Source code for pyspark.mllib.regression
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import numpy as np
from numpy import array
from pyspark import RDD
from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py, inherit_doc
from pyspark.mllib.linalg import SparseVector, Vectors, _convert_to_vector
from pyspark.mllib.util import Saveable, Loader
__all__ = ['LabeledPoint', 'LinearModel',
'LinearRegressionModel', 'LinearRegressionWithSGD',
'RidgeRegressionModel', 'RidgeRegressionWithSGD',
'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel',
'IsotonicRegression']
[docs]class LabeledPoint(object):
"""
Class that represents 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)
Note: 'label' and 'features' are accessible as class attributes.
"""
def __init__(self, label, features):
self.label = float(label)
self.features = _convert_to_vector(features)
def __reduce__(self):
return (LabeledPoint, (self.label, self.features))
def __str__(self):
return "(" + ",".join((str(self.label), str(self.features))) + ")"
def __repr__(self):
return "LabeledPoint(%s, %s)" % (self.label, self.features)
[docs]class LinearModel(object):
"""
A linear model that has a vector of coefficients and an intercept.
:param weights: Weights computed for every feature.
:param intercept: Intercept computed for this model.
"""
def __init__(self, weights, intercept):
self._coeff = _convert_to_vector(weights)
self._intercept = float(intercept)
@property
@property
def __repr__(self):
return "(weights=%s, intercept=%r)" % (self._coeff, self._intercept)
@inherit_doc
class LinearRegressionModelBase(LinearModel):
"""A linear regression model.
>>> lrmb = LinearRegressionModelBase(np.array([1.0, 2.0]), 0.1)
>>> abs(lrmb.predict(np.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.
"""
x = _convert_to_vector(x)
return self.weights.dot(x) + self.intercept
@inherit_doc
[docs]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), iterations=10,
... initialWeights=np.array([1.0]))
>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> lrm.save(sc, path)
>>> sameModel = LinearRegressionModel.load(sc, path)
>>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5
True
>>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except:
... pass
>>> 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), iterations=10,
... 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
>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
... miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2",
... intercept=True, validateData=True)
>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
"""
[docs] def save(self, sc, path):
java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel(
_py2java(sc, self._coeff), self.intercept)
java_model.save(sc._jsc.sc(), path)
@classmethod
[docs] def load(cls, sc, path):
java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel.load(
sc._jsc.sc(), path)
weights = _java2py(sc, java_model.weights())
intercept = java_model.intercept()
model = LinearRegressionModel(weights, intercept)
return model
# train_func should take two parameters, namely data and initial_weights, and
# return the result of a call to the appropriate JVM stub.
# _regression_train_wrapper is responsible for setup and error checking.
def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
from pyspark.mllib.classification import LogisticRegressionModel
first = data.first()
if not isinstance(first, LabeledPoint):
raise TypeError("data should be an RDD of LabeledPoint, but got %s" % type(first))
if initial_weights is None:
initial_weights = [0.0] * len(data.first().features)
if (modelClass == LogisticRegressionModel):
weights, intercept, numFeatures, numClasses = train_func(
data, _convert_to_vector(initial_weights))
return modelClass(weights, intercept, numFeatures, numClasses)
else:
weights, intercept = train_func(data, _convert_to_vector(initial_weights))
return modelClass(weights, intercept)
[docs]class LinearRegressionWithSGD(object):
@classmethod
[docs] def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=0.0, regType=None, intercept=False,
validateData=True):
"""
Train a linear regression model using Stochastic Gradient
Descent (SGD).
This solves the least squares regression formulation
f(weights) = 1/n ||A weights-y||^2^
(which is the mean squared error).
Here the data matrix has n rows, and the input RDD holds the
set of rows of A, each with its corresponding right hand side
label y. See also the documentation for the precise formulation.
: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: 0.0).
:param regType: The type of regularizer used for
training our model.
:Allowed values:
- "l1" for using L1 regularization (lasso),
- "l2" for using L2 regularization (ridge),
- None for no regularization
(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,
default: False).
:param validateData: Boolean parameter which indicates if
the algorithm should validate data
before training. (default: True)
"""
def train(rdd, i):
return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations),
float(step), float(miniBatchFraction), i, float(regParam),
regType, bool(intercept), bool(validateData))
return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights)
@inherit_doc
[docs]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), iterations=10, initialWeights=array([1.0]))
>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> lrm.save(sc, path)
>>> sameModel = LassoModel.load(sc, path)
>>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5
True
>>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except:
... pass
>>> 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), iterations=10,
... initialWeights=array([1.0]))
>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
... validateData=True)
>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
"""
[docs] def save(self, sc, path):
java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel(
_py2java(sc, self._coeff), self.intercept)
java_model.save(sc._jsc.sc(), path)
@classmethod
[docs] def load(cls, sc, path):
java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel.load(
sc._jsc.sc(), path)
weights = _java2py(sc, java_model.weights())
intercept = java_model.intercept()
model = LassoModel(weights, intercept)
return model
[docs]class LassoWithSGD(object):
@classmethod
[docs] def train(cls, data, iterations=100, step=1.0, regParam=0.01,
miniBatchFraction=1.0, initialWeights=None, intercept=False,
validateData=True):
"""
Train a regression model with L1-regularization using
Stochastic Gradient Descent.
