Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/19185#discussion_r138047016
--- Diff: python/pyspark/ml/classification.py ---
@@ -603,6 +614,112 @@ def featuresCol(self):
"""
return self._call_java("featuresCol")
+ @property
+ @since("2.3.0")
+ def labels(self):
+ """
+ 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.
+ """
+ return self._call_java("labels")
+
+ @property
+ @since("2.3.0")
+ def truePositiveRateByLabel(self):
+ """
+ Returns true positive rate for each label (category).
+ """
+ return self._call_java("truePositiveRateByLabel")
+
+ @property
+ @since("2.3.0")
+ def falsePositiveRateByLabel(self):
+ """
+ Returns false positive rate for each label (category).
+ """
+ return self._call_java("falsePositiveRateByLabel")
+
+ @property
+ @since("2.3.0")
+ def precisionByLabel(self):
+ """
+ Returns precision for each label (category).
+ """
+ return self._call_java("precisionByLabel")
+
+ @property
+ @since("2.3.0")
+ def recallByLabel(self):
+ """
+ Returns recall for each label (category).
+ """
+ return self._call_java("recallByLabel")
+
+ @property
+ @since("2.3.0")
+ def fMeasureByLabel(self, beta=1.0):
+ """
+ Returns f-measure for each label (category).
+ """
+ return self._call_java("fMeasureByLabel", beta)
+
+ @property
+ @since("2.3.0")
+ def accuracy(self):
+ """
+ Returns accuracy.
+ (equals to the total number of correctly classified instances
+ out of the total number of instances.)
+ """
+ return self._call_java("accuracy")
+
+ @property
+ @since("2.3.0")
+ def weightedTruePositiveRate(self):
+ """
+ Returns weighted true positive rate.
+ (equals to precision, recall and f-measure)
+ """
+ return self._call_java("weightedTruePositiveRate")
+
+ @property
+ @since("2.3.0")
+ def weightedFalsePositiveRate(self):
+ """
+ Returns weighted false positive rate.
+ """
+ return self._call_java("weightedFalsePositiveRate")
+
+ @property
+ @since("2.3.0")
+ def weightedRecall(self):
+ """
+ Returns weighted averaged recall.
+ (equals to precision, recall and f-measure)
+ """
+ return self._call_java("weightedRecall")
+
+ @property
+ @since("2.3.0")
+ def weightedPrecision(self):
+ """
+ Returns weighted averaged precision.
+ """
+ return self._call_java("weightedPrecision")
+
+ @property
--- End diff --
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