mrk-andreev commented on a change in pull request #8466: URL: https://github.com/apache/ignite/pull/8466#discussion_r535485330
########## File path: examples/src/main/java/org/apache/ignite/examples/ml/preprocessing/encoding/TargetEncoderExample.java ########## @@ -0,0 +1,138 @@ +/* + * 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 java.util.Arrays; +import java.util.HashSet; +import java.util.Set; +import org.apache.ignite.Ignite; +import org.apache.ignite.IgniteCache; +import org.apache.ignite.Ignition; +import org.apache.ignite.examples.ml.util.MLSandboxDatasets; +import org.apache.ignite.examples.ml.util.SandboxMLCache; +import org.apache.ignite.ml.composition.ModelsComposition; +import org.apache.ignite.ml.composition.boosting.GDBTrainer; +import org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceCheckerFactory; +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.boosting.GDBBinaryClassifierOnTreesTrainer; + +/** + * Example that shows how to use Target Encoder preprocessor to encode labels presented as a mean target value. + * <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 category with avarage + * target value (categories). </p> + * <p> + * Then, it trains the model based on the processed data using gradient boosing decision tree classification.</p> + * <p> + * Finally, this example uses {@link Evaluator} functionality to compute metrics from predictions.</p> + * + * <p>Daniele Miccii-Barreca (2001). A Preprocessing Scheme for High-Cardinality Categorical + * Attributes in Classification and Prediction Problems. SIGKDD Explor. Newsl. 3, 1. + * From http://dx.doi.org/10.1145/507533.507538</p> + */ +public class TargetEncoderExample { + /** + * Run example. + */ + public static void main(String[] args) { + System.out.println(); + System.out.println(">>> Train Gradient Boosing Decision Tree model on amazon-employee-access-challenge_train.csv dataset."); + + try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { + try { + IgniteCache<Integer, Object[]> dataCache = new SandboxMLCache(ignite) + .fillObjectCacheWithCategoricalData(MLSandboxDatasets.AMAZON_EMPLOYEE_ACCESS); + + Set<Integer> featuresIndexies = new HashSet<>(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9)); + Set<Integer> targetEncodedfeaturesIndexies = new HashSet<>(Arrays.asList(1, 5, 6)); + Integer targetIndex = 0; + + final Vectorizer<Integer, Object[], Integer, Object> vectorizer = new ObjectArrayVectorizer<Integer>(featuresIndexies.toArray(new Integer[0])) + .labeled(targetIndex); + + Preprocessor<Integer, Object[]> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>() + .withEncoderType(EncoderType.STRING_ENCODER) + .withEncodedFeature(0) + .withEncodedFeatures(featuresIndexies) + .fit(ignite, + dataCache, + vectorizer + ); + + Preprocessor<Integer, Object[]> targetEncoderProcessor = new EncoderTrainer<Integer, Object[]>() + .withEncoderType(EncoderType.TARGET_ENCODER) + .labeled(0) + .withEncodedFeatures(targetEncodedfeaturesIndexies) + .minSamplesLeaf(1) + .minCategorySize(1L) + .smoothing(1d) + .fit(ignite, + dataCache, + strEncoderPreprocessor + ); + + Preprocessor<Integer, Object[]> lbEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>() Review comment: I think that we want use `EncoderType.TARGET_ENCODER` only for a few columns (may be only one). In this example I use `EncoderType.STRING_ENCODER` as general propose encoder and `EncoderType.TARGET_ENCODER` for special one. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
