lindong28 commented on a change in pull request #56: URL: https://github.com/apache/flink-ml/pull/56#discussion_r840443219
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector Review comment: Other Transformer subclasses' Java docs typically start with `e.g. A model...`. And the sentence `It will combine raw features and features generated` seems a little bit redundant. Would the following Java doc be simpler? ``` /** * A feature transformer that combines a given list of input columns into a vector column. Types of * input columns must be either vector or numerical value. */ ``` ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); + if (object instanceof Number) { + map.put(offset++, ((Number) object).doubleValue()); + } else if (object instanceof Vector) { + offset = appendVector((Vector) object, map, offset); + } else { + throw new UnsupportedOperationException( + "Vector assembler : input type has not been supported yet."); + } + } catch (Exception e) { + switch (handleInvalid) { + case HasHandleInvalid.ERROR_INVALID: + throw new RuntimeException("Vector assembler failed.", e); + case HasHandleInvalid.SKIP_INVALID: + return null; + default: + } + } + } + + if (map.size() * RATIO > offset) { + DenseVector assembledVector = new DenseVector(offset); + for (int key : map.keySet()) { + assembledVector.values[key] = map.get(key); + } + return assembledVector; + } else { + return convertMapToSparseVector(offset, map); + } + } + + private static int appendVector(Vector vec, Map<Integer, Double> map, int offset) { + if (vec instanceof SparseVector) { + SparseVector sparseVector = (SparseVector) vec; + int[] indices = sparseVector.indices; + double[] values = sparseVector.values; + for (int j = 0; j < indices.length; ++j) { + map.put(offset + indices[j], values[j]); + } + offset += sparseVector.size(); + } else if (vec instanceof DenseVector) { Review comment: Would it be simpler to just do `else {...}`? ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/VectorAssemblerTest.java ########## @@ -0,0 +1,175 @@ +/* + * 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; + +import org.apache.flink.api.common.restartstrategy.RestartStrategies; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.feature.vectorassembler.VectorAssembler; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.StageTestUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions; +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.table.api.DataTypes; +import org.apache.flink.table.api.Schema; +import org.apache.flink.table.api.Table; +import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; +import org.apache.flink.test.util.AbstractTestBase; +import org.apache.flink.types.Row; + +import org.apache.commons.collections.IteratorUtils; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import java.util.Arrays; +import java.util.List; + +import static org.junit.Assert.assertArrayEquals; +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNull; + +/** Tests VectorAssembler. */ +public class VectorAssemblerTest extends AbstractTestBase { + + private StreamTableEnvironment tEnv; + private Table inputDataTable; + + private static final List<Row> INPUT_DATA = + Arrays.asList( + Row.of( + 0, + Vectors.dense(2.1, 3.1), + 1.0, + Vectors.sparse(5, new int[] {3}, new double[] {1.0})), + Row.of( + 1, + Vectors.dense(2.1, 3.1), + 1.0, + Vectors.sparse( + 5, new int[] {4, 2, 3, 1}, new double[] {4.0, 2.0, 3.0, 1.0})), + Row.of(2, null, 1.0, null)); + + private static final SparseVector EXPECTED_DATA_1 = Review comment: nits: To be more consistent with `INPUT_DATA`, how about renaming it as `EXPECTED_OUTPUT_DATA_1`? ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/VectorAssemblerTest.java ########## @@ -0,0 +1,175 @@ +/* + * 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; + +import org.apache.flink.api.common.restartstrategy.RestartStrategies; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.feature.vectorassembler.VectorAssembler; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.StageTestUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions; +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.table.api.DataTypes; +import org.apache.flink.table.api.Schema; +import org.apache.flink.table.api.Table; +import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; +import org.apache.flink.test.util.AbstractTestBase; +import org.apache.flink.types.Row; + +import org.apache.commons.collections.IteratorUtils; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import java.util.Arrays; +import java.util.List; + +import static org.junit.Assert.assertArrayEquals; +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNull; + +/** Tests VectorAssembler. */ +public class VectorAssemblerTest extends AbstractTestBase { + + private StreamTableEnvironment tEnv; + private Table inputDataTable; + + private static final List<Row> INPUT_DATA = + Arrays.asList( + Row.of( + 