d33d3c61ae
Author: Reynold Xin <rxin@apache.org> Closes #871 from rxin/mllib-pep8 and squashes the following commits: 848416f [Reynold Xin] Fixed a typo in the previous cleanup (c -> sc). a8db4cd [Reynold Xin] Fix PEP8 violations in Python mllib.
199 lines
7.1 KiB
Python
199 lines
7.1 KiB
Python
#
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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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import numpy
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from numpy import array, shape
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from pyspark import SparkContext
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from pyspark.mllib._common import \
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_dot, _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
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_serialize_double_matrix, _deserialize_double_matrix, \
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_serialize_double_vector, _deserialize_double_vector, \
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_get_initial_weights, _serialize_rating, _regression_train_wrapper, \
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_linear_predictor_typecheck, _get_unmangled_labeled_point_rdd
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from pyspark.mllib.linalg import SparseVector
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from pyspark.mllib.regression import LabeledPoint, LinearModel
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from math import exp, log
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class LogisticRegressionModel(LinearModel):
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"""A linear binary classification model derived from logistic regression.
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>>> data = [
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... LabeledPoint(0.0, [0.0]),
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... LabeledPoint(1.0, [1.0]),
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... LabeledPoint(1.0, [2.0]),
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... LabeledPoint(1.0, [3.0])
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... ]
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>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data))
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>>> lrm.predict(array([1.0])) > 0
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True
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>>> lrm.predict(array([0.0])) <= 0
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True
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>>> sparse_data = [
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... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
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... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
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... ]
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>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data))
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>>> lrm.predict(array([0.0, 1.0])) > 0
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True
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>>> lrm.predict(array([0.0, 0.0])) <= 0
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True
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>>> lrm.predict(SparseVector(2, {1: 1.0})) > 0
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True
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>>> lrm.predict(SparseVector(2, {1: 0.0})) <= 0
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True
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"""
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def predict(self, x):
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_linear_predictor_typecheck(x, self._coeff)
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margin = _dot(x, self._coeff) + self._intercept
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prob = 1/(1 + exp(-margin))
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return 1 if prob > 0.5 else 0
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class LogisticRegressionWithSGD(object):
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@classmethod
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def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None):
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"""Train a logistic regression model on the given data."""
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sc = data.context
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train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
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d._jrdd, iterations, step, miniBatchFraction, i)
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return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
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initialWeights)
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class SVMModel(LinearModel):
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"""A support vector machine.
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>>> data = [
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... LabeledPoint(0.0, [0.0]),
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... LabeledPoint(1.0, [1.0]),
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... LabeledPoint(1.0, [2.0]),
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... LabeledPoint(1.0, [3.0])
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... ]
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>>> svm = SVMWithSGD.train(sc.parallelize(data))
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>>> svm.predict(array([1.0])) > 0
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True
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>>> sparse_data = [
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... LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
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... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
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... ]
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>>> svm = SVMWithSGD.train(sc.parallelize(sparse_data))
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>>> svm.predict(SparseVector(2, {1: 1.0})) > 0
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True
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>>> svm.predict(SparseVector(2, {0: -1.0})) <= 0
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True
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"""
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def predict(self, x):
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_linear_predictor_typecheck(x, self._coeff)
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margin = _dot(x, self._coeff) + self._intercept
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return 1 if margin >= 0 else 0
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class SVMWithSGD(object):
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@classmethod
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def train(cls, data, iterations=100, step=1.0, regParam=1.0,
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miniBatchFraction=1.0, initialWeights=None):
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"""Train a support vector machine on the given data."""
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sc = data.context
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train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
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d._jrdd, iterations, step, regParam, miniBatchFraction, i)
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return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)
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class NaiveBayesModel(object):
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"""
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Model for Naive Bayes classifiers.
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Contains two parameters:
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- pi: vector of logs of class priors (dimension C)
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- theta: matrix of logs of class conditional probabilities (CxD)
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>>> data = [
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... LabeledPoint(0.0, [0.0, 0.0]),
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... LabeledPoint(0.0, [0.0, 1.0]),
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... LabeledPoint(1.0, [1.0, 0.0]),
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... ]
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>>> model = NaiveBayes.train(sc.parallelize(data))
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>>> model.predict(array([0.0, 1.0]))
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0.0
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>>> model.predict(array([1.0, 0.0]))
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1.0
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>>> sparse_data = [
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... LabeledPoint(0.0, SparseVector(2, {1: 0.0})),
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... LabeledPoint(0.0, SparseVector(2, {1: 1.0})),
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... LabeledPoint(1.0, SparseVector(2, {0: 1.0}))
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... ]
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>>> model = NaiveBayes.train(sc.parallelize(sparse_data))
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>>> model.predict(SparseVector(2, {1: 1.0}))
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0.0
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>>> model.predict(SparseVector(2, {0: 1.0}))
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1.0
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"""
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def __init__(self, labels, pi, theta):
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self.labels = labels
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self.pi = pi
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self.theta = theta
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def predict(self, x):
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"""Return the most likely class for a data vector x"""
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return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))]
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class NaiveBayes(object):
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@classmethod
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def train(cls, data, lambda_=1.0):
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"""
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Train a Naive Bayes model given an RDD of (label, features) vectors.
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This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can
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handle all kinds of discrete data. For example, by converting
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documents into TF-IDF vectors, it can be used for document
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classification. By making every vector a 0-1 vector, it can also be
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used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
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@param data: RDD of NumPy vectors, one per element, where the first
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coordinate is the label and the rest is the feature vector
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(e.g. a count vector).
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@param lambda_: The smoothing parameter
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"""
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sc = data.context
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dataBytes = _get_unmangled_labeled_point_rdd(data)
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ans = sc._jvm.PythonMLLibAPI().trainNaiveBayes(dataBytes._jrdd, lambda_)
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return NaiveBayesModel(
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_deserialize_double_vector(ans[0]),
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_deserialize_double_vector(ans[1]),
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_deserialize_double_matrix(ans[2]))
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def _test():
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import doctest
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globs = globals().copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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