[SPARK-1701] [PySpark] remove slice terminology from python examples

Author: Matthew Farrellee <matt@redhat.com>

Closes #2304 from mattf/SPARK-1701-partition-over-slice-for-python-examples and squashes the following commits:

928a581 [Matthew Farrellee] [SPARK-1701] [PySpark] remove slice terminology from python examples

examples/src/main/python/als.py

@@ -54,7 +54,7 @@ def update(i, vec, mat, ratings):
 if __name__ == "__main__":
 
     """
-    Usage: als [M] [U] [F] [iterations] [slices]"
+    Usage: als [M] [U] [F] [iterations] [partitions]"
     """
 
     print >> sys.stderr, """WARN: This is a naive implementation of ALS and is given as an
@@ -66,10 +66,10 @@ if __name__ == "__main__":
     U = int(sys.argv[2]) if len(sys.argv) > 2 else 500
     F = int(sys.argv[3]) if len(sys.argv) > 3 else 10
     ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5
-    slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
+    partitions = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
-    print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
-        (M, U, F, ITERATIONS, slices)
+    print "Running ALS with M=%d, U=%d, F=%d, iters=%d, partitions=%d\n" % \
+        (M, U, F, ITERATIONS, partitions)
 
     R = matrix(rand(M, F)) * matrix(rand(U, F).T)
     ms = matrix(rand(M, F))
@@ -80,7 +80,7 @@ if __name__ == "__main__":
     usb = sc.broadcast(us)
 
     for i in range(ITERATIONS):
-        ms = sc.parallelize(range(M), slices) \
+        ms = sc.parallelize(range(M), partitions) \
                .map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \
                .collect()
         # collect() returns a list, so array ends up being
@@ -88,7 +88,7 @@ if __name__ == "__main__":
         ms = matrix(np.array(ms)[:, :, 0])
         msb = sc.broadcast(ms)
 
-        us = sc.parallelize(range(U), slices) \
+        us = sc.parallelize(range(U), partitions) \
                .map(lambda x: update(x, usb.value[x, :], msb.value, Rb.value.T)) \
                .collect()
         us = matrix(np.array(us)[:, :, 0])

examples/src/main/python/pi.py

@@ -24,18 +24,18 @@ from pyspark import SparkContext
 
 if __name__ == "__main__":
     """
-        Usage: pi [slices]
+        Usage: pi [partitions]
     """
     sc = SparkContext(appName="PythonPi")
-    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
-    n = 100000 * slices
+    partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
+    n = 100000 * partitions
 
     def f(_):
         x = random() * 2 - 1
         y = random() * 2 - 1
         return 1 if x ** 2 + y ** 2 < 1 else 0
 
-    count = sc.parallelize(xrange(1, n + 1), slices).map(f).reduce(add)
+    count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add)
     print "Pi is roughly %f" % (4.0 * count / n)
 
     sc.stop()

examples/src/main/python/transitive_closure.py

@@ -37,11 +37,11 @@ def generateGraph():
 
 if __name__ == "__main__":
     """
-    Usage: transitive_closure [slices]
+    Usage: transitive_closure [partitions]
     """
     sc = SparkContext(appName="PythonTransitiveClosure")
-    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
-    tc = sc.parallelize(generateGraph(), slices).cache()
+    partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
+    tc = sc.parallelize(generateGraph(), partitions).cache()
 
     # Linear transitive closure: each round grows paths by one edge,
     # by joining the graph's edges with the already-discovered paths.