Coverage for pyspark/mllib/evaluation.py : 96%
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# # 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. #
'MulticlassMetrics', 'RankingMetrics']
""" Evaluator for binary classification.
.. versionadded:: 1.4.0
Parameters ---------- scoreAndLabels : :py:class:`pyspark.RDD` an RDD of score, label and optional weight.
Examples -------- >>> 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() >>> scoreAndLabelsWithOptWeight = sc.parallelize([ ... (0.1, 0.0, 1.0), (0.1, 1.0, 0.4), (0.4, 0.0, 0.2), (0.6, 0.0, 0.6), (0.6, 1.0, 0.9), ... (0.6, 1.0, 0.5), (0.8, 1.0, 0.7)], 2) >>> metrics = BinaryClassificationMetrics(scoreAndLabelsWithOptWeight) >>> metrics.areaUnderROC 0.79... >>> metrics.areaUnderPR 0.88... """
StructField("score", DoubleType(), nullable=False), StructField("label", DoubleType(), nullable=False)])
def areaUnderROC(self): """ Computes the area under the receiver operating characteristic (ROC) curve. """
def areaUnderPR(self): """ Computes the area under the precision-recall curve. """
def unpersist(self): """ Unpersists intermediate RDDs used in the computation. """
""" Evaluator for regression.
.. versionadded:: 1.4.0
Parameters ---------- predictionAndObservations : :py:class:`pyspark.RDD` an RDD of prediction, observation and optional weight.
Examples -------- >>> 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... >>> predictionAndObservationsWithOptWeight = sc.parallelize([ ... (2.5, 3.0, 0.5), (0.0, -0.5, 1.0), (2.0, 2.0, 0.3), (8.0, 7.0, 0.9)]) >>> metrics = RegressionMetrics(predictionAndObservationsWithOptWeight) >>> metrics.rootMeanSquaredError 0.68... """
StructField("prediction", DoubleType(), nullable=False), StructField("observation", DoubleType(), nullable=False)])
def explainedVariance(self): r""" Returns the explained variance regression score. explainedVariance = :math:`1 - \frac{variance(y - \hat{y})}{variance(y)}` """
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. """
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. """
def rootMeanSquaredError(self): """ Returns the root mean squared error, which is defined as the square root of the mean squared error. """
def r2(self): """ Returns R^2^, the coefficient of determination. """
""" Evaluator for multiclass classification.
.. versionadded:: 1.4.0
Parameters ---------- predictionAndLabels : :py:class:`pyspark.RDD` an RDD of prediction, label, optional weight and optional probability.
Examples -------- >>> 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... >>> predAndLabelsWithOptWeight = sc.parallelize([(0.0, 0.0, 1.0), (0.0, 1.0, 1.0), ... (0.0, 0.0, 1.0), (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), ... (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)]) >>> metrics = MulticlassMetrics(predAndLabelsWithOptWeight) >>> 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... >>> predictionAndLabelsWithProbabilities = sc.parallelize([ ... (1.0, 1.0, 1.0, [0.1, 0.8, 0.1]), (0.0, 2.0, 1.0, [0.9, 0.05, 0.05]), ... (0.0, 0.0, 1.0, [0.8, 0.2, 0.0]), (1.0, 1.0, 1.0, [0.3, 0.65, 0.05])]) >>> metrics = MulticlassMetrics(predictionAndLabelsWithProbabilities) >>> metrics.logLoss() 0.9682... """
StructField("prediction", DoubleType(), nullable=False), StructField("label", DoubleType(), nullable=False)])
def confusionMatrix(self): """ Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels". """
def truePositiveRate(self, label): """ Returns true positive rate for a given label (category). """ return self.call("truePositiveRate", label)
def falsePositiveRate(self, label): """ Returns false positive rate for a given label (category). """
def precision(self, label): """ Returns precision. """
def recall(self, label): """ Returns recall. """
""" Returns f-measure. """ return self.call("fMeasure", label) else:
def accuracy(self): """ Returns accuracy (equals to the total number of correctly classified instances out of the total number of instances). """
def weightedTruePositiveRate(self): """ Returns weighted true positive rate. (equals to precision, recall and f-measure) """ return self.call("weightedTruePositiveRate")
def weightedFalsePositiveRate(self): """ Returns weighted false positive rate. """
def weightedRecall(self): """ Returns weighted averaged recall. (equals to precision, recall and f-measure) """
def weightedPrecision(self): """ Returns weighted averaged precision. """
""" Returns weighted averaged f-measure. """ else:
""" Returns weighted logLoss. """
""" Evaluator for ranking algorithms.
.. versionadded:: 1.4.0
Parameters ---------- predictionAndLabels : :py:class:`pyspark.RDD` an RDD of (predicted ranking, ground truth set) pairs.
Examples -------- >>> 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.meanAveragePrecisionAt(1) 0.3333333333333333... >>> metrics.meanAveragePrecisionAt(2) 0.25... >>> metrics.ndcgAt(3) 0.33... >>> metrics.ndcgAt(10) 0.48... >>> metrics.recallAt(1) 0.06... >>> metrics.recallAt(5) 0.35... >>> metrics.recallAt(15) 0.66... """
schema=sql_ctx._inferSchema(predictionAndLabels))
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. """
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 warning is generated. """
def meanAveragePrecisionAt(self, k): """ Returns the mean average precision (MAP) at first k ranking of all the queries. If a query has an empty ground truth set, the average precision will be zero and a log warning is generated. """
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. """
def recallAt(self, k): """ Compute the average recall of all the queries, truncated at ranking position k.
If for a query, the ranking algorithm returns n results, the recall value will be computed as #(relevant items retrieved) / #(ground truth set). 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 recall together with a log warning. """
""" Evaluator for multilabel classification.
.. versionadded:: 1.4.0
Parameters ---------- predictionAndLabels : :py:class:`pyspark.RDD` an RDD of (predictions, labels) pairs, both are non-null Arrays, each with unique elements.
Examples -------- >>> 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... """
schema=sql_ctx._inferSchema(predictionAndLabels))
""" Returns precision or precision for a given label (category) if specified. """ else:
""" Returns recall or recall for a given label (category) if specified. """ else:
""" Returns f1Measure or f1Measure for a given label (category) if specified. """ else:
def microPrecision(self): """ Returns micro-averaged label-based precision. (equals to micro-averaged document-based precision) """
def microRecall(self): """ Returns micro-averaged label-based recall. (equals to micro-averaged document-based recall) """
def microF1Measure(self): """ Returns micro-averaged label-based f1-measure. (equals to micro-averaged document-based f1-measure) """
def hammingLoss(self): """ Returns Hamming-loss. """
def subsetAccuracy(self): """ Returns subset accuracy. (for equal sets of labels) """
def accuracy(self): """ Returns accuracy. """
# Numpy 1.14+ changed it's string format. except TypeError: pass .master("local[4]")\ .appName("mllib.evaluation tests")\ .getOrCreate() sys.exit(-1)
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