Examples with NormalizationTrainer org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer used on opensource projects

Example 1 with NormalizationTrainer

use of org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer in project ignite by apache.

the class DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    System.out.println();
    System.out.println(">>> Linear regression model over sparse distributed matrix API usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);
            System.out.println(">>> Create new normalization trainer object.");
            NormalizationTrainer<Integer, double[]> normalizationTrainer = new NormalizationTrainer<>();
            System.out.println(">>> Perform the training to get the normalization preprocessor.");
            NormalizationPreprocessor<Integer, double[]> preprocessor = normalizationTrainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), (k, v) -> Arrays.copyOfRange(v, 1, v.length), 4);
            System.out.println(">>> Create new linear regression trainer object.");
            LinearRegressionLSQRTrainer<Integer, double[]> trainer = new LinearRegressionLSQRTrainer<>();
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel mdl = trainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), preprocessor, (k, v) -> v[0], 4);
            System.out.println(">>> Linear regression model: " + mdl);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
                for (Cache.Entry<Integer, double[]> observation : observations) {
                    Integer key = observation.getKey();
                    double[] val = observation.getValue();
                    double groundTruth = val[0];
                    double prediction = mdl.apply(new DenseLocalOnHeapVector(preprocessor.apply(key, val)));
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                }
            }
            System.out.println(">>> ---------------------------------");
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 2 with NormalizationTrainer

use of org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer in project ignite by apache.

the class TrainingWithCustomPreprocessorsExample method main.

/**
 * Run example.
 *
 * @param args Command line arguments.
 * @throws Exception Exception.
 */
public static void main(String[] args) throws Exception {
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        IgniteCache<Integer, Vector> trainingSet = null;
        try {
            trainingSet = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.BOSTON_HOUSE_PRICES);
            Vectorizer<Integer, Vector, Integer, Double> basicVectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST);
            Preprocessor<Integer, Vector> imputingPreprocessor = new ImputerTrainer<Integer, Vector>().fit(ignite, trainingSet, basicVectorizer);
            // In-place definition of custom preprocessor by lambda expression.
            Preprocessor<Integer, Vector> customPreprocessor = (k, v) -> {
                LabeledVector res = imputingPreprocessor.apply(k, v);
                double fifthFeature = res.features().get(5);
                Vector updatedVector = res.features().set(5, fifthFeature > 0 ? Math.log(fifthFeature) : -1);
                return updatedVector.labeled(res.label());
            };
            Vectorizer9000 customVectorizer = new Vectorizer9000(customPreprocessor);
            PipelineMdl<Integer, Vector> mdl = new Pipeline<Integer, Vector, Integer, Double>().addVectorizer(customVectorizer).addPreprocessingTrainer(new MinMaxScalerTrainer<Integer, Vector>()).addPreprocessingTrainer(new NormalizationTrainer<Integer, Vector>().withP(1)).addPreprocessingTrainer(getCustomTrainer()).addTrainer(new DecisionTreeClassificationTrainer(5, 0)).fit(ignite, trainingSet);
            System.out.println(">>> Perform scoring.");
            double score = Evaluator.evaluate(trainingSet, mdl, mdl.getPreprocessor(), MetricName.R2);
            System.out.println(">>> R^2 score: " + score);
        } finally {
            if (trainingSet != null)
                trainingSet.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 3 with NormalizationTrainer

use of org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer in project ignite by apache.

the class Step_9_Scaling_With_Stacking method main.

/**
 * Run example.
 */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Tutorial step 9 (scaling with stacking) example started.");
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        try {
            IgniteCache<Integer, Vector> dataCache = TitanicUtils.readPassengers(ignite);
            // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
            final Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>(0, 3, 4, 5, 6, 8, 10).labeled(1);
            TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>().split(0.75);
            Preprocessor<Integer, Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Vector>().withEncoderType(EncoderType.STRING_ENCODER).withEncodedFeature(1).withEncodedFeature(// <--- Changed index here.
            6).fit(ignite, dataCache, vectorizer);
            Preprocessor<Integer, Vector> imputingPreprocessor = new ImputerTrainer<Integer, Vector>().fit(ignite, dataCache, strEncoderPreprocessor);
            Preprocessor<Integer, Vector> minMaxScalerPreprocessor = new MinMaxScalerTrainer<Integer, Vector>().fit(ignite, dataCache, imputingPreprocessor);
            Preprocessor<Integer, Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Vector>().withP(1).fit(ignite, dataCache, minMaxScalerPreprocessor);
            DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
            DecisionTreeClassificationTrainer trainer1 = new DecisionTreeClassificationTrainer(3, 0);
            DecisionTreeClassificationTrainer trainer2 = new DecisionTreeClassificationTrainer(4, 0);
            LogisticRegressionSGDTrainer aggregator = new LogisticRegressionSGDTrainer().withUpdatesStgy(new UpdatesStrategy<>(new SimpleGDUpdateCalculator(0.2), SimpleGDParameterUpdate.SUM_LOCAL, SimpleGDParameterUpdate.AVG));
            StackedModel<Vector, Vector, Double, LogisticRegressionModel> mdl = new StackedVectorDatasetTrainer<>(aggregator).addTrainerWithDoubleOutput(trainer).addTrainerWithDoubleOutput(trainer1).addTrainerWithDoubleOutput(trainer2).fit(ignite, dataCache, split.getTrainFilter(), normalizationPreprocessor);
            System.out.println("\n>>> Trained model: " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, split.getTestFilter(), mdl, normalizationPreprocessor, new Accuracy<>());
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println("\n>>> Test Error " + (1 - accuracy));
            System.out.println(">>> Tutorial step 9 (scaling with stacking) example completed.");
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        }
    } finally {
        System.out.flush();
    }
}

