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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
A logistic regression implementation that uses NumPy (http://www.numpy.org)
to act on batches of input data using efficient matrix operations.
In practice, one may prefer to use the LogisticRegression algorithm in
MLlib, as shown in examples/src/main/python/mllib/logistic_regression.py.
"""
from collections import namedtuple
from math import exp
from os.path import realpath
import sys
import numpy as np
from pyspark import SparkContext
D = 10 # Number of dimensions
# Read a batch of points from the input file into a NumPy matrix object. We operate on batches to
# make further computations faster.
# The data file contains lines of the form <label> <x1> <x2> ... <xD>. We load each block of these
# into a NumPy array of size numLines * (D + 1) and pull out column 0 vs the others in gradient().
def readPointBatch(iterator):
strs = list(iterator)
matrix = np.zeros((len(strs), D + 1))
for i in xrange(len(strs)):
matrix[i] = np.fromstring(strs[i].replace(',', ' '), dtype=np.float32, sep=' ')
return [matrix]
if __name__ == "__main__":
if len(sys.argv) != 3:
print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
exit(-1)
sc = SparkContext(appName="PythonLR")
points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache()
iterations = int(sys.argv[2])
# Initialize w to a random value
w = 2 * np.random.ranf(size=D) - 1
print "Initial w: " + str(w)
# Compute logistic regression gradient for a matrix of data points
def gradient(matrix, w):
Y = matrix[:,0] # point labels (first column of input file)
X = matrix[:,1:] # point coordinates
# For each point (x, y), compute gradient function, then sum these up
return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1)
def add(x, y):
x += y
return x
for i in range(iterations):
print "On iteration %i" % (i + 1)
w -= points.map(lambda m: gradient(m, w)).reduce(add)
print "Final w: " + str(w)
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