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import sys
from abc import abstractmethod, ABCMeta

from pyspark import since, keyword_only
from pyspark.ml.wrapper import JavaParams
from pyspark.ml.param import Param, Params, TypeConverters
from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredictionCol, \
    HasFeaturesCol, HasWeightCol
from pyspark.ml.common import inherit_doc
from pyspark.ml.util import JavaMLReadable, JavaMLWritable

__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator',
           'MulticlassClassificationEvaluator', 'ClusteringEvaluator']


@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, HasWeightCol,
                                    JavaMLReadable, JavaMLWritable):
    """
    .. note:: Experimental

    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.ml.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 = spark.createDataFrame(scoreAndLabels, ["raw", "label"])
    ...
    >>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw")
    >>> evaluator.evaluate(dataset)
    0.70...
    >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
    0.83...
    >>> bce_path = temp_path + "/bce"
    >>> evaluator.save(bce_path)
    >>> evaluator2 = BinaryClassificationEvaluator.load(bce_path)
    >>> str(evaluator2.getRawPredictionCol())
    'raw'
    >>> scoreAndLabelsAndWeight = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1], x[2]),
    ...    [(0.1, 0.0, 1.0), (0.1, 1.0, 0.9), (0.4, 0.0, 0.7), (0.6, 0.0, 0.9),
    ...     (0.6, 1.0, 1.0), (0.6, 1.0, 0.3), (0.8, 1.0, 1.0)])
    >>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"])
    ...
    >>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw", weightCol="weight")
    >>> evaluator.evaluate(dataset)
    0.70...
    >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
    0.82...

    .. versionadded:: 1.4.0
    """

    metricName = Param(Params._dummy(), "metricName",
                       "metric name in evaluation (areaUnderROC|areaUnderPR)",
                       typeConverter=TypeConverters.toString)

    @keyword_only
    def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
                 metricName="areaUnderROC", weightCol=None):
        """
        __init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
                 metricName="areaUnderROC", weightCol=None)
        """
        super(BinaryClassificationEvaluator, self).__init__()
        self._java_obj = self._new_java_obj(
            "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid)
        self._setDefault(metricName="areaUnderROC")
        kwargs = self._input_kwargs
        self._set(**kwargs)

    @since("1.4.0")
    def setMetricName(self, value):
        """
        Sets the value of :py:attr:`metricName`.
        """
        return self._set(metricName=value)

    @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", weightCol=None):
        """
        setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \
                  metricName="areaUnderROC", weightCol=None)
        Sets params for binary classification evaluator.
        """
        kwargs = self._input_kwargs
        return self._set(**kwargs)


@inherit_doc
class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol,
                          JavaMLReadable, JavaMLWritable):
    """
    .. note:: Experimental

    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 = spark.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...