lindong28 commented on a change in pull request #28: URL: https://github.com/apache/flink-ml/pull/28#discussion_r753633697
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/linalg/BLAS.java ########## @@ -0,0 +1,83 @@ +/* + * 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.common.linalg; + +import org.apache.flink.util.Preconditions; + +/** A utility class that provides BLAS routines over matrices and vectors. */ +public class BLAS { + + /** For level-1 routines, we use Java implementation. */ + private static final com.github.fommil.netlib.BLAS NATIVE_BLAS = Review comment: Spark has updated its BLAS library dependency to use e.g. `dev.ludovic.netlib.BLAS`. We can find explanation in its commit message and JIRA description. The Naive Bayes PR follows Spark's approach. Maybe we can do the same here? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/linear/HasMaxIter.java ########## @@ -0,0 +1,42 @@ +/* + * 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.common.param.linear; + +import org.apache.flink.ml.param.IntParam; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared maxIteration param. */ +public interface HasMaxIter<T> extends WithParams<T> { Review comment: Can we re-use the existing `HasMaxIter` in the package `org.apache.flink.ml.common.param`? Is there any reason we need to put these params in the package `*.linear`? It looks like more (if not all) of these parameters are general to all types of algorithms. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/linear/LogisticGradient.java ########## @@ -0,0 +1,109 @@ +/* + * 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.classification.linear; + +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.ml.common.linalg.BLAS; + +import java.io.Serializable; + +/** Logistic gradient. */ +public class LogisticGradient implements Serializable { + private static final long serialVersionUID = 1178693053439209380L; + + /** L1 regularization term. */ + private final double l1; + + /** L2 regularization term. */ + private final double l2; + + public LogisticGradient(double l1, double l2) { + this.l1 = l1; + this.l2 = l2; + } + + /** + * Computes loss and weightSum on a set of samples. + * + * @param labeledData a sample set of train data. + * @param coefficient model parameters. + * @return loss and weightSum. + */ + public final Tuple2<Double, Double> computeLoss( + Iterable<Tuple3<Double, Double, double[]>> labeledData, double[] coefficient) { + double weightSum = 0.0; + double lossSum = 0.0; + double loss; + for (Tuple3<Double, Double, double[]> dataPoint : labeledData) { + loss = computeLoss(dataPoint, coefficient); + lossSum += loss * dataPoint.f0; + weightSum += dataPoint.f0; + } + if (Double.compare(0, Math.abs(l1)) != 0) { Review comment: It looks like the LogisticRegression added in this PR asks users to separately set l1 and l2 parameter. Both values can be non-zero. And the loss is the sum of both loss. It looks like most (if not all) logistic regression algorithm (e.g. [1] and [2]) uses one type of loss function (either l1 or l2), or use one parameter with value in range [0, 1] to specify the loss function (e.g. Spark's approach). So the approach used here seems to be different from common practice of how loss function of logistic regression is specified. Could we follow the same approach as Spark, since it seems to be more consistent with commonly used logistic regression algorithm? [1] https://en.wikipedia.org/wiki/Logistic_regression [2] https://www.section.io/engineering-education/understanding-loss-functions-in-machine-learning/ ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/linear/HasBatchSize.java ########## @@ -0,0 +1,43 @@ +/* + * 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.common.param.linear; + +import org.apache.flink.ml.param.IntParam; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared batchSize param. */ +public interface HasBatchSize<T> extends WithParams<T> { Review comment: Could you explain why we need this batch size parameter but Spark's logistic regression does not? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/linear/LogisticRegression.java ########## @@ -0,0 +1,594 @@ +/* + * 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.classification.linear; + +import org.apache.flink.api.common.functions.FlatMapFunction; +import org.apache.flink.api.common.functions.RichMapFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.api.common.typeinfo.BasicTypeInfo; +import org.apache.flink.api.common.typeinfo.PrimitiveArrayTypeInfo; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.api.java.typeutils.TupleTypeInfo; +import org.apache.flink.iteration.DataStreamList; +import org.apache.flink.iteration.IterationBody; +import org.apache.flink.iteration.IterationBodyResult; +import org.apache.flink.iteration.IterationConfig; +import org.apache.flink.iteration.IterationConfig.OperatorLifeCycle; +import org.apache.flink.iteration.IterationListener; +import