path: root/python/pyspark/mllib/classification.py

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from math import exp

import numpy
from numpy import array

from pyspark import RDD
from pyspark.streaming import DStream
from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py
from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector
from pyspark.mllib.regression import (
    LabeledPoint, LinearModel, _regression_train_wrapper,
    StreamingLinearAlgorithm)
from pyspark.mllib.util import Saveable, Loader, inherit_doc


__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS',
           'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes',
           'StreamingLogisticRegressionWithSGD']


class LinearClassificationModel(LinearModel):
    """
    A private abstract class representing a multiclass classification
    model. The categories are represented by int values: 0, 1, 2, etc.
    """
    def __init__(self, weights, intercept):
        super(LinearClassificationModel, self).__init__(weights, intercept)
        self._threshold = None

    def setThreshold(self, value):
        """
        .. note:: Experimental

        Sets the threshold that separates positive predictions from
        negative predictions. An example with prediction score greater
        than or equal to this threshold is identified as an positive,
        and negative otherwise. It is used for binary classification
        only.
        """
        self._threshold = value

    @property
    def threshold(self):
        """
        .. note:: Experimental

        Returns the threshold (if any) used for converting raw
        prediction scores into 0/1 predictions. It is used for
        binary classification only.
        """
        return self._threshold

    def clearThreshold(self):
        """
        .. note:: Experimental

        Clears the threshold so that `predict` will output raw
        prediction scores. It is used for binary classification only.
        """
        self._threshold = None

    def predict(self, test):
        """
        Predict values for a single data point or an RDD of points
        using the model trained.
        """
        raise NotImplementedError


class LogisticRegressionModel(LinearClassificationModel):

    """
    Classification model trained using Multinomial/Binary Logistic
    Regression.

    :param weights: Weights computed for every feature.
    :param intercept: Intercept computed for this model. (Only used
            in Binary Logistic Regression. In Multinomial Logistic
            Regression, the intercepts will not be a single value,
            so the intercepts will be part of the weights.)
    :param numFeatures: the dimension of the features.
    :param numClasses: the number of possible outcomes for k classes
            classification problem in Multinomial Logistic Regression.
            By default, it is binary logistic regression so numClasses
            will be set to 2.

    >>> data = [
    ...     LabeledPoint(0.0, [0.0, 1.0]),
    ...     LabeledPoint(1.0, [1.0, 0.0]),
    ... ]
    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10)
    >>> lrm.predict([1.0, 0.0])
    1
    >>> lrm.predict([0.0, 1.0])
    0
    >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
    [1, 0]
    >>> lrm.clearThreshold()
    >>> lrm.predict([0.0, 1.0])
    0.279...

    >>> sparse_data = [
    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
    ...     LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
    ... ]
    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data), iterations=10)
    >>> lrm.predict(array([0.0, 1.0]))
    1
    >>> lrm.predict(array([1.0, 0.0]))
    0
    >>> lrm.predict(SparseVector(2, {1: 1.0}))
    1
    >>> lrm.predict(SparseVector(2, {0: 1.0}))
    0
    >>> import os, tempfile
    >>> path = tempfile.mkdtemp()
    >>> lrm.save(sc, path)
    >>> sameModel = LogisticRegressionModel.load(sc, path)
    >>> sameModel.predict(array([0.0, 1.0]))
    1
    >>> sameModel.predict(SparseVector(2, {0: 1.0}))
    0
    >>> from shutil import rmtree
    >>> try:
    ...    rmtree(path)
    ... except:
    ...    pass
    >>> multi_class_data = [
    ...     LabeledPoint(0.0, [0.0, 1.0, 0.0]),
    ...     LabeledPoint(1.0, [1.0, 0.0, 0.0]),
    ...     LabeledPoint(2.0, [0.0, 0.0, 1.0])
    ... ]
    >>> data = sc.parallelize(multi_class_data)
    >>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3)
    >>> mcm.predict([0.0, 0.5, 0.0])
    0
    >>> mcm.predict([0.8, 0.0, 0.0])
    1
    >>> mcm.predict([0.0, 0.0, 0.3])
    2
    """
    def __init__(self, weights, intercept, numFeatures, numClasses):
        super(LogisticRegressionModel, self).__init__(weights, intercept)
        self._numFeatures = int(numFeatures)
        self._numClasses = int(numClasses)
        self._threshold = 0.5
        if self._numClasses == 2:
            self._dataWithBiasSize = None
            self._weightsMatrix = None
        else:
            self._dataWithBiasSize = self._coeff.size / (self._numClasses - 1)
            self._weightsMatrix = self._coeff.toArray().reshape(self._numClasses - 1,
                                                                self._dataWithBiasSize)

