Source code for pyspark.mllib.classification
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from math import exp
import numpy
from numpy import array
from pyspark import RDD, since
from pyspark.streaming import DStream
from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py
from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector
from pyspark.mllib.regression import (
LabeledPoint, LinearModel, _regression_train_wrapper,
StreamingLinearAlgorithm)
from pyspark.mllib.util import Saveable, Loader, inherit_doc
__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS',
'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes',
'StreamingLogisticRegressionWithSGD']
class LinearClassificationModel(LinearModel):
"""
A private abstract class representing a multiclass classification
model. The categories are represented by int values: 0, 1, 2, etc.
"""
def __init__(self, weights, intercept):
super(LinearClassificationModel, self).__init__(weights, intercept)
self._threshold = None
@since('1.4.0')
def setThreshold(self, value):
"""
.. note:: 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. It is used for binary classification
only.
"""
self._threshold = value
@property
@since('1.4.0')
def threshold(self):
"""
.. note:: Experimental
Returns the threshold (if any) used for converting raw
prediction scores into 0/1 predictions. It is used for
binary classification only.
"""
return self._threshold
@since('1.4.0')
def clearThreshold(self):
"""
.. note:: Experimental
Clears the threshold so that `predict` will output raw
prediction scores. It is used for binary classification only.
"""
self._threshold = None
@since('1.4.0')
def predict(self, test):
"""
Predict values for a single data point or an RDD of points
using the model trained.
"""
raise NotImplementedError
[docs]class LogisticRegressionModel(LinearClassificationModel):
"""
Classification model trained using Multinomial/Binary Logistic
Regression.
:param weights: Weights computed for every feature.
:param intercept: Intercept computed for this model. (Only used
in Binary Logistic Regression. In Multinomial Logistic
Regression, the intercepts will not be a single value,
so the intercepts will be part of the weights.)
:param numFeatures: the dimension of the features.
:param numClasses: the number of possible outcomes for k classes
classification problem in Multinomial Logistic Regression.
By default, it is binary logistic regression so numClasses
will be set to 2.
>>> data = [
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10)
>>> 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])
0.279...
>>> 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), iterations=10)
>>> 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
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> lrm.save(sc, path)
>>> sameModel = LogisticRegressionModel.load(sc, path)
>>> sameModel.predict(array([0.0, 1.0]))
1
>>> sameModel.predict(SparseVector(2, {0: 1.0}))
0
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except:
... pass
>>> multi_class_data = [
... LabeledPoint(0.0, [0.0, 1.0, 0.0]),
... LabeledPoint(1.0, [1.0, 0.0, 0.0]),
... LabeledPoint(2.0, [0.0, 0.0, 1.0])
... ]
>>> data = sc.parallelize(multi_class_data)
>>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3)
>>> mcm.predict([0.0, 0.5, 0.0])
0
>>> mcm.predict([0.8, 0.0, 0.0])
1
>>> mcm.predict([0.0, 0.0, 0.3])
2
.. versionadded:: 0.9.0
"""
def __init__(self, weights, intercept, numFeatures, numClasses):
super(LogisticRegressionModel, self).__init__(weights, intercept)
self._numFeatures = int(numFeatures)
self._numClasses = int(numClasses)
self._threshold = 0.5
if self._numClasses == 2:
self._dataWithBiasSize = None
self._weightsMatrix = None
else:
self._dataWithBiasSize = self._coeff.size / (self._numClasses - 1)
self._weightsMatrix = self._coeff.toArray().reshape(self._numClasses - 1,
self._dataWithBiasSize)
@property
@since('1.4.0')
@property
@since('1.4.0')
[docs] def numClasses(self):
"""
Number of possible outcomes for k classes classification problem in Multinomial
Logistic Regression.
