"Total Carbohydrates\", \"Total Protein\", \"Minimal cost\"]",
"MODEL_DETAIL_NONZEROS": "57",
"MODEL_DETAIL_TYPE": "LP",
"PROGRESS_CURRENT_OBJECTIVE": "2.6904091716962637"
},
"latest_engine_activity": [
"[2020-07-21T16:37:36Z, INFO] Model: diet",
"[2020-07-21T16:37:36Z, INFO] - number of variables: 9",
"[2020-07-21T16:37:36Z, INFO] - binary=0, integer=0, continuous=9",
"[2020-07-21T16:37:36Z, INFO] - number of constraints: 7",
"[2020-07-21T16:37:36Z, INFO] - linear=7",
"[2020-07-21T16:37:36Z, INFO] - parameters: defaults",
"[2020-07-21T16:37:36Z, INFO] - problem type is: LP",
"[2020-07-21T16:37:36Z, INFO] Warning: Model: \"diet\" is not a MIP problem, progress listeners are disabled",
"[2020-07-21T16:37:36Z, INFO] objective: 2.690",
"[2020-07-21T16:37:36Z, INFO] \"Spaghetti W/ Sauce\"=2.155",
"[2020-07-21T16:37:36Z, INFO] \"Chocolate Chip Cookies\"=10.000",
"[2020-07-21T16:37:36Z, INFO] \"Lowfat Milk\"=1.831",
"[2020-07-21T16:37:36Z, INFO] \"Hotdog\"=0.930",
"[2020-07-21T16:37:36Z, INFO] solution.csv"
],
"solve_status": "optimal_solution"
},
"status": {
"completed_at": "2020-07-21T16:37:36.989Z",
"running_at": "2020-07-21T16:37:35.622Z",
"<strong>state": "completed</strong>"
}
},
"deployment": {
"id": "DEPLOYMENT-ID"
}
},
"metadata": {
"created_at": "2020-07-21T16:37:09.130Z",
"id": "JOB-ID",
"modified_at": "2020-07-21T16:37:37.268Z",
"name": "test-job-diet",
"space_id": "SPACE-ID"
}
}
</code></pre>
</div></li>
<li class="step stepexpand"><span class="cmd">Optional: You can <strong>delete jobs</strong> as follows:</span>
<div class="itemgroup info">
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request DELETE "<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/deployment_jobs/<strong><em>JOB-ID-HERE</em></strong>?version=<strong>2020-08-01</strong>&space_id=<strong><em>SPACE-ID-HERE</em></strong>&hard_delete=true" \
-H "Authorization: bearer <em><span class="ph hljs-callout">TOKEN-HERE</span></em>" </code></pre>
</div>
<div class="itemgroup stepresult">
If you delete a job using the API, it will still be displayed in the user interface.
</div></li>
<li class="step stepexpand"><span class="cmd">Optional: You can <strong>delete deployments</strong> as follows:</span>
<div class="itemgroup stepresult">
If you delete a deployment that contains jobs using the API, the jobs will still be displayed in the deployment space in the user interface.
</div></li>
</ol>
<section class="section result" role="region" aria-labelledby="tasktask_deploymodelREST__result__1">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deploymodelREST__result__1">Results</h2>
</div>
<p>Once your model has been deployed and job executed, the solution results are provided either inline or in the file and location that you specified, for example using an S3 reference. You can post new jobs using the deployment-ID without having to redeploy your model.</p>
</section>
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DEB599F49C3E459A08E8BF25304B063B50CAA294 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelUI-WML.html?context=cdpaas&locale=en | Deploying a Decision Optimization model by using the user interface | Deploying a Decision Optimization model by using the user interface
You can save a model for deployment in the Decision Optimization experiment UI and promote it to your Watson Machine Learning deployment space.
Procedure
To save your model for deployment:
1. In the Decision Optimization experiment UI, either from the Scenario or from the Overview pane, click the menu icon  for the scenario that you want to deploy, and select Save for deployment
2. Specify a name for your model and add a description, if needed, then click Next.
1. Review the Input and Output schema and select the tables you want to include in the schema.
2. Review the Run parameters and add, modify or delete any parameters as necessary.
3. Review the Environment and Model files that are listed in the Review and save window.
4. Click Save.
The model is then available in the Models section of your project.
To promote your model to your deployment space:
3. View your model in the Models section of your project.You can see a summary with input and output schema. Click Promote to deployment space.
4. In the Promote to space window that opens, check that the Target space field displays the name of your deployment space and click Promote.
5. Click the link deployment space in the message that you receive that confirms successful promotion. Your promoted model is displayed in the Assets tab of your Deployment space. The information pane shows you the Type, Software specification, description and any defined tags such as the Python version used.
To create a new deployment:
6. From the Assets tab of your deployment space, open your model and click New Deployment.
7. In the Create a deployment window that opens, specify a name for your deployment and select a Hardware specification.Click Create to create the deployment. Your deployment window opens from which you can later create jobs.
Creating and running Decision Optimization jobs
You can create and run jobs to your deployed model.
Procedure
1. Return to your deployment space by using the navigation path and (if the data pane isn't already open) click the data icon to open the data pane. Upload your input data tables, and solution and kpi output tables here. (You must have output tables defined in your model to be able to see the solution and kpi values.)
2. Open your deployment model, by selecting it in the Deployments tab of your deployment space and click New job.
3. Define the details of your job by entering a name, and an optional description for your job and click Next.
4. Configure your job by selecting a hardware specification and Next.You can choose to schedule you job here, or leave the default schedule option off and click Next. You can also optionally choose to turn on notifications or click Next.
5. Choose the data that you want to use in your job by clicking Select the source for each of your input and output tables. Click Next.
6. You can now review and create your model by clicking Create.When you receive a successful job creation message, you can then view it by opening it from your deployment space. There you can see the run status of your job.
7. Open the run for your job.Your job log opens and you can also view and copy the payload information.
| # Deploying a Decision Optimization model by using the user interface #
You can save a model for deployment in the Decision Optimization experiment UI and promote it to your Watson Machine Learning deployment space\.
## Procedure ##
To save your model for deployment:
<!-- <ol> -->
1. In the Decision Optimization experiment UI, either from the Scenario or from the Overview pane, click the menu icon  for the scenario that you want to deploy, and select **Save for deployment**
2. Specify a name for your model and add a description, if needed, then click **Next**\.
<!-- <ol> -->
1. Review the Input and Output schema and select the tables you want to include in the schema.
2. Review the Run parameters and add, modify or delete any parameters as necessary.
3. Review the Environment and Model files that are listed in the Review and save window.
4. Click Save.
<!-- </ol> -->
The model is then available in the **Models** section of your project.
<!-- </ol> -->
To promote your model to your deployment space:
<!-- <ol> -->
3. View your model in the Models section of your project\.You can see a summary with input and output schema\. Click **Promote to deployment space**\.
4. In the Promote to space window that opens, check that the Target space field displays the name of your deployment space and click **Promote**\.
5. Click the link **deployment space** in the message that you receive that confirms successful promotion\. Your promoted model is displayed in the Assets tab of your **Deployment space**\. The information pane shows you the Type, Software specification, description and any defined tags such as the Python version used\.
<!-- </ol> -->
To create a new deployment:
<!-- <ol> -->
6. From the **Assets tab** of your deployment space, open your model and click **New Deployment**\.
7. In the Create a deployment window that opens, specify a name for your deployment and select a **Hardware specification**\.Click **Create** to create the deployment\. Your deployment window opens from which you can later create jobs\.
<!-- </ol> -->
<!-- <article "class="topic task nested1" role="article" id="task_ktn_fkv_5mb" "> -->
## Creating and running Decision Optimization jobs ##
You can create and run jobs to your deployed model\.
### Procedure ###
<!-- <ol> -->
1. Return to your deployment space by using the navigation path and (if the data pane isn't already open) click the data icon to open the data pane\. Upload your input data tables, and solution and kpi output tables here\. (You must have output tables defined in your model to be able to see the solution and kpi values\.)
2. Open your deployment model, by selecting it in the Deployments tab of your deployment space and click **New job**\.
3. Define the details of your job by entering a name, and an optional description for your job and click **Next**\.
4. Configure your job by selecting a hardware specification and **Next**\.You can choose to schedule you job here, or leave the default schedule option off and click **Next**\. You can also optionally choose to turn on notifications or click Next\.
5. Choose the data that you want to use in your job by clicking Select the source for each of your input and output tables\. Click **Next**\.
6. You can now review and create your model by clicking **Create**\.When you receive a successful job creation message, you can then view it by opening it from your deployment space\. There you can see the run status of your job\.
7. Open the run for your job\.Your job log opens and you can also view and copy the payload information\.
<!-- </ol> -->
<!-- </article "class="topic task nested1" role="article" id="task_ktn_fkv_5mb" "> -->
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<title>Deploying a Decision Optimization model by using the user interface</title>
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<h1 class="topictitle1" id="task_deployUIWML__title__1">Deploying a <span class="keyword">Decision Optimization</span> model by using the user interface</h1>
<div class="body taskbody">
<p class="shortdesc">You can save a model for deployment in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> and promote it to your <span class="keyword">Watson Machine Learning</span> deployment space.</p>
<section role="region" class="section prereq" id="task_deployUIWML__prereq_j1y_svw_jjb" aria-label="Deploying a Decision
Optimization model by using the user interface: Before you begin">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deployUIWML__prereq_j1y_svw_jjb">Before you begin</h2>
</div>You must have a <a data-hd-product="cloud wx" href="../../wsj/wmls/wmls-deploy-overview.html">deployment space</a> associated with your project.
</section>
<section class="section context" role="region" aria-label="Deploying a Decision
Optimization model by using the user interface: About this task">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deployUIWML__context__1">About this task</h2>
</div>
<p>When you're satisfied with its results, reliability, and performance, you can deploy a model inside <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> with <span class="keyword">Watson Machine Learning</span>.</p>
<div class="p">
The main stages for deployment are as follows:
<ol id="task_deployUIWML__ol_ud5_kdd_kjb">
<li>From the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, save your model scenario as a <span class="keyword">Watson Machine Learning</span> model in your Project.</li>
<li>Promote your <span class="keyword">Watson Machine Learning</span> model to your deployment space.</li>
<li>From your deployment space, create a new deployment.</li>
<li>You can then create and run jobs to your deployed model.</li>
</ol> These stages are detailed in the following procedure.
</div>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deployUIWML__steps__1">Procedure</h2>
</div>
<p class="li stepsection">To save your model for deployment:</p>
<ol>
<li class="step stepexpand"><span class="cmd">In the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>, either from the <span class="keyword">Scenario</span> or from the <span class="keyword">Overview</span> pane, click the menu icon <img id="task_deployUIWML__image_hl2_bcd_kjb" src="images/scenariomenu.jpg" alt="Scenario menu icon"> for the scenario that you want to deploy, and select <strong><span class="ph uicontrol">Save for deployment</span></strong></span></li>
<li class="step stepexpand"><span class="cmd">Specify a name for your model and add a description, if needed, then click <strong><span class="ph uicontrol">Next</span></strong>.</span>
<ol type="a" class="ol substeps">
<li class="li substep"><span class="cmd">Review the <span class="ph uicontrol">Input </span>and <span class="ph uicontrol">Output schema</span> and select the tables you want to include in the schema.</span></li>
<li class="li substep"><span class="cmd">Review the <span class="ph uicontrol">Run parameters</span> and add, modify or delete any parameters as necessary.</span></li>
<li class="li substep"><span class="cmd">Review the <span class="ph uicontrol">Environment</span> and <span class="ph uicontrol">Model files</span> that are listed in the <span class="ph uicontrol">Review and save</span> window. </span></li>
<li class="li substep"><span class="cmd">Click <span class="ph uicontrol">Save</span>.</span></li>
</ol>
<div class="itemgroup info">
The model is then available in the <strong><span class="ph uicontrol">Models</span></strong> section of your project.
</div></li>
</ol>
<p class="li stepsection">To promote your model to your deployment space:</p>
<ol start="3">
<li class="step stepexpand"><span class="cmd">View your model in the <span class="ph uicontrol">Models</span> section of your project.</span>
<div class="itemgroup info">
You can see a summary with input and output schema. Click <strong><span class="ph uicontrol">Promote to deployment space</span></strong>.
</div></li>
<li class="step stepexpand"><span class="cmd">In the <span class="ph uicontrol">Promote to space</span> window that opens, check that the <span class="ph uicontrol">Target space</span> field displays the name of your deployment space and click <strong><span class="ph uicontrol">Promote</span></strong>.</span></li>
<li class="step stepexpand"><span class="cmd">Click the link <strong><span class="ph uicontrol">deployment space</span></strong> in the message that you receive that confirms successful promotion. </span>
<div class="itemgroup info">
Your promoted model is displayed in the <span class="ph uicontrol">Assets</span> tab of your <strong><span class="ph uicontrol">Deployment space</span></strong>. The information pane shows you the Type, Software specification, description and any defined tags such as the Python version used.
</div></li>
</ol>
<p class="li stepsection">To create a new deployment:</p>
<ol start="6">
<li class="step stepexpand"><span class="cmd">From the <strong><span class="ph uicontrol">Assets tab</span></strong> of your deployment space, open your model and click <strong><span class="ph uicontrol">New Deployment</span></strong>.</span></li>
<li class="step stepexpand"><span class="cmd">In the <span class="ph uicontrol">Create a deployment</span> window that opens, specify a name for your deployment and select a <strong><span class="ph uicontrol">Hardware specification</span></strong>.</span>
<div class="itemgroup info">
Click <strong><span class="ph uicontrol">Create</span></strong> to create the deployment. Your deployment window opens from which you can later create jobs.
</div></li>
</ol>
<section class="section result" role="region" aria-label="Deploying a Decision
Optimization model by using the user interface: Results">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_deployUIWML__result__1">Results</h2>
</div>
<p>You can access information about your deployment on the <strong><span class="ph uicontrol">Deployments</span></strong> tab of your model in your deployment space.</p>
</section>
</div>
<article class="topic task nested1" role="article" aria-labelledby="task_ktn_fkv_5mb__title__1" id="task_ktn_fkv_5mb">
<h2 class="topictitle2" id="task_ktn_fkv_5mb__title__1">Creating and running <span class="keyword">Decision Optimization</span> jobs</h2>
<div class="body taskbody">
<p class="shortdesc">You can create and run jobs to your deployed model.</p>
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_ktn_fkv_5mb__steps_ywf_hkv_5mb">Procedure</h3>
</div>
<ol class="steps" id="task_ktn_fkv_5mb__steps_ywf_hkv_5mb">
<li class="step"><span class="cmd">Return to your deployment space by using the navigation path and (if the data pane isn't already open) click the <span class="ph uicontrol">data</span> icon to open the data pane. Upload your input data tables, and solution and kpi output tables here. (You must have output tables defined in your model to be able to see the solution and kpi values.)</span></li>
<li class="step"><span class="cmd">Open your deployment model, by selecting it in the Deployments tab of your deployment space and click <strong><span class="ph uicontrol">New job</span></strong>.</span></li>
<li class="step"><span class="cmd">Define the details of your job by entering a name, and an optional description for your job and click <strong><span class="ph uicontrol">Next</span></strong>.</span></li>
<li class="step"><span class="cmd">Configure your job by selecting a hardware specification and <strong><span class="ph uicontrol">Next</span></strong>.</span>
<div class="itemgroup info">
You can choose to schedule you job here, or leave the default schedule option off and click <strong><span class="ph uicontrol">Next</span></strong>. You can also optionally choose to turn on notifications or click <span class="ph uicontrol">Next</span>.
</div></li>
<li class="step"><span class="cmd">Choose the data that you want to use in your job by clicking Select the source for each of your input and output tables. Click <strong><span class="ph uicontrol">Next</span></strong>.</span></li>
<li class="step"><span class="cmd">You can now review and create your model by clicking <strong><span class="ph uicontrol">Create</span></strong>.</span>
<div class="itemgroup info">
When you receive a successful job creation message, you can then view it by opening it from your deployment space. There you can see the run status of your job.
</div></li>
<li class="step"><span class="cmd">Open the run for your job.</span>
<div class="itemgroup info">
Your job log opens and you can also view and copy the payload information.
</div></li>
</ol>
<section class="section result" role="region" id="task_ktn_fkv_5mb__result_w5q_pkv_5mb" aria-label="Creating and running Decision
Optimization jobs: Results">
<div class="tasklabel">
<h3 class="sectiontitle tasklabel" id="tasktask_ktn_fkv_5mb__result_w5q_pkv_5mb">Results</h3>
</div>
<p>You can create and monitor jobs, and get solutions by using the <span class="keyword">Watson Machine Learning Python client</span>. See the <span class="ph filepath">RunDeployedModel</span> <span class="keyword">notebook</span> in the <a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a>. <span class="ph">Select the relevant product and version subfolder.</span></p>
</section>
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<li><a href="../wml_cpd_home.html" title="When you have created and solved your Decision Optimization models, you can deploy them using Watson Machine Learning.">Parent link: <span class="ph" data-hd-product="cloud wx"><span class="keyword">Decision Optimization</span></span>.</a></li>
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95689297B729A4186914E81A59FFB3A09289F8D8 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployPythonClient.html?context=cdpaas&locale=en | Decision Optimization Python client examples | Python client examples
You can deploy a Decision Optimization model, create and monitor jobs, and get solutions by using the Watson Machine Learning Python client.
To deploy your model, see [Model deployment](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelDeploymentTaskCloud.html).
For more information, see [Watson Machine Learning Python client documentation](https://ibm.github.io/watson-machine-learning-sdk/core_api.htmldeployments).
See also the following sample notebooks located in the jupyter folder of the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples). Select the relevant product and version subfolder..
* Deploying a DO model with WML
* RunDeployedModel
* ExtendWMLSoftwareSpec
The Deploying a DO model with WML sample shows you how to deploy a Decision Optimization model, create and monitor jobs, and get solutions by using the Watson Machine Learning Python client. This notebook uses the diet sample for the Decision Optimization model and takes you through the whole procedure without using the Decision Optimization experiment UI.
The RunDeployedModel shows you how to run jobs and get solutions from an existing deployed model. This notebook uses a model that is saved for deployment from a Decision Optimization experiment UI scenario.
The ExtendWMLSoftwareSpec notebook shows you how to extend the Decision Optimization software specification within Watson Machine Learning. By extending the software specification, you can use your own pip package to add custom code and deploy it in your model and send jobs to it.
You can also find in the samples several notebooks for deploying various models, for example CPLEX, DOcplex and OPL models with different types of data.
| # Python client examples #
You can deploy a Decision Optimization model, create and monitor jobs, and get solutions by using the Watson Machine Learning Python client\.
To deploy your model, see [Model deployment](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelDeploymentTaskCloud.html)\.
For more information, see [Watson Machine Learning Python client documentation](https://ibm.github.io/watson-machine-learning-sdk/core_api.html#deployments)\.
See also the following sample notebooks located in the jupyter folder of the **[DO\-samples](https://github.com/IBMDecisionOptimization/DO-Samples)**\. Select the relevant product and version subfolder\.\.
<!-- <ul> -->
* Deploying a DO model with WML
* RunDeployedModel
* ExtendWMLSoftwareSpec
<!-- </ul> -->
The Deploying a DO model with WML sample shows you how to deploy a Decision Optimization model, create and monitor jobs, and get solutions by using the Watson Machine Learning Python client\. This notebook uses the diet sample for the Decision Optimization model and takes you through the whole procedure without using the Decision Optimization experiment UI\.
The RunDeployedModel shows you how to run jobs and get solutions from an existing deployed model\. This notebook uses a model that is saved for deployment from a Decision Optimization experiment UI scenario\.
The ExtendWMLSoftwareSpec notebook shows you how to extend the Decision Optimization software specification within Watson Machine Learning\. By extending the software specification, you can use your own pip package to add custom code and deploy it in your model and send jobs to it\.
You can also find in the samples several notebooks for deploying various models, for example CPLEX, DOcplex and OPL models with different types of data\.
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<h1 class="topictitle1" id="topic_wmlpythonclient__title__1"><span class="ph" data-hd-product="cloud wx">Python client examples</span></h1>
<div class="body">
<p class="shortdesc">You can deploy a <span class="keyword">Decision Optimization</span> model, create and monitor jobs, and get solutions by using the <span class="keyword">Watson Machine Learning Python client</span>.</p>
<p>To deploy your model, see <a href="ModelDeploymentTaskCloud.html" title="To deploy a Decision Optimization model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.">Model deployment</a>.</p>
<p>For more information, see <a href="https://ibm.github.io/watson-machine-learning-sdk/core_api.html#deployments" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Watson Machine Learning Python client</span> documentation</a>.</p>
<div class="p">
See also the following sample <span class="keyword">notebooks</span> located in the <span class="ph filepath">jupyter</span> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>. <span class="ph">Select the relevant product and version subfolder.</span>.
<ul>
<li><span class="ph filepath">Deploying a DO model with WML</span></li>
<li><span class="ph filepath">RunDeployedModel</span></li>
<li><span class="ph filepath">ExtendWMLSoftwareSpec</span></li>
</ul>
</div>
<p>The <span class="ph filepath">Deploying a DO model with WML</span> sample shows you how to deploy a <span class="keyword">Decision Optimization</span> model, create and monitor jobs, and get solutions by using the <span class="keyword">Watson Machine Learning Python client</span>. This <span class="keyword">notebook</span> uses the diet sample for the <span class="keyword">Decision Optimization</span> model and takes you through the whole procedure without using the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>.</p>
<p>The <span class="ph filepath">RunDeployedModel</span> shows you how to run jobs and get solutions from an existing deployed model. This <span class="keyword">notebook</span> uses a model that is saved for deployment from a <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> scenario.</p>
<p id="topic_wmlpythonclient__extendWML">The <span class="ph filepath">ExtendWMLSoftwareSpec</span><span class="keyword">notebook</span> shows you how to extend the <span class="keyword">Decision Optimization</span> software specification within <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>. By extending the software specification, you can use your own pip package to add custom code and deploy it in your model and send jobs to it.</p>
<p>You can also find in the samples several <span class="keyword">notebooks</span> for deploying various models, for example CPLEX, DOcplex and OPL models with different types of data.</p>
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135AD82FAAA11FD4FEC7CE7A31516E98EE3D0EA5 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeploySolveParams.html?context=cdpaas&locale=en | Decision Optimization solve parameters | Solve parameters
To control solve behavior, you can specify Decision Optimization solve parameters in your request as named value pairs.
For example:
"solve_parameters" : {
"oaas.logAttachmentName":"log.txt",
"oaas.logTailEnabled":"true"
}
You can use this code to collect the engine log tail during the solve and the whole engine log as output at the end of the solve.
You can use these parameters in your request.
Name Type Description
oaas.timeLimit Number You can use this parameter to set a time limit in milliseconds.
oaas.resultsFormat Enum<br><br><br><br> * JSON<br> * CSV<br> * XML<br> * TEXT<br> * XLSX<br><br><br> Specifies the format for returned results. The default formats are as follows:<br><br><br><br> * CPLEX - .xml<br> * CPO - .json<br> * OPL - .csv<br> * DOcplex - .json<br><br><br><br>Other formats might or might not be supported depending on the application type.
oaas.oplRunConfig String Specifies the name of the OPL run configuration to be executed.
oaas.docplex.python 3.10 You can use this parameter to set the Python version for the run in your deployed model. If not specified, 3.10 is used by default.
oaas.logTailEnabled Boolean Use this parameter to include the log tail in the solve status.
oaas.logAttachmentName String If defined, engine logs will be defined as a job output attachment.
oaas.engineLogLevel Enum<br><br><br><br> * OFF<br> * SEVERE<br> * WARNING<br> * INFO<br> * CONFIG<br> * FINE<br> * FINER<br> * FINEST<br><br><br> You can use this parameter to define the level of detail that is provided by the engine log. The default value is INFO.
oaas.logLimit Number Maximum log-size limit in number of characters.
oaas.dumpZipName Can be viewed as Boolean (see Description) If defined, a job dump (inputs and outputs) .zip file is provided with this name as a job output attachment. The name can contain a placeholder ${job_id}. If defined with no value, dump_${job_id}.zip attachmentName is used. If not defined, by default, no job dump .zip file is attached.
oaas.dumpZipRules String If defined, ta .zip file is generated according to specific job rules (RFC 1960-based Filter). It must be used in conjunction with the {@link DUMP_ZIP_NAME} parameter. Filters can be defined on the duration and the following {@link com.ibm.optim.executionservice.model.solve.SolveState} properties:<br><br><br><br> * duration<br> * solveState.executionStatus<br> * solveState.interruptionStatus<br> * solveState.solveStatus<br> * solveState.failureInfo.type<br><br><br><br>Example:<br><br>(duration>=1000) or (&(duration<1000)(!(solveState.solveStatus=OPTIMAL_SOLUTION))) or ( (solveState.interruptionStatus=OUT_OF_MEMORY) (solveState.failureInfo.type=INFRASTRUCTURE))<br><br>(duration>=1000) or (&(duration<1000)(!(solveState.solveStatus=OPTIMAL_SOLUTION))) or (|(solveState.interruptionStatus=OUT_OF_MEMORY) (solveState.failureInfo.type=INFRASTRUCTURE))
oaas.outputUploadPeriod Number Intermediate output in minutes. This parameter can be used to set up intermediate output publication (if any).
oaas.outputUploadFiles String (RegExp) RegExp filter for files to be included in the output upload. If nothing is defined, all outputs are added.<br><br>Example:<br><br>job_${job_id}_log_${update_time}.txt
| # Solve parameters #
To control solve behavior, you can specify Decision Optimization solve parameters in your request as named value pairs\.
For example:
"solve_parameters" : {
"oaas.logAttachmentName":"log.txt",
"oaas.logTailEnabled":"true"
}
You can use this code to collect the engine log tail during the solve and the whole engine log as output at the end of the solve\.
You can use these parameters in your request\.
<!-- <table "summary="" id="topic_deploysolveparams__simpletable_kw4_n1y_h2b" class="defaultstyle" "> -->
| Name | Type | Description |
| ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `oaas.timeLimit` | Number | You can use this parameter to set a time limit in milliseconds\. |
| `oaas.resultsFormat` | Enum<br><br><!-- <ul> --><br><br> * `JSON`<br> * `CSV`<br> * `XML`<br> * `TEXT`<br> * `XLSX`<br><br><!-- </ul> --><br> | Specifies the format for returned results\. The default formats are as follows:<br><br><!-- <ul> --><br><br> * CPLEX \- `.xml`<br> * CPO \- `.json`<br> * OPL \- `.csv`<br> * DOcplex \- `.json`<br><br><!-- </ul> --><br><br>Other formats might or might not be supported depending on the application type\. |
| `oaas.oplRunConfig` | String | Specifies the name of the OPL run configuration to be executed\. |
| `oaas.docplex.python` | `3.10` | You can use this parameter to set the Python version for the run in your deployed model\. If not specified, 3\.10 is used by default\. |
| `oaas.logTailEnabled` | Boolean | Use this parameter to include the log tail in the solve status\. |
| `oaas.logAttachmentName` | String | If defined, engine logs will be defined as a job output attachment\. |
| `oaas.engineLogLevel` | Enum<br><br><!-- <ul> --><br><br> * `OFF`<br> * `SEVERE`<br> * `WARNING`<br> * `INFO`<br> * `CONFIG`<br> * `FINE`<br> * `FINER`<br> * `FINEST`<br><br><!-- </ul> --><br> | You can use this parameter to define the level of detail that is provided by the engine log\. The default value is `INFO`\. |
| `oaas.logLimit` | Number | Maximum log\-size limit in number of characters\. |
| `oaas.dumpZipName` | Can be viewed as Boolean (see Description) | If defined, a job dump (inputs and outputs) `.zip` file is provided with this name as a job output attachment\. The name can contain a placeholder `${job_id}`\. If defined with no value, `dump_${job_id}.zip attachmentName` is used\. If not defined, by default, no job dump `.zip` file is attached\. |
| `oaas.dumpZipRules` | String | If defined, ta `.zip` file is generated according to specific job rules (RFC 1960\-based Filter)\. It must be used in conjunction with the `{@link DUMP_ZIP_NAME}` parameter\. Filters can be defined on the duration and the following `{@link com.ibm.optim.executionservice.model.solve.SolveState}` properties:<br><br><!-- <ul> --><br><br> * `duration`<br> * `solveState.executionStatus`<br> * `solveState.interruptionStatus`<br> * `solveState.solveStatus`<br> * `solveState.failureInfo.type`<br><br><!-- </ul> --><br><br>Example:<br><br>`(duration>=1000) or (&(duration<1000)(!(solveState.solveStatus=OPTIMAL_SOLUTION))) or (|(solveState.interruptionStatus=OUT_OF_MEMORY) (solveState.failureInfo.type=INFRASTRUCTURE))`<br><br>(duration>=1000) or (&(duration<1000)(\!(solveState\.solveStatus=OPTIMAL\_SOLUTION))) or (\|(solveState\.interruptionStatus=OUT\_OF\_MEMORY) (solveState\.failureInfo\.type=INFRASTRUCTURE)) |
| `oaas.outputUploadPeriod` | Number | Intermediate output in minutes\. This parameter can be used to set up intermediate output publication (if any)\. |
| `oaas.outputUploadFiles` | String (RegExp) | RegExp filter for files to be included in the output upload\. If nothing is defined, all outputs are added\.<br><br>Example:<br><br>`job_${job_id}_log_${update_time}.txt` |
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<h1 class="topictitle1" id="topic_deploysolveparams__title__1"><span class="ph" data-hd-product="cloud wx">Solve parameters</span></h1>
<div class="body">
<p class="shortdesc">To control solve behavior, you can specify <span class="keyword">Decision Optimization</span> solve parameters in your request as named value pairs.</p>
<div class="p">
For example:
<pre class="codeblock"><code>"solve_parameters" : {
"oaas.logAttachmentName":"log.txt",
"oaas.logTailEnabled":"true"
}
</code></pre>You can use this code to collect the engine log tail during the solve and the whole engine log as output at the end of the solve.
</div>
<p>You can use these parameters in your request.</p>
<table summary="" id="topic_deploysolveparams__simpletable_kw4_n1y_h2b" class="defaultstyle">
<colgroup>
<col style="width:33.33333333333333%">
<col style="width:22.22222222222222%">
<col style="width:44.44444444444444%">
</colgroup>
<thead>
<tr>
<th style="vertical-align:bottom;text-align:left;" id="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1">Name</th>
<th style="vertical-align:bottom;text-align:left;" id="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Type</th>
<th style="vertical-align:bottom;text-align:left;" id="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.timeLimit</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Number</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">You can use this parameter to set a time limit in milliseconds.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.resultsFormat</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Enum
<ul id="topic_deploysolveparams__ul_vv4_sx2_ghb">
<li><code class="ph codeph">JSON</code></li>
<li><code class="ph codeph">CSV</code></li>
<li><code class="ph codeph">XML</code></li>
<li><code class="ph codeph">TEXT</code></li>
<li><code class="ph codeph">XLSX</code></li>
</ul></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">Specifies the format for returned results. The default formats are as follows:
<ul id="topic_deploysolveparams__ul_xwy_21r_jsb">
<li>CPLEX - <code class="ph codeph">.xml</code></li>
<li>CPO - <code class="ph codeph">.json</code></li>
<li>OPL - <code class="ph codeph">.csv</code></li>
<li>DOcplex - <code class="ph codeph">.json</code></li>
</ul> Other formats might or might not be supported depending on the application type.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.oplRunConfig</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">String</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">Specifies the name of the OPL run configuration to be executed.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.docplex.python</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2"><code class="ph codeph"><span class="keyword">3.10</span></code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">
<p>You can use this parameter to set the Python version for the run in your deployed model. If not specified, <span class="keyword">3.10</span> is used by default.</p></td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.logTailEnabled</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Boolean</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">Use this parameter to include the log tail in the solve status.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.logAttachmentName</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">String</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">If defined, engine logs will be defined as a job output attachment.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.engineLogLevel</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Enum
<ul id="topic_deploysolveparams__ul_y5k_cd3_j2b">
<li><code class="ph codeph">OFF</code></li>
<li><code class="ph codeph">SEVERE</code></li>
<li><code class="ph codeph">WARNING</code></li>
<li><code class="ph codeph">INFO</code></li>
<li><code class="ph codeph">CONFIG</code></li>
<li><code class="ph codeph">FINE</code></li>
<li><code class="ph codeph">FINER</code></li>
<li><code class="ph codeph">FINEST</code></li>
</ul></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">You can use this parameter to define the level of detail that is provided by the engine log. The default value is <code class="ph codeph">INFO</code>.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.logLimit</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Number</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">Maximum log-size limit in number of characters.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph" id="topic_deploysolveparams__codeph_ugl_4ps_p2b">oaas.dumpZipName</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Can be viewed as Boolean (see Description)</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">If defined, a job dump (inputs and outputs) <code class="ph codeph">.zip</code> file is provided with this name as a job output attachment. The name can contain a placeholder <code class="ph codeph">${job_id}</code>. If defined with no value, <code class="ph codeph">dump_${job_id}.zip attachmentName</code> is used. If not defined, by default, no job dump <code class="ph codeph">.zip</code> file is attached.</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.dumpZipRules</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">String</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">If defined, ta <code class="ph codeph">.zip</code> file is generated according to specific job rules (RFC 1960-based Filter). It must be used in conjunction with the <code class="ph codeph">{@link DUMP_ZIP_NAME}</code> parameter. Filters can be defined on the duration and the following <code class="ph codeph">{@link com.ibm.optim.executionservice.model.solve.SolveState}</code> properties:
<div class="p">
<ul id="topic_deploysolveparams__ul_wxt_bhs_32b">
<li><code class="ph codeph">duration</code></li>
<li><code class="ph codeph">solveState.executionStatus</code></li>
<li><code class="ph codeph">solveState.interruptionStatus</code></li>
<li><code class="ph codeph">solveState.solveStatus</code></li>
<li><code class="ph codeph">solveState.failureInfo.type</code></li>
</ul>
</div>
<div class="p">
Example:
<pre class="codeblock"><code>(duration>=1000)
or (&(duration<1000)(!(solveState.solveStatus=OPTIMAL_SOLUTION)))
or (|(solveState.interruptionStatus=OUT_OF_MEMORY)
(solveState.failureInfo.type=INFRASTRUCTURE))</code></pre>
<pre class="pre">(duration>=1000)
or (&(duration<1000)(!(solveState.solveStatus=OPTIMAL_SOLUTION)))
or (|(solveState.interruptionStatus=OUT_OF_MEMORY)
(solveState.failureInfo.type=INFRASTRUCTURE))</pre>
</div></td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.outputUploadPeriod</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">Number</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">Intermediate output in minutes. This parameter can be used to set up intermediate output publication (if any).</td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__1"><code class="ph codeph">oaas.outputUploadFiles</code></td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__2">String (RegExp)</td>
<td style="vertical-align:top;" headers="topic_deploysolveparams__simpletable_kw4_n1y_h2b__stentry__3">RegExp filter for files to be included in the output upload. If nothing is defined, all outputs are added.
<div class="p">
Example:
<pre class="codeblock"><code>job_${job_id}_log_${update_time}.txt</code></pre>
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939233F807850AE8D28246ADE7FDCCDA66E9DF03 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelDeploymentTaskCloud.html?context=cdpaas&locale=en | Decision Optimization model deployment | Model deployment
To deploy a Decision Optimization model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.
Procedure
To deploy a Decision Optimization model:
1. Package your Decision Optimization model formulation with your common data (optional) ready for deployment as a tar.gz, .zip, or .jar file. Your archive can include the following optional files:
1. Your model files
2. Settings (For more information, see [ Solve parameters](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeploySolveParams.htmltopic_deploysolveparams) )
3. Common data
Note: For Python models with multiple .py files, put all files in the same folder in your archive. The same folder must contain a main file called main.py. Do not use subfolders.
2. Create a model ready for deployment in Watson Machine Learning providing the following information:
* Machine Learning service instance
* Deployment space instance
* Software specification ( Decision Optimizationruntime version):
* do_ 22.1 runtime is based on CPLEX 22.1.1.0
* do_ 20.1 runtime is based on CPLEX 20.1.0.1
You can extend the software specification provided by Watson Machine Learning. See the [ExtendWMLSoftwareSpec](https://github.com/IBMDecisionOptimization/DO-Samples/blob/watson_studio_cloud/jupyter/watsonx.ai%20and%20Cloud%20Pak%20for%20Data%20as%20a%20Service/ExtendWMLSoftwareSpec.ipynb) notebook in the jupyter folder of the [DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples).
Updating CPLEX runtimes:
If you previously deployed your model with a CPLEX runtime that is no longer supported, you can update your existing deployed model by using either the [ REST API](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-manage-outdated.htmlupdate-soft-specs-api) or the [UI](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-manage-outdated.htmldiscont-soft-spec).
* The model type:
* opl (do-opl_<runtime version>)
* cplex (do-cplex_<runtime version>)
* cpo (do-cpo_<runtime version>)
* docplex (do-docplex_<runtime version>) using Python 3.10
(The Runtime version can be one of the available runtimes so, for example, an opl model with runtime 22.1 would have the model type do-opl_ 22.1.)
You obtain a MODEL-ID. Your Watson Machine Learning model can then be used in one or multiple deployments.
3. Upload your model archive (tar.gz, .zip, or .jar file) on Watson Machine Learning. See [Model input and output data file formats](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIOFileFormats.htmltopic_modelIOFileFormats) for information about input file types.
4. Deploy your model by using the MODEL-ID, SPACE-ID, and the hardware specification for the available configuration sizes (small S, medium M, large L, extra large XL). See [configurations](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/Paralleljobs.htmltopic_paralleljobs__34c6).You obtain a DEPLOYMENT-ID.
5. Monitor the deployment by using the DEPLOYMENT-ID. Deployment states can be: initializing, updating, ready, or failed.
6. Submit jobs to your deployment.You obtain a JOB-ID.
7. Monitor your jobs by using the JOB-ID.
| # Model deployment #
To deploy a Decision Optimization model, create a model ready for deployment in your deployment space and then upload your model as an archive\. When deployed, you can submit jobs to your model and monitor job states\.
## Procedure ##
To deploy a Decision Optimization model:
<!-- <ol> -->
1. Package your Decision Optimization model formulation with your common data (optional) ready for deployment as a `tar.gz`, `.zip`, or `.jar` file\. Your archive can include the following optional files:
<!-- <ol> -->
1. Your model files
2. Settings (For more information, see [ Solve parameters](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeploySolveParams.html#topic_deploysolveparams) )
3. Common data
<!-- </ol> -->
Note: For Python models with multiple .py files, put all files in the same folder in your archive. The same folder must contain a main file called main.py. Do not use subfolders.
2. Create a model ready for deployment in Watson Machine Learning providing the following information:
<!-- <ul> -->
* **Machine Learning** service instance
* **Deployment space** instance
* **Software specification** ( Decision Optimization**runtime version**):
<!-- <ul> -->
* do\_ 22.1 runtime is based on CPLEX 22.1.1.0
* do\_ 20.1 runtime is based on CPLEX 20.1.0.1
<!-- </ul> -->
You can extend the software specification provided by Watson Machine Learning. See the [ExtendWMLSoftwareSpec](https://github.com/IBMDecisionOptimization/DO-Samples/blob/watson_studio_cloud/jupyter/watsonx.ai%20and%20Cloud%20Pak%20for%20Data%20as%20a%20Service/ExtendWMLSoftwareSpec.ipynb) notebook in the **jupyter** folder of the **[DO-samples](https://github.com/IBMDecisionOptimization/DO-Samples)**.
Updating CPLEX runtimes:
If you previously deployed your model with a CPLEX runtime that is no longer supported, you can update your existing deployed model by using either the [ REST API](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-manage-outdated.html#update-soft-specs-api) or the [UI](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-manage-outdated.html#discont-soft-spec).
* The **model type**:
<!-- <ul> -->
* opl (do-opl\_<*runtime version*>)
* cplex (do-cplex\_<*runtime version*>)
* cpo (do-cpo\_<*runtime version*>)
* docplex (do-docplex\_<*runtime version*>) using Python 3.10
<!-- </ul> -->
(The *Runtime version* can be one of the available runtimes so, for example, an opl model with runtime 22.1 would have the model type *do-opl\_ 22.1*.)
<!-- </ul> -->
You obtain a *MODEL-ID*. Your Watson Machine Learning model can then be used in one or multiple deployments.
3. Upload your model archive (`tar.gz`, `.zip`, or `.jar` file) on Watson Machine Learning\. See [Model input and output data file formats](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIOFileFormats.html#topic_modelIOFileFormats) for information about input file types\.
4. Deploy your model by using the *MODEL\-ID*, *SPACE\-ID*, and the **hardware specification** for the available configuration sizes (small S, medium M, large L, extra large XL)\. See [configurations](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/Paralleljobs.html#topic_paralleljobs__34c6)\.You obtain a *DEPLOYMENT\-ID*\.
5. Monitor the deployment by using the *DEPLOYMENT\-ID*\. **Deployment states** can be: `initializing`, `updating`, `ready`, or `failed`\.
6. Submit jobs to your deployment\.You obtain a *JOB\-ID*\.
7. Monitor your jobs by using the JOB\-ID\.
<!-- </ol> -->
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<h1 class="topictitle1" id="task_modeldeployCloud__title__1">Model deployment</h1>
<div class="body taskbody">
<p class="shortdesc">To deploy a <span class="keyword">Decision Optimization</span> model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.</p>
<section role="region" class="section prereq" data-hd-product="cloud wx" id="task_modeldeployCloud__prereq_deploysteps" aria-labelledby="tasktask_modeldeployCloud__prereq_deploysteps">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_modeldeployCloud__prereq_deploysteps">Before you begin</h2>
</div>You must have an IBM Cloud account. See <a href="https://www.ibm.com/cloud/" rel="noopener" target="_blank" title="(Opens in a new tab or window)">https://www.ibm.com/cloud/</a>.
<div class="p">
<ol id="task_modeldeployCloud__ol_oqt_l2k_mmb">
<li>Log in to <a href="https://cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">IBM Cloud</a>.</li>
<li>Create your <a href="https://cloud.ibm.com/iam/apikeys" rel="noopener" target="_blank" title="(Opens in a new tab or window)">API key</a>. Copy or download it from the <span class="ph uicontrol">API key successfully created</span> open window (you cannot access it again when you close this window).</li>
<li>Create or select a <a href="https://cloud.ibm.com/catalog/services/machine-learning" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="keyword">Machine Learning</span> service</a>. Copy the <span class="ph uicontrol">service instance name</span>, <span class="ph uicontrol">GUID</span>, and <span class="ph uicontrol">CRN</span> from the information pane for your instance in the <span class="ph uicontrol">Resource List</span>><span class="ph uicontrol">Services</span> view on <a href="https://cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">IBM Cloud</a>. (Expand the list of services in the <span class="keyword wintitle">Resource List</span> window. Click anywhere in the row next to your Machine Learning Service name, but not on the name itself. The information pane then opens in the same window.)</li>
<li>Create or select a <a href="https://cloud.ibm.com/catalog/services/cloud-object-storage" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Cloud Object Storage</a>. Copy the <span class="ph uicontrol">Cloud Object Storage instance name</span> and <span class="ph uicontrol">CRN</span> from the information pane for your instance in the <span class="ph uicontrol">Resource List</span>><span class="ph uicontrol">Storage</span> view on <a href="https://cloud.ibm.com" rel="noopener" target="_blank" title="(Opens in a new tab or window)">IBM Cloud</a>.</li>
<li>Create a <a href="https://dataplatform.cloud.ibm.com/ml-runtime/spaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)">deployment space</a>, from the user interface. Then view it and copy your Space ID from the settings tab. For more information, see <a href="../../wsj/analyze-data/ml-spaces_local.html#create" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Deployment spaces</a>.</li>
</ol>
</div>
</section>
<section class="section context" role="region" aria-labelledby="tasktask_modeldeployCloud__context__1">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_modeldeployCloud__context__1">About this task</h2>
</div>
<p>These instructions assume that you have already built your <span class="keyword">Decision Optimization</span> model.</p>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_modeldeployCloud__steps__1">Procedure</h2>
</div>
<p class="li stepsection">To deploy a <span class="keyword">Decision Optimization</span> model:</p>
<ol class="steps">
<li class="step stepexpand"><span class="cmd">Package your <span class="keyword">Decision Optimization</span> model formulation with your common data (optional) ready for deployment as a <code class="ph codeph">tar.gz</code>, <code class="ph codeph">.zip</code>, or <code class="ph codeph">.jar</code> file. </span>
<div class="itemgroup info">
Your archive can include the following optional files:
<ol type="a" id="task_modeldeployCloud__ul_hg3_qxk_fhb">
<li>Your model files</li>
<li>Settings (For more information, see <a href="DeploySolveParams.html#topic_deploysolveparams" title="To control solve behavior, you can specify Decision Optimization solve parameters in your request as named value pairs."> Solve parameters</a> )</li>
<li>Common data</li>
</ol>
<div class="note">
<span class="notetitle">Note:</span> For Python models with multiple <span class="ph filepath">.py</span> files, put all files in the same folder in your archive. The same folder must contain a main file called <span class="ph filepath">main.py</span>. Do not use subfolders.
</div>
</div></li>
<li class="step stepexpand"><span class="cmd">Create a model ready for deployment in <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> providing the following information: </span>
<div class="itemgroup info">
<ul id="task_modeldeployCloud__ul_createdeployinfo">
<li><strong><span class="keyword">Machine Learning</span></strong> service instance</li>
<li><strong>Deployment space </strong>instance</li>
<li><strong>Software specification</strong> (<span class="keyword">Decision Optimization</span> <strong>runtime version</strong>):
<ul id="task_modeldeployCloud__ul_xly_qvz_fhb">
<li>do_<span class="keyword">22.1</span> runtime is based on CPLEX <span class="keyword">22.1.1.0</span></li>
<li>do_<span class="keyword">20.1</span> runtime is based on CPLEX <span class="keyword">20.1.0.1</span></li>
</ul>
<p>You can extend the software specification provided by <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>. See the <a href="https://github.com/IBMDecisionOptimization/DO-Samples/blob/watson_studio_cloud/jupyter/watsonx.ai%20and%20Cloud%20Pak%20for%20Data%20as%20a%20Service/ExtendWMLSoftwareSpec.ipynb" rel="noopener" target="_blank" title="(Opens in a new tab or window)"><span class="ph filepath">ExtendWMLSoftwareSpec</span></a> notebook in the <strong><span class="ph filepath">jupyter</span></strong> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>.</p>
<div class="note note">
<span class="notetitle">Updating CPLEX runtimes:</span>
<p>If you previously deployed your model with a CPLEX runtime that is no longer supported, you can update your existing deployed model by using either the <a href="../../wsj/analyze-data/ml-manage-outdated.html#update-soft-specs-api"> REST API</a> or the <a href="../../wsj/analyze-data/ml-manage-outdated.html#discont-soft-spec">UI</a>.</p>
</div></li>
<li>The <strong>model type</strong>:
<ul id="task_modeldeployCloud__ul_q4f_fwz_fhb">
<li>opl (do-opl_<<em>runtime version</em>>)</li>
<li>cplex (do-cplex_<<em>runtime version</em>>)</li>
<li>cpo (do-cpo_<<em>runtime version</em>>)</li>
<li>docplex (do-docplex_<<em>runtime version</em>>) using Python <span class="keyword">3.10</span></li>
</ul>
<p>(The <em>Runtime version</em> can be one of the available runtimes so, for example, an opl model with runtime <span class="keyword">22.1</span> would have the model type <em>do-opl_<span class="keyword">22.1</span></em>.)</p></li>
</ul>
</div>
<div class="itemgroup stepresult">
You obtain a <em>MODEL-ID</em>. Your <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> model can then be used in one or multiple deployments.
</div></li>
<li class="step stepexpand"><span class="cmd">Upload your model archive (<code class="ph codeph">tar.gz</code>, <code class="ph codeph">.zip</code>, or <code class="ph codeph">.jar</code> file) on <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>. See <a href="ModelIOFileFormats.html#topic_modelIOFileFormats" title="With your Decision Optimization model, you can use the following input and output data identifiers and extension combinations.">Model input and output data file formats</a> for information about input file types. </span></li>
<li class="step stepexpand"><span class="cmd">Deploy your model by using the <em>MODEL-ID</em>, <em>SPACE-ID</em>, and the <strong>hardware specification</strong> for the available configuration sizes (small S, medium M, large L, extra large XL). See <a href="Paralleljobs.html#topic_paralleljobs__34c6">configurations</a>.</span>
<div class="itemgroup stepresult">
You obtain a <em>DEPLOYMENT-ID</em>.
</div></li>
<li class="step stepexpand"><span class="cmd">Monitor the deployment by using the <em>DEPLOYMENT-ID</em>. <strong>Deployment states</strong> can be: <code class="ph codeph">initializing</code>, <code class="ph codeph">updating</code>, <code class="ph codeph">ready</code>, or <code class="ph codeph">failed</code>.</span></li>
<li class="step stepexpand"><span class="cmd">Submit jobs to your deployment.</span>
<div class="itemgroup stepresult">
You obtain a <em>JOB-ID</em>.
</div></li>
<li class="step stepexpand"><span class="cmd">Monitor your jobs by using the JOB-ID. </span></li>
</ol>
<section role="region" aria-labelledby="tasktask_modeldeployCloud__example__1" class="example">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_modeldeployCloud__example__1">Example</h2>
</div>
<p>See the <span class="ph filepath">Deploying a DO model with WML</span> sample for an example of how to deploy a <span class="keyword">Decision Optimization</span> model, create and monitor jobs, and get solutions by using the <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> Python Client. This <span class="keyword">notebook</span> uses the diet sample for the <span class="keyword">Decision Optimization</span> model and takes you through the whole procedure without using the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span>. This sample and the <span class="ph filepath">RunDeployedModel</span> and <span class="ph filepath">ExtendWMLSoftwareSpec</span> <span class="keyword">notebooks</span> are located in the <strong><span class="ph filepath">jupyter</span></strong> folder of the <strong><a href="https://github.com/IBMDecisionOptimization/DO-Samples" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DO-samples</a></strong>. <span class="ph">Select the relevant product and version subfolder.</span> When downloaded, you can add these Jupyter <span class="keyword">notebooks</span> to your project.</p>
<p>See also the <a href="DeployModelRest.html#task_deploymodelREST" title="You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.">REST API example</a> example.</p>
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02C5718919D676E7EA14D16AC226407CC675C95E | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelExecution.html?context=cdpaas&locale=en | Decision Optimization model execution | Model execution
Once your model is deployed, you can submit Decision Optimization jobs to this deployment.
You can submit jobs specifying the:
* Input data: the transaction data used as input by the model. This can be inline or referenced
* Output data: to define how the output data is generated by model. This is returned as inline or referenced data.
* Solve parameters: to customize the behavior of the solution engine
For more information see [Model input and output data adaptation](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIODataDefn.htmltopic_modelIOAdapt)
After submitting a job, you can use the job-id to poll the job status to collect the:
* Job execution status or error message
* Solve execution status, progress and log tail
* Inline or referenced output data
Job states can be : queued, running, completed, failed, canceled.
| # Model execution #
Once your model is deployed, you can submit Decision Optimization jobs to this deployment\.
You can submit jobs specifying the:
<!-- <ul> -->
* **Input data**: the transaction data used as input by the model\. This can be inline or referenced
* **Output data**: to define how the output data is generated by model\. This is returned as inline or referenced data\.
* **Solve parameters**: to customize the behavior of the solution engine
<!-- </ul> -->
For more information see [Model input and output data adaptation](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIODataDefn.html#topic_modelIOAdapt)
After submitting a job, you can use the job\-id to poll the job status to collect the:
<!-- <ul> -->
* Job execution status or error message
* Solve execution status, progress and log tail
* Inline or referenced output data
<!-- </ul> -->
**Job states** can be : `queued`, `running`, `completed`, `failed`, `canceled`\.
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<h1 class="topictitle1" id="topic_modelexec__title__1"><span class="ph" data-hd-product="cloud wx">Model execution</span></h1>
<div class="body">
<p class="shortdesc">Once your model is deployed, you can submit <span class="keyword">Decision Optimization</span> jobs to this deployment.</p>
<p>You can submit jobs specifying the:</p>
<ul id="topic_modelexec__ul_lgh_zwz_fhb">
<li><strong>Input data</strong>: the transaction data used as input by the model. This can be inline or referenced</li>
<li><strong>Output data</strong>: to define how the output data is generated by model. This is returned as inline or referenced data.</li>
<li><strong>Solve parameters</strong>: to customize the behavior of the solution engine</li>
</ul>
<p>For more information see <a href="ModelIODataDefn.html#topic_modelIOAdapt" title="When submitting your job you can include your data inline or reference your data in your request. This data will be mapped to a file named with data identifier and used by the model. The data identifier extension will define the format of the file used.">Model input and output data adaptation</a></p>
<p>After submitting a job, you can use the job-id to poll the job status to collect the:</p>
<ul id="topic_modelexec__ul_mgh_zwz_fhb">
<li>Job execution status or error message</li>
<li>Solve execution status, progress and log tail</li>
<li>Inline or referenced output data</li>
</ul>
<p><strong>Job states</strong> can be : <code class="ph codeph">queued</code>, <code class="ph codeph">running</code>, <code class="ph codeph">completed</code>, <code class="ph codeph">failed</code>, <code class="ph codeph">canceled</code>.</p>
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E9E9556CA0C7B258D910BB31222A78BEABB46A48 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIODataDefn.html?context=cdpaas&locale=en | Decision Optimization model input and output data | Model input and output data adaptation
When submitting your job you can include your data inline or reference your data in your request. This data will be mapped to a file named with data identifier and used by the model. The data identifier extension will define the format of the file used.
The following adaptations are supported:
* Tabular inline data to embed your data in your request. For example:
"input_data": [{
"id":"diet_food.csv",
"fields" : "name","unit_cost","qmin","qmax"],
"values" :
"Roasted Chicken", 0.84, 0, 10]
]
}]
This will generate the corresponding diet_food.csv file that is used as the model input file. Only csv adaptation is currently supported.
* Inline data, that is, non-tabular data (such as an OPL .dat file or an .lpfile) to embed data in your request. For example:
"input_data": [{
"id":"diet_food.csv",
"content":"Input data as a base64 encoded string"
}]
* URL referenced data allowing you to reference files stored at a particular URL or REST data service. For example:
"input_data_references": {
"type": "url",
"id": "diet_food.csv",
"connection": {
"verb": "GET",
"url": "https://myserver.com/diet_food.csv",
"headers": {
"Content-Type": "application/x-www-form-urlencoded"
}
},
"location": {}
}
This will copy the corresponding diet_food.csv file that is used as the model input file.
* Data assets allowing you to reference any data asset or connected data asset present in your space and benefit from the data connector integration capabilities. For example:
"input_data_references": [{
"name": "test_ref_input",
"type": "data_asset",
"connection": {},
"location": {
"href": "/v2/assets/ASSET-ID?space_id=SPACE-ID"
}
}],
"output_data_references": [{
"type": "data_asset",
"connection": {},
"location": {
"href": "/v2/assets/ASSET-ID?space_id=SPACE-ID"
}
}]
With this data asset type there are many different connections available. For more information, see [Batch deployment details](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-details.htmldo).
* Connection assets allowing you to reference any data and then refer to the connection, without having to specify credentials each time. For more information, see [Supported data sources in Decision Optimization](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html). Referencing a secure connection without having to use inline credentials in the payload also offers you better security. For more information, see [Example connection_asset payload](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets.htmlconnection_asset_payload).For example, to connect to a COS/S3 via a Connection asset:
{
"type" : "connection_asset",
"id" : "diet_food.csv",
"connection" : {
"id" : <connection_guid>
},
"location" : {
"file_name" : "FILENAME.csv",
"bucket" : "BUCKET-NAME"
}
}
For information about the parameters used in these examples, see [Deployment job definitions](https://cloud.ibm.com/apidocs/machine-learning-cpdeployment-job-definitions-create).
Another example showing you how to connect to a DB2 asset via a connection asset:
{
"type" : "connection_asset",
"id" : "diet_food.csv",
"connection" : {
"id" : <connection_guid>
},
"location" : {
"table_name" : "TABLE-NAME",
"schema_name" : "SCHEMA-NAME"
}
}
With this connection asset type there are many different connections available. For more information, see [Batch deployment details](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-details.htmldo).
You can combine different adaptations in the same request. For more information about data definitions see [Adding data to an analytics project](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/add-data-project.html).
| # Model input and output data adaptation #
When submitting your job you can include your data inline or reference your data in your request\. This data will be mapped to a file named with data identifier and used by the model\. The data identifier extension will define the format of the file used\.
The following adaptations are supported:
<!-- <ul> -->
* **Tabular inline data** to embed your data in your request\. For example:
"input_data": [{
"id":"diet_food.csv",
"fields" : "name","unit_cost","qmin","qmax"],
"values" :
"Roasted Chicken", 0.84, 0, 10]
]
}]
This will generate the corresponding `diet_food.csv` file that is used as the model input file. Only csv adaptation is currently supported.
* **Inline data**, that is, non\-tabular data (such as an OPL `.dat` file or an `.lp`file) to embed data in your request\. For example:
"input_data": [{
"id":"diet_food.csv",
"content":"Input data as a base64 encoded string"
}]
* **URL** referenced data allowing you to reference files stored at a particular URL or REST data service\. For example:
"input_data_references": {
"type": "url",
"id": "diet_food.csv",
"connection": {
"verb": "GET",
"url": "https://myserver.com/diet_food.csv",
"headers": {
"Content-Type": "application/x-www-form-urlencoded"
}
},
"location": {}
}
This will copy the corresponding `diet_food.csv` file that is used as the model input file.
* **Data assets** allowing you to reference any data asset or connected data asset present in your space and benefit from the data connector integration capabilities\. For example:
"input_data_references": [{
"name": "test_ref_input",
"type": "data_asset",
"connection": {},
"location": {
"href": "/v2/assets/ASSET-ID?space_id=SPACE-ID"
}
}],
"output_data_references": [{
"type": "data_asset",
"connection": {},
"location": {
"href": "/v2/assets/ASSET-ID?space_id=SPACE-ID"
}
}]
With this data asset type there are many different connections available. For more information, see [Batch deployment details](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-details.html#do).
* **Connection assets** allowing you to reference any data and then refer to the connection, without having to specify credentials each time\. For more information, see [Supported data sources in Decision Optimization](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html)\. Referencing a secure connection without having to use inline credentials in the payload also offers you better security\. For more information, see [Example connection\_asset payload](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets.html#connection_asset_payload)\.For example, to connect to a COS/S3 via a Connection asset:
{
"type" : "connection_asset",
"id" : "diet_food.csv",
"connection" : {
"id" : <connection_guid>
},
"location" : {
"file_name" : "FILENAME.csv",
"bucket" : "BUCKET-NAME"
}
}
For information about the parameters used in these examples, see [Deployment job definitions](https://cloud.ibm.com/apidocs/machine-learning-cp#deployment-job-definitions-create).
Another example showing you how to connect to a DB2 asset via a connection asset:
{
"type" : "connection_asset",
"id" : "diet_food.csv",
"connection" : {
"id" : <connection_guid>
},
"location" : {
"table_name" : "TABLE-NAME",
"schema_name" : "SCHEMA-NAME"
}
}
<!-- </ul> -->
With this connection asset type there are many different connections available\. For more information, see [Batch deployment details](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-details.html#do)\.
You can combine different adaptations in the same request\. For more information about data definitions see [Adding data to an analytics project](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/add-data-project.html)\.
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<title>Decision Optimization model input and output data</title>
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<h1 class="topictitle1" id="topic_modelIOAdapt__title__1"><span class="ph" data-hd-product="cloud wx">Model input and output data adaptation</span></h1>
<div class="body">
<p class="shortdesc">When submitting your job you can include your data inline or reference your data in your request. This data will be mapped to a file named with data identifier and used by the model. The data identifier extension will define the format of the file used.</p>
<div class="p">
The following adaptations are supported:
<ul id="topic_modelIOAdapt__ul_ngw_4gk_m3b">
<li><strong>Tabular inline data</strong> to embed your data in your request. For example:
<pre class="codeblock"><code>"input_data": [{
"id":"diet_food.csv",
"fields" : ["name","unit_cost","qmin","qmax"],
"values" : [
["Roasted Chicken", 0.84, 0, 10]
]
}]
</code></pre> This will generate the corresponding <code class="ph codeph">diet_food.csv</code> file that is used as the model input file. Only csv adaptation is currently supported.</li>
<li><strong>Inline data</strong>, that is, non-tabular data (such as an OPL <code class="ph codeph">.dat</code> file or an <code class="ph codeph">.lp </code>file) to embed data in your request. For example:
<pre class="codeblock"><code>"input_data": [{
"id":"diet_food.csv",
"content":"Input data as a base64 encoded string"
}]</code></pre></li>
<li><strong>URL</strong> referenced data allowing you to reference files stored at a particular URL or REST data service. For example:
<pre class="codeblock"><code>"input_data_references": {
"type": "url",
"id": "diet_food.csv",
"connection": {
"verb": "GET",
"url": "https://myserver.com/diet_food.csv",
"headers": {
"Content-Type": "application/x-www-form-urlencoded"
}
},
"location": {}
} </code></pre>This will copy the corresponding <code class="ph codeph">diet_food.csv</code> file that is used as the model input file.</li>
<li><strong>Data assets</strong> allowing you to reference any data asset or connected data asset present in your space and benefit from the data connector integration capabilities. For example:
<pre class="codeblock"><code>"input_data_references": [{
"name": "test_ref_input",
"type": "data_asset",
"connection": {},
"location": {
"href": "/v2/assets/ASSET-ID?space_id=SPACE-ID"
}
}],
"output_data_references": [{
"type": "data_asset",
"connection": {},
"location": {
"href": "/v2/assets/ASSET-ID?space_id=SPACE-ID"
}
}]</code></pre>With this data asset type there are many different connections available. For more information, see <a href="../../wsj/analyze-data/deploy-batch-details.html#do">Batch deployment details</a>.</li>
<li id="topic_modelIOAdapt__ConnectionAssets"><strong>Connection assets</strong> allowing you to reference any data and then refer to the connection, without having to specify credentials each time. For more information, see <a href="../DODS_Introduction/DOconnections.html" title="Decision Optimization supports the following relational and nonrelational data sources on .watsonx.ai.">Supported data sources in Decision Optimization</a>. Referencing a secure connection without having to use inline credentials in the payload also offers you better security. For more information, see <a href="../../wsj/analyze-data/ml-space-add-assets.html#connection_asset_payload">Example connection_asset payload</a>.
<div class="p">
For example, to connect to a COS/S3 via a Connection asset:
<pre class="codeblock"><code>{
"type" : "connection_asset",
"id" : "diet_food.csv",
"connection" : {
"id" : <connection_guid>
},
"location" : {
"file_name" : "FILENAME.csv",
"bucket" : "BUCKET-NAME"
}
}</code></pre>
</div>
<p>For information about the parameters used in these examples, see <a href="https://cloud.ibm.com/apidocs/machine-learning-cp#deployment-job-definitions-create" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Deployment job definitions</a>.</p>
<div class="p">
Another example showing you how to connect to a DB2 asset via a connection asset:
<pre class="codeblock"><code>{
"type" : "connection_asset",
"id" : "diet_food.csv",
"connection" : {
"id" : <connection_guid>
},
"location" : {
"table_name" : "TABLE-NAME",
"schema_name" : "SCHEMA-NAME"
}
}</code></pre>
</div></li>
</ul>
</div>
<p>With this connection asset type there are many different connections available. For more information, see <a href="../../wsj/analyze-data/deploy-batch-details.html#do">Batch deployment details</a>.</p>
<p>You can combine different adaptations in the same request. For more information about data definitions see <a href="../../wsj/manage-data/add-data-project.html">Adding data to an analytics project</a>.</p>
</div>
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977988398EFBDCD10DB4ACED047D8D864883614A | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIOFileFormats.html?context=cdpaas&locale=en | Decision Optimization model input and output data file formats | Model input and output data file formats
With your Decision Optimization model, you can use the following input and output data identifiers and extension combinations.
This table shows the supported file type combinations for Decision Optimization in Watson Machine Learning:
Model type Input file type Output file type Comments
cplex .lp <br>.mps <br>.sav <br>.feasibility <br>.prm<br><br>.jar for Java™ <br>models .xml <br>.json <br><br>The name of the output file must be solution The output format can be specified by using the API.<br><br>Files of type .lp, .mps, and .sav can be compressed by using gzip or bzip2, and uploaded as, for example, .lp.gz or .sav.bz2.<br><br>The schemas for the CPLEX formats for solutions, conflicts, and feasibility files are available for you to download in the cplex_xsds.zip archive from the [Decision Optimization github](https://github.com/IBMDecisionOptimization/DO-Samples/blob/watson_studio_cloud/resources/cplex_xsds.zip).
cpo .cpo<br><br>.jar for Java <br>models .xml <br>.json <br><br>The name of the output file must be solution The output format can be specified by using the solve parameter.<br><br>For the native file format for CPO models, see: [CP Optimizer file format syntax](https://www.ibm.com/docs/en/icos/20.1.0?topic=manual-cp-optimizer-file-format-syntax).
opl .mod <br>.dat <br>.oplproject <br>.xls <br>.json <br>.csv<br><br>.jar for Java <br>models .xml <br>.json <br>.txt <br>.csv <br>.xls The output format is consistent with the input type but can be specified by using the solve parameter if needed. To take advantage of data connectors, use the .csv format.<br><br>Only models that are defined with tuple sets can be deployed; other OPL structures are not supported.<br><br>To read and write input and output in OPL, see [OPL models](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.htmltopic_oplmodels).
docplex .py <br>. (input data) Any output file type that is specified in the model. Any format can be used in your Python code, but to take advantage of data connectors, use the .csv format.<br><br>To read and write input and output in Python, use the commands get_input_stream("filename") and get_output_stream("filename"). See [DOcplex API sum example](https://ibmdecisionoptimization.github.io/docplex-doc/2.23.222/mp/docplex.util.environment.html)
Data identifier restrictions
: A file name has the following restrictions:
* Is limited to 255 characters
* Can include only ASCII characters
* Cannot include the characters /?%:|"<>, the space character, or the null character
* Cannot include _ as the first character
| # Model input and output data file formats #
With your Decision Optimization model, you can use the following input and output data identifiers and extension combinations\.
This table shows the supported file type combinations for Decision Optimization in Watson Machine Learning:
<!-- <table "summary="" id="topic_modelIOFileFormats__simpletable_iys_hnq_fhb" class="defaultstyle" "> -->
| Model type | Input file type | Output file type | Comments |
| ------------- | -------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **`cplex`** | `.lp` <br>`.mps` <br>`.sav` <br>`.feasibility` <br>`.prm`<br><br>`.jar` for Java™ <br>models | `.xml` <br>`.json` <br><br>The name of the output file must be **solution** | The output format can be specified by using the API\.<br><br>Files of type `.lp`, `.mps`, and `.sav` can be compressed by using `gzip` or `bzip2`, and uploaded as, for example, `.lp.gz` or `.sav.bz2`\.<br><br>The schemas for the CPLEX formats for solutions, conflicts, and feasibility files are available for you to download in the cplex\_xsds\.zip archive from the [Decision Optimization github](https://github.com/IBMDecisionOptimization/DO-Samples/blob/watson_studio_cloud/resources/cplex_xsds.zip)\. |
| **`cpo`** | `.cpo`<br><br>`.jar` for Java <br>models | `.xml` <br>`.json` <br><br>The name of the output file must be **solution** | The output format can be specified by using the solve parameter\.<br><br>For the native file format for CPO models, see: [CP Optimizer file format syntax](https://www.ibm.com/docs/en/icos/20.1.0?topic=manual-cp-optimizer-file-format-syntax)\. |
| **`opl`** | `.mod` <br>`.dat` <br>`.oplproject` <br>`.xls` <br>`.json` <br>`.csv`<br><br>`.jar` for Java <br>models | `.xml` <br>`.json` <br>`.txt` <br>`.csv` <br>`.xls` | The output format is consistent with the input type but can be specified by using the solve parameter if needed\. To take advantage of data connectors, use the `.csv` format\.<br><br>Only models that are defined with tuple sets can be deployed; other OPL structures are not supported\.<br><br>To read and write input and output in OPL, see [OPL models](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/OPLmodels.html#topic_oplmodels)\. |
| **`docplex`** | `.py` <br>`*.*` (input data) | Any output file type that is specified in the model\. | Any format can be used in your Python code, but to take advantage of data connectors, use the `.csv` format\.<br><br>To read and write input and output in Python, use the commands `get_input_stream("filename")` and `get_output_stream("filename")`\. See [DOcplex API sum example](https://ibmdecisionoptimization.github.io/docplex-doc/2.23.222/mp/docplex.util.environment.html) |
<!-- </table "summary="" id="topic_modelIOFileFormats__simpletable_iys_hnq_fhb" class="defaultstyle" "> -->
Data identifier restrictions
: A file name has the following restrictions:
<!-- <ul> -->
* Is limited to 255 characters
* Can include only ASCII characters
* Cannot include the characters `/\?%*:|"<>`, the space character, or the null character
* Cannot include \_ as the first character
<!-- </ul> -->
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<link rel="Start" href="../WML_Deployment/DeployIntro.html">
<title>Decision Optimization model input and output data file formats</title>
<link rel="canonical" href="https://www.ibm.com/docs/en/watsonx-as-a-service?topic=steps-model-input-output-data-file-formats">
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<h1 class="topictitle1" id="topic_modelIOFileFormats__title__1"><span class="ph" data-hd-product="cloud wx">Model input and output data file formats</span></h1>
<div class="body">
<p class="shortdesc">With your <span class="keyword">Decision Optimization</span> model, you can use the following input and output data identifiers and extension combinations.</p>
<div class="p">
This table shows the supported file type combinations for <span class="keyword">Decision Optimization</span> in <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>:
<table summary="" id="topic_modelIOFileFormats__simpletable_iys_hnq_fhb" class="defaultstyle">
<colgroup>
<col style="width:9.689922480620154%">
<col style="width:21.317829457364343%">
<col style="width:24.418604651162788%">
<col style="width:44.57364341085271%">
</colgroup>
<thead>
<tr>
<th style="vertical-align:bottom;text-align:left;" id="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__1">Model type</th>
<th style="vertical-align:bottom;text-align:left;" id="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__2">Input file type</th>
<th style="vertical-align:bottom;text-align:left;" id="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__3">Output file type</th>
<th style="vertical-align:bottom;text-align:left;" id="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__4">Comments</th>
</tr>
</thead>
<tbody>
<tr>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__1"><strong><code class="ph codeph">cplex</code></strong></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__2">
<div class="lines">
<code class="ph codeph">.lp</code>
<br><code class="ph codeph">.mps</code>
<br><code class="ph codeph">.sav</code>
<br><code class="ph codeph">.feasibility</code>
<br><code class="ph codeph">.prm</code>
</div>
<div class="lines">
<code class="ph codeph">.jar</code> for <span class="keyword">Java™<br>
models</span>
</div></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__3">
<div class="lines">
<code class="ph codeph">.xml</code>
<br><code class="ph codeph">.json</code>
<br>
</div>
<p>The name of the output file must be <strong><span class="ph filepath">solution</span></strong></p></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__4">The output format can be specified by using the API.
<p>Files of type <code class="ph codeph">.lp</code>, <code class="ph codeph">.mps</code>, and <code class="ph codeph">.sav</code> can be compressed by using <code class="ph codeph">gzip</code> or <code class="ph codeph">bzip2</code>, and uploaded as, for example, <code class="ph codeph">.lp.gz</code> or <code class="ph codeph">.sav.bz2</code>.</p>
<p>The schemas for the CPLEX formats for solutions, conflicts, and feasibility files are available for you to download in the <span class="ph filepath">cplex_xsds.zip</span> archive from the <a href="https://github.com/IBMDecisionOptimization/DO-Samples/blob/watson_studio_cloud/resources/cplex_xsds.zip" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Decision Optimization github</a>.</p></td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__1"><strong><code class="ph codeph">cpo</code></strong></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__2"><code class="ph codeph">.cpo</code>
<div class="lines">
<code class="ph codeph">.jar</code> for <span class="keyword">Java<br>
models</span>
</div></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__3">
<div class="lines">
<code class="ph codeph">.xml</code>
<br><code class="ph codeph">.json</code>
<br>
</div>
<p>The name of the output file must be <strong><span class="ph filepath">solution</span></strong></p></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__4">The output format can be specified by using the solve parameter.
<p>For the native file format for CPO models, see: <a href="https://www.ibm.com/docs/en/icos/20.1.0?topic=manual-cp-optimizer-file-format-syntax" rel="noopener" target="_blank" title="(Opens in a new tab or window)">CP Optimizer file format syntax</a>.</p></td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__1"><strong><code class="ph codeph">opl</code></strong></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__2">
<div class="lines">
<code class="ph codeph">.mod</code>
<br><code class="ph codeph">.dat</code>
<br><code class="ph codeph">.oplproject</code>
<br><code class="ph codeph">.xls</code>
<br><code class="ph codeph">.json</code>
<br><code class="ph codeph">.csv</code>
</div>
<div class="lines">
<code class="ph codeph">.jar</code> for <span class="keyword">Java<br>
models</span>
</div></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__3">
<div class="lines">
<code class="ph codeph">.xml</code>
<br><code class="ph codeph">.json</code>
<br><code class="ph codeph">.txt</code>
<br><code class="ph codeph">.csv</code>
<br><code class="ph codeph">.xls</code>
</div></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__4">The output format is consistent with the input type but can be specified by using the solve parameter if needed. To take advantage of data connectors, use the <code class="ph codeph">.csv</code> format.
<p>Only models that are defined with tuple sets can be deployed; other OPL structures are not supported.</p>
<p>To read and write input and output in OPL, see <a href="../DODS_Introduction/OPLmodels.html#topic_oplmodels" title="You can build OPL models in the Decision Optimization experiment UI in watsonx.ai.">OPL models</a>.</p></td>
</tr>
<tr>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__1"><strong><code class="ph codeph">docplex</code></strong></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__2">
<div class="lines">
<code class="ph codeph">.py</code>
<br><code class="ph codeph">*.*</code> (input data)
</div></td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__3">Any output file type that is specified in the model.</td>
<td style="vertical-align:top;" headers="topic_modelIOFileFormats__simpletable_iys_hnq_fhb__stentry__4">Any format can be used in your Python code, but to take advantage of data connectors, use the <code class="ph codeph">.csv</code> format.
<p>To read and write input and output in Python, use the commands <code class="ph codeph">get_input_stream("filename")</code> and <code class="ph codeph">get_output_stream("filename")</code>. See <a href="https://ibmdecisionoptimization.github.io/docplex-doc/2.23.222/mp/docplex.util.environment.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">DOcplex API sum example</a></p></td>
</tr>
</tbody>
</table>
</div>
<dl>
<dt class="dlterm">
Data identifier restrictions
</dt>
<dd class="dlentry">
A file name has the following restrictions:
<ul id="topic_modelIOFileFormats__ul_dqb_cpq_fhb">
<li>Is limited to 255 characters</li>
<li>Can include only ASCII characters</li>
<li>Cannot include the characters <code class="ph codeph">/\?%*:|"<></code>, the space character, or the null character</li>
<li>Cannot include _ as the first character</li>
</ul>
</dd>
</dl>
<section class="section" role="region" aria-labelledby="topic_modelIOFileFormats__section_brl_tfy_1nb__title__1" id="topic_modelIOFileFormats__section_brl_tfy_1nb">
<h2 class="sectiontitle" id="topic_modelIOFileFormats__section_brl_tfy_1nb__title__1">OPL data formats</h2>
<div class="p">
The OPL model declares the tuples, decision variables, objective function, and constraints of the optimization problem by using the keywords <code class="ph codeph">tuple, dvar, minimize</code> (or <code class="ph codeph">maximize</code>) and <code class="ph codeph">subject to</code>. The following example shows an extract of an OPL model :
<pre class="codeblock"><code>tuple parameters {
int maxTrucks;
int maxVolume;
}
parameters Parameters = ...;
tuple location {
key string name;
}
{location} Hubs = ...;
tuple spoke {
key string name;
int minDepTime;
int maxArrTime;
};
{spoke} Spokes = ...;
dvar int+ TruckOnRoute[Routes][TruckTypeIds] in 0..Parameters.maxTrucks;
[...]
minimize TotalCost;
subject to {
[...]
} </code></pre>
</div>
</section>
<section class="section" role="region" aria-labelledby="topic_modelIOFileFormats__section_l3q_lgy_1nb__title__1" id="topic_modelIOFileFormats__section_l3q_lgy_1nb">
<h2 class="sectiontitle" id="topic_modelIOFileFormats__section_l3q_lgy_1nb__title__1">OPL input data</h2>
<p>The input data can be populated from an external data source. The input data for OPL models can be provided in one of these formats:</p>
<ul id="topic_modelIOFileFormats__ul_bzg_ngy_1nb">
<li><code class="ph codeph">.dat</code> file</li>
<li>JSON document</li>
<li>Microsoft Excel workbook (<code class="ph codeph">.xls</code> for Excel 2003 or <code class="ph codeph">.xlsx</code> for Excel 2007), <code class="ph codeph">.csv</code> files</li>
</ul>
<div class="div">
<dl>
<dt class="dlterm">
.dat file
</dt>
<dd class="dlentry">
All OPL data structures are supported. For example,
<pre class="codeblock"><code>{Parameters = <100, 5000>;
Hubs = { <"G">, <"H"> };
Spokes = { <"A", 360, 1080>, <"B", 400, 1150> };</code></pre>
</dd>
<dt class="dlterm">
JSON document or Microsoft Excel workbook
</dt>
<dd class="dlentry">
You can use only <code class="ph codeph">tuples</code> and <code class="ph codeph">tuple sets</code> as inputs in the OPL model.
<p>Supported types for tuple fields are <code class="ph codeph">int</code>, <code class="ph codeph">float</code> or <code class="ph codeph">string</code>.</p>
</dd>
</dl>
</div>
<div class="p">
To map the input values to your OPL model, you must follow these rules:
<ul id="topic_modelIOFileFormats__ul_exh_phy_1nb">
<li>The OPL element must have the same name as the JSON property or Excel worksheet.</li>
<li>A tuple set can be populated by a JSON property array or a worksheet.</li>
<li>A tuple element can be populated by a JSON property object, or with a single row Excel sheet.</li>
</ul>
</div>
<p>The limitation on inputs is to facilitate integration with data sources. For example, SQL data sources can be accessed and data-streamed with a minimum of effort; NoSQL data sources can be accessed and data can be transformed automatically to tables. If necessary, the optimization model developer can reformulate the data to populate other data structures during the optimization, but this manipulation must not affect the input or output data.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_modelIOFileFormats__section_cqc_vhy_1nb__title__1" id="topic_modelIOFileFormats__section_cqc_vhy_1nb">
<h2 class="sectiontitle" id="topic_modelIOFileFormats__section_cqc_vhy_1nb__title__1">JSON example</h2>
<div class="p">
JSON format can be used for OPL model integration so that it is easier to generate input data and to parse the results.
<pre class="codeblock"><code>{
"Parameters": {
"maxTrucks": 100,
"maxVolume": 5000
},
"Hubs": [
{ "name": "G" },
{ "name": "H" }
],
"Spokes": [
{ "name": "A",
"minDepTime": 360,
"maxArrTime": 1080 },
{ "name": "B",
"minDepTime": 400,
"maxArrTime": 1150 },
. . .
}</code></pre>
</div>
</section>
<section class="section" role="region" aria-labelledby="topic_modelIOFileFormats__section_gct_l3y_1nb__title__1" id="topic_modelIOFileFormats__section_gct_l3y_1nb">
<h2 class="sectiontitle" id="topic_modelIOFileFormats__section_gct_l3y_1nb__title__1">Excel file</h2>
<p>You can use an Excel file instead of using a .dat file. This option is different from IBM ILOG CPLEX Optimization Studio where the Excel file must be specified as an external source in the <code class="ph codeph">.dat</code> file. In <span class="keyword">Decision Optimization</span> the Excel file must be included with the model and cannot be called from a <code class="ph codeph">.dat</code> file.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_modelIOFileFormats__section_qdt_m3y_1nb__title__1" id="topic_modelIOFileFormats__section_qdt_m3y_1nb">
<h2 class="sectiontitle" id="topic_modelIOFileFormats__section_qdt_m3y_1nb__title__1">OPL output data</h2>
<p>If your output is a text file, then the objective function, and values of the decision variables are provided in an unstructured format.</p>
<p>If your output format is JSON, <code class="ph codeph">.csv</code> or Excel, then <strong>you must define what you want to export back to the client in the post-processing block</strong>. Post-processing is all the code that follows the <code class="ph codeph">subject to</code> section in the <code class="ph codeph">.mod</code> file. Thus to define JSON, <code class="ph codeph">.csv</code> or Excel output, you must declare tuple or tuple sets in the post-processing.</p>
<p>If you <strong>do not declare</strong> output elements in the post-processing block of the <code class="ph codeph">.mod</code> file, <strong>no output data is generated.</strong></p>
<p>In the following example, the output file will contain the value of <code class="ph codeph">Result</code> and <code class="ph codeph">NbTrucksOnRouteRes</code> and the objective function because these elements are defined in the post-processing.</p>
<pre class="codeblock"><code>subject to {
[...]
}
tuple result {
float totalCost;
}
result Result;
execute {
Result.objValue = cplex.getObjValue();
}
tuple nbTrucksOnRouteRes {
key string spoke;
key string hub;
key string truckType;
int nbTruck;
}
{nbTrucksOnRouteRes} NbTrucksOnRouteRes =
{<r.spoke, r.hub, t, TruckOnRoute[r][t]> | r in Routes, t in TruckTypeIds :
TruckOnRoute[r][t] > 0};
</code></pre>
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D476F3E93D23F52EF1D5079343D92DB793E3AD5E | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/OutputDataDefn.html?context=cdpaas&locale=en | Decision Optimization output data definition | Output data definition
When submitting your job you can define what output data you want and how you collect it (as either inline or referenced data).
For more information about output file types and names see [Model input and output data file formats](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIOFileFormats.htmltopic_modelIOFileFormats).
Some output data definition examples:
* To collect solution.csv output as inline data:
"output_data": [{
"id":"solution.csv"
}]
* Regexp can be also used as an identifier. For example to collect all csv output files as inline data:
"output_data": [{
"id":"..csv"
}]
* Similarly for reference data, to collect all csv files in COS/S3 in job specific folder, you can combine regexp and ${job_id} and ${ attachment_name } place holder
"output_data_references": [{
"id":"..csv",
"type": "connection_asset",
"connection": {
"id" : <connection_guid>
},
"location": {
"bucket": "XXXXXXXXX",
"path": "${job_id}/${attachment_name}" }
}]
For example, here if you have a job with identifier <XXXXXXXXX> to generate a solution.csv file, you will have in your COS/S3 bucket, a XXXXXXXXX / solution.csv file.
| # Output data definition #
When submitting your job you can define what output data you want and how you collect it (as either inline or referenced data)\.
For more information about output file types and names see [Model input and output data file formats](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/ModelIOFileFormats.html#topic_modelIOFileFormats)\.
Some output data definition examples:
<!-- <ul> -->
* To collect solution\.csv output as inline data:
"output_data": [{
"id":"solution.csv"
}]
* Regexp can be also used as an identifier\. For example to collect all csv output files as inline data:
"output_data": [{
"id":".*\.csv"
}]
* Similarly for reference data, to collect all csv files in COS/S3 in job specific folder, you can combine regexp and $\{job\_id\} and $\{ attachment\_name \} place holder
"output_data_references": [{
"id":".*\.csv",
"type": "connection_asset",
"connection": {
"id" : <connection_guid>
},
"location": {
"bucket": "XXXXXXXXX",
"path": "${job_id}/${attachment_name}" }
}]
For example, here if you have a job with identifier <XXXXXXXXX> to generate a solution.csv file, you will have in your COS/S3 bucket, a XXXXXXXXX / solution.csv file.
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<h1 class="topictitle1" id="topic_outputdatadefn__title__1"><span class="ph" data-hd-product="cloud wx">Output data definition</span></h1>
<div class="body">
<p class="shortdesc">When submitting your job you can define what output data you want and how you collect it (as either inline or referenced data).</p>
<p>For more information about output file types and names see <a href="ModelIOFileFormats.html#topic_modelIOFileFormats" title="With your Decision Optimization model, you can use the following input and output data identifiers and extension combinations.">Model input and output data file formats</a>.</p>
<div class="p">
Some output data definition examples:
<ul id="topic_outputdatadefn__ul_hmf_ghh_l3b">
<li>To collect solution.csv output as inline data:
<pre class="codeblock"><code>"output_data": [{
"id":"solution.csv"
}]</code></pre></li>
<li>Regexp can be also used as an identifier. For example to collect all csv output files as inline data:
<pre class="codeblock"><code>"output_data": [{
"id":".*\\.csv"
}]</code></pre></li>
<li>Similarly for reference data, to collect all csv files in COS/S3 in job specific folder, you can combine regexp and ${job_id} and ${ attachment_name } place holder
<pre class="codeblock"><code>"output_data_references": [{
"id":".*\\.csv",
"type": "connection_asset",
"connection": {
"id" : <connection_guid>
},
"location": {
"bucket": "XXXXXXXXX",
"path": "${job_id}/${attachment_name}" }
}]</code></pre>For example, here if you have a job with identifier <XXXXXXXXX> to generate a solution.csv file, you will have in your COS/S3 bucket, a XXXXXXXXX / solution.csv file.</li>
</ul>
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693BC91EAADEAE664982AA88A372590A6758F294 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/Paralleljobs.html?context=cdpaas&locale=en | Decision Optimization running jobs | Running jobs
Decision Optimization uses Watson Machine Learning asynchronous APIs to enable jobs to be run in parallel.
To solve a problem, you can create a new job from the model deployment and associate data to it. See [Deployment steps](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployIntro.htmltopic_wmldeployintro) and the [REST API example](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelRest.htmltask_deploymodelREST). You are not charged for deploying a model. Only the solving of a model with some data is charged, based on the running time.
To solve more than one job at a time, specify more than one node when you create your deployment. For example in this [REST API example](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelRest.htmltask_deploymodelREST__createdeploy), increment the number of the nodes by changing the value of the nodes property: "nodes" : 1.
1. The new job is sent to the queue.
2. If a POD is started but idle (not running a job), it immediately begins processing this job.
3. Otherwise, if the maximum number of nodes is not reached, a new POD is started. (Starting a POD can take a few seconds). The job is then assigned to this new POD for processing.
4. Otherwise, the job waits in the queue until one of the running PODs has finished and can pick up the waiting job.
The configuration of PODs of each size is as follows:
Table 1. T-shirt sizes for Decision Optimization
Definition Name Description
2 vCPU and 8 GB S Small
4 vCPU and 16 GB M Medium
8 vCPU and 32 GB L Large
16 vCPU and 64 GB XL Extra Large
For all configurations, 1 vCPU and 512 MB are reserved for internal use.
In addition to the solve time, the pricing depends on the selected size through a multiplier.
In the deployment configuration, you can also set the maximal number of nodes to be used.
Idle PODs are automatically stopped after some timeout. If a new job is submitted when no PODs are up, it takes some time (approximately 30 seconds) for the POD to restart.
| # Running jobs #
Decision Optimization uses Watson Machine Learning asynchronous APIs to enable jobs to be run in parallel\.
To solve a problem, you can create a new job from the model deployment and associate data to it\. See [Deployment steps](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployIntro.html#topic_wmldeployintro) and the [REST API example](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelRest.html#task_deploymodelREST)\. You are not charged for deploying a model\. Only the solving of a model with some data is charged, based on the running time\.
To solve **more than one job** at a time, specify more than one node when you create your deployment\. For example in this [REST API example](https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelRest.html#task_deploymodelREST__createdeploy), increment the **number of the nodes** by changing the value of the nodes property: `"nodes" : 1`\.
<!-- <ol> -->
1. The new job is sent to the queue\.
2. If a POD is started but idle (not running a job), it immediately begins processing this job\.
3. Otherwise, if the maximum number of nodes is not reached, a new POD is started\. (Starting a POD can take a few seconds)\. The job is then assigned to this new POD for processing\.
4. Otherwise, the job waits in the queue until one of the running PODs has finished and can pick up the waiting job\.
<!-- </ol> -->
The configuration of PODs of each size is as follows:
<!-- <table "summary="" id="topic_paralleljobs__table_etc_n5v_f5b" class="defaultstyle" "> -->
Table 1\. T\-shirt sizes for Decision Optimization
| Definition | Name | Description |
| ----------------- | ---- | ----------- |
| 2 vCPU and 8 GB | S | Small |
| 4 vCPU and 16 GB | M | Medium |
| 8 vCPU and 32 GB | L | Large |
| 16 vCPU and 64 GB | XL | Extra Large |
<!-- </table "summary="" id="topic_paralleljobs__table_etc_n5v_f5b" class="defaultstyle" "> -->
For all configurations, 1 vCPU and 512 MB are reserved for internal use\.
In addition to the solve time, the pricing depends on the selected size through a multiplier\.
In the deployment configuration, you can also set the maximal number of nodes to be used\.
Idle PODs are automatically stopped after some timeout\. If a new job is submitted when no PODs are up, it takes some time (approximately 30 seconds) for the POD to restart\.
