metrics.py

import re
import string
import uuid
import warnings
from abc import ABC, abstractmethod
from collections import Counter
from copy import deepcopy
from dataclasses import field
from statistics import mean
from typing import Any, Dict, Generator, List, Optional, Tuple

import evaluate
import numpy
import numpy as np
from scipy.stats import bootstrap
from scipy.stats._warnings_errors import DegenerateDataWarning

from .artifact import Artifact
from .dataclass import InternalField, OptionalField
from .logging_utils import get_logger
from .metric_utils import InstanceInput, MetricRequest, MetricResponse
from .operator import (
    MultiStreamOperator,
    SingleStreamOperator,
    StreamingOperator,
    StreamInstanceOperator,
)
from .operators import CopyFields
from .random_utils import get_seed
from .settings_utils import get_settings
from .stream import MultiStream, Stream
from .type_utils import isoftype, to_float_or_default

logger = get_logger()
settings = get_settings()

warnings.filterwarnings("ignore", category=DegenerateDataWarning)


warnings.filterwarnings("ignore", category=DegenerateDataWarning)


def abstract_factory():
    return {}


def abstract_field():
    return field(default_factory=abstract_factory)


def nan_mean(x):
    import warnings

    with warnings.catch_warnings():
        # final mean should be mean of scores, ignoring NaN, hence nanmean
        # but if the group function values is NaN for ALL values, nanmean throws a
        # RuntimeWarning that it is calculating the mean of an empty slice (with no non-Nans)
        # this is the desired behavior, but we want to avoid the warning here
        warnings.simplefilter("ignore", category=RuntimeWarning)
        return np.nanmean(x)


class UpdateStream(StreamInstanceOperator):
    update: dict

    def process(
        self, instance: Dict[str, Any], stream_name: Optional[str] = None
    ) -> Dict[str, Any]:
        instance.update(self.update)
        return instance


# TODO: currently we have two classes with this name. metric.Metric and matrics.Metric...
class Metric(Artifact):
    @property
    @abstractmethod
    def main_score(self):
        pass

    def consume_stream(self, stream: Stream):
        references = []
        predictions = []
        additional_inputs = []
        instances = []
        for instance in stream:
            references.append(instance["references"])
            predictions.append(instance["prediction"])
            additional_inputs.append(
                instance["additional_inputs"] if "additional_inputs" in instance else {}
            )
            instances.append(instance)
        return predictions, references, additional_inputs, instances

    @staticmethod
    def update_instance_scores(instances, instances_scores: List[Dict[str, Any]]):
        for instance, new_scores in zip(instances, instances_scores):
            if "score" not in instance:
                instance["score"] = {}
            scores = instance["score"]
            if "instance" not in scores:
                scores["instance"] = {}
            scores["instance"].update(new_scores)

    @staticmethod
    def set_global_score(instances, global_score: Dict[str, Any]):
        for instance in instances:
            if "score" not in instance:
                instance["score"] = {}
            scores = instance["score"]
            if "global" not in scores:
                scores["global"] = {}
            scores["global"] = global_score

    @abstractmethod
    def disable_confidence_interval_calculation(self):
        pass

    @abstractmethod
    def set_n_resamples(self, n_resample):
        pass


class MetricWithConfidenceInterval(Metric):
    # The number of resamples used to estimate the confidence intervals of this metric.
    # Use None to disable confidence interval computation.
    n_resamples: int = None
    confidence_level: float = 0.95
    ci_scores: List[str] = None

    @staticmethod
    def new_random_generator():
        # The np.random.default_rng expects a 32-bit int, while hash(..) can return a 64-bit integer.
        # So use '& MAX_32BIT' to get a 32-bit seed.
        _max_32bit = 2**32 - 1
        return np.random.default_rng(hash(get_seed()) & _max_32bit)

    def disable_confidence_interval_calculation(self):
        n = self.n_resamples
        self.n_resamples = None
        return n

    def set_n_resamples(self, n_resamples):
        self.n_resamples = n_resamples

    def _can_compute_confidence_intervals(self, num_predictions):
        return (
            self.n_resamples is not None
            and self.n_resamples > 1
            and num_predictions > 1
        )

    @staticmethod
    def average_item_scores(instances: List[dict], score_name: str):
        """Calculate mean of a set of instance scores (given by score_name), omitting NaN values.

        Args:
            instances: list of dicts of each instance's instance scores.
            score_name: score field names to compute the mean for.
        """
        return nan_mean(
            [instance["score"]["instance"][score_name] for instance in instances]
        )

    def score_based_confidence_interval(
        self,
        instances: List[dict],
        score_names: List[str],
        aggregation_func=None,
        ci_score_prefix="",
    ):
        """Compute confidence intervals based on existing scores, already computed on the input instances.

        Unlike GlobalMetric, this is simply a function of the instance scores (possibly taking into account task_data field),
         so they don't need to be recomputed after every bootstrap draw.

        Args:
            instances: The instances for which the confidence intervals are computed; should already have the relevant instance scores calculated.
            score_names: List of instance score field names to compute a confidence interval for.
            aggregation_func: A function with arguments instances, field_name; is applied on list of instances (which may include task_data
                field, as well as the prediction and references), and the field_name; default is simply to take the mean field_name from
                instances after resampling, if argument is None.
            ci_score_prefix: An optional string prefix to the score_name in the CI.  Useful in cases where the
                aggregation_func is something other than the mean

        Returns:
            Dict of confidence interval values
        """
        result = {}

        if not self._can_compute_confidence_intervals(num_predictions=len(instances)):
            return result

        ci_score_prefix = str(ci_score_prefix)
        if aggregation_func is None:
            # if aggregation_func is None, we simply take the mean of the resampled instance scores
            # otherwise, the aggregation_func needs to be applied AFTER resampling the instances;
            #   that is, re-form the groups, calculate the function, and take the mean of the group scores
            aggregation_func = self.average_item_scores
        for score_name in score_names:
            # need to redefine the statistic function within the loop because score_name is a loop variable
            def statistic(arr, axis, score_name=score_name):
                # arr is a 2d array where each row is a resampling, so we
                # iterate over the rows and compute the metric on each resampling
                scores = numpy.apply_along_axis(
                    lambda resampled_instances: aggregation_func(
                        resampled_instances, score_name
                    ),
                    axis=axis,
                    arr=arr,
                )
                return self.resample_from_non_nan(scores)

            # apply bootstrap only on the relevant field
            ci = bootstrap(
                (instances,),
                statistic=statistic,
                n_resamples=self.n_resamples,
                confidence_level=self.confidence_level,
                random_state=self.new_random_generator(),
            ).confidence_interval
            full_score_name = ci_score_prefix + score_name
            result[f"{full_score_name}_ci_low"] = ci.low
            result[f"{full_score_name}_ci_high"] = ci.high
            if score_name == self.main_score:
                result["score_ci_low"] = ci.low
                result["score_ci_high"] = ci.high
        return result

    def resample_from_non_nan(self, values):
        """Given an array values, will replace any NaN values with elements resampled with replacement from the non-NaN ones.

        here we deal with samples on which the metric could not be computed. These are
        edge cases - for example, when the sample contains only empty strings.
        CI is about the distribution around the statistic (e.g. mean), it doesn't deal with
        cases in which the metric is not computable. Therefore, we ignore these edge cases
        as part of the computation of CI.

        In theory there would be several ways to deal with this:
        1. skip the errors and return a shorter array => this fails because Scipy requires
        this callback (i.e. the statistic() callback) to return an array of the same size
        as the number of resamples
        2. Put np.nan for the errors => this fails because in such case the ci itself
        becomes np.nan. So one edge case can fail the whole CI computation.
        3. Replace the errors with a sampling from the successful cases => this is what is implemented.

        This resampling makes it so that, if possible, the bca confidence interval returned by bootstrap will not be NaN, since
        bootstrap does not ignore NaNs.  However, if there are 0 or 1 non-NaN values, or all non-NaN values are equal,
        the resulting distribution will be degenerate (only one unique value) so the CI will still be NaN since there is
        no variability.  In this case, the CI is essentially an interval of length 0 equaling the mean itself.
        """
        if values.size > 1:
            error_indices = numpy.isnan(values)
            n_errors = sum(error_indices)
            if 0 < n_errors < values.size:
                # replace NaN aggregate scores with random draws from non-NaN scores, so that confidence interval isn't NaN itself
                values[error_indices] = self.new_random_generator().choice(
                    values[~error_indices], n_errors, replace=True
                )
        return values

    def compute_global_confidence_intervals(
        self, references, predictions, task_data, score_name
    ):
        """Computed confidence intervals for a set of references and predictions."""
        random_gen = self.new_random_generator()

        def statistic(arr, axis):
            # arr is a 2d array where each row is a resampling, so we
            # iterate over the rows and compute the metric on each resampling
            def metric(sample_refs, sample_preds, sample_task_data):
                try:
                    return self._compute(
                        references=sample_refs,
                        predictions=sample_preds,
                        task_data=sample_task_data,
                    )["score"]
                except Exception as e:
                    # this happens in edge cases, for example, when the sampling creates a
                    # sample where all strings are empty and this fails bleu.
                    logger.info(f"Warning in {self.__class__.__name__}", e)
                    return np.nan

            # resample the instance scores, and then return the global score each time
            scores = numpy.apply_along_axis(
                lambda x: metric(
                    sample_refs=[references[i] for i in x],
                    sample_preds=[predictions[i] for i in x],
                    sample_task_data=[task_data[i] for i in x],
                ),
                axis=axis,
                arr=arr,
            )

            # in some resamplings of instances, the global score may be NaN since it cannot be computed;
            # in these cases, the bca confidence interval will be NaN because it does not ignore these values,
            # so we replace any NaN values with those resampled from the non-NaN ones.
            return self.resample_from_non_nan(scores)

        result = {}
        num_predictions = len(predictions)
        if self._can_compute_confidence_intervals(num_predictions=num_predictions):
            identifiers = list(range(num_predictions))
            ci = bootstrap(
                (identifiers,),
                statistic=statistic,
                n_resamples=self.n_resamples,
                confidence_level=self.confidence_level,
                random_state=random_gen,
            ).confidence_interval
            result["score_ci_low"] = ci.low
            result["score_ci_high"] = ci.high
            result[f"{score_name}_ci_low"] = ci.low
            result[f"{score_name}_ci_high"] = ci.high
        return result


class GlobalMetric(SingleStreamOperator, MetricWithConfidenceInterval):
    """A class for computing metrics that require joint calculations over all instances and are not just aggregation of scores of individuals instances.

