zhengruifeng commented on a change in pull request #28710:
URL: https://github.com/apache/spark/pull/28710#discussion_r439999029
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
##########
@@ -1396,239 +1393,34 @@ object LogisticRegressionModel extends
MLReadable[LogisticRegressionModel] {
/**
* Abstraction for logistic regression results for a given model.
*/
-sealed trait LogisticRegressionSummary extends Serializable {
-
- /**
- * Dataframe output by the model's `transform` method.
- */
- @Since("1.5.0")
- def predictions: DataFrame
+sealed trait LogisticRegressionSummary extends ClassificationSummary {
/** Field in "predictions" which gives the probability of each class as a
vector. */
@Since("1.5.0")
def probabilityCol: String
- /** Field in "predictions" which gives the prediction of each class. */
- @Since("2.3.0")
- def predictionCol: String
-
- /** Field in "predictions" which gives the true label of each instance (if
available). */
- @Since("1.5.0")
- def labelCol: String
-
/** Field in "predictions" which gives the features of each instance as a
vector. */
@Since("1.6.0")
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("2.3.0")
- def labels: Array[Double] = multiclassMetrics.labels
-
- /** Returns true positive rate for each label (category). */
- @Since("2.3.0")
- def truePositiveRateByLabel: Array[Double] = recallByLabel
-
- /** Returns false positive rate for each label (category). */
- @Since("2.3.0")
- def falsePositiveRateByLabel: Array[Double] = {
- multiclassMetrics.labels.map(label =>
multiclassMetrics.falsePositiveRate(label))
- }
-
- /** Returns precision for each label (category). */
- @Since("2.3.0")
- def precisionByLabel: Array[Double] = {
- multiclassMetrics.labels.map(label => multiclassMetrics.precision(label))
- }
-
- /** Returns recall for each label (category). */
- @Since("2.3.0")
- def recallByLabel: Array[Double] = {
- multiclassMetrics.labels.map(label => multiclassMetrics.recall(label))
- }
-
- /** Returns f-measure for each label (category). */
- @Since("2.3.0")
- def fMeasureByLabel(beta: Double): Array[Double] = {
- multiclassMetrics.labels.map(label => multiclassMetrics.fMeasure(label,
beta))
- }
-
- /** Returns f1-measure for each label (category). */
- @Since("2.3.0")
- 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("2.3.0")
- def accuracy: Double = multiclassMetrics.accuracy
-
- /**
- * Returns weighted true positive rate.
- * (equals to precision, recall and f-measure)
- */
- @Since("2.3.0")
- def weightedTruePositiveRate: Double = weightedRecall
-
- /** Returns weighted false positive rate. */
- @Since("2.3.0")
- def weightedFalsePositiveRate: Double =
multiclassMetrics.weightedFalsePositiveRate
-
- /**
- * Returns weighted averaged recall.
- * (equals to precision, recall and f-measure)
- */
- @Since("2.3.0")
- def weightedRecall: Double = multiclassMetrics.weightedRecall
-
- /** Returns weighted averaged precision. */
- @Since("2.3.0")
- def weightedPrecision: Double = multiclassMetrics.weightedPrecision
-
- /** Returns weighted averaged f-measure. */
- @Since("2.3.0")
- def weightedFMeasure(beta: Double): Double =
multiclassMetrics.weightedFMeasure(beta)
-
- /** Returns weighted averaged f1-measure. */
- @Since("2.3.0")
- def weightedFMeasure: Double = multiclassMetrics.weightedFMeasure(1.0)
-
- /**
- * Convenient method for casting to binary logistic regression summary.
- * This method will throw an Exception if the summary is not a binary
summary.
- */
- @Since("2.3.0")
- def asBinary: BinaryLogisticRegressionSummary = this match {
- case b: BinaryLogisticRegressionSummary => b
- case _ =>
- throw new RuntimeException("Cannot cast to a binary summary.")
