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Diffstat (limited to 'examples/src/main/python/mllib/logistic_regression.py')
-rwxr-xr-x | examples/src/main/python/mllib/logistic_regression.py | 50 |
1 files changed, 50 insertions, 0 deletions
diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py new file mode 100755 index 0000000000..8631051d00 --- /dev/null +++ b/examples/src/main/python/mllib/logistic_regression.py @@ -0,0 +1,50 @@ +# +# 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. +# + +""" +Logistic regression using MLlib. + +This example requires NumPy (http://www.numpy.org/). +""" + +from math import exp +import sys + +import numpy as np +from pyspark import SparkContext +from pyspark.mllib.regression import LabeledPoint +from pyspark.mllib.classification import LogisticRegressionWithSGD + + +# Parse a line of text into an MLlib LabeledPoint object +def parsePoint(line): + values = [float(s) for s in line.split(' ')] + if values[0] == -1: # Convert -1 labels to 0 for MLlib + values[0] = 0 + return LabeledPoint(values[0], values[1:]) + + +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") + points = sc.textFile(sys.argv[2]).map(parsePoint) + iterations = int(sys.argv[3]) + model = LogisticRegressionWithSGD.train(points, iterations) + print "Final weights: " + str(model.weights) + print "Final intercept: " + str(model.intercept) |