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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 math import exp
import numpy
from numpy import array
from pyspark import RDD
from pyspark.mllib.common import callMLlibFunc
from pyspark.mllib.linalg import SparseVector, _convert_to_vector
from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper
__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS',
'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes']
class LinearBinaryClassificationModel(LinearModel):
"""
Represents a linear binary classification model that predicts to whether an
example is positive (1.0) or negative (0.0).
"""
def __init__(self, weights, intercept):
super(LinearBinaryClassificationModel, self).__init__(weights, intercept)
self._threshold = None
def setThreshold(self, value):
"""
:: Experimental ::
Sets the threshold that separates positive predictions from negative
predictions. An example with prediction score greater than or equal
to this threshold is identified as an positive, and negative otherwise.
"""
self._threshold = value
def clearThreshold(self):
"""
:: Experimental ::
Clears the threshold so that `predict` will output raw prediction scores.
"""
self._threshold = None
def predict(self, test):
"""
Predict values for a single data point or an RDD of points using
the model trained.
"""
raise NotImplementedError
class LogisticRegressionModel(LinearBinaryClassificationModel):
"""A linear binary classification model derived from logistic regression.
>>> data = [
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data))
>>> lrm.predict([1.0, 0.0])
1
>>> lrm.predict([0.0, 1.0])
0
>>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
[1, 0]
>>> lrm.clearThreshold()
>>> lrm.predict([0.0, 1.0])
0.123...
>>> sparse_data = [
... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
... ]
>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data))
>>> lrm.predict(array([0.0, 1.0]))
1
>>> lrm.predict(array([1.0, 0.0]))
0
>>> lrm.predict(SparseVector(2, {1: 1.0}))
1
>>> lrm.predict(SparseVector(2, {0: 1.0}))
0
"""
def __init__(self, weights, intercept):
super(LogisticRegressionModel, self).__init__(weights, intercept)
self._threshold = 0.5
def predict(self, x):
"""
Predict values for a single data point or an RDD of points using
the model trained.
"""
if isinstance(x, RDD):
return x.map(lambda v: self.predict(v))
x = _convert_to_vector(x)
margin = self.weights.dot(x) + self._intercept
if margin > 0:
prob = 1 / (1 + exp(-margin))
else:
exp_margin = exp(margin)
prob = exp_margin / (1 + exp_margin)
if self._threshold is None:
return prob
else:
return 1 if prob > self._threshold else 0
class LogisticRegressionWithSGD(object):
@classmethod
def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=0.01, regType="l2", intercept=False):
"""
Train a logistic regression model on the given data.
:param data: The training data, an RDD of LabeledPoint.
: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: 0.01).
:param regType: The type of regularizer used for training
our model.
:Allowed values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization
- None for no regularization
(default: "l2")
: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).
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
float(step), float(miniBatchFraction), i, float(regParam), regType,
bool(intercept))
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
class LogisticRegressionWithLBFGS(object):
@classmethod
def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2",
intercept=False, corrections=10, tolerance=1e-4):
"""
Train a logistic regression model on the given data.
:param data: The training data, an RDD of LabeledPoint.
:param iterations: The number of iterations (default: 100).
:param initialWeights: The initial weights (default: None).
:param regParam: The regularizer parameter (default: 0.01).
:param regType: The type of regularizer used for training
our model.
:Allowed values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization
- None for no regularization
(default: "l2")
: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).
:param corrections: The number of corrections used in the LBFGS
update (default: 10).
:param tolerance: The convergence tolerance of iterations for
L-BFGS (default: 1e-4).
>>> data = [
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
>>> lrm.predict([1.0, 0.0])
1
>>> lrm.predict([0.0, 1.0])
0
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
float(regParam), str(regType), bool(intercept), int(corrections),
float(tolerance))
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
class SVMModel(LinearBinaryClassificationModel):
"""A support vector machine.
>>> data = [
... LabeledPoint(0.0, [0.0]),
... LabeledPoint(1.0, [1.0]),
... LabeledPoint(1.0, [2.0]),
... LabeledPoint(1.0, [3.0])
... ]
>>> svm = SVMWithSGD.train(sc.parallelize(data))
>>> svm.predict([1.0])
1
>>> svm.predict(sc.parallelize([[1.0]])).collect()
[1]
>>> svm.clearThreshold()
>>> svm.predict(array([1.0]))
1.25...