- }
}
/**
* Abstraction for multiclass logistic regression training results.
- * Currently, the training summary ignores the training weights except
- * for the objective trace.
*/
-sealed trait LogisticRegressionTrainingSummary extends
LogisticRegressionSummary {
-
- /** objective function (scaled loss + regularization) at each iteration. */
- @Since("1.5.0")
- def objectiveHistory: Array[Double]
-
- /** Number of training iterations. */
- @Since("1.5.0")
- def totalIterations: Int = objectiveHistory.length
-
+sealed trait LogisticRegressionTrainingSummary extends
LogisticRegressionSummary
+ with TrainingSummary {
}
/**
* Abstraction for binary logistic regression results for a given model.
*/
-sealed trait BinaryLogisticRegressionSummary extends LogisticRegressionSummary
{
-
- 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.
- @transient private val binaryMetrics = if
(predictions.schema.fieldNames.contains(weightCol)) {
- new BinaryClassificationMetrics(
- predictions.select(col(probabilityCol), col(labelCol).cast(DoubleType),
- checkNonNegativeWeight(col(weightCol).cast(DoubleType))).rdd.map {
- case Row(score: Vector, label: Double, weight: Double) => (score(1),
label, weight)
- }, 100
- )
- } else {
- new BinaryClassificationMetrics(
- predictions.select(col(probabilityCol), col(labelCol).cast(DoubleType),
- lit(1.0)).rdd.map {
- case Row(score: Vector, label: Double, weight: Double) => (score(1),
label, weight)
- }, 100
- )
- }
-
- /**
- * Returns the receiver operating characteristic (ROC) curve,
- * which is a Dataframe having two fields (FPR, TPR)
- * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
- * See http://en.wikipedia.org/wiki/Receiver_operating_characteristic
- */
- @Since("1.5.0")
- @transient lazy val roc: DataFrame = binaryMetrics.roc().toDF("FPR", "TPR")
+sealed trait BinaryLogisticRegressionSummary extends LogisticRegressionSummary
+ with BinaryClassificationSummary {
- /**
- * Computes the area under the receiver operating characteristic (ROC) curve.
- */
- @Since("1.5.0")
- lazy val areaUnderROC: Double = binaryMetrics.areaUnderROC()
-
- /**
- * Returns the precision-recall curve, which is a Dataframe containing
- * two fields recall, precision with (0.0, 1.0) prepended to it.
- */
- @Since("1.5.0")
- @transient lazy val pr: DataFrame = binaryMetrics.pr().toDF("recall",
"precision")
-
- /**
- * Returns a dataframe with two fields (threshold, F-Measure) curve with
beta = 1.0.
- */
- @Since("1.5.0")
- @transient lazy val fMeasureByThreshold: DataFrame = {
- binaryMetrics.fMeasureByThreshold().toDF("threshold", "F-Measure")
- }
-
- /**
- * Returns a dataframe with two fields (threshold, precision) curve.
- * Every possible probability obtained in transforming the dataset are used
- * as thresholds used in calculating the precision.
- */
- @Since("1.5.0")
- @transient lazy val precisionByThreshold: DataFrame = {
- binaryMetrics.precisionByThreshold().toDF("threshold", "precision")
- }
-
- /**
- * Returns a dataframe with two fields (threshold, recall) curve.
- * Every possible probability obtained in transforming the dataset are used
- * as thresholds used in calculating the recall.
- */
- @Since("1.5.0")
- @transient lazy val recallByThreshold: DataFrame = {
- binaryMetrics.recallByThreshold().toDF("threshold", "recall")
+ override def scoreCol: String = if (probabilityCol.nonEmpty) {
Review comment:
Since existing computation of binary did not use `rawPredictionCol`, so
I think we can just let it alone for now.
In the furture, I think we can make it a little heuristic: if
`probabilityCol.nonEmpty` use `probabilityCol`; then try `rawPredictionCol`
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