| /* |
| * 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.preprocessing.encoding; |
| |
| 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.ObjectArrayVectorizer; |
| 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.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.classification.Accuracy; |
| import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer; |
| import org.apache.ignite.ml.tree.DecisionTreeNode; |
| import org.apache.ignite.ml.util.MLSandboxDatasets; |
| import org.apache.ignite.ml.util.SandboxMLCache; |
| |
| /** |
| * Example that shows how to use String Encoder preprocessor to encode features presented as a strings. |
| * <p> |
| * Code in this example launches Ignite grid and fills the cache with test data (based on mushrooms dataset).</p> |
| * <p> |
| * After that it defines preprocessors that extract features from an upstream data and encode string values (categories) |
| * to double values in specified range.</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 EncoderExample { |
| /** |
| * 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 |
| ); |
| |
| DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0); |
| |
| // Train decision tree model. |
| DecisionTreeNode mdl = trainer.fit( |
| ignite, |
| dataCache, |
| encoderPreprocessor |
| ); |
| |
| System.out.println("\n>>> Trained model: " + mdl); |
| |
| double accuracy = Evaluator.evaluate( |
| dataCache, |
| mdl, |
| encoderPreprocessor, |
| 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(); |
| } |
| } |
| } |