| # |
| # 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 typing import Generic, List, Optional, Tuple, TypeVar, Union |
| import sys |
| |
| from pyspark import since |
| from pyspark.core.rdd import RDD |
| from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc |
| from pyspark.mllib.linalg import Matrix |
| from pyspark.sql import SQLContext |
| from pyspark.sql.types import ArrayType, DoubleType, StructField, StructType |
| |
| __all__ = [ |
| "BinaryClassificationMetrics", |
| "RegressionMetrics", |
| "MulticlassMetrics", |
| "RankingMetrics", |
| ] |
| |
| T = TypeVar("T") |
| |
| |
| class BinaryClassificationMetrics(JavaModelWrapper): |
| """ |
| 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... |
| """ |
| |
| def __init__(self, scoreAndLabels: RDD[Tuple[float, float]]): |
| sc = scoreAndLabels.ctx |
| sql_ctx = SQLContext.getOrCreate(sc) |
| numCol = len(scoreAndLabels.first()) |
| schema = StructType( |
| [ |
| StructField("score", DoubleType(), nullable=False), |
| StructField("label", DoubleType(), nullable=False), |
| ] |
| ) |
| if numCol == 3: |
| schema.add("weight", DoubleType(), False) |
| df = sql_ctx.createDataFrame(scoreAndLabels, schema=schema) |
| assert sc._jvm is not None |
| 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") |
| def areaUnderROC(self) -> float: |
| """ |
| Computes the area under the receiver operating characteristic |
| (ROC) curve. |
| """ |
| return self.call("areaUnderROC") |
| |
| @property |
| @since("1.4.0") |
| def areaUnderPR(self) -> float: |
| """ |
| Computes the area under the precision-recall curve. |
| """ |
| return self.call("areaUnderPR") |
| |
| @since("1.4.0") |
| def unpersist(self) -> None: |
| """ |
| Unpersists intermediate RDDs used in the computation. |
| """ |
| self.call("unpersist") |
| |
| |
| class RegressionMetrics(JavaModelWrapper): |
| """ |
| 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... |
| """ |
| |
| def __init__(self, predictionAndObservations: RDD[Tuple[float, float]]): |
| sc = predictionAndObservations.ctx |
| sql_ctx = SQLContext.getOrCreate(sc) |
| numCol = len(predictionAndObservations.first()) |
| schema = StructType( |
| [ |
| StructField("prediction", DoubleType(), nullable=False), |
| StructField("observation", DoubleType(), nullable=False), |
| ] |
| ) |
| if numCol == 3: |
| schema.add("weight", DoubleType(), False) |
| df = sql_ctx.createDataFrame(predictionAndObservations, schema=schema) |
| assert sc._jvm is not None |
| 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") |
| def explainedVariance(self) -> float: |
| r""" |
| Returns the explained variance regression score. |
| explainedVariance = :math:`1 - \frac{variance(y - \hat{y})}{variance(y)}` |
| """ |
| return self.call("explainedVariance") |
| |
| @property |
| @since("1.4.0") |
| def meanAbsoluteError(self) -> float: |
| """ |
| 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") |
| def meanSquaredError(self) -> float: |
| """ |
| 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") |
| def rootMeanSquaredError(self) -> float: |
| """ |
| 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") |
| def r2(self) -> float: |
| """ |
| Returns R^2^, the coefficient of determination. |
| """ |
| return self.call("r2") |
| |
| |
| class MulticlassMetrics(JavaModelWrapper): |
| """ |
| 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... |
| """ |
| |
| def __init__(self, predictionAndLabels: RDD[Tuple[float, float]]): |
| sc = predictionAndLabels.ctx |
| sql_ctx = SQLContext.getOrCreate(sc) |
| numCol = len(predictionAndLabels.first()) |
| schema = StructType( |
| [ |
| StructField("prediction", DoubleType(), nullable=False), |
| StructField("label", DoubleType(), nullable=False), |
| ] |
| ) |
| if numCol >= 3: |
| schema.add("weight", DoubleType(), False) |
| if numCol == 4: |
| schema.add("probability", ArrayType(DoubleType(), False), False) |
| df = sql_ctx.createDataFrame(predictionAndLabels, schema) |
| assert sc._jvm is not None |
| 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") |
| def confusionMatrix(self) -> Matrix: |
| """ |
| 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") |
| def truePositiveRate(self, label: float) -> float: |
| """ |
| Returns true positive rate for a given label (category). |
| """ |
| return self.call("truePositiveRate", label) |
| |
| @since("1.4.0") |
| def falsePositiveRate(self, label: float) -> float: |
| """ |
| Returns false positive rate for a given label (category). |
| """ |
| return self.call("falsePositiveRate", label) |
| |
| @since("1.4.0") |
| def precision(self, label: float) -> float: |
| """ |
| Returns precision. |
| """ |
| return self.call("precision", float(label)) |
| |
| @since("1.4.0") |
| def recall(self, label: float) -> float: |
| """ |
| Returns recall. |
| """ |
| return self.call("recall", float(label)) |
| |
| @since("1.4.0") |
| def fMeasure(self, label: float, beta: Optional[float] = None) -> float: |
| """ |
| Returns f-measure. |
| """ |
| if beta is None: |
| return self.call("fMeasure", label) |
| else: |
