Added python binding for bulk recommendation

mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala

@@ -206,6 +206,24 @@ class PythonMLLibAPI extends Serializable {
     return new Rating(user, product, rating)
   }
 
+  private[spark] def unpackTuple(tupleBytes: Array[Byte]): (Int, Int) = {
+    val bb = ByteBuffer.wrap(tupleBytes)
+    bb.order(ByteOrder.nativeOrder())
+    val v1 = bb.getInt()
+    val v2 = bb.getInt()
+    (v1, v2)
+  }
+
+  private[spark] def serializeRating(rate: Rating): Array[Byte] = {
+    val bytes = new Array[Byte](24)
+    val bb = ByteBuffer.wrap(bytes)
+    bb.order(ByteOrder.nativeOrder())
+    bb.putDouble(rate.user.toDouble)
+    bb.putDouble(rate.product.toDouble)
+    bb.putDouble(rate.rating)
+    bytes
+  }
+
   /**
    * Java stub for Python mllib ALS.train().  This stub returns a handle
    * to the Java object instead of the content of the Java object.  Extra care

mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala

@@ -19,9 +19,11 @@ package org.apache.spark.mllib.recommendation
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.api.python.PythonMLLibAPI
 
 import org.jblas._
-import java.nio.{ByteOrder, ByteBuffer}
+import org.apache.spark.api.java.JavaRDD
+
 
 /**
  * Model representing the result of matrix factorization.
@@ -65,6 +67,12 @@ class MatrixFactorizationModel(
     }
   }
 
+  def predictJavaRDD(usersProductsJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]] = {
+    val pythonAPI = new PythonMLLibAPI()
+    val usersProducts = usersProductsJRDD.rdd.map(xBytes => pythonAPI.unpackTuple(xBytes))
+    predict(usersProducts).map(rate => pythonAPI.serializeRating(rate))
+  }
+
   // TODO: Figure out what other good bulk prediction methods would look like.
   // Probably want a way to get the top users for a product or vice-versa.
 }

python/pyspark/mllib/_common.py

@@ -213,6 +213,16 @@ def _serialize_rating(r):
     intpart[0], intpart[1], doublepart[0] = r
     return ba
 
+def _deserialize_rating(ba):
+    ar = ndarray(shape=(3, ), buffer=ba, dtype="float64", order='C')
+    return ar.copy()
+
+def _serialize_tuple(t):
+    ba = bytearray(8)
+    intpart = ndarray(shape=[2], buffer=ba, dtype=int32)
+    intpart[0], intpart[1] = t
+    return ba
+
 def _test():
     import doctest
     globs = globals().copy()

python/pyspark/mllib/recommendation.py

@@ -20,7 +20,10 @@
     _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
+    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
+    _serialize_tuple, _deserialize_rating
+from pyspark.serializers import BatchedSerializer
+from pyspark.rdd import RDD
 
 class MatrixFactorizationModel(object):
     """A matrix factorisation model trained by regularized alternating
@@ -45,6 +48,11 @@ def __del__(self):
     def predict(self, user, product):
         return self._java_model.predict(user, product)
 
+    def predictAll(self, usersProducts):
+        usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple)
+        return RDD(self._java_model.predictJavaRDD(usersProductsJRDD._jrdd),
+                   self._context, BatchedSerializer(_deserialize_rating, self._context._batchSize))
+
 class ALS(object):
     @classmethod
     def train(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):