zhipeng93 commented on a change in pull request #32: URL: https://github.com/apache/flink-ml/pull/32#discussion_r748081703
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/naivebayes/NaiveBayes.java ########## @@ -0,0 +1,333 @@ +/* + * 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.naivebayes; + +import org.apache.flink.api.common.functions.AggregateFunction; +import org.apache.flink.api.common.functions.FlatMapFunction; +import org.apache.flink.api.common.functions.ReduceFunction; +import org.apache.flink.api.java.functions.KeySelector; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.api.java.tuple.Tuple4; +import org.apache.flink.ml.api.core.Estimator; +import org.apache.flink.ml.common.datastream.EndOfStreamWindows; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.functions.windowing.AllWindowFunction; +import org.apache.flink.streaming.api.windowing.windows.TimeWindow; +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.HashSet; +import java.util.Map; + +/** + * Naive Bayes classifier is a simple probability classification algorithm using + * Bayes theorem based on independent assumption. It is an independent feature model. + * The input feature can be continual or categorical. + */ +public class NaiveBayes implements Estimator<NaiveBayes, NaiveBayesModel>, + NaiveBayesParams<NaiveBayes> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + @Override + public NaiveBayesModel fit(Table... inputs) { + String[] featureColNames = getFeatureCols(); + String labelColName = getLabelCol(); + String predictionCol = getPredictionCol(); + double smoothing = getSmoothing(); + + Preconditions.checkNotNull(inputs, "input table list should not be null"); + Preconditions.checkArgument(inputs.length == 1, "input table list should contain only one argument"); + Preconditions.checkArgument( + new HashSet<>(Arrays.asList(featureColNames)).size() == featureColNames.length, + "feature columns should not duplicate"); + Preconditions.checkNotNull(labelColName, "label column should be set"); + + StreamTableEnvironment tEnv = (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<Row> input = tEnv.toDataStream(inputs[0]); + + DataStream<NaiveBayesModelData> naiveBayesModel = input + .flatMap(new FlattenFunction( + featureColNames, + labelColName + )) + .keyBy((KeySelector<Tuple4<Object, Integer, Object, Double>, Object>) value -> new Tuple3<>(value.f0, value.f1, value.f2)) + .window(EndOfStreamWindows.get()) + .reduce((ReduceFunction<Tuple4<Object, Integer, Object, Double>>) (t0, t1) -> {t0.f3 += t1.f3; return t0; }) + .keyBy((KeySelector<Tuple4<Object, Integer, Object, Double>, Object>) value -> new Tuple2<>(value.f0, value.f1)) + .window(EndOfStreamWindows.get()) + .aggregate(new ValueMapFunction()) + .keyBy((KeySelector<Tuple4<Object, Integer, Map<Object, Double>, Double>, Object>) value -> value.f0) + .window(EndOfStreamWindows.get()) + .aggregate(new MapArrayFunction(featureColNames.length)) + .windowAll(EndOfStreamWindows.get()) + .apply(new GenerateModelFunction( + smoothing, + featureColNames)); + + NaiveBayesModel model = new NaiveBayesModel() + .setPredictionCol(predictionCol) + .setFeatureCols(featureColNames); + model.setModelData( + tEnv.fromDataStream(naiveBayesModel) + ); + return model; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static NaiveBayes load(String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + /** + * Function to convert each column into tuples of label, feature column index, feature value, weight. + */ + private static class FlattenFunction implements FlatMapFunction<Row, Tuple4<Object, Integer, Object, Double>> { + private final String[] featureColNames; + private final String labelColName; + private final int featureSize; + + private FlattenFunction(String[] featureColNames, String labelColName) { + this.labelColName = labelColName; + this.featureColNames = featureColNames; + this.featureSize = featureColNames.length; + } + + @Override + public void flatMap(Row row, Collector<Tuple4<Object, Integer, Object, Double>> collector) { + Object label = row.getField(labelColName); + if (label == null) { + return; + } + + for (int i = 0; i < featureSize; i++) { + Object feature = row.getField(featureColNames[i]); + if (feature == null) { + continue; + } + collector.collect(new Tuple4<>(label, i, feature, 1.0)); Review comment: should we support weighted naive bayes or just remove 1.0 here? