Source code for pyspark.mllib.evaluation

#
# 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.
#

import warnings

from pyspark import since
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
from pyspark.sql import SQLContext
from pyspark.sql.types import StructField, StructType, DoubleType, IntegerType, ArrayType

__all__ = ['BinaryClassificationMetrics', 'RegressionMetrics',
           'MulticlassMetrics', 'RankingMetrics']


[docs]class BinaryClassificationMetrics(JavaModelWrapper): """ Evaluator for binary classification. :param scoreAndLabels: an RDD of (score, label) pairs >>> scoreAndLabels = sc.parallelize([ ... (0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)], 2) >>> metrics = BinaryClassificationMetrics(scoreAndLabels) >>> metrics.areaUnderROC 0.70... >>> metrics.areaUnderPR 0.83... >>> metrics.unpersist() .. versionadded:: 1.4.0 """ def __init__(self, scoreAndLabels): sc = scoreAndLabels.ctx sql_ctx = SQLContext.getOrCreate(sc) df = sql_ctx.createDataFrame(scoreAndLabels, schema=StructType([ StructField("score", DoubleType(), nullable=False), StructField("label", DoubleType(), nullable=False)])) java_class = sc._jvm.org.apache.spark.mllib.evaluation.BinaryClassificationMetrics java_model = java_class(df._jdf) super(BinaryClassificationMetrics, self).__init__(java_model) @property @since('1.4.0')
[docs] def areaUnderROC(self): """ Computes the area under the receiver operating characteristic (ROC) curve. """ return self.call("areaUnderROC")
@property @since('1.4.0')
[docs] def areaUnderPR(self): """ Computes the area under the precision-recall curve. """ return self.call("areaUnderPR")
@since('1.4.0')
[docs] def unpersist(self): """ Unpersists intermediate RDDs used in the computation. """ self.call("unpersist")
[docs]class RegressionMetrics(JavaModelWrapper): """ Evaluator for regression. :param predictionAndObservations: an RDD of (prediction, observation) pairs. >>> predictionAndObservations = sc.parallelize([ ... (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)]) >>> metrics = RegressionMetrics(predictionAndObservations) >>> metrics.explainedVariance 8.859... >>> metrics.meanAbsoluteError 0.5... >>> metrics.meanSquaredError 0.37... >>> metrics.rootMeanSquaredError 0.61... >>> metrics.r2 0.94... .. versionadded:: 1.4.0 """ def __init__(self, predictionAndObservations): sc = predictionAndObservations.ctx sql_ctx = SQLContext.getOrCreate(sc) df = sql_ctx.createDataFrame(predictionAndObservations, schema=StructType([ StructField("prediction", DoubleType(), nullable=False), StructField("observation", DoubleType(), nullable=False)])) java_class = sc._jvm.org.apache.spark.mllib.evaluation.RegressionMetrics java_model = java_class(df._jdf) super(RegressionMetrics, self).__init__(java_model) @property @since('1.4.0')
[docs] def explainedVariance(self): """ Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) """ return self.call("explainedVariance")
@property @since('1.4.0')
[docs] def meanAbsoluteError(self): """ Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss. """ return self.call("meanAbsoluteError")
@property @since('1.4.0')
[docs] def meanSquaredError(self): """ Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss. """ return self.call("meanSquaredError")
@property @since('1.4.0')
[docs] def rootMeanSquaredError(self): """ Returns the root mean squared error, which is defined as the square root of the mean squared error. """ return self.call("rootMeanSquaredError")
@property @since('1.4.0')
[docs] def r2(self): """ Returns R^2^, the coefficient of determination. """ return self.call("r2")
[docs]class MulticlassMetrics(JavaModelWrapper): """ Evaluator for multiclass classification. :param predictionAndLabels: an RDD of (prediction, label) pairs. >>> predictionAndLabels = sc.parallelize([(0.0, 0.0), (0.0, 1.0), (0.0, 0.0), ... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]) >>> metrics = MulticlassMetrics(predictionAndLabels) >>> metrics.confusionMatrix().toArray() array([[ 2., 1., 1.], [ 1., 3., 0.], [ 0., 0., 1.]]) >>> metrics.falsePositiveRate(0.0) 0.2... >>> metrics.precision(1.0) 0.75... >>> metrics.recall(2.0) 1.0... >>> metrics.fMeasure(0.0, 2.0) 0.52... >>> metrics.accuracy 0.66... >>> metrics.weightedFalsePositiveRate 0.19... >>> metrics.weightedPrecision 0.68... >>> metrics.weightedRecall 0.66... >>> metrics.weightedFMeasure() 0.66... >>> metrics.weightedFMeasure(2.0) 0.65... .. versionadded:: 1.4.0 """ def __init__(self, predictionAndLabels): sc = predictionAndLabels.ctx sql_ctx = SQLContext.getOrCreate(sc) df = sql_ctx.createDataFrame(predictionAndLabels, schema=StructType([ StructField("prediction", DoubleType(), nullable=False), StructField("label", DoubleType(), nullable=False)])) java_class = sc._jvm.org.apache.spark.mllib.evaluation.MulticlassMetrics java_model = java_class(df._jdf) super(MulticlassMetrics, self).__init__(java_model) @since('1.4.0')