    @property
    def numFeatures(self):
        return self._numFeatures

    @property
    def numClasses(self):
        return self._numClasses

    def predict(self, x):
        """
        Predict values for a single data point or an RDD of points
        using the model trained.
        """
        if isinstance(x, RDD):
            return x.map(lambda v: self.predict(v))

        x = _convert_to_vector(x)
        if self.numClasses == 2:
            margin = self.weights.dot(x) + self._intercept
            if margin > 0:
                prob = 1 / (1 + exp(-margin))
            else:
                exp_margin = exp(margin)
                prob = exp_margin / (1 + exp_margin)
            if self._threshold is None:
                return prob
            else:
                return 1 if prob > self._threshold else 0
        else:
            best_class = 0
            max_margin = 0.0
            if x.size + 1 == self._dataWithBiasSize:
                for i in range(0, self._numClasses - 1):
                    margin = x.dot(self._weightsMatrix[i][0:x.size]) + \
                        self._weightsMatrix[i][x.size]
                    if margin > max_margin:
                        max_margin = margin
                        best_class = i + 1
            else:
                for i in range(0, self._numClasses - 1):
                    margin = x.dot(self._weightsMatrix[i])
                    if margin > max_margin:
                        max_margin = margin
                        best_class = i + 1
            return best_class

    def save(self, sc, path):
        java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel(
            _py2java(sc, self._coeff), self.intercept, self.numFeatures, self.numClasses)
        java_model.save(sc._jsc.sc(), path)

    @classmethod
    def load(cls, sc, path):
        java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel.load(
            sc._jsc.sc(), path)
        weights = _java2py(sc, java_model.weights())
        intercept = java_model.intercept()
        numFeatures = java_model.numFeatures()
        numClasses = java_model.numClasses()
        threshold = java_model.getThreshold().get()
        model = LogisticRegressionModel(weights, intercept, numFeatures, numClasses)
        model.setThreshold(threshold)
        return model


class LogisticRegressionWithSGD(object):

    @classmethod
    def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
              initialWeights=None, regParam=0.01, regType="l2", intercept=False,
              validateData=True):
        """
        Train a logistic regression model on the given data.

        :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: 0.01).
        :param regType:           The type of regularizer used for
                                  training our model.

                                  :Allowed values:
                                     - "l1" for using L1 regularization
                                     - "l2" for using L2 regularization
                                     - None for no regularization

                                     (default: "l2")

        :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("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
                                 float(step), float(miniBatchFraction), i, float(regParam), regType,
                                 bool(intercept), bool(validateData))

        return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)


class LogisticRegressionWithLBFGS(object):

    @classmethod
    def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2",
              intercept=False, corrections=10, tolerance=1e-4, validateData=True, numClasses=2):
        """
        Train a logistic regression model on the given data.

        :param data:           The training data, an RDD of
                               LabeledPoint.
        :param iterations:     The number of iterations
                               (default: 100).
        :param initialWeights: The initial weights (default: None).
        :param regParam:       The regularizer parameter
                               (default: 0.01).
        :param regType:        The type of regularizer used for
                               training our model.

                               :Allowed values:
                                 - "l1" for using L1 regularization
                                 - "l2" for using L2 regularization
                                 - None for no regularization

                                 (default: "l2")

        :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 corrections:    The number of corrections used in the
                               LBFGS update (default: 10).
        :param tolerance:      The convergence tolerance of iterations
                               for L-BFGS (default: 1e-4).
        :param validateData:   Boolean parameter which indicates if the
                               algorithm should validate data before
                               training. (default: True)
        :param numClasses:     The number of classes (i.e., outcomes) a
                               label can take in Multinomial Logistic
                               Regression (default: 2).

        >>> data = [
        ...     LabeledPoint(0.0, [0.0, 1.0]),
        ...     LabeledPoint(1.0, [1.0, 0.0]),
        ... ]
        >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
        >>> lrm.predict([1.0, 0.0])
        1
        >>> lrm.predict([0.0, 1.0])
        0
        """
        def train(rdd, i):
            return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
                                 float(regParam), regType, bool(intercept), int(corrections),
                                 float(tolerance), bool(validateData), int(numClasses))

        if initialWeights is None:
            if numClasses == 2:
                initialWeights = [0.0] * len(data.first().features)
            else:
                if intercept:
                    initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
                else:
                    initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
        return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)


class SVMModel(LinearClassificationModel):

    """
    Model for Support Vector Machines (SVMs).

