| # |
| # 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 abc import abstractmethod, ABCMeta |
| |
| from pyspark import since |
| from pyspark.ml.wrapper import JavaWrapper |
| from pyspark.ml.param import Param, Params |
| from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredictionCol |
| from pyspark.ml.util import keyword_only |
| from pyspark.mllib.common import inherit_doc |
| |
| __all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator', |
| 'MulticlassClassificationEvaluator'] |
| |
| |
| @inherit_doc |
| class Evaluator(Params): |
| """ |
| Base class for evaluators that compute metrics from predictions. |
| |
| .. versionadded:: 1.4.0 |
| """ |
| |
| __metaclass__ = ABCMeta |
| |
| @abstractmethod |
| def _evaluate(self, dataset): |
| """ |
| Evaluates the output. |
| |
| :param dataset: a dataset that contains labels/observations and |
| predictions |
| :return: metric |
| """ |
| raise NotImplementedError() |
| |
| @since("1.4.0") |
| def evaluate(self, dataset, params=None): |
| """ |
| Evaluates the output with optional parameters. |
| |
| :param dataset: a dataset that contains labels/observations and |
| predictions |
| :param params: an optional param map that overrides embedded |
| params |
| :return: metric |
| """ |
| if params is None: |
| params = dict() |
| if isinstance(params, dict): |
| if params: |
| return self.copy(params)._evaluate(dataset) |
| else: |
| return self._evaluate(dataset) |
| else: |
| raise ValueError("Params must be a param map but got %s." % type(params)) |
| |
| @since("1.5.0") |
| def isLargerBetter(self): |
| """ |
| Indicates whether the metric returned by :py:meth:`evaluate` should be maximized |
| (True, default) or minimized (False). |
| A given evaluator may support multiple metrics which may be maximized or minimized. |
| """ |
| return True |
| |
| |
| @inherit_doc |
| class JavaEvaluator(Evaluator, JavaWrapper): |
| """ |
| Base class for :py:class:`Evaluator`s that wrap Java/Scala |
| implementations. |
| """ |
| |
| __metaclass__ = ABCMeta |
| |
| def _evaluate(self, dataset): |
| """ |
| Evaluates the output. |
| :param dataset: a dataset that contains labels/observations and predictions. |
| :return: evaluation metric |
| """ |
| self._transfer_params_to_java() |
| return self._java_obj.evaluate(dataset._jdf) |
| |
| def isLargerBetter(self): |
| self._transfer_params_to_java() |
| return self._java_obj.isLargerBetter() |
| |
| |
| @inherit_doc |
| class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol): |
| """ |
| Evaluator for binary classification, which expects two input |
| columns: rawPrediction and label. |
| |
| >>> from pyspark.mllib.linalg import Vectors |
| >>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]), |
| ... [(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)]) |
| >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"]) |
| ... |
| >>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw") |
| >>> evaluator.evaluate(dataset) |
| 0.70... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"}) |
| 0.83... |
| |
| .. versionadded:: 1.4.0 |
| """ |
| |
| # a placeholder to make it appear in the generated doc |
| metricName = Param(Params._dummy(), "metricName", |
| "metric name in evaluation (areaUnderROC|areaUnderPR)") |
| |
| @keyword_only |
| def __init__(self, rawPredictionCol="rawPrediction", labelCol="label", |
| metricName="areaUnderROC"): |
| """ |
| __init__(self, rawPredictionCol="rawPrediction", labelCol="label", \ |
| metricName="areaUnderROC") |
| """ |
| super(BinaryClassificationEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid) |
| #: param for metric name in evaluation (areaUnderROC|areaUnderPR) |
| self.metricName = Param(self, "metricName", |
| "metric name in evaluation (areaUnderROC|areaUnderPR)") |
| self._setDefault(rawPredictionCol="rawPrediction", labelCol="label", |
| metricName="areaUnderROC") |
| kwargs = self.__init__._input_kwargs |
| self._set(**kwargs) |
| |
| @since("1.4.0") |
| def setMetricName(self, value): |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| self._paramMap[self.metricName] = value |
| return self |
| |
| @since("1.4.0") |
| def getMetricName(self): |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @keyword_only |
| @since("1.4.0") |
| def setParams(self, rawPredictionCol="rawPrediction", labelCol="label", |
| metricName="areaUnderROC"): |
| """ |
| setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \ |
| metricName="areaUnderROC") |
| Sets params for binary classification evaluator. |
| """ |
| kwargs = self.setParams._input_kwargs |
| return self._set(**kwargs) |
| |
| |
| @inherit_doc |
| class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol): |
| """ |
| Evaluator for Regression, which expects two input |
| columns: prediction and label. |
| |
| >>> scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5), |
| ... (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)] |
