[SPARK-13032][ML][PYSPARK] PySpark support model export/import and take LinearRegression as example

* Implement ```MLWriter/MLWritable/MLReader/MLReadable``` for PySpark.
* Making ```LinearRegression``` to support ```save/load``` as example. After this merged, the work for other transformers/estimators will be easy, then we can list and distribute the tasks to the community.

cc mengxr jkbradley

Author: Yanbo Liang <ybliang8@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #10469 from yanboliang/spark-11939.

1 files changed, 25 insertions, 5 deletions

diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 74a2248ed0..20dc6c2db9 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -18,9 +18,9 @@
 import warnings
 
 from pyspark import since
-from pyspark.ml.util import keyword_only
-from pyspark.ml.wrapper import JavaEstimator, JavaModel
 from pyspark.ml.param.shared import *
+from pyspark.ml.util import *
+from pyspark.ml.wrapper import JavaEstimator, JavaModel
 from pyspark.mllib.common import inherit_doc
 
 
@@ -35,7 +35,7 @@ __all__ = ['AFTSurvivalRegression', 'AFTSurvivalRegressionModel',
 @inherit_doc
 class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                        HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
-                       HasStandardization, HasSolver, HasWeightCol):
+                       HasStandardization, HasSolver, HasWeightCol, MLWritable, MLReadable):
     """
     Linear regression.
 
@@ -68,6 +68,25 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     Traceback (most recent call last):
         ...
     TypeError: Method setParams forces keyword arguments.
+    >>> import os, tempfile
+    >>> path = tempfile.mkdtemp()
+    >>> lr_path = path + "/lr"
+    >>> lr.save(lr_path)
+    >>> lr2 = LinearRegression.load(lr_path)
+    >>> lr2.getMaxIter()
+    5
+    >>> model_path = path + "/lr_model"
+    >>> model.save(model_path)
+    >>> model2 = LinearRegressionModel.load(model_path)
+    >>> model.coefficients[0] == model2.coefficients[0]
+    True
+    >>> model.intercept == model2.intercept
+    True
+    >>> from shutil import rmtree
+    >>> try:
+    ...     rmtree(path)
+    ... except OSError:
+    ...     pass
 
     .. versionadded:: 1.4.0
     """
@@ -106,7 +125,7 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
         return LinearRegressionModel(java_model)
 
 
-class LinearRegressionModel(JavaModel):
+class LinearRegressionModel(JavaModel, MLWritable, MLReadable):
     """
     Model fitted by LinearRegression.
 
@@ -821,9 +840,10 @@ class AFTSurvivalRegressionModel(JavaModel):
 
 if __name__ == "__main__":
     import doctest
+    import pyspark.ml.regression
     from pyspark.context import SparkContext
     from pyspark.sql import SQLContext
-    globs = globals().copy()
+    globs = pyspark.ml.regression.__dict__.copy()
     # The small batch size here ensures that we see multiple batches,
     # even in these small test examples:
     sc = SparkContext("local[2]", "ml.regression tests")