From 05163057a1810f0a32b722e8c93e5435240636d9 Mon Sep 17 00:00:00 2001
From: Tor Myklebust <tmyklebu@gmail.com>
Date: Wed, 25 Dec 2013 00:08:05 -0500
Subject: Split the mllib bindings into a whole bunch of modules and rename
 some things.

---
 python/pyspark/mllib/recommendation.py | 74 ++++++++++++++++++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 python/pyspark/mllib/recommendation.py

(limited to 'python/pyspark/mllib/recommendation.py')

diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
new file mode 100644
index 0000000000..14d06cba21
--- /dev/null
+++ b/python/pyspark/mllib/recommendation.py
@@ -0,0 +1,74 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark import SparkContext
+from pyspark.mllib._common import \
+    _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
+    _serialize_double_matrix, _deserialize_double_matrix, \
+    _serialize_double_vector, _deserialize_double_vector, \
+    _get_initial_weights, _serialize_rating, _regression_train_wrapper
+
+class MatrixFactorizationModel(object):
+    """A matrix factorisation model trained by regularized alternating
+    least-squares.
+
+    >>> r1 = (1, 1, 1.0)
+    >>> r2 = (1, 2, 2.0)
+    >>> r3 = (2, 1, 2.0)
+    >>> ratings = sc.parallelize([r1, r2, r3])
+    >>> model = ALS.trainImplicit(sc, ratings, 1)
+    >>> model.predict(2,2) is not None
+    True
+    """
+
+    def __init__(self, sc, java_model):
+        self._context = sc
+        self._java_model = java_model
+
+    def __del__(self):
+        self._context._gateway.detach(self._java_model)
+
+    def predict(self, user, product):
+        return self._java_model.predict(user, product)
+
+class ALS(object):
+    @classmethod
+    def train(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
+        ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
+        mod = sc._jvm.PythonMLLibAPI().trainALSModel(ratingBytes._jrdd,
+                rank, iterations, lambda_, blocks)
+        return MatrixFactorizationModel(sc, mod)
+
+    @classmethod
+    def trainImplicit(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
+        ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
+        mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(ratingBytes._jrdd,
+                rank, iterations, lambda_, blocks, alpha)
+        return MatrixFactorizationModel(sc, mod)
+
+def _test():
+    import doctest
+    globs = globals().copy()
+    globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
+    (failure_count, test_count) = doctest.testmod(globs=globs,
+            optionflags=doctest.ELLIPSIS)
+    globs['sc'].stop()
+    if failure_count:
+        exit(-1)
+
+if __name__ == "__main__":
+    _test()
-- 
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