[SPARK-7648] [MLLIB] Add weights and intercept to GLM wrappers in spark.ml

Otherwise, users can only use `transform` on the models. brkyvz

Author: Xiangrui Meng <meng@databricks.com>

Closes #6156 from mengxr/SPARK-7647 and squashes the following commits:

1ae3d2d [Xiangrui Meng] add weights and intercept to LogisticRegression in Python
f49eb46 [Xiangrui Meng] add weights and intercept to LinearRegressionModel

1 files changed, 18 insertions, 0 deletions

diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 0ab5c6c3d2..2803864ff4 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -51,6 +51,10 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     -1.0
+    >>> model.weights
+    DenseVector([1.0])
+    >>> model.intercept
+    0.0
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     1.0
@@ -117,6 +121,20 @@ class LinearRegressionModel(JavaModel):
     Model fitted by LinearRegression.
     """
 
+    @property
+    def weights(self):
+        """
+        Model weights.
+        """
+        return self._call_java("weights")
+
+    @property
+    def intercept(self):
+        """
+        Model intercept.
+        """
+        return self._call_java("intercept")
+
 
 class TreeRegressorParams(object):
     """