    :param weights: Weights computed for every feature.
    :param intercept: Intercept computed for this model.

    >>> data = [
    ...     LabeledPoint(0.0, [0.0]),
    ...     LabeledPoint(1.0, [1.0]),
    ...     LabeledPoint(1.0, [2.0]),
    ...     LabeledPoint(1.0, [3.0])
    ... ]
    >>> svm = SVMWithSGD.train(sc.parallelize(data), iterations=10)
    >>> svm.predict([1.0])
    1
    >>> svm.predict(sc.parallelize([[1.0]])).collect()
    [1]
    >>> svm.clearThreshold()
    >>> svm.predict(array([1.0]))
    1.44...

    >>> sparse_data = [
    ...     LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
    ... ]
    >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data), iterations=10)
    >>> svm.predict(SparseVector(2, {1: 1.0}))
    1
    >>> svm.predict(SparseVector(2, {0: -1.0}))
    0
    >>> import os, tempfile
    >>> path = tempfile.mkdtemp()
    >>> svm.save(sc, path)
    >>> sameModel = SVMModel.load(sc, path)
    >>> sameModel.predict(SparseVector(2, {1: 1.0}))
    1
    >>> sameModel.predict(SparseVector(2, {0: -1.0}))
    0
    >>> from shutil import rmtree
    >>> try:
    ...    rmtree(path)
    ... except:
    ...    pass
    """
    def __init__(self, weights, intercept):
        super(SVMModel, self).__init__(weights, intercept)
        self._threshold = 0.0

    def predict(self, x):
        """
        Predict values for a single data point or an RDD of points
        using the model trained.
        """
        if isinstance(x, RDD):
            return x.map(lambda v: self.predict(v))

        x = _convert_to_vector(x)
        margin = self.weights.dot(x) + self.intercept
        if self._threshold is None:
            return margin
        else:
            return 1 if margin > self._threshold else 0

    def save(self, sc, path):
        java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel(
            _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.classification.SVMModel.load(
            sc._jsc.sc(), path)
        weights = _java2py(sc, java_model.weights())
        intercept = java_model.intercept()
        threshold = java_model.getThreshold().get()
        model = SVMModel(weights, intercept)
        model.setThreshold(threshold)
        return model


class SVMWithSGD(object):

    @classmethod
    def train(cls, data, iterations=100, step=1.0, regParam=0.01,
              miniBatchFraction=1.0, initialWeights=None, regType="l2",
              intercept=False, validateData=True):
        """
        Train a support vector machine on the given data.

        :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 regType:           The type of regularizer used for
                                  training our model.

                                  :Allowed values:
                                     - "l1" for using L1 regularization
                                     - "l2" for using L2 regularization
                                     - None for no regularization

                                     (default: "l2")

        :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("trainSVMModelWithSGD", rdd, int(iterations), float(step),
                                 float(regParam), float(miniBatchFraction), i, regType,
                                 bool(intercept), bool(validateData))

        return _regression_train_wrapper(train, SVMModel, data, initialWeights)


@inherit_doc
class NaiveBayesModel(Saveable, Loader):

    """
    Model for Naive Bayes classifiers.

    :param labels: list of labels.
    :param pi: log of class priors, whose dimension is C,
            number of labels.
    :param theta: log of class conditional probabilities, whose
            dimension is C-by-D, where D is number of features.