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<h1 class="topictitle1" id="topic_paralleljobs__title__1"><span class="ph" data-hd-product="cloud wx">Running jobs</span></h1>
<div class="body">
<p class="shortdesc"><span class="keyword">Decision Optimization</span> uses <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> asynchronous APIs to enable jobs to be run in parallel.</p>
<p>To solve a problem, you can create a new job from the model deployment and associate data to it. See <a href="DeployIntro.html#topic_wmldeployintro" title="With IBM Watson Machine Learning you can deploy your Decision Optimization prescriptive model and associated common data once and then submit job requests to this deployment with only the related transactional data. This deployment can be achieved by using the Watson Machine Learning REST API or by using the Watson Machine Learning Python client.">Deployment steps</a> and the <a href="DeployModelRest.html#task_deploymodelREST" title="You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.">REST API example</a>. <span class="ph" data-hd-product="cloud wx">You are not charged for deploying a model. Only the solving of a model with some data is charged, based on the running time.</span></p>
<p>To solve <strong>more than one job</strong> at a time, specify more than one node when you create your deployment. For example in this <a href="DeployModelRest.html#task_deploymodelREST__createdeploy">REST API example</a>, increment the <strong>number of the nodes</strong> by changing the value of the nodes property: <code class="ph codeph">"nodes" : 1</code>.</p>
<section class="section" role="region" aria-labelledby="topic_paralleljobs__section_deploymentflow__title__1" id="topic_paralleljobs__section_deploymentflow">
<h2 class="sectiontitle" id="topic_paralleljobs__section_deploymentflow__title__1">PODs (nodes)</h2>
<p>When a job is created and submitted, how it is handled depends on the current configuration and jobs that are running for the <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> instance. This process is shown in the following diagram.</p><img id="topic_paralleljobs__image_tmp_fcn_kkb" src="images/new_deployjobs.png" alt="Job workflor showing job queue, existing pod and new pod.">
</section>
<ol id="topic_paralleljobs__ol_jyn_lcn_kkb">
<li>The new job is sent to the queue.</li>
<li>If a POD is started but idle (not running a job), it immediately begins processing this job.</li>
<li>Otherwise, if the maximum number of nodes is not reached, a new POD is started. (Starting a POD can take a few seconds). The job is then assigned to this new POD for processing.</li>
<li>Otherwise, the job waits in the queue until one of the running PODs has finished and can pick up the waiting job.</li>
</ol>
<p id="topic_paralleljobs__34c6">The configuration of PODs of each size is as follows:</p>
<div class="tablenoborder">
<table summary="" id="topic_paralleljobs__table_etc_n5v_f5b" data-hd-product="cloud wx" class="defaultstyle">
<caption>
<span class="tablecap">Table 1. T-shirt sizes for Decision Optimization</span>
</caption>
<colgroup>
<col style="width:37.65060240963855%">
<col style="width:15.06024096385542%">
<col style="width:47.28915662650602%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="topic_paralleljobs__table_etc_n5v_f5b__entry__1">Definition</th>
<th id="topic_paralleljobs__table_etc_n5v_f5b__entry__2">Name</th>
<th id="topic_paralleljobs__table_etc_n5v_f5b__entry__3">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__1 ">2 vCPU and 8 GB</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__2 ">S</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__3 ">Small</td>
</tr>
<tr>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__1 ">4 vCPU and 16 GB</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__2 ">M</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__3 ">Medium</td>
</tr>
<tr>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__1 ">8 vCPU and 32 GB</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__2 ">L</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__3 ">Large</td>
</tr>
<tr>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__1 ">16 vCPU and 64 GB</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__2 ">XL</td>
<td headers="topic_paralleljobs__table_etc_n5v_f5b__entry__3 ">Extra Large</td>
</tr>
</tbody>
</table>
</div>
<p>For all configurations, 1 vCPU and 512 MB are reserved for internal use.</p>
<p data-hd-product="cloud wx" id="topic_paralleljobs__c32b">In addition to the solve time, the pricing depends on the selected size through a multiplier.</p>
<p id="topic_paralleljobs__7f5d">In the deployment configuration, you can also set the maximal number of nodes to be used.</p>
<p id="topic_paralleljobs__291f">Idle PODs are automatically stopped after some timeout. If a new job is submitted when no PODs are up, it takes some time (approximately 30 seconds) for the POD to restart.</p>
<section class="section" role="region" aria-labelledby="topic_paralleljobs__section_pn1_fdn_kkb__title__1" id="topic_paralleljobs__section_pn1_fdn_kkb">
<h2 class="sectiontitle" id="topic_paralleljobs__section_pn1_fdn_kkb__title__1">REST API example</h2>
<p>For the full procedure of deploying a model and links to the Swagger documentation, see <a href="DeployModelRest.html#task_deploymodelREST" title="You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.">REST API example</a>.</p>
</section>
<section class="section" role="region" aria-labelledby="topic_paralleljobs__section_iyx_jdn_kkb__title__1" id="topic_paralleljobs__section_iyx_jdn_kkb">
<h2 class="sectiontitle" id="topic_paralleljobs__section_iyx_jdn_kkb__title__1">Python API example</h2>
<p>In addition to the REST APIs, a Python API is provided with <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> so you can easily create, deploy, and use a <span class="keyword">Decision Optimization</span> model from a Python <span class="keyword" translate="no">notebook</span>.</p>
<p>For more information, see <a href="DeployPythonClient.html#topic_wmlpythonclient" title="You can deploy a Decision Optimization model, create and monitor jobs, and get solutions by using the Watson Machine Learning Python client.">Python client example</a>.</p>
<p data-hd-product="cloud wx">An <a href="https://dataplatform.cloud.ibm.com/exchange/public/entry/view/50fa9246181026cd7ae2a5bc7e4ac7bd" rel="noopener" target="_blank" title="(Opens in a new tab or window)">example <span class="keyword" translate="no">notebook</span> describing and documenting all steps </a> is available from the <span class="keyword">Samples</span>.</p>
</section>
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73DEFA42948BBE878834CA4B7C9B0395F44B9B90 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/UpdateDeployModelRest.html?context=cdpaas&locale=en | Decision Optimization REST API changing Python version in deployed model | Changing Python version for an existing deployed model with the REST API
You can update an existing Decision Optimization model using the Watson Machine Learning REST API. This can be useful, for example, if in your model you have explicitly specified a Python version that has now become deprecated.
Procedure
To change Python version for an existing deployed model:
1. Create a revision to your Decision Optimization model
All API requests require a version parameter that takes a date in the format version=YYYY-MM-DD. This code example posts a model that uses the file update_model.json. The URL will vary according to the chosen region/location for your machine learning service.
curl --location --request POST
"https://us-south.ml.cloud.ibm.com/ml/v4/models/MODEL-ID-HERE/revisions?version=2021-12-01"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
-d @revise_model.json
The revise_model.json file contains the following code:
{
"commit_message": "Save current model",
"space_id": "SPACE-ID-HERE"
}
Note the model revision number "rev" that is provided in the output for use in the next step.
2. Update an existing deployment so that current jobs will not be impacted:
curl --location --request PATCH
"https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2021-12-01&space_id=SPACE-ID-HERE"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
-d @revise_deploy.json
The revise_deploy.json file contains the following code:
[
{
"op": "add",
"path": "/asset",
"value": {
"id":"MODEL-ID-HERE",
"rev":"MODEL-REVISION-NUMBER-HERE"
}
}
]
3. Patch an existing model to explicitly specify Python version 3.10
curl --location --request PATCH
"https://us-south.ml.cloud.ibm.com/ml/v4/models/MODEL-ID-HERE?rev=MODEL-REVISION-NUMBER-HERE&version=2021-12-01&space_id=SPACE-ID-HERE"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
-d @update_model.json
The update_model.json file, with the default Python version stated explicitly, contains the following code:
[
{
"op": "add",
"path": "/custom",
"value": {
"decision_optimization":{
"oaas.docplex.python": "3.10"
}
}
}
]
Alternatively, to remove any explicit mention of a Python version so that the default version will always be used:
[
{
"op": "remove",
"path": "/custom/decision_optimization"
}
]
4. Patch the deployment to use the model that was created for Python to use version 3.10
curl --location --request PATCH
"https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2021-12-01&space_id=SPACE-ID-HERE"
-H "Authorization: bearer TOKEN-HERE"
-H "Content-Type: application/json"
-d @update_deploy.json
The update_deploy.json file contains the following code:
[
{
"op": "add",
"path": "/asset",
"value": { "id":"MODEL-ID-HERE"}
}
]
| # Changing Python version for an existing deployed model with the REST API #
You can update an existing Decision Optimization model using the Watson Machine Learning REST API\. This can be useful, for example, if in your model you have explicitly specified a Python version that has now become deprecated\.
## Procedure ##
To change Python version for an existing deployed model:
<!-- <ol> -->
1. Create a **revision to your Decision Optimization model**
All API requests require a version parameter that takes a date in the format `version=YYYY-MM-DD`. This code example posts a model that uses the file `update_model.json`. The URL will vary according to the chosen region/location for your machine learning service.
curl --location --request POST \
"https://us-south.ml.cloud.ibm.com/ml/v4/models/MODEL-ID-HERE/revisions?version=2021-12-01" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json" \
-d @revise_model.json
The revise\_model.json file contains the following code:
{
"commit_message": "Save current model",
"space_id": "SPACE-ID-HERE"
}
Note the model revision number "`rev`" that is provided in the output for use in the next step.
2. Update an existing deployment so that current jobs will not be impacted:
curl --location --request PATCH \
"https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2021-12-01&space_id=SPACE-ID-HERE" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json" \
-d @revise_deploy.json
The revise\_deploy.json file contains the following code:
[
{
"op": "add",
"path": "/asset",
"value": {
"id":"MODEL-ID-HERE",
"rev":"MODEL-REVISION-NUMBER-HERE"
}
}
]
3. Patch an existing model to explicitly specify Python version 3\.10
curl --location --request PATCH \
"https://us-south.ml.cloud.ibm.com/ml/v4/models/MODEL-ID-HERE?rev=MODEL-REVISION-NUMBER-HERE&version=2021-12-01&space_id=SPACE-ID-HERE" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json" \
-d @update_model.json
The update\_model.json file, with the default *Python version* stated explicitly, contains the following code:
[
{
"op": "add",
"path": "/custom",
"value": {
"decision_optimization":{
"oaas.docplex.python": "3.10"
}
}
}
]
Alternatively, to remove any explicit mention of a Python version so that the default version will always be used:
[
{
"op": "remove",
"path": "/custom/decision_optimization"
}
]
4. Patch the deployment to use the model that was created for Python to use version 3\.10
curl --location --request PATCH \
"https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2021-12-01&space_id=SPACE-ID-HERE" \
-H "Authorization: bearer TOKEN-HERE" \
-H "Content-Type: application/json" \
-d @update_deploy.json
The update\_deploy.json file contains the following code:
[
{
"op": "add",
"path": "/asset",
"value": { "id":"MODEL-ID-HERE"}
}
]
<!-- </ol> -->
<!-- </article "role="article" "> -->
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<title>Decision Optimization REST API changing Python version in deployed model</title>
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<h1 class="topictitle1" id="task_vbd_fg3_dtb__title__1"><span class="ph" data-hd-product="cloud wx">Changing Python version for an existing deployed model with the REST API </span></h1>
<div class="body taskbody">
<p class="shortdesc">You can update an existing <span class="keyword">Decision Optimization</span> model using the <span class="keyword">Watson Machine Learning REST API</span>. This can be useful, for example, if in your model you have explicitly specified a Python version that has now become deprecated.</p>
<section role="region" class="section prereq" id="task_vbd_fg3_dtb__prereq_wbd_fg3_dtb" aria-labelledby="tasktask_vbd_fg3_dtb__prereq_wbd_fg3_dtb">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_vbd_fg3_dtb__prereq_wbd_fg3_dtb">Before you begin</h2>
</div>You will need your <strong>SPACE-ID</strong>, <strong>MODEL-ID</strong> and <strong>DEPLOYMENT-ID</strong> to make this change. See <a href="DeployModelRest.html" title="You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.">REST API example</a> for more details.
</section>
<section class="section context" role="region" id="task_vbd_fg3_dtb__context_xbd_fg3_dtb" aria-labelledby="tasktask_vbd_fg3_dtb__context_xbd_fg3_dtb">
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_vbd_fg3_dtb__context_xbd_fg3_dtb">About this task</h2>
</div>The following steps show you how to update an existing <span class="keyword">Decision Optimization</span> deployed model using the <span class="keyword">Watson Machine Learning REST API</span>. The REST API example uses curl, a command line tool and library for transferring data with URL syntax. You can download curl and read more about it at <a href="http://curl.haxx.se/" rel="noopener" target="_blank" title="(Opens in a new tab or window)">http://curl.haxx.se</a>.
<p>For <strong>Windows</strong> users, use ^ instead of \ for the multi-line separator and double quotation marks " throughout these code examples. Windows users also need to use indentation of at least one character space in the header lines.</p>
<p>For clarity, some code examples in this procedure have been placed in a <span class="ph filepath">json</span> file to make the commands more readable and easier to use.</p>
</section>
<div class="tasklabel">
<h2 class="sectiontitle tasklabel" id="tasktask_vbd_fg3_dtb__steps_ybd_fg3_dtb">Procedure</h2>
</div>
<p class="li stepsection">To change Python version for an existing deployed model:</p>
<ol class="steps" id="task_vbd_fg3_dtb__steps_ybd_fg3_dtb">
<li class="step stepexpand"><span class="cmd">Create a <strong>revision to your <span class="keyword">Decision Optimization</span> model</strong></span>
<div class="itemgroup info">
<p>All API requests require a version parameter that takes a date in the format <code class="ph codeph">version=YYYY-MM-DD</code>. This code example posts a model that uses the file <code class="ph codeph">update_model.json</code>. The URL will vary according to the chosen region/location for your machine learning service.</p>
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request POST \
"<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/models/<strong><em><span class="ph hljs-callout">MODEL-ID-HERE</span></em></strong>/revisions?version=<strong>2021-12-01</strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json" \
-d @revise_model.json</code></pre>The <span class="ph filepath">revise_model.json</span> file contains the following code:
</div>
<div class="itemgroup info">
<pre class="codeblock"><code>{
"commit_message": "Save current model",
"space_id": "<em><strong><span class="ph hljs-callout">SPACE-ID-HERE</span></strong></em>"
}</code></pre>
</div>
<div class="itemgroup stepresult">
Note the model revision number "<code class="ph codeph">rev</code>" that is provided in the output for use in the next step.
</div></li>
<li class="step stepexpand"><span class="cmd">Update an existing deployment so that current jobs will not be impacted:</span>
<div class="itemgroup info">
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request PATCH \
"<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/deployments/<strong><em><span class="ph hljs-callout">DEPLOYMENT-ID-HERE</span></em></strong>?version=<strong>2021-12-01</strong>&space_id=<strong><em><span class="ph hljs-callout">SPACE-ID-HERE</span></em></strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json" \
-d @revise_deploy.json</code></pre>
</div>
<div class="itemgroup info">
The <span class="ph filepath">revise_deploy.json</span> file contains the following code:
</div>
<div class="itemgroup info">
<pre class="codeblock"><code>[
{
"op": "add",
"path": "/asset",
"value": {
"id":"MODEL-ID-HERE",
"rev":"MODEL-REVISION-NUMBER-HERE"
}
}
]</code></pre>
</div></li>
<li class="step stepexpand"><span class="cmd">Patch an existing model to explicitly specify Python version <span class="keyword">3.10</span></span>
<div class="itemgroup info">
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request PATCH \
"<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/models/<strong><em><span class="ph hljs-callout">MODEL-ID-HERE</span></em></strong>?rev=<strong><em><span class="ph hljs-callout">MODEL-REVISION-NUMBER-HERE</span></em></strong>&version=<strong>2021-12-01</strong>&space_id=<strong><em><span class="ph hljs-callout">SPACE-ID-HERE</span></em></strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json" \
-d @update_model.json</code></pre>
</div>
<div class="itemgroup info">
The <span class="ph filepath">update_model.json</span> file, with the default <em>Python version</em> stated explicitly, contains the following code:
</div>
<div class="itemgroup info">
<pre class="codeblock"><code>[
{
"op": "add",
"path": "/custom",
"value": {
"decision_optimization":{
"oaas.docplex.python": "<span class="keyword">3.10</span>"
}
}
}
]</code></pre>
</div>
<div class="itemgroup info">
<div class="p">
Alternatively, to remove any explicit mention of a Python version so that the default version will always be used:
<pre class="codeblock"><code>[
{
"op": "remove",
"path": "/custom/decision_optimization"
}
]</code></pre>
</div>
</div></li>
<li class="step stepexpand"><span class="cmd">Patch the deployment to use the model that was created for Python to use version <span class="keyword">3.10</span></span>
<div class="itemgroup info">
<pre class="codeblock" data-hd-product="cloud wx"><code>curl <span class="keyword">--location</span> --request PATCH \
"<span class="keyword">https://us-south.ml.cloud.ibm.com</span>/ml/v4/deployments/<strong><em><span class="ph hljs-callout">DEPLOYMENT-ID-HERE</span></em></strong>?version=<strong>2021-12-01</strong>&space_id=<strong><em><span class="ph hljs-callout">SPACE-ID-HERE</span></em></strong>" \
-H "Authorization: bearer <strong><em><span class="ph hljs-callout">TOKEN-HERE</span></em></strong>" \
-H "Content-Type: application/json" \
-d @update_deploy.json</code></pre>
</div>
<div class="itemgroup info">
The <span class="ph filepath">update_deploy.json</span> file contains the following code:
</div>
<div class="itemgroup info">
<pre class="codeblock"><code>[
{
"op": "add",
"path": "/asset",
"value": { "id":"<strong>MODEL-ID-HERE</strong>"}
}
]</code></pre>
</div></li>
</ol>
<section class="section result" role="region" id="task_vbd_fg3_dtb__result_zbd_fg3_dtb" aria-labelledby="tasktask_vbd_fg3_dtb__result_zbd_fg3_dtb">
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<h2 class="sectiontitle tasklabel" id="tasktask_vbd_fg3_dtb__result_zbd_fg3_dtb">Results</h2>
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<p>You can post new jobs using the DEPLOYMENT-ID without having to redeploy your model.</p>
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1BB1684259F93D91580690D898140D98F12611ED | https://dataplatform.cloud.ibm.com/docs/content/DO/wml_cpd_home.html?context=cdpaas&locale=en | Deploying Decision Optimization models | Decision Optimization
When you have created and solved your Decision Optimization models, you can deploy them using Watson Machine Learning.
See the [Decision Optimization experiment UI](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/buildingmodels.htmltopic_buildingmodels) for building and solving models. The following sections describe how you can deploy your models.
| # Decision Optimization #
When you have created and solved your Decision Optimization models, you can deploy them using Watson Machine Learning\.
See the [Decision Optimization experiment UI](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/buildingmodels.html#topic_buildingmodels) for building and solving models\. The following sections describe how you can deploy your models\.
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<p class="shortdesc">When you have created and solved your <span class="keyword">Decision Optimization</span> models, you can deploy them using <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span>.</p>
<p>See the <a href="DODS_Introduction/buildingmodels.html#topic_buildingmodels" title="If you use the Decision Optimization experiment UI, you can take advantage of its many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with Watson Machine Learning."><span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span></a> for building and solving models. The following sections describe how you can deploy your models.</p>
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<li class="ulchildlink"><strong><a href="WML_Deployment/DeployModelUI-WML.html#task_deployUIWML">Deploying a Decision Optimization model by using the user interface</a></strong><br>
You can save a model for deployment in the <span class="keyword">Decision Optimization</span> <span class="keyword">experiment UI</span> and promote it to your <span class="keyword">Watson Machine Learning</span> deployment space.</li>
<li class="ulchildlink"><strong><a href="WML_Deployment/DeployIntro.html">Deployment steps</a></strong><br>
With IBM <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> you can deploy your <span class="keyword">Decision Optimization</span> prescriptive model and associated common data once and then submit job requests to this deployment with only the related transactional data. This deployment can be achieved by using the <span class="keyword">Watson Machine Learning REST API</span> or by using the <span class="keyword">Watson Machine Learning Python client</span>.</li>
<li class="ulchildlink"><strong><a href="WML_Deployment/Paralleljobs.html">Running jobs</a></strong><br><span class="keyword">Decision Optimization</span> uses <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> asynchronous APIs to enable jobs to be run in parallel.</li>
<li class="ulchildlink"><strong><a href="WML_Deployment/DeployModelRest.html">REST API example</a></strong><br>
You can deploy a <span class="keyword">Decision Optimization</span> model, create and monitor jobs and get solutions using the <span class="keyword">Watson Machine Learning REST API</span>.</li>
<li class="ulchildlink"><strong><a href="WML_Deployment/DeployJava.html">Deploying Java models for Decision Optimization</a></strong><br>
You can deploy <span class="keyword">Decision Optimization</span> <span class="keyword">Java™ models</span> in <span class="keyword">Watson Machine Learning</span> by using the <span class="keyword">Watson Machine Learning</span> REST API.</li>
<li class="ulchildlink"><strong><a href="WML_Deployment/UpdateDeployModelRest.html">Changing Python version for an existing deployed model with the REST API</a></strong><br>
You can update an existing <span class="keyword">Decision Optimization</span> model using the <span class="keyword">Watson Machine Learning REST API</span>. This can be useful, for example, if in your model you have explicitly specified a Python version that has now become deprecated.</li>
<li class="ulchildlink"><strong><a href="WML_Deployment/DeployPythonClient.html">Python client examples</a></strong><br>
You can deploy a <span class="keyword">Decision Optimization</span> model, create and monitor jobs, and get solutions by using the <span class="keyword">Watson Machine Learning Python client</span>.</li>
<li class="ulchildlink"><strong><a href="WML_Deployment/CPLEXSolveWML.html">Delegating the Decision Optimization solve to run on Watson Machine Learning from Java or .NET CPLEX or CPO models</a></strong><br>
You can delegate the <span class="keyword">Decision Optimization</span> solve to run on <span class="keyword" data-hd-product="cloud wx">Watson Machine Learning</span> from your Java or .NET (CPLEX or CPO) models.</li>
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A255BB890CA287C5A91765B71832DAA45BA4132B | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_appearance_tab.html?context=cdpaas&locale=en | Global visualization preferences | Global visualization preferences
You can override the default settings for titles, range slider, grid lines, and mouse tracking. You can also specify a different color scheme template.
1. In Visualizations, click the Global visualization preferences control in the Actions section.
The Global visualization preferences dialog provides the following settings.
Titles
: Provides global chart title settings.
Global titles
: Enables or disables the global titles for all charts.
Global primary title
: Enables or disables the display of global, primary chart titles. When enabled, the top-level chart title that you enter here is applied to all chart's, effectively overriding each chart's individual Primary title setting.
Global subtitle
: Enables or disables the display of global chart subtitles. When enabled, the chart subtitle that you enter here is applied to all chart's, effectively overriding each chart's individual Subtitle setting.
Default titles
: Enables or disables the default titles for all charts.
Title alignment
: Provides the title alignment options Left, Center (the default setting), and Right.
Tools
: Provides options that control chart behavior.
Range slider
: Enables or disables the range slider for each chart. When enabled, you can control the amount of chart data that displays with a range slider that is provided for each chart.
Grid lines
: Controls the display of X axis (vertical) and Y axis (horizontal) grid lines.
Mouse tracker
: When enabled, the mouse cursor location, in relation to the chart data, is tracked and displayed when placed anywhere over the chart.
Toolbox
: Enables or disables the toolbox for each chart. Depending on the chart type, the toolbox on the right of the screen provides tools such as zoom, save as image, restore, select data, and clear selection.
ARIA
: When enabled, web content and web applications are more accessible to users with disabilities.
Filter out null
: Enables or disables the filtering of null chart data.
X axis on zero
: When enabled, the X axis lies on the other's origin position. When not enabled, the X axis always starts at 0.
Y axis on zero
: When enabled, the Y axis lies on the other's origin position. When not enabled, the Y axis always starts at 0.
Show xAxis Label
: Enables or disables the xAxis label.
Show yAxis Label
: Enables or disables the yAxis label.
Show xAxis Line
: Enables or disables the xAxis line.
Show yAxis Line
: Enables or disables the yAxis line.
Show xAxis Name
: Enables or disables the xAxis name.
Show yAxis Name
: Enables or disables the yAxis name.
yAxis Name Location
: The drop-down list provides options for specifying the yAxis name location. Options include Start, Middle, and End.
Truncation length
: The specified value sets the string length. Strings that are longer than the specified length are truncated. The default value is 10. When 0 is specified, truncation is turned off.
xAxis tick label decimal
: Sets the tick label decimal value for the xAxis. The default value is 3.
yAxis tick label decimal
: Sets the tick label decimal value for the yAxis. The default value is 3.
xAxis tick label rotate
: Sets the xAxis tick label rotation value. The default value is 0 (no rotation). You can specify value in the range -90 to 90 degrees.
Theme
: Select a template to change the colors that are used in charts that have a grouping or stacking variable. Any element attributes defined in the selected template file override the default template settings for those element attributes.
2. Click Apply to save your settings or Cancel to disregard the changes.
| # Global visualization preferences #
You can override the default settings for titles, range slider, grid lines, and mouse tracking\. You can also specify a different color scheme template\.
<!-- <ol> -->
1. In Visualizations, click the Global visualization preferences control in the Actions section\.
The Global visualization preferences dialog provides the following settings.
Titles
: Provides global chart title settings.
Global titles
: Enables or disables the global titles for all charts.
Global primary title
: Enables or disables the display of global, primary chart titles. When enabled, the top-level chart title that you enter here is applied to all chart's, effectively overriding each chart's individual Primary title setting.
Global subtitle
: Enables or disables the display of global chart subtitles. When enabled, the chart subtitle that you enter here is applied to all chart's, effectively overriding each chart's individual Subtitle setting.
Default titles
: Enables or disables the default titles for all charts.
Title alignment
: Provides the title alignment options Left, Center (the default setting), and Right.
Tools
: Provides options that control chart behavior.
Range slider
: Enables or disables the range slider for each chart. When enabled, you can control the amount of chart data that displays with a range slider that is provided for each chart.
Grid lines
: Controls the display of X axis (vertical) and Y axis (horizontal) grid lines.
Mouse tracker
: When enabled, the mouse cursor location, in relation to the chart data, is tracked and displayed when placed anywhere over the chart.
Toolbox
: Enables or disables the toolbox for each chart. Depending on the chart type, the toolbox on the right of the screen provides tools such as zoom, save as image, restore, select data, and clear selection.
ARIA
: When enabled, web content and web applications are more accessible to users with disabilities.
Filter out null
: Enables or disables the filtering of null chart data.
X axis on zero
: When enabled, the X axis lies on the other's origin position. When not enabled, the X axis always starts at 0.
Y axis on zero
: When enabled, the Y axis lies on the other's origin position. When not enabled, the Y axis always starts at 0.
Show xAxis Label
: Enables or disables the xAxis label.
Show yAxis Label
: Enables or disables the yAxis label.
Show xAxis Line
: Enables or disables the xAxis line.
Show yAxis Line
: Enables or disables the yAxis line.
Show xAxis Name
: Enables or disables the xAxis name.
Show yAxis Name
: Enables or disables the yAxis name.
yAxis Name Location
: The drop-down list provides options for specifying the yAxis name location. Options include Start, Middle, and End.
Truncation length
: The specified value sets the string length. Strings that are longer than the specified length are truncated. The default value is 10. When 0 is specified, truncation is turned off.
xAxis tick label decimal
: Sets the tick label decimal value for the xAxis. The default value is 3.
yAxis tick label decimal
: Sets the tick label decimal value for the yAxis. The default value is 3.
xAxis tick label rotate
: Sets the xAxis tick label rotation value. The default value is 0 (no rotation). You can specify value in the range -90 to 90 degrees.
Theme
: Select a template to change the colors that are used in charts that have a grouping or stacking variable. Any element attributes defined in the selected template file override the default template settings for those element attributes.
2. Click Apply to save your settings or Cancel to disregard the changes\.
<!-- </ol> -->
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<h1 class="topictitle1" id="chart_appearance_tab__title__1">Global visualization preferences</h1>
<div class="body">
<p>You can override the default settings for titles, range slider, grid lines, and mouse tracking. You can also specify a different color scheme template.</p>
<ol>
<li><span class="ph" data-hd-product="spssbase">In Visualizations, </span>click the Global visualization preferences control in the <span class="ph uicontrol">Actions</span> section.
<p>The Global visualization preferences dialog provides the following settings.</p>
<dl>
<dt class="dlterm">
Titles
</dt>
<dd class="dlentry">
Provides global chart title settings.
<dl>
<dt class="dlterm" id="chart_appearance_tab__dlentry_bjt_cc3_lfb">
Global titles
</dt>
<dd class="dlentry">
Enables or disables the global titles for all charts.
</dd>
<dt class="dlterm">
Global primary title
</dt>
<dd class="dlentry">
Enables or disables the display of global, primary chart titles. When enabled, the top-level chart title that you enter here is applied to all chart's, effectively overriding each chart's individual <span class="ph uicontrol">Primary title</span> setting.
</dd>
<dt class="dlterm">
Global subtitle
</dt>
<dd class="dlentry">
Enables or disables the display of global chart subtitles. When enabled, the chart subtitle that you enter here is applied to all chart's, effectively overriding each chart's individual <span class="ph uicontrol">Subtitle</span> setting.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dlentry_k3p_cc3_lfb">
Default titles
</dt>
<dd class="dlentry">
Enables or disables the default titles for all charts.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_az4_vtn_mrb">
Title alignment
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_bz4_vtn_mrb">
Provides the title alignment options <span class="ph uicontrol">Left</span>, <span class="ph uicontrol">Center</span> (the default setting), and <span class="ph uicontrol">Right</span>.
</dd>
</dl>
</dd>
<dt class="dlterm">
Tools
</dt>
<dd class="dlentry">
Provides options that control chart behavior.
<dl>
<dt class="dlterm" id="chart_appearance_tab__zoom">
Range slider
</dt>
<dd class="dlentry">
Enables or disables the range slider for each chart. When enabled, you can control the amount of chart data that displays with a range slider that is provided for each chart.
</dd>
<dt class="dlterm" id="chart_appearance_tab__gridlines">
Grid lines
</dt>
<dd class="dlentry">
Controls the display of X axis (vertical) and Y axis (horizontal) grid lines.
</dd>
<dt class="dlterm" id="chart_appearance_tab__mouse">
Mouse tracker
</dt>
<dd class="dlentry">
When enabled, the mouse cursor location, in relation to the chart data, is tracked and displayed when placed anywhere over the chart.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dlentry_ftn_gc3_lfb">
Toolbox
</dt>
<dd class="dlentry">
Enables or disables the toolbox for each chart. Depending on the chart type, the toolbox on the right of the screen provides tools such as zoom, save as image, restore, select data, and clear selection.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_cb5_zk3_5kb">
ARIA
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_db5_zk3_5kb">
When enabled, web content and web applications are more accessible to users with disabilities.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_ocn_1l3_5kb">
Filter out null
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_pcn_1l3_5kb">
Enables or disables the filtering of null chart data.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_wln_1l3_5kb">
X axis on zero
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_xln_1l3_5kb">
When enabled, the X axis lies on the other's origin position. When not enabled, the X axis always starts at 0.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_z5n_1l3_5kb">
Y axis on zero
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_avn_1l3_5kb">
When enabled, the Y axis lies on the other's origin position. When not enabled, the Y axis always starts at 0.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_rc4_1l3_5kb">
Show xAxis Label
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_sc4_1l3_5kb">
Enables or disables the xAxis label.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_vl4_1l3_5kb">
Show yAxis Label
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_wl4_1l3_5kb">
Enables or disables the yAxis label.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_s54_1l3_5kb">
Show xAxis Line
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_t54_1l3_5kb">
Enables or disables the xAxis line.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_ndp_1l3_5kb">
Show yAxis Line
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_odp_1l3_5kb">
Enables or disables the yAxis line.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_xrz_scw_wqb">
Show xAxis Name
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_yrz_scw_wqb">
Enables or disables the xAxis name.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_ozz_tcw_wqb">
Show yAxis Name
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_pzz_tcw_wqb">
Enables or disables the yAxis name.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_gjq_vcw_wqb">
yAxis Name Location
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_hjq_vcw_wqb">
The drop-down list provides options for specifying the yAxis name location. Options include <span class="ph uicontrol">Start</span>, <span class="ph uicontrol">Middle</span>, and <span class="ph uicontrol">End</span>.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_prr_wcw_wqb">
Truncation length
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_qrr_wcw_wqb">
The specified value sets the string length. Strings that are longer than the specified length are truncated. The default value is 10. When 0 is specified, truncation is turned off.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_tdm_zcw_wqb">
xAxis tick label decimal
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_udm_zcw_wqb">
Sets the tick label decimal value for the xAxis. The default value is 3.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_dfc_bdw_wqb">
yAxis tick label decimal
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_efc_bdw_wqb">
Sets the tick label decimal value for the yAxis. The default value is 3.
</dd>
<dt class="dlterm" id="chart_appearance_tab__dt_qyq_bdw_wqb">
xAxis tick label rotate
</dt>
<dd class="dlentry" id="chart_appearance_tab__dd_ryq_bdw_wqb">
Sets the xAxis tick label rotation value. The default value is 0 (no rotation). You can specify value in the range -90 to 90 degrees.
</dd>
</dl>
</dd>
<dt class="dlterm">
Theme
</dt>
<dd class="dlentry">
Select a template to change the colors that are used in charts that have a grouping or stacking variable. Any element attributes defined in the selected template file override the default template settings for those element attributes.
</dd>
</dl></li>
<li>Click <span class="ph uicontrol">Apply</span> to save your settings or <span class="ph uicontrol">Cancel</span> to disregard the changes.</li>
</ol>
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5D043091B2F2398611A819743FC83688D7658B22 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_create_layout.html?context=cdpaas&locale=en | Visualizations layout and terms | Visualizations layout and terms
Canvas
: The canvas is the area of the Visualizations dialog where you build the chart.
Chart type
: Lists the available chart types. The graphic elements are the items in the chart that represent data (bars, points, lines, and so on).
Details pane
: The Details pane provides the basic chart building blocks.
Chart settings
: Provides options for selecting which variables are used to build the chart, distribution method, title and subtitle fields, and so on. Depending on the selected chart type, the Details pane options might vary. For more information, see [Chart types](https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_charttypes.html).
Actions
: Provides options for downloading chart configuration files, downloading charts as image files, resetting charts, and setting the global chart preferences.
| # Visualizations layout and terms #
Canvas
: The canvas is the area of the Visualizations dialog where you build the chart\.
Chart type
: Lists the available chart types\. The graphic elements are the items in the chart that represent data (bars, points, lines, and so on)\.
Details pane
: The Details pane provides the basic chart building blocks\.
Chart settings
: Provides options for selecting which variables are used to build the chart, distribution method, title and subtitle fields, and so on. Depending on the selected chart type, the Details pane options might vary. For more information, see [Chart types](https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_charttypes.html).
Actions
: Provides options for downloading chart configuration files, downloading charts as image files, resetting charts, and setting the global chart preferences\.
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<h1 class="topictitle1" id="chart_create_layout__title__1"><span class="ph" data-hd-product="spssbase"><span class="ph"><span class="ph">Visualizations</span></span> layout and terms</span></h1>
<div class="body">
<dl>
<dt class="dlterm">
Canvas
</dt>
<dd class="dlentry">
The canvas is the area of the <span class="ph" data-hd-product="spssbase"><span class="ph">Visualizations</span></span> dialog where you build the chart.
</dd>
<dt class="dlterm">
Chart type
</dt>
<dd class="dlentry">
Lists the available chart types. The graphic elements are the items in the chart that represent data (bars, points, lines, and so on).
</dd>
<dt class="dlterm">
Details pane
</dt>
<dd class="dlentry">
The Details pane provides the basic chart building blocks.
<dl>
<dt class="dlterm">
Chart settings
</dt>
<dd class="dlentry">
Provides options for selecting which variables are used to build the chart, distribution method, title and subtitle fields, and so on. Depending on the selected chart type, the Details pane options might vary. For more information, see <a href="chart_creation_charttypes.html">Chart types</a>.
</dd>
</dl>
</dd>
<dt class="dlterm">
Actions
</dt>
<dd class="dlentry">
Provides options for downloading chart configuration files, downloading charts as image files, resetting charts, and setting the global chart preferences.
</dd>
</dl>
</div>
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9F5D44B3A96F8418BE317AD258E4932E468551BE | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_3d.html?context=cdpaas&locale=en | 3D charts | 3D charts
3D charts are commonly used to represent multiple-variable functions and include a z-axis variable that is a function of both the x and y-axis variables.
| # 3D charts #
3D charts are commonly used to represent multiple\-variable functions and include a z\-axis variable that is a function of both the x and y\-axis variables\.
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<h1 class="topictitle1" id="chart_creation_3d__title__1">3D charts</h1>
<div class="body">
<div class="abstract">
3D charts are commonly used to represent multiple-variable functions and include a z-axis variable that is a function of both the x and y-axis variables.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_3d__section_tsd_ljb_qdb__title__1" id="chart_creation_3d__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_3d__section_tsd_ljb_qdb__title__1">Creating a simple 3D chart</h2>
<ol id="chart_creation_3d__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">3D</span> icon.
<p>The canvas updates to display a 3D chart template.</p></li>
<li>Select the chart <span class="ph uicontrol">Type</span> from the drop-down list.</li>
<li>Select an <span class="ph uicontrol">X-axis</span> variable from the drop-down list.</li>
<li>Select an <span class="ph uicontrol">Y-axis</span> variable from the drop-down list.</li>
<li>Select an <span class="ph uicontrol">Z-axis</span> variable from the drop-down list.</li>
<li data-hd-product="spssbase" id="chart_creation_3d__li-save">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase" id="chart_creation_3d__li-apply">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_3d__section_fj1_mjb_qdb__title__1" id="chart_creation_3d__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_3d__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Type
</dt>
<dd class="dlentry">
Lists the chart types that are available to represent the data.
</dd>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Y-axis.
</dd>
<dt class="dlterm">
Z-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Z-axis.
</dd>
<dt class="dlterm">
Tooltip info
</dt>
<dd class="dlentry">
Lists the variables that can be used the generate tooltip information when the cursor hovers over a data point.
</dd>
<dt class="dlterm" id="chart_creation_3d__d9e198">
Color map
</dt>
<dd class="dlentry" id="chart_creation_3d__d9e201">
Lists available color map variables. These variables use color progression, based on the range of values in the specified column, to represent themselves in the plot points. Color maps are also known as choropleth maps.
</dd>
<dt class="dlterm" id="chart_creation_3d__d9e207">
Size map
</dt>
<dd class="dlentry" id="chart_creation_3d__d9e210">
Lists available size map variables. These variables use differing sizes to represent themselves in the plot points.
</dd>
<dt class="dlterm">
Z ratio
</dt>
<dd class="dlentry">
Sets the scale of the Z-axis data values, relative to the X and Y axes.
</dd>
<dt class="dlterm">
Rotate
</dt>
<dd class="dlentry">
Enables and disables chart rotation.
</dd>
<dt class="dlterm">
Data point tooltips
</dt>
<dd class="dlentry">
Controls where the data point tooltips display (right of data points, top-right of chart, or hide).
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
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823EB607207DFD62D80671AF48451CCE1C44153F | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_barcharts.html?context=cdpaas&locale=en | Bar charts | Bar charts
Bar charts are useful for summarizing categorical variables. For example, you can use a bar chart to show the number of men and the number of women who participated in a survey. You can also use a bar chart to show the mean salary for men and the mean salary for women.
| # Bar charts #
Bar charts are useful for summarizing categorical variables\. For example, you can use a bar chart to show the number of men and the number of women who participated in a survey\. You can also use a bar chart to show the mean salary for men and the mean salary for women\.
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<h1 class="topictitle1" id="chart_creation_barcharts__title__1">Bar charts</h1>
<div class="body">
<div class="abstract">
Bar charts are useful for summarizing categorical variables. For example, you can use a bar chart to show the number of men and the number of women who participated in a survey. You can also use a bar chart to show the mean salary for men and the mean salary for women.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_barcharts__section_tsd_ljb_qdb__title__1" id="chart_creation_barcharts__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_barcharts__section_tsd_ljb_qdb__title__1">Creating a simple bar chart</h2>
<ol id="chart_creation_barcharts__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Bar</span> icon.
<p>The canvas updates to display a bar chart template.</p></li>
<li>Select a categorical (nominal or ordinal) variable as the <span class="ph uicontrol">Category</span> variable. You can use a scale variable, but the results are useful in only a few special cases. A bar chart looks best with a limited number of distinct values. If you create a bar chart with a scale <span class="ph uicontrol">Category</span> axis, the bars are thin because each bar is drawn at an exact value, and the bar cannot overlap other continuous values.</li>
<li>Select a statistic from the <span class="ph uicontrol">Summary</span> list. The result of any statistic determines the height of the bars. If the statistic you want does not appear in the <span class="ph uicontrol">Summary</span> list, it might require a variable. Select a variable from the <span class="ph uicontrol">Value</span> list and check if the statistic is now available. Other chart type limitations might exist. For example, error bar charts can be calculated only for specific statistics.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_barcharts__section_fj1_mjb_qdb__title__1" id="chart_creation_barcharts__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_barcharts__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_barcharts__category">
Category
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__orderby">
Order based on
</dt>
<dd class="dlentry">
Select a sorting option for the categories within the variable.
<dl>
<dt class="dlterm">
Category name
</dt>
<dd class="dlentry">
Use the category labels for sorting the variable's categories. The labels appear in the chart, usually as tick or legend labels.
</dd>
<dt class="dlterm">
Category value
</dt>
<dd class="dlentry">
Use the value that is stored in the data set for sorting the variable's categories. The category's value is what identifies the category in the data set. It often differs from its label and is not necessarily descriptive. For example, the value might be a number (for example, <em>1</em>), while the label is a text description of the category (for example, <em>Female</em>).
</dd>
</dl>
</dd>
<dt class="dlterm" id="chart_creation_barcharts__catorder">
Category order
</dt>
<dd class="dlentry">
Select the order in which variable categories are sorted.
<dl>
<dt class="dlterm">
As read
</dt>
<dd class="dlentry">
Variable categories are presented as they appear in the data set.
</dd>
<dt class="dlterm">
Ascending
</dt>
<dd class="dlentry">
Sort variable categories in ascending order.
</dd>
<dt class="dlterm">
Descending
</dt>
<dd class="dlentry">
Sort variable categories in descending order.
</dd>
</dl>
</dd>
<dt class="dlterm" id="chart_creation_barcharts__summary">
Summary
</dt>
<dd class="dlentry">
Select a statistical summary function for the graphic element. The result of the statistic determines the position of the graphic elements on the Y-axis. In a 2-D chart, the statistic is calculated for each value on the X-axis. In a 3-D chart, it is calculated for the intersection of values on the X-axis and Z-axis.
<div class="p" id="chart_creation_barcharts__p_ofh_jw4_blb">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<ul id="chart_creation_barcharts__ul_o3f_grc_rdb">
<li id="chart_creation_barcharts__li_qfh_jw4_blb"><strong>Functions that do not require a value variable.</strong> Functions that do not require a variable. All count and percentage statistics are in this category. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
<li id="chart_creation_barcharts__li_rfh_jw4_blb"><strong>Functions that do require a value variable.</strong> Functions that do require a <span class="ph uicontrol">Value</span> variable. For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
</ul>
</div>
</dd>
<dt class="dlterm" id="chart_creation_barcharts__value">
Value
</dt>
<dd class="dlentry">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable, is selected. Select a variable to serve as the value.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__splitby">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__dt_ywg_vk4_blb">
<a id="chart_creation_barcharts__dlentry_xwg_vk4_blb"></a>Split type
</dt>
<dd class="dlentry" id="chart_creation_barcharts__dd_zwg_vk4_blb">
When a <span class="ph uicontrol">Split by</span> variable is selected, you can choose to display the resulting category bars as either stacked or clustered. Clustering and stacking add dimensionality within the chart. Clustering splits one bar into multiple bars, and stacking creates segments in each bar. Be careful that you choose the right statistic for stacking. When the values are added (stacked), the result must make sense. For example, adding and stacking mean (averaged) values is not usually meaningful.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__dt_yhl_534_blb">
<a id="chart_creation_barcharts__dlentry_rvw_5k4_blb"></a>Bar type
</dt>
<dd class="dlentry" id="chart_creation_barcharts__dd_zhl_534_blb">
Select the bar chart type from the provided options.
<ul id="chart_creation_barcharts__ul_dr1_y34_blb">
<li id="chart_creation_barcharts__li_er1_y34_blb">X-axis</li>
<li id="chart_creation_barcharts__li_ll3_z34_blb">Y-axis</li>
<li id="chart_creation_barcharts__li_ocq_z34_blb">X-axis inverse</li>
<li id="chart_creation_barcharts__li_vm1_1j4_blb">Y-axis inverse</li>
<li id="chart_creation_barcharts__li_vzm_1j4_blb">Polar-angle axis</li>
<li id="chart_creation_barcharts__li_kvh_bj4_blb">Polar-radius axis</li>
<li id="chart_creation_barcharts__li_dgz_bj4_blb">Polar-rainbow</li>
</ul>
</dd>
<dt class="dlterm" id="chart_creation_barcharts__dt_rtv_dj4_blb">
<a id="chart_creation_barcharts__dlentry_fkp_5k4_blb"></a>Label position
</dt>
<dd class="dlentry" id="chart_creation_barcharts__dd_stv_dj4_blb">
Select the chart's label position from the drop-down menu.