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/naivebayes/NaiveBayes.java ########## @@ -0,0 +1,333 @@ +/* + * 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.naivebayes; + +import org.apache.flink.api.common.functions.AggregateFunction; +import org.apache.flink.api.common.functions.FlatMapFunction; +import org.apache.flink.api.common.functions.ReduceFunction; +import org.apache.flink.api.java.functions.KeySelector; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.api.java.tuple.Tuple4; +import org.apache.flink.ml.api.core.Estimator; +import org.apache.flink.ml.common.datastream.EndOfStreamWindows; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.functions.windowing.AllWindowFunction; +import org.apache.flink.streaming.api.windowing.windows.TimeWindow; +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.HashSet; +import java.util.Map; + +/** + * Naive Bayes classifier is a simple probability classification algorithm using + * Bayes theorem based on independent assumption. It is an independent feature model. + * The input feature can be continual or categorical. + */ +public class NaiveBayes implements Estimator<NaiveBayes, NaiveBayesModel>, + NaiveBayesParams<NaiveBayes> { Review comment: Shall we use different param classes for `NaiveBayes` and the `NaiveBayesModel`? it may be confusing to users when call `new NaiveBayesModel().setSmoothing(...)` since `smoothing` is useless during the prediction. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/datastream/EndOfStreamWindows.java ########## @@ -0,0 +1,82 @@ +/* + * 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.common.datastream; + +import org.apache.flink.annotation.PublicEvolving; +import org.apache.flink.api.common.ExecutionConfig; +import org.apache.flink.api.common.typeutils.TypeSerializer; +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.streaming.api.windowing.assigners.WindowAssigner; +import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; +import org.apache.flink.streaming.api.windowing.triggers.Trigger; +import org.apache.flink.streaming.api.windowing.windows.TimeWindow; + +import java.util.Collection; +import java.util.Collections; + +/** + * A {@link WindowAssigner} that windows elements into windows based on the timestamp of the + * elements. Windows cannot overlap. + * + * <p>For example, in order to window into windows of 1 minute: + * + * <pre>{@code + * DataStream<Tuple2<String, Integer>> in = ...; + * KeyedStream<Tuple2<String, Integer>, String> keyed = in.keyBy(...); + * WindowedStream<Tuple2<String, Integer>, String, TimeWindow> windowed = + * keyed.window(TumblingEventTimeWindows.of(Time.minutes(1))); + * }</pre> + */ +@PublicEvolving +public class EndOfStreamWindows extends WindowAssigner<Object, TimeWindow> { + + private static final EndOfStreamWindows INSTANCE = new EndOfStreamWindows(); + + private EndOfStreamWindows() {} + + public static EndOfStreamWindows get() { + return INSTANCE; + } + + @Override + public Collection<TimeWindow> assignWindows( + Object element, long timestamp, WindowAssignerContext context) { + return Collections.singletonList(new TimeWindow(Long.MIN_VALUE, Long.MAX_VALUE)); Review comment: why do you create a new TimeWindow on each call? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/naivebayes/NaiveBayesModelData.java ########## @@ -0,0 +1,99 @@ +/* + * 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.naivebayes; + +import com.esotericsoftware.kryo.Kryo; +import com.esotericsoftware.kryo.io.Input; +import com.esotericsoftware.kryo.io.Output; +import org.apache.flink.api.common.serialization.Encoder; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.ObjectArrayTypeInfo; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.connector.file.src.reader.SimpleStreamFormat; +import org.apache.flink.core.fs.FSDataInputStream; +import org.apache.flink.table.api.DataTypes; +import org.apache.flink.table.api.Schema; + +import java.io.IOException; +import java.io.OutputStream; +import java.io.Serializable; +import java.util.Map; + +/** + * The model data of {@link NaiveBayesModel}. + */ +public class NaiveBayesModelData implements Serializable { + private static final long serialVersionUID = 3919917903722286395L; + public final String[] featureNames; + public final Map<Object, Double>[][] theta; + public final double[] piArray; + public final Object[] label; + + public static final Schema SCHEMA = + Schema.newBuilder() + .column("f0", DataTypes.of(NaiveBayesModelData.class)) + .build(); + + public NaiveBayesModelData(String[] featureNames, Map<Object, Double>[][] theta, double[] piArray, Object[] label) { + this.featureNames = featureNames; + this.theta = theta; + this.piArray = piArray; + this.label = label; + } + + /** Encoder for the KMeans model data. */ Review comment: update the comment here. