[GitHub] [flink-ml] yunfengzhou-hub commented on a change in pull request #70: [FLINK-26313] Add Transformer and Estimator of OnlineKMeans

[GitBox <gi...@apache.org>]

yunfengzhou-hub commented on a change in pull request #70:
URL: https://github.com/apache/flink-ml/pull/70#discussion_r831986897



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File path: flink-ml-lib/src/main/java/org/apache/flink/ml/clustering/kmeans/StreamingKMeans.java
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@@ -0,0 +1,404 @@
+/*
+ * 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.clustering.kmeans;
+
+import org.apache.flink.api.common.functions.AggregateFunction;
+import org.apache.flink.api.common.functions.MapFunction;
+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.java.tuple.Tuple2;
+import org.apache.flink.api.java.typeutils.ObjectArrayTypeInfo;
+import org.apache.flink.api.java.typeutils.TupleTypeInfo;
+import org.apache.flink.iteration.DataStreamList;
+import org.apache.flink.iteration.IterationBody;
+import org.apache.flink.iteration.IterationBodyResult;
+import org.apache.flink.iteration.Iterations;
+import org.apache.flink.ml.api.Estimator;
+import org.apache.flink.ml.common.distance.DistanceMeasure;
+import org.apache.flink.ml.common.param.HasBatchStrategy;
+import org.apache.flink.ml.linalg.BLAS;
+import org.apache.flink.ml.linalg.DenseVector;
+import org.apache.flink.ml.linalg.Vectors;
+import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo;
+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.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.TwoInputStreamOperator;
+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.bridge.java.internal.StreamTableEnvironmentImpl;
+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 org.apache.commons.lang3.ArrayUtils;
+
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+
+/**
+ * StreamingKMeans extends the function of {@link KMeans}, supporting to train a K-Means model
+ * continuously according to an unbounded stream of train data.
+ */
+public class StreamingKMeans
+        implements Estimator<StreamingKMeans, StreamingKMeansModel>,
+                StreamingKMeansParams<StreamingKMeans> {
+    private final Map<Param<?>, Object> paramMap = new HashMap<>();
+    private Table initModelDataTable;
+
+    public StreamingKMeans() {
+        ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+    }
+
+    public StreamingKMeans(Table... initModelDataTables) {
+        Preconditions.checkArgument(initModelDataTables.length == 1);
+        this.initModelDataTable = initModelDataTables[0];
+        ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+        setInitMode("direct");
+    }
+
+    @Override
+    public StreamingKMeansModel fit(Table... inputs) {
+        Preconditions.checkArgument(inputs.length == 1);
+        Preconditions.checkArgument(HasBatchStrategy.COUNT_STRATEGY.equals(getBatchStrategy()));
+
+        StreamTableEnvironment tEnv =
+                (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
+        StreamExecutionEnvironment env = ((StreamTableEnvironmentImpl) tEnv).execEnv();
+
+        DataStream<DenseVector> points =
+                tEnv.toDataStream(inputs[0]).map(new FeaturesExtractor(getFeaturesCol()));
+        points.getTransformation().setParallelism(1);

Review comment:
       According to offline discussion, I'll add a single-threaded operator for mini batch distribution, and have the rest training process still working in parallel.




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