"""
return self._numClasses
@since('0.9.0')
[docs] 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)
if self.numClasses == 2:
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
else:
best_class = 0
max_margin = 0.0
if x.size + 1 == self._dataWithBiasSize:
for i in range(0, self._numClasses - 1):
margin = x.dot(self._weightsMatrix[i][0:x.size]) + \
self._weightsMatrix[i][x.size]
if margin > max_margin:
max_margin = margin
best_class = i + 1
else:
for i in range(0, self._numClasses - 1):
margin = x.dot(self._weightsMatrix[i])
if margin > max_margin:
max_margin = margin
best_class = i + 1
return best_class
@since('1.4.0')
[docs] def save(self, sc, path):
"""
Save this model to the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel(
_py2java(sc, self._coeff), self.intercept, self.numFeatures, self.numClasses)
java_model.save(sc._jsc.sc(), path)
@classmethod
@since('1.4.0')
[docs] def load(cls, sc, path):
"""
Load a model from the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel.load(
sc._jsc.sc(), path)
weights = _java2py(sc, java_model.weights())
intercept = java_model.intercept()
numFeatures = java_model.numFeatures()
numClasses = java_model.numClasses()
threshold = java_model.getThreshold().get()
model = LogisticRegressionModel(weights, intercept, numFeatures, numClasses)
model.setThreshold(threshold)
return model
[docs]class LogisticRegressionWithSGD(object):
"""
.. versionadded:: 0.9.0
"""
@classmethod
@since('0.9.0')
[docs] def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=0.01, regType="l2", intercept=False,
validateData=True, convergenceTol=0.001):
"""
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 (default: 1.0).
: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,
default: False).
:param validateData: Boolean parameter which indicates if
the algorithm should validate data
before training. (default: True)
:param convergenceTol: A condition which decides iteration termination.
(default: 0.001)
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
float(step), float(miniBatchFraction), i, float(regParam), regType,
bool(intercept), bool(validateData), float(convergenceTol))
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
[docs]class LogisticRegressionWithLBFGS(object):
"""
.. versionadded:: 1.2.0
"""
@classmethod
@since('1.2.0')
[docs] def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2",
intercept=False, corrections=10, tolerance=1e-4, validateData=True, numClasses=2):
"""
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,
default: False).
: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).
:param validateData: Boolean parameter which indicates if the
algorithm should validate data before
training. (default: True)
:param numClasses: The number of classes (i.e., outcomes) a
label can take in Multinomial Logistic
Regression (default: 2).
>>> data = [
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
>>> 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), regType, bool(intercept), int(corrections),
float(tolerance), bool(validateData), int(numClasses))
if initialWeights is None:
if numClasses == 2:
initialWeights = [0.0] * len(data.first().features)
else:
if intercept:
initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
else:
initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
[docs]class SVMModel(LinearClassificationModel):
"""
Model for Support Vector Machines (SVMs).
:param weights: Weights computed for every feature.
:param intercept: Intercept computed for this model.
>>> 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), iterations=10)
>>> svm.predict([1.0])
1
>>> svm.predict(sc.parallelize([[1.0]])).collect()
[1]
>>> svm.clearThreshold()
>>> svm.predict(array([1.0]))
1.44...
>>> 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), iterations=10)
>>> svm.predict(SparseVector(2, {1: 1.0}))
1
>>> svm.predict(SparseVector(2, {0: -1.0}))
0
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> svm.save(sc, path)
>>> sameModel = SVMModel.load(sc, path)
>>> sameModel.predict(SparseVector(2, {1: 1.0}))
1
>>> sameModel.predict(SparseVector(2, {0: -1.0}))
0
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except:
... pass
.. versionadded:: 0.9.0
"""
def __init__(self, weights, intercept):
super(SVMModel, self).__init__(weights, intercept)
self._threshold = 0.0
@since('0.9.0')
[docs] 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
@since('1.4.0')
[docs] def save(self, sc, path):
"""
Save this model to the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel(
_py2java(sc, self._coeff), self.intercept)
java_model.save(sc._jsc.sc(), path)
@classmethod
@since('1.4.0')
[docs] def load(cls, sc, path):
"""
Load a model from the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel.load(
sc._jsc.sc(), path)
weights = _java2py(sc, java_model.weights())
intercept = java_model.intercept()
threshold = java_model.getThreshold().get()
model = SVMModel(weights, intercept)
model.setThreshold(threshold)
return model
[docs]class SVMWithSGD(object):
"""
.. versionadded:: 0.9.0
"""
@classmethod
@since('0.9.0')
[docs] def train(cls, data, iterations=100, step=1.0, regParam=0.01,
miniBatchFraction=1.0, initialWeights=None, regType="l2",
intercept=False, validateData=True, convergenceTol=0.001):
"""
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 (default: 1.0).
: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,
default: False).
:param validateData: Boolean parameter which indicates if
the algorithm should validate data
before training. (default: True)
:param convergenceTol: A condition which decides iteration termination.
