examples/src/main/python/mllib/logistic_regression.py

#
# 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 __future__ import print_function

import sys

from pyspark import SparkContext
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import LogisticRegressionWithSGD


def parsePoint(line):
    """
    Parse a line of text into an MLlib LabeledPoint object.
    """
    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) != 3:
        print("Usage: logistic_regression <file> <iterations>", file=sys.stderr)
        exit(-1)
    sc = SparkContext(appName="PythonLR")
    points = sc.textFile(sys.argv[1]).map(parsePoint)
    iterations = int(sys.argv[2])
    model = LogisticRegressionWithSGD.train(points, iterations)
    print("Final weights: " + str(model.weights))
    print("Final intercept: " + str(model.intercept))
    sc.stop()