huaxingao commented on a change in pull request #28710:
URL: https://github.com/apache/spark/pull/28710#discussion_r436790826
##########
File path: project/MimaExcludes.scala
##########
@@ -49,7 +49,34 @@ object MimaExcludes {
//[SPARK-31840] Add instance weight support in LogisticRegressionSummary
// weightCol in
org.apache.spark.ml.classification.LogisticRegressionSummary is present only in
current version
-
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightCol")
+
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightCol"),
+
+ //[SPARK-31893] Add a generic ClassificationSummary trait
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$_setter_$org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics_="),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics"),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.weightCol"),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$BinaryClassificationSummary$_setter_$org$apache$spark$ml$classification$BinaryClassificationSummary$$sparkSession_="),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$BinaryClassificationSummary$_setter_$org$apache$spark$ml$classification$BinaryClassificationSummary$$binaryMetrics_="),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$BinaryClassificationSummary$$binaryMetrics"),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.scoreCol"),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$BinaryClassificationSummary$$sparkSession"),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$_setter_$org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics_="),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics"),
+
ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.weightCol"),
+
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.asBinary"),
Review comment:
@viirya Thanks for your comment. I think we are still OK, because
summary is still an instance of BinaryLogisticRegressionSummary
```
val blorModel = lr.fit(smallBinaryDataset)
assert(blorModel.summary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
assert(blorModel.summary.asBinary.isInstanceOf[BinaryLogisticRegressionSummary])
assert(blorModel.binarySummary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
```
Similarly, in multinomial case, summary is still an instance of
LogisticRegressionTrainingSummary
```
val mlorModel = lr.setFamily("multinomial").fit(smallMultinomialDataset)
assert(mlorModel.summary.isInstanceOf[LogisticRegressionTrainingSummary])
```
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala
##########
@@ -0,0 +1,265 @@
+/*
+ * 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.spark.ml.classification
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml.functions.checkNonNegativeWeight
+import org.apache.spark.ml.linalg.Vector
+import org.apache.spark.mllib.evaluation.{BinaryClassificationMetrics,
MulticlassMetrics}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.{col, lit}
+import org.apache.spark.sql.types.DoubleType
+
+
+/**
+ * Abstraction for multiclass classification results for a given model.
+ */
+private[classification] trait ClassificationSummary extends Serializable {
+
+ /**
+ * Dataframe output by the model's `transform` method.
+ */
+ @Since("3.1.0")
+ def predictions: DataFrame
+
+ /**
+ * Field in "predictions" which gives the probability or rawPrediction of
each class as a vector.
+ */
+ @Since("3.1.0")
+ def scoreCol: String
+
+ /** Field in "predictions" which gives the prediction of each class. */
+ @Since("3.1.0")
+ def predictionCol: String
+
+ /** Field in "predictions" which gives the true label of each instance (if
available). */
+ @Since("3.1.0")
+ def labelCol: String
+
+ /** Field in "predictions" which gives the features of each instance as a
vector. */
+ @Since("3.1.0")
+ def featuresCol: String
+
+ /** Field in "predictions" which gives the weight of each instance as a
vector. */
+ @Since("3.1.0")
+ def weightCol: String
+
+ @transient private val multiclassMetrics = {
+ if (predictions.schema.fieldNames.contains(weightCol)) {
+ new MulticlassMetrics(
+ predictions.select(
+ col(predictionCol),
+ col(labelCol).cast(DoubleType),
+ checkNonNegativeWeight(col(weightCol).cast(DoubleType))).rdd.map {
+ case Row(prediction: Double, label: Double, weight: Double) =>
(prediction, label, weight)
+ })
+ } else {
+ new MulticlassMetrics(
+ predictions.select(
+ col(predictionCol),
+ col(labelCol).cast(DoubleType),
+ lit(1.0)).rdd.map {
+ case Row(prediction: Double, label: Double, weight: Double) =>
(prediction, label, weight)
+ })
+ }
+ }
+
+ /**
+ * Returns the sequence of labels in ascending order. This order matches the
order used
+ * in metrics which are specified as arrays over labels, e.g.,
truePositiveRateByLabel.
+ *
+ * Note: In most cases, it will be values {0.0, 1.0, ..., numClasses-1},
However, if the
+ * training set is missing a label, then all of the arrays over labels
+ * (e.g., from truePositiveRateByLabel) will be of length numClasses-1
instead of the
+ * expected numClasses.