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/naivebayes/NaiveBayesModelData.java ########## @@ -0,0 +1,99 @@ +/* + * 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.naivebayes; + +import com.esotericsoftware.kryo.Kryo; +import com.esotericsoftware.kryo.io.Input; +import com.esotericsoftware.kryo.io.Output; +import org.apache.flink.api.common.serialization.Encoder; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.ObjectArrayTypeInfo; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.connector.file.src.reader.SimpleStreamFormat; +import org.apache.flink.core.fs.FSDataInputStream; +import org.apache.flink.table.api.DataTypes; +import org.apache.flink.table.api.Schema; + +import java.io.IOException; +import java.io.OutputStream; +import java.io.Serializable; +import java.util.Map; + +/** + * The model data of {@link NaiveBayesModel}. + */ +public class NaiveBayesModelData implements Serializable { + private static final long serialVersionUID = 3919917903722286395L; + public final String[] featureNames; + public final Map<Object, Double>[][] theta; + public final double[] piArray; + public final Object[] label; + + public static final Schema SCHEMA = + Schema.newBuilder() + .column("f0", DataTypes.of(NaiveBayesModelData.class)) + .build(); + + public NaiveBayesModelData(String[] featureNames, Map<Object, Double>[][] theta, double[] piArray, Object[] label) { + this.featureNames = featureNames; + this.theta = theta; + this.piArray = piArray; + this.label = label; + } + + /** Encoder for the KMeans model data. */ + public static class ModelDataEncoder implements Encoder<NaiveBayesModelData> { + @Override + public void encode(NaiveBayesModelData modelData, OutputStream outputStream) { + Kryo kryo = new Kryo(); + Output output = new Output(outputStream); + kryo.writeObject(output, modelData); + output.flush(); + } + } + + /** Decoder for the KMeans model data. */ Review comment: update the comment here. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/naivebayes/NaiveBayes.java ########## @@ -0,0 +1,333 @@ +/* + * 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.naivebayes; + +import org.apache.flink.api.common.functions.AggregateFunction; +import org.apache.flink.api.common.functions.FlatMapFunction; +import org.apache.flink.api.common.functions.ReduceFunction; +import org.apache.flink.api.java.functions.KeySelector; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.api.java.tuple.Tuple4; +import org.apache.flink.ml.api.core.Estimator; +import org.apache.flink.ml.common.datastream.EndOfStreamWindows; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.functions.windowing.AllWindowFunction; +import org.apache.flink.streaming.api.windowing.windows.TimeWindow; +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.HashSet; +import java.util.Map; + +/** + * Naive Bayes classifier is a simple probability classification algorithm using + * Bayes theorem based on independent assumption. It is an independent feature model. + * The input feature can be continual or categorical. + */ +public class NaiveBayes implements Estimator<NaiveBayes, NaiveBayesModel>, + NaiveBayesParams<NaiveBayes> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + @Override + public NaiveBayesModel fit(Table... inputs) { + String[] featureColNames = getFeatureCols(); + String labelColName = getLabelCol(); + String predictionCol = getPredictionCol(); + double smoothing = getSmoothing(); + + Preconditions.checkNotNull(inputs, "input table list should not be null"); + Preconditions.checkArgument(inputs.length == 1, "input table list should contain only one argument"); + Preconditions.checkArgument( + new HashSet<>(Arrays.asList(featureColNames)).size() == featureColNames.length, + "feature columns should not duplicate"); + Preconditions.checkNotNull(labelColName, "label column should be set"); + + StreamTableEnvironment tEnv = (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<Row> input = tEnv.toDataStream(inputs[0]); + + DataStream<NaiveBayesModelData> naiveBayesModel = input + .flatMap(new FlattenFunction( + featureColNames, + labelColName + )) + .keyBy((KeySelector<Tuple4<Object, Integer, Object, Double>, Object>) value -> new Tuple3<>(value.f0, value.f1, value.f2)) + .window(EndOfStreamWindows.get()) + .reduce((ReduceFunction<Tuple4<Object, Integer, Object, Double>>) (t0, t1) -> {t0.f3 += t1.f3; return t0; }) + .keyBy((KeySelector<Tuple4<Object, Integer, Object, Double>, Object>) value -> new Tuple2<>(value.f0, value.f1)) + .window(EndOfStreamWindows.get()) + .aggregate(new ValueMapFunction()) + .keyBy((KeySelector<Tuple4<Object, Integer, Map<Object, Double>, Double>, Object>) value -> value.f0) + .window(EndOfStreamWindows.get()) + .aggregate(new MapArrayFunction(featureColNames.length)) + .windowAll(EndOfStreamWindows.get()) Review comment: When we use `windowAll`, we are actually changing paralllelism to one and using one task to process all the elements. 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