<ul id="chart_creation_barcharts__ul_afn_mk4_blb">
<li id="chart_creation_barcharts__li_bfn_mk4_blb">none</li>
<li id="chart_creation_barcharts__li_gkd_nk4_blb">top</li>
<li id="chart_creation_barcharts__li_zmx_nk4_blb">left</li>
<li id="chart_creation_barcharts__li_odc_4k4_blb">right</li>
<li id="chart_creation_barcharts__li_cfn_4k4_blb">bottom</li>
<li id="chart_creation_barcharts__li_sxg_4k4_blb">inside</li>
<li id="chart_creation_barcharts__li_az5_pk4_blb">insideLeft</li>
<li id="chart_creation_barcharts__li_ulg_qk4_blb">insideRight</li>
<li id="chart_creation_barcharts__li_w1r_qk4_blb">insideTop</li>
<li id="chart_creation_barcharts__li_tyb_rk4_blb">insideBottom</li>
<li id="chart_creation_barcharts__li_zwr_rk4_blb">insideTopLeft</li>
<li id="chart_creation_barcharts__li_in2_sk4_blb">insideBottomLeft</li>
<li id="chart_creation_barcharts__li_jk4_sk4_blb">insideTopRight</li>
<li id="chart_creation_barcharts__li_kyl_tk4_blb">insideBottomRight</li>
</ul>
</dd>
<dt class="dlterm" id="chart_creation_barcharts__dt_xth_ds4_blb">
<a id="chart_creation_barcharts__dlentry_i4d_2s4_blb"></a>Show reference line
</dt>
<dd class="dlentry" id="chart_creation_barcharts__dd_yth_ds4_blb">
The toggle control enables and disables the display of reference lines in the chart. Available options are <span class="ph uicontrol">Min</span>, <span class="ph uicontrol">Max</span>, and <span class="ph uicontrol">Average</span>, which display reference lines at the chart's minimum, maximum and average values.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__dt_jjq_qs4_blb">
<a id="chart_creation_barcharts__dlentry_pxw_xs4_blb"></a>Enter a reference line value
</dt>
<dd class="dlentry" id="chart_creation_barcharts__dd_kjq_qs4_blb">
When <span class="ph uicontrol">Show reference line</span> is enabled, this setting provides the option of specifying a reference line value. Click <span class="ph uicontrol">Add another column</span> to specify more reference line values.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__transpose">
Transpose
</dt>
<dd class="dlentry">
When enabled, the chart's X and Y axes are transposed.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__title">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__subtitle">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__footnote">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__xaxis-label">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm" id="chart_creation_barcharts__yaxis-label">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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BECCA4C839A0BCF01ADCB6A5CE31A3B1168D3548 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_boxplots.html?context=cdpaas&locale=en | Box plots | Box plots
A box plot chart shows the five statistics (minimum, first quartile, median, third quartile, and maximum). It is useful for displaying the distribution of a scale variable and pinpointing outliers.
| # Box plots #
A box plot chart shows the five statistics (minimum, first quartile, median, third quartile, and maximum)\. It is useful for displaying the distribution of a scale variable and pinpointing outliers\.
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<h1 class="topictitle1" id="chart_creation_boxplots__title__1">Box plots</h1>
<div class="body">
<div class="abstract">
A box plot chart shows the five statistics (minimum, first quartile, median, third quartile, and maximum). It is useful for displaying the distribution of a scale variable and pinpointing outliers.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_boxplots__section_tsd_ljb_qdb__title__1" id="chart_creation_boxplots__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_boxplots__section_tsd_ljb_qdb__title__1">Creating a simple box plot</h2>
<ol id="chart_creation_boxplots__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Box plot</span> icon.
<p>The canvas updates to display a box plot chart template.</p></li>
<li>Select one or more scale variables as the <span class="ph uicontrol">Columns</span> variable.
<div class="note">
<span class="notetitle">Note:</span> The statistic for a dot plot is Box plot. You cannot change this setting.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_boxplots__section_fj1_mjb_qdb__title__1" id="chart_creation_boxplots__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_boxplots__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dd class="ddexpand">
Click <span class="ph uicontrol">Add another column</span> to add more columns.
</dd>
<dt class="dlterm">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm">
Category order
</dt>
<dd class="dlentry">
Select the order in which variable categories are sorted.
<dl>
<dt class="dlterm">
As read
</dt>
<dd class="dlentry">
Variable categories are presented as they appear in the data set.
</dd>
<dt class="dlterm">
Ascending
</dt>
<dd class="dlentry">
Sort variable categories in ascending order.
</dd>
<dt class="dlterm">
Descending
</dt>
<dd class="dlentry">
Sort variable categories in descending order.
</dd>
</dl>
</dd>
<dt class="dlterm" id="chart_creation_boxplots__iqr-strength">
Strength holder of IQR
</dt>
<dd class="dlentry">
The strength holder of the inter-quartile range (<code class="ph codeph">N*IQR</code>). The <code class="ph codeph">N</code> default value is 1.5.
</dd>
<dt class="dlterm" id="chart_creation_boxplots__normalize">
Normalize data
</dt>
<dd class="dlentry">
When enabled, this setting transforms data into a normal distribution compares data from multiple data sets or multiple columns. This setting creates 100% stacking for counts and converts statistics to percents.
</dd>
<dt class="dlterm">
Transpose
</dt>
<dd class="dlentry">
When enabled, the chart's X and Y axes are transposed.
</dd>
<dt class="dlterm" id="chart_creation_boxplots__outlier">
Show the outlier
</dt>
<dd class="dlentry">
When enabled, outliers display on the chart.
</dd>
<dt class="dlterm" id="chart_creation_boxplots__outlier-extreme">
Show extreme outlier style
</dt>
<dd class="dlentry">
When enabled, extreme outlier style displays on the chart.
</dd>
<dt class="dlterm" id="chart_creation_boxplots__show-label">
Show label
</dt>
<dd class="dlentry">
When enabled, column labels display on the chart. Only scatter series data is supported.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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5466D9A71E87BB01000DC957683E9CD3C10AD8BC | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_bubble.html?context=cdpaas&locale=en | Bubble charts | Bubble charts
Bubble charts display categories in your groups as nonhierarchical packed circles. The size of each circle (bubble) is proportional to its value. Bubble charts are useful for comparing relationships in your data.
| # Bubble charts #
Bubble charts display categories in your groups as nonhierarchical packed circles\. The size of each circle (bubble) is proportional to its value\. Bubble charts are useful for comparing relationships in your data\.
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Bubble charts display categories in your groups as nonhierarchical packed circles. The size of each circle (bubble) is proportional to its value. Bubble charts are useful for comparing relationships in your data.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_bubble__section_tsd_ljb_qdb__title__1" id="chart_creation_bubble__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_bubble__section_tsd_ljb_qdb__title__1">Creating a simple bubble chart</h2>
<ol id="chart_creation_bubble__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Bubble</span> icon.
<p>The canvas updates to display a bubble chart template.</p></li>
<li>Select a <span class="ph uicontrol">Columns</span> variable from the drop-down list.
<div class="note">
<span class="notetitle">Note:</span> Click <span class="ph uicontrol">Add another column</span> to include more column variables.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_bubble__section_fj1_mjb_qdb__title__1" id="chart_creation_bubble__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_bubble__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Lists variables that are available for the chart.
</dd>
<dd class="ddexpand">
Click <span class="ph uicontrol">Add another column</span> to add more columns.
</dd>
<dt class="dlterm" id="chart_creation_bubble__color">
Group color
</dt>
<dd class="dlentry">
Turn on or off color groupings.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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F7D94E6CD13F36EB9B1FE7653C436DC5745250B1 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_candlestick.html?context=cdpaas&locale=en | Candlestick charts | Candlestick charts
Candlestick charts are a style of financial charts that are used to describe price movements of a security, derivative, or currency. Each candlestick element typically shows one day. A one-month chart might show the 20 trading days as 20 candlesticks elements. Candlestick charts are most often used in the analysis of equity and currency price patterns and are similar to box plots.
The data set that is used to create a candlestick chart must contain open, high, low, and close values for each time period you want to display.
| # Candlestick charts #
Candlestick charts are a style of financial charts that are used to describe price movements of a security, derivative, or currency\. Each candlestick element typically shows one day\. A one\-month chart might show the 20 trading days as 20 candlesticks elements\. Candlestick charts are most often used in the analysis of equity and currency price patterns and are similar to box plots\.
The data set that is used to create a candlestick chart must contain open, high, low, and close values for each time period you want to display\.
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<h1 class="topictitle1" id="chart_creation_candlestick__title__1">Candlestick charts</h1>
<div class="body">
<div class="abstract">
Candlestick charts are a style of financial charts that are used to describe price movements of a security, derivative, or currency. Each candlestick element typically shows one day. A one-month chart might show the 20 trading days as 20 candlesticks elements. Candlestick charts are most often used in the analysis of equity and currency price patterns and are similar to box plots.
</div>
<p>The data set that is used to create a candlestick chart must contain open, high, low, and close values for each time period you want to display.</p>
<section class="section" role="region" aria-labelledby="chart_creation_candlestick__section_tsd_ljb_qdb__title__1" id="chart_creation_candlestick__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_candlestick__section_tsd_ljb_qdb__title__1">Creating a simple Candlestick chart</h2>
<ol id="chart_creation_candlestick__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Candlestick</span> icon.
<p>The canvas updates to display a Candlestick chart template.</p></li>
<li>Select a variable as the <span class="ph uicontrol">X-axis</span> variable.</li>
<li>Select a variable as the <span class="ph uicontrol">High</span> variable.</li>
<li>Select a variable as the <span class="ph uicontrol">Low</span> variable.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_candlestick__section_fj1_mjb_qdb__title__1" id="chart_creation_candlestick__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_candlestick__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Summary
</dt>
<dd class="dlentry">
When enabled, summary calculations for available for the following options.
</dd>
<dt class="dlterm">
High
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's high price value.
</dd>
<dt class="dlterm">
High field summary
</dt>
<dd class="dlentry">
Select a statistical summary function for the selected high variable.
</dd>
<dt class="dlterm">
Low
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's low price value.
</dd>
<dt class="dlterm">
Open
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's opening price value.
</dd>
<dt class="dlterm">
Close
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's closing price value.
</dd>
<dt class="dlterm">
Volume
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's volume bars.
</dd>
<dt class="dlterm">
Category order
</dt>
<dd class="dlentry">
Select the order in which variable categories are sorted.
<dl>
<dt class="dlterm">
As read
</dt>
<dd class="dlentry">
Variable categories are presented as they appear in the data set.
</dd>
<dt class="dlterm">
Ascending
</dt>
<dd class="dlentry">
Sort variable categories in ascending order.
</dd>
<dt class="dlterm">
Descending
</dt>
<dd class="dlentry">
Sort variable categories in descending order.
</dd>
</dl>
</dd>
<dt class="dlterm">
Candlestick
</dt>
<dd class="dlentry">
Toggles the chart data to display as either candlestick or line.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
</div>
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2C9D0D0309E01FF2EE0D298A16011857DE068038 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_charttypes.html?context=cdpaas&locale=en | Chart types | Chart types
The gallery contains a collection of the most commonly used charts.
| # Chart types #
The gallery contains a collection of the most commonly used charts\.
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<h1 class="topictitle1" id="chart_creation_charttypes__title__1">Chart types</h1>
<div class="body">
<p>The gallery contains a collection of the most commonly used charts.</p>
</div>
<aside role="complementary" aria-labelledby="chart_creation_charttypes__title__1">
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<ul class="ullinks">
<li class="ulchildlink"><strong><a href="chart_creation_3d.html">3D charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_barcharts.html">Bar charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_boxplots.html">Box plots</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_bubble.html">Bubble charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_candlestick.html">Candlestick charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_circlepacking.html">Circle packing charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_customize.html">Custom charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_dendrogram.html">Dendrogram charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_dualy.html">Dual Y-axes charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_errorbar.html">Error bar charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_evaluation.html">Evaluation charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_heatmap.html">Heat map charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_histograms.html">Histogram charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_linecharts.html">Line charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_map.html">Map charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_mathcurve.html">Math curve charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_multichart.html">Multi-chart charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_multiseries.html">Multiple series charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_parallel.html">Parallel charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_pareto.html">Pareto charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_piecharts.html">Pie charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_pyramid.html">Population pyramid charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_qqplot.html">Q-Q plots</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_radar.html">Radar charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_relation.html">Relationship charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_scatterdot.html">Scatter plots and dot plots</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_matrixscatter.html">Scatter matrix charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_seriesarray.html">Series array charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_sunburst.html">Sunburst charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_tsne.html">t-SNE charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_timeplot.html">Time plots</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_themeriver.html">Theme River charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_tree.html">Tree charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_treemap.html">Treemap charts</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_wordcloud.html">Word cloud charts</a></strong><br></li>
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035430AFAC1E73483636073C5BF48BCF8B4F5E1D | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_circlepacking.html?context=cdpaas&locale=en | Circle packing charts | Circle packing charts
Circle packing charts display hierarchical data as a set of nested areas to visualize a large amount of hierarchically structured data. It's similar to a treemap, but uses circles instead of rectangles. Circle packing charts use containment (nesting) to display hierarchy data.
| # Circle packing charts #
Circle packing charts display hierarchical data as a set of nested areas to visualize a large amount of hierarchically structured data\. It's similar to a treemap, but uses circles instead of rectangles\. Circle packing charts use containment (nesting) to display hierarchy data\.
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<div class="abstract">
Circle packing charts display hierarchical data as a set of nested areas to visualize a large amount of hierarchically structured data. It's similar to a treemap, but uses circles instead of rectangles. Circle packing charts use containment (nesting) to display hierarchy data.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_circlepacking__section_tsd_ljb_qdb__title__1" id="chart_creation_circlepacking__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_circlepacking__section_tsd_ljb_qdb__title__1">Creating a simple circle packing chart</h2>
<ol id="chart_creation_circlepacking__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Time plot</span> icon.
<p>The canvas updates to display a circle packing chart template.</p></li>
<li>Select a <span class="ph uicontrol">Columns</span> variable from the drop-down list.
<div class="note">
<span class="notetitle">Note:</span> Click <span class="ph uicontrol">Add another column</span> to include more column variables.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_circlepacking__section_fj1_mjb_qdb__title__1" id="chart_creation_circlepacking__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_circlepacking__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Lists variables that are available for the chart.
</dd>
<dd class="ddexpand">
Click <span class="ph uicontrol">Add another column</span> to add more columns.
</dd>
<dt class="dlterm" id="chart_creation_circlepacking__color">
Group color
</dt>
<dd class="dlentry">
Turn on or off color groupings.
</dd>
<dt class="dlterm" id="chart_creation_circlepacking__summary">
Summary
</dt>
<dd class="dlentry">
Select a statistical summary function (the method that is used for summarizing each category).
<div class="p" id="chart_creation_circlepacking__p_hrx_4w4_blb">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<ul id="chart_creation_circlepacking__d31e204">
<li id="chart_creation_circlepacking__d31e206"><strong>Functions that do not require a value variable.</strong> Functions that do not require a variable. All count and percentage statistics are in this category. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
<li id="chart_creation_circlepacking__d31e214"><strong>Functions that do require a value variable.</strong> Functions that do require a <span class="ph uicontrol">Value</span> variable. For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
</ul>
</div>
</dd>
<dt class="dlterm">
Value
</dt>
<dd class="dlentry">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable, is selected. Select a variable to serve as the value.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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49724D4B7690D4B215FE6F1C0A49C8B347F0C9A1 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_customize.html?context=cdpaas&locale=en | Custom charts | Custom charts
The custom charts option provides options for pasting or editing JSON code to create the wanted chart.
| # Custom charts #
The custom charts option provides options for pasting or editing JSON code to create the wanted chart\.
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<div class="abstract">
The custom charts option provides options for pasting or editing JSON code to create the wanted chart.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_customize__section_tsd_ljb_qdb__title__1" id="chart_creation_customize__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_customize__section_tsd_ljb_qdb__title__1">Creating a custom chart</h2>
<ol id="chart_creation_customize__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Customized</span> icon.</li>
<li>Paste the JSON code that contains the chart specifications into the provided <span class="ph uicontrol">JSON script</span> field in the Details pane.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
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91B834E69C2153740973C59CF6B4D66260640342 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_dendrogram.html?context=cdpaas&locale=en | Dendrogram charts | Dendrogram charts
Dendrogram charts are similar to tree charts and are typically used to illustrate a network structure (for example, a hierarchical structure). Dendrogram charts consist of a root node that is connected to subordinate nodes through edges or branches. The last nodes in the hierarchy are called leaves.
| # Dendrogram charts #
Dendrogram charts are similar to tree charts and are typically used to illustrate a network structure (for example, a hierarchical structure)\. Dendrogram charts consist of a root node that is connected to subordinate nodes through edges or branches\. The last nodes in the hierarchy are called leaves\.
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Dendrogram charts are similar to tree charts and are typically used to illustrate a network structure (for example, a hierarchical structure). Dendrogram charts consist of a root node that is connected to subordinate nodes through edges or branches. The last nodes in the hierarchy are called leaves.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_dendrogram__section_tsd_ljb_qdb__title__1" id="chart_creation_dendrogram__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_dendrogram__section_tsd_ljb_qdb__title__1">Creating a simple dendrogram chart</h2>
<ol id="chart_creation_dendrogram__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Dendrogram</span> icon.
<p>The canvas updates to display a Dendrogram chart template.</p></li>
<li>Select at least two variables from the <span class="ph uicontrol">Field name</span> list. You can select <span class="ph uicontrol">SELECT ALL</span> to select all available variables. A maximum number 300 variables are recommended.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_dendrogram__section_fj1_mjb_qdb__title__1" id="chart_creation_dendrogram__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_dendrogram__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_dendrogram__dt_cgj_t3v_wqb">
Field name
</dt>
<dd class="dlentry" id="chart_creation_dendrogram__dd_dgj_t3v_wqb">
The list provides all available variables that represent the leave nodes. You must select at least two variables.
</dd>
<dt class="dlterm">
Show axis
</dt>
<dd class="dlentry">
The toggle control enables or disables the display of the distance value axis.
</dd>
<dt class="dlterm">
Linkage
</dt>
<dd class="dlentry">
The linkage criterion determines the distance between sets of observations as a function of the pairwise distances between observations. Select <span class="ph uicontrol">Average distance</span>, <span class="ph uicontrol">Min distance</span>, or <span class="ph uicontrol">Max distance</span>.
</dd>
<dt class="dlterm" id="chart_creation_dendrogram__d48e144">
Tree layout
</dt>
<dd class="dlentry" id="chart_creation_dendrogram__d48e147">
<dl id="chart_creation_dendrogram__d48e149">
<dt class="dlterm" id="chart_creation_dendrogram__d48e153">
Left to right
</dt>
<dd class="dlentry" id="chart_creation_dendrogram__d48e156">
The root node displays on the left and the leaf nodes display on the right.
</dd>
<dt class="dlterm" id="chart_creation_dendrogram__d48e162">
Right to left
</dt>
<dd class="dlentry" id="chart_creation_dendrogram__d48e165">
The root node displays on the right and the leaf nodes display on the left.
</dd>
<dt class="dlterm" id="chart_creation_dendrogram__d48e171">
Top to bottom
</dt>
<dd class="dlentry" id="chart_creation_dendrogram__d48e174">
The root node displays on the top and the leaf nodes display on the bottom.
</dd>
<dt class="dlterm" id="chart_creation_dendrogram__d48e180">
Bottom to top
</dt>
<dd class="dlentry" id="chart_creation_dendrogram__d48e183">
The root node displays on the bottom and the leaf nodes display on the top.
</dd>
<dt class="dlterm" id="chart_creation_dendrogram__d48e189">
Radial
</dt>
<dd class="dlentry" id="chart_creation_dendrogram__d48e192">
The root node displays in the middle and the leaf nodes radiate from the root.
</dd>
</dl>
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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2910B7C4CD65F8E4ADD1607791DD22BED468B61D | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_dualy.html?context=cdpaas&locale=en | Dual Y-axes charts | Dual Y-axes charts
A dual Y-axes chart summarizes or plots two Y-axes variables that have different domains. For example, you can plot the number of cases on one axis and the mean salary on another. This chart can also be a mix of different graphic elements so that the dual Y-axes chart encompasses several of the different chart types. Dual Y-axes charts can display the counts as a line and the mean of each category as a bar.
| # Dual Y\-axes charts #
A dual Y\-axes chart summarizes or plots two Y\-axes variables that have different domains\. For example, you can plot the number of cases on one axis and the mean salary on another\. This chart can also be a mix of different graphic elements so that the dual Y\-axes chart encompasses several of the different chart types\. Dual Y\-axes charts can display the counts as a line and the mean of each category as a bar\.
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<div class="body">
<div class="abstract">
A dual Y-axes chart summarizes or plots two Y-axes variables that have different domains. For example, you can plot the number of cases on one axis and the mean salary on another. This chart can also be a mix of different graphic elements so that the dual Y-axes chart encompasses several of the different chart types. Dual Y-axes charts can display the counts as a line and the mean of each category as a bar.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_dualy__section_tsd_ljb_qdb__title__1" id="chart_creation_dualy__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_dualy__section_tsd_ljb_qdb__title__1">Creating a simple Dual Y-axes chart</h2>
<ol id="chart_creation_dualy__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Dual Y-axes</span> icon.
<p>The canvas updates to display a Dual Y-axes chart template.</p></li>
<li>Select a variable as the <span class="ph uicontrol">X-axis</span> variable.</li>
<li>Select variable for the first <span class="ph uicontrol">Y-axis</span> variable and then select a chart type to represent the variable (<span class="ph uicontrol">Bar</span>, <span class="ph uicontrol">Line</span>, or <span class="ph uicontrol">Scatter plot</span>).</li>
<li>Select variable for the second <span class="ph uicontrol">Y-axis</span> variable and then select a chart type to represent the variable (<span class="ph uicontrol">Bar</span>, <span class="ph uicontrol">Line</span>, or <span class="ph uicontrol">Scatter plot</span>).
<div class="note">
<span class="notetitle">Note:</span> You can use the up and down arrow controls to change the Y-axes order.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_dualy__section_fj1_mjb_qdb__title__1" id="chart_creation_dualy__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_dualy__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's dual Y-axes.
</dd>
<dt class="dlterm">
Summary
</dt>
<dd class="dlentry">
When enabled, options for selecting the method that is used for summarizing each category are displayed.
<dl>
<dt class="dlterm">
Left Y-axis summary
</dt>
<dd class="dlentry">
Sets the summary method for the Y-axis that displays on the left side of the chart. Options include <span class="ph uicontrol">Sum</span>, <span class="ph uicontrol">Mean</span>, <span class="ph uicontrol">Maximum</span>, and <span class="ph uicontrol">Minimum</span>.
</dd>
<dt class="dlterm">
Right Y-axis summary
</dt>
<dd class="dlentry">
Sets the summary method for the Y-axis that displays on the right side of the chart. Options include <span class="ph uicontrol">Sum</span>, <span class="ph uicontrol">Mean</span>, <span class="ph uicontrol">Maximum</span>, and <span class="ph uicontrol">Minimum</span>.
</dd>
</dl>
</dd>
<dt class="dlterm">
Normalize data
</dt>
<dd class="dlentry">
When enabled, data is transformed into a normal distribution, which allows data from multiple data sets or columns to be easily compared.
</dd>
<dt class="dlterm">
Second axis lines
</dt>
<dd class="dlentry">
When enabled, the chart's second axis line is shown.
</dd>
<dt class="dlterm" id="chart_creation_dualy__reorder">
Reorder
</dt>
<dd class="dlentry">
When enabled, the chart's data is reordered based on the X and Y axis values.
</dd>
<dt class="dlterm" id="chart_creation_dualy__legend-orient">
Legend orient
</dt>
<dd class="dlentry">
Sets the chart legend orientation. Available options are <span class="ph uicontrol">Horizontal</span>, <span class="ph uicontrol">Vertical</span>, and <span class="ph uicontrol">Vertical bottom</span>.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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97492A97F355A95D56BCF768A62CA7FD75718086 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_errorbar.html?context=cdpaas&locale=en | Error bar charts | Error bar charts
Error bar charts represent the variability of data and indicate the error (or uncertainty) in a reported measurement. Error bars help determine whether differences are statistically significant. Error bars can also suggest goodness of fit for a specific function.
| # Error bar charts #
Error bar charts represent the variability of data and indicate the error (or uncertainty) in a reported measurement\. Error bars help determine whether differences are statistically significant\. Error bars can also suggest goodness of fit for a specific function\.
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<div class="body">
<div class="abstract">
Error bar charts represent the variability of data and indicate the error (or uncertainty) in a reported measurement. Error bars help determine whether differences are statistically significant. Error bars can also suggest goodness of fit for a specific function.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_errorbar__section_tsd_ljb_qdb__title__1" id="chart_creation_errorbar__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_errorbar__section_tsd_ljb_qdb__title__1">Creating a simple error bar chart</h2>
<ol id="chart_creation_errorbar__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Error bar</span> icon.
<p>The canvas updates to display an error bar chart template.</p></li>
<li id="chart_creation_errorbar__li_rtj_fy4_blb">Select a scale variable as the <span class="ph uicontrol">Category</span> variable (the variable whose data is represented on the X-axis).</li>
<li>Select a variable as the <span class="ph uicontrol">Y-axis</span> variable (the variable whose data is represented on the Y-axis).</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_errorbar__section_fj1_mjb_qdb__title__1" id="chart_creation_errorbar__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_errorbar__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Category
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Y-axis.
</dd>
<dt class="dlterm">
Category order
</dt>
<dd class="dlentry">
Select the order in which variable categories are sorted.
<dl>
<dt class="dlterm">
As read
</dt>
<dd class="dlentry">
Variable categories are presented as they appear in the data set.
</dd>
<dt class="dlterm">
Ascending
</dt>
<dd class="dlentry">
Sort variable categories in ascending order.
</dd>
<dt class="dlterm">
Descending
</dt>
<dd class="dlentry">
Sort variable categories in descending order.
</dd>
</dl>
</dd>
<dt class="dlterm">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm">
Reference line
</dt>
<dd class="dlentry">
When enabled, displays a reference line on the chart. The reference line correlates with the selected <span class="ph uicontrol">Statistical method</span>.
</dd>
<dt class="dlterm">
Error bars
</dt>
<dd class="dlentry">
When enabled, the lines that represent the range of error are displayed in the chart.
</dd>
<dt class="dlterm">
Measure
</dt>
<dd class="dlentry">
Select the measure type that is represented by the error bars:
<dl>
<dt class="dlterm">
Confidence intervals
</dt>
<dd class="dlentry">
Sets the confidence intervals for the selected variables. The default value is 0.95 (95%), as reflected in the <span class="ph uicontrol">Represent value</span> field.
</dd>
<dt class="dlterm">
Standard error
</dt>
<dd class="dlentry">
Measures the standard error of the selected variables.
</dd>
<dt class="dlterm">
Standard deviation
</dt>
<dd class="dlentry">
Measures the standard deviations of the selected variables.
</dd>
</dl>
</dd>
<dt class="dlterm">
Confidence level
</dt>
<dd class="dlentry">
This value represents the confidence intervals for the selected <span class="ph uicontrol">Measure</span>. The default value is 0.95 (95%).
</dd>
<dt class="dlterm">
Statistical method
</dt>
<dd class="dlentry">
Select the method for describing the central tendency:
<dl>
<dt class="dlterm">
Mean
</dt>
<dd class="dlentry">
The result of summing the ratios and dividing the result by the total number ratios.
</dd>
<dt class="dlterm">
Median
</dt>
<dd class="dlentry">
The value such that number of ratios less than this value and the number of ratios greater than this value are the same.
</dd>
</dl>
</dd>
<dt class="dlterm">
Display mode
</dt>
<dd class="dlentry">
Select how the <span class="ph uicontrol">Statistical method</span> selection displays (bar, line, or circle).
</dd>
<dt class="dlterm">
Legend orient
</dt>
<dd class="dlentry">
Sets the chart legend orientation. Available options are <span class="ph uicontrol">Horizontal</span>, <span class="ph uicontrol">Vertical</span>, and <span class="ph uicontrol">Vertical bottom</span>.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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41167E3AD363B416D508B03A300E5ACFAF83F042 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_evaluation.html?context=cdpaas&locale=en | Evaluation charts | Evaluation charts
Evaluation charts are similar to histograms or collection graphs. Evaluation charts show how accurate models are in predicting particular outcomes. They work by sorting records based on the predicted value and confidence of the prediction, splitting the records into groups of equal size (quantiles), and then plotting the value of the criterion for each quantile, from highest to lowest. Multiple models are shown as separate lines in the plot.
Outcomes are handled by defining a specific value or range of values as a "hit". Hits usually indicate success of some sort (such as a sale to a customer) or an event of interest (such as a specific medical diagnosis).
Flag
: Output fields are straightforward; hits correspond to true values.
Nominal
: For nominal output fields, the first value in the set defines a hit.
Continuous
: For continuous output fields, hits equal values greater than the midpoint of the field's range.
Evaluation charts can also be cumulative so that each point equals the value for the corresponding quantile plus all higher quantiles. Cumulative charts usually convey the overall performance of models better, whereas noncumulative charts often excel at indicating particular problem areas for models.
| # Evaluation charts #
Evaluation charts are similar to histograms or collection graphs\. Evaluation charts show how accurate models are in predicting particular outcomes\. They work by sorting records based on the predicted value and confidence of the prediction, splitting the records into groups of equal size (quantiles), and then plotting the value of the criterion for each quantile, from highest to lowest\. Multiple models are shown as separate lines in the plot\.
Outcomes are handled by defining a specific value or range of values as a "hit"\. Hits usually indicate success of some sort (such as a sale to a customer) or an event of interest (such as a specific medical diagnosis)\.
Flag
: Output fields are straightforward; hits correspond to `true` values\.
Nominal
: For nominal output fields, the first value in the set defines a hit\.
Continuous
: For continuous output fields, hits equal values greater than the midpoint of the field's range\.
Evaluation charts can also be cumulative so that each point equals the value for the corresponding quantile plus all higher quantiles\. Cumulative charts usually convey the overall performance of models better, whereas noncumulative charts often excel at indicating particular problem areas for models\.
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<h1 class="topictitle1" id="chart_creation_evaluation__title__1">Evaluation charts</h1>
<div class="body">
<div class="abstract">
Evaluation charts are similar to histograms or collection graphs. Evaluation charts show how accurate models are in predicting particular outcomes. They work by sorting records based on the predicted value and confidence of the prediction, splitting the records into groups of equal size (quantiles), and then plotting the value of the criterion for each quantile, from highest to lowest. Multiple models are shown as separate lines in the plot.
</div>
<p>Outcomes are handled by defining a specific value or range of values as a "hit". Hits usually indicate success of some sort (such as a sale to a customer) or an event of interest (such as a specific medical diagnosis).</p>
<dl>
<dt class="dlterm">
Flag
</dt>
<dd class="dlentry">
Output fields are straightforward; hits correspond to <code class="ph codeph">true</code> values.
</dd>
<dt class="dlterm">
Nominal
</dt>
<dd class="dlentry">
For nominal output fields, the first value in the set defines a hit.
</dd>
<dt class="dlterm">
Continuous
</dt>
<dd class="dlentry">
For continuous output fields, hits equal values greater than the midpoint of the field's range.
</dd>
</dl>
<p>Evaluation charts can also be cumulative so that each point equals the value for the corresponding quantile plus all higher quantiles. Cumulative charts usually convey the overall performance of models better, whereas noncumulative charts often excel at indicating particular problem areas for models.</p>
<section class="section" role="region" aria-labelledby="chart_creation_evaluation__section_tsd_ljb_qdb__title__1" id="chart_creation_evaluation__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_evaluation__section_tsd_ljb_qdb__title__1">Creating a simple Evaluation chart</h2>
<ol id="chart_creation_evaluation__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Evaluation</span> icon.
<p>The canvas updates to display an Evaluation chart template.</p></li>
<li>Set the <span class="ph uicontrol">Target field</span>, <span class="ph uicontrol">Predict field</span> and <span class="ph uicontrol">Confidence field</span> variables. The target field can be any instantiated flag or nominal field with two or more values. The predict field defines the variable that is used as the predicted value. The confidence field defines the variable that is used to establish the confidence of the prediction.
<div class="note" id="chart_creation_evaluation__note_cyq_zy4_blb">
<span class="notetitle">Note:</span> The <span class="ph uicontrol">Predict field</span> variable type must match the variable type that is selected for the <span class="ph uicontrol">Target field</span>.
</div></li>
<li id="chart_creation_evaluation__li_uy5_bz4_blb">Specify a custom condition used to indicate the <span class="ph uicontrol">User defined hit</span>. This option is useful for defining the outcome of interest rather than deducing it from the type of target field and the order of values.
<p id="chart_creation_evaluation__p_fxm_m1p_blb">You must specify a CLEM expression for a hit condition. For example, <code class="ph codeph">@TARGET = "YES"</code> is a valid condition that indicates a value of <code class="ph codeph">Yes</code> for the target field is counted as a hit in the evaluation. The specified condition is used for all target fields.</p></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_evaluation__section_fj1_mjb_qdb__title__1" id="chart_creation_evaluation__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_evaluation__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Target field
</dt>
<dd class="dlentry">
Lists instantiated flag or nominal field variables with two or more values.
</dd>
<dt class="dlterm">
User defined hit
</dt>
<dd class="dlentry">
Specify a hit value. Hits indicate events of interest (for example, a specific medical diagnosis).
</dd>
<dt class="dlterm">
Predict field
</dt>
<dd class="dlentry">
Lists variables that can be used as the predicted value.
</dd>
<dt class="dlterm">
Confidence field
</dt>
<dd class="dlentry">
Lists variables that can establish the confidence of the prediction.
</dd>
<dt class="dlterm" id="chart_creation_evaluation__dt_njf_tz4_blb">
Cumulative plot
</dt>
<dd class="dlentry" id="chart_creation_evaluation__dd_ojf_tz4_blb">
Create a cumulative chart when enabled. Values in cumulative charts are plotted for each quantile plus all higher quantiles.
</dd>
<dt class="dlterm">
Display mode
</dt>
<dd class="dlentry">
The settings control which charts display in preview mode and in the output.
<dl>
<dt class="dlterm">
Single mode
</dt>
<dd class="dlentry">
When selected, the Model Classification Tuning chart is in the only chart that displays in preview mode and in the output.
</dd>
<dt class="dlterm" id="chart_creation_evaluation__dt_yrw_tz4_blb">
Classical mode
</dt>
<dd class="dlentry" id="chart_creation_evaluation__dd_zrw_tz4_blb">
When selected, the Model Classification Tuning, Cutoff, Matrix Bar, ROC, Gains, ROI, and Profit charts display in preview mode and in the output.
</dd>
<dt class="dlterm">
Full mode
</dt>
<dd class="dlentry">
When selected, the Model Classification Tuning, Cutoff, Matrix Bar, ROC, Gains, ROI, Profit, GINI, Lift, and Response charts display in preview mode and in the output.
</dd>
</dl>
</dd>
<dt class="dlterm">
Evaluation charts
</dt>
<dd class="dlentry">
<dl>
<dt class="dlterm">
Cutoff
</dt>
<dd class="dlentry">
The cutoff chart shows the predicted versus actual values for selected variables for a specified cutoff value.
</dd>
<dt class="dlterm">
Matrix Bar
</dt>
<dd class="dlentry">
Matrix Bar charts are a good way to determine whether linear correlations exist between multiple variables.
</dd>
<dt class="dlterm">
ROC
</dt>
<dd class="dlentry">
ROC (Receiver Operating Characteristic) evaluates the performance of classification schemes where subjects are classified for one variable with two categories.
</dd>
<dt class="dlterm">
Gains
</dt>
<dd class="dlentry">
Gains are defined as the proportion of total hits that occurs in each quantile. Gains are computed as <code class="ph codeph">(number of hits in quantile / total number of hits) × 100%</code>.
</dd>
<dt class="dlterm">
ROI
</dt>
<dd class="dlentry">
ROI (return on investment) is similar to profit in that it involves defining revenues and costs. ROI compares profits to costs for the quantile. ROI is computed as <code class="ph codeph">(profits for quantile / costs for quantile) × 100%</code>.
</dd>
<dt class="dlterm">
Profit
</dt>
<dd class="dlentry">
Profit equals the revenue for each record minus the cost for the record. Profits for a quantile are the sum of profits for all records in the quantile. Revenues are assumed to apply only to hits, but costs apply to all records. Profits and costs can be fixed or can be defined by fields in the data. Profits are computed as (sum of revenue for records in quantile − sum of costs for records in quantile).
</dd>
<dt class="dlterm" id="chart_creation_evaluation__dt_fnh_zz4_blb">
Kolmogorov-Smirnov
</dt>
<dd class="dlentry" id="chart_creation_evaluation__dd_gnh_zz4_blb">
Compares the observed cumulative distribution function for a variable with a specified theoretical distribution, which can be normal, uniform, exponential, or Poisson.
</dd>
<dt class="dlterm">
GINI
</dt>
<dd class="dlentry">
GINI measures statistical dispersion and is intended to represent the income or wealth distribution. It is the most commonly used measurement of inequality.
</dd>
<dt class="dlterm">
Lift
</dt>
<dd class="dlentry">
Lift compares the percentage of records in each quantile that are hits with the overall percentage of hits in the training data. It is computed as <code class="ph codeph">(hits in quantile / records in quantile) / (total hits / total records)</code>.
</dd>
<dt class="dlterm">
Response
</dt>
<dd class="dlentry">
Response is the percentage of records in the quantile that are hits. Response is computed as <code class="ph codeph">(hits in quantile / records in quantile) × 100%</code>.
</dd>
</dl>
</dd>
<dt class="dlterm">
Evaluation chart settings
</dt>
<dd class="dlentry">
The following settings apply only to profit and ROI charts.
<dl>
<dt class="dlterm">
Costs
</dt>
<dd class="dlentry">
Specify the fixed cost associated with each record.
</dd>
<dt class="dlterm">
Revenue
</dt>
<dd class="dlentry">
Specify the fixed revenue associated with each record that represents a hit.
</dd>
<dt class="dlterm">
Weight
</dt>
<dd class="dlentry">
If the records in your data represent more than one unit, you can use frequency weights to adjust the results. Specify the fixed weight associated with each record.
</dd>
</dl>
</dd>
</dl>
</section>
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57AB3726FA10435D26878C626F61988F7305B9E8 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_fromgallery.html?context=cdpaas&locale=en | Building a chart from the chart type gallery | Building a chart from the chart type gallery
Use chart type gallery for building charts. Following are general steps for building a chart from the gallery.
1. In the Chart Type section, select a chart category. A preview version of the selected chart type is shown on the chart canvas.
2. If the canvas already displays a chart, the new chart replaces the chart's axis set and graphic elements.
1. Depending on the selected chart type, the available variables are presented under a number of different headings in the Details pane (for example, Category for bar charts, X-axis and Y-axis for line charts). Select the appropriate variables for the selected chart type.
3. Click the Save visualization to project control to save the visualization to the project. You can select to also Create a new asset from the visualization, provide a visualization asset name, description, and chart name.
4. Click Apply to save the visualization to the project. The new visualization asset is now available under the Assets tab.
| # Building a chart from the chart type gallery #
Use chart type gallery for building charts\. Following are general steps for building a chart from the gallery\.
<!-- <ol> -->
1. In the Chart Type section, select a chart category\. A preview version of the selected chart type is shown on the chart canvas\.
2. If the canvas already displays a chart, the new chart replaces the chart's axis set and graphic elements\.
<!-- <ol> -->
1. Depending on the selected chart type, the available variables are presented under a number of different headings in the Details pane (for example, Category for bar charts, X-axis and Y-axis for line charts). Select the appropriate variables for the selected chart type.
<!-- </ol> -->
3. Click the Save visualization to project control to save the visualization to the project\. You can select to also Create a new asset from the visualization, provide a visualization asset name, description, and chart name\.
4. Click Apply to save the visualization to the project\. The new visualization asset is now available under the Assets tab\.
<!-- </ol> -->
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<h1 class="topictitle1" id="chart_creation_fromgallery__title__1">Building a chart from the chart type gallery</h1>
<div class="body">
<p>Use chart type gallery for building charts. Following are general steps for building a chart from the gallery.</p>
<ol>
<li>In the <span class="ph uicontrol">Chart Type</span> section, select a chart category. A preview version of the selected chart type is shown on the chart canvas.</li>
<li>If the canvas already displays a chart, the new chart replaces the chart's axis set and graphic elements.
<ol type="a">
<li>Depending on the selected chart type, the available variables are presented under a number of different headings in the Details pane (for example, <span class="ph uicontrol">Category</span> for bar charts, <span class="ph uicontrol">X-axis</span> and <span class="ph uicontrol">Y-axis</span> for line charts). Select the appropriate variables for the selected chart type.</li>
</ol></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization to project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
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CC0ADF041F1628221CAC49A1BAEC1D497D762DC4 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_heatmap.html?context=cdpaas&locale=en | Heat map charts | Heat map charts
Heat map charts present data where the individual values that are contained in a matrix are represented as colors.
| # Heat map charts #
Heat map charts present data where the individual values that are contained in a matrix are represented as colors\.
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<div class="body">
<div class="abstract">
Heat map charts present data where the individual values that are contained in a matrix are represented as colors.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_heatmap__section_tsd_ljb_qdb__title__1" id="chart_creation_heatmap__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_heatmap__section_tsd_ljb_qdb__title__1">Creating a simple heat map chart</h2>
<ol id="chart_creation_heatmap__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Heat map</span> icon.
<p>The canvas updates to display a heat map chart template.</p></li>
<li>Select a variable as the <span class="ph uicontrol">Column</span> variable. Each variable category is represented as an individual chart column.</li>
<li>Select a variable as the <span class="ph uicontrol">Row</span> variable. Each variable category is represented as an individual chart row.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_heatmap__section_fj1_mjb_qdb__title__1" id="chart_creation_heatmap__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_heatmap__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Column
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's columns. Each variable category is represented as an individual chart column.
</dd>
<dt class="dlterm">
Row
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's rows. Each variable category is represented as an individual chart row.
</dd>
<dt class="dlterm">
Category order
</dt>
<dd class="dlentry">
Select the order in which variable categories are sorted.
<dl>
<dt class="dlterm">
As read
</dt>
<dd class="dlentry">
Variable categories are presented as they appear in the data set.
</dd>
<dt class="dlterm">
Ascending
</dt>
<dd class="dlentry">
Sort variable categories in ascending order.
</dd>
<dt class="dlterm">
Descending
</dt>
<dd class="dlentry">
Sort variable categories in descending order.
</dd>
</dl>
</dd>
<dt class="dlterm">
Summary
</dt>
<dd class="dlentry">
Select a statistical summary function for the graphic element. The result of the statistic determines the position of the graphic elements on the Y-axis. In a 2-D chart, the statistic is calculated for each value on the X-axis. In a 3-D chart, it is calculated for the intersection of values on the X-axis and Z-axis.
<div class="p" id="chart_creation_heatmap__d31e199">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<ul id="chart_creation_heatmap__d31e204">
<li id="chart_creation_heatmap__d31e206"><strong>Functions that do not require a value variable.</strong> Functions that do not require a variable. All count and percentage statistics are in this category. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
<li id="chart_creation_heatmap__d31e214"><strong>Functions that do require a value variable.</strong> Functions that do require a <span class="ph uicontrol">Value</span> variable. For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
</ul>
</div>
</dd>
<dt class="dlterm">
Value
</dt>
<dd class="dlentry">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable, is selected. Select a variable to serve as the value.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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1453D1CAD565842EEA24C8D92963BD73338EF0F1 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_histograms.html?context=cdpaas&locale=en | Histogram charts | Histogram charts
A histogram is similar in appearance to a bar chart, but instead of comparing categories or looking for trends over time, each bar represents how data is distributed in a single category. Each bar represents a continuous range of data or the number of frequencies for a specific data point.
Histograms are useful for showing the distribution of a single scale variable. Data are binned and summarized by using a count or percentage statistic. A variation of a histogram is a frequency polygon, which is like a typical histogram except that the area graphic element is used instead of the bar graphic element.
Another variation of the histogram is the population pyramid. Its name is derived from its most common use: summarizing population data. When used with population data, it is split by gender to provide two back-to-back, horizontal histograms of age data. In countries with a young population, the shape of the resulting graph resembles a pyramid.
Footnote
: The chart footnote, which is placed beneath the chart.
XAxis label
: The x-axis label, which is placed beneath the x-axis.
YAxis label
: The y-axis label, which is placed above the y-axis.
| # Histogram charts #
A histogram is similar in appearance to a bar chart, but instead of comparing categories or looking for trends over time, each bar represents how data is distributed in a single category\. Each bar represents a continuous range of data or the number of frequencies for a specific data point\.
Histograms are useful for showing the distribution of a single scale variable\. Data are binned and summarized by using a count or percentage statistic\. A variation of a histogram is a frequency polygon, which is like a typical histogram except that the area graphic element is used instead of the bar graphic element\.
Another variation of the histogram is the population pyramid\. Its name is derived from its most common use: summarizing population data\. When used with population data, it is split by gender to provide two back\-to\-back, horizontal histograms of age data\. In countries with a young population, the shape of the resulting graph resembles a pyramid\.
Footnote
: The chart footnote, which is placed beneath the chart\.
XAxis label
: The x\-axis label, which is placed beneath the x\-axis\.
YAxis label
: The y\-axis label, which is placed above the y\-axis\.
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<h1 class="topictitle1" id="chart_creation_histograms__title__1">Histogram charts</h1>
<div class="body">
<div class="abstract">
A histogram is similar in appearance to a bar chart, but instead of comparing categories or looking for trends over time, each bar represents how data is distributed in a single category. Each bar represents a continuous range of data or the number of frequencies for a specific data point.
</div>
<p>Histograms are useful for showing the distribution of a single scale variable. Data are binned and summarized by using a count or percentage statistic. A variation of a histogram is a frequency polygon, which is like a typical histogram except that the area graphic element is used instead of the bar graphic element.</p>
<p>Another variation of the histogram is the population pyramid. Its name is derived from its most common use: summarizing population data. When used with population data, it is split by gender to provide two back-to-back, horizontal histograms of age data. In countries with a young population, the shape of the resulting graph resembles a pyramid.</p>
<section class="section" role="region" aria-labelledby="chart_creation_histograms__section_tsd_ljb_qdb__title__1" id="chart_creation_histograms__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_histograms__section_tsd_ljb_qdb__title__1">Creating a histogram chart</h2>
<ol id="chart_creation_histograms__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Histogram</span> icon.
