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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/12/09 05:30:36 UTC
[GitHub] [flink-ml] lindong28 commented on a diff in pull request #187: [FLINK-29601] Add Estimator and Transformer for UnivariateFeatureSelector
lindong28 commented on code in PR #187:
URL: https://github.com/apache/flink-ml/pull/187#discussion_r1044092761
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/univariatefeatureselector/UnivariateFeatureSelector.java:
##########
@@ -0,0 +1,305 @@
+/*
+ * 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.univariatefeatureselector;
+
+import org.apache.flink.api.common.state.ListState;
+import org.apache.flink.api.common.state.ListStateDescriptor;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.ml.api.Estimator;
+import org.apache.flink.ml.param.Param;
+import org.apache.flink.ml.stats.anovatest.ANOVATest;
+import org.apache.flink.ml.stats.chisqtest.ChiSqTest;
+import org.apache.flink.ml.stats.fvaluetest.FValueTest;
+import org.apache.flink.ml.util.ParamUtils;
+import org.apache.flink.ml.util.ReadWriteUtils;
+import org.apache.flink.runtime.state.StateInitializationContext;
+import org.apache.flink.runtime.state.StateSnapshotContext;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.operators.AbstractStreamOperator;
+import org.apache.flink.streaming.api.operators.BoundedOneInput;
+import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+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.commons.collections.IteratorUtils;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.IntStream;
+
+/**
+ * An Estimator which selects features based on univariate statistical tests against labels.
+ *
+ * <p>Currently, Flink supports three Univariate Feature Selectors: chi-squared, ANOVA F-test and
+ * F-value. User can choose Univariate Feature Selector by setting `featureType` and `labelType`,
+ * and Flink will pick the score function based on the specified `featureType` and `labelType`.
+ *
+ * <p>The following combination of `featureType` and `labelType` are supported:
+ *
+ * <ul>
+ * <li>`featureType` `categorical` and `labelType` `categorical`: Flink uses chi-squared, i.e.
+ * chi2 in sklearn.
+ * <li>`featureType` `continuous` and `labelType` `categorical`: Flink uses ANOVA F-test,
+ * f_classif in sklearn.
Review Comment:
nits: add `i.e.` for consistency.
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/feature/univariatefeatureselector/UnivariateFeatureSelectorModel.java:
##########
@@ -0,0 +1,201 @@
+/*
+ * 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.univariatefeatureselector;
+
+import org.apache.flink.api.common.functions.RichMapFunction;
+import org.apache.flink.api.java.typeutils.RowTypeInfo;
+import org.apache.flink.ml.api.Model;
+import org.apache.flink.ml.common.broadcast.BroadcastUtils;
+import org.apache.flink.ml.common.datastream.TableUtils;
+import org.apache.flink.ml.linalg.DenseVector;
+import org.apache.flink.ml.linalg.SparseVector;
+import org.apache.flink.ml.linalg.Vector;
+import org.apache.flink.ml.linalg.Vectors;
+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.commons.lang3.ArrayUtils;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * A Model which transforms data using the model data computed by {@link UnivariateFeatureSelector}.
+ */
+public class UnivariateFeatureSelectorModel
+ implements Model<UnivariateFeatureSelectorModel>,
+ UnivariateFeatureSelectorModelParams<UnivariateFeatureSelectorModel> {
+
+ private final Map<Param<?>, Object> paramMap = new HashMap<>();
+ private Table modelDataTable;
+
+ public UnivariateFeatureSelectorModel() {
+ ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+ }
+
+ @Override
+ public UnivariateFeatureSelectorModel setModelData(Table... inputs) {
+ Preconditions.checkArgument(inputs.length == 1);
+ modelDataTable = inputs[0];
+ return this;
+ }
+
+ @Override
+ public Table[] getModelData() {
+ return new Table[] {modelDataTable};
+ }
+
+ @SuppressWarnings({"unchecked", "rawtypes"})
+ @Override
+ public Table[] transform(Table... inputs) {
+ Preconditions.checkArgument(inputs.length == 1);
+
+ StreamTableEnvironment tEnv =
+ (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
+ DataStream<Row> data = tEnv.toDataStream(inputs[0]);
+ DataStream<UnivariateFeatureSelectorModelData> modelData =
+ UnivariateFeatureSelectorModelData.getModelDataStream(modelDataTable);
+
+ final String broadcastModelKey = "broadcastModelKey";
+ RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema());
+ RowTypeInfo outputTypeInfo =
+ new RowTypeInfo(
+ ArrayUtils.addAll(inputTypeInfo.getFieldTypes(), VectorTypeInfo.INSTANCE),
+ ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol()));
+
+ DataStream<Row> outputStream =
+ BroadcastUtils.withBroadcastStream(
+ Collections.singletonList(data),
+ Collections.singletonMap(broadcastModelKey, modelData),
+ inputList -> {
+ DataStream input = inputList.get(0);
+ return input.map(
+ new PredictOutputFunction(getFeaturesCol(), broadcastModelKey),
+ outputTypeInfo);
+ });
+
+ return new Table[] {tEnv.fromDataStream(outputStream)};
+ }
+
+ /** This operator loads model data and predicts result. */
+ private static class PredictOutputFunction extends RichMapFunction<Row, Row> {
+
+ private final String inputCol;
+ private final String broadcastKey;
+ private int[] indices;
+
+ public PredictOutputFunction(String inputCol, String broadcastKey) {
+ this.inputCol = inputCol;
+ this.broadcastKey = broadcastKey;
+ }
+
+ @Override
+ public Row map(Row row) {
+ if (indices == null) {
+ UnivariateFeatureSelectorModelData modelData =
+ (UnivariateFeatureSelectorModelData)
+ getRuntimeContext().getBroadcastVariable(broadcastKey).get(0);
+ indices = Arrays.stream(modelData.indices).sorted().toArray();
+ }
+
+ if (indices.length == 0) {
+ return Row.join(row, Row.of(Vectors.dense()));
+ } else {
+ Vector inputVec = ((Vector) row.getField(inputCol));
+ Preconditions.checkArgument(
+ inputVec.size() > indices[indices.length - 1],
+ "Input %s features, but UnivariateFeatureSelector is "
+ + "expecting at least %s features as input.",
+ inputVec.size(),
+ indices[indices.length - 1] + 1);
+ Vector outputVec = selectByIndices(inputVec, indices);
+ return Row.join(row, Row.of(outputVec));
+ }
+ }
+
+ /**
+ * Selects a subset of the vector base on the indices. Note that the input indices must be
+ * sorted in ascending order if the input vector is sparse.
+ */
+ private Vector selectByIndices(Vector vector, int[] selectedIndices) {
Review Comment:
Since this method looks pretty generic and it is re-used by `VarianceThresholdSelectorModel.java`, how about we move this method to `org.apache.flink.ml.common.util.VectorUtils` and make it static?
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