0, + Vectors.dense(2.1, 3.1), + 1.0, + Vectors.sparse(5, new int[] {3}, new double[] {1.0})), + Row.of( + 1, + Vectors.dense(2.1, 3.1), + 1.0, + Vectors.sparse( + 5, new int[] {4, 2, 3, 1}, new double[] {4.0, 2.0, 3.0, 1.0})), + Row.of(2, null, 1.0, null)); + + private static final SparseVector EXPECTED_DATA_1 = + Vectors.sparse(8, new int[] {0, 1, 2, 6}, new double[] {2.1, 3.1, 1.0, 1.0}); + private static final DenseVector EXPECTED_DATA_2 = + Vectors.dense(2.1, 3.1, 1.0, 0.0, 1.0, 2.0, 3.0, 4.0); + + @Before + public void before() { + Configuration config = new Configuration(); + config.set(ExecutionCheckpointingOptions.ENABLE_CHECKPOINTS_AFTER_TASKS_FINISH, true); + StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(config); + env.setParallelism(4); + env.enableCheckpointing(100); + env.setRestartStrategy(RestartStrategies.noRestart()); + tEnv = StreamTableEnvironment.create(env); + Schema schema = + Schema.newBuilder() + .column("f0", DataTypes.INT()) + .column("f1", DataTypes.of(DenseVector.class)) + .column("f2", DataTypes.DOUBLE()) + .column("f3", DataTypes.of(SparseVector.class)) + .build(); + DataStream<Row> dataStream = env.fromCollection(INPUT_DATA); + inputDataTable = Review comment: We can just do the following without using `schema`. ``` inputDataTable = tEnv.fromDataStream(dataStream).as("id", "vec", "num", "sparseVec") ``` ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); + if (object instanceof Number) { + map.put(offset++, ((Number) object).doubleValue()); + } else if (object instanceof Vector) { + offset = appendVector((Vector) object, map, offset); + } else { + throw new UnsupportedOperationException( + "Vector assembler : input type has not been supported yet."); + } + } catch (Exception e) { + switch (handleInvalid) { + case HasHandleInvalid.ERROR_INVALID: + throw new RuntimeException("Vector assembler failed.", e); + case HasHandleInvalid.SKIP_INVALID: + return null; Review comment: In this case, the returned table will contain a row whose outputColumn value is null. This sounds like `keep` instead of `skip`. Spark uses `keep` as the skip_invalid param value in this case. Should we add a `keep` option in `HasHandleInvalid`? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); + if (object instanceof Number) { + map.put(offset++, ((Number) object).doubleValue()); + } else if (object instanceof Vector) { + offset = appendVector((Vector) object, map, offset); + } else { + throw new UnsupportedOperationException( Review comment: Should it be `IllegalArgumentException`? And we probably don't need to explicitly specify `Vector assembler` in the error message since the stracktrace should already contain this information. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); + if (object instanceof Number) { + map.put(offset++, ((Number) object).doubleValue()); + } else if (object instanceof Vector) { + offset = appendVector((Vector) object, map, offset); + } else { + throw new UnsupportedOperationException( + "Vector assembler : input type has not been supported yet."); + } + } catch (Exception e) { + switch (handleInvalid) { + case HasHandleInvalid.ERROR_INVALID: + throw new RuntimeException("Vector assembler failed.", e); + case HasHandleInvalid.SKIP_INVALID: + return null; + default: + } + } + } + + if (map.size() * RATIO > offset) { + DenseVector assembledVector = new DenseVector(offset); + for (int key : map.keySet()) { + assembledVector.values[key] = map.get(key); + } + return assembledVector; + } else { + return convertMapToSparseVector(offset, map); + } + } + + private static int appendVector(Vector vec, Map<Integer, Double> map, int offset) { + if (vec instanceof SparseVector) { + SparseVector sparseVector = (SparseVector) vec; + int[] indices = sparseVector.indices; + double[] values = sparseVector.values; + for (int j = 0; j < indices.length; ++j) { + map.put(offset + indices[j], values[j]); + } + offset += sparseVector.size(); + } else if (vec instanceof DenseVector) { + DenseVector denseVector = (DenseVector) vec; + for (int j = 0; j < denseVector.size(); ++j) { + map.put(offset++, denseVector.values[j]); + } + } + return offset; + } + + private static SparseVector convertMapToSparseVector(int size, Map<Integer, Double> map) { + int nnz = map.size(); Review comment: It is not obvious what `nnz` means. Would it be more self-explanatory to rename it as `map_size`? It could be even simpler to do the following: ``` int[] indices = new int[map.size()]; double[] values = new double[map.size()]; ``` ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); + if (object instanceof Number) { + map.put(offset++, ((Number) object).doubleValue()); + } else if (object instanceof Vector) { + offset = appendVector((Vector) object, map, offset); + } else { + throw new UnsupportedOperationException( + "Vector assembler : input type has not been supported yet."); + } + } catch (Exception e) { + switch (handleInvalid) { + case