4c28a2bad8
We've used camel case in other Spark methods so it felt reasonable to keep using it here and make the code match Scala/Java as much as possible. Note that parameter names matter in Python because it allows passing optional parameters by name.
114 lines
4.6 KiB
Python
114 lines
4.6 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
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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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_linear_predictor_typecheck
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class LinearModel(object):
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"""Something that has a vector of coefficients and an intercept."""
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def __init__(self, coeff, intercept):
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self._coeff = coeff
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self._intercept = intercept
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class LinearRegressionModelBase(LinearModel):
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"""A linear regression model.
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>>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1)
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>>> abs(lrmb.predict(array([-1.03, 7.777])) - 14.624) < 1e-6
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True
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"""
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def predict(self, x):
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"""Predict the value of the dependent variable given a vector x"""
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"""containing values for the independent variables."""
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_linear_predictor_typecheck(x, self._coeff)
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return dot(self._coeff, x) + self._intercept
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class LinearRegressionModel(LinearRegressionModelBase):
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"""A linear regression model derived from a least-squares fit.
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>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
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>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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"""
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class LinearRegressionWithSGD(object):
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@classmethod
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def train(cls, data, iterations=100, step=1.0,
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miniBatchFraction=1.0, initialWeights=None):
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"""Train a linear regression model on the given data."""
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sc = data.context
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return _regression_train_wrapper(sc, lambda d, i:
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sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
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d._jrdd, iterations, step, miniBatchFraction, i),
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LinearRegressionModel, data, initialWeights)
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class LassoModel(LinearRegressionModelBase):
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"""A linear regression model derived from a least-squares fit with an
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l_1 penalty term.
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>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
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>>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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"""
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class LassoWithSGD(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 Lasso regression model on the given data."""
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sc = data.context
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return _regression_train_wrapper(sc, lambda d, i:
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sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd,
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iterations, step, regParam, miniBatchFraction, i),
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LassoModel, data, initialWeights)
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class RidgeRegressionModel(LinearRegressionModelBase):
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"""A linear regression model derived from a least-squares fit with an
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l_2 penalty term.
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>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
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>>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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"""
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class RidgeRegressionWithSGD(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 ridge regression model on the given data."""
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sc = data.context
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return _regression_train_wrapper(sc, lambda d, i:
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sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd,
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iterations, step, regParam, miniBatchFraction, i),
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RidgeRegressionModel, data, initialWeights)
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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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