| /* |
| * 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.tutorial; |
| |
| import java.io.FileNotFoundException; |
| import org.apache.ignite.Ignite; |
| import org.apache.ignite.IgniteCache; |
| import org.apache.ignite.Ignition; |
| 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.preprocessing.Preprocessor; |
| import org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer; |
| import org.apache.ignite.ml.preprocessing.encoding.EncoderType; |
| import org.apache.ignite.ml.preprocessing.imputing.ImputerTrainer; |
| import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer; |
| import org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.classification.Accuracy; |
| import org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter; |
| import org.apache.ignite.ml.selection.split.TrainTestSplit; |
| import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer; |
| import org.apache.ignite.ml.tree.DecisionTreeNode; |
| |
| /** |
| * The highest accuracy in the previous example ({@link Step_6_KNN}) is the result of |
| * <a href="https://en.wikipedia.org/wiki/Overfitting">overfitting</a>. |
| * For real model estimation is better to use test-train split via {@link TrainTestDatasetSplitter}. |
| * <p> |
| * Code in this example launches Ignite grid and fills the cache with test data (based on Titanic passengers data).</p> |
| * <p> |
| * After that it defines how to split the data to train and test sets and configures preprocessors that extract features |
| * from an upstream data and perform other desired changes over the extracted data.</p> |
| * <p> |
| * Then, it trains the model based on the processed data using decision tree classification.</p> |
| * <p> |
| * Finally, this example uses {@link Evaluator} functionality to compute metrics from predictions.</p> |
| */ |
| public class Step_7_Split_train_test { |
| /** |
| * 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(6) // <--- Changed index here. |
| .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. |
| DecisionTreeNode 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(); |
| } |
| } |
| } |