/*
 * 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.ignite.examples.ml.inference.exchange;

import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.dataset.feature.extractor.Vectorizer;
import org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer;
import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
import org.apache.ignite.ml.selection.scoring.metric.MetricName;

/**
 * Run linear regression model based on <a href="http://web.stanford.edu/group/SOL/software/lsqr/">LSQR algorithm</a>
 * ({@link LinearRegressionLSQRTrainer}) over cached dataset.
 * <p>
 * Code in this example launches Ignite grid and fills the cache with simple test data.</p>
 * <p>
 * After that it trains the linear regression model based on the specified data.</p>
 * <p>
 * Finally, this example loops over the test set of data points, applies the trained model to predict the target value
 * and compares prediction to expected outcome (ground truth).</p>
 * <p>
 * You can change the test data used in this example and re-run it to explore this algorithm further.</p>
 */
public class LinearRegressionExportImportExample {
    /**
     * Run example.
     */
    public static void main(String[] args) throws IOException {
        System.out.println();
        System.out.println(">>> Linear regression model over cache based dataset usage example started.");
        // Start ignite grid.
        try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
            System.out.println(">>> Ignite grid started.");

            IgniteCache<Integer, Vector> dataCache = null;
            Path jsonMdlPath = null;
            try {
                dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);

                System.out.println("\n>>> Create new linear regression trainer object.");
                LinearRegressionLSQRTrainer trainer = new LinearRegressionLSQRTrainer();

                System.out.println("\n>>> Perform the training to get the model.");

                LinearRegressionModel mdl = trainer.fit(
                        ignite,
                        dataCache,
                        new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST)
                    );

                System.out.println("\n>>> Exported LinearRegression model: " + mdl);

                double rmse = Evaluator.evaluate(
                    dataCache,
                    mdl,
                    new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST),
                    MetricName.RMSE
                );

                System.out.println("\n>>> RMSE for exported LinearRegression model: " + rmse);

                jsonMdlPath = Files.createTempFile(null, null);
                mdl.toJSON(jsonMdlPath);

                LinearRegressionModel modelImportedFromJSON = LinearRegressionModel.fromJSON(jsonMdlPath);

                System.out.println("\n>>> Imported LinearRegression model: " + modelImportedFromJSON);

                rmse = Evaluator.evaluate(
                    dataCache,
                    mdl,
                    new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST),
                    MetricName.RMSE
                );

                System.out.println("\n>>> RMSE for imported LinearRegression model: " + rmse);

                System.out.println("\n>>> Linear regression model over cache based dataset usage example completed.");
            }
            finally {
                if (dataCache != null)
                    dataCache.destroy();
                if (jsonMdlPath != null)
                    Files.deleteIfExists(jsonMdlPath);
            }
        }
        finally {
            System.out.flush();
        }
    }
}