    For example, macro_F1 requires
    calculation requires calculation of recall and precision per class, so all instances of the class
    need to be considered.  Accuracy, on the other hand, is just an average of the accuracy of all the instances.
    """

    n_resamples: int = OptionalField(
        default_factory=lambda: settings.num_resamples_for_global_metrics
    )
    process_single_instances = True

    def process(self, stream: Stream, stream_name: Optional[str] = None) -> Generator:
        references = []
        predictions = []
        task_data = []
        global_score = {}

        instances = []

        for instance in stream:
            if "score" not in instance:
                instance["score"] = {"global": global_score, "instance": {}}
            else:
                global_score = instance["score"]["global"]

            instance_references, instance_prediction = (
                instance["references"],
                instance["prediction"],
            )
            references.append(instance_references)
            predictions.append(instance_prediction)
            instances.append(instance)

            instance_task_data = (
                instance["task_data"] if "task_data" in instance else {}
            )
            task_data.append(instance_task_data)
            instance_score = None
            # for backward compatibility
            no_score_value = np.nan
            if self.process_single_instances:
                try:
                    instance_score = self._compute(
                        [instance_references],
                        [instance_prediction],
                        [instance_task_data],
                    )
                except:
                    no_score_value = None
            if not instance_score:
                instance_score = {
                    "score": no_score_value,
                    "score_name": self.main_score,
                }

                if isinstance(self.main_score, str):
                    instance_score[self.main_score] = no_score_value

            instance["score"]["instance"].update(instance_score)

        result = self._compute(references, predictions, task_data)

        global_score.update(result)

        score_name = global_score["score_name"]
        confidence_interval = self.compute_global_confidence_intervals(
            references, predictions, task_data, score_name
        )
        global_score.update(confidence_interval)

        for instance in instances:
            instance["score"]["global"] = global_score
            yield instance

    def _compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Any],
    ) -> dict:
        result = self.compute(references, predictions, task_data)
        result["score"] = result[self.main_score]
        result["score_name"] = self.main_score
        return result

    @abstractmethod
    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Any],
    ) -> dict:
        """Computes a scores dictionary on a list of references, predictions and input.

        This function is called once per instance, and then another time
        over all data instances.

        Returns:
            a dictionary of scores that is set as:
              the instance scores when called on a single data instance
              the global score when called on the all data instances
        """
        pass


class BulkInstanceMetric(SingleStreamOperator, MetricWithConfidenceInterval):
    n_resamples: int = OptionalField(
        default_factory=lambda: settings.num_resamples_for_instance_metrics
    )
    main_score: str
    reduction_map: Dict[str, List[str]]

    implemented_reductions: List[str] = field(default_factory=lambda: ["mean"])

    def process(self, stream: Stream, stream_name: Optional[str] = None) -> Generator:
        global_score = {}
        instances = []

        # consume the stream
        references, predictions = map(
            list,
            zip(
                *[
                    (instance["references"], instance["prediction"])
                    for instance in stream
                ]
            ),
        )

        task_data = [
            instance["task_data"] if "task_data" in instance else {}
            for instance in stream
        ]

        # compute the metric over all refs and preds
        instance_scores = self.compute(
            references=references,
            predictions=predictions,
            task_data=task_data,
        )

        # add the score and score_name fields
        for instance_score in instance_scores:
            instance_score["score"] = instance_score[self.main_score]
            instance_score["score_name"] = self.main_score

        for instance, score in zip(stream, instance_scores):
            if "score" not in instance:
                instance["score"] = {"global": global_score, "instance": {}}
            else:
                global_score = instance["score"]["global"]

            instance["score"]["instance"].update(score)

            instances.append(instance)

        for reduction, fields in self.reduction_map.items():
            assert (
                reduction in self.implemented_reductions
            ), f"Reduction {reduction} is not implemented, use one of {self.implemented_reductions}"

            if reduction == "mean":
                for field_name in fields:
                    global_score[field_name] = mean(
                        [
                            instance["score"]["instance"][field_name]
                            for instance in instances
                        ]
                    )
                    if field_name == self.main_score:
                        global_score["score"] = global_score[field_name]
                        global_score["score_name"] = self.main_score

                ci_fields = (
                    list(set(self.ci_scores))
                    if self.ci_scores is not None
                    else [self.main_score]
                )
                confidence_interval = self.score_based_confidence_interval(
                    instances=instances, score_names=ci_fields
                )
                global_score.update(confidence_interval)

        for instance in instances:
            yield instance

    @abstractmethod
    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Dict],
    ) -> List[Dict[str, Any]]:
        pass


class InstanceMetric(SingleStreamOperator, MetricWithConfidenceInterval):
    """Class for metrics for which a global score can be calculated by aggregating the instance scores (possibly with additional instance inputs).

    InstanceMetric currently allows two reductions:
    1. 'mean', which calculates the mean of instance scores,
    2. 'group_mean', which first applies an aggregation function specified in the reduction_map
        to instance scores grouped by the field grouping_field (which must not be None), and returns the mean
        of the group scores; if grouping_field is None, grouping is disabled.
        See _validate_group_mean_reduction for formatting instructions.
    """

    n_resamples: int = OptionalField(
        default_factory=lambda: settings.num_resamples_for_instance_metrics
    )

    # some group_mean aggregation functions (3rd element of "agg_func" list in the reduction)
    # only require a list of instance scores (e.g., mean, median, etc.).  Others aggregation functions
    # require an additional column (e.g., a subgroup identifier) by which the instance scores will be grouped
    # if subgroup_column is not None, a column by the specified name will be required in task_data
    subgroup_column = None
    implemented_reductions: List[str] = field(
        default_factory=lambda: ["mean", "group_mean"]
    )

    @property
    @abstractmethod
    def reduction_map(self) -> dict:
        pass

    def _validate_group_mean_reduction(self, instances: List[dict]):
        """Ensure that group_mean reduction_map is properly formatted.

        Example: Apply the variance (np.var) to group Accuracy instance scores.  This class would be specified as follows:

        class GroupVarianceAccuracy(Accuracy):
            reduction_map = {'group_mean': {'agg_func': ['variance', np.var, True]}}

        reduction_map must be a dict with values containing
        - an 'agg_func' field with value being a 3-element list where
            - 1st element is a string name of the aggregation function (used in naming the CI report)
            - 2nd element is the callable aggregation function
            - 3rd element is a Boolean indicator of whether, during boostrap CI calculation, the groups are to be sampled as single units.
                If True, the group scores are calculated and then resampled.  This treats the group units as the unit of
                interest for which the CI is being compared.
                If False, the instances are resampled individually, and the groups determined
                (meaning the groups may be of slightly different size or composition from the original
                depending on the resampling of the instances).
        - Optional: 'score_fields' key with list value containing the string names of fields to apply the aggregation to
            - If not present, the parent class main_score is used.

        The aggregation function (2nd element of agg_func) can be one of two types:
        1. simple: calculate a summary statistic from a single group of values (e.g. mean, median, etc.).
            This is best suited for cases where the instances are independent of each other, other than belonging to the same group
        2. comparison: requires subgroup_column to be specified.  This function conducts
            a comparison between scores for differing values of subgroup_column (e.g., 'original' vs 'paraphrase').
            An example is where the original instance is a question, and the others are various paraphrases
            or perturbations of this question.  Here, the function would return, say, a comparison of the instance accuracies
            rather than, say, the average instance accuracy.
            In these cases, we recommend setting the 3rd parameter to be True so that the groups are resampled together.

        Example:
            class GroupVsBaselineDiffAccuracy(Accuracy):
                subgroup_column = 'variant_type'
                reduction_map = {'group_mean': {'agg_func': ['accuracy_diff', accuracy_diff, True],}}

            # where the function is defined as
            def accuracy_diff(subgroup_scores_dict, expected_subgroup_types=['original', 'paraphrase']):
                validate_subgroup_types(subgroup_scores_dict, expected_subgroup_types)
                from statistics import mean
                return mean(subgroup_scores_dict['paraphrase']) - mean(subgroup_scores_dict['original'])
            The input dataset should look like:

            'group_id'  'question'                                   'variant_type'
            1           'How do you fix a car engine?'               'original'
            1           'What is the best way to fix an engine?'     'paraphrase'
            1           'How do you repair a car engine?'            'paraphrase'
            1           'How do I repair my engine?'                 'paraphrase'
            2           'Why are ants eating my food?'               'original'
        """
        # instances need to all have task_data field with field group_id
        assert all(
            "task_data" in instance for instance in instances
        ), "each instance must have an task_data field"
        assert all(
            isinstance(instance["task_data"], dict) for instance in instances
        ), "each instance must have an task_data field that is a dict"
        assert all(
            "group_id" in instance["task_data"] for instance in instances
        ), "each instance task_data dict must have a key group_id"

        # validate the reduction_map
        assert (
            "group_mean" in self.reduction_map
        ), "reduction_map must have a 'group_mean' key"
        fields = self.reduction_map["group_mean"]
        # for group_mean, expects a dict
        assert isinstance(fields, dict)
        assert (
            "agg_func" in fields
        ), "fields should have a key 'agg_func' whose value is a 3-element list of a function name, function definition, and a boolean indicator"
        assert isinstance(
            fields["agg_func"], list
        ), "fields['agg_func'] should be a list"
        assert (
            len(fields["agg_func"]) == 3
        ), "fields['agg_func'] should be a 3-element list"
        assert isinstance(
            fields["agg_func"][0], str
        ), "first item in fields['agg_func'] should be a string name of a function"
        assert callable(
            fields["agg_func"][1]
        ), "second item in fields['agg_func'] should be a callable function"
        assert isinstance(
            fields["agg_func"][2], bool
        ), "third item in fields['agg_func'] should be a boolean value"
        if "score_fields" in fields:
            assert isinstance(fields["score_fields"], list)

