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#
# 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.
#
from numpy import array, dot
from pyspark import SparkContext
from pyspark.mllib._common import \
_get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
_serialize_double_matrix, _deserialize_double_matrix, \
_serialize_double_vector, _deserialize_double_vector, \
_get_initial_weights, _serialize_rating, _regression_train_wrapper, \
_linear_predictor_typecheck
class LinearModel(object):
"""Something that has a vector of coefficients and an intercept."""
def __init__(self, coeff, intercept):
self._coeff = coeff
self._intercept = intercept
class LinearRegressionModelBase(LinearModel):
"""A linear regression model.
>>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1)
>>> abs(lrmb.predict(array([-1.03, 7.777])) - 14.624) < 1e-6
True
"""
def predict(self, x):
"""Predict the value of the dependent variable given a vector x"""
"""containing values for the independent variables."""
_linear_predictor_typecheck(x, self._coeff)
return dot(self._coeff, x) + self._intercept
class LinearRegressionModel(LinearRegressionModelBase):
"""A linear regression model derived from a least-squares fit.
>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
>>> lrm = LinearRegressionWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
"""
class LinearRegressionWithSGD(object):
@classmethod
def train(cls, sc, data, iterations=100, step=1.0,
mini_batch_fraction=1.0, initial_weights=None):
"""Train a linear regression model on the given data."""
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
d._jrdd, iterations, step, mini_batch_fraction, i),
LinearRegressionModel, data, initial_weights)
class LassoModel(LinearRegressionModelBase):
"""A linear regression model derived from a least-squares fit with an
l_1 penalty term.
>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
>>> lrm = LassoWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
"""
class LassoWithSGD(object):
@classmethod
def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
mini_batch_fraction=1.0, initial_weights=None):
"""Train a Lasso regression model on the given data."""
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd,
iterations, step, reg_param, mini_batch_fraction, i),
LassoModel, data, initial_weights)
class RidgeRegressionModel(LinearRegressionModelBase):
"""A linear regression model derived from a least-squares fit with an
l_2 penalty term.
>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
>>> lrm = RidgeRegressionWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0]))
"""
class RidgeRegressionWithSGD(object):
@classmethod
def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
mini_batch_fraction=1.0, initial_weights=None):
"""Train a ridge regression model on the given data."""
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd,
iterations, step, reg_param, mini_batch_fraction, i),
RidgeRegressionModel, data, initial_weights)
def _test():
import doctest
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
(failure_count, test_count) = doctest.testmod(globs=globs,
optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
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