/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.flink.ml.feature.minmaxscaler;

import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.typeutils.RowTypeInfo;
import org.apache.flink.ml.api.Model;
import org.apache.flink.ml.common.broadcast.BroadcastUtils;
import org.apache.flink.ml.common.datastream.TableUtils;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.param.Param;
import org.apache.flink.ml.util.ParamUtils;
import org.apache.flink.ml.util.ReadWriteUtils;
import org.apache.flink.ml.util.RowUtils;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.internal.TableImpl;
import org.apache.flink.types.Row;
import org.apache.flink.util.Preconditions;

import org.apache.commons.lang3.ArrayUtils;

import java.io.IOException;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

/** A Model which transforms data using the model data computed by {@link MinMaxScaler}. */
public class MinMaxScalerModel
        implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> {
    private final Map<Param<?>, Object> paramMap = new HashMap<>();
    private Table modelDataTable;

    public MinMaxScalerModel() {
        ParamUtils.initializeMapWithDefaultValues(paramMap, this);
    }

    @Override
    public MinMaxScalerModel setModelData(Table... inputs) {
        modelDataTable = inputs[0];
        return this;
    }

    @Override
    public Table[] getModelData() {
        return new Table[] {modelDataTable};
    }

    @Override
    @SuppressWarnings("unchecked")
    public Table[] transform(Table... inputs) {
        Preconditions.checkArgument(inputs.length == 1);
        StreamTableEnvironment tEnv =
                (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
        DataStream<Row> data = tEnv.toDataStream(inputs[0]);
        DataStream<MinMaxScalerModelData> minMaxScalerModel =
                MinMaxScalerModelData.getModelDataStream(modelDataTable);
        final String broadcastModelKey = "broadcastModelKey";
        RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema());
        RowTypeInfo outputTypeInfo =
                new RowTypeInfo(
                        ArrayUtils.addAll(
                                inputTypeInfo.getFieldTypes(),
                                TypeInformation.of(DenseVector.class)),
                        ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol()));
        DataStream<Row> output =
                BroadcastUtils.withBroadcastStream(
                        Collections.singletonList(data),
                        Collections.singletonMap(broadcastModelKey, minMaxScalerModel),
                        inputList -> {
                            DataStream input = inputList.get(0);
                            return input.map(
                                    new PredictOutputFunction(
                                            broadcastModelKey, getMax(), getMin(), getInputCol()),
                                    outputTypeInfo);
                        });
        return new Table[] {tEnv.fromDataStream(output)};
    }

    @Override
    public Map<Param<?>, Object> getParamMap() {
        return paramMap;
    }

    @Override
    public void save(String path) throws IOException {
        ReadWriteUtils.saveMetadata(this, path);
        ReadWriteUtils.saveModelData(
                MinMaxScalerModelData.getModelDataStream(modelDataTable),
                path,
                new MinMaxScalerModelData.ModelDataEncoder());
    }

    /**
     * Loads model data from path.
     *
     * @param tEnv Stream table environment.
     * @param path Model path.
     * @return MinMaxScalerModel model.
     */
    public static MinMaxScalerModel load(StreamTableEnvironment tEnv, String path)
            throws IOException {
        MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path);
        Table modelDataTable =
                ReadWriteUtils.loadModelData(
                        tEnv, path, new MinMaxScalerModelData.ModelDataDecoder());
        return model.setModelData(modelDataTable);
    }

    /** This operator loads model data and predicts result. */
    private static class PredictOutputFunction extends RichMapFunction<Row, Row> {
        private final String inputCol;
        private final String broadcastKey;
        private final double upperBound;
        private final double lowerBound;
        private DenseVector scaleVector;
        private DenseVector offsetVector;

        public PredictOutputFunction(
                String broadcastKey, double upperBound, double lowerBound, String inputCol) {
            this.upperBound = upperBound;
            this.lowerBound = lowerBound;
            this.broadcastKey = broadcastKey;
            this.inputCol = inputCol;
        }

        @Override
        public Row map(Row row) {
            if (scaleVector == null) {
                MinMaxScalerModelData minMaxScalerModelData =
                        (MinMaxScalerModelData)
                                getRuntimeContext().getBroadcastVariable(broadcastKey).get(0);
                DenseVector minVector = minMaxScalerModelData.minVector;
                DenseVector maxVector = minMaxScalerModelData.maxVector;
                scaleVector = new DenseVector(minVector.size());
                offsetVector = new DenseVector(minVector.size());
                for (int i = 0; i < maxVector.size(); ++i) {
                    if (Math.abs(minVector.values[i] - maxVector.values[i]) < 1.0e-5) {
                        scaleVector.values[i] = 0.0;
                        offsetVector.values[i] = (upperBound + lowerBound) / 2;
                    } else {
                        scaleVector.values[i] =
                                (upperBound - lowerBound)
                                        / (maxVector.values[i] - minVector.values[i]);
                        offsetVector.values[i] =
                                lowerBound - minVector.values[i] * scaleVector.values[i];
                    }
                }
            }
            DenseVector inputVec = ((Vector) row.getField(inputCol)).toDense();
            DenseVector outputVec = new DenseVector(scaleVector.size());
            for (int i = 0; i < scaleVector.size(); ++i) {
                outputVec.values[i] =
                        inputVec.values[i] * scaleVector.values[i] + offsetVector.values[i];
            }
            return RowUtils.append(row, outputVec);
        }
    }
}