    >>> data = [
    ...     LabeledPoint(0.0, [0.0, 0.0]),
    ...     LabeledPoint(0.0, [0.0, 1.0]),
    ...     LabeledPoint(1.0, [1.0, 0.0]),
    ... ]
    >>> model = NaiveBayes.train(sc.parallelize(data))
    >>> model.predict(array([0.0, 1.0]))
    0.0
    >>> model.predict(array([1.0, 0.0]))
    1.0
    >>> model.predict(sc.parallelize([[1.0, 0.0]])).collect()
    [1.0]
    >>> sparse_data = [
    ...     LabeledPoint(0.0, SparseVector(2, {1: 0.0})),
    ...     LabeledPoint(0.0, SparseVector(2, {1: 1.0})),
    ...     LabeledPoint(1.0, SparseVector(2, {0: 1.0}))
    ... ]
    >>> model = NaiveBayes.train(sc.parallelize(sparse_data))
    >>> model.predict(SparseVector(2, {1: 1.0}))
    0.0
    >>> model.predict(SparseVector(2, {0: 1.0}))
    1.0
    >>> import os, tempfile
    >>> path = tempfile.mkdtemp()
    >>> model.save(sc, path)
    >>> sameModel = NaiveBayesModel.load(sc, path)
    >>> sameModel.predict(SparseVector(2, {0: 1.0})) == model.predict(SparseVector(2, {0: 1.0}))
    True
    >>> from shutil import rmtree
    >>> try:
    ...     rmtree(path)
    ... except OSError:
    ...     pass
    """

    def __init__(self, labels, pi, theta):
        self.labels = labels
        self.pi = pi
        self.theta = theta

    def predict(self, x):
        """
        Return the most likely class for a data vector
        or an RDD of vectors
        """
        if isinstance(x, RDD):
            return x.map(lambda v: self.predict(v))
        x = _convert_to_vector(x)
        return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))]

    def save(self, sc, path):
        java_labels = _py2java(sc, self.labels.tolist())
        java_pi = _py2java(sc, self.pi.tolist())
        java_theta = _py2java(sc, self.theta.tolist())
        java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel(
            java_labels, java_pi, java_theta)
        java_model.save(sc._jsc.sc(), path)

    @classmethod
    def load(cls, sc, path):
        java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel.load(
            sc._jsc.sc(), path)
        # Can not unpickle array.array from Pyrolite in Python3 with "bytes"
        py_labels = _java2py(sc, java_model.labels(), "latin1")
        py_pi = _java2py(sc, java_model.pi(), "latin1")
        py_theta = _java2py(sc, java_model.theta(), "latin1")
        return NaiveBayesModel(py_labels, py_pi, numpy.array(py_theta))


class NaiveBayes(object):

    @classmethod
    def train(cls, data, lambda_=1.0):
        """
        Train a Naive Bayes model given an RDD of (label, features)
        vectors.

        This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which
        can handle all kinds of discrete data.  For example, by
        converting documents into TF-IDF vectors, it can be used for
        document classification. By making every vector a 0-1 vector,
        it can also be used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
        The input feature values must be nonnegative.

        :param data: RDD of LabeledPoint.
        :param lambda_: The smoothing parameter (default: 1.0).
        """
        first = data.first()
        if not isinstance(first, LabeledPoint):
            raise ValueError("`data` should be an RDD of LabeledPoint")
        labels, pi, theta = callMLlibFunc("trainNaiveBayesModel", data, lambda_)
        return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))


@inherit_doc
class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
    """
    Run LogisticRegression with SGD on a batch of data.

    The weights obtained at the end of training a stream are used as initial
    weights for the next batch.

    :param stepSize: Step size for each iteration of gradient descent.
    :param numIterations: Number of iterations run for each batch of data.
    :param miniBatchFraction: Fraction of data on which SGD is run for each
                              iteration.
    :param regParam: L2 Regularization parameter.
    """
    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01):
        self.stepSize = stepSize
        self.numIterations = numIterations
        self.regParam = regParam
        self.miniBatchFraction = miniBatchFraction
        self._model = None
        super(StreamingLogisticRegressionWithSGD, self).__init__(
            model=self._model)

    def setInitialWeights(self, initialWeights):
        """
        Set the initial value of weights.

        This must be set before running trainOn and predictOn.
        """
        initialWeights = _convert_to_vector(initialWeights)

        # LogisticRegressionWithSGD does only binary classification.
        self._model = LogisticRegressionModel(
            initialWeights, 0, initialWeights.size, 2)
        return self

    def trainOn(self, dstream):
        """Train the model on the incoming dstream."""
        self._validate(dstream)

        def update(rdd):
            # LogisticRegressionWithSGD.train raises an error for an empty RDD.
            if not rdd.isEmpty():
                self._model = LogisticRegressionWithSGD.train(
                    rdd, self.numIterations, self.stepSize,
                    self.miniBatchFraction, self._model.weights)

        dstream.foreachRDD(update)


def _test():
    import doctest
    from pyspark import SparkContext
    import pyspark.mllib.classification
    globs = pyspark.mllib.classification.__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()