[GitHub] [ignite] mrk-andreev commented on a change in pull request #8466: IGNITE-13713 [ML]: Add target encoding preprocessor

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mrk-andreev commented on a change in pull request #8466:
URL: https://github.com/apache/ignite/pull/8466#discussion_r535485330



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File path: examples/src/main/java/org/apache/ignite/examples/ml/preprocessing/encoding/TargetEncoderExample.java
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@@ -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.




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