+ */
+ @Since("3.1.0")
+ def labels: Array[Double] = multiclassMetrics.labels
+
+ /** Returns true positive rate for each label (category). */
+ @Since("3.1.0")
+ def truePositiveRateByLabel: Array[Double] = recallByLabel
+
+ /** Returns false positive rate for each label (category). */
+ @Since("3.1.0")
+ def falsePositiveRateByLabel: Array[Double] = {
+ multiclassMetrics.labels.map(label =>
multiclassMetrics.falsePositiveRate(label))
+ }
+
+ /** Returns precision for each label (category). */
+ @Since("3.1.0")
+ def precisionByLabel: Array[Double] = {
+ multiclassMetrics.labels.map(label => multiclassMetrics.precision(label))
+ }
+
+ /** Returns recall for each label (category). */
+ @Since("3.1.0")
+ def recallByLabel: Array[Double] = {
+ multiclassMetrics.labels.map(label => multiclassMetrics.recall(label))
+ }
+
+ /** Returns f-measure for each label (category). */
+ @Since("3.1.0")
+ def fMeasureByLabel(beta: Double): Array[Double] = {
+ multiclassMetrics.labels.map(label => multiclassMetrics.fMeasure(label,
beta))
+ }
+
+ /** Returns f1-measure for each label (category). */
+ @Since("3.1.0")
+ def fMeasureByLabel: Array[Double] = fMeasureByLabel(1.0)
+
+ /**
+ * Returns accuracy.
+ * (equals to the total number of correctly classified instances
+ * out of the total number of instances.)
+ */
+ @Since("3.1.0")
+ def accuracy: Double = multiclassMetrics.accuracy
+
+ /**
+ * Returns weighted true positive rate.
+ * (equals to precision, recall and f-measure)
+ */
+ @Since("3.1.0")
+ def weightedTruePositiveRate: Double = weightedRecall
+
+ /** Returns weighted false positive rate. */
+ @Since("3.1.0")
+ def weightedFalsePositiveRate: Double =
multiclassMetrics.weightedFalsePositiveRate
+
+ /**
+ * Returns weighted averaged recall.
+ * (equals to precision, recall and f-measure)
+ */
+ @Since("3.1.0")
+ def weightedRecall: Double = multiclassMetrics.weightedRecall
+
+ /** Returns weighted averaged precision. */
+ @Since("3.1.0")
+ def weightedPrecision: Double = multiclassMetrics.weightedPrecision
+
+ /** Returns weighted averaged f-measure. */
+ @Since("3.1.0")
+ def weightedFMeasure(beta: Double): Double =
multiclassMetrics.weightedFMeasure(beta)
+
+ /** Returns weighted averaged f1-measure. */
+ @Since("3.1.0")
+ def weightedFMeasure: Double = multiclassMetrics.weightedFMeasure(1.0)
+
+ /**
+ * Convenient method for casting to binary classification summary.
+ * This method will throw an Exception if the summary is not a binary
summary.
+ */
+ @Since("3.1.0")
+ def asBinary: BinaryClassificationSummary = this match {
+ case b: BinaryClassificationSummary => b
+ case _ =>
+ throw new RuntimeException("Cannot cast to a binary summary.")
+ }
+}
+
+/**
+ * Abstraction for training results.
+ */
+private[classification] trait TrainingSummary {
+
+ /** objective function (scaled loss + regularization) at each iteration. */
+ @Since("3.1.0")
+ def objectiveHistory: Array[Double]
+
+ /** Number of training iterations. */
+ @Since("3.1.0")
+ def totalIterations: Int = objectiveHistory.length
+}
+
+/**
+ * Abstraction for binary classification results for a given model.
+ */
+trait BinaryClassificationSummary extends ClassificationSummary {
+
+ private val sparkSession = predictions.sparkSession
+ import sparkSession.implicits._
+
+ // TODO: Allow the user to vary the number of bins using a setBins method in
+ // BinaryClassificationMetrics. For now the default is set to 100.
Review comment:
This piece of code was used in ```LogisticRegression``` and I moved it
here. I'm not really sure what the comment means, but it might mean "Allow the
user to vary the number of bins using a setBins method in
BinaryClassificationMetrics. For now set it to 100."
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala
##########
@@ -0,0 +1,265 @@
+/*
+ * 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.spark.ml.classification
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml.functions.checkNonNegativeWeight
+import org.apache.spark.ml.linalg.Vector
+import org.apache.spark.mllib.evaluation.{BinaryClassificationMetrics,
MulticlassMetrics}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.{col, lit}
+import org.apache.spark.sql.types.DoubleType
+
+
+/**
+ * Abstraction for multiclass classification results for a given model.
+ */
+private[classification] trait ClassificationSummary extends Serializable {
+
+ /**
+ * Dataframe output by the model's `transform` method.
+ */
+ @Since("3.1.0")
+ def predictions: DataFrame
+
+ /**
+ * Field in "predictions" which gives the probability or rawPrediction of
each class as a vector.
+ */
+ @Since("3.1.0")
+ def scoreCol: String
Review comment:
still need to keep ```scoreCol``` in this generic trait because it is
needed in ```BinaryClassificationSummary```
```
new BinaryClassificationMetrics(
predictions.select(col(scoreCol), col(labelCol).cast(DoubleType),
```
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