        # for aggregation functions that use the subgroup_column (expect a dict of lists), check that
        # this field exists
        if self.subgroup_column is not None:
            assert all(
                self.subgroup_column in instance["task_data"] for instance in instances
            ), f"each instance task_data dict must have a key {self.subgroup_column}"

    def process(self, stream: Stream, stream_name: Optional[str] = None) -> Generator:
        instances, global_score = self.compute_instance_scores(stream)

        for reduction_type, reduction_params in self.reduction_map.items():
            assert (
                reduction_type in self.implemented_reductions
            ), f"Reduction {reduction_type} is not implemented, use one of {self.implemented_reductions}"

            field_name_full_prefix = ""
            # used for passing to the bootstrapping, depends on whether the groups are fixed or not
            aggregation_function = self.average_item_scores
            if reduction_type == "mean":
                reduction_fields = list(set(reduction_params))
                # no group reduction, so resample instances individually
                scores_to_resample = instances
            elif reduction_type == "group_mean":
                self._validate_group_mean_reduction(instances=instances)
                reduction_fields = (
                    [self.main_score]
                    if "score_fields" not in reduction_params
                    else list(set(reduction_params["score_fields"]))
                )
                aggregation_function_name = str(reduction_params["agg_func"][0])
                field_name_full_prefix = "group_" + aggregation_function_name + "_"
                do_resample_as_group = reduction_params["agg_func"][2]
                if do_resample_as_group:
                    # append fixed_ to name because resamples the groups as fixed units
                    field_name_full_prefix = "fixed_" + field_name_full_prefix
                (
                    scores_to_resample,
                    aggregation_function,
                ) = self._set_up_group_mean_aggregation(
                    instances, reduction_params, reduction_fields
                )
            else:
                raise ValueError(
                    f"Reduction {reduction_type} is not supported, please specify a valid reduction method in reduction_map {self.reduction_map}."
                )

            # calculate global scores for each reduction field
            for field_name in reduction_fields:
                field_name_full = field_name_full_prefix + field_name
                # if group resampling (3rd element of agg_func parameter) is True, then
                #   1. scores_to_resample are the group scores, and
                #   2. aggregation_function is to take the raw mean
                # if no group resampling (3rd element of agg_func parameter) is False, then
                #   1. scores_to_resample are the original instance scores, and
                #   2. aggregation_function is to apply the group aggregation from the instance scores
                # either way, the application of aggregation_function to scores_to_resample yields the global score
                global_score[field_name_full] = aggregation_function(
                    scores_to_resample, field_name
                )
                if field_name == self.main_score:
                    global_score["score"] = global_score[field_name_full]
                    global_score["score_name"] = field_name_full

            # need to specify which fields should have CIs calculated for them through ci_scores
            # (will not automatically calculate CIs for fields in reduction map)
            if self.ci_scores is not None:
                confidence_interval = self.score_based_confidence_interval(
                    instances=scores_to_resample,
                    score_names=list(set(self.ci_scores)),
                    ci_score_prefix=field_name_full_prefix,
                    aggregation_func=aggregation_function,
                )
                global_score.update(confidence_interval)

        yield from instances

    def compute_instance_scores(
        self, stream: Stream, stream_name: Optional[str] = None
    ):
        global_score = {}
        instances = []

        for instance in stream:
            refs, pred = instance["references"], instance["prediction"]
            task_data = instance["task_data"] if "task_data" in instance else {}

            instance_score = self.compute(
                references=refs, prediction=pred, task_data=task_data
            )
            instance_score["score"] = instance_score[self.main_score]
            instance_score["score_name"] = self.main_score
            if "score" not in instance:
                instance["score"] = {"global": global_score, "instance": {}}
            else:
                global_score = instance["score"]["global"]

            instance["score"]["instance"].update(instance_score)

            instances.append(instance)

        return instances, global_score

    def get_group_scores(
        self, instances: List[dict], score_names: List[str], group_aggregation_func
    ):
        """Group scores by the group_id and subgroup_type fields of each instance, and compute group_aggregation_func by group.

        Args:
            instances: List of observation instances with instance-level scores (fields) computed.
            score_names: List of instance score names in each instance to apply the aggregation function.
            group_aggregation_func: Callable aggregation function accepting a list of numeric scores;
                or, if self.subgroup_column is not None, a dict of subgroup types scores by subgroup_column value.
                callable function returns a single score for the group

        Returns:
            List of dicts, each corresponding to a group of instances (defined by 'group_id'),
                with an aggregate group score for each score_name
        """
        from collections import defaultdict

        # three-level defaultdict:
        # first is the grouping, second is the field name, the third is the subgroup_type (by default 'default')
        group_to_instance_scores = defaultdict(
            lambda: defaultdict(lambda: defaultdict(list))
        )

        # check if function has fields for subgroup_column
        uses_subgroups = self.subgroup_column is not None
        default_subgroup_name = "default"
        # loop through the instances and group the scores
        for instance in instances:
            task_data = instance["task_data"]
            group_key = task_data["group_id"]
            # for functions that do comparisons between subgroup_column groups
            # if function doesn't use subgroup_column, or none is present, set "default" as default value, and pass all scores
            subgroup_type = (
                task_data[self.subgroup_column]
                if uses_subgroups
                else default_subgroup_name
            )
            for score_name in score_names:
                group_to_instance_scores[group_key][score_name][subgroup_type].append(
                    instance["score"]["instance"][score_name]
                )

        # if group_aggregation_func expects a subgroup-types score dict, pass it; otherwise pass the default type list of scores
        return [
            {
                "score": {
                    "instance": {
                        score_name: group_aggregation_func(
                            score_dict
                            if uses_subgroups
                            else score_dict[default_subgroup_name]
                        )
                        for score_name, score_dict in group_scores.items()
                    }
                }
            }
            for group_scores in group_to_instance_scores.values()
        ]

    def _set_up_group_mean_aggregation(
        self, instances, reduction_params, reduction_fields
    ):
        group_aggregation_func = reduction_params["agg_func"][1]
        # if treat groups as units
        do_resample_as_group = reduction_params["agg_func"][2]
        if do_resample_as_group:
            # pass the group aggregate---not instance---scores to resample as usual
            aggregation_function = self.average_item_scores
            scores_to_resample = self.get_group_scores(
                instances, reduction_fields, group_aggregation_func
            )
        else:
            # pass the instance scores to resample, and calculate the group aggregation on the resamplings
            scores_to_resample = instances

            def aggregation_function(
                instances,
                field_name,
                group_aggregation_func=group_aggregation_func,
            ):
                group_scores = self.get_group_scores(
                    instances, [field_name], group_aggregation_func
                )
                return nan_mean(
                    [group["score"]["instance"][field_name] for group in group_scores]
                )

        return scores_to_resample, aggregation_function

    @abstractmethod
    def compute(self, references: List[Any], prediction: Any, task_data: Dict) -> dict:
        pass


class Squad(GlobalMetric):
    _metric = None
    main_score = "f1"
    metric = "squad"

    def prepare(self):
        super().prepare()
        self._metric = evaluate.load(self.metric)

    def compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Dict],
    ) -> dict:
        ids = [str(uuid.uuid4()).replace("-", "") for _ in range(len(predictions))]
        formatted_predictions = [
            {"prediction_text": prediction, "id": ids[i]}
            for i, prediction in enumerate(predictions)
        ]
        formatted_references = [
            {"answers": {"answer_start": [-1], "text": reference}, "id": ids[i]}
            for i, reference in enumerate(references)
        ]

        return self._metric.compute(
            predictions=formatted_predictions,
            references=formatted_references,
        )


class Accuracy(InstanceMetric):
    reduction_map = {"mean": ["accuracy"]}
    main_score = "accuracy"
    ci_scores = ["accuracy"]

    def compute(
        self, references: List[Any], prediction: Any, task_data: List[Dict]
    ) -> dict:
        result = {
            self.main_score: float(
                str(prediction) in [str(reference) for reference in references]
            )
        }
        result["score"] = result[self.main_score]
        result["score_name"] = self.main_score
        return result


class StringContainment(InstanceMetric):
    reduction_map = {"mean": ["string_containment"]}
    main_score = "string_containment"
    ci_scores = ["string_containment"]

    def compute(
        self, references: List[Any], prediction: Any, task_data: List[Dict]
    ) -> dict:
        result = {
            self.main_score: float(
                any(str(reference) in str(prediction) for reference in references)
            )
        }
        result["score"] = result[self.main_score]
        result["score_name"] = self.main_score
        return result


class MetricPipeline(MultiStreamOperator, Metric):
    main_score: str = None
    preprocess_steps: Optional[List[StreamingOperator]] = field(default_factory=list)
    postpreprocess_steps: Optional[List[StreamingOperator]] = field(
        default_factory=list
    )
    metric: Metric = None

    def disable_confidence_interval_calculation(self):
        return self.metric.disable_confidence_interval_calculation()

    def set_n_resamples(self, n_resample):
        if isinstance(self.metric, MetricWithConfidenceInterval):
            self.metric.set_n_resamples(n_resample)

    def verify(self):
        assert self.main_score is not None, "main_score is not set"

    def prepare(self):
        super().prepare()
        self.prepare_score = CopyFields(
            field_to_field=[
                [f"score/instance/{self.main_score}", "score/instance/score"],
                [f"score/global/{self.main_score}", "score/global/score"],
            ],
            use_query=True,
        )

    def process(self, multi_stream: MultiStream) -> MultiStream:
        for step in self.preprocess_steps:
            multi_stream = step(multi_stream)
        multi_stream = self.metric(multi_stream)
        for step in self.postpreprocess_steps:
            multi_stream = step(multi_stream)
        return self.prepare_score(multi_stream)


class HuggingfaceMetric(GlobalMetric):
    hf_metric_name: str = None
    main_score: str = None  # The main score returned from the metric
    hf_main_score: str = (
        None  # USed if HF returns uses a different score name for the main metric
    )

    scale: float = 1.0  # optional scaling of main results
    scaled_fields: list = None
    # This are fixed arguments  passed to compute method
    hf_compute_args: Dict[str, Any] = OptionalField(default_factory=dict)
    # These are additional input fields passed to HF compute method (a list with one value per instance)
    hf_additional_input_fields: List = OptionalField(default_factory=list)
    # These are additional input fields that are passed as one value
    hf_additional_input_fields_pass_one_value: List = OptionalField(
        default_factory=list
    )

    experiment_id: str = OptionalField(default_factory=lambda: str(uuid.uuid4()))

    def verify(self):
        assert (
            self.hf_additional_input_fields is None
            or isoftype(self.hf_additional_input_fields, List[str])
        ), f"Argument hf_additional_input_fields should be either None or List[str]. It is now: {self.hf_additional_input_fields}."
        assert (
            self.hf_additional_input_fields_pass_one_value is None
            or isoftype(self.hf_additional_input_fields_pass_one_value, List[str])
        ), f"Argument hf_additional_input_fields_pass_one_value should be either None or List[str]. It is now: {self.hf_additional_input_fields_pass_one_value}."