| return self.call("fMeasure", label, beta) |
| |
| @property |
| @since("2.0.0") |
| def accuracy(self) -> float: |
| """ |
| 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") |
| def weightedTruePositiveRate(self) -> float: |
| """ |
| Returns weighted true positive rate. |
| (equals to precision, recall and f-measure) |
| """ |
| return self.call("weightedTruePositiveRate") |
| |
| @property |
| @since("1.4.0") |
| def weightedFalsePositiveRate(self) -> float: |
| """ |
| Returns weighted false positive rate. |
| """ |
| return self.call("weightedFalsePositiveRate") |
| |
| @property |
| @since("1.4.0") |
| def weightedRecall(self) -> float: |
| """ |
| Returns weighted averaged recall. |
| (equals to precision, recall and f-measure) |
| """ |
| return self.call("weightedRecall") |
| |
| @property |
| @since("1.4.0") |
| def weightedPrecision(self) -> float: |
| """ |
| Returns weighted averaged precision. |
| """ |
| return self.call("weightedPrecision") |
| |
| @since("1.4.0") |
| def weightedFMeasure(self, beta: Optional[float] = None) -> float: |
| """ |
| Returns weighted averaged f-measure. |
| """ |
| if beta is None: |
| return self.call("weightedFMeasure") |
| else: |
| return self.call("weightedFMeasure", beta) |
| |
| @since("3.0.0") |
| def logLoss(self, eps: float = 1e-15) -> float: |
| """ |
| Returns weighted logLoss. |
| """ |
| return self.call("logLoss", eps) |
| |
| |
| class RankingMetrics(JavaModelWrapper, Generic[T]): |
| """ |
| Evaluator for ranking algorithms. |
| |
| .. versionadded:: 1.4.0 |
| |
| Parameters |
| ---------- |
| predictionAndLabels : :py:class:`pyspark.RDD` |
| an RDD of (predicted ranking, ground truth set) pairs |
| or (predicted ranking, ground truth set, |
| relevance value of ground truth set). |
| Since 3.4.0, it supports ndcg evaluation with relevance value. |
| |
| 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... |
| """ |
| |
| def __init__( |
| self, |
| predictionAndLabels: Union[ |
| RDD[Tuple[List[T], List[T]]], RDD[Tuple[List[T], List[T], List[float]]] |
| ], |
| ): |
| sc = predictionAndLabels.ctx |
| sql_ctx = SQLContext.getOrCreate(sc) |
| df = sql_ctx.createDataFrame( |
| predictionAndLabels, schema=sql_ctx.sparkSession._inferSchema(predictionAndLabels) |
| ) |
| java_model = callMLlibFunc("newRankingMetrics", df._jdf) |
| super(RankingMetrics, self).__init__(java_model) |
| |
| @since("1.4.0") |
| def precisionAt(self, k: int) -> float: |
| """ |
| 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") |
| def meanAveragePrecision(self) -> float: |
| """ |
| 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. |
| """ |
| return self.call("meanAveragePrecision") |
| |
| @since("3.0.0") |
| def meanAveragePrecisionAt(self, k: int) -> float: |
| """ |
| 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. |
| """ |
| return self.call("meanAveragePrecisionAt", int(k)) |
| |
| @since("1.4.0") |
| def ndcgAt(self, k: int) -> float: |
| """ |
| 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)) |
| |
| @since("3.0.0") |
| def recallAt(self, k: int) -> float: |
| """ |
| 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. |
| """ |
| return self.call("recallAt", int(k)) |
| |
| |
| class MultilabelMetrics(JavaModelWrapper): |
| """ |
| 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... |
| """ |
| |
| def __init__(self, predictionAndLabels: RDD[Tuple[List[float], List[float]]]): |
| sc = predictionAndLabels.ctx |
| sql_ctx = SQLContext.getOrCreate(sc) |
| df = sql_ctx.createDataFrame( |
| predictionAndLabels, schema=sql_ctx.sparkSession._inferSchema(predictionAndLabels) |
| ) |
| assert sc._jvm is not None |
| 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: Optional[float] = None) -> float: |
| """ |
| 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: Optional[float] = None) -> float: |
| """ |
| 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: Optional[float] = None) -> float: |
| """ |
| 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) -> float: |
| """ |
| 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) -> float: |
| """ |
| 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) -> float: |
| """ |
| 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) -> float: |
| """ |
| Returns Hamming-loss. |
| """ |
| return self.call("hammingLoss") |
| |
| @property |
| @since("1.4.0") |
| def subsetAccuracy(self) -> float: |
| """ |
| Returns subset accuracy. |
| (for equal sets of labels) |
| """ |
| return self.call("subsetAccuracy") |
| |
| @property |
| @since("1.4.0") |
| def accuracy(self) -> float: |
| """ |
| Returns accuracy. |
| """ |
| return self.call("accuracy") |
| |
| |
| def _test() -> None: |
| import doctest |
| import numpy |
| from pyspark.sql import SparkSession |
| import pyspark.mllib.evaluation |
| |
| try: |
| # Numpy 1.14+ changed it's string format. |
| numpy.set_printoptions(legacy="1.13") |
| except TypeError: |
| pass |
| 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: |
| sys.exit(-1) |
| |
| |
| if __name__ == "__main__": |
| _test() |
