282a15f78e
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8684 from yu-iskw/SPARK-10277.
800 lines
30 KiB
Python
800 lines
30 KiB
Python
#
|
|
# 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.
|
|
#
|
|
|
|
import numpy as np
|
|
from numpy import array
|
|
|
|
from pyspark import RDD, since
|
|
from pyspark.streaming.dstream import DStream
|
|
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', 'StreamingLinearAlgorithm',
|
|
'StreamingLinearRegressionWithSGD']
|
|
|
|
|
|
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.
|
|
|
|
.. versionadded:: 1.0.0
|
|
"""
|
|
|
|
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)
|
|
|
|
|
|
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.
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
|
|
def __init__(self, weights, intercept):
|
|
self._coeff = _convert_to_vector(weights)
|
|
self._intercept = float(intercept)
|
|
|
|
@property
|
|
@since("1.0.0")
|
|
def weights(self):
|
|
"""Weights computed for every feature."""
|
|
return self._coeff
|
|
|
|
@property
|
|
@since("1.0.0")
|
|
def intercept(self):
|
|
"""Intercept computed for this model."""
|
|
return self._intercept
|
|
|
|
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
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
|
|
@since("0.9.0")
|
|
def predict(self, x):
|
|
"""
|
|
Predict the value of the dependent variable given a vector or
|
|
an RDD of vectors containing values for the independent variables.
|
|
"""
|
|
if isinstance(x, RDD):
|
|
return x.map(self.predict)
|
|
x = _convert_to_vector(x)
|
|
return self.weights.dot(x) + self.intercept
|
|
|
|
|
|
@inherit_doc
|
|
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
|
|
>>> abs(lrm.predict(sc.parallelize([[1.0]])).collect()[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
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
@since("1.4.0")
|
|
def save(self, sc, path):
|
|
"""Save a LinearRegressionModel."""
|
|
java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel(
|
|
_py2java(sc, self._coeff), self.intercept)
|
|
java_model.save(sc._jsc.sc(), path)
|
|
|
|
@classmethod
|
|
@since("1.4.0")
|
|
def load(cls, sc, path):
|
|
"""Load a LinearRegressionModel."""
|
|
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)
|
|
|
|
|
|
class LinearRegressionWithSGD(object):
|
|
"""
|
|
Train a linear regression model with no regularization using Stochastic Gradient Descent.
|
|
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.
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
|
|
@classmethod
|
|
@since("0.9.0")
|
|
def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
|
|
initialWeights=None, regParam=0.0, regType=None, intercept=False,
|
|
validateData=True, convergenceTol=0.001):
|
|
"""
|
|
Train a linear regression model using Stochastic Gradient
|
|
Descent (SGD).
|
|
This solves the least squares regression formulation
|
|
|
|
f(weights) = 1/(2n) ||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)
|
|
:param convergenceTol: A condition which decides iteration termination.
|
|
(default: 0.001)
|
|
"""
|
|
def train(rdd, i):
|
|
return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations),
|
|
float(step), float(miniBatchFraction), i, float(regParam),
|
|
regType, bool(intercept), bool(validateData),
|
|
float(convergenceTol))
|
|
|
|
return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights)
|
|
|
|
|
|
@inherit_doc
|
|
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
|
|
>>> abs(lrm.predict(sc.parallelize([[1.0]])).collect()[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
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
@since("1.4.0")
|
|
def save(self, sc, path):
|
|
"""Save a LassoModel."""
|
|
java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel(
|
|
_py2java(sc, self._coeff), self.intercept)
|
|
java_model.save(sc._jsc.sc(), path)
|
|
|
|
@classmethod
|
|
@since("1.4.0")
|
|
def load(cls, sc, path):
|
|
"""Load a LassoModel."""
|
|
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
|
|
|
|
|
|
class LassoWithSGD(object):
|
|
"""
|
|
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.
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
|
|
@classmethod
|
|
@since("0.9.0")
|
|
def train(cls, data, iterations=100, step=1.0, regParam=0.01,
|
|
miniBatchFraction=1.0, initialWeights=None, intercept=False,
|
|
validateData=True, convergenceTol=0.001):
|
|
"""
|
|
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)
|
|
:param convergenceTol: A condition which decides iteration termination.
|
|
(default: 0.001)
|
|
"""
|
|
def train(rdd, i):
|
|
return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step),
|
|
float(regParam), float(miniBatchFraction), i, bool(intercept),
|
|
bool(validateData), float(convergenceTol))
|
|
|
|
return _regression_train_wrapper(train, LassoModel, data, initialWeights)
|
|
|
|
|
|
@inherit_doc
|
|
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
|
|
>>> abs(lrm.predict(sc.parallelize([[1.0]])).collect()[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
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
@since("1.4.0")
|
|
def save(self, sc, path):
|
|
"""Save a RidgeRegressionMode."""
|
|
java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel(
|
|
_py2java(sc, self._coeff), self.intercept)
|
|
java_model.save(sc._jsc.sc(), path)
|
|
|
|
@classmethod
|
|
@since("1.4.0")
|
|
def load(cls, sc, path):
|
|
"""Load a RidgeRegressionMode."""
|
|
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
|
|
|
|
|
|
class RidgeRegressionWithSGD(object):
|
|
"""
|
|
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.