        return super().verify()

    def prepare(self):
        super().prepare()
        self.metric = evaluate.load(
            self.hf_metric_name, experiment_id=self.experiment_id
        )

    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Dict],
    ) -> dict:
        passed_task_data = {}
        for additional_input_field in self.hf_additional_input_fields:
            assert (
                additional_input_field in task_data[0]
            ), f"'{additional_input_field}' field required by {__class__.__name__} is not in passed in task_data: {task_data[0]}"
            passed_task_data[additional_input_field] = [
                additional_input[additional_input_field]
                for additional_input in task_data
            ]
        for additional_input_field in self.hf_additional_input_fields_pass_one_value:
            assert (
                additional_input_field in task_data[0]
            ), f"'{additional_input_field}' field required by {__class__.__name__} is not in passed in task_data: {task_data[0]}"

            values = {
                additional_input[additional_input_field]
                for additional_input in task_data
            }
            assert (
                len(values) == 1
            ), f"Values of '{additional_input_field}' field required by {__class__.__name__}  should all be the same, but have multiple values {values}"

            passed_task_data[additional_input_field] = next(iter(values))

        # add check that all required fields in self.metrics are in passed_task_data       print(passed_task_data)
        result = self.metric.compute(
            predictions=predictions,
            references=references,
            **passed_task_data,
            **self.hf_compute_args,
        )
        if self.hf_main_score:
            result[self.main_score] = result[self.hf_main_score]
            del result[self.hf_main_score]
        if self.scale != 1.0:
            assert (
                self.scaled_fields is not None
            ), f"Scaling factor was set to {self.scale}, but no fields specified"
            for key in self.scaled_fields:
                assert (
                    key in result
                ), f"Trying to scale field '{key}' which is not in results of metrics: {result}"
                if isinstance(result[key], list):
                    assert all(
                        isinstance(v, float) for v in result[key]
                    ), "Not all scaled field '{key}' values are floats: {result[key]}"
                    result[key] = [v / self.scale for v in result[key]]
                else:
                    assert isinstance(
                        result[key], float
                    ), "Scaled field '{key}' is not float: {result[key]}"
                    result[key] /= self.scale
        return result


class HuggingfaceBulkMetric(BulkInstanceMetric):
    hf_metric_name: str

    hf_metric_fields: List[str]
    hf_compute_args: dict = {}
    hf_additional_input_fields: List = OptionalField(default_factory=list)

    def prepare(self):
        super().prepare()
        self.metric = evaluate.load(self.hf_metric_name)

    def compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Any],
    ) -> List[Dict[str, Any]]:
        passed_task_data = {}
        for additional_input_field in self.hf_additional_input_fields:
            assert (
                additional_input_field in task_data[0]
            ), f"'{additional_input_field}' field required by {__class__.__name__} is not in passed in task_data: {task_data[0]}"
            passed_task_data[additional_input_field] = [
                additional_input[additional_input_field]
                for additional_input in task_data
            ]
        # add check that all required fields in self.metrics are in passed_task_data

        scores = self.metric.compute(
            predictions=predictions,
            references=references,
            **passed_task_data,
            **self.hf_compute_args,
        )

        # convert dict of lists to a list of dicts
        results = [{} for _ in range(len(scores[self.hf_metric_fields[0]]))]
        for key in self.hf_metric_fields:
            values = scores[key]
            for result_id, result in enumerate(results):
                result[key] = values[result_id]

        return results


class F1(GlobalMetric):
    _metric = None
    main_score = "f1_macro"
    average = None  # Report per class then aggregate by mean
    metric = "f1"

    def prepare(self):
        super().prepare()
        self._metric = evaluate.load(self.metric)

    def get_str_id(self, str):
        if str not in self.str_to_id:
            id = len(self.str_to_id)
            self.str_to_id[str] = id
            self.id_to_str[id] = str
        return self.str_to_id[str]

    def compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Dict],
    ) -> dict:
        assert all(
            len(reference) == 1 for reference in references
        ), "Only a single reference per prediction is allowed in F1 metric"
        self.str_to_id = {}
        self.id_to_str = {}
        formatted_references = [
            self.get_str_id(reference[0]) for reference in references
        ]
        self.str_to_id.keys()
        formatted_predictions = [
            self.get_str_id(prediction) for prediction in predictions
        ]
        labels = list(set(formatted_references))
        result = self._metric.compute(
            predictions=formatted_predictions,
            references=formatted_references,
            labels=labels,
            average=self.average,
        )
        if isinstance(result["f1"], numpy.ndarray):
            final_result = {self.main_score: mean(result["f1"])}
            for i, label in enumerate(labels):
                final_result["f1_" + self.id_to_str[label]] = result["f1"][i]
        else:
            final_result = {self.main_score: result["f1"]}
        return final_result


class F1Micro(F1):
    main_score = "f1_micro"
    average = "micro"


class F1Macro(F1):
    main_score = "f1_macro"


class F1Weighted(F1):
    main_score = "f1_weighted"
    average = "weighted"


class F1MultiLabel(GlobalMetric):
    _metric = None
    main_score = "f1_macro"
    average = None  # Report per class then aggregate by mean
    metric = "f1"

    def prepare(self):
        super().prepare()
        self._metric = evaluate.load(self.metric, "multilabel")

    def add_str_to_id(self, str):
        if str not in self.str_to_id:
            id = len(self.str_to_id)
            self.str_to_id[str] = id
            self.id_to_str[id] = str
        return

    def get_one_hot_vector(self, labels: List[str]):
        result = [0] * len(self.str_to_id)
        for label in labels:
            if label in self.str_to_id:
                result[self.str_to_id[label]] = 1
        return result

    def compute(
        self,
        references: List[List[str]],
        predictions: List[List[str]],
        task_data: List[Dict],
    ) -> dict:
        self.str_to_id = {}
        self.id_to_str = {}

        self._validate_references_and_prediction(references, predictions)
        references = [reference[0] for reference in references]

        labels = list({label for reference in references for label in reference})

        # if no classes are left then F1 is not defined
        if len(labels) == 0:
            return {self.main_score: float("nan")}

        for label in labels:
            self.add_str_to_id(label)
        formatted_references = [
            self.get_one_hot_vector(reference) for reference in references
        ]
        formatted_predictions = [
            self.get_one_hot_vector(prediction) for prediction in predictions
        ]

        # There is odd behavior in scikit-learn that when passing a one-hot vector with a single
        # element, it is treated a class identifier. Therefore, we add labels=[1] to limit to only
        # to this class.
        if len(labels) == 1:
            labels_param = [1]
        else:
            labels_param = None

        result = self._metric.compute(
            predictions=formatted_predictions,
            references=formatted_references,
            average=self.average,
            labels=labels_param,
        )
        if isinstance(result[self.metric], numpy.ndarray):
            assert (
                len(result[self.metric]) == len(labels)
            ), f"F1 result ({result[self.metric]}) has more entries than labels ({labels})"
            final_result = {self.main_score: mean(result[self.metric])}
            for i, label in enumerate(labels):
                final_result[self.metric + "_" + label] = result[self.metric][i]
        else:
            final_result = {self.main_score: result[self.metric]}
        return final_result

    def _validate_references_and_prediction(self, references, predictions):
        for reference in references:
            if not len(reference) == 1:
                raise ValueError(
                    f"Only a single reference per prediction is allowed in F1 multi label metric. Received reference: {reference}"
                )
            if not isoftype(reference[0], List[str]):
                raise ValueError(
                    f"Each reference is expected to be a list of strings in F1 multi label metric. Received reference: '{reference[0]}'"
                )

        for prediction in predictions:
            if not isoftype(prediction, List[str]):
                raise ValueError(
                    f"Each prediction is expected to be a list of strings in F1 multi label metric. Received prediction: '{prediction}'"
                )


class PrecisionMacroMultiLabel(F1MultiLabel):
    main_score = "precision_macro"
    metric = "precision"
    average = "macro"


class PrecisionMicroMultiLabel(F1MultiLabel):
    main_score = "precision_micro"
    metric = "precision"
    average = "micro"


class RecallMacroMultiLabel(F1MultiLabel):
    main_score = "recall_macro"
    metric = "recall"
    average = "macro"


class RecallMicroMultiLabel(F1MultiLabel):
    main_score = "recall_micro"
    metric = "recall"
    average = "micro"


class F1MicroMultiLabel(F1MultiLabel):
    main_score = "f1_micro"
    average = "micro"


class F1MacroMultiLabel(F1MultiLabel):
    main_score = "f1_macro"
    average = None


class Rouge(HuggingfaceMetric):
    hf_metric_name = "rouge"
    main_score = "rougeL"
    scale = 1.0

    use_aggregator: bool = True
    rouge_types: List[str] = ["rouge1", "rouge2", "rougeL", "rougeLsum"]

    sent_split_newline: bool = True

    _requirements_list: List[str] = ["nltk", "rouge_score"]

    def prepare(self):
        super().prepare()

        self.hf_compute_args.update(
            {"use_aggregator": self.use_aggregator, "rouge_types": self.rouge_types}
        )

        import nltk

        nltk.download("punkt")
        self.sent_tokenize = nltk.sent_tokenize

    def compute(self, references, predictions, task_data: List[Dict]):
        if self.sent_split_newline:
            predictions = [
                "\n".join(self.sent_tokenize(prediction.strip()))
                for prediction in predictions
            ]
            references = [
                ["\n".join(self.sent_tokenize(r.strip())) for r in reference]
                for reference in references
            ]
        return super().compute(references, predictions, task_data)


