[SPARK-15316][PYSPARK][ML] Add linkPredictionCol to GeneralizedLinearRegression

## What changes were proposed in this pull request?

Add linkPredictionCol to GeneralizedLinearRegression and fix the PyDoc to generate the bullet list

## How was this patch tested?

doctests & built docs locally

Author: Holden Karau <holden@us.ibm.com>

Closes #13106 from holdenk/SPARK-15316-add-linkPredictionCol-toGeneralizedLinearRegression.

1 files changed, 35 insertions, 11 deletions

diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index cfcbbfc98e..25640b1f85 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -1245,10 +1245,14 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
     predictor (link function) and a description of the error distribution (family). It supports
     "gaussian", "binomial", "poisson" and "gamma" as family. Valid link functions for each family
     is listed below. The first link function of each family is the default one.
-    - "gaussian" -> "identity", "log", "inverse"
-    - "binomial" -> "logit", "probit", "cloglog"
-    - "poisson"  -> "log", "identity", "sqrt"
-    - "gamma"    -> "inverse", "identity", "log"
+
+    * "gaussian" -> "identity", "log", "inverse"
+
+    * "binomial" -> "logit", "probit", "cloglog"
+
+    * "poisson"  -> "log", "identity", "sqrt"
+
+    * "gamma"    -> "inverse", "identity", "log"
 
     .. seealso:: `GLM <https://en.wikipedia.org/wiki/Generalized_linear_model>`_
 
@@ -1258,9 +1262,12 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
     ...     (1.0, Vectors.dense(1.0, 2.0)),
     ...     (2.0, Vectors.dense(0.0, 0.0)),
     ...     (2.0, Vectors.dense(1.0, 1.0)),], ["label", "features"])
-    >>> glr = GeneralizedLinearRegression(family="gaussian", link="identity")
+    >>> glr = GeneralizedLinearRegression(family="gaussian", link="identity", linkPredictionCol="p")
     >>> model = glr.fit(df)
-    >>> abs(model.transform(df).head().prediction - 1.5) < 0.001
+    >>> transformed = model.transform(df)
+    >>> abs(transformed.head().prediction - 1.5) < 0.001
+    True
+    >>> abs(transformed.head().p - 1.5) < 0.001
     True
     >>> model.coefficients
     DenseVector([1.5..., -1.0...])
@@ -1290,20 +1297,23 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
                  "relationship between the linear predictor and the mean of the distribution " +
                  "function. Supported options: identity, log, inverse, logit, probit, cloglog " +
                  "and sqrt.", typeConverter=TypeConverters.toString)
+    linkPredictionCol = Param(Params._dummy(), "linkPredictionCol", "link prediction (linear " +
+                              "predictor) column name", typeConverter=TypeConverters.toString)
 
     @keyword_only
     def __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction",
                  family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,
-                 regParam=0.0, weightCol=None, solver="irls"):
+                 regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=""):
         """
         __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction", \
                  family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, \
-                 regParam=0.0, weightCol=None, solver="irls")
+                 regParam=0.0, weightCol=None, solver="irls", linkPredictionCol="")
         """
         super(GeneralizedLinearRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.regression.GeneralizedLinearRegression", self.uid)
-        self._setDefault(family="gaussian", maxIter=25, tol=1e-6, regParam=0.0, solver="irls")
+        self._setDefault(family="gaussian", maxIter=25, tol=1e-6, regParam=0.0, solver="irls",
+                         linkPredictionCol="")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -1311,11 +1321,11 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
     @since("2.0.0")
     def setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction",
                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,
-                  regParam=0.0, weightCol=None, solver="irls"):
+                  regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=""):
         """
         setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction", \
                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, \
-                  regParam=0.0, weightCol=None, solver="irls")
+                  regParam=0.0, weightCol=None, solver="irls", linkPredictionCol="")
         Sets params for generalized linear regression.
         """
         kwargs = self.setParams._input_kwargs
@@ -1339,6 +1349,20 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
         return self.getOrDefault(self.family)
 
     @since("2.0.0")
+    def setLinkPredictionCol(self, value):
+        """
+        Sets the value of :py:attr:`linkPredictionCol`.
+        """
+        return self._set(linkPredictionCol=value)
+
+    @since("2.0.0")
+    def getLinkPredictionCol(self):
+        """
+        Gets the value of linkPredictionCol or its default value.
+        """
+        return self.getOrDefault(self.linkPredictionCol)
+
+    @since("2.0.0")
     def setLink(self, value):
         """
         Sets the value of :py:attr:`link`.