7b1457839b
https://issues.apache.org/jira/browse/SPARK-6267 Author: Yanbo Liang <ybliang8@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #5890 from yanboliang/spark-6267 and squashes the following commits: f20541d [Yanbo Liang] Merge pull request #3 from mengxr/SPARK-6267 7f202f9 [Xiangrui Meng] use Vector to have the best Python 2&3 compatibility 4bccfee [Yanbo Liang] fix doctest ec09412 [Yanbo Liang] fix typos 8214bbb [Yanbo Liang] fix code style 5c8ebe5 [Yanbo Liang] Python API for IsotonicRegression
481 lines
18 KiB
Python
481 lines
18 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
|
|
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']
|
|
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
|
|
class LinearModel(object):
|
|
|
|
"""A linear model that has a vector of coefficients and an intercept."""
|
|
|
|
def __init__(self, weights, intercept):
|
|
self._coeff = _convert_to_vector(weights)
|
|
self._intercept = float(intercept)
|
|
|
|
@property
|
|
def weights(self):
|
|
return self._coeff
|
|
|
|
@property
|
|
def intercept(self):
|
|
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
|
|
"""
|
|
|
|
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
|
|
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
|
|
>>> try:
|
|
... os.removedirs(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
|
|
"""
|
|
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
|
|
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)
|
|
|
|
|
|
class LinearRegressionWithSGD(object):
|
|
|
|
@classmethod
|
|
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 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: 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
|
|
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
|
|
>>> try:
|
|
... os.removedirs(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
|
|
"""
|
|
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
|
|
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
|
|
|
|
|
|
class LassoWithSGD(object):
|
|
|
|
@classmethod
|
|
def train(cls, data, iterations=100, step=1.0, regParam=0.01,
|
|
miniBatchFraction=1.0, initialWeights=None, intercept=False,
|
|
validateData=True):
|
|
"""Train a Lasso regression model on the given data."""
|
|
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
|
|
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
|
|
>>> try:
|
|
... os.removedirs(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
|
|
"""
|
|
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
|
|
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
|
|
|
|
|
|
class RidgeRegressionWithSGD(object):
|
|
|
|
@classmethod
|
|
def train(cls, data, iterations=100, step=1.0, regParam=0.01,
|
|
miniBatchFraction=1.0, initialWeights=None, intercept=False,
|
|
validateData=True):
|
|
"""Train a ridge regression model on the given data."""
|
|
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)
|
|
|
|
|
|
class IsotonicRegressionModel(Saveable, Loader):
|
|
|
|
"""Regression model for isotonic regression.
|
|
|
|
>>> 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
|
|
>>> try:
|
|
... os.removedirs(path)
|
|
... except OSError:
|
|
... pass
|
|
"""
|
|
|
|
def __init__(self, boundaries, predictions, isotonic):
|
|
self.boundaries = boundaries
|
|
self.predictions = predictions
|
|
self.isotonic = isotonic
|
|
|
|
def predict(self, x):
|
|
if isinstance(x, RDD):
|
|
return x.map(lambda v: self.predict(v))
|
|
return np.interp(x, self.boundaries, self.predictions)
|
|
|
|
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
|
|
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)
|
|
|
|
|
|
class IsotonicRegression(object):
|
|
"""
|
|
Run IsotonicRegression algorithm to obtain isotonic regression model.
|
|
|
|
:param data: RDD of (label, feature, weight) tuples.
|
|
:param isotonic: Whether this is isotonic or antitonic.
|
|
"""
|
|
@classmethod
|
|
def train(cls, data, isotonic=True):
|
|
"""Train a isotonic regression model on the given data."""
|
|
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()
|