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Diffstat (limited to 'examples/src/main/python/logistic_regression.py')
-rwxr-xr-x | examples/src/main/python/logistic_regression.py | 76 |
1 files changed, 76 insertions, 0 deletions
diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py new file mode 100755 index 0000000000..fe5373cf79 --- /dev/null +++ b/examples/src/main/python/logistic_regression.py @@ -0,0 +1,76 @@ +# +# 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) != 4: + print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>" + exit(-1) + sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)]) + points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache() + iterations = int(sys.argv[3]) + + # 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) |