87 lines
3.5 KiB
Python
87 lines
3.5 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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from numpy import array, dot, shape
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from pyspark import SparkContext
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from pyspark.mllib._common import \
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_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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LinearModel, _linear_predictor_typecheck
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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 = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
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>>> lrm = LogisticRegressionWithSGD.train(sc, sc.parallelize(data))
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>>> lrm.predict(array([1.0])) != None
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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, sc, data, iterations=100, step=1.0,
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mini_batch_fraction=1.0, initial_weights=None):
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"""Train a logistic regression model on the given data."""
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return _regression_train_wrapper(sc, lambda d, i:
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sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd,
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iterations, step, mini_batch_fraction, i),
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LogisticRegressionModel, data, initial_weights)
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class SVMModel(LinearModel):
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"""A support vector machine.
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>>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2)
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>>> svm = SVMWithSGD.train(sc, sc.parallelize(data))
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>>> svm.predict(array([1.0])) != None
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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, sc, data, iterations=100, step=1.0, reg_param=1.0,
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mini_batch_fraction=1.0, initial_weights=None):
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"""Train a support vector machine on the given data."""
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return _regression_train_wrapper(sc, lambda d, i:
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sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
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iterations, step, reg_param, mini_batch_fraction, i),
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SVMModel, data, initial_weights)
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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,
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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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