vacaly commented on code in PR #192: URL: https://github.com/apache/flink-ml/pull/192#discussion_r1053913128
########## flink-ml-lib/src/main/java/org/apache/flink/ml/recommendation/Swing.java: ########## @@ -0,0 +1,407 @@ +/* + * 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.recommendation; + +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.Types; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.clustering.kmeans.KMeans; +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.SingleOutputStreamOperator; +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.table.catalog.ResolvedSchema; +import org.apache.flink.table.runtime.typeutils.InternalTypeInfo; +import org.apache.flink.table.runtime.typeutils.TypeCheckUtils; +import org.apache.flink.table.types.logical.LogicalType; +import org.apache.flink.types.Row; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.Comparator; +import java.util.HashMap; +import java.util.HashSet; +import java.util.List; +import java.util.Map; +import java.util.Map.Entry; + +/** + * An Estimator which implements the Swing algorithm. + * + * <p>Swing is an item recall model. The topology of user-item graph usually can be described as user-item-user or + * item-user-item, which are like 'swing'. For example, if both user <em>u</em> and user <em>v</em> have purchased the + * same commodity <em>i</em> , they will form a relationship diagram similar to a swing. If <em>u</em> and <em>v</em> + * have purchased commodity <em>j</em> in addition to <em>i</em>, it is supposed <em>i</em> and <em>j</em> are + * similar.</p> + */ +public class Swing implements Estimator <Swing, SwingModel>, SwingParams <Swing> { + private final Map <Param <?>, Object> paramMap = new HashMap <>(); + + public Swing() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public SwingModel fit(Table... inputs) { + + final String userCol = getUserCol(); + final String itemCol = getItemCol(); + final int minUserItems = getMinUserItems(); + final int maxUserItems = getMaxUserItems(); + final ResolvedSchema schema = ((TableImpl) inputs[0]).getResolvedSchema(); + final LogicalType userColType = schema.getColumn(userCol).get().getDataType().getLogicalType(); + final LogicalType itemColType = schema.getColumn(itemCol).get().getDataType().getLogicalType(); + + if (!TypeCheckUtils.isCharacterString(InternalTypeInfo.of(userColType).toLogicalType()) || + !TypeCheckUtils.isCharacterString(InternalTypeInfo.of(itemColType).toLogicalType())) { + throw new IllegalArgumentException("Type of user and item column must be string."); + } + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]) + .getTableEnvironment(); + + SingleOutputStreamOperator <Tuple2 <String, String>> itemUsers = + tEnv.toDataStream(inputs[0]) + .map(row -> Tuple2.of((String) row.getFieldAs(userCol), (String) row.getFieldAs(itemCol))) + .returns(Types.TUPLE(Types.STRING, Types.STRING)); + + SingleOutputStreamOperator <Tuple3 <String, String, List <String>>> userAllItemsStream = itemUsers + .keyBy(tuple -> tuple.f0) + .transform("fillUserItemsTable", + Types.TUPLE(Types.STRING, Types.STRING, Types.LIST(Types.STRING)), + new BuildSwingData(minUserItems, maxUserItems)); + + SingleOutputStreamOperator <SwingModelData> similarity = userAllItemsStream + .keyBy(tuple -> tuple.f1) + .transform("calculateSimilarity", + Types.ROW(Types.STRING, Types.LIST(Types.STRING), Types.LIST(Types.FLOAT)), + new CalculateSimilarity(getTopN())) + .map(new MapFunction <Row, SwingModelData>() { + @Override + public SwingModelData map(Row value) throws Exception { + return new SwingModelData(value.getFieldAs(0), value.getFieldAs(1), value.getFieldAs(2)); + } + }); + + SwingModel model = new SwingModel().setModelData(tEnv.fromDataStream(similarity)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + @Override + public Map <Param <?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static Swing load(StreamTableEnvironment tEnv, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + /** + * Append one column that records all items the user has purchased to the input table. + * + * <p>During the process, this operator collect users and all items a user has purchased into a map of list. + * When the input is finished, this operator append the certain user-purchased-items list to each row. </p> + */ + private static class BuildSwingData + extends AbstractStreamOperator <Tuple3 <String, String, List <String>>> + implements OneInputStreamOperator < + Tuple2 <String, String>, + Tuple3 <String, String, List <String>>>, + BoundedOneInput { + final int minUserItems; + final int maxUserItems; + + private Map <String, List <String>> userItemsMap = new HashMap <>(); + + private ListState <Map <String, List <String>>> userItemsMapState; + + private BuildSwingData(int minUserItems, int maxUserItems) { + this.minUserItems = minUserItems; + this.maxUserItems = maxUserItems; + } + + @Override + public void endInput() { + + for (Entry <String, List <String>> entry : + userItemsMap.entrySet()) { + List <String> items = entry.getValue(); + String user = entry.getKey(); + if (items.size() < minUserItems || items.size() > maxUserItems) { + continue; + } + for (String item : items) { + output.collect( + new StreamRecord <>( + new Tuple3 <>( + user, item, items))); + } + } + + userItemsMapState.clear(); + } + + @Override + public void processElement(StreamRecord <Tuple2 <String, String>> element) { + Tuple2 <String, String> userAndItem = + element.getValue(); + String user = userAndItem.f0; + String item = userAndItem.f1; + List <String> items = userItemsMap.get(user); + + if (items == null) { + ArrayList <String> value = new ArrayList <>(); + value.add(item); + userItemsMap.put(user, value); + } else { + if (!items.contains(item)) { + items.add(item); + } + } + } + + @Override + public void initializeState(StateInitializationContext context) throws Exception { + super.initializeState(context); + userItemsMapState = + context.getOperatorStateStore() + .getListState( + new ListStateDescriptor <>( + "userItemsMapState", + Types.MAP( + Types.STRING, + Types.LIST( + Types.STRING))) + ); + + OperatorStateUtils.getUniqueElement(userItemsMapState, "userItemsMapState") + .ifPresent(x -> userItemsMap = x); + + } + + @Override + public void snapshotState(StateSnapshotContext context) throws Exception { + super.snapshotState(context); + userItemsMapState.update(Collections.singletonList(userItemsMap)); + } + } + + /** + * Calculate top N similar items of each item. + */ + private static class CalculateSimilarity + extends AbstractStreamOperator <Row> + implements OneInputStreamOperator < + Tuple3 <String, String, List <String>>, + Row>, + BoundedOneInput { + + private Map <String, HashSet <String>> userItemsMap = new HashMap <>(); + private Map <String, HashSet <String>> itemUsersMap = new HashMap <>(); + private ListState <Map <String, List <String>>> userItemsMapState; + private ListState <Map <String, List <String>>> itemUsersMapState; + + final private double alpha = 1.0; + final private double userAlpha = 5.0; + final private double userBeta = -0.35; + final private int topN; + + private CalculateSimilarity(int topN) {this.topN = topN;} + + @Override + public void endInput() throws Exception { + + Map <String, Float> userWeights = new HashMap <>(userItemsMap.size()); + userItemsMap.forEach((k, v) -> { + int count = v.size(); + userWeights.put(k, calculateWeight(count)); + }); + + for (String mainItem : itemUsersMap.keySet()) { + List <String> userList = new ArrayList(itemUsersMap.get(mainItem)); + HashMap <String, Float> id2swing = new HashMap <>(); + + for (int i = 0; i < userList.size(); i++) { + String u = userList.get(i); + for (int j = i + 1; j < userList.size(); j++) { + String v = userList.get(j); + HashSet <String> interaction = (HashSet <String>) userItemsMap.get(u).clone(); + interaction.retainAll(userItemsMap.get(v)); + if (interaction.size() == 0) { + continue; + } + float similarity = + (float) (userWeights.get(u) * userWeights.get(v) / (alpha + interaction.size())); + for (String simItem : interaction) { + if (simItem.equals(mainItem)) { + continue; + } + float itemSimilarity = id2swing.getOrDefault(simItem, (float) 0) + similarity; + id2swing.put(simItem, itemSimilarity); + } + } + } + + ArrayList <Tuple2 <String, Float>> itemAndScore = new ArrayList <>(); + id2swing.forEach( + (key, value) -> itemAndScore.add(Tuple2.of(key, value)) + ); + + itemAndScore.sort(new Comparator <Tuple2 <String, Float>>() { + @Override + public int compare(Tuple2 <String, Float> o1, Tuple2 <String, Float> o2) { + return 0 - Float.compare(o1.f1, o2.f1); + } + }); + + if (itemAndScore.size() == 0) { + continue; + } + + int itemNums = topN > itemAndScore.size() ? itemAndScore.size() : topN; + String[] itemIds = new String[itemNums]; + Float[] itemScores = new Float[itemNums]; + for (int i = 0; i < itemNums; i++) { + itemIds[i] = itemAndScore.get(i).f0; + itemScores[i] = itemAndScore.get(i).f1; + } + + output.collect( + new StreamRecord <>( + Row.of( + mainItem, new ArrayList <String>(Arrays.asList(itemIds)), + new ArrayList <Float>(Arrays.asList(itemScores)) + ) + ) + ); + } + userItemsMapState.clear(); + itemUsersMapState.clear(); + } + + private float calculateWeight(int size) { + return (float) (1.0 / Math.pow(userAlpha + size, userBeta)); + } + + @Override + public void processElement(StreamRecord <Tuple3 <String, String, List <String>>> streamRecord) + throws Exception { + Tuple3 <String, String, List <String>> tuple3 = streamRecord.getValue(); + String user = tuple3.f0; + String item = tuple3.f1; + List <String> items = tuple3.f2; + + if (!userItemsMap.containsKey(user)) { + HashSet <String> itemSet = new HashSet <>(items.size()); + itemSet.addAll(items); + userItemsMap.put(user, itemSet); + } + + if (!itemUsersMap.containsKey(item)) { + itemUsersMap.put(item, new HashSet <>()); + } + itemUsersMap.get(item).add(user); + } + + @Override + public void initializeState(StateInitializationContext context) throws Exception { + super.initializeState(context); + userItemsMapState = + context.getOperatorStateStore() + .getListState( + new ListStateDescriptor <>( + "userItemsMapState", + Types.MAP( + Types.STRING, + Types.LIST( + Types.STRING))) + ); + + OperatorStateUtils.getUniqueElement(userItemsMapState, "userItemsMapState") + .ifPresent(x -> { + userItemsMap = new HashMap <>(x.size()); Review Comment: Because `x` and `userItemsMap` are different types. -- This is an automated message from the Apache Git Service. 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