<p>The canvas updates to display a histogram chart template.</p></li>
<li>Select a scale variable as the <span class="ph uicontrol">X-axis</span> variable.
<div class="note">
<span class="notetitle">Note:</span> The statistic for a histogram is Histogram or Histogram Percent. These statistics bin the data and calculate a count for each bin.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_histograms__section_fj1_mjb_qdb__title__1" id="chart_creation_histograms__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_histograms__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm">
Bin method
</dt>
<dd class="dlentry">
Specify a bin method that is used to create the chart bars. Available option include <span class="ph uicontrol">Auto bin</span>, <span class="ph uicontrol">By bin width</span>, and <span class="ph uicontrol">By bin num</span>.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_idd_pbp_blb">
<a id="chart_creation_histograms__dlentry_wgh_x5c_clb"></a>Show kde curve
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_jdd_pbp_blb">
When enabled, the kernel density estimate curve is shown on the chart.
</dd>
<dt class="dlterm" id="chart_creation_histograms__curve">
Show distribution curve
</dt>
<dd class="dlentry">
When enabled, the distribution fitting curve is shown on the chart.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_j1s_tbp_blb">
Distribution
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_k1s_tbp_blb">
The drop-down list provides to the following distribution options.
<dl id="chart_creation_histograms__dl_wtw_hcp_blb">
<dt class="dlterm" id="chart_creation_histograms__dt_xtw_hcp_blb">
<a id="chart_creation_histograms__dlentry_zyn_kwv_blb"></a>Auto fit distribution
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_ytw_hcp_blb">
Automatically fits the distribution (the default setting).
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_slz_3cp_blb">
<a id="chart_creation_histograms__dlentry_dzn_kwv_blb"></a>Beta
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_tlz_3cp_blb">
Returns the value from a Beta distribution with specified shape parameters.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_xzg_jcp_blb">
<a id="chart_creation_histograms__dlentry_gzn_kwv_blb"></a>Exponential
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_yzg_jcp_blb">
Returns the value from an exponential distribution.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_ckh_jcp_blb">
<a id="chart_creation_histograms__dlentry_hzn_kwv_blb"></a>Gamma
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_dkh_jcp_blb">
Returns the value from the Gamma distribution, with the specified shape and scale parameters.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_esh_jcp_blb">
<a id="chart_creation_histograms__dlentry_jzn_kwv_blb"></a>Log-normal
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_fsh_jcp_blb">
Returns the value from a log-normal distribution with specified parameters.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_xzh_jcp_blb">
<a id="chart_creation_histograms__dlentry_kzn_kwv_blb"></a>Normal
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_yzh_jcp_blb">
Returns the value from a normal distribution with specified mean and standard deviation.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_hvk_jcp_blb">
<a id="chart_creation_histograms__dlentry_lzn_kwv_blb"></a>Triangular
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_ivk_jcp_blb">
Returns the value from a triangular distribution with specified parameters.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_fnn_lcp_blb">
<a id="chart_creation_histograms__dlentry_mzn_kwv_blb"></a>Uniform
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_gnn_lcp_blb">
Returns the value from the uniform distribution between the minimum and maximum.
</dd>
<dt class="dlterm" id="chart_creation_histograms__dt_xzq_lcp_blb">
<a id="chart_creation_histograms__dlentry_ozn_kwv_blb"></a>Weibull
</dt>
<dd class="dlentry" id="chart_creation_histograms__dd_yzq_lcp_blb">
Returns the value from a Weibull distribution with specified parameters.
</dd>
</dl>
</dd>
<dt class="dlterm" id="chart_creation_histograms__binwidth">
Bin width
</dt>
<dd class="dlentry">
The slider controls the size of the interval that is used to split the data into groups.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
</dl>
</section>
<dl>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</div>
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9DF72C2325CE5BACA0CC7D2A884695D115557C40 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_linecharts.html?context=cdpaas&locale=en | Line charts | Line charts
A line chart plots a series of data points on a graph and connects them with lines. A line chart is useful for showing trend lines with subtle differences, or with data lines that cross one another. You can use a line chart to summarize categorical variables, in which case it is similar to a bar chart (see [Bar charts](https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_barcharts.htmlchart_creation_barcharts) ). Line charts are also useful for time-series data.
| # Line charts #
A line chart plots a series of data points on a graph and connects them with lines\. A line chart is useful for showing trend lines with subtle differences, or with data lines that cross one another\. You can use a line chart to summarize categorical variables, in which case it is similar to a bar chart (see [Bar charts](https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_barcharts.html#chart_creation_barcharts) )\. Line charts are also useful for time\-series data\.
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<div class="abstract">
A line chart plots a series of data points on a graph and connects them with lines. A line chart is useful for showing trend lines with subtle differences, or with data lines that cross one another. You can use a line chart to summarize categorical variables, in which case it is similar to a bar chart (see <a href="chart_creation_barcharts.html#chart_creation_barcharts">Bar charts</a> ). Line charts are also useful for time-series data.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_linecharts__section_tsd_ljb_qdb__title__1" id="chart_creation_linecharts__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_linecharts__section_tsd_ljb_qdb__title__1">Creating a simple time-series line chart</h2>
<ol id="chart_creation_linecharts__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Line</span> icon.
<p>The canvas updates to display a line chart template.</p></li>
<li>Select a date variable as the <span class="ph uicontrol">X-axis</span> variable.</li>
<li>Select a scale variable as the <span class="ph uicontrol">Y-axis</span> variable (the variable whose values were recorded over time).</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_linecharts__section_fj1_mjb_qdb__title__1" id="chart_creation_linecharts__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_linecharts__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_linecharts__xfield">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm" id="chart_creation_linecharts__yfield">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Y-axis.
</dd>
<dt class="dlterm">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm" id="chart_creation_linecharts__area">
Area
</dt>
<dd class="dlentry">
When enabled, the area beneath the line is shown in a different color.
</dd>
<dt class="dlterm" id="chart_creation_linecharts__dt_bjl_d2p_blb">
<a id="chart_creation_linecharts__dlentry_p1b_s2p_blb"></a>Smooth
</dt>
<dd class="dlentry" id="chart_creation_linecharts__dd_cjl_d2p_blb">
When enabled, the chart shows a smooth curve.
</dd>
<dt class="dlterm" id="chart_creation_linecharts__dt_gl5_r2p_blb">
<a id="chart_creation_linecharts__dlentry_zw2_s2p_blb"></a>Show data points
</dt>
<dd class="dlentry" id="chart_creation_linecharts__dd_hl5_r2p_blb">
When enabled, the data point is shown in the chart.
</dd>
<dt class="dlterm" id="chart_creation_linecharts__dt_n5w_52p_blb">
<a id="chart_creation_linecharts__dlentry_fhc_v2p_blb"></a>Reorder
</dt>
<dd class="dlentry" id="chart_creation_linecharts__dd_o5w_52p_blb">
The toggle control reorders data based on X and Y-axis values.
</dd>
<dt class="dlterm">
Fit line
</dt>
<dd class="dlentry">
In a fit line, the data points are fitted to a line that usually does not pass through all of the data points. The fit line represents the trend of the data. Some fits lines are regression-based. Others are based on iterative weighted least squares. Select a fit line option from the drop-down list.
</dd>
<dt class="dlterm" id="chart_creation_linecharts__dt_h14_vgp_blb">
<a id="chart_creation_linecharts__dlentry_kps_vgp_blb"></a>Show reference line
</dt>
<dd class="dlentry" id="chart_creation_linecharts__dd_i14_vgp_blb">
When enabled, the option shows a reference line on the chart that is based on the specified <span class="ph uicontrol">xAxis</span> and <span class="ph uicontrol">yAxis</span> values.
<dl id="chart_creation_linecharts__dl_d3h_khp_blb">
<dt class="dlterm" id="chart_creation_linecharts__dt_e3h_khp_blb">
Enter a reference line value on xAxis
</dt>
<dd class="dlentry" id="chart_creation_linecharts__dd_f3h_khp_blb">
When <span class="ph uicontrol">Show reference line</span> is enabled, this setting provides the option of specifying a specific reference line value for the X-axis. Click <span class="ph uicontrol">Add another column</span> to specify more reference line values.
</dd>
<dt class="dlterm" id="chart_creation_linecharts__dt_nsc_3kp_blb">
Enter a reference line value on yAxis
</dt>
<dd class="dlentry" id="chart_creation_linecharts__dd_osc_3kp_blb">
When <span class="ph uicontrol">Show reference line</span> is enabled, this setting provides the option of specifying a specific reference line value for the Y-axis. Click <span class="ph uicontrol">Add another column</span> to specify more reference line values.
</dd>
</dl>
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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F5AF4BCC2D0168D2698BEB2A858C24F81A476610 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_map.html?context=cdpaas&locale=en | Map charts | Map charts
Map charts are commonly used to compare values and show categories across geographical regions. Map charts are most beneficial when the data contains geographic information (countries, regions, states, counties, postal codes, and so on).
| # Map charts #
Map charts are commonly used to compare values and show categories across geographical regions\. Map charts are most beneficial when the data contains geographic information (countries, regions, states, counties, postal codes, and so on)\.
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<title>Map charts</title>
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<h1 class="topictitle1" id="chart_creation_map__title__1">Map charts</h1>
<div class="body">
<div class="abstract">
Map charts are commonly used to compare values and show categories across geographical regions. Map charts are most beneficial when the data contains geographic information (countries, regions, states, counties, postal codes, and so on).
</div>
<section class="section" role="region" aria-labelledby="chart_creation_map__section_tsd_ljb_qdb__title__1" id="chart_creation_map__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_map__section_tsd_ljb_qdb__title__1">Creating a simple map chart</h2>
<ol id="chart_creation_map__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Map</span> icon.
<p>The canvas updates to display a map chart template.</p></li>
<li id="chart_creation_map__li_tbs_ykp_blb">Select a service to use for serving map images from the <span class="ph uicontrol">Map service</span> drop-down list. The list provides options that cover specific global regions.</li>
<li id="chart_creation_map__li_yrd_glp_blb">Select a map chart <span class="ph uicontrol">Type</span> from the drop-down menu. The following options are available, dependent on the selected chart type:
<dl id="chart_creation_map__dl_h31_r45_blb">
<dt class="dlterm" id="chart_creation_map__dt_i31_r45_blb">
Longitude
</dt>
<dd class="dlentry" id="chart_creation_map__dd_j31_r45_blb">
Select a variable to serve as the longitudinal value from the drop-down list.
</dd>
<dt class="dlterm" id="chart_creation_map__dt_dpx_r45_blb">
Latitude
</dt>
<dd class="dlentry" id="chart_creation_map__dd_epx_r45_blb">
Select a variable to serve as the latitudinal value from the drop-down list.
</dd>
<dt class="dlterm" id="chart_creation_map__dt_sxj_pp5_blb">
Group
</dt>
<dd class="dlentry" id="chart_creation_map__dd_txj_pp5_blb">
Select a variable that groups the data point locations from the drop-down menu.
</dd>
<dt class="dlterm" id="chart_creation_map__dt_fzt_y45_blb">
Category
</dt>
<dd class="dlentry" id="chart_creation_map__dd_gzt_y45_blb">
Select a column variable that you want to visualize.
</dd>
<dt class="dlterm">
Summary
</dt>
<dd class="dlentry">
Select a statistical summary function (the method that is used for summarizing each category).
<div class="p" id="chart_creation_map__p_hrx_4w4_blb">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<ul id="chart_creation_map__d31e204">
<li id="chart_creation_map__d31e206"><strong>Functions that do not require a value variable.</strong> Functions that do not require a variable. All count and percentage statistics are in this category. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
<li id="chart_creation_map__d31e214"><strong>Functions that do require a value variable.</strong> Functions that do require a <span class="ph uicontrol">Value</span> variable. For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
</ul>
</div>
</dd>
<dt class="dlterm">
Value
</dt>
<dd class="dlentry">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable, is selected. Select a variable to serve as the value.
</dd>
</dl></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_map__section_fj1_mjb_qdb__title__1" id="chart_creation_map__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_map__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Map service
</dt>
<dd class="dlentry">
Lists the services that are available for providing map images.
</dd>
<dt class="dlterm" id="chart_creation_map__type">
Type
</dt>
<dd class="dlentry">
Lists the chart types that are available to represent the data.
</dd>
<dt class="dlterm">
Longitude
</dt>
<dd class="dlentry">
Lists the variables that are available to serve as the longitudinal value.
</dd>
<dt class="dlterm">
Latitude
</dt>
<dd class="dlentry">
Lists the variables that are available to serve as the latitudinal value.
</dd>
<dt class="dlterm" id="chart_creation_map__dt_xsk_1q5_blb">
Group
</dt>
<dd class="dlentry" id="chart_creation_map__dd_ysk_1q5_blb">
Lists the variables that can be used to group the data point locations.
</dd>
<dt class="dlterm" id="chart_creation_map__dt_zsk_1q5_blb">
Category
</dt>
<dd class="dlentry" id="chart_creation_map__dd_atk_1q5_blb">
Lists column variables.
</dd>
<dt class="dlterm">
Summary
</dt>
<dd class="dlentry">
Select a statistical summary function (the method that is used for summarizing each category).
<div class="p" id="chart_creation_map__p_hrx_4w4_blb-d29e244">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<ul id="chart_creation_map__d31e204-d29e249">
<li id="chart_creation_map__d31e206-d29e251"><strong>Functions that do not require a value variable.</strong> Functions that do not require a variable. All count and percentage statistics are in this category. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
<li id="chart_creation_map__d31e214-d29e259"><strong>Functions that do require a value variable.</strong> Functions that do require a <span class="ph uicontrol">Value</span> variable. For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
</ul>
</div>
</dd>
<dt class="dlterm">
Value
</dt>
<dd class="dlentry">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable, is selected. Select a variable to serve as the value.
</dd>
<dt class="dlterm" id="chart_creation_map__tooltip">
Tooltip info
</dt>
<dd class="dlentry">
Lists the variables that can be used the generate tooltip information when the cursor hovers over a data point.
</dd>
<dt class="dlterm" id="chart_creation_map__d25e207">
Size map
</dt>
<dd class="dlentry" id="chart_creation_map__d25e210">
Lists available size map variables. These variables use differing sizes to represent themselves in the plot points.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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0C836867DD758509B908532F35CFC5E160D81A19 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_mathcurve.html?context=cdpaas&locale=en | Math curve charts | Math curve charts
A math curve chart plots mathematical equation curves that are based on user-entered expressions.
| # Math curve charts #
A math curve chart plots mathematical equation curves that are based on user\-entered expressions\.
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<h1 class="topictitle1" id="chart_creation_mathcurve__title__1">Math curve charts</h1>
<div class="body">
<div class="abstract">
A math curve chart plots mathematical equation curves that are based on user-entered expressions.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_mathcurve__section_tsd_ljb_qdb__title__1" id="chart_creation_mathcurve__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_mathcurve__section_tsd_ljb_qdb__title__1">Creating a simple math curve chart</h2>
<ol id="chart_creation_mathcurve__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Math curve</span> icon.
<p>The canvas updates to display a math curve chart template.</p></li>
<li>Enter a starting value for the X-axis in the <span class="ph uicontrol">X value starts from</span> field.</li>
<li id="chart_creation_mathcurve__li_f5c_mx5_blb">Enter an ending value for the X-axis in the <span class="ph uicontrol">X value ends with</span> field.</li>
<li id="chart_creation_mathcurve__li_tnl_tx5_blb">Enter an equation that plots the graph curve in the <span class="ph uicontrol">equations</span> field. Click <span class="ph uicontrol">Add another column</span> to include more equations.
<div class="p" id="chart_creation_mathcurve__p_o3r_by5_blb">
Each equation treats <code class="ph codeph">x</code> as an independent variable. The following equations are allowed.
<ul id="chart_creation_mathcurve__ul_vtv_xy5_blb">
<li id="chart_creation_mathcurve__li_wtv_xy5_blb"><code class="ph codeph">+, -, *, /, %, and ^</code></li>
<li id="chart_creation_mathcurve__li_txv_yy5_blb"><code class="ph codeph">abs(x)</code></li>
<li id="chart_creation_mathcurve__li_xdb_hz5_blb"><code class="ph codeph">ceil(x)</code></li>
<li id="chart_creation_mathcurve__li_lch_hz5_blb"><code class="ph codeph">floor(x)</code></li>
<li id="chart_creation_mathcurve__li_rrh_hz5_blb"><code class="ph codeph">log(x)</code></li>
<li id="chart_creation_mathcurve__li_szh_hz5_blb"><code class="ph codeph">max(a,b,c...)</code></li>
<li id="chart_creation_mathcurve__li_v5k_hz5_blb"><code class="ph codeph">min(a,b,c...)</code></li>
<li id="chart_creation_mathcurve__li_wwk_hz5_blb"><code class="ph codeph">random()</code></li>
<li id="chart_creation_mathcurve__li_uyk_hz5_blb"><code class="ph codeph">round(x)</code></li>
<li id="chart_creation_mathcurve__li_kzk_hz5_blb"><code class="ph codeph">sqrt(x)</code></li>
<li id="chart_creation_mathcurve__li_ezr_kz5_blb"><code class="ph codeph">sin</code></li>
<li id="chart_creation_mathcurve__li_p4s_kz5_blb"><code class="ph codeph">cos</code></li>
<li id="chart_creation_mathcurve__li_zbt_kz5_blb"><code class="ph codeph">exp</code></li>
<li id="chart_creation_mathcurve__li_iqt_kz5_blb"><code class="ph codeph">tan</code></li>
<li id="chart_creation_mathcurve__li_id5_kz5_blb"><code class="ph codeph">atan</code></li>
<li id="chart_creation_mathcurve__li_ix4_4z5_blb"><code class="ph codeph">atan2</code></li>
<li id="chart_creation_mathcurve__li_fbp_4z5_blb"><code class="ph codeph">asin</code></li>
<li id="chart_creation_mathcurve__li_jdp_4z5_blb"><code class="ph codeph">acos</code></li>
</ul>
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_mathcurve__section_fj1_mjb_qdb__title__1" id="chart_creation_mathcurve__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_mathcurve__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_mathcurve__xfield">
X value starts from
</dt>
<dd class="dlentry">
The X-axis starting value.
</dd>
<dt class="dlterm" id="chart_creation_mathcurve__yfield">
X value ends with
</dt>
<dd class="dlentry">
The X-axis ending value.
</dd>
<dt class="dlterm" id="chart_creation_mathcurve__area">
equations
</dt>
<dd class="dlentry">
User-entered equations that plot the graph curve.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
</div>
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66E7B1F986535FCE165F0CB5C553A6305339204E | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_matrixscatter.html?context=cdpaas&locale=en | Scatter matrix charts | Scatter matrix charts
Scatter plot matrices are a good way to determine whether linear correlations exist between multiple variables.
| # Scatter matrix charts #
Scatter plot matrices are a good way to determine whether linear correlations exist between multiple variables\.
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<div class="body">
<div class="abstract">
Scatter plot matrices are a good way to determine whether linear correlations exist between multiple variables.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_matrixscatter__section_lxw_wxd_rdb__title__1" id="chart_creation_matrixscatter__section_lxw_wxd_rdb">
<h2 class="sectiontitle" id="chart_creation_matrixscatter__section_lxw_wxd_rdb__title__1">Creating a scatter matrix chart</h2>
<ol id="chart_creation_matrixscatter__ol_mxw_wxd_rdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Scatter matrix</span> icon.
<p>The canvas updates to display a scatter matrix chart template.</p></li>
<li>Select at least two scale <span class="ph uicontrol">Columns</span> variables.
<div class="note" id="chart_creation_matrixscatter__note_ntf_gpc_clb">
<span class="notetitle">Note:</span> Click <span class="ph uicontrol">Add another column</span> to include more column variables.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
<p>Each selected variable is plotted against every other variable to create a matrix of individual scatter plots.</p>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_matrixscatter__section_oxw_wxd_rdb__title__1" id="chart_creation_matrixscatter__section_oxw_wxd_rdb">
<h2 class="sectiontitle" id="chart_creation_matrixscatter__section_oxw_wxd_rdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_matrixscatter__dt_n5t_35c_clb">
Columns
</dt>
<dd class="dlentry" id="chart_creation_matrixscatter__dd_o5t_35c_clb">
Select at least two matrix variables. The variables must be numeric (but not date format).
</dd>
<dd class="ddexpand" id="chart_creation_matrixscatter__dd_p5t_35c_clb">
Click <span class="ph uicontrol">Add another column</span> to add more columns.
</dd>
<dt class="dlterm" id="chart_creation_matrixscatter__d25e198">
Color map
</dt>
<dd class="dlentry" id="chart_creation_matrixscatter__d25e201">
Lists available color map variables. These variables use color progression, based on the range of values in the specified column, to represent themselves in the plot points. Color maps are also known as choropleth maps.
</dd>
<dt class="dlterm">
Correlation
</dt>
<dd class="dlentry">
When enabled, linear correlation information (Strong, Medium, Weak) is shown for the selected variables.
</dd>
<dt class="dlterm" id="chart_creation_matrixscatter__d30e127">
Show kde curve
</dt>
<dd class="dlentry" id="chart_creation_matrixscatter__d30e130">
When enabled, the kernel density estimate curve is shown on the chart.
</dd>
<dt class="dlterm">
Show histogram
</dt>
<dd class="dlentry">
When enabled, histogram charts display for the selected column variables.
</dd>
<dt class="dlterm" id="chart_creation_matrixscatter__d25e235">
Gradient bubble
</dt>
<dd class="dlentry" id="chart_creation_matrixscatter__d25e238">
The toggle control enables and disables the display of color gradients and 3D effects in the chart bubbles. The setting is not available when a <span class="ph uicontrol">Color map</span> variable is selected.
</dd>
<dt class="dlterm" id="chart_creation_matrixscatter__d25e247">
Minimum bubble size
</dt>
<dd class="dlentry" id="chart_creation_matrixscatter__d25e250">
Sets the minimum bubble size. Enter a value the range 5 - 20.
</dd>
<dt class="dlterm" id="chart_creation_matrixscatter__d25e256">
Maximum bubble size
</dt>
<dd class="dlentry" id="chart_creation_matrixscatter__d25e259">
Sets the maximum bubble size. Enter a value in the range 20 - 80.
</dd>
<dt class="dlterm">
Show label
</dt>
<dd class="dlentry">
When enabled, column labels display on the chart. Only scatter series data is supported.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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3094E343D06DA6AE0D0D5D4865C7B0D806DC61A1 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_multichart.html?context=cdpaas&locale=en | Multi-chart charts | Multi-chart charts
Multi-chart charts provide options for creating multiple charts. The charts can be of the same or different types, and can include different variables from the same data set.
| # Multi\-chart charts #
Multi\-chart charts provide options for creating multiple charts\. The charts can be of the same or different types, and can include different variables from the same data set\.
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<h1 class="topictitle1" id="chart_creation_multichart__title__1">Multi-chart charts</h1>
<div class="body">
<div class="abstract">
Multi-chart charts provide options for creating multiple charts. The charts can be of the same or different types, and can include different variables from the same data set.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_multichart__section_tsd_ljb_qdb__title__1" id="chart_creation_multichart__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_multichart__section_tsd_ljb_qdb__title__1">Creating a simple multi-chart chart</h2>
<ol id="chart_creation_multichart__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Multi-chart</span> icon.
<p>The canvas updates to display a multi-chart chart template.</p></li>
<li id="chart_creation_multichart__li_f4v_v1v_blb">Click <span class="ph uicontrol">Add another sub-chart</span> to add a subchart.</li>
<li>Select a chart type from the <span class="ph uicontrol">Type</span> drop-down list.</li>
<li>Depending on the selected chart type, the following options are available:
<dl>
<dt class="dlterm">
Bar and Pie charts
</dt>
<dd class="dlentry">
Select a <span class="ph uicontrol">Category</span> variable from the drop-down list.
</dd>
<dt class="dlterm">
Line and Scatter plot charts
</dt>
<dd class="dlentry">
Select an <span class="ph uicontrol">X-axis</span> variable from the drop-down list.
</dd>
<dd class="ddexpand">
Select an <span class="ph uicontrol">Y-axis</span> variable from the drop-down list.
</dd>
</dl></li>
<li>Click <span class="ph uicontrol">Add another sub-chart</span> to include more charts types.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_multichart__section_fj1_mjb_qdb__title__1" id="chart_creation_multichart__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_multichart__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_multichart__summary">
Title
</dt>
<dd class="dlentry">
The subchart title.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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E777A9C7D0450D572431F168374224179C1AE7C4 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_multiseries.html?context=cdpaas&locale=en | Multiple series charts | Multiple series charts
Multiple series charts are similar to line charts, with the exception that you can chart multiple variables on the Y-axis.
| # Multiple series charts #
Multiple series charts are similar to line charts, with the exception that you can chart multiple variables on the Y\-axis\.
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<h1 class="topictitle1" id="chart_creation_multiseries__title__1">Multiple series charts</h1>
<div class="body">
<div class="abstract">
Multiple series charts are similar to line charts, with the exception that you can chart multiple variables on the Y-axis.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_multiseries__section_tsd_ljb_qdb__title__1" id="chart_creation_multiseries__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_multiseries__section_tsd_ljb_qdb__title__1">Creating a simple multiple series chart</h2>
<ol id="chart_creation_multiseries__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Multi-series</span> icon.
<p>The canvas updates to display a multiple series chart template.</p></li>
<li>Select a variable as the <span class="ph uicontrol">X-axis</span> variable.</li>
<li>Select at least two scale variables as the <span class="ph uicontrol">Y-axis</span> variables.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_multiseries__section_fj1_mjb_qdb__title__1" id="chart_creation_multiseries__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_multiseries__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Y-axes.
</dd>
<dd class="ddexpand">
Select the chart type (bar, line, or scatter) from the drop-down list.
</dd>
<dd class="ddexpand">
Click <span class="ph uicontrol">Add another column</span> to include more columns on the chart.
</dd>
<dt class="dlterm" id="chart_creation_multiseries__dt_chw_wcv_blb">
Series style
</dt>
<dd class="dlentry" id="chart_creation_multiseries__dd_dhw_wcv_blb">
Provides options for defining the Y-axes orientation. The following options are available.
<ul id="chart_creation_multiseries__ul_dyh_kdv_blb">
<li id="chart_creation_multiseries__li_eyh_kdv_blb">Default</li>
<li id="chart_creation_multiseries__li_ct4_kdv_blb">Separate Y axes</li>
<li id="chart_creation_multiseries__li_cwh_ldv_blb">Show a secondary Y axis</li>
<li id="chart_creation_multiseries__li_v5k_ldv_blb">Dual Y axes</li>
</ul>
</dd>
<dt class="dlterm">
Secondary Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's secondary Y-axes.
</dd>
<dd class="ddexpand">
Select the chart type (bar, line, or scatter) from the drop-down list.
</dd>
<dd class="ddexpand">
Click <span class="ph uicontrol">Add another column</span> to include more columns on the chart.
</dd>
<dt class="dlterm">
Normalize data
</dt>
<dd class="dlentry">
When enabled, this setting transforms data into a normal distribution compares data from multiple data sets or multiple columns. This setting creates 100% stacking for counts and converts statistics to percents.
</dd>
<dt class="dlterm">
Reorder
</dt>
<dd class="dlentry">
When enabled, the chart's data is reordered based on the X and Y axis values.
</dd>
<dt class="dlterm">
Show label
</dt>
<dd class="dlentry">
When enabled, column labels display on the chart. Only scatter series data is supported.
</dd>
<dt class="dlterm" id="chart_creation_multiseries__label-field">
Label field
</dt>
<dd class="dlentry">
The field menu provides variables to display as chart labels.
</dd>
<dt class="dlterm">
Legend orient
</dt>
<dd class="dlentry">
Sets the chart legend orientation. Available options are <span class="ph uicontrol">Horizontal</span>, <span class="ph uicontrol">Vertical</span>, and <span class="ph uicontrol">Vertical bottom</span>.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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DE359E77F61C11B6F759E8DFE8EA69AAC3D0514A | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_parallel.html?context=cdpaas&locale=en | Parallel charts | Parallel charts
Parallel charts are useful for visualizing high dimensional geometry and for analyzing multivariate data. Parallel charts resemble line charts for time-series data, but the axes do not correspond to points in time (a natural order is not present).
| # Parallel charts #
Parallel charts are useful for visualizing high dimensional geometry and for analyzing multivariate data\. Parallel charts resemble line charts for time\-series data, but the axes do not correspond to points in time (a natural order is not present)\.
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<h1 class="topictitle1" id="chart_creation_parallel__title__1">Parallel charts</h1>
<div class="body">
<div class="abstract">
Parallel charts are useful for visualizing high dimensional geometry and for analyzing multivariate data. Parallel charts resemble line charts for time-series data, but the axes do not correspond to points in time (a natural order is not present).
</div>
<section class="section" role="region" aria-labelledby="chart_creation_parallel__section_tsd_ljb_qdb__title__1" id="chart_creation_parallel__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_parallel__section_tsd_ljb_qdb__title__1">Creating a simple parallel chart</h2>
<ol id="chart_creation_parallel__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Parallel</span> icon.
<p>The canvas updates to display a parallel chart template.</p></li>
<li>Select at least two variables as the <span class="ph uicontrol">Columns</span> variables. Each column represents a vertical, parallel axis in the chart.
<div class="note">
<span class="notetitle">Note:</span> The column order is important for finding features. In a typical data analysis, you might need to reorder the columns numerous times.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_parallel__section_fj1_mjb_qdb__title__1" id="chart_creation_parallel__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_parallel__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Y-axes.
</dd>
<dd class="ddexpand">
Click <span class="ph uicontrol">Add another column</span> to add more columns.
</dd>
<dt class="dlterm" id="chart_creation_parallel__d25e198">
Color map
</dt>
<dd class="dlentry" id="chart_creation_parallel__d25e201">
Lists available color map variables. These variables use color progression, based on the range of values in the specified column, to represent themselves in the plot points. Color maps are also known as choropleth maps.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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6B4213FC5352021865E77592EBC27242E746B5AA | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_pareto.html?context=cdpaas&locale=en | Pareto charts | Pareto charts
Pareto charts contain both bars and a line graph. The bars represent individual variable categories and the line graph represents the cumulative total.
| # Pareto charts #
Pareto charts contain both bars and a line graph\. The bars represent individual variable categories and the line graph represents the cumulative total\.
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<h1 class="topictitle1" id="chart_creation_pareto__title__1">Pareto charts</h1>
<div class="body">
<div class="abstract">
Pareto charts contain both bars and a line graph. The bars represent individual variable categories and the line graph represents the cumulative total.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_pareto__section_tsd_ljb_qdb__title__1" id="chart_creation_pareto__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_pareto__section_tsd_ljb_qdb__title__1">Creating a simple Pareto chart</h2>
<ol id="chart_creation_pareto__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Pareto</span> icon.
<p>The canvas updates to display a Pareto chart template.</p></li>
<li>Select a variable as the <span class="ph uicontrol">Category</span> variable. The selected variable categories are drawn on the chart's X-axis.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_pareto__section_fj1_mjb_qdb__title__1" id="chart_creation_pareto__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_pareto__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Category
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
YAxis align
</dt>
<dd class="dlentry">
The toggle control enables and disables the alignment of the two Y-axes on the chart.
</dd>
<dt class="dlterm" id="chart_creation_pareto__dt_nzm_xqc_clb">
Gradient bar
</dt>
<dd class="dlentry" id="chart_creation_pareto__dd_ozm_xqc_clb">
The toggle control enables and disables the display of color gradients in the chart bars.
</dd>
<dt class="dlterm">
Highlight vital few
</dt>
<dd class="dlentry">
The toggle control enables and disables the highlighting of variable categories that are considered vital.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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A2B0DB014389285D9ABCA9FE0D4035F85DE6D102 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_piecharts.html?context=cdpaas&locale=en | Pie charts | Pie charts
A pie chart is useful for comparing proportions. For example, you can use a pie chart to demonstrate that a greater proportion of Europeans is enrolled in a certain class.
| # Pie charts #
A pie chart is useful for comparing proportions\. For example, you can use a pie chart to demonstrate that a greater proportion of Europeans is enrolled in a certain class\.
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<title>Pie charts</title>
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<h1 class="topictitle1" id="chart_creation_piecharts__title__1">Pie charts</h1>
<div class="body">
<div class="abstract">
A pie chart is useful for comparing proportions. For example, you can use a pie chart to demonstrate that a greater proportion of Europeans is enrolled in a certain class.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_piecharts__section_tsd_ljb_qdb__title__1" id="chart_creation_piecharts__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_piecharts__section_tsd_ljb_qdb__title__1">Creating a simple pie chart</h2>
<ol id="chart_creation_piecharts__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Pie</span> icon.
<p>The canvas updates to display a pie chart template.</p></li>
<li>Select a categorical (nominal or ordinal) variable from the <span class="ph uicontrol">Category</span> list. The categories in this variable determine the number of slices in the pie chart.</li>
<li>Select a statistical summary function for the graphic element. For pie charts, you typically want a count-based statistic or a sum. The result of the statistic determines the size of each slice.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_piecharts__section_fj1_mjb_qdb__title__1" id="chart_creation_piecharts__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_piecharts__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Category
</dt>
<dd class="dlentry">
Select a categorical (nominal or ordinal) variable that determines the number of slices in the pie chart.
</dd>
<dt class="dlterm" id="chart_creation_piecharts__dt_dwk_whv_blb">
Summary
</dt>
<dd class="dlentry" id="chart_creation_piecharts__dd_ewk_whv_blb">
Select a statistical summary function for the graphic element. For pie charts, you typically want a count-based statistic or a sum. The result of the statistic determines the size of each slice.
<div class="p" id="chart_creation_piecharts__p_hrx_4w4_blb">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<ul id="chart_creation_piecharts__d31e204">
<li id="chart_creation_piecharts__d31e206"><strong>Functions that do not require a value variable.</strong> Functions that do not require a variable. All count and percentage statistics are in this category. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
<li id="chart_creation_piecharts__d31e214"><strong>Functions that do require a value variable.</strong> Functions that do require a <span class="ph uicontrol">Value</span> variable. For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
</ul>
</div>
</dd>
<dt class="dlterm" id="chart_creation_piecharts__dt_xrl_xhv_blb">
Value
</dt>
<dd class="dlentry" id="chart_creation_piecharts__dd_yrl_xhv_blb">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a scale variable, is selected. Select a variable to serve as the scale variable.
</dd>
<dt class="dlterm" id="chart_creation_piecharts__pietype">
Pie type
</dt>
<dd class="dlentry">
The following styles are available.
<dl>
<dt class="dlterm">
Normal
</dt>
<dd class="dlentry">
The pie segments display as normal slices.
</dd>
<dt class="dlterm">
Ring
</dt>
<dd class="dlentry">
The pie segments display as a ring. This style is also known as a doughnut chart.
</dd>
<dt class="dlterm">
Rose
</dt>
<dd class="dlentry">
Unlike the normal pie chart, which uses a common radius, the pie segment sizes vary depending on their value.
</dd>
<dt class="dlterm" id="chart_creation_piecharts__dt_omh_f3v_blb">
Rose area
</dt>
<dd class="dlentry" id="chart_creation_piecharts__dd_pmh_f3v_blb">
Unlike the normal pie chart, which uses a common radius, the pie segment sizes vary depending on their area.
</dd>
<dt class="dlterm" id="chart_creation_piecharts__dt_msz_f3v_blb">
Rose ring
</dt>
<dd class="dlentry" id="chart_creation_piecharts__dd_nsz_f3v_blb">
Unlike the normal pie chart, which uses a common radius, the pie segment sizes vary depending on their value and the segments display as a ring.
</dd>
<dt class="dlterm" id="chart_creation_piecharts__dt_csp_g3v_blb">
Half rose
</dt>
<dd class="dlentry" id="chart_creation_piecharts__dd_dsp_g3v_blb">
Same as Rose, except the chart is represented as one-half of a pie.
</dd>
</dl>
</dd>
<dt class="dlterm">
Show value
</dt>
<dd class="dlentry">
When enabled, the pie slice values display in the legend.
</dd>
<dt class="dlterm">
Outer field
</dt>
<dd class="dlentry">
The list provides variables that can be used as the count value of the outer ring.
</dd>
<dt class="dlterm">
Outer label angle
</dt>
<dd class="dlentry">
The value species the location of the outer field variable value on the outer ring.
</dd>
<dt class="dlterm">
Legend orient
</dt>
<dd class="dlentry">
Sets the chart legend orientation. Available options are <span class="ph uicontrol">Horizontal</span>, <span class="ph uicontrol">Vertical</span>, and <span class="ph uicontrol">Vertical bottom</span>.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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81F297B28D1978EB0D0B1985D6F44B45DFE53542 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_pyramid.html?context=cdpaas&locale=en | Population pyramid charts | Population pyramid charts
Population pyramid charts (also known as "age-sex pyramids") are commonly used to present and analyze population information based on age and gender.
| # Population pyramid charts #
Population pyramid charts (also known as "age\-sex pyramids") are commonly used to present and analyze population information based on age and gender\.
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<h1 class="topictitle1" id="chart_creation_pyramid__title__1">Population pyramid charts</h1>
<div class="body">
<div class="abstract">
Population pyramid charts (also known as "age-sex pyramids") are commonly used to present and analyze population information based on age and gender.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_pyramid__section_tsd_ljb_qdb__title__1" id="chart_creation_pyramid__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_pyramid__section_tsd_ljb_qdb__title__1">Creating a simple Population pyramid chart</h2>
<ol id="chart_creation_pyramid__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Population pyramid</span> icon.
<p>The canvas updates to display a Population pyramid chart template.</p></li>
<li>Select a <span class="ph uicontrol">Y-axis</span> variable from the drop-down list.</li>
<li>Select a <span class="ph uicontrol">Split by</span> variable from the drop-down list.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_pyramid__section_fj1_mjb_qdb__title__1" id="chart_creation_pyramid__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_pyramid__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Y-axis.
</dd>
<dt class="dlterm">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm">
Bin width
</dt>
<dd class="dlentry">
The slider controls the size of the interval that is used to split the data into groups.
</dd>
<dt class="dlterm">
Show distribution curve
</dt>
<dd class="dlentry">
When enabled, the distribution fitting curve is shown on the chart.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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BA8A6820B3DBFAA703679B19BE070F7BD0CCA3D1 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_qqplot.html?context=cdpaas&locale=en | Q-Q plots | Q-Q plots
Q-Q (quantile-quantile) plots compare two probability distributions by plotting their quantiles against each other. A Q–Q plot is used to compare the shapes of distributions, providing a graphical view of how properties such as location, scale, and skewness are similar or different in the two distributions.
| # Q\-Q plots #
Q\-Q (quantile\-quantile) plots compare two probability distributions by plotting their quantiles against each other\. A Q–Q plot is used to compare the shapes of distributions, providing a graphical view of how properties such as location, scale, and skewness are similar or different in the two distributions\.
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<h1 class="topictitle1" id="chart_creation_qqplot__title__1">Q-Q plots</h1>
<div class="body">
<div class="abstract">
Q-Q (quantile-quantile) plots compare two probability distributions by plotting their quantiles against each other. A Q–Q plot is used to compare the shapes of distributions, providing a graphical view of how properties such as location, scale, and skewness are similar or different in the two distributions.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_qqplot__section_tsd_ljb_qdb__title__1" id="chart_creation_qqplot__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_qqplot__section_tsd_ljb_qdb__title__1">Creating a simple Q-Q plot chart</h2>
<ol id="chart_creation_qqplot__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Q-Q plot</span> icon.
<p>The canvas updates to display a Q-Q plot chart template.</p></li>
<li>Select a variable as the <span class="ph uicontrol">X-axis</span> variable.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_qqplot__section_fj1_mjb_qdb__title__1" id="chart_creation_qqplot__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_qqplot__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Distribution
</dt>
<dd class="dlentry">
The drop-down list provides all available distribution methods.
<dl id="chart_creation_qqplot__dl_lj1_2wv_blb">
<dt class="dlterm" id="chart_creation_qqplot__d30e155">
Auto fit distribution
</dt>
<dd class="dlentry" id="chart_creation_qqplot__d30e158">
Automatically fits the distribution (the default setting).
</dd>
<dt class="dlterm" id="chart_creation_qqplot__d30e164">
Beta
</dt>
<dd class="dlentry" id="chart_creation_qqplot__d30e167">
Returns the value from a Beta distribution with specified shape parameters.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__d30e173">
Exponential
</dt>
<dd class="dlentry" id="chart_creation_qqplot__d30e176">
Returns the value from an exponential distribution.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__d30e182">
Gamma
</dt>
<dd class="dlentry" id="chart_creation_qqplot__d30e185">
Returns the value from the Gamma distribution, with the specified shape and scale parameters.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__d30e191">
Log-normal
</dt>
<dd class="dlentry" id="chart_creation_qqplot__d30e194">
Returns the value from a log-normal distribution with specified parameters.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__d30e201">
Normal
</dt>
<dd class="dlentry" id="chart_creation_qqplot__d30e204">
Returns the value from a normal distribution with specified mean and standard deviation.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__d30e219">
Uniform
</dt>
<dd class="dlentry" id="chart_creation_qqplot__d30e222">
Returns the value from the uniform distribution between the minimum and maximum.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_x2x_dxv_blb">
Student t
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_y2x_dxv_blb">
Returns the value from Student's t distribution, with specified degrees of freedom.
</dd>
</dl>
</dd>
<dt class="dlterm">
Plot type
</dt>
<dd class="dlentry">
Select either a Q-Q (quantile-quantile) plot or a P-P (percent-percent) plot.
</dd>
<dt class="dlterm">
Auto fit
</dt>
<dd class="dlentry">
When enabled, data parameters for the selected <span class="ph uicontrol">Distribution</span> are automatically estimated. When not selected, the <span class="ph uicontrol">Shape</span> and <span class="ph uicontrol">Scale</span> distribution values display.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_r1s_myv_blb">
Shape1
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_s1s_myv_blb">
Sets the shape1 value for the <span class="ph uicontrol">Beta</span> distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Beta</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_bj5_nyv_blb">
Shape2
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_cj5_nyv_blb">
Sets the shape2 value for the <span class="ph uicontrol">Beta</span> distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Beta</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_xdp_1kw_blb">
Shape
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_ydp_1kw_blb">
Sets the shape value for the selected distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Gamma</span> or <span class="ph uicontrol">Log-normal</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_fb2_vjw_blb">
Scale
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_gb2_vjw_blb">
Sets the scale value for the selected distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Exponential</span>, <span class="ph uicontrol">Gamma</span>, or <span class="ph uicontrol">Log-normal</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_a12_3kw_blb">
Mean
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_b12_3kw_blb">
Sets the means value for the <span class="ph uicontrol">Normal</span> distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Normal</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_a13_3kw_blb">
Std. dev.
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_b13_3kw_blb">
Sets the standard deviation value for the <span class="ph uicontrol">Normal</span> distribution. This setting is available only when <span class="ph uicontrol">Normal</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_wt5_pkw_blb">
Min
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_xt5_pkw_blb">
Sets the minimum value for the <span class="ph uicontrol">Uniform</span> distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Uniform</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_lz1_qkw_blb">
Max
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_mz1_qkw_blb">
Sets the maximum value for the <span class="ph uicontrol">Uniform</span> distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Uniform</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm" id="chart_creation_qqplot__dt_elc_xnw_blb">
Degrees of freedom(df)
</dt>
<dd class="dlentry" id="chart_creation_qqplot__dd_flc_xnw_blb">
Set the degrees of freedom value for the <span class="ph uicontrol">Student t</span> distribution. This setting is available only when <span class="ph uicontrol">Auto fit</span> is not enabled and <span class="ph uicontrol">Student t</span> is selected as the <span class="ph uicontrol">Distribution</span>.
</dd>
<dt class="dlterm">
Detrend
</dt>
<dd class="dlentry">
When enabled, detrended plots display on the chart.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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61F714F5629AD260B0D9776FC53CDA2EAA10DF24 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_radar.html?context=cdpaas&locale=en | Radar charts | Radar charts
Radar charts compare multiple quantitative variables and are useful for visualizing which variables have similar values, or if outliers exist among the variables. Radar charts consists of a sequence of spokes, with each spoke representing a single variable. Radar Charts are also useful for determining which variables are scoring high or low within a data set.
| # Radar charts #
Radar charts compare multiple quantitative variables and are useful for visualizing which variables have similar values, or if outliers exist among the variables\. Radar charts consists of a sequence of spokes, with each spoke representing a single variable\. Radar Charts are also useful for determining which variables are scoring high or low within a data set\.
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<h1 class="topictitle1" id="chart_creation_radar__title__1">Radar charts</h1>
<div class="body">
<div class="abstract">
Radar charts compare multiple quantitative variables and are useful for visualizing which variables have similar values, or if outliers exist among the variables. Radar charts consists of a sequence of spokes, with each spoke representing a single variable. Radar Charts are also useful for determining which variables are scoring high or low within a data set.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_radar__section_tsd_ljb_qdb__title__1" id="chart_creation_radar__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_radar__section_tsd_ljb_qdb__title__1">Creating a simple radar chart</h2>
<ol id="chart_creation_radar__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Radar</span> icon.
<p>The canvas updates to display a radar chart template.</p></li>
<li>Select a <span class="ph uicontrol">Columns</span> variable from the drop-down list.
