| /* |
| * 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 java.util.Arrays; |
| 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.pipeline.Pipeline; |
| import org.apache.ignite.ml.preprocessing.imputing.ImputerTrainer; |
| import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer; |
| import org.apache.ignite.ml.selection.cv.CrossValidation; |
| import org.apache.ignite.ml.selection.cv.CrossValidationResult; |
| import org.apache.ignite.ml.selection.paramgrid.ParamGrid; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.MetricName; |
| 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; |
| |
| /** |
| * To choose the best hyper-parameters the cross-validation with {@link ParamGrid} will be used in this example. |
| * <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 tunes hyper-parameters with K-fold Cross-Validation on the split training set and trains the model based on the |
| * processed data using decision tree classification and the obtained hyper-parameters.</p> |
| * <p> |
| * Finally, this example uses {@link Evaluator} functionality to compute metrics from predictions.</p> |
| * <p> |
| * The purpose of cross-validation is model checking, not model building.</p> |
| * <p> |
| * We train {@code k} different models.</p> |
| * <p> |
| * They differ in that {@code 1/(k-1)}th of the training data is exchanged against other cases.</p> |
| * <p> |
| * These models are sometimes called surrogate models because the (average) performance measured for these models is |
| * taken as a surrogate of the performance of the model trained on all cases.</p> |
| * <p> |
| * All scenarios are described there: https://sebastianraschka.com/faq/docs/evaluate-a-model.html</p> |
| */ |
| public class Step_8_CV_with_Param_Grid_and_metrics_and_pipeline { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) { |
| System.out.println(); |
| System.out.println(">>> Tutorial step 8 (cross-validation with param grid) example started."); |
| |
| try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { |
| try { |
| IgniteCache<Integer, Vector> dataCache = TitanicUtils.readPassengers(ignite); |
| |
| // Extracts "pclass", "sibsp", "parch", "age", "fare". |
| final Vectorizer<Integer, Vector, Integer, Double> vectorizer |
| = new DummyVectorizer<Integer>(0, 4, 5, 6, 8).labeled(1); |
| |
| TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>() |
| .split(0.75); |
| |
| DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0); |
| |
| Pipeline<Integer, Vector, Integer, Double> pipeline = new Pipeline<Integer, Vector, Integer, Double>() |
| .addVectorizer(vectorizer) |
| .addPreprocessingTrainer(new ImputerTrainer<Integer, Vector>()) |
| .addPreprocessingTrainer(new MinMaxScalerTrainer<Integer, Vector>()) |
| .addTrainer(trainer); |
| |
| // Tune hyper-parameters with K-fold Cross-Validation on the split training set. |
| |
| CrossValidation<DecisionTreeNode, Integer, Vector> scoreCalculator |
| = new CrossValidation<>(); |
| |
| ParamGrid paramGrid = new ParamGrid() |
| .addHyperParam("maxDeep", trainer::withMaxDeep, new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 10.0}) |
| .addHyperParam("minImpurityDecrease", trainer::withMinImpurityDecrease, new Double[] {0.0, 0.25, 0.5}); |
| |
| scoreCalculator |
| .withIgnite(ignite) |
| .withUpstreamCache(dataCache) |
| .withPipeline(pipeline) |
| .withMetric(MetricName.ACCURACY) |
| .withFilter(split.getTrainFilter()) |
| .withPreprocessor(vectorizer) |
| .withAmountOfFolds(3) |
| .withParamGrid(paramGrid); |
| |
| CrossValidationResult crossValidationRes = scoreCalculator.tuneHyperParameters(); |
| |
| System.out.println("Train with maxDeep: " + crossValidationRes.getBest("maxDeep") |
| + " and minImpurityDecrease: " + 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)); |
| |
| } |
| catch (FileNotFoundException e) { |
| e.printStackTrace(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |