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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/04/02 00:25:34 UTC
[GitHub] [flink-ml] lindong28 commented on a change in pull request #73: [FLINK-26626] Add Transformer and Estimator for StandardScaler
lindong28 commented on a change in pull request #73:
URL: https://github.com/apache/flink-ml/pull/73#discussion_r840980426
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File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/standardscaler/StandardScaler.java
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+/*
+ * 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.standardscaler;
+
+import org.apache.flink.api.common.state.ListState;
+import org.apache.flink.api.common.state.ListStateDescriptor;
+import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.api.java.typeutils.TupleTypeInfo;
+import org.apache.flink.iteration.operator.OperatorStateUtils;
+import org.apache.flink.ml.api.Estimator;
+import org.apache.flink.ml.linalg.BLAS;
+import org.apache.flink.ml.linalg.DenseVector;
+import org.apache.flink.ml.linalg.Vector;
+import org.apache.flink.ml.linalg.Vectors;
+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.runtime.state.StateInitializationContext;
+import org.apache.flink.runtime.state.StateSnapshotContext;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+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 java.io.IOException;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * An Estimator which implements the standard scaling algorithm.
+ *
+ * <p>Standardization is a common requirement for machine learning training because they may behave
+ * badly if the individual features of a input do not look like standard normally distributed data
+ * (e.g. Gaussian with 0 mean and unit variance).
+ *
+ * <p>This estimator standardizes the input features by removing the mean and scaling each dimension
+ * to unit variance.
+ */
+public class StandardScaler
+ implements Estimator<StandardScaler, StandardScalerModel>,
+ StandardScalerParams<StandardScaler> {
+ private final Map<Param<?>, Object> paramMap = new HashMap<>();
+
+ public StandardScaler() {
+ ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+ }
+
+ @Override
+ public StandardScalerModel fit(Table... inputs) {
+ Preconditions.checkArgument(inputs.length == 1);
+ StreamTableEnvironment tEnv =
+ (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
+ DataStream<Tuple3<DenseVector, DenseVector, Long>> sumAndSquaredSumAndWeight =
+ tEnv.toDataStream(inputs[0])
+ .transform(
+ "computeMeta",
+ new TupleTypeInfo<>(
+ TypeInformation.of(DenseVector.class),
+ TypeInformation.of(DenseVector.class),
+ BasicTypeInfo.LONG_TYPE_INFO),
+ new ComputeMetaOperator(getFeaturesCol()));
+
+ DataStream<StandardScalerModelData> modelData =
+ sumAndSquaredSumAndWeight
+ .transform(
+ "buildModel",
+ TypeInformation.of(StandardScalerModelData.class),
+ new BuildModelOperator())
+ .setParallelism(1);
+
+ StandardScalerModel model =
+ new StandardScalerModel().setModelData(tEnv.fromDataStream(modelData));
+ ReadWriteUtils.updateExistingParams(model, paramMap);
+ return model;
+ }
+
+ /**
+ * Builds the {@link StandardScalerModelData} using the meta data computed on each partition.
+ */
+ private static class BuildModelOperator extends AbstractStreamOperator<StandardScalerModelData>
+ implements OneInputStreamOperator<
+ Tuple3<DenseVector, DenseVector, Long>, StandardScalerModelData>,
+ BoundedOneInput {
+ private ListState<DenseVector> sumState;
+ private ListState<DenseVector> squaredSumState;
+ private ListState<Long> numElementsState;
+ private DenseVector sum;
+ private DenseVector squaredSum;
+ private long numElements;
+
+ @Override
+ public void endInput() {
+ if (numElements > 0) {
+ BLAS.scal(1.0 / numElements, sum);
+ double[] mean = sum.values;
+ double[] std = squaredSum.values;
+ if (numElements > 1) {
+ for (int i = 0; i < mean.length; i++) {
+ std[i] =
+ Math.sqrt(
+ (squaredSum.values[i] - numElements * mean[i] * mean[i])
+ / (numElements - 1));
+ }
+ } else {
+ Arrays.fill(std, 0.0);
+ }
+
+ output.collect(
+ new StreamRecord<>(
+ new StandardScalerModelData(
+ Vectors.dense(mean), Vectors.dense(std))));
+ }
+ }
+
+ @Override
+ public void processElement(StreamRecord<Tuple3<DenseVector, DenseVector, Long>> element) {
+ Tuple3<DenseVector, DenseVector, Long> value = element.getValue();
+ if (sum == null) {
+ sum = value.f0;
+ squaredSum = value.f1;
+ numElements = value.f2;
+ } else {
+ BLAS.axpy(1, value.f0, sum);
+ BLAS.axpy(1, value.f1, squaredSum);
+ numElements += value.f2;
+ }
+ }
+
+ @Override
+ public void initializeState(StateInitializationContext context) throws Exception {
+ super.initializeState(context);
+ sumState =
+ context.getOperatorStateStore()
+ .getListState(
+ new ListStateDescriptor<>(
+ "sumState", TypeInformation.of(DenseVector.class)));
+ squaredSumState =
+ context.getOperatorStateStore()
+ .getListState(
+ new ListStateDescriptor<>(
+ "squaredSumState",
+ TypeInformation.of(DenseVector.class)));
+ numElementsState =
+ context.getOperatorStateStore()
+ .getListState(
+ new ListStateDescriptor<>(
+ "numElementsState", BasicTypeInfo.LONG_TYPE_INFO));
+
+ sum = OperatorStateUtils.getUniqueElement(sumState, "sumState").orElse(null);
+ squaredSum =
+ OperatorStateUtils.getUniqueElement(squaredSumState, "squaredSumState")
+ .orElse(null);
+ numElements =
+ OperatorStateUtils.getUniqueElement(numElementsState, "numElementsState")
+ .orElse(0L);
+ }
+
+ @Override
+ public void snapshotState(StateSnapshotContext context) throws Exception {
+ super.snapshotState(context);
+ if (numElements > 0) {
+ sumState.update(Collections.singletonList(sum));
+ squaredSumState.update(Collections.singletonList(squaredSum));
+ numElementsState.update(Collections.singletonList(numElements));
+ }
+ }
+ }
+
+ /** Computes sum, squared sum and number of elements in each partition. */
+ private static class ComputeMetaOperator
+ extends AbstractStreamOperator<Tuple3<DenseVector, DenseVector, Long>>
+ implements OneInputStreamOperator<Row, Tuple3<DenseVector, DenseVector, Long>>,
+ BoundedOneInput {
+ private ListState<DenseVector> sumState;
+ private ListState<DenseVector> squaredSumState;
+ private ListState<Long> numElementsState;
+ private DenseVector sum;
+ private DenseVector squaredSum;
+ private long numElements;
+
+ private final String featuresCol;
+
+ public ComputeMetaOperator(String featuresCol) {
+ this.featuresCol = featuresCol;
+ }
+
+ @Override
+ public void endInput() {
+ if (numElements > 0) {
Review comment:
Sounds good.
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