<div class="note">
<span class="notetitle">Note:</span> Click <span class="ph uicontrol">Add another column</span> to include more columns. At least three columns variables must be defined.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_radar__section_fj1_mjb_qdb__title__1" id="chart_creation_radar__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_radar__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_radar__dt_gb5_5kc_clb">
Category
</dt>
<dd class="dlentry" id="chart_creation_radar__dd_hb5_5kc_clb">
Select a categorical (nominal or ordinal) variable. If you select <span class="ph uicontrol">None</span> as the category values, all values are shown separately, and no summary method is applied.
</dd>
<dt class="dlterm" id="chart_creation_radar__dt_trv_glc_clb">
Summary
</dt>
<dd class="dlentry" id="chart_creation_radar__dd_urv_glc_clb">
When a categorical variable is selected, the following summary statistics are available.
<dl id="chart_creation_radar__dl_vrv_glc_clb">
<dt class="dlterm" id="chart_creation_radar__dt_wrv_glc_clb">
Count
</dt>
<dd class="dlentry" id="chart_creation_radar__dd_xrv_glc_clb">
Total number of cases.
</dd>
<dt class="dlterm" id="chart_creation_radar__dt_yrv_glc_clb">
Sum
</dt>
<dd class="dlentry" id="chart_creation_radar__dd_zrv_glc_clb">
Sum of the values.
</dd>
<dt class="dlterm" id="chart_creation_radar__dt_asv_glc_clb">
Mean
</dt>
<dd class="dlentry" id="chart_creation_radar__dd_bsv_glc_clb">
Arithmetic average; the sum divided by the number of cases.
</dd>
<dt class="dlterm" id="chart_creation_radar__dt_csv_glc_clb">
Maximum
</dt>
<dd class="dlentry" id="chart_creation_radar__dd_dsv_glc_clb">
Largest (highest) value.
</dd>
<dt class="dlterm" id="chart_creation_radar__dt_esv_glc_clb">
Minimum
</dt>
<dd class="dlentry" id="chart_creation_radar__dd_fsv_glc_clb">
Smallest (lowest) value.
</dd>
</dl>
</dd>
<dt class="dlterm">
Radar Layout
</dt>
<dd class="dlentry">
Determines the background image layout for the radar chart:
<dl>
<dt class="dlterm">
Circle
</dt>
<dd class="dlentry">
When selected, the radar chart is drawn over a circular layout.
</dd>
<dt class="dlterm">
Polygon
</dt>
<dd class="dlentry">
When selected, the radar chart is drawn over a polygonal layout.
</dd>
</dl>
</dd>
<dt class="dlterm">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
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5A812008B8370853F0C151FDE4DFEDA4A39193CB | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_relation.html?context=cdpaas&locale=en | Relationship charts | Relationship charts
A relationship chart is useful for determining how variables relate to each other.
| # Relationship charts #
A relationship chart is useful for determining how variables relate to each other\.
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<div class="abstract">
A relationship chart is useful for determining how variables relate to each other.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_relation__section_tsd_ljb_qdb__title__1" id="chart_creation_relation__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_relation__section_tsd_ljb_qdb__title__1">Creating a simple relationship chart</h2>
<ol id="chart_creation_relation__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Relationship</span> icon.
<p>The canvas updates to display a relational chart template.</p></li>
<li>Select at least two variables as <span class="ph uicontrol">Columns</span> variables.
<div class="note" id="chart_creation_relation__note_ntf_gpc_clb">
<span class="notetitle">Note:</span> Click <span class="ph uicontrol">Add another column</span> to include more column variables.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_relation__section_fj1_mjb_qdb__title__1" id="chart_creation_relation__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_relation__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Lists the available variables.
</dd>
<dd class="ddexpand">
Click <span class="ph uicontrol">Add another column</span> to add more columns.
</dd>
<dt class="dlterm">
Line style
</dt>
<dd class="dlentry">
Controls the line style between related data points.
<dl>
<dt class="dlterm">
Curve
</dt>
<dd class="dlentry">
When selected, curved lines are drawn between related data points.
</dd>
<dt class="dlterm">
Straight
</dt>
<dd class="dlentry">
When selected, straight lines are drawn between related data points.
</dd>
</dl>
</dd>
<dt class="dlterm">
Label threshold
</dt>
<dd class="dlentry">
Displays labels for data points whose values exceed the defined value.
</dd>
<dt class="dlterm">
Legend orient
</dt>
<dd class="dlentry">
Sets the chart legend orientation. Available options are <span class="ph uicontrol">Horizontal</span>, <span class="ph uicontrol">Vertical</span>, and <span class="ph uicontrol">Vertical bottom</span>.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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<nav role="navigation">
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67C56AAC7DA2232E4DA2B8AEDEC41B9D8755E22A | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_scatterdot.html?context=cdpaas&locale=en | Scatter plots and dot plots | Scatter plots and dot plots
Several broad categories of charts are created with the point graphic element.
Scatter plots
: Scatter plots are useful for plotting multivariate data. They can help you determine potential relationships among scale variables. A simple scatter plot uses a 2-D coordinate system to plot two variables. A 3-D scatter plot uses a 3-D coordinate system to plot three variables. When you need to plot more variables, you can try overlay scatter plots and scatter plot matrices (SPLOMs). An overlay scatter plot displays overlaid pairs of X-Y variables, with each pair distinguished by color or shape. A SPLOM creates a matrix of 2-D scatter plots, with each variable plotted against every other variable in the SPLOM.
Dot plots
: Like histograms, dot plots are useful for showing the distribution of a single scale variable. The data are binned, but, instead of one value for each bin (like a count), all of the points in each bin are displayed and stacked. These graphs are sometimes called density plots.
Summary point plots
: Summary point plots are similar to bar charts, except that points are drawn in place of the top of the bars. For more information, see [Bar charts](https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_barcharts.htmlchart_creation_barcharts).
Drop-line charts
: Drop-line charts are a special type of summary point plot. The points are grouped and a line is drawn through the points in each category. The drop-line chart is useful for comparing a statistic across categorical variables.
| # Scatter plots and dot plots #
Several broad categories of charts are created with the point graphic element\.
Scatter plots
: Scatter plots are useful for plotting multivariate data\. They can help you determine potential relationships among scale variables\. A simple scatter plot uses a 2\-D coordinate system to plot two variables\. A 3\-D scatter plot uses a 3\-D coordinate system to plot three variables\. When you need to plot more variables, you can try overlay scatter plots and scatter plot matrices (SPLOMs)\. An overlay scatter plot displays overlaid pairs of X\-Y variables, with each pair distinguished by color or shape\. A SPLOM creates a matrix of 2\-D scatter plots, with each variable plotted against every other variable in the SPLOM\.
Dot plots
: Like histograms, dot plots are useful for showing the distribution of a single scale variable\. The data are binned, but, instead of one value for each bin (like a count), all of the points in each bin are displayed and stacked\. These graphs are sometimes called density plots\.
Summary point plots
: Summary point plots are similar to bar charts, except that points are drawn in place of the top of the bars\. For more information, see [Bar charts](https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_barcharts.html#chart_creation_barcharts)\.
Drop\-line charts
: Drop\-line charts are a special type of summary point plot\. The points are grouped and a line is drawn through the points in each category\. The drop\-line chart is useful for comparing a statistic across categorical variables\.
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<h1 class="topictitle1" id="chart_creation_scatterdot__title__1">Scatter plots and dot plots</h1>
<div class="body">
<div class="abstract">
Several broad categories of charts are created with the point graphic element.
</div>
<dl>
<dt class="dlterm">
Scatter plots
</dt>
<dd class="dlentry">
Scatter plots are useful for plotting multivariate data. They can help you determine potential relationships among scale variables. A simple scatter plot uses a 2-D coordinate system to plot two variables. A 3-D scatter plot uses a 3-D coordinate system to plot three variables. When you need to plot more variables, you can try overlay scatter plots and scatter plot matrices (SPLOMs). An overlay scatter plot displays overlaid pairs of X-Y variables, with each pair distinguished by color or shape. A SPLOM creates a matrix of 2-D scatter plots, with each variable plotted against every other variable in the SPLOM.
</dd>
<dt class="dlterm">
Dot plots
</dt>
<dd class="dlentry">
Like histograms, dot plots are useful for showing the distribution of a single scale variable. The data are binned, but, instead of one value for each bin (like a count), all of the points in each bin are displayed and stacked. These graphs are sometimes called density plots.
</dd>
<dt class="dlterm">
Summary point plots
</dt>
<dd class="dlentry">
Summary point plots are similar to bar charts, except that points are drawn in place of the top of the bars. For more information, see <a href="chart_creation_barcharts.html#chart_creation_barcharts">Bar charts</a>.
</dd>
<dt class="dlterm">
Drop-line charts
</dt>
<dd class="dlentry">
Drop-line charts are a special type of summary point plot. The points are grouped and a line is drawn through the points in each category. The drop-line chart is useful for comparing a statistic across categorical variables.
</dd>
</dl>
<section class="section" role="region" aria-labelledby="chart_creation_scatterdot__section_tsd_ljb_qdb__title__1" id="chart_creation_scatterdot__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_scatterdot__section_tsd_ljb_qdb__title__1">Creating a simple scatter plot</h2>
<ol id="chart_creation_scatterdot__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Scatter plot</span> icon.
<p>The canvas updates to display a scatter plot chart template.</p></li>
<li>Select a scale variable as the <span class="ph uicontrol">X-axis</span> variable.</li>
<li>Select a scale variable as the <span class="ph uicontrol">Y-axis</span> variable. You do not need to specify a statistic because scatter plots typically show raw values.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_scatterdot__section_fj1_mjb_qdb__title__1" id="chart_creation_scatterdot__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_scatterdot__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's X-axis.
</dd>
<dt class="dlterm">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the chart's Y-axis.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__dt_blj_wqc_clb">
<a id="chart_creation_scatterdot__color"></a>Color map
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__dd_clj_wqc_clb">
Lists available color map variables. These variables use color progression, based on the range of values in the specified column, to represent themselves in the plot points. Color maps are also known as choropleth maps.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__dt_dlj_wqc_clb">
<a id="chart_creation_scatterdot__size"></a>Size map
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__dd_elj_wqc_clb">
Lists available size map variables. These variables use differing sizes to represent themselves in the plot points.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__dt_flj_wqc_clb">
<a id="chart_creation_scatterdot__shape"></a>Shape map
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__dd_glj_wqc_clb">
Lists available shape map variables. These variables use differing shapes to represent themselves in the plot points.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__fitline">
Fit line
</dt>
<dd class="dlentry">
In a fit line, the data points are fitted to a line that usually does not pass through all of the data points. The fit line represents the trend of the data. Some fits lines are regression-based. Others are based on iterative weighted least squares. Select a fit line option from the drop-down list.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__dt_nzm_xqc_clb">
<a id="chart_creation_scatterdot__dlentry_pcc_m5c_clb"></a>Gradient bubble
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__dd_ozm_xqc_clb">
The toggle control enables and disables the display of color gradients and 3D effects in the chart bubbles. The setting is not available when a <span class="ph uicontrol">Color map</span> variable is selected.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__dt_nys_2sc_clb">
<a id="chart_creation_scatterdot__dlentry_scc_m5c_clb"></a>Minimum bubble size
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__dd_oys_2sc_clb">
Sets the minimum bubble size. Enter a value the range 5 - 20.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__dt_oyb_fsc_clb">
<a id="chart_creation_scatterdot__dlentry_vcc_m5c_clb"></a>Maximum bubble size
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__dd_pyb_fsc_clb">
Sets the maximum bubble size. Enter a value in the range 20 - 80.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__d17e158">
Show reference line
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__d17e161">
When enabled, the option shows a reference line on the chart that is based on the specified <span class="ph uicontrol">xAxis</span> and <span class="ph uicontrol">yAxis</span> values.
<dl id="chart_creation_scatterdot__d17e169">
<dt class="dlterm" id="chart_creation_scatterdot__d17e173">
Enter a reference line value on xAxis
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__d17e176">
When <span class="ph uicontrol">Show reference line</span> is enabled, this setting provides the option of specifying a specific reference line value for the X-axis. Click <span class="ph uicontrol">Add another column</span> to specify more reference line values.
</dd>
<dt class="dlterm" id="chart_creation_scatterdot__d17e188">
Enter a reference line value on yAxis
</dt>
<dd class="dlentry" id="chart_creation_scatterdot__d17e191">
When <span class="ph uicontrol">Show reference line</span> is enabled, this setting provides the option of specifying a specific reference line value for the Y-axis. Click <span class="ph uicontrol">Add another column</span> to specify more reference line values.
</dd>
</dl>
</dd>
<dt class="dlterm">
Show label
</dt>
<dd class="dlentry">
When enabled, column labels display on the chart. Only scatter series data is supported.
</dd>
<dt class="dlterm">
Label field
</dt>
<dd class="dlentry">
The field menu provides variables to display as chart labels.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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7B3616D29E7AC720B73EF3E24C9C807DA05C4DA3 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_seriesarray.html?context=cdpaas&locale=en | Series array charts | Series array charts
Series array charts include individual sub charts and display the Y-axis for all sub charts in the legend.
| # Series array charts #
Series array charts include individual sub charts and display the Y\-axis for all sub charts in the legend\.
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<div class="body">
<div class="abstract">
Series array charts include individual sub charts and display the Y-axis for all sub charts in the legend.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_seriesarray__section_tsd_ljb_qdb__title__1" id="chart_creation_seriesarray__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_seriesarray__section_tsd_ljb_qdb__title__1">Creating a series array chart</h2>
<ol id="chart_creation_seriesarray__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Series array</span> icon.
<p>The canvas updates to display a series array chart template.</p></li>
<li id="chart_creation_seriesarray__li_ddr_c3v_wqb">Click <span class="ph uicontrol">Add another sub-chart</span> to define the first series array sub chart.</li>
<li>Select a variable as the <span class="ph uicontrol">X-axis</span> variable.</li>
<li>Select a variable as the <span class="ph uicontrol">Y-axis</span> variable.</li>
<li id="chart_creation_seriesarray__li_vqq_fjv_wqb">Click <span class="ph uicontrol">Add another sub-chart</span> to define more series array sub charts.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_seriesarray__section_fj1_mjb_qdb__title__1" id="chart_creation_seriesarray__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_seriesarray__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm" id="chart_creation_seriesarray__xfield">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the sub chart's x-axis.
</dd>
<dt class="dlterm" id="chart_creation_seriesarray__yfield">
Y-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the sub chart's y-axis.
</dd>
<dt class="dlterm">
Split by
</dt>
<dd class="dlentry">
Select a categorical variable that creates a table of charts, with a cell for each category in the Split by variable. Like grouping, split by variables essentially add more dimensions to your chart by displaying information for each variable category.
</dd>
<dt class="dlterm">
Legend label
</dt>
<dd class="dlentry">
Specify a sub-chart legend title.
</dd>
<dt class="dlterm">
Title
</dt>
<dd class="dlentry">
Specify a sub-chart title.
</dd>
<dt class="dlterm">
Type
</dt>
<dd class="dlentry">
Select a sub-chart type. Available options are <span class="ph uicontrol">line</span>, <span class="ph uicontrol">bar</span>, and <span class="ph uicontrol">scatter</span>.
</dd>
<dt class="dlterm" id="chart_creation_seriesarray__d12e123">
Smooth
</dt>
<dd class="dlentry" id="chart_creation_seriesarray__d12e126">
When enabled, the chart shows a smooth curve.
</dd>
<dt class="dlterm" id="chart_creation_seriesarray__d12e133">
Show data points
</dt>
<dd class="dlentry" id="chart_creation_seriesarray__d12e136">
When enabled, the data point is shown in the chart.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
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5CF2FE478862FCAA1745D5B0770CE6486B3B71F8 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_sunburst.html?context=cdpaas&locale=en | Sunburst charts | Sunburst charts
A sunburst chart is useful for visualizing hierarchical data structures. A sunburst chart consists of an inner circle that is surrounded by rings of deeper hierarchy levels. The angle of each segment proportional to either a value or divided equally under its inner segment. The chart segments are colored based on the category or hierarchical level to which they belong.
| # Sunburst charts #
A sunburst chart is useful for visualizing hierarchical data structures\. A sunburst chart consists of an inner circle that is surrounded by rings of deeper hierarchy levels\. The angle of each segment proportional to either a value or divided equally under its inner segment\. The chart segments are colored based on the category or hierarchical level to which they belong\.
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<div class="abstract">
A sunburst chart is useful for visualizing hierarchical data structures. A sunburst chart consists of an inner circle that is surrounded by rings of deeper hierarchy levels. The angle of each segment proportional to either a value or divided equally under its inner segment. The chart segments are colored based on the category or hierarchical level to which they belong.
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<section class="section" role="region" aria-labelledby="chart_creation_sunburst__section_tsd_ljb_qdb__title__1" id="chart_creation_sunburst__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_sunburst__section_tsd_ljb_qdb__title__1">Creating a simple sunburst chart</h2>
<ol id="chart_creation_sunburst__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Sunburst</span> icon.
<p>The canvas updates to display a sunburst chart template.</p></li>
<li>Select a categorical (nominal or ordinal) variable from the <span class="ph uicontrol">Columns</span> list. The categories in this variable determine the number of segments in the chart.</li>
<li id="chart_creation_sunburst__li_jjp_4mh_5kb">Click <span class="ph uicontrol">Add another column</span> and select another categorical (nominal or ordinal) variable from the <span class="ph uicontrol">Columns</span> list. The categories in this variable determine the number of segments in the chart's second ring and represent a hierarchical level.</li>
<li>Select a statistical summary function for the graphic element (count-based statistic or a sum). The result of the statistic determines the size of each segment. When <span class="ph uicontrol">Sum</span> is selected, choose a scale variable from the <span class="ph uicontrol">Value</span> list to represent the value in the data set to summarize.</li>
<li id="chart_creation_sunburst__li_etl_44h_5kb">Select a <span class="ph uicontrol">Sunburst layout</span> option (either <span class="ph uicontrol">Traditional</span> or <span class="ph uicontrol">Divergent</span>).</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_sunburst__section_fj1_mjb_qdb__title__1" id="chart_creation_sunburst__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_sunburst__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Select a categorical (nominal or ordinal) variable that determines the number of segments in the chart.
</dd>
<dt class="dlterm">
Summary
</dt>
<dd class="dlentry">
Select a statistical summary function for the graphic element (count-based statistic or a sum). The result of the statistic determines the size of each slice.
<div class="p">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<dl>
<dt class="dlterm">
Functions that do not require a value variable
</dt>
<dd class="dlentry">
All count and percentage statistics are in this category. These statistics are available when there is no defined <span class="ph uicontrol">Value</span> variable.
</dd>
<dt class="dlterm">
Functions that do require a value variable
</dt>
<dd class="dlentry">
For example, the <span class="keyword cmdname">Sum</span> function requires a variable on which the summary is calculated.
</dd>
</dl>
</div>
</dd>
<dt class="dlterm">
Value
</dt>
<dd class="dlentry">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a scale variable, is selected. Select a variable to serve as the scale variable.
</dd>
<dt class="dlterm" id="chart_creation_sunburst__dt_jpt_vph_5kb">
Sunburst layout
</dt>
<dd class="dlentry" id="chart_creation_sunburst__dd_kpt_vph_5kb">
Available options are <span class="ph uicontrol">Traditional</span> and <span class="ph uicontrol">Divergent</span>.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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BAE3302FC87E1BBFA604BAA2D003069E4233A517 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_themeriver.html?context=cdpaas&locale=en | Theme River charts | Theme River charts
A theme river is a specialized flow graph that shows changes over time.
| # Theme River charts #
A theme river is a specialized flow graph that shows changes over time\.
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<div class="abstract">
A theme river is a specialized flow graph that shows changes over time.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_themeriver__section_tsd_ljb_qdb__title__1" id="chart_creation_themeriver__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_themeriver__section_tsd_ljb_qdb__title__1">Creating a simple theme river chart</h2>
<ol id="chart_creation_themeriver__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Theme River</span> icon.
<p>The canvas updates to display a theme river chart template.</p></li>
<li>Select an <span class="ph uicontrol">X-axis</span> variable.</li>
<li>Select a <span class="ph uicontrol">Category</span> variable.
<div class="note restriction">
<span class="restrictiontitle">Restriction:</span> If a category field has more than 50 distinct categories, only the first 50 maximum categories are used as events in the chart.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_themeriver__section_fj1_mjb_qdb__title__1" id="chart_creation_themeriver__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_themeriver__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
X-axis
</dt>
<dd class="dlentry">
Lists variables that are available for the X-axis.
</dd>
<dt class="dlterm">
Order based on
</dt>
<dd class="dlentry">
Depending on the X-axis value you select, you might be able to specify whether the category order is based on category name or category value.
</dd>
</dl>
<dl>
<dt class="dlterm">
Category order
</dt>
<dd class="dlentry">
Depending on the X-axis value you select, you might be able to specify whether the category order is ascending, descending, or as read from the data set.
</dd>
<dt class="dlterm">
Category
</dt>
<dd class="dlentry">
Lists variables that are available as categories.
</dd>
</dl>
<dl>
<dt class="dlterm" id="chart_creation_themeriver__dt_bqd_5wc_clb">
<a id="chart_creation_themeriver__summary"></a>Summary
</dt>
<dd class="dlentry" id="chart_creation_themeriver__dd_cqd_5wc_clb">
Select a statistical summary function (the method that is used for summarizing each category).
<div class="p" id="chart_creation_themeriver__p_hrx_4w4_blb">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<ul id="chart_creation_themeriver__d31e204">
<li id="chart_creation_themeriver__d31e206"><strong>Functions that do not require a value variable.</strong> Functions that do not require a variable. All count and percentage statistics are in this category. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
<li id="chart_creation_themeriver__d31e214"><strong>Functions that do require a value variable.</strong> Functions that do require a <span class="ph uicontrol">Value</span> variable. For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when the <span class="ph uicontrol">Value</span> variable is not defined.</li>
</ul>
</div>
</dd>
<dt class="dlterm">
Value
</dt>
<dd class="dlentry">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable, is selected. Select a variable to serve as the value.
</dd>
<dt class="dlterm">
Legend orient
</dt>
<dd class="dlentry">
Sets the chart legend orientation. Available options are <span class="ph uicontrol">Horizontal</span>, <span class="ph uicontrol">Vertical</span>, and <span class="ph uicontrol">Vertical bottom</span>.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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B49F37BD511123A94FCAD3C6E826E60FC61DB446 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_timeplot.html?context=cdpaas&locale=en | Time plots | Time plots
Time plots illustrate data points at successive intervals of time. The time series you plot must contain numeric values and are assumed to occur over a range of time in which the periods are uniform. Time plots provide a preliminary analysis of the characteristics of time series data on basic statistics and test, and thus generate useful insights about your data before modeling. Time plots include analysis methods such as decomposition, augmented Dickey-Fuller test (ADF), correlations (ACF/PACF), and spectral analysis.
| # Time plots #
Time plots illustrate data points at successive intervals of time\. The time series you plot must contain numeric values and are assumed to occur over a range of time in which the periods are uniform\. Time plots provide a preliminary analysis of the characteristics of time series data on basic statistics and test, and thus generate useful insights about your data before modeling\. Time plots include analysis methods such as decomposition, augmented Dickey\-Fuller test (ADF), correlations (ACF/PACF), and spectral analysis\.
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<title>Time plots</title>
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<h1 class="topictitle1" id="chart_creation_timeplot__title__1">Time plots</h1>
<div class="body">
<div class="abstract">
Time plots illustrate data points at successive intervals of time. The time series you plot must contain numeric values and are assumed to occur over a range of time in which the periods are uniform. Time plots provide a preliminary analysis of the characteristics of time series data on basic statistics and test, and thus generate useful insights about your data before modeling. Time plots include analysis methods such as decomposition, augmented Dickey-Fuller test (ADF), correlations (ACF/PACF), and spectral analysis.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_timeplot__section_tsd_ljb_qdb__title__1" id="chart_creation_timeplot__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_timeplot__section_tsd_ljb_qdb__title__1">Creating a simple time plot</h2>
<ol id="chart_creation_timeplot__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Time plot</span> icon.
<p>The canvas updates to display a time plot chart template.</p></li>
<li>From the <span class="ph uicontrol">Values</span> drop-down, select a value for the y-axis.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_timeplot__section_fj1_mjb_qdb__title__1" id="chart_creation_timeplot__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_timeplot__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Values
</dt>
<dd class="dlentry">
Lists variables that are available for the time plot values.
</dd>
<dt class="dlterm" id="chart_creation_timeplot__date">
Date
</dt>
<dd class="dlentry">
Select a date from the drop-down, if applicable. Each observation is separated by the same time interval. If you select a date variable, a resample option is shown. You can use this option to aggregate value fields to match the specified interval.
</dd>
<dt class="dlterm" id="chart_creation_timeplot__timeplotalgorithm">
Time plot algorithm
</dt>
<dd class="dlentry">
The time plot algorithm to use for analyzing the time series data.
<dl>
<dt class="dlterm">
Decomposition
</dt>
<dd class="dlentry">
Decompose a time series as three components (trend-cycle, seasonal, and irregular). Decomposition is run in an additive fashion.
</dd>
<dt class="dlterm">
ADF test
</dt>
<dd class="dlentry">
Augmented Dickey–Fuller (ADF) tests the null hypothesis that a unit root exists in the series and the series is not stationary. If the test result rejects the null hypothesis, the series is stationary, or can be represented to be stationary with a difference model.
</dd>
<dt class="dlterm">
ACF/PACF
</dt>
<dd class="dlentry">
Correlations of series.
</dd>
<dt class="dlterm">
Spectral analysis
</dt>
<dd class="dlentry">
An analytic tool in the frequency domain. The highest frequency is marked as diamond.
</dd>
</dl>
</dd>
<dt class="dlterm" id="chart_creation_timeplot__swap">
Swap chart position
</dt>
<dd class="dlentry">
Reverses the positions of the charts on the planimetric rectangular coordinate system and the polar coordinate system.
</dd>
<dt class="dlterm" id="chart_creation_timeplot__shape">
Show the turning point
</dt>
<dd class="dlentry">
Shows or hides the turning points in the charts. The series is explored to determine whether it has an overall trend or has some turning points that change the direction of the trend pattern.
</dd>
<dt class="dlterm" id="chart_creation_timeplot__outlier">
Show the outlier
</dt>
<dd class="dlentry">
Shows or hides any outliers. The outliers of the time series are analyzed from the irregular component of time series decomposition.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
<dt class="dlterm">
XAxis label
</dt>
<dd class="dlentry">
The x-axis label, which is placed beneath the x-axis.
</dd>
<dt class="dlterm">
YAxis label
</dt>
<dd class="dlentry">
The y-axis label, which is placed above the y-axis.
</dd>
</dl>
</section>
</div>
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D872C74770B5729E037E841679F741CF3D8C20AD | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_tree.html?context=cdpaas&locale=en | Tree charts | Tree charts
Tree charts represent hierarchy in a tree-like structure. The structure of a Tree chart consists of a root node (has no parent node), line connections (named branches), and leaf nodes (have no child nodes). Line connections represent the relationships and connections between the members.
| # Tree charts #
Tree charts represent hierarchy in a tree\-like structure\. The structure of a Tree chart consists of a root node (has no parent node), line connections (named branches), and leaf nodes (have no child nodes)\. Line connections represent the relationships and connections between the members\.
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<h1 class="topictitle1" id="chart_creation_tree__title__1">Tree charts</h1>
<div class="body">
<div class="abstract">
Tree charts represent hierarchy in a tree-like structure. The structure of a Tree chart consists of a root node (has no parent node), line connections (named branches), and leaf nodes (have no child nodes). Line connections represent the relationships and connections between the members.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_tree__section_tsd_ljb_qdb__title__1" id="chart_creation_tree__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_tree__section_tsd_ljb_qdb__title__1">Creating a simple tree chart</h2>
<ol id="chart_creation_tree__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Tree</span> icon.
<p>The canvas updates to display a Tree chart template.</p></li>
<li>Select a <span class="ph uicontrol">Columns</span> variable from the drop-down list.
<div class="note">
<span class="notetitle">Note:</span> Click <span class="ph uicontrol">Add another column</span> to include more column variables.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_tree__section_fj1_mjb_qdb__title__1" id="chart_creation_tree__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_tree__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Lists variables that are available to represent chart columns.
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_p4g_1th_5kb">
Summary
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_q4g_1th_5kb">
Select a statistical summary function for the graphic element. The result of the statistic determines the position of the graphic elements.
<div class="p" id="chart_creation_tree__p_mvm_cth_5kb">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<dl>
<dt class="dlterm">
Functions that do not require a value variable
</dt>
<dd class="dlentry">
All count and percentage statistics are in this category. These statistics are available when there is no defined <span class="ph uicontrol">Value</span> variable.
</dd>
<dt class="dlterm">
Functions that do require a value variable
</dt>
<dd class="dlentry">
For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when there is a defined <span class="ph uicontrol">Value</span> variable.
</dd>
</dl>
</div>
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_vxp_fth_5kb">
Value
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_wxp_fth_5kb">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable, is selected. Select a variable to serve as the basis for the summary value.
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_lwv_psh_5kb">
<a id="chart_creation_tree__dlentry_ymd_myh_wqb"></a>Tree layout
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_mwv_psh_5kb">
<dl id="chart_creation_tree__dl_nwv_psh_5kb">
<dt class="dlterm" id="chart_creation_tree__dt_owv_psh_5kb">
Left to right
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_pwv_psh_5kb">
The root node displays on the left and the leaf nodes display on the right.
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_qwv_psh_5kb">
Right to left
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_rwv_psh_5kb">
The root node displays on the right and the leaf nodes display on the left.
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_swv_psh_5kb">
Top to bottom
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_twv_psh_5kb">
The root node displays on the top and the leaf nodes display on the bottom.
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_uwv_psh_5kb">
Bottom to top
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_vwv_psh_5kb">
The root node displays on the bottom and the leaf nodes display on the top.
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_wwv_psh_5kb">
Radial
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_xwv_psh_5kb">
The root node displays in the middle and the leaf nodes radiate from the root.
</dd>
</dl>
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_es4_5sh_5kb">
Leaf depth
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_fs4_5sh_5kb">
Sets the drill-down level value for the leaf nodes.
</dd>
<dt class="dlterm" id="chart_creation_tree__dt_k23_qsh_5kb">
Show leaves label
</dt>
<dd class="dlentry" id="chart_creation_tree__dd_l23_qsh_5kb">
When enabled, labels display for each leaf node.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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<nav role="navigation">
<div class="familylinks">
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9B6386C6C291665ACA0892481681A94A70185E9D | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_treemap.html?context=cdpaas&locale=en | Treemap charts | Treemap charts
Treemap charts are an alternative method for visualizing the hierarchical structure of tree diagrams while also displaying quantities for each category. Treemap charts are useful for identifying patterns in data. Tree branches are represented by rectangles, with each sub branch represented by smaller rectangles.
| # Treemap charts #
Treemap charts are an alternative method for visualizing the hierarchical structure of tree diagrams while also displaying quantities for each category\. Treemap charts are useful for identifying patterns in data\. Tree branches are represented by rectangles, with each sub branch represented by smaller rectangles\.
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<h1 class="topictitle1" id="chart_creation_treemap__title__1">Treemap charts</h1>
<div class="body">
<div class="abstract">
Treemap charts are an alternative method for visualizing the hierarchical structure of tree diagrams while also displaying quantities for each category. Treemap charts are useful for identifying patterns in data. Tree branches are represented by rectangles, with each sub branch represented by smaller rectangles.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_treemap__section_tsd_ljb_qdb__title__1" id="chart_creation_treemap__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_treemap__section_tsd_ljb_qdb__title__1">Creating a simple Treemap chart</h2>
<ol id="chart_creation_treemap__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Treemap</span> icon.
<p>The canvas updates to display a Treemap chart template.</p></li>
<li>Select a <span class="ph uicontrol">Columns</span> variable from the drop-down list.
<div class="note">
<span class="notetitle">Note:</span> Click <span class="ph uicontrol">Add another column</span> to include more column variables.
</div></li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_treemap__section_fj1_mjb_qdb__title__1" id="chart_creation_treemap__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_treemap__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Columns
</dt>
<dd class="dlentry">
Lists variables that are available to represent chart columns.
</dd>
<dt class="dlterm" id="chart_creation_treemap__dt_p4g_1th_5kb">
Summary
</dt>
<dd class="dlentry" id="chart_creation_treemap__dd_q4g_1th_5kb">
Select a statistical summary function for the graphic element. The result of the statistic determines the position of the graphic elements.
<div class="p" id="chart_creation_treemap__p_mvm_cth_5kb">
Two types of statistical summary functions are available. The distinction is important because it determines whether you need to specify a <span class="ph uicontrol">Value</span> variable.
<dl>
<dt class="dlterm">
Functions that do not require a value variable
</dt>
<dd class="dlentry">
All count and percentage statistics are in this category. These statistics are available when there is no defined <span class="ph uicontrol">Value</span> variable.
</dd>
<dt class="dlterm">
Functions that do require a value variable
</dt>
<dd class="dlentry">
For example, the <span class="keyword cmdname">Mean</span> function requires a variable on which the mean is calculated. These statistics are available when there is a defined <span class="ph uicontrol">Value</span> variable.
</dd>
</dl>
</div>
</dd>
<dt class="dlterm" id="chart_creation_treemap__dt_vxp_fth_5kb">
Value
</dt>
<dd class="dlentry" id="chart_creation_treemap__dd_wxp_fth_5kb">
This field displays when a <span class="ph uicontrol">Summary</span> function that requires a value variable is selected. Select a variable to serve as the basis for the summary value.
</dd>
<dt class="dlterm" id="chart_creation_treemap__dt_es4_5sh_5kb">
Leaf depth
</dt>
<dd class="dlentry" id="chart_creation_treemap__dd_fs4_5sh_5kb">
Sets the drill-down level value for the leaf nodes.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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<nav role="navigation">
<div class="familylinks">
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99B0C1C962E0642E5B877747ED37E9BB27238664 | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_tsne.html?context=cdpaas&locale=en | t-SNE charts | t-SNE charts
T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization. t-SNE charts model each high-dimensional object by a two-or-three dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.
| # t\-SNE charts #
T\-distributed Stochastic Neighbor Embedding (t\-SNE) is a machine learning algorithm for visualization\. t\-SNE charts model each high\-dimensional object by a two\-or\-three dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability\.
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<h1 class="topictitle1" id="chart_creation_tsne__title__1">t-SNE charts</h1>
<div class="body">
<div class="abstract">
T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization. t-SNE charts model each high-dimensional object by a two-or-three dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_tsne__section_tsd_ljb_qdb__title__1" id="chart_creation_tsne__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_tsne__section_tsd_ljb_qdb__title__1">Creating a simple t-SNE chart</h2>
<ol id="chart_creation_tsne__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">t-SNE</span> icon.
<p>The canvas updates to display a t-SNE chart template.</p></li>
<li>Set the <span class="ph uicontrol">Perplexity</span>, <span class="ph uicontrol">Learning rate</span>, and <span class="ph uicontrol">Maximum iterations</span> values.</li>
<li>Optional: Select a <span class="ph uicontrol">Color map</span> variable.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_tsne__section_fj1_mjb_qdb__title__1" id="chart_creation_tsne__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_tsne__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Perplexity
</dt>
<dd class="dlentry">
Sets a number that establishes an educated guess as to the number of close neighbors for each data point. The purpose is to balance the local and global aspects for your data.
</dd>
<dt class="dlterm">
Learning rate
</dt>
<dd class="dlentry">
This value affects the speed of learning by specifying the weight size changes at each iteration.
</dd>
<dt class="dlterm">
Maximum iterations
</dt>
<dd class="dlentry">
The maximum number of iterations to run.
</dd>
<dt class="dlterm" id="chart_creation_tsne__d9e198">
Color map
</dt>
<dd class="dlentry" id="chart_creation_tsne__d9e201">
Lists available color map variables. These variables use color progression, based on the range of values in the specified column, to represent themselves in the plot points. Color maps are also known as choropleth maps.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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3873A285DCB38EF4B4ED663BFA0DF4047AB7692D | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_wordcloud.html?context=cdpaas&locale=en | Word cloud charts | Word cloud charts
Word cloud charts present data as words, where the size and placement of any individual word is determined by how it is weighted.
| # Word cloud charts #
Word cloud charts present data as words, where the size and placement of any individual word is determined by how it is weighted\.
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<h1 class="topictitle1" id="chart_creation_wordcloud__title__1">Word cloud charts</h1>
<div class="body">
<div class="abstract">
Word cloud charts present data as words, where the size and placement of any individual word is determined by how it is weighted.
</div>
<section class="section" role="region" aria-labelledby="chart_creation_wordcloud__section_tsd_ljb_qdb__title__1" id="chart_creation_wordcloud__section_tsd_ljb_qdb">
<h2 class="sectiontitle" id="chart_creation_wordcloud__section_tsd_ljb_qdb__title__1">Creating a simple word cloud chart</h2>
<ol id="chart_creation_wordcloud__ol_q3s_njb_qdb">
<li>In the <span class="ph uicontrol">Chart Type</span> section, click the <span class="ph uicontrol">Word cloud</span> icon.
<p>The canvas updates to display a word cloud chart template.</p></li>
<li>Select a variable as the <span class="ph uicontrol">Source</span> variable. Each variable category is represented in the chart based on its weight value.</li>
<li>Select a <span class="ph uicontrol">Shape</span> value for the chart. The chart data is presented in the selected shape.</li>
<li data-hd-product="spssbase">Click the <span class="ph uicontrol">Save visualization in the project</span> control to save the visualization to the project. You can select to also <span class="ph uicontrol">Create a new asset</span> from the visualization, provide a visualization asset name, description, and chart name.</li>
<li data-hd-product="spssbase">Click <span class="ph uicontrol">Apply</span> to save the visualization to the project. The new visualization asset is now available under the <span class="ph uicontrol">Assets</span> tab.</li>
</ol>
</section>
<section class="section" role="region" aria-labelledby="chart_creation_wordcloud__section_fj1_mjb_qdb__title__1" id="chart_creation_wordcloud__section_fj1_mjb_qdb">
<h2 class="sectiontitle" id="chart_creation_wordcloud__section_fj1_mjb_qdb__title__1">Options</h2>
<dl>
<dt class="dlterm">
Source
</dt>
<dd class="dlentry">
Lists variables that are available as the chart's source. Each variable category is represented in the chart based on its weight value.
</dd>
<dt class="dlterm">
Shape
</dt>
<dd class="dlentry">
Lists the available chart shapes. The chart data is presented in the selected shape.
</dd>
<dt class="dlterm">
Primary title
</dt>
<dd class="dlentry">
The chart title.
</dd>
<dt class="dlterm">
Subtitle
</dt>
<dd class="dlentry">
The chart subtitle, which is placed directly beneath the chart title.
</dd>
<dt class="dlterm">
Footnote
</dt>
<dd class="dlentry">
The chart footnote, which is placed beneath the chart.
</dd>
</dl>
</section>
</div>
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3BB91EBACC556700F955C3E6E01D90E5256207CF | https://dataplatform.cloud.ibm.com/docs/content/dataview/idh_idc_cg_help_main.html?context=cdpaas&locale=en | Visualizing your data | Visualizing your data
You can discover insights from your data by creating visualizations. By exploring data from different perspectives with visualizations, you can identify patterns, connections, and relationships within that data and quickly understand large amounts of information.
Data format
: Tabular: Avro, CSV, JSON, Parquet, TSV, SAV, Microsoft Excel .xls and .xlsx files, SAS, delimited text files, and connected data.
For more information about supported data sources, see [Connectors](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/conn_types.html).
Data size
: No limit
You can create graphics similar to the following example that shows how humidity values over time.
| # Visualizing your data #
You can discover insights from your data by creating visualizations\. By exploring data from different perspectives with visualizations, you can identify patterns, connections, and relationships within that data and quickly understand large amounts of information\.
Data format
: Tabular: Avro, CSV, JSON, Parquet, TSV, SAV, Microsoft Excel \.xls and \.xlsx files, SAS, delimited text files, and connected data\.
For more information about supported data sources, see [Connectors](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/conn_types.html).
Data size
: No limit
You can create graphics similar to the following example that shows how humidity values over time\.
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<h1 class="topictitle1" id="idh_idc_cg_help_main__title__1"><span class="ph" data-hd-product="spssbase">Visualizing your data</span></h1>
<div class="body">
<p><span class="ph" data-hd-product="spssbase">You can discover insights from your data by creating visualizations. By exploring data from different perspectives with visualizations, you can identify patterns, connections, and relationships within that data and quickly understand large amounts of information.</span></p>
<div class="p" data-hd-product="spssbase clemclient clembatch clemdesktop clempublisher clemserver">
<dl>
<dt class="dlterm">
Data format
</dt>
<dd class="dlentry">
Tabular: Avro, CSV, JSON, Parquet, TSV, SAV, Microsoft Excel .xls and .xlsx files, SAS, delimited text files, and connected data.
<p>For more information about supported data sources, see <a href="../wsj/manage-data/conn_types.html">Connectors</a>.</p>
</dd>
<dt class="dlterm">
Data size
</dt>
<dd class="dlentry">
No limit
</dd>
</dl>
</div>
<p>You can create graphics similar to the following example that shows how humidity values over time.</p>
<p><img src="viz_main.png" alt="Example visualization"></p>
<section class="section" role="region" aria-labelledby="idh_idc_cg_help_main__title__2" data-hd-product="spssbase">
<h2 class="sectiontitle" id="idh_idc_cg_help_main__title__2">Creating visualizations</h2>
<p>You can build a chart by selecting a predefined chart type from a gallery or by combining basic elements from the provided chart type options (for example, axes and bars). See <a href="chart_create_layout.html">Visualizations layout and terms</a> for descriptions. Using the gallery is the preferred method for new users. Start by using the predefined charts. For more information, see <a href="chart_creation_fromgallery.html">Building a chart from the chart type gallery</a> .</p>
<p>To create visualizations that use an asset from your project:</p>
<ol>
<li>On the <strong>Assets</strong> tab of your project, click <strong>Data asset</strong> in the list of asset types, and select a data asset.</li>
<li>Click the <strong>Visualization</strong> tab.</li>
<li>Select a chart type from the options that are listed and input your preferences in the graphical options pane.