# Computes char edit distance, ignoring whitespace
class CharEditDistanceAccuracy(InstanceMetric):
    reduction_map = {"mean": ["char_edit_dist_accuracy"]}
    main_score = "char_edit_dist_accuracy"
    ci_scores = ["char_edit_dist_accuracy"]

    _requirements_list: List[str] = ["editdistance"]

    def prepare(self):
        super().prepare()
        import editdistance

        self.eval = editdistance.eval

    def compute(self, references, prediction: str, task_data: List[Dict]) -> dict:
        assert (
            len(references) == 1
        ), f"Expected only one reference , but received: {references}"

        formatted_prediction = "".join(prediction.split())
        formatted_reference = "".join(references[0].split())
        max_length = max(len(formatted_reference), len(formatted_prediction))
        if max_length == 0:
            return {"char_edit_dist_accuracy": 0.0}
        edit_dist = self.eval(formatted_reference, formatted_prediction)
        return {"char_edit_dist_accuracy": (1 - edit_dist / max_length)}


class Wer(HuggingfaceMetric):
    hf_metric_name = "wer"
    main_score = "wer"

    _requirements_list: List[str] = ["jiwer"]

    def compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Dict],
    ) -> dict:
        assert all(
            len(reference) == 1 for reference in references
        ), "Only single reference per prediction is allowed in wer metric"
        formatted_references = [reference[0] for reference in references]
        result = self.metric.compute(
            predictions=predictions, references=formatted_references
        )
        return {self.main_score: result}


class Spearmanr(HuggingfaceMetric):
    hf_metric_name = "spearmanr"
    main_score = "spearmanr"
    process_single_instances = False


class KendallTauMetric(GlobalMetric):
    main_score = "kendalltau_b"
    variant = "b"
    process_single_instances = False

    _requirements_list: List[str] = ["scipy"]

    def prepare(self):
        from scipy.stats import kendalltau

        self.kendalltau = kendalltau

    def compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Dict],
    ) -> dict:
        if isinstance(references[0], list):
            references = [reference[0] for reference in references]
        references = [to_float_or_default(r) for r in references]
        predictions = [to_float_or_default(p) for p in predictions]

        kendall_results = self.kendalltau(references, predictions, variant=self.variant)
        corr = kendall_results.correlation
        return {
            self.main_score: corr,
            f"{self.main_score}_p_val": kendall_results.pvalue,
        }


class MatthewsCorrelation(HuggingfaceMetric):
    hf_metric_name = "matthews_correlation"
    main_score = "matthews_correlation"
    str_to_id: dict = InternalField(default_factory=dict)

    def get_str_id(self, str):
        if str not in self.str_to_id:
            id = len(self.str_to_id)
            self.str_to_id[str] = id
        return self.str_to_id[str]

    def compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Dict],
    ) -> dict:
        formatted_references = [
            self.get_str_id(reference[0]) for reference in references
        ]
        formatted_predictions = [
            self.get_str_id(prediction) for prediction in predictions
        ]
        return self.metric.compute(
            predictions=formatted_predictions, references=formatted_references
        )


class RocAuc(GlobalMetric):
    main_score = "roc_auc"
    process_single_instances = False
    _requirements_list: List[str] = ["sklearn"]

    def prepare(self):
        from sklearn import metrics

        self.roc_curve = metrics.roc_curve
        self.auc = metrics.auc

    def compute(
        self,
        references: List[List[str]],
        predictions: List[str],
        task_data: List[Dict],
    ) -> dict:
        if isinstance(references[0], list):
            references = [reference[0] for reference in references]
        references = [to_float_or_default(r) for r in references]
        predictions = [to_float_or_default(p) for p in predictions]

        fpr, tpr, thrs = self.roc_curve(y_true=references, y_score=predictions)
        roc_auc = self.auc(fpr, tpr)
        return {self.main_score: roc_auc}


class CustomF1(GlobalMetric):
    main_score = "f1_micro"
    groups = None
    zero_division = 0.0

    @abstractmethod
    def get_element_group(self, element, additional_input):
        pass

    @abstractmethod
    def get_element_representation(self, element, additional_input):
        pass

    def should_ignore_element(self, element, additional_input):
        return False

    def group_elements(self, elements_list, additional_input):
        if not isinstance(elements_list, list):
            elements_list = [elements_list]
        return {
            k: Counter(
                [
                    self.get_element_representation(value, additional_input)
                    for value in elements_list
                    if self.get_element_group(value, additional_input) == k
                ]
            )
            for k in {
                self.get_element_group(e, additional_input)
                for e in elements_list
                if not self.should_ignore_element(e, additional_input)
            }
        }

    def calculate_groups_ratio(self, actual_group, total_group):
        return sum(
            [min(actual_group[k], total_group[k]) for k in actual_group.keys()]
        ), sum(actual_group.values())

    def precision(self, pn, pd, rn, rd):
        return self.zero_division if pn == 0 and pd == 0 else pn / pd

    def recall(self, pn, pd, rn, rd):
        return self.zero_division if rn == 0 and rd == 0 else rn / rd

    def f1(self, pn, pd, rn, rd):
        precision = self.precision(pn, pd, rn, rd)
        recall = self.recall(pn, pd, rn, rd)
        try:
            return 2 * precision * recall / (precision + recall)
        except ZeroDivisionError:
            return self.zero_division

    def get_groups(self, elements, task_data):
        groups = set()
        for sublist, additional_input in zip(elements, task_data):
            for e in sublist:
                if self.should_ignore_element(e, additional_input):
                    continue
                groups.add(self.get_element_group(e, additional_input))
        return groups

    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Dict],
    ) -> dict:
        # in case reference are List[List[List[Any]]] and predictions are List[List[Any]]:
        if (
            isinstance(references[0], list)
            and len(references[0]) > 0
            and isinstance(references[0][0], list)
        ):
            references = [element[0] for element in references]

        assert len(references) == len(predictions), (
            f"references size ({len(references)})"
            f" doesn't mach predictions sise ({len(references)})."
        )

        if self.groups is None:
            groups = self.get_groups(references, task_data)
        else:
            groups = self.groups
        groups_statistics = {}
        for references_batch, predictions_batch, additional_input in zip(
            references, predictions, task_data
        ):
            grouped_references = self.group_elements(references_batch, additional_input)
            grouped_predictions = self.group_elements(
                predictions_batch, additional_input
            )
            all_groups = set(grouped_references.keys()).union(
                grouped_predictions.keys()
            )
            for group in all_groups:
                if group not in groups_statistics:
                    groups_statistics[group] = {
                        "precision_numerator": 0,
                        "precision_denominator": 0,
                        "recall_numerator": 0,
                        "recall_denominator": 0,
                    }
                references_by_group = grouped_references.get(group, Counter([]))
                predictions_by_group = grouped_predictions.get(group, Counter([]))
                pn, pd = self.calculate_groups_ratio(
                    actual_group=predictions_by_group, total_group=references_by_group
                )
                rn, rd = self.calculate_groups_ratio(
                    actual_group=references_by_group, total_group=predictions_by_group
                )
                groups_statistics[group]["precision_numerator"] += pn
                groups_statistics[group]["precision_denominator"] += pd
                groups_statistics[group]["recall_numerator"] += rn
                groups_statistics[group]["recall_denominator"] += rd

        num_of_unknown_class_predictions = 0
        pn_total = pd_total = rn_total = rd_total = 0
        f1_result = {}
        recall_result = {}
        precision_result = {}
        for group in groups_statistics.keys():
            pn, pd, rn, rd = (
                groups_statistics[group]["precision_numerator"],
                groups_statistics[group]["precision_denominator"],
                groups_statistics[group]["recall_numerator"],
                groups_statistics[group]["recall_denominator"],
            )
            pn_total, pd_total, rn_total, rd_total = (
                pn_total + pn,
                pd_total + pd,
                rn_total + rn,
                rd_total + rd,
            )
            if group in groups:
                f1_result[f"f1_{group}"] = self.f1(pn, pd, rn, rd)
                recall_result[f"recall_{group}"] = self.recall(pn, pd, rn, rd)
                precision_result[f"precision_{group}"] = self.precision(pn, pd, rn, rd)
            else:
                num_of_unknown_class_predictions += pd

        result = f1_result
        try:
            result["f1_macro"] = sum(f1_result.values()) / len(result.keys())
            result["recall_macro"] = sum(recall_result.values()) / len(
                recall_result.keys()
            )
            result["precision_macro"] = sum(precision_result.values()) / len(
                precision_result.keys()
            )
        except ZeroDivisionError:
            result["f1_macro"] = self.zero_division
            result["recall_macro"] = self.zero_division
            result["precision_macro"] = self.zero_division

        amount_of_predictions = pd_total
        if amount_of_predictions == 0:
            result["in_classes_support"] = 1.0
        else:
            result["in_classes_support"] = (
                1.0 - num_of_unknown_class_predictions / amount_of_predictions
            )
        result["f1_micro"] = self.f1(pn_total, pd_total, rn_total, rd_total)
        result["recall_micro"] = self.recall(pn_total, pd_total, rn_total, rd_total)
        result["precision_micro"] = self.precision(
            pn_total, pd_total, rn_total, rd_total
        )
        return result


class NER(CustomF1):
    def get_element_group(self, element, additional_input):
        return element[1]

    def get_element_representation(self, element, additional_input):
        return str(element)


def normalize_answer(s):
    """Lower text and remove punctuation, articles and extra whitespace."""

