Examples with DecisionTreeModel org.apache.ignite.ml.tree.DecisionTreeModel used on opensource projects

Example 21 with DecisionTreeModel

use of org.apache.ignite.ml.tree.DecisionTreeModel in project ignite by apache.

the class DecisionTreeFromSparkExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws FileNotFoundException {
    System.out.println();
    System.out.println(">>> Decision Tree model loaded from Spark through serialization over partitioned 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;
        try {
            dataCache = TitanicUtils.readPassengersWithoutNulls(ignite);
            final Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>(0, 5, 6, 4).labeled(1);
            DecisionTreeModel mdl = (DecisionTreeModel) SparkModelParser.parse(SPARK_MDL_PATH, SupportedSparkModels.DECISION_TREE, env);
            System.out.println(">>> DT: " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, mdl, vectorizer, new Accuracy<>());
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println("\n>>> Test Error " + (1 - accuracy));
        } finally {
            dataCache.destroy();
        }
    }
}

Example 22 with DecisionTreeModel

use of org.apache.ignite.ml.tree.DecisionTreeModel in project ignite by apache.

the class DecisionTreeClassificationTrainerExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String... args) {
    System.out.println(">>> Decision tree classification trainer example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create cache with training data.
        CacheConfiguration<Integer, LabeledVector<Double>> trainingSetCfg = new CacheConfiguration<>();
        trainingSetCfg.setName("TRAINING_SET");
        trainingSetCfg.setAffinity(new RendezvousAffinityFunction(false, 10));
        IgniteCache<Integer, LabeledVector<Double>> trainingSet = null;
        try {
            trainingSet = ignite.createCache(trainingSetCfg);
            Random rnd = new Random(0);
            // Fill training data.
            for (int i = 0; i < 1000; i++) trainingSet.put(i, generatePoint(rnd));
            // Create classification trainer.
            DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(4, 0);
            // Train decision tree model.
            LabeledDummyVectorizer<Integer, Double> vectorizer = new LabeledDummyVectorizer<>();
            DecisionTreeModel mdl = trainer.fit(ignite, trainingSet, vectorizer);
            System.out.println(">>> Decision tree classification model: " + mdl);
            // Calculate score.
            int correctPredictions = 0;
            for (int i = 0; i < 1000; i++) {
                LabeledVector<Double> pnt = generatePoint(rnd);
                double prediction = mdl.predict(pnt.features());
                double lbl = pnt.label();
                if (i % 50 == 1)
                    System.out.printf(">>> test #: %d\t\t predicted: %.4f\t\tlabel: %.4f\n", i, prediction, lbl);
                if (Precision.equals(prediction, lbl, Precision.EPSILON))
                    correctPredictions++;
            }
            System.out.println(">>> Accuracy: " + correctPredictions / 10.0 + "%");
            System.out.println(">>> Decision tree classification trainer example completed.");
        } finally {
            trainingSet.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 23 with DecisionTreeModel

use of org.apache.ignite.ml.tree.DecisionTreeModel in project ignite by apache.

the class DecisionTreeClassificationTrainerSQLTableExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws IgniteCheckedException, IOException {
    System.out.println(">>> Decision tree classification trainer example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Dummy cache is required to perform SQL queries.
        CacheConfiguration<?, ?> cacheCfg = new CacheConfiguration<>(DUMMY_CACHE_NAME).setSqlSchema("PUBLIC");
        IgniteCache<?, ?> cache = null;
        try {
            cache = ignite.getOrCreateCache(cacheCfg);
            System.out.println(">>> Creating table with training data...");
            cache.query(new SqlFieldsQuery("create table titanic_train (\n" + "    passengerid int primary key,\n" + "    pclass int,\n" + "    survived int,\n" + "    name varchar(255),\n" + "    sex varchar(255),\n" + "    age float,\n" + "    sibsp int,\n" + "    parch int,\n" + "    ticket varchar(255),\n" + "    fare float,\n" + "    cabin varchar(255),\n" + "    embarked varchar(255)\n" + ") with \"template=partitioned\";")).getAll();
            System.out.println(">>> Creating table with test data...");
            cache.query(new SqlFieldsQuery("create table titanic_test (\n" + "    passengerid int primary key,\n" + "    pclass int,\n" + "    survived int,\n" + "    name varchar(255),\n" + "    sex varchar(255),\n" + "    age float,\n" + "    sibsp int,\n" + "    parch int,\n" + "    ticket varchar(255),\n" + "    fare float,\n" + "    cabin varchar(255),\n" + "    embarked varchar(255)\n" + ") with \"template=partitioned\";")).getAll();
            loadTitanicDatasets(ignite, cache);
            System.out.println(">>> Prepare trainer...");
            DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(4, 0);
            System.out.println(">>> Perform training...");
            DecisionTreeModel mdl = trainer.fit(new SqlDatasetBuilder(ignite, "SQL_PUBLIC_TITANIC_TRAIN"), new BinaryObjectVectorizer<>("pclass", "age", "sibsp", "parch", "fare").withFeature("sex", BinaryObjectVectorizer.Mapping.create().map("male", 1.0).defaultValue(0.0)).labeled("survived"));
            System.out.println("Tree is here: " + mdl.toString(true));
            System.out.println(">>> Perform inference...");
            try (QueryCursor<List<?>> cursor = cache.query(new SqlFieldsQuery("select " + "pclass, " + "sex, " + "age, " + "sibsp, " + "parch, " + "fare from titanic_test"))) {
                for (List<?> passenger : cursor) {
                    Vector input = VectorUtils.of(new Double[] { asDouble(passenger.get(0)), "male".equals(passenger.get(1)) ? 1.0 : 0.0, asDouble(passenger.get(2)), asDouble(passenger.get(3)), asDouble(passenger.get(4)), asDouble(passenger.get(5)) });
                    double prediction = mdl.predict(input);
                    System.out.printf("Passenger %s will %s.\n", passenger, prediction == 0 ? "die" : "survive");
                }
            }
            System.out.println(">>> Example completed.");
        } finally {
            cache.query(new SqlFieldsQuery("DROP TABLE titanic_train"));
            cache.query(new SqlFieldsQuery("DROP TABLE titanic_test"));
            cache.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 24 with DecisionTreeModel

use of org.apache.ignite.ml.tree.DecisionTreeModel in project ignite by apache.