<p>Available chart types are ordered from most relevant to least relevant, based on the selected columns. If there are no columns in the data set with a data type that is supported for a chart type, that chart will not be available. If a column's data type is not supported for a chart, that column is not available for selection for that chart. Dots next to the charts' names suggest the best charts for your data.</p>
<p>As you are building the chart, the canvas displays a preview of the chart. The preview uses the actual variable labels and measurement levels that are representative of your actual data.</p></li>
<li>To save your visualization, select <strong>ACTIONS > Save visualization to project</strong>.</li>
</ol>
<p>Your saved visualization is listed as a Visualization asset in your project. Graphical charts are generated based on a sample data set of up to 5000 records. To generate a chart based on a full data set, edit the visualization asset.</p>
<p>You can view or edit the visualization by clicking the name of the visualization that is listed in the Visualization assets of your project. On the <strong>Layout</strong> tab, you can arrange a layout of multiple charts and generate an output in PDF format.</p>
</section>
<section class="section" role="region" aria-labelledby="idh_idc_cg_help_main__title__3" data-hd-product="spssbase">
<h2 class="sectiontitle" id="idh_idc_cg_help_main__title__3">Learn more</h2>
</section>
</div>
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<li class="ulchildlink"><strong><a href="chart_creation_fromgallery.html">Building a chart from the chart type gallery</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_creation_charttypes.html">Chart types</a></strong><br></li>
<li class="ulchildlink"><strong><a href="chart_appearance_tab.html">Global visualization preferences</a></strong><br></li>
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9D9188E6383DB5F7038B98A688CB2DC9CF5A336C | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/aiopenscale.html?context=cdpaas&locale=en | watsonx.governance on IBM watsonx | watsonx.governance on IBM® watsonx
| # watsonx\.governance on IBM® watsonx #
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<h1 class="topictitle1" id="welcome-aiopenscale__title__1"><span class="keyword" data-hd-audience="wx" translate="no">watsonx.governance</span> on <span class="keyword" data-hd-audience="wx" translate="no">IBM® watsonx</span></h1>
<div class="body" id="body">
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<h2 class="sectiontitle" id="welcome-aiopenscale__body__title__1">Description</h2>
<p>Use <span class="keyword" translate="no">watsonx.governance</span> to analyze your AI with trust and transparency and understand how your AI models make decisions. Detect and mitigate bias and drift. Increase the quality and accuracy of your predictions. Explain transactions and perform what-if analysis. Extend the governance capabilities with support for tracking predictive and generative model assets in AI use cases. Details about the tracked assets are recorded in fact sheets to use for your governance and compliance requirements.</p>
<p><span class="keyword" translate="no">Watsonx.governance</span> provides an enterprise-grade environment for AI applications that provides your enterprise visibility into how your AI is built, is used, and delivers return on investment. Its open platform enables businesses to operate and automate AI at scale with transparent, explainable outcomes that are free from harmful bias and drift. <span class="keyword" translate="no">Watsonx.governance</span> supports external models that are developed in third-party providers, including <span class="keyword" translate="no">Amazon Web Services</span> or <span class="keyword" translate="no">Microsoft Azure</span>.</p>
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<h2 class="sectiontitle" id="welcome-aiopenscale__sl-quick__title__1">Quick links</h2>
<ul id="welcome-aiopenscale__sl-quick-links">
<li><a data-hd-audience="wx" href="../wsj/model/wos-config-ovr.html">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li><a href="../wsj/model/getting-started.html">Use</a><span class="ph">: Work with the service</span></li>
<li><a data-hd-audience="wx" href="https://cloud.ibm.com/apidocs/ai-openscale" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Develop</a><span class="ph">: Write code and build applications</span></li>
<li><a data-hd-audience="wx" href="../wsj/getting-started/whats-new.html">What's new</a><span class="ph" data-hd-audience="cloud wx">: See what's new each week</span></li>
<li><a href="../wsj/troubleshoot/wos-troubleshoot.html">Troubleshoot</a><span class="ph">: Find solutions to problems</span></li>
</ul>
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CF88BCC09A32B2D6D65F2C2A831E2960ACA1E347 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloud-object-storage.html?context=cdpaas&locale=en | Cloud Object Storage on IBM watsonx | Cloud Object Storage on IBM® watsonx
| # Cloud Object Storage on IBM® watsonx #
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<h1 class="topictitle1" id="welcome-cloud-object-storage__title__1"><span class="keyword">Cloud Object Storage</span> on <span class="ph" data-hd-audience="wx">IBM® watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-cloud-object-storage__sl-desc__title__1" id="welcome-cloud-object-storage__sl-desc">
<h2 class="sectiontitle" id="welcome-cloud-object-storage__sl-desc__title__1">Description</h2>
<p>IBM Cloud Object Storage provides storage for projects, catalogs, and deployment spaces. You can also create connections to access data in an IBM Cloud Object Storage instance.</p>
<p>When you create a project, catalog, or deployment space, you must choose an IBM Cloud Object Storage instance. You can use a single instance for all projects, catalogs, and deployment spaces, or you can use multiple instances. Each project, catalog, and deployment space has its own dedicated bucket. The bucket stores files for assets, such as uploaded data files or notebook files. The connection between the project, catalog, or deployment space and its storage bucket is implicit.</p>
<p>You create an IBM Cloud Object Storage connection when you want to connect to data stored in any IBM Cloud Object Storage instance that you have access to. After you create a connection to a Cloud Object Storage bucket, you can access the files in the bucket by creating connected data assets.</p>
<p>This service provides storage for projects, catalogs, and deployment spaces. The Lite plan instance is free to use for storage capacity up to 25 GB per month.</p>
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<h2 class="sectiontitle" id="welcome-cloud-object-storage__sl-quick__title__1">Quick links</h2>
<ul id="welcome-cloud-object-storage__sl-quick-links">
<li><a href="https://cloud.ibm.com/docs/cloud-object-storage?topic=cloud-object-storage-getting-started-cloud-object-storage" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li><a href="../wsj/admin/storage-options.html">Use</a><span class="ph">: Work with the service</span></li>
<li><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li><a href="../wsj/manage-data/conn-cos.html">Connect</a><span class="ph">: Create a connection</span></li>
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59DF73D502B5F62E3837464E81AC6BC9FDF07014 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloud-services.html?context=cdpaas&locale=en | IBM Cloud services in the IBM watsonx services catalog | IBM Cloud services in the IBM watsonx services catalog
You can provision IBM® Cloud service instances for the watsonx platform.
The IBM watsonx.ai component provides the following services that provide key functionality, including tools and compute resources:
* [Watson™ Studio](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/wsl.html)
* [Watson Machine Learning](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/wml.html)
If you signed up for watsonx.ai, you already have these services. Otherwise, you can create instances of these services from the Services catalog.
If you signed up for watsonx.governance, you already have this service. Otherwise, you can create an instance of this service from the Services catalog.
The [IBM Cloud Object Storage](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloud-object-storage.html) provides storage for projects and deployment spaces on the IBM watsonx platform.
The [Secure Gateway](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/secure-gateway.html) service provides secure connections to on-premises date sources.
These services provide databases that you can access in IBM watsonx by creating connections:
* [IBM Analytics Engine](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/spark.html)
* [Cloudant](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloudant.html)
* [Databases for Elasticsearch](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/elasticsearch.html)
* [Databases for EDB](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/edb.html)
* [Databases for MongoDB](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/mongodb.html)
* [Databases for PostgreSQL](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/postgresql.html)
* [Db2®](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/db2oltp.html)
* [Db2 Warehouse](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/db2wh.html)
| # IBM Cloud services in the IBM watsonx services catalog #
You can provision IBM® Cloud service instances for the watsonx platform\.
The IBM watsonx\.ai component provides the following services that provide key functionality, including tools and compute resources:
<!-- <ul> -->
* [Watson™ Studio](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/wsl.html)
* [Watson Machine Learning](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/wml.html)
<!-- </ul> -->
If you signed up for watsonx\.ai, you already have these services\. Otherwise, you can create instances of these services from the Services catalog\.
If you signed up for watsonx\.governance, you already have this service\. Otherwise, you can create an instance of this service from the Services catalog\.
The [IBM Cloud Object Storage](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloud-object-storage.html) provides storage for projects and deployment spaces on the IBM watsonx platform\.
The [Secure Gateway](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/secure-gateway.html) service provides secure connections to on\-premises date sources\.
These services provide databases that you can access in IBM watsonx by creating connections:
<!-- <ul> -->
* [IBM Analytics Engine](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/spark.html)
* [Cloudant](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloudant.html)
* [Databases for Elasticsearch](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/elasticsearch.html)
* [Databases for EDB](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/edb.html)
* [Databases for MongoDB](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/mongodb.html)
* [Databases for PostgreSQL](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/postgresql.html)
* [Db2®](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/db2oltp.html)
* [Db2 Warehouse](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/db2wh.html)
<!-- </ul> -->
<!-- </article "role="article" "> -->
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<title>IBM Cloud services in the IBM watsonx services catalog</title>
<link rel="canonical" href="https://www.ibm.com/docs/en/watsonx-as-a-service?topic=integrations-cloud-services">
<meta name="viewport" content="width=device-width,initial-scale=1">
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<article role="article" aria-labelledby="cloud-services__title__1">
<h1 class="topictitle1" id="cloud-services__title__1"><span class="keyword">IBM Cloud</span> services in the <span class="ph" data-hd-audience="wx">IBM watsonx</span> services catalog</h1>
<div class="body">
<div class="section" data-hd-audience="wx">
<div class="sectiondiv" data-hd-audience="wx">
<p>You can provision <span class="keyword" translate="no">IBM® Cloud</span> service instances for the <span class="keyword" translate="no">watsonx</span> platform.</p>
<div class="p">
The <span class="keyword" translate="no">IBM watsonx.ai</span> component provides the following services that provide key functionality, including tools and compute resources:
<ul>
<li><a href="wsl.html"><span class="keyword" translate="no">Watson™ Studio</span></a></li>
<li><a href="wml.html"><span class="keyword" translate="no">Watson Machine Learning</span></a></li>
</ul>
</div>
<p>If you signed up for <span class="keyword" translate="no">watsonx.ai</span>, you already have these services. Otherwise, you can create instances of these services from the Services catalog.</p>
<p>If you signed up for <span class="keyword" translate="no">watsonx.governance</span>, you already have this service. Otherwise, you can create an instance of this service from the Services catalog.</p>
</div>
</div>
<section class="section" role="region" aria-labelledby="cloud-services__title__2" data-hd-audience="wx">
<h2 class="sectiontitle" id="cloud-services__title__2">Storage service</h2>
</section>
<div class="section" data-hd-audience="wx">
<div class="sectiondiv" data-hd-audience="wx">
<p>The <a href="cloud-object-storage.html"><span class="keyword" translate="no">IBM Cloud Object Storage</span></a> provides storage for projects and deployment spaces on the <span class="keyword" translate="no">IBM watsonx</span> platform.</p>
</div>
</div>
<section class="section" role="region" aria-labelledby="cloud-services__title__3" data-hd-audience="wx">
<h2 class="sectiontitle" id="cloud-services__title__3">Security service</h2>
</section>
<div class="section" data-hd-audience="wx">
<div class="sectiondiv" data-hd-audience="wx">
<p>The <a href="secure-gateway.html"><span class="keyword" translate="no">Secure Gateway</span></a> service provides secure connections to on-premises date sources.</p>
</div>
</div>
<section class="section" role="region" aria-labelledby="cloud-services__title__4" data-hd-audience="wx">
<h2 class="sectiontitle" id="cloud-services__title__4">Data source services</h2>
</section>
<div class="section" data-hd-audience="wx">
<div class="sectiondiv" data-hd-audience="wx">
<div class="p">
These services provide databases that you can access in <span class="keyword" translate="no">IBM watsonx</span> by creating connections:
<ul>
<li><a href="spark.html"><span class="keyword" data-hd-audience="cloud wx">IBM Analytics Engine</span></a></li>
<li><a href="cloudant.html"><span class="keyword">Cloudant</span></a></li>
<li><a href="elasticsearch.html"><span class="keyword">Databases for Elasticsearch</span></a></li>
<li><a href="edb.html"><span class="keyword">Databases for EDB</span></a></li>
<li><a href="mongodb.html"><span class="keyword" data-hd-audience="cloud wx">Databases for MongoDB</span></a></li>
<li><a href="postgresql.html"><span class="keyword">Databases for PostgreSQL</span></a></li>
<li><a href="db2oltp.html"><span class="keyword" translate="no">Db2®</span></a></li>
<li><a href="db2wh.html"><span class="keyword" translate="no">Db2 Warehouse</span></a></li>
</ul>
</div>
</div>
</div>
<section class="section" role="region" aria-labelledby="cloud-services__title__5">
<h2 class="sectiontitle" id="cloud-services__title__5">Learn more</h2>
<ul>
<li><a href="../wsj/admin/create-services.html">Creating services</a></li>
<li><a href="../wsj/admin/endpoints-vrf.html">Securing connections to services with private service endpoints</a></li>
</ul>
</section>
</div>
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<nav role="navigation">
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<strong>Parent topic:</strong> <a href="../wsj/admin/svc-int.html">Services and integrations</a>
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A56686454E771E5FDDA0315DD38313F9FCB31AAC | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloudant.html?context=cdpaas&locale=en | Cloudant on IBM watsonx | Cloudant on IBM® watsonx
| # Cloudant on IBM® watsonx #
<!-- </article "role="article" "> -->
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<title>Cloudant on IBM watsonx</title>
<link rel="canonical" href="https://www.ibm.com/docs/en/watsonx-as-a-service?topic=services-cloudant">
<meta name="viewport" content="width=device-width,initial-scale=1">
</head>
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<main role="main">
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<h1 class="topictitle1" id="welcome-cloudant__title__1"><span class="keyword">Cloudant</span> on <span class="ph" data-hd-audience="wx">IBM® watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-cloudant__sl-desc__title__1" id="welcome-cloudant__sl-desc">
<h2 class="sectiontitle" id="welcome-cloudant__sl-desc__title__1">Description</h2>
<p>IBM Cloudant is a fully managed JSON document database that offers independent serverless scaling of provisioned throughput capacity and storage. Cloudant is compatible with Apache CouchDB and accessible through a simple to use HTTPS API for web, mobile, and IoT applications.</p>
<p>This service provides a database that you can connect to from Watson Studio.</p>
</section>
<section class="section" role="region" aria-labelledby="welcome-cloudant__sl-quick__title__1" id="welcome-cloudant__sl-quick">
<h2 class="sectiontitle" id="welcome-cloudant__sl-quick__title__1">Quick links</h2>
<ul id="welcome-cloudant__sl-quick-links">
<li><a href="https://cloud.ibm.com/docs/Cloudant?topic=Cloudant-getting-started-with-cloudant" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li><a href="https://cloud.ibm.com/docs/Cloudant?topic=Cloudant-getting-started-with-cloudant" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Use</a><span class="ph">: Work with the service</span></li>
<li><a href="https://cloud.ibm.com/apidocs/cloudant" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Develop</a><span class="ph">: Write code and build applications</span></li>
<li><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li><a href="../wsj/manage-data/conn-cloudant.html">Connect</a><span class="ph">: Create a connection</span></li>
</ul>
</section>
</div>
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F3BA8CCB1E55BB6535944CB5ACDB19EFAEB1C3F9 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/db2oltp.html?context=cdpaas&locale=en | Db2 on IBM watsonx | Db2 on IBM watsonx
| # Db2 on IBM watsonx #
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<title>Db2 on IBM watsonx</title>
<link rel="canonical" href="https://www.ibm.com/docs/en/watsonx-as-a-service?topic=services-db2">
<meta name="viewport" content="width=device-width,initial-scale=1">
</head>
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<main role="main">
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<h1 class="topictitle1" id="welcome-db2oltp__title__1"><span class="keyword" translate="no">Db2</span> on <span class="ph" data-hd-audience="wx">IBM watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-db2oltp__sl-desc__title__1" id="welcome-db2oltp__sl-desc">
<h2 class="sectiontitle" id="welcome-db2oltp__sl-desc__title__1">Description</h2>
<p>IBM Db2 provides advanced data management and analytics capabilities for transactional workloads. Db2 has no processor, memory, or database size limits, which makes it ideal for any size workload. The Db2 service enables you to create these databases in your <span class="ph" data-hd-audience="wx">IBM® watsonx</span> cluster so that you can govern the data and use it for more in-depth analysis.</p>
<p>Integrating a Db2 database with <span class="ph" data-hd-audience="wx">IBM watsonx</span> can be useful in the following situations:</p>
<ul>
<li>You need your transactional data to be governed, such as data from a website, bank, or retail store.</li>
<li>You want to create a replica of your transactional database so that you can run analytics without impacting regular business operations.</li>
<li>You need to ensure the integrity of your data by using an ACID-compliant database.</li>
<li>You need a low-latency database.</li>
<li>You need real-time insight into your business operations.</li>
</ul>
<p data-hd-audience="cloud wx">This service provides a database that you can connect to from Watson Studio .</p>
</section>
<section class="section" role="region" aria-labelledby="welcome-db2oltp__sl-quick__title__1" id="welcome-db2oltp__sl-quick">
<h2 class="sectiontitle" id="welcome-db2oltp__sl-quick__title__1">Quick links</h2>
<ul id="welcome-db2oltp__sl-quick-links">
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/docs/Db2onCloud?topic=Db2onCloud-getting-started" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Use</a><span class="ph">: Work with the service</span></li>
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/apidocs/db2-on-cloud/db2-on-cloud-v3" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Develop</a><span class="ph">: Write code and build applications</span></li>
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/docs/Db2onCloud?topic=Db2onCloud-interfaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/manage-data/conn-db2.html">Connect</a><span class="ph">: Create a connection</span></li>
</ul>
</section>
</div>
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E81F1FD08E472AF1516E6C6B0C936A2DCA55CC20 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/db2wh.html?context=cdpaas&locale=en | Db2 Warehouse on IBM watsonx | Db2 Warehouse on IBM watsonx
| # Db2 Warehouse on IBM watsonx #
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<title>Db2 Warehouse on IBM watsonx</title>
<link rel="canonical" href="https://www.ibm.com/docs/en/watsonx-as-a-service?topic=services-db2-warehouse">
<meta name="viewport" content="width=device-width,initial-scale=1">
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<main role="main">
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<h1 class="topictitle1" id="welcome-db2wh__title__1"><span class="keyword" translate="no">Db2 Warehouse</span> on <span class="ph" data-hd-audience="wx">IBM watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-db2wh__sl-desc__title__1" id="welcome-db2wh__sl-desc">
<h2 class="sectiontitle" id="welcome-db2wh__sl-desc__title__1">Description</h2>
<p>IBM Db2 Warehouse is an analytics data warehouse that features in-memory data processing and in-database analytics. It is client-managed and optimized for fast and flexible deployment, with automated scaling that supports analytics workloads. Based on the number of worker nodes selected, <span class="ph" data-hd-audience="wx">IBM® watsonx</span> automatically creates the appropriate data warehouse environment. For a single node, the warehouse uses symmetric multiprocessing (SMP) architecture for cost-efficiency. For two or more nodes, the warehouse is deployed using a massively parallel processing (MPP) architecture for high availability and improved performance.</p>
<p>Integrating a Db2 Warehouse database with <span class="ph" data-hd-audience="wx">IBM watsonx</span> can be useful in the following situations:</p>
<ul>
<li>You have developers who need to create small-scale database management systems for development and test work. For example, if you need to test new applications and data sources in a development environment before you move them to a production environment.</li>
<li>You want to accelerate line-of-business analytics projects by creating a data mart service that combines a governed data source with analytic techniques.</li>
<li>You need to deliver self-service analytics solutions and applications that leverage data that is generated from new sources and is ingested directly into the private cloud warehouse.</li>
<li>You want to migrate a subset of applications or data from an on-premises data warehouse to a private cloud.</li>
<li>You want to save money or improve performance by migrating on-premises data marts or an on-premises data warehouse to a cloud-native data warehouse.</li>
<li>You want to support data scientists who are coding and need to store data locally and need to use a logical representation.</li>
<li>You want to reduce network traffic and improve analytic performance by storing your data near your analytics engine.</li>
<li>You have multiple departments, and each department requires their own database management system.</li>
</ul>
<p data-hd-audience="cloud wx">This service provides a database that you can connect to from Watson Studio.</p>
</section>
<section class="section" role="region" aria-labelledby="welcome-db2wh__sl-quick__title__1" id="welcome-db2wh__sl-quick">
<h2 class="sectiontitle" id="welcome-db2wh__sl-quick__title__1">Quick links</h2>
<ul id="welcome-db2wh__sl-quick-links">
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/docs/Db2whc?topic=Db2whc-getting-started" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Use</a><span class="ph">: Work with the service</span></li>
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/docs/Db2whc?topic=Db2whc-interfaces" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/apidocs/db2-warehouse-on-cloud/db2-warehouse-on-cloud-v4" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Develop</a><span class="ph">: Write code and build applications</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/manage-data/conn-db2-wh.html">Connect</a><span class="ph">: Create a connection</span></li>
</ul>
</section>
</div>
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<nav role="navigation">
<div class="familylinks">
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32217F5F0DEE4A95C64B2BD92C25366706CC7E0C | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/edb.html?context=cdpaas&locale=en | Databases for EDB on IBM watsonx | Databases for EDB on IBM watsonx
| # Databases for EDB on IBM watsonx #
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<title>Databases for EDB on IBM watsonx</title>
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<h1 class="topictitle1" id="welcome-edb__title__1"><span class="keyword">Databases for EDB</span> on <span class="ph" data-hd-audience="wx">IBM watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-edb__sl-desc__title__1" id="welcome-edb__sl-desc">
<h2 class="sectiontitle" id="welcome-edb__sl-desc__title__1">Description</h2>
<p>The Databases for EDB service provides the EDB Postgres Advanced Server database engine that optimizes the built-in features of PostgreSQL. EDB Postgres Advanced Server (formerly known as EnterpriseDB) is a PostgreSQL-based database engine optimized for performance, developer productivity, and compatibility with Oracle. Databases for EDB is a fully managed offering with 24x7 operations and support. Features include high availability, automated backup orchestration, and de-coupled scaling of storage, RAM, and vCPUs.</p>
<p><span class="ph" data-hd-audience="wx">IBM® watsonx</span> does not yet include a connection to EDB Postgres Advanced Server, however, you can use the PostgreSQL connector to create connections to EDB Postgres Advanced Server from Watson Studio.</p>
<p>This service provides a database that you can connect to from Watson Studio.</p>
<p><strong>Restriction:</strong> When you connect to EDB through <span class="ph" data-hd-audience="wx">IBM watsonx</span>, you can use PostgreSQL features, but not EDB Postgres Advanced Server features. See the <a href="https://www.enterprisedb.com/compare-postgres-databases" rel="noopener" target="_blank" title="(Opens in a new tab or window)">list of differences</a> between PostgreSQL and EDB Postgres Advanced Server. This restriction applies only when you access EDB through <span class="ph" data-hd-audience="wx">IBM watsonx</span>. This restriction does not apply when you have an external service that uses the EDB Postgres Advanced Server driver to access the EDB Postgres Advanced Server service provisioned through <span class="ph" data-hd-audience="wx">IBM watsonx</span>.</p>
</section>
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<h2 class="sectiontitle" id="welcome-edb__sl-quick__title__1">Quick links</h2>
<ul id="welcome-edb__sl-quick-links">
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/docs/databases-for-enterprisedb?topic=databases-for-enterprisedb-getting-started" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Use</a><span class="ph">: Work with the service</span></li>
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/apidocs/cloud-databases-api" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Develop</a><span class="ph">: Write code and build applications</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/manage-data/conn-postgresql.html">Connect</a><span class="ph">: Create a connection</span></li>
</ul>
</section>
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868801EC73691D31B90C8611E934AA5DD3B17EA7 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/elasticsearch.html?context=cdpaas&locale=en | Databases for Elasticsearch on IBM watsonx | Databases for Elasticsearch on IBM® watsonx
| # Databases for Elasticsearch on IBM® watsonx #
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<h1 class="topictitle1" id="welcome-elasticsearch__title__1"><span class="keyword">Databases for Elasticsearch</span> on <span class="ph" data-hd-audience="wx">IBM® watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-elasticsearch__sl-desc__title__1" id="welcome-elasticsearch__sl-desc">
<h2 class="sectiontitle" id="welcome-elasticsearch__sl-desc__title__1">Description</h2>
<p>Elasticsearch is an open source search and analytics engine based on the <span class="keyword" translate="no">Apache Lucene</span> library. It combines the power of a scalable full text search engine with the indexing strengths of a schema-free JSON document database.</p>
<p>Elasticsearch is a powerful tool for rich data analysis of large volumes of data, catalogs, autocompletion, log analysis, monitoring, blockchain analysis and more. IBM Cloud Databases for Elasticsearch provide the benefits of high availability, automated backup orchestration, autoscaling, and de-coupled allocation of storage, RAM, and vCPUs. Databases for Elasticsearch pricing is based on underlying disk, RAM, and optional vCPU allocation, as well as backup storage usage.</p>
<p>This service provides a database that you can connect to from Watson Studio.</p>
</section>
<section class="section" role="region" aria-labelledby="welcome-elasticsearch__sl-quick__title__1" id="welcome-elasticsearch__sl-quick">
<h2 class="sectiontitle" id="welcome-elasticsearch__sl-quick__title__1">Quick links</h2>
<ul id="welcome-elasticsearch__sl-quick-links">
<li><a href="https://cloud.ibm.com/docs/databases-for-elasticsearch?topic=databases-for-elasticsearch-getting-started" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li><a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Use</a><span class="ph">: Work with the service</span></li>
<li><a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/rest-apis.html" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Develop</a><span class="ph">: Write code and build applications</span></li>
<li><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li><a href="../wsj/manage-data/conn-elastic.html">Connect</a><span class="ph">: Create a connection</span></li>
</ul>
</section>
</div>
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408FDAB4F452AB2C207EE3416332D315598E3456 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/mongodb.html?context=cdpaas&locale=en | Databases for MongoDB on IBM watsonx | Databases for MongoDB on IBM watsonx
| # Databases for MongoDB on IBM watsonx #
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<h1 class="topictitle1" id="welcome-mongodb__title__1"><span class="keyword" data-hd-audience="cloud wx">Databases for MongoDB</span> on <span class="ph" data-hd-audience="wx">IBM watsonx</span></h1>
<div class="body" id="body">
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<h2 class="sectiontitle" id="welcome-mongodb__sl-desc__title__1">Description</h2>
<p>MongoDB Enterprise Advanced is a cross-platform, document-oriented, NoSQL database. Rather than table-based relational database structure, it uses JSON-like documents with dynamic schemas, which makes it easier and faster to integrate data in certain types of applications. With the <span class="keyword" data-hd-audience="cloud wx">Databases for MongoDB</span> service you can create highly performant, highly available databases with automatic scaling in your <span class="ph" data-hd-audience="wx">IBM® watsonx</span> cluster so that you can govern the data and use it for in-depth analysis.</p>
<p>Integrating a MongoDB database into <span class="ph" data-hd-audience="wx">IBM watsonx</span> can be useful in the following situations:</p>
<ul>
<li>You need an operational database that supports a rapidly changing data model.</li>
<li>You want lightweight, low-latency analytics integrated into your operational database.</li>
<li>You need real-time views of your business, even if your data is in silos.</li>
<li>You develop applications and need a database that can:
<ul>
<li>Store large amounts of data with different data types, such as structure, unstructured, and polymorphic data</li>
<li>Support millions of users.</li>
<li>Personalize the content that you deliver to customers.</li>
</ul></li>
<li>You need to store large amounts of data from Internet of Things devices or sensors.</li>
<li>You need to maintain a catalog.</li>
<li>You need to store and serve many different types of content.</li>
</ul>
<p data-hd-audience="cloud wx">This service provides a database that you can connect to from Watson Studio .</p>
</section>
<section class="section" role="region" aria-labelledby="welcome-mongodb__sl-quick__title__1" id="welcome-mongodb__sl-quick">
<h2 class="sectiontitle" id="welcome-mongodb__sl-quick__title__1">Quick links</h2>
<ul id="welcome-mongodb__sl-quick-links">
<li data-hd-audience="cloud wx"><a href="https://cloud.ibm.com/docs/databases-for-mongodb?topic=databases-for-mongodb-getting-started" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Use</a><span class="ph">: Work with the service</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/manage-data/conn-mongodb.html">Connect</a><span class="ph">: Create a connection</span></li>
</ul>
</section>
</div>
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649119A6EF3F5AA2B1B0C63E0973532D4C950F48 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/postgresql.html?context=cdpaas&locale=en | Databases for PostgreSQL on IBM watsonx | Databases for PostgreSQL on IBM® watsonx
| # Databases for PostgreSQL on IBM® watsonx #
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<h1 class="topictitle1" id="welcome-postgresql__title__1"><span class="keyword">Databases for PostgreSQL</span> on <span class="ph" data-hd-audience="wx">IBM® watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-postgresql__sl-desc__title__1" id="welcome-postgresql__sl-desc">
<h2 class="sectiontitle" id="welcome-postgresql__sl-desc__title__1">Description</h2>
<p>The Databases for PostgreSQL service provides the PostgreSQL database. PostgreSQL is an object-relational database management system (ORDBMS) with an emphasis on extensibility and on standards-compliance.</p>
<p>This service provides a database that you can connect to from Watson Studio .</p>
</section>
<section class="section" role="region" aria-labelledby="welcome-postgresql__sl-quick__title__1" id="welcome-postgresql__sl-quick">
<h2 class="sectiontitle" id="welcome-postgresql__sl-quick__title__1">Quick links</h2>
<ul id="welcome-postgresql__sl-quick-links">
<li><a href="https://cloud.ibm.com/docs/databases-for-postgresql?topic=databases-for-postgresql-getting-started" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Use</a><span class="ph">: Work with the service</span></li>
<li><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
<li><a href="../wsj/manage-data/conn-dbase-postgresql.html">Connect</a><span class="ph">: Create a connection</span></li>
</ul>
</section>
</div>
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B9D44BBCF205103BF01619D31CFEBE31A725BA5A | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/secure-gateway.html?context=cdpaas&locale=en | Secure Gateway on IBM watsonx | Secure Gateway on IBM® watsonx
| # Secure Gateway on IBM® watsonx #
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<h1 class="topictitle1" id="welcome-secure-gateway__title__1"><span class="keyword">Secure Gateway</span> on <span class="ph" data-hd-audience="wx">IBM® watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-secure-gateway__sl-desc__title__1" id="welcome-secure-gateway__sl-desc">
<h2 class="sectiontitle" id="welcome-secure-gateway__sl-desc__title__1">Description</h2>
<div class="p">
<div class="note attention">
<span class="attentiontitle">Attention:</span> IBM Cloud® is announcing the full deprecation of Secure Gateway. See the <a href="https://cloud.ibm.com/docs/SecureGateway?topic=SecureGateway-dep-overview" rel="noopener" target="_blank" title="(Opens in a new tab or window)">deprecation dates, details, and specific implications</a>.
</div>
</div>
<p>IBM Secure Gateway for IBM Cloud service provides a quick, easy, and secure solution for connecting anything to anything. By deploying the light-weight and natively installed Secure Gateway Client, a secure, persistent connection can be established between your environment and the cloud. With this, you can safely connect all of your applications and resources regardless of their location. Rather than bridging your environments at the network level like a traditional VPN, Secure Gateway provides granular resource control operating with the principle of least privilege as its core tenet.</p>
<p>This service provides secure connections to on-premises data sources.</p>
</section>
<section class="section" role="region" aria-labelledby="welcome-secure-gateway__sl-quick__title__1" id="welcome-secure-gateway__sl-quick">
<h2 class="sectiontitle" id="welcome-secure-gateway__sl-quick__title__1">Quick links</h2>
<ul id="welcome-secure-gateway__sl-quick-links">
<li><a href="https://cloud.ibm.com/docs/SecureGateway?topic=SecureGateway-getting-started-with-sg&locale=en#getting-started-with-sg" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li><a href="../wsj/manage-data/create-conn.html#configure-a-secure-gateway-service">Use</a><span class="ph">: Work with the service</span></li>
<li><a href="https://cloud.ibm.com/apidocs/secure-gateway" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Develop</a><span class="ph">: Write code and build applications</span></li>
<li><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
</ul>
</section>
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6AC4A29FEBF419002BDBA62D99D997CF55E9FCF2 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/spark.html?context=cdpaas&locale=en | IBM Analytics Engine on IBM watsonx | IBM Analytics Engine on IBM® watsonx
| # IBM Analytics Engine on IBM® watsonx #
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<h1 class="topictitle1" id="welcome-spark__title__1"><span class="keyword" data-hd-audience="cloud wx">IBM Analytics Engine</span> on <span class="ph" data-hd-audience="wx">IBM® watsonx</span></h1>
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<h2 class="sectiontitle" id="welcome-spark__sl-desc__title__1">Description</h2>
<div class="div" data-hd-audience="cloud wx">
<p>With IBM Analytics Engine, you can run Jupyter notebooks and jobs from tools in Watson Studio by selecting IBM Analytics Engine as your runtime environment. You are offered Hortonworks Data Platform on IBM Cloud. You get one VM per cluster compute node and your own local HDFS. You get Spark and the entire Hadoop ecosystem. You are given shell access and can also create notebooks.</p>
<p>This service adds a tool and compute resources in projects.</p>
</div>
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<h2 class="sectiontitle" id="welcome-spark__sl-quick__title__1">Quick links</h2>
<ul id="welcome-spark__sl-quick-links">
<li><a data-hd-audience="cloud wx" href="../wsj/getting-started/assoc-services.html">Use</a><span class="ph">: Work with the service</span></li>
<li><a data-hd-audience="cloud wx" href="https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-best-practices" rel="noopener" target="_blank" title="(Opens in a new tab or window)">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li><a data-hd-audience="cloud wx" href="../wsj/analyze-data/creating-notebooks.html">Develop</a><span class="ph">: Write code and build applications</span></li>
<li data-hd-audience="cloud wx"><a href="../wsj/admin/create-services.html">Create</a><span class="ph">: Create the service instance</span></li>
</ul>
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40DEFBE604B3629CAF8855A6D00EC14A0A6C92F3 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/wml.html?context=cdpaas&locale=en | Watson Machine Learning on IBM watsonx | Watson Machine Learning on IBM watsonx
Watson Machine Learning is part of IBM® watsonx.ai. Watson Machine Learning provides a full range of tools for your team to build, train, and deploy Machine Learning models. You can choose the tool with the level of automation or autonomy that matches your needs. Watson Machine Learning provides the following tools:
* AutoAI experiment builder for automatically processing structured data to generate model-candidate pipelines. The best-performing pipelines can be saved as a machine learning model and deployed for scoring.
* Deployment spaces give you the tools to view and manage model deployments.
* Tools to view and manage model deployments.
| # Watson Machine Learning on IBM watsonx #
Watson Machine Learning is part of IBM® watsonx\.ai\. Watson Machine Learning provides a full range of tools for your team to build, train, and deploy Machine Learning models\. You can choose the tool with the level of automation or autonomy that matches your needs\. Watson Machine Learning provides the following tools:
<!-- <ul> -->
* AutoAI experiment builder for automatically processing structured data to generate model\-candidate pipelines\. The best\-performing pipelines can be saved as a machine learning model and deployed for scoring\.
* Deployment spaces give you the tools to view and manage model deployments\.
* Tools to view and manage model deployments\.
<!-- </ul> -->
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<h1 class="topictitle1" id="welcome-wml__title__1"><span class="keyword" translate="no">Watson Machine Learning</span> on <span class="ph" data-hd-audience="wx">IBM watsonx</span></h1>
<div class="body" id="body">
<div class="section" data-hd-audience="wx">
<div class="sectiondiv" data-hd-audience="wx">
<span class="keyword" translate="no">Watson Machine Learning</span> is part of <span class="ph">IBM® watsonx.ai</span>. <span class="keyword" translate="no">Watson Machine Learning</span> provides a full range of tools for your team to build, train, and deploy Machine Learning models. You can choose the tool with the level of automation or autonomy that matches your needs.
<div class="p">
<span class="keyword" translate="no">Watson Machine Learning</span> provides the following tools:
<ul>
<li>AutoAI experiment builder for automatically processing structured data to generate model-candidate pipelines. The best-performing pipelines can be saved as a machine learning model and deployed for scoring.</li>
<li>Deployment spaces give you the tools to view and manage model deployments.</li>
<li>Tools to view and manage model deployments.</li>
</ul>
</div>
</div>
</div>
<section class="section" role="region" aria-labelledby="welcome-wml__body__title__1" data-hd-audience="wx">
<h2 class="sectiontitle" id="welcome-wml__body__title__1">Quick links</h2>
<ul data-hd-audience="wx">
<li><a href="../wsj/wmls/wmls-deploy-overview.html">Use</a><span class="ph">: Work with the service</span></li>
<li><a href="../wsj/getting-started/whats-new.html">What's new</a><span class="ph" data-hd-audience="cloud wx">: See what's new each week</span></li>
<li><a href="../wsj/getting-started/known-issues.html#machine-learning-issues">Known issues</a><span class="ph">: View limitations</span></li>
<li><a href="../wsj/troubleshoot/ml_troubleshooting.html">Troubleshoot</a><span class="ph">: Find solutions to problems</span></li>
<li><a href="../wsj/getting-started/wml-plans.html">Offering plans</a><span class="ph">: Capabilities for each plan</span></li>
</ul>
</section>
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C4BB814768F5D91D2C6AA90B34FDDD944AA1EB91 | https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/wsl.html?context=cdpaas&locale=en | Watson Studio on IBM watsonx | Watson Studio on IBM watsonx
| # Watson Studio on IBM watsonx #
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<h1 class="topictitle1" id="welcome-wsl__title__1"><span class="keyword" translate="no">Watson Studio</span> on <span class="ph" data-hd-audience="wx">IBM watsonx</span></h1>
<div class="body" id="body">
<section class="section" role="region" aria-labelledby="welcome-wsl__body__title__1" data-hd-audience="wx">
<h2 class="sectiontitle" id="welcome-wsl__body__title__1">Description</h2>
<div class="sectiondiv" data-hd-audience="wx">
<span class="keyword" translate="no">Watson Studio</span> is part of <span class="ph">IBM® watsonx.ai</span>. <span class="keyword" translate="no">Watson Studio</span> provides the environment and tools for your team to collaboratively solve your business problems. You can choose the tools you need to analyze and visualize data, to cleanse and shape data, to experiment with prompting foundation models, to tune foundation models, or to create and train machine learning models.
<div class="p">
<span class="keyword" translate="no">Watson Studio</span> provides the following tools:
<ul>
<li><span class="keyword" translate="no">Data Refinery</span>: Prepare and visualize data.</li>
<li>Prompt Lab: Experiment with prompting foundation models.</li>
<li>Tuning Studio: Tune a foundation model to guide the foundation model to return useful output.</li>
<li>Jupyter notebook editor: Code Jupyter notebooks in Python or R.</li>
<li><span class="keyword" translate="no">SPSS® Modeler</span>: Automate the flow of data through a model with SPSS algorithms.</li>
<li><span class="keyword" translate="no">Decision Optimization</span> model builder: Optimize solving business problem scenarios.</li>
<li>Federated learning: Train models on remote parties without sharing data.</li>
<li><span class="keyword" translate="no">RStudio®</span>: Code Jupyter notebooks in R.</li>
<li>Pipelines: Automate end-to-end flows of data or models.</li>
<li>Synthetic Data Generator: Generate tabular data to use in training models.</li>
</ul>
</div>
</div>
</section>
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<h2 class="sectiontitle" id="welcome-wsl__body__title__2">Quick links</h2>
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<li><a href="../wsj/getting-started/set-up-ws.html">Administer</a><span class="ph">: Manage and maintain the service</span></li>
<li><a href="../wsj/analyze-data/data-science.html">Use</a><span class="ph">: Work with the service</span></li>
<li><a href="../wsj/getting-started/whats-new.html">What's new</a><span class="ph" data-hd-audience="cloud wx">: See what's new each week</span></li>
<li><a href="../wsj/getting-started/known-issues.html">Known issues</a><span class="ph">: View limitations</span></li>
<li><a href="../wsj/getting-started/ws-plans.html">Offering plans</a><span class="ph">: Capabilities for each plan</span></li>
</ul>
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189F970CF3B162E67B98B2A928B36193169E3CAF | https://dataplatform.cloud.ibm.com/docs/content/wsd/dataview.html?context=cdpaas&locale=en | Working with your data (SPSS Modeler) | Working with your data
To see a quick sample of a flow's data, right-click a node a select Preview. To more thoroughly examine your data, use a Charts node to launch the chart builder.
With the chart builder, you can use advanced visualizations to explore your data from different perspectives and identify patterns, connections, and relationships within your data. You can also visualize your data with these same charts in a Data Refinery flow.
Figure 1. Sample visualizations available for a flow
For more information, see [Visualizing your data](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/visualizations.html).
| # Working with your data #
To see a quick sample of a flow's data, right\-click a node a select Preview\. To more thoroughly examine your data, use a Charts node to launch the chart builder\.
With the chart builder, you can use advanced visualizations to explore your data from different perspectives and identify patterns, connections, and relationships within your data\. You can also visualize your data with these same charts in a Data Refinery flow\.
Figure 1\. Sample visualizations available for a flow
For more information, see [Visualizing your data](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/visualizations.html)\.
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<div class="body">
<p class="shortdesc">To see a quick sample of a flow's data, right-click a node a select <span class="ph uicontrol">Preview</span>. To more thoroughly examine your data, use a Charts node to launch the chart builder.</p>
<div class="p">
With the chart builder, you can use advanced visualizations to explore your data from different perspectives and identify patterns, connections, and relationships within your data. You can also visualize your data with these same charts in a Data Refinery flow.
<figure class="fignone" id="dataview__fig_ms4_lfc_4hb">
<figcaption>
Figure 1. Sample visualizations available for a flow
</figcaption>
<div class="image">
<img id="dataview__image_ns4_lfc_4hb" src="images/charts_thumbnail4.png" alt="Shows four example charts available in Visualizations">
</div>
</figure>
</div>
<p>For more information, see <a href="../wsj/refinery/visualizations.html">Visualizing your data</a>.</p>
</div>
<aside role="complementary" aria-labelledby="dataview__title__1">
<nav role="navigation">
<ul class="ullinks">
<li class="ulchildlink"><strong><a href="nodes/missingvalues_overview.html">Missing data values</a></strong><br>
During the data preparation phase of data mining, you will often want to replace missing values in the data.</li>
</ul>
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<div class="parentlink">
<strong>Parent topic:</strong> <a href="spss-modeler.html" title="With SPSS Modeler flows, you can quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. Designed around the long-established SPSS Modeler client software and the industry-standard CRISP-DM model it uses, the flows interface supports the entire data mining process, from data to better business results.">Creating SPSS Modeler flows</a>
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6A32659DF809F04F9A670634129FC75CC9140729 | https://dataplatform.cloud.ibm.com/docs/content/wsd/flow_properties.html?context=cdpaas&locale=en | Setting properties for SPSS Modeler flows | Setting properties for flows
You can specify properties to apply to the current flow.
To set flow properties, click the Flow Properties icon:
The following properties are available.
| # Setting properties for flows #
You can specify properties to apply to the current flow\.
To set flow properties, click the Flow Properties icon:
The following properties are available\.
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<h1 class="topictitle1" id="flow_properties__title__1">Setting properties for flows</h1>
<div class="body">
<p class="shortdesc">You can specify properties to apply to the current flow.</p>
<div class="p">
To set flow properties, click the <span class="ph uicontrol">Flow Properties</span> icon:
<div class="image">
<img src="images/flow_properties.png" alt="Flow properties icon">
</div>
</div>
<p>The following properties are available.</p>
<section class="section" role="region" aria-labelledby="flow_properties__title__2">
<h2 class="sectiontitle" id="flow_properties__title__2">Options</h2>
<dl>
<dt class="dlterm">
General
</dt>
<dd class="dlentry">
<ul>
<li><span class="ph uicontrol">Maximum number of rows to show in Data Preview</span>. Specify the number of rows to be shown when a preview of the data is requested for a node.</li>
<li><span class="ph uicontrol">Limit members for nominal fields</span>. Select this option and specify a maximum number of members for nominal (set) fields after which the data type of the field becomes <span class="ph uicontrol">Typeless</span>. This option is useful when working with large nominal fields. But when the measurement level of a field is set to <span class="ph uicontrol">Typeless</span>, its role is automatically set to <span class="ph uicontrol">None</span>. This means that the fields aren't available for modeling.</li>
<li><span class="ph uicontrol">Refresh source nodes on execution</span>. Select this option to automatically refresh all source (import) nodes when running the current flow. This action is analogous to clicking the <span class="ph uicontrol">Refresh</span> button in an import node's properties, except that this option automatically refreshes all import nodes (except User Input nodes) for the current flow.</li>
</ul>
</dd>
</dl>
<dl>
<dt class="dlterm">
Date/Time
</dt>
<dd class="dlentry">
<ul>
<li><span class="ph uicontrol">Import date/time as</span>. Select whether to use date/time storage for date/time fields or whether to import them as string variables.</li>
<li><span class="ph uicontrol">Date format</span>. Select a date format to use for date storage fields or when strings are interpreted as dates by CLEM date functions.</li>
<li><span class="ph uicontrol">Time format</span>. Select a time format to use for time storage fields or when strings are interpreted as times by CLEM time functions.</li>
<li><span class="ph uicontrol">Rollover days/mins</span>. For time formats, select whether negative time differences are interpreted as referring to the previous day or hour.</li>
<li><span class="ph uicontrol">Date baseline (1st Jan)</span>. Select the baseline years (always 1 January) to be used by CLEM date functions that work with a single date.</li>
<li><span class="ph uicontrol">2-digit dates start from</span>. Specify the cutoff year to add century digits for years that are denoted with only 2 digits. For example, specifying 1930 as the cutoff year assumes that 05/11/02 is in the year 2002. The same setting will use the 20th century for dates after 30; thus 05/11/73 is assumed to be in 1973.</li>
<li><span class="ph uicontrol">Time zone</span>. Select how the time zone is chosen for use with the <code class="ph codeph">datetime_now</code> CLEM expression.
<ul>
<li>If you select <span class="ph uicontrol">Server</span>, the time zone is used from where the SPSS Modeler run-time is running (in some cases this may be the same as the <span class="ph uicontrol">Client</span> option). Or if your flow uses data from a database and the supported database uses SQL pushback, the <code class="ph codeph">datetime_now</code> expression will use the time of the database.</li>
<li>If you select <span class="ph uicontrol">Client</span>, the time zone is used from the machine where SPSS Modeler is installed.</li>
<li>Alternatively, you can select any of the Coordinated Universal Time values for the time zone.</li>
</ul></li>
</ul>
</dd>
</dl>
<dl>
<dt class="dlterm">
Number Formats
</dt>
<dd class="dlentry">
For standard, scientific, and currency display formats, specify the number of decimal places to use when displaying real numbers.
</dd>
</dl>
<dl>
<dt class="dlterm">
Optimization
</dt>
<dd class="dlentry">
You can use these settings to optimize flow performance.
<ul>
<li><span class="ph uicontrol">Enable flow rewriting</span>. Select this option to enable flow rewriting. Flow rewriting reorders the nodes in a flow behind the scenes for more efficient operation, without altering flow semantics.</li>
<li><span class="ph uicontrol">Optimize CLEM expressions</span>. This option enables the optimizer to search for CLEM expressions that can be preprocessed before the flow runs, to increase the processing speed. As a simple example, if you have an expression such as <code class="ph codeph">log(salary)</code>, the optimizer will calculate the actual salary value and pass that on for processing. This can be used to improve both SQL pushback and <span class="keyword">SPSS Modeler</span> performance.</li>
<li><span class="ph uicontrol">Optimize syntax execution</span>. This method of flow rewriting increases the efficiency of operations that incorporate more than one node containing SPSS Statistics syntax. Optimization is achieved by combining the syntax commands into a single operation, instead of running each as a separate operation.</li>
<li><span class="ph uicontrol">Optimize other execution</span>. This method of flow rewriting increases the efficiency of operations that can't be delegated to the database. Optimization is achieved by reducing the amount of data in the flow as early as possible. While maintaining data integrity, the flow is rewritten to push operations closer to the data source, thus reducing data downstream for costly operations, such as joins.</li>
<li><span class="ph uicontrol">Enable parallel processing</span>. When running on a computer with multiple processors, this option allows the system to balance the load across those processors, which may result in faster performance. Use of multiple nodes or use of the following individual nodes may benefit from parallel processing: C5.0, Merge (by key), Sort, Bin (rank and tile methods), and Aggregate (using one or more key fields).</li>
<li><span class="ph uicontrol">Generate SQL</span>. This option pushes SQL processing back to the database. Note that turning this option on or off affects only the new flows that you create. You cannot switch the setting for an existing flow. For more information about using this option with flows, see <a href="sql_overview.html#sql_overview__note_OptimizationTips">SQL optimization</a>.
