An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation
Paper • 2505.03452 • Published • 3
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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>
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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 \n\nSeveral examples are presented in this (...TRUNCATED) | "# Sample models and notebooks for Decision Optimization #\n\nSeveral examples are presented in thi(...TRUNCATED) | "<!doctype html>\n<html lang=\"en-us\">\n <head>\n <meta http-equiv=\"Content-Type\" content=\"text(...TRUNCATED) |
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watsonxDocsQA Dataset
Overview
watsonxDocsQA is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and is designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation. The dataset consists of two components:
- Documents: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation (main page - crawl March 2024).
- Benchmark: A set of 75 question-answer (QA) pairs with gold document labels and answers. The QA pairs are crafted as follows:
- 25 questions: Human-generated by two subject matter experts.
- 50 questions: Synthetically generated using the
tiiuae/falcon-180bmodel, then manually filtered and reviewed for quality. The methodology is detailed in Yehudai et al. 2024.
Data Description
Corpus Dataset
The corpus dataset contains the following fields:
| Field | Description |
|---|---|
doc_id |
Unique identifier for the document |
title |
Document title as it appears on the HTML page |
document |
Textual representation of the content |
md_document |
Markdown representation of the content |
url |
Origin URL of the document |
Question-Answers Dataset
The QA dataset includes these fields:
| Field | Description |
|---|---|
question_id |
Unique identifier for the question |
question |
Text of the question |
correct_answer |
Ground-truth answer |
ground_truths_contexts_ids |
List of ground-truth document IDs |
ground_truths_contexts |
List of grounding texts on which the answer is based |
Samples
Below is an example from the question_answers dataset:
- question_id: watsonx_q_2
- question: What foundation models have been built by IBM?
- correct_answer:
"Foundation models built by IBM include:- granite-13b-chat-v2
- granite-13b-chat-v1
- granite-13b-instruct-v1"
- ground_truths_contexts_ids: B2593108FA446C4B4B0EF5ADC2CD5D9585B0B63C
- ground_truths_contexts: Foundation models built by IBM \n\nIn IBM watsonx.ai, ...
Citation
If you decide to use this dataset, please consider citing our preprint
@misc{orbach2025analysishyperparameteroptimizationmethods,
title={An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation},
author={Matan Orbach and Ohad Eytan and Benjamin Sznajder and Ariel Gera and Odellia Boni and Yoav Kantor and Gal Bloch and Omri Levy and Hadas Abraham and Nitzan Barzilay and Eyal Shnarch and Michael E. Factor and Shila Ofek-Koifman and Paula Ta-Shma and Assaf Toledo},
year={2025},
eprint={2505.03452},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.03452},
}
Contact
For questions or feedback, please:
- Email: benjams@il.ibm.com
- Or, open an pull request/discussion in this repository.
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