the class EncoderExampleWithNormalization method main.

/**
 * Run example.
 */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Train Decision Tree model on mushrooms.csv dataset.");
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        try {
            IgniteCache<Integer, Object[]> dataCache = new SandboxMLCache(ignite).fillObjectCacheWithDoubleLabels(MLSandboxDatasets.MUSHROOMS);
            final Vectorizer<Integer, Object[], Integer, Object> vectorizer = new ObjectArrayVectorizer<Integer>(1, 2, 3).labeled(0);
            Preprocessor<Integer, Object[]> encoderPreprocessor = new EncoderTrainer<Integer, Object[]>().withEncoderType(EncoderType.STRING_ENCODER).withEncodedFeature(0).withEncodedFeature(1).withEncodedFeature(2).fit(ignite, dataCache, vectorizer);
            // Defines second preprocessor that normalizes features.
            Preprocessor<Integer, Object[]> normalizer = new NormalizationTrainer<Integer, Object[]>().withP(1).fit(ignite, dataCache, encoderPreprocessor);
            DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
            // Train decision tree model.
            DecisionTreeModel mdl = trainer.fit(ignite, dataCache, normalizer);
            System.out.println("\n>>> Trained model: " + mdl);
            double accuracy = Evaluator.evaluate(dataCache, mdl, normalizer, new Accuracy<>());
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println("\n>>> Test Error " + (1 - accuracy));
            System.out.println(">>> Tutorial step 3 (categorial with One-hot encoder) example started.");
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        }
    } finally {
        System.out.flush();
    }
}

Example 25 with DecisionTreeModel

use of org.apache.ignite.ml.tree.DecisionTreeModel in project ignite by apache.

the class Step_13_RandomSearch method main.

/**
 * Run example.
 */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Tutorial step 13 (Random Search) 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(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);
            NormalizationTrainer<Integer, Vector> normalizationTrainer = new NormalizationTrainer<Integer, Vector>().withP(1);
            Preprocessor<Integer, Vector> normalizationPreprocessor = normalizationTrainer.fit(ignite, dataCache, minMaxScalerPreprocessor);
            // Tune hyperparams with K-fold Cross-Validation on the split training set.
            DecisionTreeClassificationTrainer trainerCV = new DecisionTreeClassificationTrainer();
            CrossValidation<DecisionTreeModel, Integer, Vector> scoreCalculator = new CrossValidation<>();
            ParamGrid paramGrid = new ParamGrid().withParameterSearchStrategy(new RandomStrategy().withMaxTries(10).withSeed(12L)).addHyperParam("p", normalizationTrainer::withP, new Double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 }).addHyperParam("maxDeep", trainerCV::withMaxDeep, new Double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 }).addHyperParam("minImpurityDecrease", trainerCV::withMinImpurityDecrease, new Double[] { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 });
            scoreCalculator.withIgnite(ignite).withUpstreamCache(dataCache).withTrainer(trainerCV).withMetric(MetricName.ACCURACY).withFilter(split.getTrainFilter()).isRunningOnPipeline(false).withPreprocessor(normalizationPreprocessor).withAmountOfFolds(3).withParamGrid(paramGrid);
            CrossValidationResult crossValidationRes = scoreCalculator.tuneHyperParameters();
            System.out.println("Train with maxDeep: " + crossValidationRes.getBest("maxDeep") + " and minImpurityDecrease: " + crossValidationRes.getBest("minImpurityDecrease"));
            DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer().withMaxDeep(crossValidationRes.getBest("maxDeep")).withMinImpurityDecrease(crossValidationRes.getBest("minImpurityDecrease"));
            System.out.println(crossValidationRes);
            System.out.println("Best score: " + Arrays.toString(crossValidationRes.getBestScore()));
            System.out.println("Best hyper params: " + crossValidationRes.getBestHyperParams());
            System.out.println("Best average score: " + crossValidationRes.getBestAvgScore());
            crossValidationRes.getScoringBoard().forEach((hyperParams, score) -> System.out.println("Score " + Arrays.toString(score) + " for hyper params " + hyperParams));
            // Train decision tree model.
            DecisionTreeModel bestMdl = trainer.fit(ignite, dataCache, split.getTrainFilter(), normalizationPreprocessor);
            System.out.println("\n>>> Trained model: " + bestMdl);
            double accuracy = Evaluator.evaluate(dataCache, split.getTestFilter(), bestMdl, normalizationPreprocessor, new Accuracy<>());
            System.out.println("\n>>> Accuracy " + accuracy);
            System.out.println("\n>>> Test Error " + (1 - accuracy));
            System.out.println(">>> Tutorial step 13 (Random Search) example completed.");
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        }
    } finally {
        System.out.flush();
    }
}