This solves the l1-regularized least squares regression
formulation
f(weights) = 1/2n ||A weights-y||^2^ + regParam ||weights||_1
Here the data matrix has n rows, and the input RDD holds the
set of rows of A, each with its corresponding right hand side
label y. See also the documentation for the precise formulation.
: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 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)
"""
def train(rdd, i):
return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step),
float(regParam), float(miniBatchFraction), i, bool(intercept),
bool(validateData))
return _regression_train_wrapper(train, LassoModel, data, initialWeights)
@inherit_doc
[docs]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), iterations=10,
... initialWeights=array([1.0]))
>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> lrm.save(sc, path)
>>> sameModel = RidgeRegressionModel.load(sc, path)
>>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5
True
>>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except:
... pass
>>> 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), iterations=10,
... initialWeights=array([1.0]))
>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
>>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
... validateData=True)
>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
True
>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
True
"""
[docs] def save(self, sc, path):
java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel(
_py2java(sc, self._coeff), self.intercept)
java_model.save(sc._jsc.sc(), path)
@classmethod
[docs] def load(cls, sc, path):
java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel.load(
sc._jsc.sc(), path)
weights = _java2py(sc, java_model.weights())
intercept = java_model.intercept()
model = RidgeRegressionModel(weights, intercept)
return model
[docs]class RidgeRegressionWithSGD(object):
@classmethod
[docs] def train(cls, data, iterations=100, step=1.0, regParam=0.01,
miniBatchFraction=1.0, initialWeights=None, intercept=False,
validateData=True):
"""
Train a regression model with L2-regularization using
Stochastic Gradient Descent.
This solves the l2-regularized least squares regression
formulation
f(weights) = 1/2n ||A weights-y||^2^ + regParam/2 ||weights||^2^
Here the data matrix has n rows, and the input RDD holds the
set of rows of A, each with its corresponding right hand side
label y. See also the documentation for the precise formulation.
: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 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)
"""
def train(rdd, i):
return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step),
float(regParam), float(miniBatchFraction), i, bool(intercept),
bool(validateData))
return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)
[docs]class IsotonicRegressionModel(Saveable, Loader):
"""
Regression model for isotonic regression.
:param boundaries: Array of boundaries for which predictions are
known. Boundaries must be sorted in increasing order.
:param predictions: Array of predictions associated to the
boundaries at the same index. Results of isotonic
regression and therefore monotone.
:param isotonic: indicates whether this is isotonic or antitonic.
>>> data = [(1, 0, 1), (2, 1, 1), (3, 2, 1), (1, 3, 1), (6, 4, 1), (17, 5, 1), (16, 6, 1)]
>>> irm = IsotonicRegression.train(sc.parallelize(data))
>>> irm.predict(3)
2.0
>>> irm.predict(5)
16.5
>>> irm.predict(sc.parallelize([3, 5])).collect()
[2.0, 16.5]
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> irm.save(sc, path)
>>> sameModel = IsotonicRegressionModel.load(sc, path)
>>> sameModel.predict(3)
2.0
>>> sameModel.predict(5)
16.5
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except OSError:
... pass
"""
def __init__(self, boundaries, predictions, isotonic):
self.boundaries = boundaries
self.predictions = predictions
self.isotonic = isotonic
[docs] def predict(self, x):
"""
Predict labels for provided features.
Using a piecewise linear function.
1) If x exactly matches a boundary then associated prediction
is returned. In case there are multiple predictions with the
same boundary then one of them is returned. Which one is
undefined (same as java.util.Arrays.binarySearch).
2) If x is lower or higher than all boundaries then first or
last prediction is returned respectively. In case there are
multiple predictions with the same boundary then the lowest
or highest is returned respectively.
3) If x falls between two values in boundary array then
prediction is treated as piecewise linear function and
interpolated value is returned. In case there are multiple
values with the same boundary then the same rules as in 2)
are used.
:param x: Feature or RDD of Features to be labeled.
"""
if isinstance(x, RDD):
return x.map(lambda v: self.predict(v))
return np.interp(x, self.boundaries, self.predictions)
[docs] def save(self, sc, path):
java_boundaries = _py2java(sc, self.boundaries.tolist())
java_predictions = _py2java(sc, self.predictions.tolist())
java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel(
java_boundaries, java_predictions, self.isotonic)
java_model.save(sc._jsc.sc(), path)
@classmethod
[docs] def load(cls, sc, path):
java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel.load(
sc._jsc.sc(), path)
py_boundaries = _java2py(sc, java_model.boundaryVector()).toArray()
py_predictions = _java2py(sc, java_model.predictionVector()).toArray()
return IsotonicRegressionModel(py_boundaries, py_predictions, java_model.isotonic)
[docs]class IsotonicRegression(object):
@classmethod
[docs] def train(cls, data, isotonic=True):
"""
Train a isotonic regression model on the given data.
:param data: RDD of (label, feature, weight) tuples.
:param isotonic: Whether this is isotonic or antitonic.
"""
boundaries, predictions = callMLlibFunc("trainIsotonicRegressionModel",
data.map(_convert_to_vector), bool(isotonic))
return IsotonicRegressionModel(boundaries.toArray(), predictions.toArray(), isotonic)
def _test():
import doctest
from pyspark import SparkContext
import pyspark.mllib.regression
globs = pyspark.mllib.regression.__dict__.copy()
globs['sc'] = SparkContext('local[2]', '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()
