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/*
 * 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.classification.linearsvc;

import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
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.BLAS;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo;
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.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 classifies data using the model data computed by {@link LinearSVC}. */
public class LinearSVCModel implements Model, LinearSVCModelParams {

    private final Map, Object> paramMap = new HashMap<>();

    private Table modelDataTable;

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

    @Override
    @SuppressWarnings("unchecked")
    public Table[] transform(Table... inputs) {
        Preconditions.checkArgument(inputs.length == 1);
        StreamTableEnvironment tEnv =
                (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
        DataStream inputStream = tEnv.toDataStream(inputs[0]);

        final String broadcastModelKey = "broadcastModelKey";
        DataStream modelDataStream =
                LinearSVCModelData.getModelDataStream(modelDataTable);

        RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema());
        RowTypeInfo outputTypeInfo =
                new RowTypeInfo(
                        ArrayUtils.addAll(
                                inputTypeInfo.getFieldTypes(),
                                BasicTypeInfo.DOUBLE_TYPE_INFO,
                                DenseVectorTypeInfo.INSTANCE),
                        ArrayUtils.addAll(
                                inputTypeInfo.getFieldNames(),
                                getPredictionCol(),
                                getRawPredictionCol()));

        DataStream predictionResult =
                BroadcastUtils.withBroadcastStream(
                        Collections.singletonList(inputStream),
                        Collections.singletonMap(broadcastModelKey, modelDataStream),
                        inputList -> {
                            DataStream inputData = inputList.get(0);
                            return inputData.map(
                                    new PredictLabelFunction(
                                            broadcastModelKey, getFeaturesCol(), getThreshold()),
                                    outputTypeInfo);
                        });
        return new Table[] {tEnv.fromDataStream(predictionResult)};
    }

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

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

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

    public static LinearSVCModel load(StreamTableEnvironment tEnv, String path) throws IOException {
        LinearSVCModel model = ReadWriteUtils.loadStageParam(path);
        Table modelDataTable =
                ReadWriteUtils.loadModelData(tEnv, path, new LinearSVCModelData.ModelDataDecoder());
        return model.setModelData(modelDataTable);
    }

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

    /** A utility function used for prediction. */
    private static class PredictLabelFunction extends RichMapFunction {

        private final String broadcastModelKey;

        private final String featuresCol;

        private final double threshold;

        private DenseVector coefficient;

        public PredictLabelFunction(
                String broadcastModelKey, String featuresCol, double threshold) {
            this.broadcastModelKey = broadcastModelKey;
            this.featuresCol = featuresCol;
            this.threshold = threshold;
        }

        @Override
        public Row map(Row dataPoint) {
            if (coefficient == null) {
                LinearSVCModelData modelData =
                        (LinearSVCModelData)
                                getRuntimeContext().getBroadcastVariable(broadcastModelKey).get(0);
                coefficient = modelData.coefficient;
            }
            DenseVector features = ((Vector) dataPoint.getField(featuresCol)).toDense();
            Row predictionResult = predictOneDataPoint(features, coefficient, threshold);
            return Row.join(dataPoint, predictionResult);
        }
    }

    /**
     * The main logic that predicts one input data point.
     *
     * @param feature The input feature.
     * @param coefficient The model parameters.
     * @param threshold The threshold for prediction.
     * @return The prediction label and the raw predictions.
     */
    private static Row predictOneDataPoint(
            DenseVector feature, DenseVector coefficient, double threshold) {
        double dotValue = BLAS.dot(feature, coefficient);
        return Row.of(dotValue >= threshold ? 1.0 : 0.0, Vectors.dense(dotValue, -dotValue));
    }
}