2013-07-16 20:21:33 -04:00
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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2012-12-29 20:52:47 -05:00
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"""
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This example requires numpy (http://www.numpy.org/)
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"""
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from collections import namedtuple
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from math import exp
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from os.path import realpath
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import sys
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import numpy as np
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2013-01-01 16:52:14 -05:00
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from pyspark import SparkContext
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2012-12-29 20:52:47 -05:00
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N = 100000 # Number of data points
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D = 10 # Number of dimensions
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R = 0.7 # Scaling factor
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ITERATIONS = 5
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np.random.seed(42)
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DataPoint = namedtuple("DataPoint", ['x', 'y'])
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2013-07-27 21:11:28 -04:00
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from logistic_regression import DataPoint # So that DataPoint is properly serialized
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2012-12-29 20:52:47 -05:00
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def generateData():
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def generatePoint(i):
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y = -1 if i % 2 == 0 else 1
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x = np.random.normal(size=D) + (y * R)
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return DataPoint(x, y)
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return [generatePoint(i) for i in range(N)]
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if __name__ == "__main__":
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if len(sys.argv) == 1:
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2013-07-27 21:11:28 -04:00
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print >> sys.stderr, "Usage: logistic_regression <master> [<slices>]"
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2012-12-29 20:52:47 -05:00
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exit(-1)
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sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
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slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
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points = sc.parallelize(generateData(), slices).cache()
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# Initialize w to a random value
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w = 2 * np.random.ranf(size=D) - 1
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print "Initial w: " + str(w)
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def add(x, y):
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x += y
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return x
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for i in range(1, ITERATIONS + 1):
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print "On iteration %i" % i
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gradient = points.map(lambda p:
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(1.0 / (1.0 + exp(-p.y * np.dot(w, p.x)))) * p.y * p.x
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).reduce(add)
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w -= gradient
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print "Final w: " + str(w)
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