    def remove_articles(text):
        return re.sub(r"\b(a|an|the)\b", " ", text)

    def white_space_fix(text):
        return " ".join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return "".join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))


class TokenOverlap(InstanceMetric):
    reduction_map = {"mean": ["f1", "precision", "recall"]}
    main_score = "f1"
    ci_scores = ["f1", "precision", "recall"]

    def compute(
        self, references: List[Any], prediction: Any, task_data: List[Dict]
    ) -> dict:
        results = [
            self._compute_single_ref(str(reference), str(prediction))
            for reference in references
        ]
        return {
            measure: max(r[i] for r in results)
            for i, measure in enumerate(["precision", "recall", "f1"])
        }

    def _compute_single_ref(
        self, reference: Any, prediction: Any
    ) -> Tuple[float, float, float]:
        prediction_tokens = normalize_answer(str(prediction)).split()
        reference_tokens = normalize_answer(str(reference)).split()
        common = Counter(prediction_tokens) & Counter(reference_tokens)
        num_same = sum(common.values())
        if num_same == 0:
            pr, rc, f1 = 0, 0, 0
        else:
            pr = 1.0 * num_same / len(prediction_tokens)
            rc = 1.0 * num_same / len(reference_tokens)
            f1 = (2 * pr * rc) / (pr + rc)
        return pr, rc, f1


class BertScore(HuggingfaceBulkMetric):
    hf_metric_name = "bertscore"
    main_score = "f1"
    reduction_map = {"mean": ["f1", "precision", "recall"]}
    hf_metric_fields = ["f1", "precision", "recall"]
    ci_scores = ["f1", "precision", "recall"]
    model_name: str

    _requirements_list: List[str] = ["bert_score"]

    def prepare(self):
        super().prepare()
        self.hf_compute_args = {"model_type": self.model_name, "batch_size": 16}


class SentenceBert(BulkInstanceMetric):
    reduction_map = {"mean": ["score"]}
    main_score = "score"
    batch_size: int = 32

    model_name: str

    _requirements_list: List[str] = ["sentence_transformers"]

    def prepare(self):
        super().prepare()
        import torch
        from sentence_transformers import SentenceTransformer
        from sentence_transformers import util as sbert_util

        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
        self.model = SentenceTransformer(self.model_name, device=self.device)
        self.util = sbert_util

    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Dict],
    ) -> List[Dict[str, Any]]:
        scores = []

        # we are in a multi-reference case (each prediction may have multiple
        # references), so we need to flatten the refs in order to compute the
        # embeddings in one batch, but first we have to store the spans of
        # reference groups, so we can recover it later on.
        ref_group_boundaries = []
        count = 0
        for ref_group in references:
            ref_group_boundaries.append((count, count + len(ref_group)))
            count += len(ref_group)

        # compute s-bert embeddings
        preds_emb = self.model.encode(predictions, device=self.device)
        refs_emb = self.model.encode(
            [ref for ref_group in references for ref in ref_group], device=self.device
        )

        # for each candidate, pick the reference with the highest score
        for pred_emb, ref_group_bounds in zip(preds_emb, ref_group_boundaries):
            refs_group_emb = refs_emb[ref_group_bounds[0] : ref_group_bounds[1]]
            scores.append(self.util.cos_sim(pred_emb, refs_group_emb).max().item())

        return [{"score": score} for score in scores]


class Reward(BulkInstanceMetric):
    reduction_map = {"mean": ["score"]}
    main_score = "score"
    batch_size: int = 32

    model_name: str

    _requirements_list: List[str] = ["transformers"]

    def prepare(self):
        super().prepare()
        import torch
        from transformers import pipeline

        device = "cuda:0" if torch.cuda.is_available() else "cpu"
        self.pipe = pipeline(
            "text-classification", model=self.model_name, device=device
        )

    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Dict],
    ) -> List[Dict[str, Any]]:
        # treat the references as the questions and the predictions as answers
        # assume a single reference
        questions = [refs[0] for refs in references]
        answers = predictions

        # prepare for computation
        inputs = [{"text": q, "text_pair": a} for q, a in zip(questions, answers)]

        # compute the metric
        # add function_to_apply="none" to disable sigmoid
        return self.pipe(inputs, batch_size=self.batch_size)


class Perplexity(BulkInstanceMetric):
    """Computes the likelihood of generating text Y after text X - P(Y|X)."""

    main_score = "perplexity"
    reduction_map = {"mean": ["perplexity"]}

    perplexity_prompt: str

    batch_size: int = 32
    model_name: str

    _requirements_list: List[str] = ["transformers"]

    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Dict],
    ) -> List[Dict[str, Any]]:
        """Computes the likelihood of generating text Y after text X - P(Y|X).

        :param predictions: the list of Y texts = the targets of the generation
        :param references: the list of list of X texts = the sources of the generation

        :return: the likelihood of generating text Y_i after each text X_i_j = P(Y_i|X_i_1), ..., P(Y_i|X_i_n)  for every i.
        """
        sources = []
        targets = []
        for prediction, instance_references in zip(predictions, references):
            for instance_reference in instance_references:
                sources.append(f"{self.perplexity_prompt} {instance_reference}")
                targets.append(prediction)

        from transformers import AutoConfig

        config = AutoConfig.from_pretrained(self.model_name, trust_remote_code=True)
        lm = (
            self.EncoderDecoderLM(model_name=self.model_name)
            if config.is_encoder_decoder is True
            else self.DecoderOnlyLM(model_name=self.model_name)
        )

        # compute P(Q|P) and store in queue
        scores = lm.compute_lm(
            source=sources, target=targets, batch_size=self.batch_size
        )

        index = 0
        all_instances_scores = []
        for instance_references in references:
            instance_scores = {}
            instance_scores_list = []
            for _ in range(len(instance_references)):
                instance_scores_list.append(scores[index])
                index += 1
            instance_scores["reference_scores"] = instance_scores_list

            # max seems more useful than mean for common use cases like
            # context relevance, where what we want to know is if there
            # is at least one good result in the context. Using mean will
            # bring the score down due to bad contexts at the tail.
            instance_scores[self.main_score] = max(instance_scores_list)
            all_instances_scores.append(instance_scores)

        return all_instances_scores

    class AbstractLM(ABC):
        def __init__(self, model_name):
            import torch
            from transformers import AutoTokenizer

            self.model_name = model_name
            self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
            self.model = (
                self.model_class().from_pretrained(self.model_name).to(self.device)
            )
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)

        def compute_lm(
            self, source: List[str], target: List[str], batch_size: int
        ) -> List[float]:
            import torch

            scores = []

            with torch.no_grad():
                # break the documents to batches
                n_batches = int(len(source) / batch_size)
                batch_range = range(n_batches + 1)
                for batch in batch_range:
                    batch_source = source[batch * batch_size : (batch + 1) * batch_size]
                    batch_target = target[batch * batch_size : (batch + 1) * batch_size]
                    if len(batch_source) > 0:
                        # tokenize the source and target
                        tokens_source = self.tokenizer(
                            batch_source, padding=True, return_tensors="pt"
                        )
                        tokens_target = self.tokenizer(
                            batch_target, padding=True, return_tensors="pt"
                        )

                        # compute the logits
                        logits, labels = self.compute_batch(
                            tokens_source, tokens_target
                        )

                        # logits is a tensor of size: batch_size * len(target) * vocab_size
                        # because for each example in the batch, the model predicted the
                        # logit at every position in the target, for every vocab item.

                        # the model returns mean over all batch. We run the CE again without reduction
                        # and extract the mean for each document
                        loss_fct = torch.nn.CrossEntropyLoss(
                            ignore_index=-100, reduction="none"
                        )

                        # logits.size(-1) = the dimension of the vocabulary
                        # labels.view(-1) = flattens the labels tensor to 1d
                        loss = loss_fct(
                            logits.view(-1, logits.size(-1)), labels.view(-1)
                        )
                        loss = loss.view(len(batch_source), -1)

                        # for each document, do mean only over the non zero values (sum(labels>0))
                        batch_loss = torch.sum(loss, dim=1) / torch.sum(
                            labels > 0, dim=1
                        )

                        # e^-average(cross-entropy-loss(logits) == geometric mean of the probabilities
                        # proof:
                        # * CE-loss of logits is computed by transforming the logits to
                        #   probabilities by softmax, and then -log(p) is returned, where
                        #   p is the probability of the gold label.
                        # * Averaging the CE loss is computed by summing over -log(p) and
                        #   then dividing by the length of the gold labels.
                        # * Thus, pr_score = (-log(p_1) +  ... + -log(p_n)) / n
                        #                  = -log(p_1 * ... * p_n) * 1/n
                        # * Therefore,
                        #   e^(-pr_score) = e^(log(p_1 * ... * p_n) * 1/n)
                        #                 = (e^(log(p_1 * ... * p_n))) ^ 1/n
                        #                 = p_1 * ... * p_n) ^ 1/n
                        #                 = geometric mean of [p_1, ..., p_n]
                        #
                        # in principle we could have computed the geometric mean directly over the
                        # probabilities instead of e^(average cross entropy loss of the logits),
                        # but the current approach is more stable numerically.  See for example:
                        # https://stackoverflow.com/questions/59722983/how-to-calculate-geometric-mean-in-a-differentiable-way
                        geometric_mean = (-batch_loss).exp()

                        # append the batch scores to the list of all scores
                        scores.append(geometric_mean)

            return torch.cat(scores, dim=0).tolist()

        @abstractmethod
        def model_class(self):
            pass

        @abstractmethod
        def compute_batch(self, tokens_source, tokens_target):
            pass

    class EncoderDecoderLM(AbstractLM):
        def model_class(self):
            from transformers import AutoModelForSeq2SeqLM

            return AutoModelForSeq2SeqLM

        def compute_batch(self, tokens_source, tokens_target):
            tokens_docs_ids = tokens_source["input_ids"].to(self.device)
            attention = tokens_source["attention_mask"].to(self.device)
            labels = tokens_target["input_ids"].to(self.device)

            logits = self.model(
                input_ids=tokens_docs_ids.long(),
                attention_mask=attention.long(),
                labels=labels.long(),
            ).logits

            # replace the padding token in the labels by -100
            labels[labels == self.tokenizer.pad_token_id] = -100

            return logits, labels

    class DecoderOnlyLM(AbstractLM):
        def model_class(self):
            from transformers import AutoModelForCausalLM

            return AutoModelForCausalLM

        def compute_batch(self, tokens_source, tokens_target):
            import torch

            tokens = torch.cat(
                [tokens_source["input_ids"], tokens_target["input_ids"]], dim=1
            )
            attention = torch.cat(
                [tokens_source["attention_mask"], tokens_target["attention_mask"]],
                dim=1,
            )
            labels = torch.cat(
                [
                    torch.zeros_like(tokens_source["input_ids"]).fill_(-100),
                    tokens_target["input_ids"],
                ],
                dim=1,
            )

            # replace the padding token in the labels by -100
            labels[labels == self.tokenizer.pad_token_id] = -100

            tokens = tokens.to(self.device)
            attention = attention.to(self.device)
            labels = labels.to(self.device)

            # no need to pass labels as we calculate the loss below per document
            model_output = self.model(
                input_ids=tokens.long(), attention_mask=attention.long()
            )
            logits = model_output.logits

            # in decoder only, the first token is not being generated, it is taken from the input,
            # so the model is generating from token 2 to n+1. therefore, we need to skip the last
            # logit and the first label.
            shifted_logits = logits[..., :-1, :].contiguous()
            shifted_labels = labels[..., 1:].contiguous()

            return shifted_logits, shifted_labels


class NDCG(GlobalMetric):
    """Normalized Discounted Cumulative Gain: measures the quality of ranking with respect to ground truth ranking scores.