org.apache.flink.iteration.Iterations; +import org.apache.flink.iteration.ReplayableDataStreamList; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.core.Estimator; +import org.apache.flink.ml.common.broadcast.BroadcastUtils; +import org.apache.flink.ml.common.datastream.AllReduceUtils; +import org.apache.flink.ml.common.datastream.DataStreamUtils; +import org.apache.flink.ml.common.linalg.BLAS; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.util.ParamUtils; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.runtime.state.StateInitializationContext; +import org.apache.flink.runtime.state.StateSnapshotContext; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator; +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +import org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator; +import org.apache.flink.streaming.api.operators.BoundedMultiInput; +import org.apache.flink.streaming.api.operators.BoundedOneInput; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.api.operators.TwoInputStreamOperator; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +import org.apache.flink.table.api.Table; +import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; +import org.apache.flink.table.api.internal.TableImpl; +import org.apache.flink.util.Collector; +import org.apache.flink.util.OutputTag; +import org.apache.flink.util.Preconditions; + +import org.apache.commons.collections.IteratorUtils; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Random; + +/** This class implements methods to train a logistic regression model. */ +public class LogisticRegression + implements Estimator<LogisticRegression, LogisticRegressionModel>, + LogisticRegressionParams<LogisticRegression> { + + Map<Param<?>, Object> paramMap; + + private static final OutputTag<Tuple2<double[], double[]>> MODEL_OUTPUT = + new OutputTag<Tuple2<double[], double[]>>("MODEL_OUTPUT") {}; + + public LogisticRegression(Map<Param<?>, Object> paramMap) { + this.paramMap = paramMap; + ParamUtils.initializeMapWithDefaultValues(this.paramMap, this); + } + + public LogisticRegression() { + this(new HashMap<>()); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static LogisticRegression load(StreamExecutionEnvironment env, String path) + throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + @SuppressWarnings("unchecked") + public LogisticRegressionModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Tuple3<Double, Double, double[]>> trainData = + tEnv.toDataStream(inputs[0]) + .map( + row -> + Tuple3.of( + getWeightCol() == null + ? new Double(1.0) + : (Double) row.getField(getWeightCol()), + (Double) row.getField(getLabelCol()), + (double[]) row.getField(getVectorCol()))) + .returns( + new TupleTypeInfo<>( + BasicTypeInfo.DOUBLE_TYPE_INFO, + BasicTypeInfo.DOUBLE_TYPE_INFO, + PrimitiveArrayTypeInfo.DOUBLE_PRIMITIVE_ARRAY_TYPE_INFO)); + + DataStream<Double> labelValues = DataStreamUtils.distinct(trainData.map(x -> x.f1)); + + final String broadcastLabelsName = "broadcastLabels"; + trainData = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(trainData), + Collections.singletonMap(broadcastLabelsName, labelValues), + inputList -> { + DataStream data = inputList.get(0); + return data.transform( + "preprocess", + new TupleTypeInfo<>( + BasicTypeInfo.DOUBLE_TYPE_INFO, + BasicTypeInfo.DOUBLE_TYPE_INFO, + TypeInformation.of(double[].class)), + new PreprocessDataOp( + new PreprocessOneRecord(broadcastLabelsName))); + }); + + DataStream<double[]> initModel = + trainData + .transform( + "getInitModel", + TypeInformation.of(double[].class), + new GetInitModel()) + .returns(TypeInformation.of(double[].class)); + + DataStream<Tuple2<double[], double[]>> modelAndLossCurve = train(trainData, initModel); + + DataStream<LogisticRegressionModelData> modelData = + modelAndLossCurve + .connect(labelValues) + .transform( + "composeModelData", + TypeInformation.of(LogisticRegressionModelData.class), + new ComposeModelDataOp()) + .setParallelism(1); + + LogisticRegressionModel model = + new LogisticRegressionModel(new HashMap<>()) + .setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, paramMap); + return model; + } + + /** Pre-processes the training data. */ + private static class PreprocessDataOp + extends AbstractUdfStreamOperator< + Tuple3<Double, Double, double[]>, + RichMapFunction< + Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>>> + implements OneInputStreamOperator< + Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>> { + public PreprocessDataOp( + RichMapFunction<Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>> + userFunction) { + super(userFunction); + } + + @Override + public void processElement(StreamRecord<Tuple3<Double, Double, double[]>> streamRecord) + throws Exception { + streamRecord.replace(userFunction.map(streamRecord.getValue())); + output.collect(streamRecord); + } + } + + /** Pre-processes the training data. */ + private static class