| # |
| # 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 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'] |
| |
| |
| 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() |
| """ |
| |
| def __init__(self, scoreAndLabels): |
| sc = scoreAndLabels.ctx |
| sql_ctx = SQLContext(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 |
| def areaUnderROC(self): |
| """ |
| Computes the area under the receiver operating characteristic |
| (ROC) curve. |
| """ |
| return self.call("areaUnderROC") |
| |
| @property |
| def areaUnderPR(self): |
| """ |
| Computes the area under the precision-recall curve. |
| """ |
| return self.call("areaUnderPR") |
| |
| def unpersist(self): |
| """ |
| Unpersists intermediate RDDs used in the computation. |
| """ |
| self.call("unpersist") |
| |
| |
| 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... |
| """ |
| |
| def __init__(self, predictionAndObservations): |
| sc = predictionAndObservations.ctx |
| sql_ctx = SQLContext(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 |
| def explainedVariance(self): |
| """ |
| Returns the explained variance regression score. |
| explainedVariance = 1 - variance(y - \hat{y}) / variance(y) |
| """ |
| return self.call("explainedVariance") |
| |
| @property |
| 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 |
| 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 |
| 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 |
| def r2(self): |
| """ |
| Returns R^2^, the coefficient of determination. |
| """ |
| return self.call("r2") |
| |
| |
| 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.precision() |
| 0.66... |
| >>> metrics.recall() |
| 0.66... |
| >>> metrics.weightedFalsePositiveRate |
| 0.19... |
| >>> metrics.weightedPrecision |
| 0.68... |
| >>> metrics.weightedRecall |
| 0.66... |
| >>> metrics.weightedFMeasure() |
| 0.66... |
| >>> metrics.weightedFMeasure(2.0) |
| 0.65... |
| """ |
| |
| def __init__(self, predictionAndLabels): |
| sc = predictionAndLabels.ctx |
| sql_ctx = SQLContext(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) |
| |
| 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") |
| |
| 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). |
| """ |
| return self.call("falsePositiveRate", label) |
| |
| 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)) |
| |
| 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)) |
| |
| 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: |
| 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 |
| def weightedTruePositiveRate(self): |
| """ |
| Returns weighted true positive rate. |
| (equals to precision, recall and f-measure) |
| """ |
| return self.call("weightedTruePositiveRate") |
| |
| @property |
| def weightedFalsePositiveRate(self): |
| """ |
| Returns weighted false positive rate. |
| """ |
| return self.call("weightedFalsePositiveRate") |
| |
| @property |
| def weightedRecall(self): |
| """ |
| Returns weighted averaged recall. |
| (equals to precision, recall and f-measure) |
| """ |
| return self.call("weightedRecall") |
| |
| @property |
| def weightedPrecision(self): |
| """ |
| Returns weighted averaged precision. |
| """ |
| return self.call("weightedPrecision") |
| |
| def weightedFMeasure(self, beta=None): |
| """ |
| Returns weighted averaged f-measure. |
| """ |
| if beta is None: |
| return self.call("weightedFMeasure") |
| else: |
| return self.call("weightedFMeasure", beta) |
| |
| |
| 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... |
| |
| """ |
| |
| def __init__(self, predictionAndLabels): |
| sc = predictionAndLabels.ctx |
| sql_ctx = SQLContext(sc) |
| df = sql_ctx.createDataFrame(predictionAndLabels, |
| schema=sql_ctx._inferSchema(predictionAndLabels)) |
| java_model = callMLlibFunc("newRankingMetrics", df._jdf) |
| super(RankingMetrics, self).__init__(java_model) |
| |
| 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 |
| 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") |
| |
| 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... |
| """ |
| |
| def __init__(self, predictionAndLabels): |
| sc = predictionAndLabels.ctx |
| sql_ctx = SQLContext(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) |
| |
| 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)) |
| |
| 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)) |
| |
| 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 |
| def microPrecision(self): |
| """ |
| Returns micro-averaged label-based precision. |
| (equals to micro-averaged document-based precision) |
| """ |
| return self.call("microPrecision") |
| |
| @property |
| def microRecall(self): |
| """ |
| Returns micro-averaged label-based recall. |
| (equals to micro-averaged document-based recall) |
| """ |
| return self.call("microRecall") |
| |
| @property |
| def microF1Measure(self): |
| """ |
| Returns micro-averaged label-based f1-measure. |
| (equals to micro-averaged document-based f1-measure) |
| """ |
| return self.call("microF1Measure") |
| |
| @property |
| def hammingLoss(self): |
| """ |
| Returns Hamming-loss. |
| """ |
| return self.call("hammingLoss") |
| |
| @property |
| def subsetAccuracy(self): |
| """ |
| Returns subset accuracy. |
| (for equal sets of labels) |
| """ |
| return self.call("subsetAccuracy") |
| |
| @property |
| def accuracy(self): |
| """ |
| Returns accuracy. |
| """ |
| return self.call("accuracy") |
| |
| |
| def _test(): |
| import doctest |
| from pyspark import SparkContext |
| import pyspark.mllib.evaluation |
| globs = pyspark.mllib.evaluation.__dict__.copy() |
| globs['sc'] = SparkContext('local[4]', 'PythonTest') |
| (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) |
| globs['sc'].stop() |
| if failure_count: |
| exit(-1) |
| |
| |
| if __name__ == "__main__": |
| _test() |