[GitHub] [flink-ml] weibozhao commented on a diff in pull request #133: [FLINK-28601] Add Transformer for FeatureHasher

[GitBox <gi...@apache.org>]

weibozhao commented on code in PR #133:
URL: https://github.com/apache/flink-ml/pull/133#discussion_r930783748


##########
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/featurehasher/FeatureHasher.java:
##########
@@ -0,0 +1,143 @@
+/*
+ * 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.flink.ml.feature.featurehasher;
+
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.java.typeutils.RowTypeInfo;
+import org.apache.flink.ml.api.Transformer;
+import org.apache.flink.ml.common.datastream.TableUtils;
+import org.apache.flink.ml.linalg.SparseVector;
+import org.apache.flink.ml.linalg.typeinfo.VectorTypeInfo;
+import org.apache.flink.ml.param.Param;
+import org.apache.flink.ml.util.ParamUtils;
+import org.apache.flink.ml.util.ReadWriteUtils;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.table.api.Table;
+import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
+import org.apache.flink.table.api.internal.TableImpl;
+import org.apache.flink.types.Row;
+import org.apache.flink.util.Preconditions;
+
+import org.apache.flink.shaded.guava30.com.google.common.hash.HashFunction;
+
+import org.apache.commons.lang3.ArrayUtils;
+
+import java.io.IOException;
+import java.util.HashMap;
+import java.util.Map;
+import java.util.TreeMap;
+
+import static org.apache.flink.shaded.guava30.com.google.common.hash.Hashing.murmur3_32;
+
+/**
+ * FeatureHasher projects a set of categorical or numerical features into a vector with fixed
+ * dimension. This is done using the hashing trick (https://en.wikipedia.org/wiki/Feature_hashing)
+ * to map features to indices in the feature vector.
+ */
+public class FeatureHasher
+        implements Transformer<FeatureHasher>, FeatureHasherParams<FeatureHasher> {
+    private final Map<Param<?>, Object> paramMap = new HashMap<>();
+    private static final HashFunction HASH = murmur3_32(0);
+
+    public FeatureHasher() {
+        ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+    }
+
+    @Override
+    public Table[] transform(Table... inputs) {
+        Preconditions.checkArgument(inputs.length == 1);
+        StreamTableEnvironment tEnv =
+                (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
+        RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema());
+        RowTypeInfo outputTypeInfo =
+                new RowTypeInfo(
+                        ArrayUtils.addAll(inputTypeInfo.getFieldTypes(), VectorTypeInfo.INSTANCE),
+                        ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol()));
+        DataStream<Row> output =
+                tEnv.toDataStream(inputs[0])
+                        .map(
+                                new HashFunc(
+                                        getInputCols(), getCategoricalCols(), getNumFeatures()),
+                                outputTypeInfo);
+        Table outputTable = tEnv.fromDataStream(output);
+        return new Table[] {outputTable};
+    }
+
+    private static class HashFunc implements MapFunction<Row, Row> {
+        private final String[] categoricalCols;
+        private final int numFeatures;
+        private final String[] numericCols;
+
+        public HashFunc(String[] inputCols, String[] handleInvalid, int numFeatures) {
+            this.categoricalCols = handleInvalid;
+            this.numFeatures = numFeatures;
+            this.numericCols = ArrayUtils.removeElements(inputCols, categoricalCols);
+        }
+
+        @Override
+        public Row map(Row row) {
+            TreeMap<Integer, Double> feature = new TreeMap<>();
+            for (String col : numericCols) {
+                if (null != row.getField(col)) {
+                    double value = ((Number) row.getFieldAs(col)).doubleValue();
+                    updateMap(col, value, feature, numFeatures);
+                }
+            }
+            for (String col : categoricalCols) {
+                if (null != row.getField(col)) {
+                    updateMap(col + "=" + row.getField(col), 1.0, feature, numFeatures);
+                }
+            }
+            return Row.join(row, Row.of(new SparseVector(numFeatures, feature)));

Review Comment:
   I agree with you @zhipeng93, I will just add the transformation code in my FeatureHasher algorithm, if some other algorithm use this transform, then make it a common function.



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