(default: 0.001)
"""
def train(rdd, i):
return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step),
float(regParam), float(miniBatchFraction), i, regType,
bool(intercept), bool(validateData), float(convergenceTol))
return _regression_train_wrapper(train, SVMModel, data, initialWeights)
@inherit_doc
[docs]class NaiveBayesModel(Saveable, Loader):
"""
Model for Naive Bayes classifiers.
:param labels: list of labels.
:param pi: log of class priors, whose dimension is C,
number of labels.
:param theta: log of class conditional probabilities, whose
dimension is C-by-D, where D is number of features.
>>> 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
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> model.save(sc, path)
>>> sameModel = NaiveBayesModel.load(sc, path)
>>> sameModel.predict(SparseVector(2, {0: 1.0})) == model.predict(SparseVector(2, {0: 1.0}))
True
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except OSError:
... pass
.. versionadded:: 0.9.0
"""
def __init__(self, labels, pi, theta):
self.labels = labels
self.pi = pi
self.theta = theta
@since('0.9.0')
[docs] 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()))]
[docs] def save(self, sc, path):
"""
Save this model to the given path.
"""
java_labels = _py2java(sc, self.labels.tolist())
java_pi = _py2java(sc, self.pi.tolist())
java_theta = _py2java(sc, self.theta.tolist())
java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel(
java_labels, java_pi, java_theta)
java_model.save(sc._jsc.sc(), path)
@classmethod
@since('1.4.0')
[docs] def load(cls, sc, path):
"""
Load a model from the given path.
"""
java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel.load(
sc._jsc.sc(), path)
# Can not unpickle array.array from Pyrolite in Python3 with "bytes"
py_labels = _java2py(sc, java_model.labels(), "latin1")
py_pi = _java2py(sc, java_model.pi(), "latin1")
py_theta = _java2py(sc, java_model.theta(), "latin1")
return NaiveBayesModel(py_labels, py_pi, numpy.array(py_theta))
[docs]class NaiveBayes(object):
"""
.. versionadded:: 0.9.0
"""
@classmethod
@since('0.9.0')
[docs] 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}).
The input feature values must be nonnegative.
:param data: RDD of LabeledPoint.
:param lambda_: The smoothing parameter (default: 1.0).
"""
first = data.first()
if not isinstance(first, LabeledPoint):
raise ValueError("`data` should be an RDD of LabeledPoint")
labels, pi, theta = callMLlibFunc("trainNaiveBayesModel", data, lambda_)
return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
@inherit_doc
[docs]class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
"""
Train or predict a logistic regression model on streaming data. Training uses
Stochastic Gradient Descent to update the model based on each new batch of
incoming data from a DStream.
Each batch of data is assumed to be an RDD of LabeledPoints.
The number of data points per batch can vary, but the number
of features must be constant. An initial weight
vector must be provided.
:param stepSize:
Step size for each iteration of gradient descent.
(default: 0.1)
:param numIterations:
Number of iterations run for each batch of data.
(default: 50)
:param miniBatchFraction:
Fraction of each batch of data to use for updates.
(default: 1.0)
:param regParam:
L2 Regularization parameter.
(default: 0.0)
:param convergenceTol:
Value used to determine when to terminate iterations.
(default: 0.001)
.. versionadded:: 1.5.0
"""
def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.0,
convergenceTol=0.001):
self.stepSize = stepSize
self.numIterations = numIterations
self.regParam = regParam
self.miniBatchFraction = miniBatchFraction
self.convergenceTol = convergenceTol
self._model = None
super(StreamingLogisticRegressionWithSGD, self).__init__(
model=self._model)
@since('1.5.0')
[docs] def setInitialWeights(self, initialWeights):
"""
Set the initial value of weights.
This must be set before running trainOn and predictOn.
"""
initialWeights = _convert_to_vector(initialWeights)
# LogisticRegressionWithSGD does only binary classification.
self._model = LogisticRegressionModel(
initialWeights, 0, initialWeights.size, 2)
return self
@since('1.5.0')
[docs] def trainOn(self, dstream):
"""Train the model on the incoming dstream."""
self._validate(dstream)
def update(rdd):
# LogisticRegressionWithSGD.train raises an error for an empty RDD.
if not rdd.isEmpty():
self._model = LogisticRegressionWithSGD.train(
rdd, self.numIterations, self.stepSize,
self.miniBatchFraction, self._model.weights,
regParam=self.regParam, convergenceTol=self.convergenceTol)
dstream.foreachRDD(update)
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()
