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[FLINK-27877] Add benchmark configuration for StringIndexer, StandardScaler and Bucketizer
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‎flink-ml-benchmark/src/main/java/org/apache/flink/ml/benchmark/datagenerator/common/DoubleGenerator.java

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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.flink.ml.benchmark.datagenerator.common;
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import org.apache.flink.api.common.typeinfo.TypeInformation;
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import org.apache.flink.api.common.typeinfo.Types;
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import org.apache.flink.api.java.typeutils.RowTypeInfo;
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import org.apache.flink.types.Row;
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import org.apache.flink.util.Preconditions;
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import java.util.Arrays;
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/** A DataGenerator which creates a table of doubles. */
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public class DoubleGenerator extends InputTableGenerator<DoubleGenerator> {
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@Override
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protected RowGenerator[] getRowGenerators() {
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String[][] colNames = getColNames();
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Preconditions.checkState(colNames.length == 1);
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int numOutputCols = colNames[0].length;
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return new RowGenerator[] {
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new RowGenerator(getNumValues(), getSeed()) {
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@Override
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public Row nextRow() {
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Row r = new Row(numOutputCols);
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for (int i = 0; i < numOutputCols; i++) {
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r.setField(i, random.nextDouble());
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}
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return r;
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}
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@Override
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protected RowTypeInfo getRowTypeInfo() {
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TypeInformation[] outputTypes = new TypeInformation[colNames[0].length];
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Arrays.fill(outputTypes, Types.DOUBLE);
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return new RowTypeInfo(outputTypes, colNames[0]);
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}
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}
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};
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}
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}

‎flink-ml-benchmark/src/main/java/org/apache/flink/ml/benchmark/datagenerator/common/RandomStringGenerator.java

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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.flink.ml.benchmark.datagenerator.common;
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import org.apache.flink.api.common.typeinfo.TypeInformation;
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import org.apache.flink.api.common.typeinfo.Types;
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import org.apache.flink.api.java.typeutils.RowTypeInfo;
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import org.apache.flink.ml.param.IntParam;
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import org.apache.flink.ml.param.Param;
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import org.apache.flink.ml.param.ParamValidators;
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import org.apache.flink.types.Row;
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import org.apache.flink.util.Preconditions;
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import java.util.Arrays;
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/** A DataGenerator which creates a table of random strings. */
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public class RandomStringGenerator extends InputTableGenerator<RandomStringGenerator> {
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public static final Param<Integer> NUM_DISTINCT_VALUE =
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new IntParam(
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"numDistinctValue",
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"Number of distinct values of the data to be generated.",
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10,
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ParamValidators.gt(0));
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public int getNumDistinctValue() {
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return get(NUM_DISTINCT_VALUE);
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}
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public RandomStringGenerator setNumDistinctValue(int value) {
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return set(NUM_DISTINCT_VALUE, value);
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}
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@Override
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protected RowGenerator[] getRowGenerators() {
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String[][] colNames = getColNames();
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Preconditions.checkState(colNames.length == 1);
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int numOutputCols = colNames[0].length;
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int numDistinctValues = getNumDistinctValue();
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return new RowGenerator[] {
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new RowGenerator(getNumValues(), getSeed()) {
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@Override
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public Row nextRow() {
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Row r = new Row(numOutputCols);
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for (int i = 0; i < numOutputCols; i++) {
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r.setField(i, Integer.toString(random.nextInt(numDistinctValues)));
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}
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return r;
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}
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@Override
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protected RowTypeInfo getRowTypeInfo() {
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TypeInformation[] outputTypes = new TypeInformation[colNames[0].length];
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Arrays.fill(outputTypes, Types.STRING);
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return new RowTypeInfo(outputTypes, colNames[0]);
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}
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}
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};
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}
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}

‎flink-ml-benchmark/src/main/resources/bucketizer-benchmark.json

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// Licensed to the Apache Software Foundation (ASF) under one or more
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// contributor license agreements. See the NOTICE file distributed with
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// this work for additional information regarding copyright ownership.
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// The ASF licenses this file to You under the Apache License, Version 2.0
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// (the "License"); you may not use this file except in compliance with
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// the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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{
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"version": 1,
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"bucketizer100000000": {
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"inputData": {
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"className": "org.apache.flink.ml.benchmark.datagenerator.common.DoubleGenerator",
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"paramMap": {
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"colNames": [
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[
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"col0"
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]
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],
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"seed": 2,
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"numValues": 100000000
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}
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},
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"stage": {
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"className": "org.apache.flink.ml.feature.bucketizer.Bucketizer",
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"paramMap": {
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"outputCols": [
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"outputCol0"
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],
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"handleInvalid": "skip",
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"inputCols": [
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"col0"
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],
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"splitsArray": [
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[
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-1.0,
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0.0,
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0.5,
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1.0,
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2.0
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]
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]
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}
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}
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}
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}