[docs] def confusionMatrix(self): """ Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels". """ return self.call("confusionMatrix")
@since('1.4.0')
[docs] def truePositiveRate(self, label): """ Returns true positive rate for a given label (category). """ return self.call("truePositiveRate", label)
@since('1.4.0')
[docs] def falsePositiveRate(self, label): """ Returns false positive rate for a given label (category). """ return self.call("falsePositiveRate", label)
@since('1.4.0')
[docs] def precision(self, label=None): """ Returns precision or precision for a given label (category) if specified. """ if label is None: # note:: Deprecated in 2.0.0. Use accuracy. warnings.warn("Deprecated in 2.0.0. Use accuracy.") return self.call("precision") else: return self.call("precision", float(label))
@since('1.4.0')
[docs] def recall(self, label=None): """ Returns recall or recall for a given label (category) if specified. """ if label is None: # note:: Deprecated in 2.0.0. Use accuracy. warnings.warn("Deprecated in 2.0.0. Use accuracy.") return self.call("recall") else: return self.call("recall", float(label))
@since('1.4.0')
[docs] def fMeasure(self, label=None, beta=None): """ Returns f-measure or f-measure for a given label (category) if specified. """ if beta is None: if label is None: # note:: Deprecated in 2.0.0. Use accuracy. warnings.warn("Deprecated in 2.0.0. Use accuracy.") return self.call("fMeasure") else: return self.call("fMeasure", label) else: if label is None: raise Exception("If the beta parameter is specified, label can not be none") else: return self.call("fMeasure", label, beta)
@property @since('2.0.0')
[docs] def accuracy(self): """ Returns accuracy (equals to the total number of correctly classified instances out of the total number of instances). """ return self.call("accuracy")
@property @since('1.4.0')
[docs] def weightedTruePositiveRate(self): """ Returns weighted true positive rate. (equals to precision, recall and f-measure) """ return self.call("weightedTruePositiveRate")
@property @since('1.4.0')
[docs] def weightedFalsePositiveRate(self): """ Returns weighted false positive rate. """ return self.call("weightedFalsePositiveRate")
@property @since('1.4.0')
[docs] def weightedRecall(self): """ Returns weighted averaged recall. (equals to precision, recall and f-measure) """ return self.call("weightedRecall")
@property @since('1.4.0')
[docs] def weightedPrecision(self): """ Returns weighted averaged precision. """ return self.call("weightedPrecision")
@since('1.4.0')
[docs] def weightedFMeasure(self, beta=None): """ Returns weighted averaged f-measure. """ if beta is None: return self.call("weightedFMeasure") else: return self.call("weightedFMeasure", beta)
[docs]class RankingMetrics(JavaModelWrapper): """ Evaluator for ranking algorithms. :param predictionAndLabels: an RDD of (predicted ranking, ground truth set) pairs. >>> predictionAndLabels = sc.parallelize([ ... ([1, 6, 2, 7, 8, 3, 9, 10, 4, 5], [1, 2, 3, 4, 5]), ... ([4, 1, 5, 6, 2, 7, 3, 8, 9, 10], [1, 2, 3]), ... ([1, 2, 3, 4, 5], [])]) >>> metrics = RankingMetrics(predictionAndLabels) >>> metrics.precisionAt(1) 0.33... >>> metrics.precisionAt(5) 0.26... >>> metrics.precisionAt(15) 0.17... >>> metrics.meanAveragePrecision 0.35... >>> metrics.ndcgAt(3) 0.33... >>> metrics.ndcgAt(10) 0.48... .. versionadded:: 1.4.0 """ def __init__(self, predictionAndLabels): sc = predictionAndLabels.ctx sql_ctx = SQLContext.getOrCreate(sc) df = sql_ctx.createDataFrame(predictionAndLabels, schema=sql_ctx._inferSchema(predictionAndLabels)) java_model = callMLlibFunc("newRankingMetrics", df._jdf) super(RankingMetrics, self).__init__(java_model) @since('1.4.0')