Example 4 with NormalizationTrainer

use of org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer in project ignite by apache.

the class Step_10_Bagging method main.

/**
 * Run example.
 */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Tutorial step 10 (Bagging) example started.");
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        try {
            IgniteCache<Integer, Vector> dataCache = TitanicUtils.readPassengers(ignite);
            // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
            final Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>(0, 3, 4, 5, 6, 8, 10).labeled(1);
            TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>().split(0.75);
            Preprocessor<Integer, Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Vector>().withEncoderType(EncoderType.STRING_ENCODER).withEncodedFeature(1).withEncodedFeature(// <--- Changed index here.
            6).fit(ignite, dataCache, vectorizer);
            Preprocessor<Integer, Vector> imputingPreprocessor = new ImputerTrainer<Integer, Vector>().fit(ignite, dataCache, strEncoderPreprocessor);
            Preprocessor<Integer, Vector> minMaxScalerPreprocessor = new MinMaxScalerTrainer<Integer, Vector>().fit(ignite, dataCache, imputingPreprocessor);
            Preprocessor<Integer, Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Vector>().withP(1).fit(ignite, dataCache, minMaxScalerPreprocessor);
            DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
            BaggedTrainer<Double> baggedTrainer = TrainerTransformers.makeBagged(trainer, 10, 0.6, 4, 3, new OnMajorityPredictionsAggregator()).withEnvironmentBuilder(LearningEnvironmentBuilder.defaultBuilder().withRNGSeed(1));
            BaggedModel mdl = baggedTrainer.fit(ignite, dataCache, split.getTrainFilter(), normalizationPreprocessor);
            System.out.println("\n>>> Trained model: " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, split.getTestFilter(), mdl, normalizationPreprocessor, MetricName.ACCURACY);
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println("\n>>> Test Error " + (1 - accuracy));
            System.out.println(">>> Tutorial step 10 (Bagging) example completed.");
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        }
    } finally {
        System.out.flush();
    }
}

Example 5 with NormalizationTrainer

use of org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer in project ignite by apache.

the class Step_6_KNN method main.

/**
 * Run example.
 */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Tutorial step 6 (kNN) example started.");
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        try {
            IgniteCache<Integer, Vector> dataCache = TitanicUtils.readPassengers(ignite);
            // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
            final Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>(0, 3, 4, 5, 6, 8, 10).labeled(1);
            Preprocessor<Integer, Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Vector>().withEncoderType(EncoderType.STRING_ENCODER).withEncodedFeature(1).withEncodedFeature(// <--- Changed index here.
            6).fit(ignite, dataCache, vectorizer);
            Preprocessor<Integer, Vector> imputingPreprocessor = new ImputerTrainer<Integer, Vector>().fit(ignite, dataCache, strEncoderPreprocessor);
            Preprocessor<Integer, Vector> minMaxScalerPreprocessor = new MinMaxScalerTrainer<Integer, Vector>().fit(ignite, dataCache, imputingPreprocessor);
            Preprocessor<Integer, Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Vector>().withP(1).fit(ignite, dataCache, minMaxScalerPreprocessor);
            KNNClassificationTrainer trainer = new KNNClassificationTrainer().withK(1).withWeighted(true);
            // Train decision tree model.
            KNNClassificationModel mdl = trainer.fit(ignite, dataCache, normalizationPreprocessor);
            System.out.println("\n>>> Trained model: " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, mdl, normalizationPreprocessor, new Accuracy<>());
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println("\n>>> Test Error " + (1 - accuracy));
            System.out.println(">>> Tutorial step 6 (kNN) example completed.");
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        }
    } finally {
        System.out.flush();
    }
}