Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/11621#discussion_r57965918
--- Diff: python/pyspark/ml/classification.py ---
@@ -233,6 +234,210 @@ def intercept(self):
"""
return self._call_java("intercept")
+ @property
+ @since("2.0.0")
+ def summary(self):
+ """
+ Gets summary (e.g. residuals, mse, r-squared ) of model on
+ training set. An exception is thrown if
+ `trainingSummary == None`.
+ """
+ java_blrt_summary = self._call_java("summary")
+ return BinaryLogisticRegressionTrainingSummary(java_blrt_summary)
+
+ @property
+ @since("2.0.0")
+ def hasSummary(self):
+ """
+ Indicates whether a training summary exists for this model
+ instance.
+ """
+ return self._call_java("hasSummary")
+
+ @since("2.0.0")
+ def evaluate(self, dataset):
+ """
+ Evaluates the model on a test dataset.
+
+ :param dataset:
+ Test dataset to evaluate model on.
+ """
+ java_blr_summary = self._call_java("evaluate", dataset)
+ return BinaryLogisticRegressionSummary(java_blr_summary)
+
+
+class LogisticRegressionSummary(JavaCallable):
+ """
+ Abstraction for Logistic Regression Results for a given model.
+
+ .. versionadded:: 2.0.0
+ """
+
+ @property
+ @since("2.0.0")
+ def predictions(self):
+ """
+ Dataframe outputted by the model's `transform` method.
+ """
+ return self._call_java("predictions")
+
+ @property
+ @since("2.0.0")
+ def probabilityCol(self):
+ """
+ Field in "predictions" which gives the calibrated probability
+ of each instance as a vector.
+ """
+ return self._call_java("probabilityCol")
+
+ @property
+ @since("2.0.0")
+ def labelCol(self):
+ """
+ Field in "predictions" which gives the true label of each
+ instance.
--- End diff --
Add: "(if available)" Same for Scala doc
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