| >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"]) |
| ... |
| >>> evaluator = RegressionEvaluator(predictionCol="raw") |
| >>> evaluator.evaluate(dataset) |
| 2.842... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"}) |
| 0.993... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"}) |
| 2.649... |
| |
| .. versionadded:: 1.4.0 |
| """ |
| # Because we will maximize evaluation value (ref: `CrossValidator`), |
| # when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`), |
| # we take and output the negative of this metric. |
| metricName = Param(Params._dummy(), "metricName", |
| "metric name in evaluation (mse|rmse|r2|mae)") |
| |
| @keyword_only |
| def __init__(self, predictionCol="prediction", labelCol="label", |
| metricName="rmse"): |
| """ |
| __init__(self, predictionCol="prediction", labelCol="label", \ |
| metricName="rmse") |
| """ |
| super(RegressionEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.RegressionEvaluator", self.uid) |
| #: param for metric name in evaluation (mse|rmse|r2|mae) |
| self.metricName = Param(self, "metricName", |
| "metric name in evaluation (mse|rmse|r2|mae)") |
| self._setDefault(predictionCol="prediction", labelCol="label", |
| metricName="rmse") |
| kwargs = self.__init__._input_kwargs |
| self._set(**kwargs) |
| |
| @since("1.4.0") |
| def setMetricName(self, value): |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| self._paramMap[self.metricName] = value |
| return self |
| |
| @since("1.4.0") |
| def getMetricName(self): |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @keyword_only |
| @since("1.4.0") |
| def setParams(self, predictionCol="prediction", labelCol="label", |
| metricName="rmse"): |
| """ |
| setParams(self, predictionCol="prediction", labelCol="label", \ |
| metricName="rmse") |
| Sets params for regression evaluator. |
| """ |
| kwargs = self.setParams._input_kwargs |
| return self._set(**kwargs) |
| |
| |
| @inherit_doc |
| class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol): |
| """ |
| Evaluator for Multiclass Classification, which expects two input |
| columns: prediction and label. |
| >>> scoreAndLabels = [(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)] |
| >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["prediction", "label"]) |
| ... |
| >>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction") |
| >>> evaluator.evaluate(dataset) |
| 0.66... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "precision"}) |
| 0.66... |
| >>> evaluator.evaluate(dataset, {evaluator.metricName: "recall"}) |
| 0.66... |
| |
| .. versionadded:: 1.5.0 |
| """ |
| # a placeholder to make it appear in the generated doc |
| metricName = Param(Params._dummy(), "metricName", |
| "metric name in evaluation " |
| "(f1|precision|recall|weightedPrecision|weightedRecall)") |
| |
| @keyword_only |
| def __init__(self, predictionCol="prediction", labelCol="label", |
| metricName="f1"): |
| """ |
| __init__(self, predictionCol="prediction", labelCol="label", \ |
| metricName="f1") |
| """ |
| super(MulticlassClassificationEvaluator, self).__init__() |
| self._java_obj = self._new_java_obj( |
| "org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator", self.uid) |
| # param for metric name in evaluation (f1|precision|recall|weightedPrecision|weightedRecall) |
| self.metricName = Param(self, "metricName", |
| "metric name in evaluation" |
| " (f1|precision|recall|weightedPrecision|weightedRecall)") |
| self._setDefault(predictionCol="prediction", labelCol="label", |
| metricName="f1") |
| kwargs = self.__init__._input_kwargs |
| self._set(**kwargs) |
| |
| @since("1.5.0") |
| def setMetricName(self, value): |
| """ |
| Sets the value of :py:attr:`metricName`. |
| """ |
| self._paramMap[self.metricName] = value |
| return self |
| |
| @since("1.5.0") |
| def getMetricName(self): |
| """ |
| Gets the value of metricName or its default value. |
| """ |
| return self.getOrDefault(self.metricName) |
| |
| @keyword_only |
| @since("1.5.0") |
| def setParams(self, predictionCol="prediction", labelCol="label", |
| metricName="f1"): |
| """ |
| setParams(self, predictionCol="prediction", labelCol="label", \ |
| metricName="f1") |
| Sets params for multiclass classification evaluator. |
| """ |
| kwargs = self.setParams._input_kwargs |
| return self._set(**kwargs) |
| |
| if __name__ == "__main__": |
| import doctest |
| from pyspark.context import SparkContext |
| from pyspark.sql import SQLContext |
| globs = globals().copy() |
| # The small batch size here ensures that we see multiple batches, |
| # even in these small test examples: |
| sc = SparkContext("local[2]", "ml.evaluation tests") |
| sqlContext = SQLContext(sc) |
| globs['sc'] = sc |
| globs['sqlContext'] = sqlContext |
| (failure_count, test_count) = doctest.testmod( |
| globs=globs, optionflags=doctest.ELLIPSIS) |
| sc.stop() |
| if failure_count: |
| exit(-1) |
