zhipeng93 commented on a change in pull request #73: URL: https://github.com/apache/flink-ml/pull/73#discussion_r840212185
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/standardscaler/StandardScaler.java ########## @@ -0,0 +1,288 @@ +/* + * 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) { + output.collect(new StreamRecord<>(Tuple3.of(sum, squaredSum, numElements))); + } + } + + @Override + public void processElement(StreamRecord<Row> element) throws Exception { + Vector feature = (Vector) element.getValue().getField(featuresCol); + if (sum == null) { + sum = new DenseVector(feature.size()); + squaredSum = new DenseVector(feature.size()); + } + BLAS.axpy(1, feature, sum); + BLAS.hDot(feature, feature); + BLAS.axpy(1, feature, squaredSum); + numElements++; + } + + @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) { Review comment: Good point. I agree with the behavior that when the train data is empty, we should output a `special model data` to indicate that we cannot do inference. I have updated the code to handle this case and also added one test case. (See for StandardScalerTest#testFitOnEmptyData). For other checks about when numElements is zero or not, we probably need to keep it. For example, when the training data is not empty, there could be one worker that contains zero data points. If we remove the check, it throws an nullpointer exception. -- 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. To unsubscribe, e-mail: issues-unsubscr...@flink.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
