spark-instrumented-optimizer/python/pyspark/mllib/regression.py

#
# 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
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from numpy import array, ndarray
from pyspark import SparkContext
from pyspark.mllib._common import \
    _dot, _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
    _serialize_double_matrix, _deserialize_double_matrix, \
    _serialize_double_vector, _deserialize_double_vector, \
    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
    _linear_predictor_typecheck, _have_scipy, _scipy_issparse
from pyspark.mllib.linalg import SparseVector, Vectors


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)
    """
    def __init__(self, label, features):
        self.label = label
        if (type(features) == ndarray or type(features) == SparseVector
                or (_have_scipy and _scipy_issparse(features))):
            self.features = features
        elif type(features) == list:
            self.features = array(features)
        else:
            raise TypeError("Expected NumPy array, list, SparseVector, or scipy.sparse matrix")

    def __str__(self):
        return "(" + ",".join((str(self.label), Vectors.stringify(self.features))) + ")"


class LinearModel(object):
    """A linear model that has a vector of coefficients and an intercept."""
    def __init__(self, weights, intercept):
        self._coeff = weights
        self._intercept = intercept

    @property
    def weights(self):
        return self._coeff

    @property
    def intercept(self):
        return self._intercept


class LinearRegressionModelBase(LinearModel):
    """A linear regression model.

    >>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1)
    >>> abs(lrmb.predict(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."""
        _linear_predictor_typecheck(x, self._coeff)
        return _dot(x, self._coeff) + self._intercept


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=array([1.0]))
    >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
    True
    >>> abs(lrm.predict(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
    """


class LinearRegressionWithSGD(object):
    @classmethod
    def train(cls, data, iterations=100, step=1.0,
              miniBatchFraction=1.0, initialWeights=None):
        """Train a linear regression model on the given data."""
        sc = data.context
        train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
            d._jrdd, iterations, step, miniBatchFraction, i)
        return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights)


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(array([0.0])) - 0) < 0.5
    True
    >>> abs(lrm.predict(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
    """


class LassoWithSGD(object):
    @classmethod
    def train(cls, data, iterations=100, step=1.0, regParam=1.0,
              miniBatchFraction=1.0, initialWeights=None):
        """Train a Lasso regression model on the given data."""
        sc = data.context
        train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(
            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
        return _regression_train_wrapper(sc, train_f, LassoModel, data, initialWeights)


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(array([0.0])) - 0) < 0.5
    True
    >>> abs(lrm.predict(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
    """


class RidgeRegressionWithSGD(object):
    @classmethod
    def train(cls, data, iterations=100, step=1.0, regParam=1.0,
              miniBatchFraction=1.0, initialWeights=None):
        """Train a ridge regression model on the given data."""
        sc = data.context
        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(
            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
        return _regression_train_wrapper(sc, train_func, RidgeRegressionModel, data, initialWeights)


def _test():
    import doctest
    globs = globals().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()