Source code for pyspark.mllib.regression

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import sys
import warnings

import numpy as np

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 _convert_to_vector
from pyspark.mllib.util import Saveable, Loader

__all__ = ['LabeledPoint', 'LinearModel',
           'LinearRegressionModel', 'LinearRegressionWithSGD',
           'RidgeRegressionModel', 'RidgeRegressionWithSGD',
           'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel',
           'IsotonicRegression', 'StreamingLinearAlgorithm',
           'StreamingLinearRegressionWithSGD']


[docs]class LabeledPoint(object): """ Class that represents the features and labels of a data point. .. versionadded:: 1.0.0 Parameters ---------- label : int Label for this data point. features : :py:class:`pyspark.mllib.linalg.Vector` or convertible Vector of features for this point (NumPy array, list, pyspark.mllib.linalg.SparseVector, or scipy.sparse column matrix). Notes ----- '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. .. versionadded:: 0.9.0 Parameters ---------- weights : :py:class:`pyspark.mllib.linalg.Vector` Weights computed for every feature. intercept : float Intercept computed for this model. """ 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. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> 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 """ @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
[docs]@inherit_doc class LinearRegressionModel(LinearRegressionModelBase): """A linear regression model derived from a least-squares fit. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> 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=np.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 = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0, ... miniBatchFraction=1.0, initialWeights=np.array([1.0]), regParam=0.1, regType="l2", ... 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 """
[docs] @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)
[docs] @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)
[docs]class LinearRegressionWithSGD(object): """ Train a linear regression model with no regularization using Stochastic Gradient Descent. .. versionadded:: 0.9.0 .. deprecated:: 2.0.0 Use :py:class:`pyspark.ml.regression.LinearRegression`. """
[docs] @classmethod 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. .. versionadded:: 0.9.0 Parameters ---------- data : :py:class:`pyspark.RDD` The training data, an RDD of LabeledPoint. iterations : int, optional The number of iterations. (default: 100) step : float, optional The step parameter used in SGD. (default: 1.0) miniBatchFraction : float, optional Fraction of data to be used for each SGD iteration. (default: 1.0) initialWeights : :py:class:`pyspark.mllib.linalg.Vector` or convertible, optional The initial weights. (default: None) regParam : float, optional The regularizer parameter. (default: 0.0) regType : str, optional The type of regularizer used for training our model. Supported values: - "l1" for using L1 regularization - "l2" for using L2 regularization - None for no regularization (default) intercept : bool, optional 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) validateData : bool, optional Boolean parameter which indicates if the algorithm should validate data before training. (default: True) convergenceTol : float, optional A condition which decides iteration termination. (default: 0.001) """ warnings.warn( "Deprecated in 2.0.0. Use ml.regression.LinearRegression.", DeprecationWarning) 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)
[docs]@inherit_doc class LassoModel(LinearRegressionModelBase): """A linear regression model derived from a least-squares fit with an l_1 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> 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=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 = 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=np.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=np.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 """
[docs] @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)
[docs] @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
[docs]class LassoWithSGD(object): """ Train a regression model with L1-regularization using Stochastic Gradient Descent. .. versionadded:: 0.9.0 .. deprecated:: 2.0.0 Use :py:class:`pyspark.ml.regression.LinearRegression` with elasticNetParam = 1.0. Note the default regParam is 0.01 for LassoWithSGD, but is 0.0 for LinearRegression. """
[docs] @classmethod 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. .. versionadded:: 0.9.0 Parameters ---------- data : :py:class:`pyspark.RDD` The training data, an RDD of LabeledPoint. iterations : int, optional The number of iterations. (default: 100) step : float, optional The step parameter used in SGD. (default: 1.0) regParam : float, optional The regularizer parameter. (default: 0.01) miniBatchFraction : float, optional Fraction of data to be used for each SGD iteration. (default: 1.0) initialWeights : :py:class:`pyspark.mllib.linalg.Vector` or convertible, optional The initial weights. (default: None) intercept : bool, optional 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) validateData : bool, optional Boolean parameter which indicates if the algorithm should validate data before training. (default: True) convergenceTol : float, optional A condition which decides iteration termination. (default: 0.001) """ warnings.warn( "Deprecated in 2.0.0. Use ml.regression.LinearRegression with elasticNetParam = 1.0. " "Note the default regParam is 0.01 for LassoWithSGD, but is 0.0 for LinearRegression.", DeprecationWarning) 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)
[docs]@inherit_doc class RidgeRegressionModel(LinearRegressionModelBase): """A linear regression model derived from a least-squares fit with an l_2 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> 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=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 = 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=np.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=np.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 """