HasHandleInvalid.ERROR_INVALID: + throw new RuntimeException("Vector assembler failed.", e); + case HasHandleInvalid.SKIP_INVALID: + return null; + default: + } + } + } + + if (map.size() * RATIO > offset) { + DenseVector assembledVector = new DenseVector(offset); + for (int key : map.keySet()) { + assembledVector.values[key] = map.get(key); + } + return assembledVector; + } else { + return convertMapToSparseVector(offset, map); + } + } + + private static int appendVector(Vector vec, Map<Integer, Double> map, int offset) { + if (vec instanceof SparseVector) { + SparseVector sparseVector = (SparseVector) vec; + int[] indices = sparseVector.indices; + double[] values = sparseVector.values; + for (int j = 0; j < indices.length; ++j) { + map.put(offset + indices[j], values[j]); + } + offset += sparseVector.size(); + } else if (vec instanceof DenseVector) { + DenseVector denseVector = (DenseVector) vec; + for (int j = 0; j < denseVector.size(); ++j) { Review comment: nits: Could we use `for (int i = 0; i < indices.length; i++)` to be consistent with other for loop code style? ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/VectorAssemblerTest.java ########## @@ -0,0 +1,175 @@ +/* + * 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; + +import org.apache.flink.api.common.restartstrategy.RestartStrategies; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.feature.vectorassembler.VectorAssembler; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.StageTestUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions; +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.table.api.DataTypes; +import org.apache.flink.table.api.Schema; +import org.apache.flink.table.api.Table; +import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; +import org.apache.flink.test.util.AbstractTestBase; +import org.apache.flink.types.Row; + +import org.apache.commons.collections.IteratorUtils; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import java.util.Arrays; +import java.util.List; + +import static org.junit.Assert.assertArrayEquals; +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNull; + +/** Tests VectorAssembler. */ +public class VectorAssemblerTest extends AbstractTestBase { + + private StreamTableEnvironment tEnv; + private Table inputDataTable; + + private static final List<Row> INPUT_DATA = + Arrays.asList( + Row.of( + 0, + Vectors.dense(2.1, 3.1), + 1.0, + Vectors.sparse(5, new int[] {3}, new double[] {1.0})), + Row.of( + 1, + Vectors.dense(2.1, 3.1), + 1.0, + Vectors.sparse( + 5, new int[] {4, 2, 3, 1}, new double[] {4.0, 2.0, 3.0, 1.0})), + Row.of(2, null, 1.0, null)); + + private static final SparseVector EXPECTED_DATA_1 = + Vectors.sparse(8, new int[] {0, 1, 2, 6}, new double[] {2.1, 3.1, 1.0, 1.0}); + private static final DenseVector EXPECTED_DATA_2 = + Vectors.dense(2.1, 3.1, 1.0, 0.0, 1.0, 2.0, 3.0, 4.0); + + @Before + public void before() { + Configuration config = new Configuration(); + config.set(ExecutionCheckpointingOptions.ENABLE_CHECKPOINTS_AFTER_TASKS_FINISH, true); + StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(config); + env.setParallelism(4); + env.enableCheckpointing(100); + env.setRestartStrategy(RestartStrategies.noRestart()); + tEnv = StreamTableEnvironment.create(env); + Schema schema = + Schema.newBuilder() + .column("f0", DataTypes.INT()) + .column("f1", DataTypes.of(DenseVector.class)) + .column("f2", DataTypes.DOUBLE()) + .column("f3", DataTypes.of(SparseVector.class)) + .build(); + DataStream<Row> dataStream = env.fromCollection(INPUT_DATA); + inputDataTable = + tEnv.fromDataStream(dataStream, schema).as("id", "vec", "num", "sparseVec"); + } + + private void verifyOutputResult(Table output, String outputCol) throws Exception { + DataStream<Row> dataStream = tEnv.toDataStream(output); + List<Row> results = IteratorUtils.toList(dataStream.executeAndCollect()); + assertEquals(3, results.size()); + for (Row result : results) { + if (result.getField(0) == (Object) 0) { + assertEquals(EXPECTED_DATA_1, result.getField(outputCol)); + } else if (result.getField(0) == (Object) 1) { + assertEquals(EXPECTED_DATA_2, result.getField(outputCol)); + } else { + assertNull(result.getField(outputCol)); + } + } + } + + @Test + public void testParam() { + VectorAssembler vectorAssembler = new VectorAssembler(); + assertEquals(HasHandleInvalid.ERROR_INVALID, vectorAssembler.getHandleInvalid()); + assertEquals("output", vectorAssembler.getOutputCol()); + vectorAssembler + .setInputCols("vec", "num", "sparseVec") + .setOutputCol("assembledVec") + .setHandleInvalid(HasHandleInvalid.SKIP_INVALID); + assertArrayEquals(new String[] {"vec", "num", "sparseVec"}, vectorAssembler.getInputCols()); + assertEquals(HasHandleInvalid.SKIP_INVALID, vectorAssembler.getHandleInvalid()); + assertEquals("assembledVec", vectorAssembler.getOutputCol()); + } + + @Test + public void testTransform() throws Exception { + VectorAssembler vectorAssembler = + new VectorAssembler() + .setInputCols("vec", "num", "sparseVec") + .setOutputCol("assembledVec") + .setHandleInvalid(HasHandleInvalid.SKIP_INVALID); + Table output = vectorAssembler.transform(inputDataTable)[0]; Review comment: Could we also test the schema of the output table, similar to what we did in `KMeansTest::testFitAndPredict()`? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); + if (object instanceof Number) { + map.put(offset++, ((Number) object).doubleValue()); + } else if (object instanceof Vector) { + offset = appendVector((Vector) object, map, offset); + } else { + throw new UnsupportedOperationException( + "Vector assembler : input type has not been supported yet."); + } + } catch (Exception e) { + switch (handleInvalid) { + case HasHandleInvalid.ERROR_INVALID: + throw new RuntimeException("Vector assembler failed.", e); Review comment: Would it be simpler to just do `throw e` here? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); Review comment: `VectorAssembler Error` seems redundant given that the stacktrace should also contain `VectorAssember`. How about changing the error to `input column value should not be null`? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/vectorassembler/VectorAssembler.java ########## @@ -0,0 +1,179 @@ +/* + * 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.vectorassembler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.common.param.HasHandleInvalid; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.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 org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.HashMap; +import java.util.LinkedHashMap; +import java.util.Map; + +/** + * Vector assembler is a transformer that combines a given list of columns into a single vector + * column. It will combine raw features and features generated by different feature transformers + * into a single feature vector. The input features of this transformer must be a vector feature or + * a numerical feature. + */ +public class VectorAssembler + implements Transformer<VectorAssembler>, VectorAssemblerParams<VectorAssembler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private static final double RATIO = 1.5; + + public VectorAssembler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), TypeInformation.of(Vector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + DataStream<Row> output = + tEnv.toDataStream(inputs[0]) + .map(new AssemblerFunc(getInputCols(), getHandleInvalid()), outputTypeInfo); + Table outputTable = tEnv.fromDataStream(output); + return new Table[] {outputTable}; + } + + private static class AssemblerFunc implements MapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + + public AssemblerFunc(String[] inputCols, String handleInvalid) { + this.inputCols = inputCols; + this.handleInvalid = handleInvalid; + } + + @Override + public Row map(Row value) { + Object[] objects = new Object[inputCols.length]; + for (int i = 0; i < objects.length; ++i) { + objects[i] = value.getField(inputCols[i]); + } + return Row.join(value, Row.of(assemble(objects, handleInvalid))); + } + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static VectorAssembler load(StreamTableEnvironment env, String path) throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + private static Vector assemble(Object[] objects, String handleInvalid) { + int offset = 0; + Map<Integer, Double> map = new LinkedHashMap<>(objects.length); + for (Object object : objects) { + try { + Preconditions.checkNotNull(object, "VectorAssembler Error: input data is null."); + if (object instanceof Number) { + map.put(offset++, ((Number) object).doubleValue()); + } else if (object instanceof Vector) { + offset = appendVector((Vector) object, map, offset); + } else { + throw new UnsupportedOperationException( + "Vector assembler : input type has not been supported yet."); + } + } catch (Exception e) { + switch (handleInvalid) { + case HasHandleInvalid.ERROR_INVALID: + throw new RuntimeException("Vector assembler failed.", e); + case HasHandleInvalid.SKIP_INVALID: + return null; + default: + } + } + } + + if (map.size() * RATIO > offset) { + DenseVector assembledVector = new DenseVector(offset); + for (int key : map.keySet()) { + assembledVector.values[key] = map.get(key); + } + return assembledVector; + } else { + return convertMapToSparseVector(offset, map); + } + } + + private static int appendVector(Vector vec, Map<Integer, Double> map, int offset) { + if (vec instanceof SparseVector) { + SparseVector sparseVector = (SparseVector) vec; + int[] indices = sparseVector.indices; + double[] values = sparseVector.values; + for (int j = 0; j < indices.length; ++j) { Review comment: nits: Could we use `for (int i = 0; i < indices.length; i++)` to be consistent with other for loop code style? -- This is an automated message from the Apache Git Service. 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