[docs] def precisionAt(self, k): """ Compute the average precision of all the queries, truncated at ranking position k. If for a query, the ranking algorithm returns n (n < k) results, the precision value will be computed as #(relevant items retrieved) / k. This formula also applies when the size of the ground truth set is less than k. If a query has an empty ground truth set, zero will be used as precision together with a log warning. """ return self.call("precisionAt", int(k))
@property @since('1.4.0')
[docs] def meanAveragePrecision(self): """ Returns the mean average precision (MAP) of all the queries. If a query has an empty ground truth set, the average precision will be zero and a log warining is generated. """ return self.call("meanAveragePrecision")
@since('1.4.0')
[docs] def ndcgAt(self, k): """ Compute the average NDCG value of all the queries, truncated at ranking position k. The discounted cumulative gain at position k is computed as: sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1), and the NDCG is obtained by dividing the DCG value on the ground truth set. In the current implementation, the relevance value is binary. If a query has an empty ground truth set, zero will be used as NDCG together with a log warning. """ return self.call("ndcgAt", int(k))
class MultilabelMetrics(JavaModelWrapper): """ Evaluator for multilabel classification. :param predictionAndLabels: an RDD of (predictions, labels) pairs, both are non-null Arrays, each with unique elements. >>> predictionAndLabels = sc.parallelize([([0.0, 1.0], [0.0, 2.0]), ([0.0, 2.0], [0.0, 1.0]), ... ([], [0.0]), ([2.0], [2.0]), ([2.0, 0.0], [2.0, 0.0]), ... ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])]) >>> metrics = MultilabelMetrics(predictionAndLabels) >>> metrics.precision(0.0) 1.0 >>> metrics.recall(1.0) 0.66... >>> metrics.f1Measure(2.0) 0.5 >>> metrics.precision() 0.66... >>> metrics.recall() 0.64... >>> metrics.f1Measure() 0.63... >>> metrics.microPrecision 0.72... >>> metrics.microRecall 0.66... >>> metrics.microF1Measure 0.69... >>> metrics.hammingLoss 0.33... >>> metrics.subsetAccuracy 0.28... >>> metrics.accuracy 0.54... .. versionadded:: 1.4.0 """ def __init__(self, predictionAndLabels): sc = predictionAndLabels.ctx sql_ctx = SQLContext.getOrCreate(sc) df = sql_ctx.createDataFrame(predictionAndLabels, schema=sql_ctx._inferSchema(predictionAndLabels)) java_class = sc._jvm.org.apache.spark.mllib.evaluation.MultilabelMetrics java_model = java_class(df._jdf) super(MultilabelMetrics, self).__init__(java_model) @since('1.4.0') def precision(self, label=None): """ Returns precision or precision for a given label (category) if specified. """ if label is None: return self.call("precision") else: return self.call("precision", float(label)) @since('1.4.0') def recall(self, label=None): """ Returns recall or recall for a given label (category) if specified. """ if label is None: return self.call("recall") else: return self.call("recall", float(label)) @since('1.4.0') def f1Measure(self, label=None): """ Returns f1Measure or f1Measure for a given label (category) if specified. """ if label is None: return self.call("f1Measure") else: return self.call("f1Measure", float(label)) @property @since('1.4.0') def microPrecision(self): """ Returns micro-averaged label-based precision. (equals to micro-averaged document-based precision) """ return self.call("microPrecision") @property @since('1.4.0') def microRecall(self): """ Returns micro-averaged label-based recall. (equals to micro-averaged document-based recall) """ return self.call("microRecall") @property @since('1.4.0') def microF1Measure(self): """ Returns micro-averaged label-based f1-measure. (equals to micro-averaged document-based f1-measure) """ return self.call("microF1Measure") @property @since('1.4.0') def hammingLoss(self): """ Returns Hamming-loss. """ return self.call("hammingLoss") @property @since('1.4.0') def subsetAccuracy(self): """ Returns subset accuracy. (for equal sets of labels) """ return self.call("subsetAccuracy") @property @since('1.4.0') def accuracy(self): """ Returns accuracy. """ return self.call("accuracy") def _test(): import doctest from pyspark.sql import SparkSession import pyspark.mllib.evaluation globs = pyspark.mllib.evaluation.__dict__.copy() spark = SparkSession.builder\ .master("local[4]")\ .appName("mllib.evaluation tests")\ .getOrCreate() globs['sc'] = spark.sparkContext (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) spark.stop() if failure_count: exit(-1) if __name__ == "__main__": _test()