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"""
This example requires numpy (http://www.numpy.org/)
"""
from os.path import realpath
import sys
import numpy as np
from numpy.random import rand
from numpy import matrix
from pyspark import SparkContext
LAMBDA = 0.01 # regularization
np.random.seed(42)
def rmse(R, ms, us):
diff = R - ms * us.T
return np.sqrt(np.sum(np.power(diff, 2)) / M * U)
def update(i, vec, mat, ratings):
uu = mat.shape[0]
ff = mat.shape[1]
XtX = matrix(np.zeros((ff, ff)))
Xty = np.zeros((ff, 1))
for j in range(uu):
v = mat[j, :]
XtX += v.T * v
Xty += v.T * ratings[i, j]
XtX += np.eye(ff, ff) * LAMBDA * uu
return np.linalg.solve(XtX, Xty)
if __name__ == "__main__":
if len(sys.argv) < 2:
print >> sys.stderr, \
"Usage: PythonALS <master> <M> <U> <F> <iters> <slices>"
exit(-1)
sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)])
M = int(sys.argv[2]) if len(sys.argv) > 2 else 100
U = int(sys.argv[3]) if len(sys.argv) > 3 else 500
F = int(sys.argv[4]) if len(sys.argv) > 4 else 10
ITERATIONS = int(sys.argv[5]) if len(sys.argv) > 5 else 5
slices = int(sys.argv[6]) if len(sys.argv) > 6 else 2
print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
(M, U, F, ITERATIONS, slices)
R = matrix(rand(M, F)) * matrix(rand(U, F).T)
ms = matrix(rand(M ,F))
us = matrix(rand(U, F))
Rb = sc.broadcast(R)
msb = sc.broadcast(ms)
usb = sc.broadcast(us)
for i in range(ITERATIONS):
ms = sc.parallelize(range(M), slices) \
.map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \
.collect()
ms = matrix(np.array(ms)[:, :, 0]) # collect() returns a list, so array ends up being
# a 3-d array, we take the first 2 dims for the matrix
msb = sc.broadcast(ms)
us = sc.parallelize(range(U), slices) \
.map(lambda x: update(x, usb.value[x, :], msb.value, Rb.value.T)) \
.collect()
us = matrix(np.array(us)[:, :, 0])
usb = sc.broadcast(us)
error = rmse(R, ms, us)
print "Iteration %d:" % i
print "\nRMSE: %5.4f\n" % error
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