<ul>
<li><span class="ph uicontrol">Database caching (SQL only)</span>. For flows that generate SQL to be run in the database, data can be cached mid flow to a temporary table in the database rather than to the file system. When combined with SQL optimization, this may result in significant gains in performance. For example, the output from a flow that merges multiple tables to create a data mining view may be cached and reused as needed. With database caching enabled, simply right-click any nonterminal node to cache data at that point, and the cache is automatically created directly in the database the next time the flow runs. This allows SQL to be generated for downstream nodes, further improving performance. Alternatively, this option can be disabled if needed, such as when policies or permissions preclude data being written to the database. If database caching or SQL optimization is not enabled, the cache will be written to the file system instead.</li>
<li><span class="ph uicontrol">Use relaxed conversion (SQL only)</span>. This option enables the conversion of data from either strings to numbers, or numbers to strings, if stored in a suitable format. For example, if the data is kept in the database as a string, but actually contains a meaningful number, the data can be converted for use when the pushback occurs.</li>
</ul></li>
</ul>
</dd>
</dl>
<dl>
<dt class="dlterm">
Logging
</dt>
<dd class="dlentry">
<ul>
<li><span class="ph uicontrol">Display SQL in the messages log at run time</span>. Specifies whether SQL generated while running the flow is passed to the messages log.</li>
<li><span class="ph uicontrol">Display SQL generation in the message log during preparation</span>. During flow preview, specifies whether a preview of the SQL that would be generated is passed to the messages log.</li>
<li><span class="ph uicontrol">SQL format</span> Specifies whether any SQL that's displayed in the log should contain native SQL functions or standard ODBC functions of the form <code class="ph codeph">{fn FUNC(…)}</code>, as generated by <span class="keyword">SPSS Modeler</span>. The former relies on ODBC driver functionality that may not be implemented.</li>
<li><span class="ph uicontrol">Reformat SQL for improved readability</span>. Specifies whether SQL displayed in the log should be formatted for readability.</li>
<li><span class="ph uicontrol">Show status for records</span>. Specifies when records should be reported as they arrive at terminal nodes. Specify a number to use for updating the status every <em>N</em> records.</li>
</ul>
</dd>
</dl>
</section>
<section class="section" role="region" aria-labelledby="flow_properties__title__3">
<h2 class="sectiontitle" id="flow_properties__title__3">Parameters</h2>
<p>You can define parameters for use in CLEM expressions and in scripting. They function as user-defined variables that are saved and persisted with the current flow, session, or SuperNode, and can be accessed from the user interface or through scripting. If you save a flow, for example, any parameters set for that flow are also saved. (This distinguishes them from local script variables, which can be used only in the script in which they are declared.) Parameters are often used in scripting to control the behavior of the script, by providing information about fields and values that don't need to be hard coded in the script.</p>
<p>If you set a parameter here in the flow properties, it's available to all nodes in the flow. Click <span class="ph uicontrol">Add value</span> and enter the following information.</p>
<dl>
<dt class="dlterm">
Name
</dt>
<dd class="dlentry">
Parameter names are listed here. For example, to create a parameter for the minimum temperature, you could type <kbd class="ph userinput">minvalue</kbd>. Do not include the <code class="ph codeph">$P-</code> prefix that denotes a parameter in CLEM expressions. This name is how the parameter is referenced in expressions.
</dd>
</dl>
<dl>
<dt class="dlterm">
Label
</dt>
<dd class="dlentry">
Lists a descriptive name for each parameter created.
</dd>
</dl>
<dl>
<dt class="dlterm">
Storage
</dt>
<dd class="dlentry">
Select a storage type from the list. Storage indicates how the data values are stored in the parameter. For example, when working with values containing leading zeros that you want to preserve (such as <code class="ph codeph">008</code>), you should select <span class="ph uicontrol">String</span> as the storage type. Otherwise, the zeros will be stripped from the value. Available storage types are string, integer, real, time, date, and timestamp. Values for date parameters must be specified in ISO standard notation (YYYY-MM-DD).
</dd>
</dl>
<dl>
<dt class="dlterm">
Value
</dt>
<dd class="dlentry">
Lists the current value for each parameter. Adjust the parameter as required. Values for date parameters must be specified in ISO standard notation (YYYY-MM-DD). Dates specified in other formats aren't accepted.
</dd>
</dl>
<dl>
<dt class="dlterm">
Measure
</dt>
<dd class="dlentry">
Select the measurement level, which is used to describe characteristics of the parameter.
</dd>
</dl>
<dl>
<dt class="dlterm">
Prompt?
</dt>
<dd class="dlentry">
Select this option if you want the user to be prompted at runtime to enter a value for this parameter.
</dd>
</dl>
</section>
<section class="section" role="region" aria-labelledby="flow_properties__title__4">
<h2 class="sectiontitle" id="flow_properties__title__4">Globals</h2>
<p>In the <span class="ph uicontrol">Globals</span> tab of the flow properties, you can view the global values set for the current flow. Global values are created using a Set Globals node to determine statistics such as mean, sum, or standard deviation for selected fields.</p>
<p>After a Set Globals node runs, these values are then available for a variety of uses in flow operations.</p>
<p>You can't edit global values in the table here in the flow properties, but you can clear all global values for a flow using the button to the right of the table.</p>
</section>
<section class="section" role="region" aria-labelledby="flow_properties__title__5">
<h2 class="sectiontitle" id="flow_properties__title__5">Messages</h2>
<p>In the <span class="ph uicontrol">Messages</span> tab of the flow properties, you can easily view messages regarding flow operations, such as running, optimization, and time elapsed for model building and evaluation. Error messages are also reported in this table.</p>
</section>
<section class="section" role="region" aria-labelledby="flow_properties__title__6">
<h2 class="sectiontitle" id="flow_properties__title__6">Annotations</h2>
<p>If you need to describe a flow to others in your organization, you can attach explanatory comments to flows, nodes, and model nuggets. Others can then view these comments on-screen, or you might even print out an image of the flow that includes the comments.</p>
<p>Use the <span class="ph uicontrol">Annotations</span> tab of the flow properties to add text annotations to your flow. These notes are visible only when the <span class="ph uicontrol">Annotations</span> tab is open, except that flow annotations can also be shown as on-screen comments.</p>
</section>
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81045ED1B34827B3BD74D2546185C3BD3163B37E | https://dataplatform.cloud.ibm.com/docs/content/wsd/flow_scripting.html?context=cdpaas&locale=en | Flow scripting (SPSS Modeler) | Flow scripting
You can use scripts to customize operations within a particular flow, and they're saved with that flow. For example, you might use a script to specify a particular run order for terminal nodes. You use the flow properties page to edit the script that's saved with the current flow.
To access scripting in a flow's properties:
1. Right-click your flow's canvas and select Flow properties.
2. Open the Scripting section to work with scripts for the current flow.
Tips:
* By default, the Python scripting language is used. If you'd rather use a scripting language unique to old versions of SPSS Modeler desktop, select Legacy.
* For complete details about scripting, see the [Scripting and automation](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/scripting_overview.html) guide.
You can specify whether or not the script runs when the flow runs. To run the script each time the flow runs, respecting the run order of the script, select Run the script. This setting provides automation at the flow level for quicker model building. Or, to ignore the script, you can select the option to only Run all terminal nodes when the flow runs.
The script editor includes the following features that help with script authoring:
* Syntax highlighting; keywords, literal values (such as strings and numbers), and comments are highlighted
* Line numbering
* Block matching; when the cursor is placed by the start of a program block, the corresponding end block is also highlighted
* Suggested auto-completion
A list of suggested syntax completions can be accessed by selecting Auto-Suggest from the context menu, or pressing Ctrl + Space. Use the cursor keys to move up and down the list, then press Enter to insert the selected text. To exit from auto-suggest mode without modifying the existing text, press Esc.
| # Flow scripting #
You can use scripts to customize operations within a particular flow, and they're saved with that flow\. For example, you might use a script to specify a particular run order for terminal nodes\. You use the flow properties page to edit the script that's saved with the current flow\.
To access scripting in a flow's properties:
<!-- <ol> -->
1. Right\-click your flow's canvas and select Flow properties\.
2. Open the Scripting section to work with scripts for the current flow\.
<!-- </ol> -->
Tips:
<!-- <ul> -->
* By default, the Python scripting language is used\. If you'd rather use a scripting language unique to old versions of SPSS Modeler desktop, select Legacy\.
* For complete details about scripting, see the [Scripting and automation](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/scripting_overview.html) guide\.
<!-- </ul> -->
You can specify whether or not the script runs when the flow runs\. To run the script each time the flow runs, respecting the run order of the script, select Run the script\. This setting provides automation at the flow level for quicker model building\. Or, to ignore the script, you can select the option to only Run all terminal nodes when the flow runs\.
The script editor includes the following features that help with script authoring:
<!-- <ul> -->
* Syntax highlighting; keywords, literal values (such as strings and numbers), and comments are highlighted
* Line numbering
* Block matching; when the cursor is placed by the start of a program block, the corresponding end block is also highlighted
* Suggested auto\-completion
<!-- </ul> -->
A list of suggested syntax completions can be accessed by selecting Auto\-Suggest from the context menu, or pressing Ctrl \+ Space\. Use the cursor keys to move up and down the list, then press Enter to insert the selected text\. To exit from auto\-suggest mode without modifying the existing text, press Esc\.
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<h1 class="topictitle1" id="flow_scripting__title__1">Flow scripting</h1>
<div class="body">
<p class="shortdesc">You can use scripts to customize operations within a particular flow, and they're saved with that flow. For example, you might use a script to specify a particular run order for terminal nodes. You use the flow properties page to edit the script that's saved with the current flow.</p>
<p>To access scripting in a flow's properties:</p>
<ol>
<li>Right-click your flow's canvas and select <span class="ph uicontrol">Flow properties</span>.</li>
<li>Open the <span class="ph uicontrol">Scripting</span> section to work with scripts for the current flow.</li>
</ol>
<div class="note note">
<span class="notetitle">Tips:</span>
<ul>
<li>By default, the Python scripting language is used. If you'd rather use a scripting language unique to old versions of <span class="keyword">SPSS Modeler</span> desktop, select <span class="ph uicontrol">Legacy</span>.</li>
<li>For complete details about scripting, see the <a href="nodes/scripting_guide/clementine/scripting_overview.html">Scripting and automation</a> guide.</li>
</ul>
</div>
<p>You can specify whether or not the script runs when the flow runs. To run the script each time the flow runs, respecting the run order of the script, select <span class="ph uicontrol">Run the script</span>. This setting provides automation at the flow level for quicker model building. Or, to ignore the script, you can select the option to only <span class="ph uicontrol">Run all terminal nodes</span> when the flow runs.</p>
<p>The script editor includes the following features that help with script authoring:</p>
<ul>
<li>Syntax highlighting; keywords, literal values (such as strings and numbers), and comments are highlighted</li>
<li>Line numbering</li>
<li>Block matching; when the cursor is placed by the start of a program block, the corresponding end block is also highlighted</li>
<li>Suggested auto-completion</li>
</ul>
<p>A list of suggested syntax completions can be accessed by selecting <span class="ph uicontrol">Auto-Suggest</span> from the context menu, or pressing Ctrl + Space. Use the cursor keys to move up and down the list, then press Enter to insert the selected text. To exit from auto-suggest mode without modifying the existing text, press Esc.</p>
</div>
<aside role="complementary" aria-labelledby="flow_scripting__title__1">
<nav role="navigation">
<ul class="ullinks">
<li class="ulchildlink"><strong><a href="flow_scripting_example.html">Flow scripting example</a></strong><br>
You can use a flow to train a model when it runs. Normally, to test the model, you might run the modeling node to add the model to the flow, make the appropriate connections, and run an Analysis node.</li>
<li class="ulchildlink"><strong><a href="parameters.html">Flow and SuperNode parameters</a></strong><br>
You can define parameters for use in CLEM expressions and in scripting. They are, in effect, user-defined variables that are saved and persisted with the current flow or SuperNode and can be accessed from the user interface as well as through scripting.</li>
</ul>
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<strong>Parent topic:</strong> <a href="spss-modeler.html" title="With SPSS Modeler flows, you can quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. Designed around the long-established SPSS Modeler client software and the industry-standard CRISP-DM model it uses, the flows interface supports the entire data mining process, from data to better business results.">Creating SPSS Modeler flows</a>
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D3084BFB07D425EBACE9F538D800E08DAEA97594 | https://dataplatform.cloud.ibm.com/docs/content/wsd/flow_scripting_example.html?context=cdpaas&locale=en | SPSS Modeler flow scripting example | Flow scripting example
You can use a flow to train a model when it runs. Normally, to test the model, you might run the modeling node to add the model to the flow, make the appropriate connections, and run an Analysis node.
Using a script, you can automate the process of testing the model nugget after you create it. For example, you might use a script such as the following to train a neural network model:
stream = modeler.script.stream()
neuralnetnode = stream.findByType("neuralnetwork", None)
results = []
neuralnetnode.run(results)
appliernode = stream.createModelApplierAt(results[0], "Drug", 594, 187)
analysisnode = stream.createAt("analysis", "Drug", 688, 187)
typenode = stream.findByType("type", None)
stream.linkBetween(appliernode, typenode, analysisnode)
analysisnode.run([])
The following bullets describe each line in this script example.
* The first line defines a variable that points to the current flow.
* In line 2, the script finds the Neural Net builder node.
* In line 3, the script creates a list where the execution results can be stored.
* In line 4, the Neural Net model nugget is created. This is stored in the list defined on line 3.
* In line 5, a model apply node is created for the model nugget and placed on the flow canvas.
* In line 6, an analysis node called Drug is created.
* In line 7, the script finds the Type node.
* In line 8, the script connects the model apply node created in line 5 between the Type node and the Analysis node.
* Finally, the Analysis node runs to produce the Analysis report.
Tips:
* It's possible to use a script to build and run a flow from scratch, starting with a blank canvas.
* For complete details about scripting, see the [Scripting and automation](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/scripting_overview.html) guide.
| # Flow scripting example #
You can use a flow to train a model when it runs\. Normally, to test the model, you might run the modeling node to add the model to the flow, make the appropriate connections, and run an Analysis node\.
Using a script, you can automate the process of testing the model nugget after you create it\. For example, you might use a script such as the following to train a neural network model:
stream = modeler.script.stream()
neuralnetnode = stream.findByType("neuralnetwork", None)
results = []
neuralnetnode.run(results)
appliernode = stream.createModelApplierAt(results[0], "Drug", 594, 187)
analysisnode = stream.createAt("analysis", "Drug", 688, 187)
typenode = stream.findByType("type", None)
stream.linkBetween(appliernode, typenode, analysisnode)
analysisnode.run([])
The following bullets describe each line in this script example\.
<!-- <ul> -->
* The first line defines a variable that points to the current flow\.
* In line 2, the script finds the Neural Net builder node\.
* In line 3, the script creates a list where the execution results can be stored\.
* In line 4, the Neural Net model nugget is created\. This is stored in the list defined on line 3\.
* In line 5, a model apply node is created for the model nugget and placed on the flow canvas\.
* In line 6, an analysis node called `Drug` is created\.
* In line 7, the script finds the Type node\.
* In line 8, the script connects the model apply node created in line 5 between the Type node and the Analysis node\.
* Finally, the Analysis node runs to produce the Analysis report\.
<!-- </ul> -->
Tips:
<!-- <ul> -->
* It's possible to use a script to build and run a flow from scratch, starting with a blank canvas\.
* For complete details about scripting, see the [Scripting and automation](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/scripting_overview.html) guide\.
<!-- </ul> -->
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<h1 class="topictitle1" id="flow_scripting_example__title__1">Flow scripting example</h1>
<div class="body">
<p class="shortdesc">You can use a flow to train a model when it runs. Normally, to test the model, you might run the modeling node to add the model to the flow, make the appropriate connections, and run an Analysis node.</p>
<p>Using a script, you can automate the process of testing the model nugget after you create it. For example, you might use a script such as the following to train a neural network model:</p>
<pre class="codeblock language-shell"><code class="language-shell">stream = modeler.script.stream()
neuralnetnode = stream.findByType("neuralnetwork", None)
results = []
neuralnetnode.run(results)
appliernode = stream.createModelApplierAt(results[0], "Drug", 594, 187)
analysisnode = stream.createAt("analysis", "Drug", 688, 187)
typenode = stream.findByType("type", None)
stream.linkBetween(appliernode, typenode, analysisnode)
analysisnode.run([])</code></pre>
<p>The following bullets describe each line in this script example.</p>
<ul>
<li>The first line defines a variable that points to the current flow.</li>
<li>In line 2, the script finds the Neural Net builder node.</li>
<li>In line 3, the script creates a list where the execution results can be stored.</li>
<li>In line 4, the Neural Net model nugget is created. This is stored in the list defined on line 3.</li>
<li>In line 5, a model apply node is created for the model nugget and placed on the flow canvas.</li>
<li>In line 6, an analysis node called <code class="ph codeph">Drug</code> is created.</li>
<li>In line 7, the script finds the Type node.</li>
<li>In line 8, the script connects the model apply node created in line 5 between the Type node and the Analysis node.</li>
<li>Finally, the Analysis node runs to produce the Analysis report.</li>
</ul>
<div class="note note">
<span class="notetitle">Tips:</span>
<ul>
<li>It's possible to use a script to build and run a flow from scratch, starting with a blank canvas.</li>
<li>For complete details about scripting, see the <a href="nodes/scripting_guide/clementine/scripting_overview.html">Scripting and automation</a> guide.</li>
</ul>
</div>
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C8B4A993CB8642BC87432FCB305EEE744C16A154 | https://dataplatform.cloud.ibm.com/docs/content/wsd/migration.html?context=cdpaas&locale=en | Importing a stream (SPSS Modeler) | Importing an SPSS Modeler stream
You can import a stream ( .str) that was created in SPSS Modeler Subscription or SPSS Modeler client.
1. From your project's Assets tab, click .
2. Select Local file, select the .str file you want to import, and click Create.
If the imported stream contains one or more source (import) or export nodes, you'll be prompted to convert the nodes. Watsonx.ai will walk you through the migration process.
Watch the following video for an example of this easy process:
This video provides a visual method to learn the concepts and tasks in this documentation.
Video disclaimer: Some minor steps and graphical elements in this video might differ from your platform.
[https://www.ustream.tv/embed/recorded/127732173](https://www.ustream.tv/embed/recorded/127732173)
If the stream contains multiple import nodes that use the same data file, then you must first add that file to your project as a data asset before migrating because the conversion can't upload the same file to more than one import node. After adding the data asset to your project, reopen the flow and proceed with the migration using the new data asset. Nodes with the same name will be automatically mapped to project assets.
Configure export nodes to export to your project or to a connection. The following export nodes are supported:
Table 1. Export nodes that can be migrated
Supported SPSS Modeler export nodes
Analytic Server
Database
Flat File
Statistics Export
Data Collection
Excel
IBM Cognos Analytics Export
TM1 Export
SAS
XML Export
Notes: Keep the following information in mind when migrating nodes.
* When migrating export nodes, you're converting node types that don't exist in watsonx.ai. The nodes are converted to Data Asset export nodes or a connection. Due to a current limitation for automatically migrating nodes, only existing project assets or connections can be selected as export targets. These assets will be overwritten during export when the flow runs.
* To preserve any type or filter information, when an import node is replaced with Data Asset nodes, they're converted to a SuperNode.
* After migration, you can go back later and use the Convert button if you want to migrate a node that you skipped previously.
* If the stream you imported uses scripting, you may encounter an error when you run the flow even after completing a migration. This could be due to the flow script containing a reference to an unsupported import or export node. To avoid such errors, you must remove the scripting code that references the unsupported node.
* If the stream you're importing contains unsupported data file types, you need to convert them to a supported type (CSV, Excel, or SPSS Statistics .sav).
* In some cases, some settings from your original stream may not be restored during migration. For example, if the field delimiter in your original stream was tabs, it may be changed to commas after migration. Settings such as custom SQL also aren't migrated currently. Compare the new migrated flow to your original stream and making adjustments as needed.
| # Importing an SPSS Modeler stream #
You can import a stream ( \.str) that was created in SPSS Modeler Subscription or SPSS Modeler client\.
<!-- <ol> -->
1. From your project's Assets tab, click \.
2. Select Local file, select the \.str file you want to import, and click Create\.
<!-- </ol> -->
If the imported stream contains one or more source (import) or export nodes, you'll be prompted to convert the nodes\. Watsonx\.ai will walk you through the migration process\.
Watch the following video for an example of this easy process:
This video provides a visual method to learn the concepts and tasks in this documentation\.
Video disclaimer: Some minor steps and graphical elements in this video might differ from your platform\.
[https://www\.ustream\.tv/embed/recorded/127732173](https://www.ustream.tv/embed/recorded/127732173)
If the stream contains multiple import nodes that use the same data file, then you must first add that file to your project as a data asset before migrating because the conversion can't upload the same file to more than one import node\. After adding the data asset to your project, reopen the flow and proceed with the migration using the new data asset\. Nodes with the same name will be automatically mapped to project assets\.
Configure export nodes to export to your project or to a connection\. The following export nodes are supported:
<!-- <table "summary="" id="migration__table_hlc_ngk_thb" class="defaultstyle" "> -->
Table 1\. Export nodes that can be migrated
| Supported SPSS Modeler export nodes |
| ----------------------------------- |
| Analytic Server |
| Database |
| Flat File |
| Statistics Export |
| Data Collection |
| Excel |
| IBM Cognos Analytics Export |
| TM1 Export |
| SAS |
| XML Export |
<!-- </table "summary="" id="migration__table_hlc_ngk_thb" class="defaultstyle" "> -->
Notes: Keep the following information in mind when migrating nodes\.
<!-- <ul> -->
* When migrating export nodes, you're converting node types that don't exist in watsonx\.ai\. The nodes are converted to Data Asset export nodes or a connection\. Due to a current limitation for automatically migrating nodes, only existing project assets or connections can be selected as export targets\. These assets will be overwritten during export when the flow runs\.
* To preserve any type or filter information, when an import node is replaced with Data Asset nodes, they're converted to a SuperNode\.
* After migration, you can go back later and use the Convert button if you want to migrate a node that you skipped previously\.
* If the stream you imported uses scripting, you may encounter an error when you run the flow even after completing a migration\. This could be due to the flow script containing a reference to an unsupported import or export node\. To avoid such errors, you must remove the scripting code that references the unsupported node\.
* If the stream you're importing contains unsupported data file types, you need to convert them to a supported type (CSV, Excel, or SPSS Statistics \.sav)\.
* In some cases, some settings from your original stream may not be restored during migration\. For example, if the field delimiter in your original stream was tabs, it may be changed to commas after migration\. Settings such as custom SQL also aren't migrated currently\. Compare the new migrated flow to your original stream and making adjustments as needed\.
<!-- </ul> -->
<!-- </article "role="article" "> -->
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<h1 class="topictitle1" id="migration__title__1">Importing an SPSS Modeler stream</h1>
<div class="body conbody">
<p class="shortdesc">You can import a stream (<span class="ph filepath">.str</span>) that was created in <span class="keyword">SPSS Modeler Subscription</span> or <span class="keyword">SPSS Modeler</span> client.</p>
<ol>
<li data-hd-audience="wx">From your project's <span class="ph uicontrol">Assets</span> tab, click <span class="ph menucascade"><span class="ph uicontrol">New asset</span><abbr title="and then"> > </abbr><span class="ph uicontrol">Build models as a visual flow</span></span>.</li>
<li>Select <span class="ph uicontrol">Local file</span>, select the <span class="ph filepath">.str</span> file you want to import, and click <span class="ph uicontrol">Create</span>.</li>
</ol>
<p>If the imported stream contains one or more source (import) or export nodes, you'll be prompted to convert the nodes. <span class="keyword" data-hd-audience="wx" translate="no">Watsonx.ai</span> will walk you through the migration process.</p>
<p data-hd-audience="cloud wx">Watch the following video for an example of this easy process:</p>
<p data-hd-audience="cloud wx">This video provides a visual method to learn the concepts and tasks in this documentation.</p>
<p class="smaller" data-hd-audience="wx">Video disclaimer: Some minor steps and graphical elements in this video might differ from your platform.</p>
<p data-hd-audience="cloud wx"><iframe webkitallowfullscreen="" allowfullscreen src="https://www.ustream.tv/embed/recorded/127739531" width="640" height="480" title="Import an SPSS Modeler flow into a project"></iframe></p>
<div class="lines">
</div><a href="https://www.ustream.tv/embed/recorded/127732173" rel="noopener" target="_blank" title="(Opens in a new tab or window)">https://www.ustream.tv/embed/recorded/127732173</a>
<div class="lines">
</div>
<p></p>
<p>If the stream contains multiple import nodes that use the same data file, then you must first add that file to your project as a data asset before migrating because the conversion can't upload the same file to more than one import node. After adding the data asset to your project, reopen the flow and proceed with the migration using the new data asset. Nodes with the same name will be automatically mapped to project assets.</p>
<p>Configure export nodes to export to your project or to a connection. The following export nodes are supported:</p>
<div class="tablenoborder">
<table summary="" id="migration__table_hlc_ngk_thb" class="defaultstyle">
<caption>
<span class="tablecap">Table 1. Export nodes that can be migrated</span>
</caption>
<colgroup>
<col style="width:100%">
</colgroup>
<thead style="text-align:left;">
<tr>
<th id="migration__table_hlc_ngk_thb__entry__1">Supported SPSS Modeler export nodes</th>
</tr>
</thead>
<tbody>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">Analytic Server</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">Database</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">Flat File</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">Statistics Export</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">Data Collection</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">Excel</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">IBM Cognos Analytics Export</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">TM1 Export</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">SAS</td>
</tr>
<tr>
<td headers="migration__table_hlc_ngk_thb__entry__1 ">XML Export</td>
</tr>
</tbody>
</table>
</div>
<div class="note note">
<span class="notetitle">Notes:</span> Keep the following information in mind when migrating nodes.
<ul id="migration__ul_kq2_23k_thb">
<li>When migrating export nodes, you're converting node types that don't exist in <span class="keyword" data-hd-audience="wx" translate="no">watsonx.ai</span>. The nodes are converted to Data Asset export nodes or a connection. Due to a current limitation for automatically migrating nodes, only existing project assets or connections can be selected as export targets. These assets will be overwritten during export when the flow runs.</li>
<li>To preserve any type or filter information, when an import node is replaced with Data Asset nodes, they're converted to a SuperNode.</li>
<li>After migration, you can go back later and use the <span class="ph uicontrol">Convert</span> button if you want to migrate a node that you skipped previously.</li>
<li>If the stream you imported uses scripting, you may encounter an error when you run the flow even after completing a migration. This could be due to the flow script containing a reference to an unsupported import or export node. To avoid such errors, you must remove the scripting code that references the unsupported node.</li>
<li>If the stream you're importing contains unsupported data file types, you need to convert them to a supported type (CSV, Excel, or SPSS Statistics <span class="ph filepath">.sav</span>).</li>
<li>In some cases, some settings from your original stream may not be restored during migration. For example, if the field delimiter in your original stream was tabs, it may be changed to commas after migration. Settings such as custom SQL also aren't migrated currently. Compare the new migrated flow to your original stream and making adjustments as needed.</li>
</ul>
</div>
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B851271C134A1B282412BD7A667C1C9813B4E8B2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/TMWBModelApplier.html?context=cdpaas&locale=en | Text Mining model nuggets (SPSS Modeler) | Text Mining model nuggets
You can run a Text Mining node to automatically generate a concept model nugget using the Generate directly option in the node settings. Or you can use a more hands-on, exploratory approach using the Build interactively mode to generate category model nuggets from within the Text Analytics Workbench.
| # Text Mining model nuggets #
You can run a Text Mining node to automatically generate a concept model nugget using the Generate directly option in the node settings\. Or you can use a more hands\-on, exploratory approach using the Build interactively mode to generate category model nuggets from within the Text Analytics Workbench\.
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<p class="shortdesc">You can run a Text Mining node to automatically generate a <dfn class="term">concept</dfn> model nugget using the <span class="ph uicontrol">Generate directly</span> option in the node settings. Or you can use a more hands-on, exploratory approach using the <span class="ph uicontrol">Build interactively</span> mode to generate <dfn class="term">category</dfn> model nuggets from within the Text Analytics Workbench.</p>
<section class="section" role="region" aria-labelledby="TMWBModelApplier__section_usq_y52_d3b__title__1" id="TMWBModelApplier__section_usq_y52_d3b">
<h2 class="sectiontitle" id="TMWBModelApplier__section_usq_y52_d3b__title__1">Category model nugget</h2>
<p id="TMWBModelApplier__p_w54_mzj_fdb">A Text Mining category model nugget is created whenever you generate a category model from within the Text Analytics Workbench. This modeling nugget contains a set of categories, whose definition is made up of concepts, types, TLA patterns, and/or category rules. The nugget is used to categorize survey responses, blog entries, other web feeds, and any other text data.</p>
<p id="TMWBModelApplier__p_x54_mzj_fdb">If you launch a Text Analytics Workbench session in the modeling node, you can explore the extraction results, refine the resources, and fine-tune your categories before you generate category models. When you run a flow that contains a Text Mining model nugget, new fields are added to the data according to the build mode selected in the settings of the Text Mining node.</p>
<p>If the model nugget was generated using translated documents, the scoring is performed in the translated language. Similarly, if the model nugget was generated using English as the language, you can specify a translation language in the model nugget, since the documents will then be translated into English.</p>
<p>Text Mining model nuggets are placed in the Outputs pane when they're generated.</p>
</section>
<section class="section" role="region" aria-labelledby="TMWBModelApplier__section_y45_x52_d3b__title__1" id="TMWBModelApplier__section_y45_x52_d3b">
<h2 class="sectiontitle" id="TMWBModelApplier__section_y45_x52_d3b__title__1">Concept model nugget</h2>
<p id="TMWBModelApplier__p_y51_qzj_fdb">A Text Mining concept model nugget is created whenever you successfully run a Text Mining node where you've selected the option to <span class="ph uicontrol">Generate directly</span> in the node settings. Use a text mining concept model nugget for the real-time discovery of key concepts in other text data, such as scratch-pad data from a call center.</p>
<p id="TMWBModelApplier__p_z51_qzj_fdb">The concept model nugget itself comprises a list of concepts, which have been assigned to types. You can select any or all of the concepts in that model for scoring against other data. When you run a flow containing a Text Mining model nugget, new fields are added to the data according to the build mode selected in the settings of the Text Mining modeling node.</p>
<p>If the model nugget was generated using translated documents, the scoring will be performed in the translated language. Similarly, if the model nugget was generated using English as the language, you can specify a translation language in the model nugget, since the documents will then be translated into English.</p>
<p>Text Mining model nuggets are placed in the Outputs pane when they're generated.</p>
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BBD1F022A8393101199ABB731534C10BE99CF1E4 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/TextMiningWorkbench.html?context=cdpaas&locale=en | Mining for concepts and categories (SPSS Modeler) | Mining for concepts and categories
The Text Mining node uses linguistic and frequency techniques to extract key concepts from the text and create categories with these concepts and other data. Use the node to explore the text data contents or to produce either a concept model nugget or category model nugget.
When you run this node, an internal linguistic extraction engine extracts and organizes the concepts, patterns, and categories by using natural language processing methods. Two build modes are available in the Text Mining node's properties:
* The Generate directly (concept model nugget) mode automatically produces a concept or category model nugget when you run the node.
* The Build interactively (category model nugget) is a more hands-on, exploratory approach. You can use this mode to not only extract concepts, create categories, and refine your linguistic resources, but also run text link analysis and explore clusters. This build mode launches the Text Analytics Workbench.
And you can use the Text Mining node to generate one of two text mining model nuggets:
* Concept model nuggets uncover and extract important concepts from your structured or unstructured text data.
* Category model nuggets score and assign documents and records to categories, which are made up of the extracted concepts (and patterns).
The extracted concepts and patterns and the categories from your model nuggets can all be combined with existing structured data, such as demographics, to yield better and more-focused decisions. For example, if customers frequently list login issues as the primary impediment to completing online account management tasks, you might want to incorporate "login issues" into your models.
| # Mining for concepts and categories #
The Text Mining node uses linguistic and frequency techniques to extract key concepts from the text and create categories with these concepts and other data\. Use the node to explore the text data contents or to produce either a concept model nugget or category model nugget\.
When you run this node, an internal linguistic extraction engine extracts and organizes the concepts, patterns, and categories by using natural language processing methods\. Two build modes are available in the Text Mining node's properties:
<!-- <ul> -->
* The Generate directly (concept model nugget) mode automatically produces a concept or category model nugget when you run the node\.
* The Build interactively (category model nugget) is a more hands\-on, exploratory approach\. You can use this mode to not only extract concepts, create categories, and refine your linguistic resources, but also run text link analysis and explore clusters\. This build mode launches the Text Analytics Workbench\.
<!-- </ul> -->
And you can use the Text Mining node to generate one of two text mining model nuggets:
<!-- <ul> -->
* Concept model nuggets uncover and extract important concepts from your structured or unstructured text data\.
* Category model nuggets score and assign documents and records to categories, which are made up of the extracted concepts (and patterns)\.
<!-- </ul> -->
The extracted concepts and patterns and the categories from your model nuggets can all be combined with existing structured data, such as demographics, to yield better and more\-focused decisions\. For example, if customers frequently list login issues as the primary impediment to completing online account management tasks, you might want to incorporate "login issues" into your models\.
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<div class="body" id="body_vzh_vzj_fdb">
<p class="shortdesc">The Text Mining node uses linguistic and frequency techniques to extract key concepts from the text and create categories with these concepts and other data. Use the node to explore the text data contents or to produce either a concept model nugget or category model nugget.</p>
<div class="image">
<img src="../images/ta_textmining.png" alt="Text Mining node">
</div>
<div class="p" id="TextMiningWorkbench__p_wzh_vzj_fdb">
When you run this node, an internal linguistic extraction engine extracts and organizes the concepts, patterns, and categories by using natural language processing methods. Two build modes are available in the Text Mining node's properties:
<ul>
<li>The <span class="ph uicontrol">Generate directly (concept model nugget)</span> mode automatically produces a concept or category model nugget when you run the node.</li>
<li>The <span class="ph uicontrol">Build interactively (category model nugget)</span> is a more hands-on, exploratory approach. You can use this mode to not only extract concepts, create categories, and refine your linguistic resources, but also run text link analysis and explore clusters. This build mode launches the Text Analytics Workbench.</li>
</ul>
</div>
<p>And you can use the Text Mining node to generate one of two text mining model nuggets:</p>
<ul id="TextMiningWorkbench__ul_xk4_tzj_fdb">
<li id="TextMiningWorkbench__li_yk4_tzj_fdb"><dfn class="term">Concept model nuggets</dfn> uncover and extract important concepts from your structured or unstructured text data.</li>
<li id="TextMiningWorkbench__li_zk4_tzj_fdb"><dfn class="term">Category model nuggets</dfn> score and assign documents and records to categories, which are made up of the extracted concepts (and patterns).</li>
</ul>
<p id="TextMiningWorkbench__p_al4_tzj_fdb">The extracted concepts and patterns and the categories from your model nuggets can all be combined with existing structured data, such as demographics, to yield better and more-focused decisions. For example, if customers frequently list login issues as the primary impediment to completing online account management tasks, you might want to incorporate "login issues" into your models.</p>
<section class="section" role="region" aria-labelledby="TextMiningWorkbench__body_vzh_vzj_fdb__title__1">
<h2 class="sectiontitle" id="TextMiningWorkbench__body_vzh_vzj_fdb__title__1">Data sources and linguistic resources</h2>
<p id="TextMiningWorkbench__p_zzh_vzj_fdb">Text Mining modeling nodes accept text data from Import nodes.</p>
<p>You can also upload custom templates and text analysis packages directly in the Text Mining node to use in the extraction process.</p>
</section>
<section class="section" role="region" aria-labelledby="TextMiningWorkbench__section_aqb_qmq_c3b__title__1" id="TextMiningWorkbench__section_aqb_qmq_c3b">
<h2 class="sectiontitle" id="TextMiningWorkbench__section_aqb_qmq_c3b__title__1">Concepts and concept model nuggets</h2>
<p id="TextMiningWorkbench__p_fl4_tzj_fdb">During the extraction process, text data is scanned and analyzed to identify important single words, such as <code class="ph codeph">election</code> or <code class="ph codeph">peace</code>, and word phrases such as <code class="ph codeph">presidential election</code>, <code class="ph codeph">election of the president</code>, or <code class="ph codeph">peace treaties</code>. These words and phrases are collectively referred to as <dfn class="term">terms</dfn>. Using the linguistic resources, the relevant terms are extracted, and similar terms are grouped under a lead term that is called a <dfn class="term">concept</dfn>.</p>
<p>This grouping means that a concept might represent multiple underlying terms. For example, the concept <code class="ph codeph">salary</code> was extracted from an employee satisfaction survey. When you looked at the records associated with <code class="ph codeph">salary</code>, you noticed that <code class="ph codeph">salary</code> isn't always present in the text but instead certain records contained something similar, such as the terms <code class="ph codeph">wage</code>, <code class="ph codeph">wages</code>, and <code class="ph codeph">salaries</code>. These terms are grouped under <code class="ph codeph">salary</code> since the extraction engine deemed them as similar or determined they were synonyms based on processing rules or linguistic resources. In this case, any documents or records containing any of those terms would be treated as if they contained the word <code class="ph codeph">salary</code>.</p>
<p id="TextMiningWorkbench__p_hl4_tzj_fdb">If you want to see what terms are grouped under a concept, you can explore the concept in the Text Analytics Workbench or look at which synonyms are shown in the concept model.</p>
<div class="p" id="TextMiningWorkbench__p_il4_tzj_fdb">
A <dfn class="term">concept model nugget</dfn> contains a set of concepts, which you can use to identify records or documents that also contain the concept (including any of its synonyms or grouped terms). A concept model can be used in two ways:
<ul>
<li>To explore and analyze the concepts that were discovered in the original source text or to quickly identify documents of interest.</li>
<li>To apply this model to new text records or documents to quickly identify the same key concepts in the new documents/records. For example, you can apply the model to the real-time discovery of key concepts in scratch-pad data from a call center.</li>
</ul>
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<section class="section" role="region" aria-labelledby="TextMiningWorkbench__section_rgd_cnq_c3b__title__1" id="TextMiningWorkbench__section_rgd_cnq_c3b">
<h2 class="sectiontitle" id="TextMiningWorkbench__section_rgd_cnq_c3b__title__1">Categories and category model nuggets</h2>
<p id="TextMiningWorkbench__p_ll4_tzj_fdb">You can create <dfn class="term">categories</dfn> that represent higher-level concepts or topics to capture the key ideas, knowledge, and attitudes expressed in the text. Categories are made up of a set of descriptors, such as <dfn class="term">concepts</dfn>, <dfn class="term">types</dfn>, and <dfn class="term">rules</dfn>. Together, these descriptors are used to identify whether or not a record or document belongs in a category. A document or record can be scanned to see whether any of its text matches a descriptor. If a match is found, the document is assigned to that category. This process is called <dfn class="term">categorization</dfn>.</p>
<p id="TextMiningWorkbench__p_ml4_tzj_fdb">Categories can be built automatically by using SPSS Modeler's robust set of automated techniques. You can also manually build them using any additional insight that you might have regarding the data, or a combination of both. You can also load a set of prebuilt categories from a text analysis package through the Model settings of this node. Manual creation of categories or refining categories can only be done through the Text Analytics Workbench.</p>
<p id="TextMiningWorkbench__p_nl4_tzj_fdb">A <dfn class="term">category model nugget</dfn> contains a set of categories along with its descriptors. The model can be used to categorize a set of documents or records based on the text in each document or record. Every document or record is read and then assigned to each category for which a descriptor match was found. In this way, a document or record could be assigned to more than one category. For example, you can use category model nuggets to see the essential ideas in open-ended survey responses or in a set of blog entries.</p>
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<li class="ulchildlink"><strong><a href="../nodes/TMWBModelApplier.html">Text Mining model nuggets</a></strong><br>
You can run a Text Mining node to automatically generate a <dfn class="term">concept</dfn> model nugget using the <span class="ph uicontrol">Generate directly</span> option in the node settings. Or you can use a more hands-on, exploratory approach using the <span class="ph uicontrol">Build interactively</span> mode to generate <dfn class="term">category</dfn> model nuggets from within the Text Analytics Workbench.</li>
<li class="ulchildlink"><strong><a href="../nodes/tmwb_intro.html">Text Analytics Workbench</a></strong><br>
From a Text Mining modeling node, you can choose to launch an interactive Text Analytics Workbench session when your flow runs. In this workbench, you can extract key concepts from your text data, build categories, explore patterns in text link analysis, and generate category models.</li>
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D73C52B16EC33CAA6D1F51EFFA5A6E37052D6110 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/_nodes.html?context=cdpaas&locale=en | Nodes palette (SPSS Modeler) | Nodes palette
The following sections describe all the nodes available on the palette in SPSS Modeler. Drag-and-drop or double-click a node in the list to add it to your flow canvas. You can then double-click any node icon in your flow to set its properties. Hover over a property to see information about it, or click the information icon to see Help.
When first creating a flow, you select which runtime to use. By default, the flow will use the IBM SPSS Modeler runtime. If you want to use native Spark algorithms instead of SPSS algorithms, select the Spark runtime. Properties for some nodes will vary depending on which runtime option you choose.
| # Nodes palette #
The following sections describe all the nodes available on the palette in SPSS Modeler\. Drag\-and\-drop or double\-click a node in the list to add it to your flow canvas\. You can then double\-click any node icon in your flow to set its properties\. Hover over a property to see information about it, or click the information icon to see Help\.
When first creating a flow, you select which runtime to use\. By default, the flow will use the IBM SPSS Modeler runtime\. If you want to use native Spark algorithms instead of SPSS algorithms, select the Spark runtime\. Properties for some nodes will vary depending on which runtime option you choose\.
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<h1 class="topictitle1" id="title_wyf_l25_zcb">Nodes palette</h1>
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<p class="shortdesc">The following sections describe all the nodes available on the palette in SPSS Modeler. Drag-and-drop or double-click a node in the list to add it to your flow canvas. You can then double-click any node icon in your flow to set its properties. Hover over a property to see information about it, or click the information icon to see Help.</p>
<p id="_nodes__p_yyf_l25_zcb"></p>
<p>When first creating a flow, you select which runtime to use. By default, the flow will use the <span class="ph uicontrol">IBM SPSS Modeler</span> runtime. If you want to use native Spark algorithms instead of SPSS algorithms, select the <span class="ph uicontrol">Spark</span> runtime. Properties for some nodes will vary depending on which runtime option you choose.</p>
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Use Import nodes to import data stored in various formats, or to generate your own synthetic data.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_record_operations.html">Record Operations</a></strong><br>
Record Operations nodes are useful for making changes to data at the record level. These operations are important during the <dfn class="term">data understanding</dfn> and <dfn class="term">data preparation</dfn> phases of data mining because they allow you to tailor the data to your particular business need.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_field_operations.html">Field Operations</a></strong><br>
After an initial data exploration, you will probably need to select, clean, or construct data in preparation for analysis. The Field Operations palette contains many nodes useful for this transformation and preparation.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_graphs.html">Graphs</a></strong><br>
Several phases of the data mining process use graphs and charts to explore data brought in to <span class="keyword" data-hd-audience="wx" translate="no">watsonx.ai</span>.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_modeling.html">Modeling</a></strong><br><span class="keyword" data-hd-audience="wx" translate="no">Watsonx.ai</span> offers a variety of modeling methods taken from machine learning, artificial intelligence, and statistics.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_TA.html">Text Analytics</a></strong><br><span class="keyword">SPSS Modeler</span> offers nodes that are specialized for handling text.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_outputs.html">Outputs</a></strong><br>
Output nodes provide the means to obtain information about your data and models. They also provide a mechanism for exporting data in various formats to interface with your other software tools.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_export.html">Export</a></strong><br>
Export nodes provide a mechanism for exporting data in various formats to interface with your other software tools.</li>
<li class="ulchildlink"><strong><a href="../nodes/_nodes_extension.html">Extension nodes</a></strong><br><span class="keyword">SPSS Modeler</span> supports the languages R and Apache Spark (via Python).</li>
<li class="ulchildlink"><strong><a href="../nodes/supernodes.html">SuperNodes</a></strong><br>
One of the reasons the <span class="keyword">SPSS Modeler</span> visual interface is so easy to learn is that each node has a clearly defined function. However, for complex processing, a long sequence of nodes may be necessary. Eventually, this may clutter your flow canvas and make it difficult to follow flow diagrams.</li>
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<strong>Parent topic:</strong> <a href="../spss-modeler.html" title="With SPSS Modeler flows, you can quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. Designed around the long-established SPSS Modeler client software and the industry-standard CRISP-DM model it uses, the flows interface supports the entire data mining process, from data to better business results.">Creating SPSS Modeler flows</a>
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