>>> sparse_data = [
... LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
... ]
>>> svm = SVMWithSGD.train(sc.parallelize(sparse_data))
>>> svm.predict(SparseVector(2, {1: 1.0}))
1
>>> svm.predict(SparseVector(2, {0: -1.0}))
0
"""
def __init__(self, weights, intercept):
super(SVMModel, self).__init__(weights, intercept)
self._threshold = 0.0
def predict(self, x):
"""
Predict values for a single data point or an RDD of points using
the model trained.
"""
if isinstance(x, RDD):
return x.map(lambda v: self.predict(v))
x = _convert_to_vector(x)
margin = self.weights.dot(x) + self.intercept
if self._threshold is None:
return margin
else:
return 1 if margin > self._threshold else 0
class SVMWithSGD(object):
@classmethod
def train(cls, data, iterations=100, step=1.0, regParam=0.01,
miniBatchFraction=1.0, initialWeights=None, regType="l2", intercept=False):
"""
Train a support vector machine on the given data.
:param data: The training data, an RDD of LabeledPoint.
:param iterations: The number of iterations (default: 100).
:param step: The step parameter used in SGD
(default: 1.0).
:param regParam: The regularizer parameter (default: 0.01).
:param miniBatchFraction: Fraction of data to be used for each SGD
iteration.
:param initialWeights: The initial weights (default: None).
:param regType: The type of regularizer used for training
our model.
:Allowed values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization
- None for no regularization
(default: "l2")
: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).
"""
def train(rdd, i):
return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step),
float(regParam), float(miniBatchFraction), i, regType,
bool(intercept))
return _regression_train_wrapper(train, SVMModel, data, initialWeights)
class NaiveBayesModel(object):
"""
Model for Naive Bayes classifiers.
Contains two parameters:
- pi: vector of logs of class priors (dimension C)
- theta: matrix of logs of class conditional probabilities (CxD)
>>> data = [
... LabeledPoint(0.0, [0.0, 0.0]),
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> model = NaiveBayes.train(sc.parallelize(data))
>>> model.predict(array([0.0, 1.0]))
0.0
>>> model.predict(array([1.0, 0.0]))
1.0
>>> model.predict(sc.parallelize([[1.0, 0.0]])).collect()
[1.0]
>>> sparse_data = [
... LabeledPoint(0.0, SparseVector(2, {1: 0.0})),
... LabeledPoint(0.0, SparseVector(2, {1: 1.0})),
... LabeledPoint(1.0, SparseVector(2, {0: 1.0}))
... ]
>>> model = NaiveBayes.train(sc.parallelize(sparse_data))
>>> model.predict(SparseVector(2, {1: 1.0}))
0.0
>>> model.predict(SparseVector(2, {0: 1.0}))
1.0
"""
def __init__(self, labels, pi, theta):
self.labels = labels
self.pi = pi
self.theta = theta
def predict(self, x):
"""Return the most likely class for a data vector or an RDD of vectors"""
if isinstance(x, RDD):
return x.map(lambda v: self.predict(v))
x = _convert_to_vector(x)
return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))]
class NaiveBayes(object):
@classmethod
def train(cls, data, lambda_=1.0):
"""
Train a Naive Bayes model given an RDD of (label, features) vectors.
This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can
handle all kinds of discrete data. For example, by converting
documents into TF-IDF vectors, it can be used for document
classification. By making every vector a 0-1 vector, it can also be
used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
:param data: RDD of LabeledPoint.
:param lambda_: The smoothing parameter
"""
first = data.first()
if not isinstance(first, LabeledPoint):
raise ValueError("`data` should be an RDD of LabeledPoint")
labels, pi, theta = callMLlibFunc("trainNaiveBayes", data, lambda_)
return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
def _test():
import doctest
from pyspark import SparkContext
import pyspark.mllib.classification
globs = pyspark.mllib.classification.__dict__.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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