Source code for pyspark.mllib.regression

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import numpy as np
from numpy import array

from pyspark.mllib.common import callMLlibFunc, inherit_doc
from pyspark.mllib.linalg import SparseVector, _convert_to_vector

__all__ = ['LabeledPoint', 'LinearModel',
           'LinearRegressionModel', 'LinearRegressionWithSGD',
           'RidgeRegressionModel', 'RidgeRegressionWithSGD',
           'LassoModel', 'LassoWithSGD']


[docs]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)
[docs]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
[docs] def weights(self): return self._coeff
@property
[docs] 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
[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), 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 >>> 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), 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 """ # 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): first = data.first() if not isinstance(first, LabeledPoint): raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first) initial_weights = initial_weights or [0.0] * len(data.first().features) 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): """ 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) """ def train(rdd, i): return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), regType, bool(intercept)) 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), 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 >>> 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), 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 """
[docs]class LassoWithSGD(object): @classmethod
[docs] def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None): """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) 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), 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 >>> 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), 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 """
[docs]class RidgeRegressionWithSGD(object): @classmethod
[docs] def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None): """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) return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)
def _test(): import doctest from pyspark import SparkContext import pyspark.mllib.regression globs = pyspark.mllib.regression.__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()