[docs] @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)
[docs] @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
[docs]class RidgeRegressionWithSGD(object): """ Train a regression model with L2-regularization using Stochastic Gradient Descent. .. versionadded:: 0.9.0 .. deprecated:: 2.0.0 Use :py:class:`pyspark.ml.regression.LinearRegression` with elasticNetParam = 0.0. Note the default regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression. """
[docs] @classmethod 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. .. versionadded:: 0.9.0 Parameters ---------- data : :py:class:`pyspark.RDD` The training data, an RDD of LabeledPoint. iterations : int, optional The number of iterations. (default: 100) step : float, optional The step parameter used in SGD. (default: 1.0) regParam : float, optional The regularizer parameter. (default: 0.01) miniBatchFraction : float, optional Fraction of data to be used for each SGD iteration. (default: 1.0) initialWeights : :py:class:`pyspark.mllib.linalg.Vector` or convertible, optional The initial weights. (default: None) intercept : bool, optional 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) validateData : bool, optional Boolean parameter which indicates if the algorithm should validate data before training. (default: True) convergenceTol : float, optional A condition which decides iteration termination. (default: 0.001) """ warnings.warn( "Deprecated in 2.0.0. Use ml.regression.LinearRegression with elasticNetParam = 0.0. " "Note the default regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for " "LinearRegression.", DeprecationWarning) 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)
[docs]class IsotonicRegressionModel(Saveable, Loader): """ Regression model for isotonic regression. .. versionadded:: 1.4.0 Parameters ---------- boundaries : ndarray Array of boundaries for which predictions are known. Boundaries must be sorted in increasing order. predictions : ndarray Array of predictions associated to the boundaries at the same index. Results of isotonic regression and therefore monotone. isotonic : true Indicates whether this is isotonic or antitonic. Examples -------- >>> 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 """ def __init__(self, boundaries, predictions, isotonic): self.boundaries = boundaries self.predictions = predictions self.isotonic = isotonic
[docs] 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. .. versionadded:: 1.4.0 Parameters ---------- x : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD` 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)
[docs] @since("1.4.0") def save(self, sc, path): """Save an 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)
[docs] @classmethod @since("1.4.0") def load(cls, sc, path): """Load an 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)
[docs]class IsotonicRegression(object): """ Isotonic regression. Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported. .. versionadded:: 1.4.0 Notes ----- Sequential PAV implementation based on Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani (2011) [1]_ Sequential PAV parallelization based on Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset (1996) [2]_ See also `Isotonic regression (Wikipedia) <http://en.wikipedia.org/wiki/Isotonic_regression>`_. .. [1] 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 .. [2] 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 """
[docs] @classmethod def train(cls, data, isotonic=True): """ Train an isotonic regression model on the given data. .. versionadded:: 1.4.0 Parameters ---------- data : :py:class:`pyspark.RDD` RDD of (label, feature, weight) tuples. isotonic : bool, optional Whether this is isotonic (which is default) or antitonic. (default: True) """ boundaries, predictions = callMLlibFunc("trainIsotonicRegressionModel", data.map(_convert_to_vector), bool(isotonic)) return IsotonicRegressionModel(boundaries.toArray(), predictions.toArray(), isotonic)
[docs]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
[docs] @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 initialized using setInitialWeights")
[docs] def predictOn(self, dstream): """ Use the model to make predictions on batches of data from a DStream. .. versionadded:: 1.5.0 Returns ------- :py:class:`pyspark.streaming.DStream` DStream containing predictions. """ self._validate(dstream) return dstream.map(lambda x: self._model.predict(x))
[docs] def predictOnValues(self, dstream): """ Use the model to make predictions on the values of a DStream and carry over its keys. .. versionadded:: 1.5.0 Returns ------- :py:class:`pyspark.streaming.DStream` DStream containing predictions. """ self._validate(dstream) return dstream.mapValues(lambda x: self._model.predict(x))
[docs]@inherit_doc class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm): """ Train or predict a linear regression model on streaming data. Training uses Stochastic Gradient Descent to update the model based on each new batch of incoming data from a DStream (see `LinearRegressionWithSGD` for model equation). Each batch of data is assumed to be an RDD of LabeledPoints. The number of data points per batch can vary, but the number of features must be constant. An initial weight vector must be provided. .. versionadded:: 1.5.0 Parameters ---------- stepSize : float, optional Step size for each iteration of gradient descent. (default: 0.1) numIterations : int, optional Number of iterations run for each batch of data. (default: 50) miniBatchFraction : float, optional Fraction of each batch of data to use for updates. (default: 1.0) convergenceTol : float, optional Value used to determine when to terminate iterations. (default: 0.001) """ 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)
[docs] @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
[docs] @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.sql import SparkSession import pyspark.mllib.regression globs = pyspark.mllib.regression.__dict__.copy() spark = SparkSession.builder\ .master("local[2]")\ .appName("mllib.regression tests")\ .getOrCreate() globs['sc'] = spark.sparkContext (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()