# # 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. # from math import exp import sys import warnings import numpy from pyspark import RDD, since from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py from pyspark.mllib.linalg import _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 @since('1.4.0') def setThreshold(self, value): """ 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 a positive, and negative otherwise. It is used for binary classification only. """ self._threshold = value @property @since('1.4.0') def threshold(self): """ 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 @since('1.4.0') def clearThreshold(self): """ Clears the threshold so that `predict` will output raw prediction scores. It is used for binary classification only. """ self._threshold = None @since('1.4.0') def predict(self, test): """ Predict values for a single data point or an RDD of points using the model trained. """ raise NotImplementedError [docs]class LogisticRegressionModel(LinearClassificationModel): """ Classification model trained using Multinomial/Binary Logistic Regression. .. versionadded:: 0.9.0 Parameters ---------- weights : :py:class:`pyspark.mllib.linalg.Vector` Weights computed for every feature. intercept : float 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.) numFeatures : int The dimension of the features. numClasses : int 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. Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> 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(numpy.array([0.0, 1.0])) 1 >>> lrm.predict(numpy.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(numpy.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 @since('1.4.0') def numFeatures(self): """ Dimension of the features. """ return self._numFeatures @property @since('1.4.0') def numClasses(self): """ Number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. """ return self._numClasses [docs] @since('0.9.0') 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) 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 [docs] @since('1.4.0') def save(self, sc, path): """ Save this model to the given 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) [docs] @classmethod @since('1.4.0') def load(cls, sc, path): """ Load a model from the given 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 def __repr__(self): return self._call_java("toString") [docs]class LogisticRegressionWithSGD(object): """ Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent. .. versionadded:: 0.9.0 .. deprecated:: 2.0.0 Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS. """ [docs] @classmethod def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None, regParam=0.01, regType="l2", intercept=False, validateData=True, convergenceTol=0.001): """ Train a logistic regression model on the given data. .. versionadded:: 0.9.0 Parameters ---------- data : :py:class:`pyspark.RDD` The training data, an RDD of :py:class:`pyspark.mllib.regression.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: 0.01) regType : str, optional The type of regularizer used for training our model. Supported values: - "l1" for using L1 regularization - "l2" for using L2 regularization (default) - None for no regularization 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.classification.LogisticRegression or " "LogisticRegressionWithLBFGS.", FutureWarning) def train(rdd, i): return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), regType, bool(intercept), bool(validateData), float(convergenceTol)) return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) [docs]class LogisticRegressionWithLBFGS(object): """ Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS. Standard feature scaling and L2 regularization are used by default. .. versionadded:: 1.2.0 """ [docs] @classmethod def train(cls, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2", intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2): """ Train a logistic regression model on the given data. .. versionadded:: 1.2.0 Parameters ---------- data : :py:class:`pyspark.RDD` The training data, an RDD of :py:class:`pyspark.mllib.regression.LabeledPoint`. iterations : int, optional The number of iterations. (default: 100) initialWeights : :py:class:`pyspark.mllib.linalg.Vector` or convertible, optional The initial weights. (default: None) regParam : float, optional The regularizer parameter. (default: 0.01) regType : str, optional The type of regularizer used for training our model. Supported values: - "l1" for using L1 regularization - "l2" for using L2 regularization (default) - None for no regularization 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) corrections : int, optional The number of corrections used in the LBFGS update. If a known updater is used for binary classification, it calls the ml implementation and this parameter will have no effect. (default: 10) tolerance : float, optional The convergence tolerance of iterations for L-BFGS. (default: 1e-6) validateData : bool, optional Boolean parameter which indicates if the algorithm should validate data before training. (default: True) numClasses : int, optional The number of classes (i.e., outcomes) a label can take in Multinomial Logistic Regression. (default: 2) Examples -------- >>> 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) [docs]class SVMModel(LinearClassificationModel): """ Model for Support Vector Machines (SVMs). .. versionadded:: 0.9.0 Parameters ---------- weights : :py:class:`pyspark.mllib.linalg.Vector` Weights computed for every feature. intercept : float Intercept computed for this model. Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> 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(numpy.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 [docs] @since('0.9.0') 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 [docs] @since('1.4.0') def save(self, sc, path): """ Save this model to the given path. """ java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel( _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 model from the given 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 [docs]class SVMWithSGD(object): """ Train a Support Vector Machine (SVM) using Stochastic Gradient Descent. .. versionadded:: 0.9.0 """ [docs] @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, miniBatchFraction=1.0, initialWeights=None, regType="l2", intercept=False, validateData=True, convergenceTol=0.001): """ Train a support vector machine on the given data. .. versionadded:: 0.9.0 Parameters ---------- data : :py:class:`pyspark.RDD` The training data, an RDD of :py:class:`pyspark.mllib.regression.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) regType : str, optional The type of regularizer used for training our model. Allowed values: - "l1" for using L1 regularization - "l2" for using L2 regularization (default) - None for no regularization 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) """ def train(rdd, i): return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, regType, bool(intercept), bool(validateData), float(convergenceTol)) return _regression_train_wrapper(train, SVMModel, data, initialWeights) [docs]@inherit_doc class NaiveBayesModel(Saveable, Loader): """ Model for Naive Bayes classifiers. .. versionadded:: 0.9.0 Parameters ---------- labels : :py:class:`numpy.ndarray` List of labels. pi : :py:class:`numpy.ndarray` Log of class priors, whose dimension is C, number of labels. theta : :py:class:`numpy.ndarray` Log of class conditional probabilities, whose dimension is C-by-D, where D is number of features. Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> 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(numpy.array([0.0, 1.0])) 0.0 >>> model.predict(numpy.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 [docs] @since('0.9.0') 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()))] [docs] def save(self, sc, path): """ Save this model to the given 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) [docs] @classmethod @since('1.4.0') def load(cls, sc, path): """ Load a model from the given 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)) [docs]class NaiveBayes(object): """ Train a Multinomial Naive Bayes model. .. versionadded:: 0.9.0 """ [docs] @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 <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 <http://tinyurl.com/p7c96j6>`_. The input feature values must be nonnegative. .. versionadded:: 0.9.0 Parameters ---------- data : :py:class:`pyspark.RDD` The training data, an RDD of :py:class:`pyspark.mllib.regression.LabeledPoint`. lambda\\_ : float, optional 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)) [docs]@inherit_doc class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm): """ Train or predict a logistic 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. 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) regParam : float, optional L2 Regularization parameter. (default: 0.0) 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, regParam=0.0, convergenceTol=0.001): self.stepSize = stepSize self.numIterations = numIterations self.regParam = regParam self.miniBatchFraction = miniBatchFraction self.convergenceTol = convergenceTol self._model = None super(StreamingLogisticRegressionWithSGD, 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) # LogisticRegressionWithSGD does only binary classification. self._model = LogisticRegressionModel( initialWeights, 0, initialWeights.size, 2) return self [docs] @since('1.5.0') 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, regParam=self.regParam, convergenceTol=self.convergenceTol) dstream.foreachRDD(update) def _test(): import doctest from pyspark.sql import SparkSession import pyspark.mllib.classification globs = pyspark.mllib.classification.__dict__.copy() spark = SparkSession.builder\ .master("local[4]")\ .appName("mllib.classification 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()