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[SPARK-7036][MLLIB] ALS.train should support DataFrames in PySpark #5619

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[SPARK-7036][MLLIB] ALS.train should support DataFrames in PySpark #5619

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36 changes: 26 additions & 10 deletions python/pyspark/mllib/recommendation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
from pyspark.rdd import RDD
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
from pyspark.mllib.util import JavaLoader, JavaSaveable
from pyspark.sql import DataFrame

__all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']

Expand Down Expand Up @@ -78,18 +79,23 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
True

>>> model = ALS.train(ratings, 1, nonnegative=True, seed=10)
>>> model.predict(2,2)
>>> model.predict(2, 2)
3.8...

>>> df = sqlContext.createDataFrame([Rating(1, 1, 1.0), Rating(1, 2, 2.0), Rating(2, 1, 2.0)])
>>> model = ALS.train(df, 1, nonnegative=True, seed=10)
>>> model.predict(2, 2)
3.8...

>>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10)
>>> model.predict(2,2)
>>> model.predict(2, 2)
0.4...

>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> model.save(sc, path)
>>> sameModel = MatrixFactorizationModel.load(sc, path)
>>> sameModel.predict(2,2)
>>> sameModel.predict(2, 2)
0.4...
>>> sameModel.predictAll(testset).collect()
[Rating(...
Expand Down Expand Up @@ -125,13 +131,20 @@ class ALS(object):

@classmethod
def _prepare(cls, ratings):
assert isinstance(ratings, RDD), "ratings should be RDD"
if isinstance(ratings, RDD):
pass
elif isinstance(ratings, DataFrame):
ratings = ratings.rdd
else:
raise TypeError("Ratings should be represented by either an RDD or a DataFrame, "
"but got %s." % type(ratings))
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a minor issue: maybe better to use "%s is provided"

first = ratings.first()
if not isinstance(first, Rating):
if isinstance(first, (tuple, list)):
ratings = ratings.map(lambda x: Rating(*x))
else:
raise ValueError("rating should be RDD of Rating or tuple/list")
if isinstance(first, Rating):
pass
elif isinstance(first, (tuple, list)):
ratings = ratings.map(lambda x: Rating(*x))
else:
raise TypeError("Expect a Rating or a tuple/list, but got %s." % type(first))
return ratings

@classmethod
Expand All @@ -152,8 +165,11 @@ def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alp
def _test():
import doctest
import pyspark.mllib.recommendation
from pyspark.sql import SQLContext
globs = pyspark.mllib.recommendation.__dict__.copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest')
sc = SparkContext('local[4]', 'PythonTest')
globs['sc'] = sc
globs['sqlContext'] = SQLContext(sc)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
Expand Down