weibozhao commented on a change in pull request #24: URL: https://github.com/apache/flink-ml/pull/24#discussion_r766586090
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/knn/Knn.java ########## @@ -0,0 +1,182 @@ +/* + * 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.classification.knn; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.functions.RichMapPartitionFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.common.datastream.DataStreamUtils; +import org.apache.flink.ml.linalg.DenseMatrix; +import org.apache.flink.ml.linalg.DenseVector; +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.streaming.api.environment.StreamExecutionEnvironment; +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.Collector; +import org.apache.flink.util.Preconditions; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +/** + * An Estimator which implements the KNN algorithm. + * + * <p>See: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm. + */ +public class Knn implements Estimator<Knn, KnnModel>, KnnParams<Knn> { + + protected Map<Param<?>, Object> params = new HashMap<>(); + + public Knn() { + ParamUtils.initializeMapWithDefaultValues(params, this); + } + + @Override + public KnnModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + /* Tuple2 : <sampleVector, label> */ + DataStream<Tuple2<DenseVector, Double>> inputData = + tEnv.toDataStream(inputs[0]) + .map( + new MapFunction<Row, Tuple2<DenseVector, Double>>() { + @Override + public Tuple2<DenseVector, Double> map(Row value) { + Double label = (Double) value.getField(getLabelCol()); + DenseVector feature = + (DenseVector) value.getField(getFeaturesCol()); + return Tuple2.of(feature, label); + } + }); + DataStream<KnnModelData> modelData = prepareModelData(inputData); + KnnModel model = new KnnModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return this.params; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static Knn load(StreamExecutionEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + /** + * Prepares knn model data. Constructs the sample matrix and computes norm of vectors. + * + * @param inputData Input vector data with label. + * @return Knn model. + */ + private static DataStream<KnnModelData> prepareModelData( + DataStream<Tuple2<DenseVector, Double>> inputData) { + return DataStreamUtils.mapPartition( + inputData, + new RichMapPartitionFunction<Tuple2<DenseVector, Double>, KnnModelData>() { + @Override + public void mapPartition( + Iterable<Tuple2<DenseVector, Double>> values, + Collector<KnnModelData> out) { + Tuple3<DenseMatrix, DenseVector, DenseVector> model = prepareData(values); + if (model != null) { + out.collect(new KnnModelData(model)); + } + } + }, + TypeInformation.of(KnnModelData.class)); + } + + /** + * Prepares knn model data, the output is a Tuple3, which includes matrix, vector norms and + * labels. + * + * @param trainData Input train data. + * @return Model data in format of tuple3. + */ + private static Tuple3<DenseMatrix, DenseVector, DenseVector> prepareData( + Iterable<Tuple2<DenseVector, Double>> trainData) { + List<Tuple2<DenseVector, Double>> buffer = new ArrayList<>(0); + int vecSize = -1; + for (Tuple2<DenseVector, Double> tuple2 : trainData) { + if (vecSize == -1) { + vecSize = tuple2.f0.size(); + } + buffer.add(tuple2); + } + if (vecSize == -1) { + return null; + } + DenseMatrix matrix = new DenseMatrix(vecSize, buffer.size()); + DenseVector label = new DenseVector(buffer.size()); + for (int i = 0; i < buffer.size(); ++i) { + Tuple2<DenseVector, Double> tuple2 = buffer.get(i); + label.values[i] = tuple2.f1; + double[] vectorData = tuple2.f0.toArray(); + double[] matrixData = matrix.values; + System.arraycopy(vectorData, 0, matrixData, i * vecSize, vecSize); + } + DenseVector norm = computeNorm(matrix); Review comment: OK -- This is an automated message from the Apache Git Service. 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