    As this measures ranking, it is a global metric that can only be calculated over groups of instances. In the
    common use case where the instances are grouped by different queries, i.e., where the task is to provide a
    relevance score for a search result w.r.t. a query, an nDCG score is calculated per each query (specified in the
    "query" input field of an instance) and the final score is the average across all queries.
    Note that the expected scores are relevance scores (i.e., higher is better) and not rank indices. The absolute
    value of the scores is only meaningful for the reference scores; for the predictions, only the ordering of the
    scores affects the outcome - for example, predicted scores of [80, 1, 2] and [0.8, 0.5, 0.6] will receive
    the same nDCG score w.r.t. a given set of reference scores.

    See also https://en.wikipedia.org/wiki/Discounted_cumulative_gain
    """

    main_score = "nDCG"

    _requirements_list: List[str] = ["sklearn"]

    def prepare(self):
        from sklearn.metrics import ndcg_score

        super().prepare()
        self.eval = ndcg_score

    def compute(
        self,
        references: List[List[Any]],
        predictions: List[Any],
        task_data: List[Any],
    ) -> dict:
        from collections import defaultdict

        query_to_predictions_and_references = defaultdict(lambda: [[], []])
        for reference, pred, inputs_dict in zip(references, predictions, task_data):
            query = inputs_dict.get("query")
            query_to_predictions_and_references[query][0].append(pred)
            query_to_predictions_and_references[query][1].append(reference)

        scores = []
        for q_predictions, q_references in query_to_predictions_and_references.values():
            if len(q_references) == 1:
                continue

            if (
                None in q_predictions
            ):  # model failed to predict numeric scores for some instances
                numeric_predictions = [
                    pred for pred in q_predictions if pred is not None
                ]
                if len(numeric_predictions) <= 1:  # no meaningful ranking
                    scores.append(0)
                    continue
                # consider non-numeric model predictions as ranked last
                min_value = min(numeric_predictions)
                q_predictions = [
                    1 + (pred - min_value) if pred is not None else 0
                    for pred in q_predictions
                ]
            scores.append(self.eval([q_references], [q_predictions]))
        return {self.main_score: mean(scores) if len(scores) > 0 else np.nan}


class RetrievalMetric(InstanceMetric):
    def compute(self, references: List[Any], prediction: Any, task_data: Dict) -> dict:
        # digest input
        pred_ids: List[Any] = prediction
        ref_ids: List[Any] = list(dict.fromkeys(references))

        # relevance_at_k: 1-based dictionary of indicators (0/1), telling whether
        # the doc id retrieved at position k (assuming it is 1-based, so k starts
        # from 1) is in the gold doc ids or not.
        # For example, assuming that in the retrieved docs we have correct predictions
        # at positions 2, 4 and 5 (1-based), the dict will look like:
        # {1: 0, 2: 1, 3: 0, 4: 1, 5: 1, ...}
        relevance_at_k = {
            k + 1: 1 if doc_id in ref_ids else 0 for k, doc_id in enumerate(pred_ids)
        }

        # relevance_sum_at_k: 1-based dictionary of counts, where the value at k determines
        # how many gold doc ids have been observed up to index k.
        relevance_sum_at_k = {}
        for k, value in relevance_at_k.items():
            relevance_sum_at_k[k] = relevance_sum_at_k.get(k - 1, 0) + value

        # precision_at_k: the precision of the top k retrieved documents. For example,
        # assuming that only 1 out of the first 4 retrieved documents is correct, the
        # value at 4 will be 1/4.
        precision_at_k = {k: value / k for k, value in relevance_sum_at_k.items()}

        # recall_at_k: the recall of the top k retrieved documents. For example,
        # assuming that only 2 out of the 3 gold documents are in the top 5 results,
        # the value at 5 will be 2/3.
        n_refs = len(ref_ids)
        recall_at_k = {
            k: value / n_refs if n_refs > 0 else 0
            for k, value in relevance_sum_at_k.items()
        }

        # rank - the 1-based index of the first hit of a gold doc id. So 1
        # means first position.
        rank = 0
        for k, relevance in relevance_at_k.items():
            if relevance == 1:
                rank = k
                break

        # match_at_k: whether we have a match at the top k retrieved documents
        match_at_k = {
            k: 1.0 if value > 0 else 0.0 for k, value in relevance_sum_at_k.items()
        }

        return self._compute(
            relevance_at_k,
            relevance_sum_at_k,
            precision_at_k,
            recall_at_k,
            match_at_k,
            rank,
        )

    @abstractmethod
    def _compute(
        self,
        relevance_at_k,
        relevance_sum_at_k,
        precision_at_k,
        recall_at_k,
        match_at_k,
        rank,
    ) -> dict:
        pass


class MRR(RetrievalMetric):
    reduction_map = {"mean": ["mrr"]}
    main_score = "mrr"
    ci_scores = ["mrr"]

    def _compute(
        self,
        relevance_at_k,
        relevance_sum_at_k,
        precision_at_k,
        recall_at_k,
        match_at_k,
        rank,
    ) -> dict:
        return {self.main_score: 1 / rank if rank > 0 else 0}


class MAP(RetrievalMetric):
    reduction_map = {"mean": ["map"]}
    main_score = "map"
    ci_scores = ["map"]

    def _compute(
        self,
        relevance_at_k,
        relevance_sum_at_k,
        precision_at_k,
        recall_at_k,
        match_at_k,
        rank,
    ) -> dict:
        result = 0
        if len(relevance_at_k) > 0:
            total = sum(relevance_at_k.values())
            if total > 0:
                dot = sum(relevance_at_k[k] * precision_at_k[k] for k in relevance_at_k)
                result = dot / total
        return {self.main_score: result}


class RetrievalAtK(RetrievalMetric):
    k_list: List[int]
    main_score: str = None
    reduction_map: Dict[str, List[str]] = None

    def prepare(self):
        super().prepare()
        self.main_score = self.score_name("match", self.k_list[0])
        self.ci_scores = [
            self.score_name(measure, k)
            for measure in ["precision", "recall", "match"]
            for k in self.k_list
        ]
        self.reduction_map = {"mean": self.ci_scores}

    @staticmethod
    def score_name(measure: str, k: int):
        return f"{measure}_at_{k}"

    def _compute(
        self,
        relevance_at_k,
        relevance_sum_at_k,
        precision_at_k,
        recall_at_k,
        match_at_k,
        rank,
    ) -> dict:
        result = {}
        for measure_array, measure_name in [
            (precision_at_k, "precision"),
            (recall_at_k, "recall"),
            (match_at_k, "match"),
        ]:
            max_k = max(measure_array.keys())
            for k in self.k_list:
                result[self.score_name(measure_name, k)] = measure_array[min(k, max_k)]
        return result


class KPA(CustomF1):
    def get_element_group(self, element, additional_input):
        return additional_input["keypoint"]

    def get_element_representation(self, element, additional_input):
        return additional_input["keypoint"]

    def should_ignore_element(self, element, additional_input):
        return element == "none"


class RemoteMetric(SingleStreamOperator, Metric):
    """A metric that runs another metric remotely.

    main_score: the score updated by this metric.
    endpoint: the remote host that supports the remote metric execution.
    metric_name: the name of the metric that is executed remotely.
    api_key: optional, passed to the remote metric with the input, allows secure authentication.
    """

    main_score: str = None
    endpoint: str
    metric_name: str
    api_key: str = None

    @staticmethod
    def wrap_inner_metric_pipeline_metric(
        metric_pipeline: MetricPipeline, remote_metrics_endpoint: str
    ) -> MetricPipeline:
        """Wrap the inner metric in a MetricPipeline with a RemoteMetric.

        When executing the returned MetricPipeline, the inner metric will be computed
        remotely (pre and post processing steps in the MetricPipeline will be computed locally).
        """
        local_inner_metric = metric_pipeline.metric
        metric_pipeline = deepcopy(
            metric_pipeline
        )  # To avoid unintentional changes to the catalog contents
        metric_pipeline.metric = RemoteMetric(
            main_score=local_inner_metric.main_score,
            metric_name=local_inner_metric.artifact_identifier,
            endpoint=remote_metrics_endpoint,
        )
        return metric_pipeline

    def get_metric_url(self) -> str:
        return f"{self.endpoint}/{self.metric_name}"

    def process(self, stream: Stream, stream_name: Optional[str] = None) -> Generator:
        predictions, references, additional_inputs, instances = self.consume_stream(
            stream
        )
        metric_request = self.create_metric_request(
            predictions, references, additional_inputs
        )
        metric_response = self.get_metric_response(metric_request)
        self.update_instance_scores(instances, metric_response.instances_scores)
        self.set_global_score(instances, metric_response.global_score)
        yield from instances

    @staticmethod
    def create_metric_request(predictions, references, additional_inputs):
        instance_inputs = [
            InstanceInput(
                prediction=prediction,
                references=reference,
                additional_inputs=additional_input,
            )
            for prediction, reference, additional_input in zip(
                predictions, references, additional_inputs
            )
        ]
        return MetricRequest(instance_inputs=instance_inputs)

    def get_metric_response(self, metric_request: MetricRequest) -> MetricResponse:
        import requests

        response = requests.post(
            url=self.get_metric_url(),
            json=metric_request.to_dict(),
            headers={"Authorization": f"Bearer {self.api_key}"},
        )
        response.raise_for_status()
        response_json = response.json()
        return MetricResponse(**response_json)

    def disable_confidence_interval_calculation(self):
        """Confidence intervals are always disabled for RemoteMetric.

