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author | Josh Rosen <joshrosen@eecs.berkeley.edu> | 2012-12-29 17:52:47 -0800 |
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committer | Josh Rosen <joshrosen@eecs.berkeley.edu> | 2012-12-29 18:00:28 -0800 |
commit | 099898b43955d99351ec94d4a373de854bf7edf7 (patch) | |
tree | d08d475fee10fe9769cba18551c721b9bc3f5456 /pyspark/examples/lr.py | |
parent | 39dd953fd88e9aa7335603ab452d9c1bed4ba67a (diff) | |
download | spark-099898b43955d99351ec94d4a373de854bf7edf7.tar.gz spark-099898b43955d99351ec94d4a373de854bf7edf7.tar.bz2 spark-099898b43955d99351ec94d4a373de854bf7edf7.zip |
Port LR example to PySpark using numpy.
This version of the example crashes after the first iteration with
"OverflowError: math range error" because Python's math.exp()
behaves differently than Scala's; see SPARK-646.
Diffstat (limited to 'pyspark/examples/lr.py')
-rwxr-xr-x | pyspark/examples/lr.py | 57 |
1 files changed, 57 insertions, 0 deletions
diff --git a/pyspark/examples/lr.py b/pyspark/examples/lr.py new file mode 100755 index 0000000000..5fca0266b8 --- /dev/null +++ b/pyspark/examples/lr.py @@ -0,0 +1,57 @@ +""" +This example requires numpy (http://www.numpy.org/) +""" +from collections import namedtuple +from math import exp +from os.path import realpath +import sys + +import numpy as np +from pyspark.context import SparkContext + + +N = 100000 # Number of data points +D = 10 # Number of dimensions +R = 0.7 # Scaling factor +ITERATIONS = 5 +np.random.seed(42) + + +DataPoint = namedtuple("DataPoint", ['x', 'y']) +from lr import DataPoint # So that DataPoint is properly serialized + + +def generateData(): + def generatePoint(i): + y = -1 if i % 2 == 0 else 1 + x = np.random.normal(size=D) + (y * R) + return DataPoint(x, y) + return [generatePoint(i) for i in range(N)] + + +if __name__ == "__main__": + if len(sys.argv) == 1: + print >> sys.stderr, \ + "Usage: PythonLR <host> [<slices>]" + exit(-1) + sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)]) + slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 + points = sc.parallelize(generateData(), slices).cache() + + # Initialize w to a random value + w = 2 * np.random.ranf(size=D) - 1 + print "Initial w: " + str(w) + + def add(x, y): + x += y + return x + + for i in range(1, ITERATIONS + 1): + print "On iteration %i" % i + + gradient = points.map(lambda p: + (1.0 / (1.0 + exp(-p.y * np.dot(w, p.x)))) * p.y * p.x + ).reduce(add) + w -= gradient + + print "Final w: " + str(w) |