PreprocessOneRecord + extends RichMapFunction< + Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>> { + + String broadcastName; + double[] labelValues; + + public PreprocessOneRecord(String broadcastName) { + this.broadcastName = broadcastName; + } + + @Override + public Tuple3<Double, Double, double[]> map(Tuple3<Double, Double, double[]> value) { + if (labelValues == null) { + List<Double> labelList = getRuntimeContext().getBroadcastVariable(broadcastName); + labelValues = labelList.stream().mapToDouble(Double::doubleValue).toArray(); + } + value.f1 = getLabel(labelValues, value.f1); + return value; + } + + private double getLabel(double[] labels, double label) { + if (Math.abs(label - labels[0]) < 1e-7) { + return 1.; + } else { + return -1.; + } + } + } + + /** + * Gets initialized model, note that the parallelism is same as the input train data, not one. + */ + private static class GetInitModel extends AbstractStreamOperator<double[]> + implements OneInputStreamOperator<Tuple3<Double, Double, double[]>, double[]>, + BoundedOneInput { + + int dim = 0; + + @Override + public void endInput() { + output.collect(new StreamRecord<>(new double[dim])); + } + + @Override + public void processElement(StreamRecord<Tuple3<Double, Double, double[]>> streamRecord) { + dim = Math.max(dim, streamRecord.getValue().f2.length); + } + } + + /** + * Trains a machine learning model on the input data and initialized model, return the trained + * model and loss curve. + * + * @param trainData the training data + * @param initModel the init model + * @return the trained model and loss during the training + */ + private DataStream<Tuple2<double[], double[]>> train( + DataStream<Tuple3<Double, Double, double[]>> trainData, + DataStream<double[]> initModel) { + LogisticGradient logisticGradient = new LogisticGradient(getL1(), getL2()); + + DataStreamList resultList = + Iterations.iterateBoundedStreamsUntilTermination( + DataStreamList.of(initModel), + ReplayableDataStreamList.notReplay(trainData), + IterationConfig.newBuilder() + .setOperatorLifeCycle(OperatorLifeCycle.ALL_ROUND) + .build(), + new TrainIterationBody( + logisticGradient, + getBatchSize(), + getLearningRate(), + getMaxIter(), + getEpsilon())); + + return resultList.get(0); + } + + /** The iteration implementation for training process. */ + private static class TrainIterationBody implements IterationBody { + + private final LogisticGradient logisticGradient; + + private final int batchSize; + + private final double learningRate; + + private final int maxIter; + + private final double epsilon; + + public TrainIterationBody( + LogisticGradient logisticGradient, + int batchSize, + double learningRate, + int maxIter, + double epsilon) { + this.logisticGradient = logisticGradient; + this.batchSize = batchSize; + this.learningRate = learningRate; + this.maxIter = maxIter; + this.epsilon = epsilon; + } + + @Override + public IterationBodyResult process( + DataStreamList variableStreams, DataStreamList dataStreams) { + DataStream<double[]> initModelOrGradientsAndWeightAndLoss = variableStreams.get(0); + DataStream<Tuple3<Double, Double, double[]>> trainData = dataStreams.get(0); + SingleOutputStreamOperator<double[]> gradientAndWeightAndLoss = + trainData + .connect(initModelOrGradientsAndWeightAndLoss) + .transform( + "updateModelAndComputeGradients", + TypeInformation.of(double[].class), + new CacheDataAndUpdateModelAndComputeGradient( + logisticGradient, + batchSize, + learningRate, + maxIter, + epsilon)); + + DataStreamList outputs = + IterationBody.forEachRound( + DataStreamList.of(gradientAndWeightAndLoss), + input -> { + DataStream<double[]> feedback = + AllReduceUtils.allReduce(input.get(0)); + return DataStreamList.of(feedback); + }); + DataStream<Integer> terminatation = + outputs.get(0) + .map( + x -> { + double[] value = (double[]) x; + return value[value.length - 1] / value[value.length - 2]; + }) + .flatMap(new TerminationCriteria(maxIter, epsilon)); + + return new IterationBodyResult( + DataStreamList.of(outputs.get(0)), + DataStreamList.of(gradientAndWeightAndLoss.getSideOutput(MODEL_OUTPUT)), + terminatation); + } + } + + /** Terminates if epochId is greater than {maxIter} or loss smaller than {epsilon}. */ + private static class TerminationCriteria + implements IterationListener<Integer>, FlatMapFunction<Double, Integer> { + + private final int maxIter; + private final double epsilon; + double loss = 0; + + public TerminationCriteria(int maxIter, double epsilon) { + this.maxIter = maxIter; + this.epsilon = epsilon; + } + + @Override + public void flatMap(Double value, Collector<Integer> out) { + this.loss = value; + } + + @Override + public void onEpochWatermarkIncremented( + int epochWatermark, Context context, Collector<Integer> collector) { + if ((epochWatermark + 1) < maxIter && this.loss > epsilon) { + collector.collect(0); + } + } + + @Override + public void onIterationTerminated(Context context, Collector<Integer> collector) {} + } + + /** First input is data, second input is initModelOrGradientAndweightAndLoss. */ + private static class CacheDataAndUpdateModelAndComputeGradient + extends AbstractStreamOperator<double[]> + implements TwoInputStreamOperator<Tuple3<Double, Double, double[]>, double[], double[]>, + IterationListener<double[]> { + + /** training specific parameters. */ + double[] model; Review comment: Can we access` model` from `modelState` directly instead of creating this variable? ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/classification/linear/LogisticRegressionTest.java ########## @@ -0,0 +1,275 @@ +/* + * 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.classification.linear; + +import org.apache.flink.api.common.typeinfo.PrimitiveArrayTypeInfo; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.common.typeinfo.Types; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.utils.TestBaseImpl; +import org.apache.flink.streaming.api.functions.sink.SinkFunction; +import org.apache.flink.table.api.Table; +import org.apache.flink.types.Row; + +import org.junit.Before; +import org.junit.Test; + +import java.nio.file.Files; +import java.util.Arrays; +import java.util.Collections; +import java.util.List; +import java.util.Objects; + +import static org.junit.Assert.assertArrayEquals; +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.assertNull; + +/** Tests {@link LogisticRegression} and {@link LogisticRegressionModel}. */ +public class LogisticRegressionTest extends TestBaseImpl { + + private static List<Row> trainData = + Arrays.asList( + Row.of(new double[] {1, 2, 3, 4}, 1., 1.), + Row.of(new double[] {1, 2, 3, 4}, 1., 1.), + Row.of(new double[] {1, 2, 3, 4}, 1., 1.), + Row.of(new double[] {1, 2, 3, 4}, 1., 1.), + Row.of(new double[] {1, 2, 3, 4}, 1., 1.), + Row.of(new double[] {3, 2, 3, 4}, -1., 1.), + Row.of(new double[] {1, 2, 3, 4}, 1., 1.), + Row.of(new double[] {3, 2, 3, 4}, -1., 1.)); + + private static double[] expectedWeight = new double[] {-2.2, 0.3, 0.5, 0.7}; + + private static final double TOLERANCE = 1e-7; + + private Table dataTable; + + @Before + public void before() { + super.before(); + dataTable = + tEnv.fromDataStream( + env.fromCollection( + trainData, + new RowTypeInfo( + new TypeInformation[] { + PrimitiveArrayTypeInfo.DOUBLE_PRIMITIVE_ARRAY_TYPE_INFO, + Types.DOUBLE, + Types.DOUBLE + }, + new String[] {"vec", "label", "weight"}))); + } + + private void verifyPredictionResult( + Table output, String vecCol, String predCol, String predDetailCol) throws Exception { + tEnv.toDataStream(output) + .addSink( + new SinkFunction<Row>() { + @Override + public void invoke(Row value, Context context) { + double[] feature = (double[]) value.getField(vecCol); + assertNotNull(feature); + if (Math.abs(feature[0] - 1) < TOLERANCE) { + assertEquals(1, (Double) value.getField(predCol), TOLERANCE); + assert ((double[]) Review comment: Can we use `assertTrue(...)` here to follow the same style as `assertEquals`? Same for other usages. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/linear/HasVectorCol.java ########## @@ -0,0 +1,39 @@ +/* + * 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.common.param.linear; + +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.StringParam; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared vector column param. */ +public interface HasVectorCol<T> extends WithParams<T> { Review comment: Can you explain the use-case for this column? For example, is the value of this column going to be a string? How are the values of this column going to be calculated and interpreted? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/linear/HasLearningRate.java ########## @@ -0,0 +1,43 @@ +/* + * 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.common.param.linear; + +import org.apache.flink.ml.param.DoubleParam; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared learning rate param. */ +public interface HasLearningRate<T> extends WithParams<T> { Review comment: I didn't find this learning rate parameter in the logistic regression wiki [1] or in Spark [2]. Could you explain why we need this learning rate parameter but Spark's logistic regression does not? [1] https://en.wikipedia.org/wiki/Logistic_regression ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/linear/HasPredictionDetailCol.java ########## @@ -0,0 +1,39 @@ +/* + * 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.common.param.linear; + +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.StringParam; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared prediction detail param. */ +public interface HasPredictionDetailCol<T> extends WithParams<T> { Review comment: Can you explain the use-case for this column? For example, is the value of this column going to be a string? How are the values of this column going to be calculated and interpreted? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
