path: root/python/pyspark/ml/evaluation.py

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# The ASF licenses this file to You under the Apache License, Version 2.0
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#    http://www.apache.org/licenses/LICENSE-2.0
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from abc import abstractmethod, ABCMeta

from pyspark import since
from pyspark.ml.wrapper import JavaParams
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(JavaParams, Evaluator):
    """
    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.
    The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label
    1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).

    >>> 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
    """

    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)
        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)
        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
    """
    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)
        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)