use of org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer in project ignite by apache.
the class Step_7_Split_train_test method main.
/**
* Run example.
*/
public static void main(String[] args) {
System.out.println();
System.out.println(">>> Tutorial step 7 (split to train and test) 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);
// Train decision tree model.
DecisionTreeModel mdl = trainer.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 7 (split to train and test) example completed.");
} catch (FileNotFoundException e) {
e.printStackTrace();
}
} finally {
System.out.flush();
}
}
use of org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer in project ignite by apache.
the class Step_8_CV method main.
/**
* Run example.
*/
public static void main(String[] args) {
System.out.println();
System.out.println(">>> Tutorial step 8 (cross-validation) 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);
// Tune hyper-parameters with K-fold Cross-Validation on the split training set.
int[] pSet = new int[] { 1, 2 };
int[] maxDeepSet = new int[] { 1, 2, 3, 4, 5, 10, 20 };
int bestP = 1;
int bestMaxDeep = 1;
double avg = Double.MIN_VALUE;
for (int p : pSet) {
for (int maxDeep : maxDeepSet) {
Preprocessor<Integer, Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Vector>().withP(p).fit(ignite, dataCache, minMaxScalerPreprocessor);
DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(maxDeep, 0);
CrossValidation<DecisionTreeModel, Integer, Vector> scoreCalculator = new CrossValidation<>();
double[] scores = scoreCalculator.withIgnite(ignite).withUpstreamCache(dataCache).withTrainer(trainer).withMetric(MetricName.ACCURACY).withFilter(split.getTrainFilter()).withPreprocessor(normalizationPreprocessor).withAmountOfFolds(3).isRunningOnPipeline(false).scoreByFolds();
System.out.println("Scores are: " + Arrays.toString(scores));
final double currAvg = Arrays.stream(scores).average().orElse(Double.MIN_VALUE);
if (currAvg > avg) {
avg = currAvg;
bestP = p;
bestMaxDeep = maxDeep;
}
System.out.println("Avg is: " + currAvg + " with p: " + p + " with maxDeep: " + maxDeep);
}
}
System.out.println("Train with p: " + bestP + " and maxDeep: " + bestMaxDeep);
Preprocessor<Integer, Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Vector>().withP(bestP).fit(ignite, dataCache, minMaxScalerPreprocessor);
DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(bestMaxDeep, 0);
// 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 8 (cross-validation) example completed.");
} catch (FileNotFoundException e) {
e.printStackTrace();
}
} finally {
System.out.flush();
}
}
use of org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer in project ignite by apache.
the class LabelEncoderExample 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).fillObjectCacheWithCategoricalData(MLSandboxDatasets.MUSHROOMS);
final Vectorizer<Integer, Object[], Integer, Object> vectorizer = new ObjectArrayVectorizer<Integer>(1, 2).labeled(0);
Preprocessor<Integer, Object[]> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>().withEncoderType(EncoderType.STRING_ENCODER).withEncodedFeature(0).withEncodedFeature(1).fit(ignite, dataCache, vectorizer);
Preprocessor<Integer, Object[]> lbEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>().withEncoderType(EncoderType.LABEL_ENCODER).fit(ignite, dataCache, strEncoderPreprocessor);
DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
// Train decision tree model.
DecisionTreeModel mdl = trainer.fit(ignite, dataCache, lbEncoderPreprocessor);
System.out.println("\n>>> Trained model: " + mdl);
double accuracy = Evaluator.evaluate(dataCache, mdl, lbEncoderPreprocessor, new Accuracy());
System.out.println("\n>>> Accuracy " + accuracy);
System.out.println("\n>>> Test Error " + (1 - accuracy));
System.out.println(">>> Train Decision Tree model on mushrooms.csv dataset.");
} catch (FileNotFoundException e) {
e.printStackTrace();
}
} finally {
System.out.flush();
}
}
use of org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer in project ignite by apache.
the class Step_12_Model_Update method main.
/**
* Run example.
*/
public static void main(String[] args) {
System.out.println();
System.out.println(">>> Tutorial step 12 (Model update) 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(0.5);
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);
LogisticRegressionSGDTrainer trainer = new LogisticRegressionSGDTrainer().withUpdatesStgy(new UpdatesStrategy<>(new SimpleGDUpdateCalculator(0.2), SimpleGDParameterUpdate.SUM_LOCAL, SimpleGDParameterUpdate.AVG)).withMaxIterations(100000).withLocIterations(100).withBatchSize(10).withSeed(123L);
// Train LogReg model.
LogisticRegressionModel mdl = trainer.fit(ignite, dataCache, split.getTrainFilter(), normalizationPreprocessor);
// Update LogReg model with new portion of data.
LogisticRegressionModel mdl2 = trainer.update(mdl, ignite, dataCache, split.getTestFilter(), normalizationPreprocessor);
System.out.println("\n>>> Trained model: " + mdl);
double accuracy = Evaluator.evaluate(dataCache, mdl2, normalizationPreprocessor, MetricName.ACCURACY);
System.out.println("\n>>> Accuracy " + accuracy);
System.out.println("\n>>> Test Error " + (1 - accuracy));
System.out.println(">>> Tutorial step 12 (Model update) example completed.");
} catch (FileNotFoundException e) {
e.printStackTrace();
}
} finally {
System.out.flush();
}
}
use of org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer in project ignite by apache.
the class Step_5_Scaling method main.
/**
* Run example.
*/
public static void main(String[] args) {
System.out.println();
System.out.println(">>> Tutorial step 5 (scaling) 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);
DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
// Train decision tree model.
DecisionTreeModel 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 5 (scaling) example completed.");
} catch (FileNotFoundException e) {
e.printStackTrace();
}
} finally {
System.out.flush();
}
}
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