[SPARK-13037][ML][PYSPARK] PySpark ml.recommendation support export/import

PySpark ml.recommendation support export/import.

Author: Kai Jiang <jiangkai@gmail.com>

Closes #11044 from vectorijk/spark-13037.

python/pyspark/ml/recommendation.py

@@ -16,7 +16,7 @@
 #
 
 from pyspark import since
-from pyspark.ml.util import keyword_only
+from pyspark.ml.util import *
 from pyspark.ml.wrapper import JavaEstimator, JavaModel
 from pyspark.ml.param.shared import *
 from pyspark.mllib.common import inherit_doc
@@ -26,7 +26,8 @@ __all__ = ['ALS', 'ALSModel']
 
 
 @inherit_doc
-class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, HasRegParam, HasSeed):
+class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, HasRegParam, HasSeed,
+          MLWritable, MLReadable):
     """
     Alternating Least Squares (ALS) matrix factorization.
 
@@ -81,6 +82,27 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
     Row(user=1, item=0, prediction=2.6258413791656494)
     >>> predictions[2]
     Row(user=2, item=0, prediction=-1.5018409490585327)
+    >>> import os, tempfile
+    >>> path = tempfile.mkdtemp()
+    >>> als_path = path + "/als"
+    >>> als.save(als_path)
+    >>> als2 = ALS.load(als_path)
+    >>> als.getMaxIter()
+    5
+    >>> model_path = path + "/als_model"
+    >>> model.save(model_path)
+    >>> model2 = ALSModel.load(model_path)
+    >>> model.rank == model2.rank
+    True
+    >>> sorted(model.userFactors.collect()) == sorted(model2.userFactors.collect())
+    True
+    >>> sorted(model.itemFactors.collect()) == sorted(model2.itemFactors.collect())
+    True
+    >>> from shutil import rmtree
+    >>> try:
+    ...     rmtree(path)
+    ... except OSError:
+    ...     pass
 
     .. versionadded:: 1.4.0
     """
@@ -274,7 +296,7 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
         return self.getOrDefault(self.nonnegative)
 
 
-class ALSModel(JavaModel):
+class ALSModel(JavaModel, MLWritable, MLReadable):
     """
     Model fitted by ALS.
 
@@ -308,9 +330,10 @@ class ALSModel(JavaModel):
 
 if __name__ == "__main__":
     import doctest
+    import pyspark.ml.recommendation
     from pyspark.context import SparkContext
     from pyspark.sql import SQLContext
-    globs = globals().copy()
+    globs = pyspark.ml.recommendation.__dict__.copy()
     # The small batch size here ensures that we see multiple batches,
     # even in these small test examples:
     sc = SparkContext("local[2]", "ml.recommendation tests")