|
|
|
|
.. versionadded:: 0.9.0
|
|
"""
|
|
|
|
@classmethod
|
|
@since("0.9.0")
|
|
def train(cls, data, iterations=100, step=1.0, regParam=0.01,
|
|
miniBatchFraction=1.0, initialWeights=None, intercept=False,
|
|
validateData=True, convergenceTol=0.001):
|
|
"""
|
|
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)
|
|
:param convergenceTol: A condition which decides iteration termination.
|
|
(default: 0.001)
|
|
"""
|
|
def train(rdd, i):
|
|
return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step),
|
|
float(regParam), float(miniBatchFraction), i, bool(intercept),
|
|
bool(validateData), float(convergenceTol))
|
|
|
|
return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)
|
|
|
|
|
|
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
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
def __init__(self, boundaries, predictions, isotonic):
|
|
self.boundaries = boundaries
|
|
self.predictions = predictions
|
|
self.isotonic = isotonic
|
|
|
|
@since("1.4.0")
|
|
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)
|
|
|
|
@since("1.4.0")
|
|
def save(self, sc, path):
|
|
"""Save a IsotonicRegressionModel."""
|
|
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
|
|
@since("1.4.0")
|
|
def load(cls, sc, path):
|
|
"""Load a IsotonicRegressionModel."""
|
|
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)
|
|
|
|
|
|
class IsotonicRegression(object):
|
|
"""
|
|
Isotonic regression.
|
|
Currently implemented using parallelized pool adjacent violators algorithm.
|
|
Only univariate (single feature) algorithm supported.
|
|
|
|
Sequential PAV implementation based on:
|
|
Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani.
|
|
"Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61.
|
|
Available from [[http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf]]
|
|
|
|
Sequential PAV parallelization based on:
|
|
Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset.
|
|
"An approach to parallelizing isotonic regression."
|
|
Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147.
|
|
Available from [[http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf]]
|
|
|
|
@see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
@classmethod
|
|
@since("1.4.0")
|
|
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)
|
|
|
|
|
|
class StreamingLinearAlgorithm(object):
|
|
"""
|
|
Base class that has to be inherited by any StreamingLinearAlgorithm.
|
|
|
|
Prevents reimplementation of methods predictOn and predictOnValues.
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
def __init__(self, model):
|
|
self._model = model
|
|
|
|
@since("1.5.0")
|
|
def latestModel(self):
|
|
"""
|
|
Returns the latest model.
|
|
"""
|
|
return self._model
|
|
|
|
def _validate(self, dstream):
|
|
if not isinstance(dstream, DStream):
|
|
raise TypeError(
|
|
"dstream should be a DStream object, got %s" % type(dstream))
|
|
if not self._model:
|
|
raise ValueError(
|
|
"Model must be intialized using setInitialWeights")
|
|
|
|
@since("1.5.0")
|
|
def predictOn(self, dstream):
|
|
"""
|
|
Make predictions on a dstream.
|
|
|
|
:return: Transformed dstream object.
|
|
"""
|
|
self._validate(dstream)
|
|
return dstream.map(lambda x: self._model.predict(x))
|
|
|
|
@since("1.5.0")
|
|
def predictOnValues(self, dstream):
|
|
"""
|
|
Make predictions on a keyed dstream.
|
|
|
|
:return: Transformed dstream object.
|
|
"""
|
|
self._validate(dstream)
|
|
return dstream.mapValues(lambda x: self._model.predict(x))
|
|
|
|
|
|
@inherit_doc
|
|
class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm):
|
|
"""
|
|
Run LinearRegression with SGD on a batch of data.
|
|
|
|
The problem minimized is (1 / n_samples) * (y - weights'X)**2.
|
|
After training on a batch of data, the weights obtained at the end of
|
|
training are used as initial weights for the next batch.
|
|
|
|
:param stepSize: Step size for each iteration of gradient descent.
|
|
:param numIterations: Total number of iterations run.
|
|
:param miniBatchFraction: Fraction of data on which SGD is run for each
|
|
iteration.
|
|
:param convergenceTol: A condition which decides iteration termination.
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, convergenceTol=0.001):
|
|
self.stepSize = stepSize
|
|
self.numIterations = numIterations
|
|
self.miniBatchFraction = miniBatchFraction
|
|
self.convergenceTol = convergenceTol
|
|
self._model = None
|
|
super(StreamingLinearRegressionWithSGD, self).__init__(
|
|
model=self._model)
|
|
|
|
@since("1.5.0")
|
|
def setInitialWeights(self, initialWeights):
|
|
"""
|
|
Set the initial value of weights.
|
|
|
|
This must be set before running trainOn and predictOn
|
|
"""
|
|
initialWeights = _convert_to_vector(initialWeights)
|
|
self._model = LinearRegressionModel(initialWeights, 0)
|
|
return self
|
|
|
|
@since("1.5.0")
|
|
def trainOn(self, dstream):
|
|
"""Train the model on the incoming dstream."""
|
|
self._validate(dstream)
|
|
|
|
def update(rdd):
|
|
# LinearRegressionWithSGD.train raises an error for an empty RDD.
|
|
if not rdd.isEmpty():
|
|
self._model = LinearRegressionWithSGD.train(
|
|
rdd, self.numIterations, self.stepSize,
|
|
self.miniBatchFraction, self._model.weights,
|
|
intercept=self._model.intercept, convergenceTol=self.convergenceTol)
|
|
|
|
dstream.foreachRDD(update)
|
|
|
|
|
|
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()
|