‎flink-ml-benchmark/src/main/resources/standardscaler-benchmark.json

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// Licensed to the Apache Software Foundation (ASF) under one or more
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// contributor license agreements. See the NOTICE file distributed with
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// this work for additional information regarding copyright ownership.
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// The ASF licenses this file to You under the Apache License, Version 2.0
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// (the "License"); you may not use this file except in compliance with
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// the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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{
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"version": 1,
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"standardscaler10000000": {
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"inputData": {
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"className": "org.apache.flink.ml.benchmark.datagenerator.common.DenseVectorGenerator",
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"paramMap": {
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"vectorDim": 100,
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"colNames": [
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[
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"features"
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]
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],
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"seed": 2,
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"numValues": 10000000
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}
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},
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"stage": {
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"className": "org.apache.flink.ml.feature.standardscaler.StandardScaler",
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"paramMap": {
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"inputCol": "features",
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"withMean": true,
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"withStd": true,
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"outputCol": "outputCol"
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}
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}
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}
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}

‎flink-ml-benchmark/src/main/resources/stringindexer-benchmark.json

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// Licensed to the Apache Software Foundation (ASF) under one or more
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// contributor license agreements. See the NOTICE file distributed with
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// this work for additional information regarding copyright ownership.
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// The ASF licenses this file to You under the Apache License, Version 2.0
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// (the "License"); you may not use this file except in compliance with
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// the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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{
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"version": 1,
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"stringindexer100000000": {
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"inputData": {
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"className": "org.apache.flink.ml.benchmark.datagenerator.common.RandStringGenerator",
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"paramMap": {
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"colNames": [
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[
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"col0"
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]
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],
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"seed": 2,
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"numValues": 100000000,
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"numDistinctValue": 100
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}
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},
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"stage": {
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"className": "org.apache.flink.ml.feature.stringindexer.StringIndexer",
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"paramMap": {
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"outputCols": [
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"outputCol0"
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],
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"handleInvalid": "skip",
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"inputCols": [
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"col0"
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],
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"stringOrderType": "arbitrary"
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}
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}
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}
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}

‎flink-ml-benchmark/src/test/java/org/apache/flink/ml/benchmark/DataGeneratorTest.java

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import org.apache.flink.configuration.Configuration;
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import org.apache.flink.ml.benchmark.datagenerator.common.DenseVectorArrayGenerator;
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import org.apache.flink.ml.benchmark.datagenerator.common.DenseVectorGenerator;
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import org.apache.flink.ml.benchmark.datagenerator.common.DoubleGenerator;
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import org.apache.flink.ml.benchmark.datagenerator.common.LabeledPointWithWeightGenerator;
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import org.apache.flink.ml.benchmark.datagenerator.common.RandomStringGenerator;
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import org.apache.flink.ml.linalg.DenseVector;
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import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions;
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import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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}
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assertEquals(generator.getNumValues(), count);
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}
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@Test
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public void testRandomStringGenerator() {
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String col1 = "col1";
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String col2 = "col2";
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RandomStringGenerator generator =
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new RandomStringGenerator()
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.setColNames(new String[] {col1, col2})
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.setSeed(2L)
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.setNumValues(5)
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.setNumDistinctValue(2);
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int count = 0;
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for (CloseableIterator<Row> it = generator.getData(tEnv)[0].execute().collect();
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it.hasNext(); ) {
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Row row = it.next();
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count++;
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String value1 = (String) row.getField(col1);
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String value2 = (String) row.getField(col2);
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assertTrue(Integer.parseInt(value1) < generator.getNumDistinctValue());
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assertTrue(Integer.parseInt(value2) < generator.getNumDistinctValue());
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}
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assertEquals(generator.getNumValues(), count);
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}
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@Test
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public void testDoubleGenerator() {
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String col1 = "col1";
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String col2 = "col2";
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DoubleGenerator generator =
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new DoubleGenerator()
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.setColNames(new String[] {"col1", "col2"})
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.setSeed(2L)
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.setNumValues(5);
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int count = 0;
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for (CloseableIterator<Row> it = generator.getData(tEnv)[0].execute().collect();
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it.hasNext(); ) {
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Row row = it.next();
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count++;
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double value1 = (Double) row.getField(col1);
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double value2 = (Double) row.getField(col2);
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assertTrue(value1 <= 1 && value1 >= 0);
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assertTrue(value2 <= 1 && value2 >= 0);
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}
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assertEquals(generator.getNumValues(), count);
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}
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}

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