Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/11621#discussion_r58467841
--- Diff: python/pyspark/ml/regression.py ---
@@ -151,6 +151,209 @@ 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_lrt_summary = self._call_java("summary")
+ return LinearRegressionTrainingSummary(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.
+ """
+ java_lr_summary = self._call_java("evaluate", dataset)
+ return LinearRegressionSummary(java_lr_summary)
+
+
+class LinearRegressionSummary(JavaCallable):
+ """
+ .. note:: Experimental
+
+ Linear regression results evaluated on a dataset.
+
+ .. 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 predictionCol(self):
+ """
+ Field in "predictions" which gives the predicted value of
+ the label at each instance.
+ """
+ return self._call_java("predictionCol")
+
+ @property
+ @since("2.0.0")
+ def labelCol(self):
+ """
+ Field in "predictions" which gives the true label of each
+ instance.
+ """
+ return self._call_java("labelCol")
+
+ @property
+ @since("2.0.0")
+ def explainedVariance(self):
+ """
+ Returns the explained variance regression score.
+ explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
+ Reference: http://en.wikipedia.org/wiki/Explained_variation
+
+ Note: This ignores instance weights (setting all to 1.0) from
+ `LinearRegression.weightCol`. This will change in later Spark
+ versions.
+ """
+ return self._call_java("explainedVariance")
+
+ @property
+ @since("2.0.0")
+ def meanAbsoluteError(self):
+ """
+ Returns the mean absolute error, which is a risk function
+ corresponding to the expected value of the absolute error
+ loss or l1-norm loss.
+
+ Note: This ignores instance weights (setting all to 1.0) from
+ `LinearRegression.weightCol`. This will change in later Spark
+ versions.
+ """
+ return self._call_java("meanAbsoluteError")
+
+ @property
+ @since("2.0.0")
+ def meanSquaredError(self):
+ """
+ Returns the mean squared error, which is a risk function
+ corresponding to the expected value of the squared error
+ loss or quadratic loss.
+
+ Note: This ignores instance weights (setting all to 1.0) from
+ `LinearRegression.weightCol`. This will change in later Spark
+ versions.
+ """
+ return self._call_java("meanSquaredError")
+
+ @property
+ @since("2.0.0")
+ def rootMeanSquaredError(self):
+ """
+ Returns the root mean squared error, which is defined as the
+ square root of the mean squared error.
+
+ Note: This ignores instance weights (setting all to 1.0) from
+ `LinearRegression.weightCol`. This will change in later Spark
+ versions.
+ """
+ return self._call_java("rootMeanSquaredError")
+
+ @property
+ @since("2.0.0")
+ def r2(self):
+ """
+ Returns R^2^, the coefficient of determination.
+ Reference:
http://en.wikipedia.org/wiki/Coefficient_of_determination
+
+ Note: This ignores instance weights (setting all to 1.0) from
+ `LinearRegression.weightCol`. This will change in later Spark
+ versions.
+ """
+ return self._call_java("r2")
+
+ @property
+ @since("2.0.0")
+ def residuals(self):
+ """
+ Residuals (label - predicted value)
+ """
+ return self._call_java("residuals")
+
+ @property
+ @since("2.0.0")
+ def numInstances(self):
+ """
+ Number of instances in DataFrame predictions
+ """
+ return self._call_java("numInstances")
+
+ @property
+ @since("2.0.0")
+ def devianceResiduals(self):
+ """
+ The weighted residuals, the usual residuals rescaled by the
+ square root of the instance weights.
+ """
+ return self._call_java("devianceResiduals")
+
+ @property
+ @since("2.0.0")
+ def coefficientStandardErrors(self):
+ """
+ Standard error of estimated coefficients and intercept.
+ """
+ return self._call_java("coefficientStandardErrors")
+
+ @property
+ @since("2.0.0")
+ def tValues(self):
+ """
+ T-statistic of estimated coefficients and intercept.
+ """
+ return self._call_java("tValues")
+
+ @property
+ @since("2.0.0")
+ def pValues(self):
+ """
+ Two-sided p-value of estimated coefficients and intercept.
+ """
+ return self._call_java("pValues")
--- End diff --
+1 for noting that they may not be available depending on the solver. How
about:
```
This value is only available when using the "normal" solver.
.. seealso:: :py:attr:`LinearRegression.solver`
```
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