#
# 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.
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from numpy import array, ndarray
from pyspark import SparkContext
from pyspark.mllib._common import \
_dot, _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, _have_scipy, _scipy_issparse
from pyspark.mllib.linalg import SparseVector, Vectors
class LabeledPoint(object):
"""
The features and labels of a data point.
@param label: Label for this data point.
@param features: Vector of features for this point (NumPy array, list,
pyspark.mllib.linalg.SparseVector, or scipy.sparse column matrix)
"""
def __init__(self, label, features):
self.label = label
if (type(features) == ndarray or type(features) == SparseVector
or (_have_scipy and _scipy_issparse(features))):
self.features = features
elif type(features) == list:
self.features = array(features)
else:
raise TypeError("Expected NumPy array, list, SparseVector, or scipy.sparse matrix")
def __str__(self):
return "(" + ",".join((str(self.label), Vectors.stringify(self.features))) + ")"
class LinearModel(object):
"""A linear model that has a vector of coefficients and an intercept."""
def __init__(self, weights, intercept):
self._coeff = weights
self._intercept = intercept
@property
def weights(self):
return self._coeff
@property
def intercept(self):
return self._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
>>> abs(lrmb.predict(SparseVector(2, {0: -1.03, 1: 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(x, self._coeff) + self._intercept
class LinearRegressionModel(LinearRegressionModelBase):
"""A linear regression model derived from a least-squares fit.
>>> from pyspark.mllib.regression import LabeledPoint
>>> data = [
... LabeledPoint(0.0, [0.0]),
... LabeledPoint(1.0, [1.0]),
... LabeledPoint(3.0, [2.0]),
... LabeledPoint(2.0, [3.0])
... ]
>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(array([1.0])) - 1) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> data = [
... LabeledPoint(0.0, SparseVector(1, {0: 0.0})),
... LabeledPoint(1.0, SparseVector(1, {0: 1.0})),
... LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
... LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
... ]
>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
"""
class LinearRegressionWithSGD(object):
@classmethod
def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=1.0, regType=None, intercept=False):
"""
Train a linear regression model on the given data.
@param data: The training data.
@param iterations: The number of iterations (default: 100).
@param step: The step parameter used in SGD
(default: 1.0).
@param miniBatchFraction: Fraction of data to be used for each SGD
iteration.
@param initialWeights: The initial weights (default: None).
@param regParam: The regularizer parameter (default: 1.0).
@param regType: The type of regularizer used for training
our model.
Allowed values: "l1" for using L1Updater,
"l2" for using
SquaredL2Updater,
"none" for no regularizer.
(default: "none")
@param intercept: Boolean parameter which indicates the use
or not of the augmented representation for
training data (i.e. whether bias features
are activated or not).
"""
sc = data.context
if regType is None:
regType = "none"
train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
d._jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights)
class LassoModel(LinearRegressionModelBase):
"""A linear regression model derived from a least-squares fit with an
l_1 penalty term.
>>> from pyspark.mllib.regression import LabeledPoint
>>> data = [
... LabeledPoint(0.0, [0.0]),
... LabeledPoint(1.0, [1.0]),
... LabeledPoint(3.0, [2.0]),
... LabeledPoint(2.0, [3.0])
... ]
>>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(array([1.0])) - 1) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> data = [
... LabeledPoint(0.0, SparseVector(1, {0: 0.0})),
... LabeledPoint(1.0, SparseVector(1, {0: 1.0})),
... LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
... LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
... ]
>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
"""
class LassoWithSGD(object):
@classmethod
def train(cls, data, iterations=100, step=1.0, regParam=1.0,
miniBatchFraction=1.0, initialWeights=None):
"""Train a Lasso regression model on the given data."""
sc = data.context
train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(
d._jrdd, iterations, step, regParam, miniBatchFraction, i)
return _regression_train_wrapper(sc, train_f, LassoModel, data, initialWeights)
class RidgeRegressionModel(LinearRegressionModelBase):
"""A linear regression model derived from a least-squares fit with an
l_2 penalty term.
>>> from pyspark.mllib.regression import LabeledPoint
>>> data = [
... LabeledPoint(0.0, [0.0]),
... LabeledPoint(1.0, [1.0]),
... LabeledPoint(3.0, [2.0]),
... LabeledPoint(2.0, [3.0])
... ]
>>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(array([1.0])) - 1) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> data = [
... LabeledPoint(0.0, SparseVector(1, {0: 0.0})),
... LabeledPoint(1.0, SparseVector(1, {0: 1.0})),
... LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
... LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
... ]
>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
"""
class RidgeRegressionWithSGD(object):
@classmethod
def train(cls, data, iterations=100, step=1.0, regParam=1.0,
miniBatchFraction=1.0, initialWeights=None):
"""Train a ridge regression model on the given data."""
sc = data.context
train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(
d._jrdd, iterations, step, regParam, miniBatchFraction, i)
return _regression_train_wrapper(sc, train_func, RidgeRegressionModel, data, initialWeights)
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()
