[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, 16 insertions, 17 deletions

diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index dd1d4b076e..d4d48eb215 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -21,21 +21,10 @@ from pyspark import SparkContext
 from pyspark.sql import DataFrame
 from pyspark.ml.param import Params
 from pyspark.ml.pipeline import Estimator, Transformer, Model
+from pyspark.ml.util import _jvm
 from pyspark.mllib.common import inherit_doc, _java2py, _py2java
 
 
-def _jvm():
-    """
-    Returns the JVM view associated with SparkContext. Must be called
-    after SparkContext is initialized.
-    """
-    jvm = SparkContext._jvm
-    if jvm:
-        return jvm
-    else:
-        raise AttributeError("Cannot load _jvm from SparkContext. Is SparkContext initialized?")
-
-
 @inherit_doc
 class JavaWrapper(Params):
     """
@@ -159,15 +148,24 @@ class JavaModel(Model, JavaTransformer):
 
     __metaclass__ = ABCMeta
 
-    def __init__(self, java_model):
+    def __init__(self, java_model=None):
         """
         Initialize this instance with a Java model object.
         Subclasses should call this constructor, initialize params,
         and then call _transformer_params_from_java.
+
+        This instance can be instantiated without specifying java_model,
+        it will be assigned after that, but this scenario only used by
+        :py:class:`JavaMLReader` to load models.  This is a bit of a
+        hack, but it is easiest since a proper fix would require
+        MLReader (in pyspark.ml.util) to depend on these wrappers, but
+        these wrappers depend on pyspark.ml.util (both directly and via
+        other ML classes).
         """
         super(JavaModel, self).__init__()
-        self._java_obj = java_model
-        self.uid = java_model.uid()
+        if java_model is not None:
+            self._java_obj = java_model
+            self.uid = java_model.uid()
 
     def copy(self, extra=None):
         """
@@ -182,8 +180,9 @@ class JavaModel(Model, JavaTransformer):
         if extra is None:
             extra = dict()
         that = super(JavaModel, self).copy(extra)
-        that._java_obj = self._java_obj.copy(self._empty_java_param_map())
-        that._transfer_params_to_java()
+        if self._java_obj is not None:
+            that._java_obj = self._java_obj.copy(self._empty_java_param_map())
+            that._transfer_params_to_java()
         return that
 
     def _call_java(self, name, *args):