Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/12961#discussion_r63009814
--- Diff: python/pyspark/ml/regression.py ---
@@ -1337,6 +1338,204 @@ def intercept(self):
"""
return self._call_java("intercept")
+ @property
+ @since("2.0.0")
+ def summary(self):
+ """
+ Gets summary (e.g. residuals, deviance, pValues) of model on
+ training set. An exception is thrown if
+ `trainingSummary is None`.
+ """
+ java_lrt_summary = self._call_java("summary")
+ return GeneralizedLinearRegressionTrainingSummary(java_lrt_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, where dataset is an
+ instance of :py:class:`pyspark.sql.DataFrame`
+ """
+ if not isinstance(dataset, DataFrame):
+ raise ValueError("dataset must be a DataFrame but got %s." %
type(dataset))
+ java_glr_summary = self._call_java("evaluate", dataset)
+ return GeneralizedLinearRegressionSummary(java_glr_summary)
+
+
+class GeneralizedLinearRegressionSummary(JavaWrapper):
+ """
+ .. note:: Experimental
+
+ Generalized linear regression results evaluated on a dataset.
+
+ .. versionadded:: 2.0.0
+ """
+
+ @property
+ @since("2.0.0")
+ def predictions(self):
+ """
+ Predictions output by the model's `transform` method.
+ """
+ return self._call_java("predictions")
+
+ @property
+ @since("2.0.0")
+ def predictionCol(self):
+ """
+ Field in :py:attr:`predictions` which gives the predicted value of
each instance.
+ This is set to a new column name if the original model's
`predictionCol` is not set.
+ """
+ return self._call_java("predictionCol")
+
+ @property
+ @since("2.0.0")
+ def rank(self):
+ """
+ The numeric rank of the fitted linear model.
+ """
+ return self._call_java("rank")
+
+ @property
+ @since("2.0.0")
+ def degreesOfFreedom(self):
+ """
+ Degrees of freedom.
+ """
+ return self._call_java("degreesOfFreedom")
+
+ @property
+ @since("2.0.0")
+ def residualDegreeOfFreedom(self):
+ """
+ The residual degrees of freedom.
+ """
+ return self._call_java("residualDegreeOfFreedom")
+
+ @property
+ @since("2.0.0")
+ def residualDegreeOfFreedomNull(self):
+ """
+ The residual degrees of freedom for the null model.
+ """
+ return self._call_java("residualDegreeOfFreedomNull")
+
+ @since("2.0.0")
+ def residuals(self, residualsType="deviance"):
+ """
+ Get the residuals of the fitted model by type.
--- End diff --
Minor: Add doc for default returned residuals (deviance residuals).
---
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