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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/04/01 11:24:21 UTC
[GitHub] [flink-ml] lindong28 commented on a change in pull request #56: [FLINK-25616] Add Transformer for VectorAssembler
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?
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