        No need to do anything.
        """
        pass

    def set_n_resamples(self, n_resample):
        """Since confidence intervals are always disabled for remote metrics, this is a no-op."""
        pass


def validate_subgroup_types(
    subgroup_scores_dict: Dict[str, List],
    control_subgroup_types: List[str],
    comparison_subgroup_types: List[str],
):
    """Validate a dict of subgroup type instance score lists, and subgroup type lists.

    Args:
        subgroup_scores_dict: dict where keys are subgroup types and values are lists of instance scores.
        control_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the control (baseline) group
        comparison_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the group
            to be compared to the control group.

    Returns:
        dict with all NaN scores removed; control_subgroup_types and comparison_subgroup_types will have non-unique elements removed
    """
    # note: subgroup_scores_dict is already a defaultdict of lists, so don't need to check that keys in control_ and comparison_subgroup_types exist in it
    # remove any NaNs
    subgroup_scores_dict.update(
        {
            subgroup_name: [score for score in score_list if not np.isnan(score)]
            for subgroup_name, score_list in subgroup_scores_dict.items()
        }
    )
    assert isinstance(
        control_subgroup_types, list
    ), "control_subgroup_types must be a list"
    assert isinstance(
        comparison_subgroup_types, list
    ), "comparison_subgroup_types must be a list"
    # make sure each list is unique, so that labels aren't double-counted
    control_subgroup_types = list(set(control_subgroup_types))
    comparison_subgroup_types = list(set(comparison_subgroup_types))

    return subgroup_scores_dict, control_subgroup_types, comparison_subgroup_types


def performance_drop_rate(
    subgroup_scores_dict: Dict[str, List],
    control_subgroup_types: List[str],
    comparison_subgroup_types: List[str],
):
    """Percentage decrease of mean performance on test elements relative to that on a baseline (control).

    from https://arxiv.org/pdf/2306.04528.pdf.

    Args:
        subgroup_scores_dict: dict where keys are subgroup types and values are lists of instance scores.
        control_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the control (baseline) group
        comparison_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the group
            to be compared to the control group.

    Returns:
        numeric PDR metric.
        If only one element (no test set) or the first is 0 (percentage change is undefined) return NaN
        otherwise, calculate PDR
    """
    (
        subgroup_scores_dict,
        control_subgroup_types,
        comparison_subgroup_types,
    ) = validate_subgroup_types(
        subgroup_scores_dict, control_subgroup_types, comparison_subgroup_types
    )

    # combine all scores from each label (if there are more than 1 in each group) into a list
    group_scores_list = [
        np.concatenate(
            [subgroup_scores_dict[subgroup_name] for subgroup_name in name_list]
        )
        for name_list in [control_subgroup_types, comparison_subgroup_types]
    ]
    if any(len(scores) == 0 for scores in group_scores_list):
        # no comparison can be made since there is not at least one score per type
        return np.nan
    control_mean = mean(group_scores_list[0])
    comparison_mean = mean(group_scores_list[1])
    if control_mean == 0:
        # return 0 if comparison is also 0
        if comparison_mean == 0:
            return 0
        return np.nan
    # otherwise, take the percentage change (which may also be 0)
    return 1 - comparison_mean / control_mean


def interpret_effect_size(x: float):
    """Return a string rule-of-thumb interpretation of an effect size value, as defined by Cohen/Sawilowsky.

    See https://en.wikipedia.org/wiki/Effect_size;
    Cohen, Jacob (1988). Statistical Power Analysis for the Behavioral Sciences; and
    Sawilowsky, S (2009). "New effect size rules of thumb". Journal of Modern Applied Statistical Methods. 8 (2): 467-474.

    Value has interpretation of
    - essentially 0 if |x| < 0.01
    - very small if 0.01 <= |x| < 0.2
    - small difference if 0.2 <= |x| < 0.5
    - a medium difference if 0.5 <= |x| < 0.8
    - a large difference if 0.8 <= |x| < 1.2
    - a very large difference if 1.2 <= |x| < 2.0
    - a huge difference if 2.0 <= |x|

    Args:
        x: float effect size value

    Returns:
        string interpretation
    """
    import pandas as pd

    # assign a label according to threshold of the absolute value
    return pd.cut(
        x=[np.abs(x)],
        right=False,
        bins=[-1, 0.01, 0.2, 0.5, 0.8, 1.2, 2.0, np.Inf],
        labels=[
            "essentially zero",
            "very small",
            "small",
            "medium",
            "large",
            "very large",
            "huge",
        ],
    )[0]


def normalized_cohens_h(
    subgroup_scores_dict: Dict[str, List],
    control_subgroup_types: List[str],
    comparison_subgroup_types: List[str],
    interpret=False,
):
    """Cohen's h effect size between two proportions, normalized to interval [-1,1].

    Allows for change-type metric when the baseline is 0 (percentage change, and thus PDR, is undefined)
    https://en.wikipedia.org/wiki/Cohen%27s_h

    Cohen's h effect size metric between two proportions p2 and p1 is 2 * (arcsin(sqrt(p2)) - arcsin(sqrt(p1))).
    h in -pi, pi, with +/-pi representing the largest increase/decrease (p1=0, p2=1), or (p1=1, p2=0).
    h=0 is no change. Unlike percentage change, h is defined even if the baseline (p1) is 0.
    Assumes the scores are in [0,1], either continuous or binary; hence taking the average of a group of scores yields a proportion..
    Calculates the change in the average of the other_scores relative to the average of the baseline_scores.    We rescale this to [-1,1] from [-pi,pi] for clarity, where +- 1 are the most extreme changes, and 0 is no change

    Interpretation: the original unscaled Cohen's h can be interpreted according to function interpret_effect_size

    Thus, the rule of interpreting the effect of the normalized value is to use the same thresholds divided by pi
        - essentially 0 if |norm h| < 0.0031831
        - very small if 0.0031831 <= |norm h| < 0.06366198
        - small difference if 0.06366198 <= |norm h| < 0.15915494
        - a medium difference if 0.15915494 <= |norm h| < 0.25464791
        - a large difference if 0.25464791 <= |norm h| < 0.38197186
        - a very large difference if 0.38197186 <= |norm h| < 0.63661977
        - a huge difference if 0.63661977 <= |norm h|
    Args:
        subgroup_scores_dict: dict where keys are subgroup types and values are lists of instance scores.
        control_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the control (baseline) group
        comparison_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the group
            to be compared to the control group.
        interpret: boolean, whether to interpret the significance of the score or not
    Returns:
        float score between -1 and 1, and a string interpretation if interpret=True
    """
    (
        subgroup_scores_dict,
        control_subgroup_types,
        comparison_subgroup_types,
    ) = validate_subgroup_types(
        subgroup_scores_dict, control_subgroup_types, comparison_subgroup_types
    )

    # requires scores to be in [0,1]
    for subgroup_name, score_list in subgroup_scores_dict.items():
        assert all(
            0 <= score <= 1 for score in score_list
        ), f"all {subgroup_name} scores must be in [0,1]"

    # combine all scores from each label (if there are more than 1 in each group) into a list
    group_scores_list = [
        np.concatenate(
            [subgroup_scores_dict[subgroup_name] for subgroup_name in name_list]
        )
        for name_list in [control_subgroup_types, comparison_subgroup_types]
    ]

    if any(len(scores) == 0 for scores in group_scores_list):
        # no comparison can be made since there is not at least one score per type
        h, norm_h = np.nan, np.nan
    else:
        control_mean = mean(group_scores_list[0])
        comparison_mean = mean(group_scores_list[1])
        h = 2 * (np.arcsin(np.sqrt(comparison_mean)) - np.arcsin(np.sqrt(control_mean)))
        norm_h = np.clip(a=h / np.pi, a_min=-1, a_max=1)

    if not interpret:
        return norm_h

    return norm_h, interpret_effect_size(h)


def normalized_hedges_g(
    subgroup_scores_dict: Dict[str, List[float]],
    control_subgroup_types: List[str],
    comparison_subgroup_types: List[str],
    interpret=False,
):
    """Hedge's g effect size between mean of two samples, normalized to interval [-1,1].  Better than Cohen's d for small sample sizes.

    Takes into account the variances within the samples, not just the means.

    Args:
        subgroup_scores_dict: dict where keys are subgroup types and values are lists of instance scores.
        control_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the control (baseline) group
        comparison_subgroup_types: list of subgroup types (potential keys of subgroup_scores_dict) that are the group
            to be compared to the control group.
        interpret: boolean, whether to interpret the significance of the score or not
    Returns:
        float score between -1 and 1, and a string interpretation if interpret=True
    """
    (
        subgroup_scores_dict,
        control_subgroup_types,
        comparison_subgroup_types,
    ) = validate_subgroup_types(
        subgroup_scores_dict, control_subgroup_types, comparison_subgroup_types
    )

    # combine all scores from each label (if there are more than 1 in each group) into a list
    group_scores_list = [
        np.concatenate(
            [subgroup_scores_dict[subgroup_name] for subgroup_name in name_list]
        )
        for name_list in [control_subgroup_types, comparison_subgroup_types]
    ]

    group_n = [len(scores) for scores in group_scores_list]
    if any(nn == 0 for nn in group_n) or all(nn <= 1 for nn in group_n):
        # if at least one sample size is 0 for one type, no comparison can be made at all
        # if both sample sizes are 1, then the denominator is undefined since divide by n1 + n2 - 2
        # so require at least one sample to have > 1 observation, and both to have >= 1.
        g, norm_g = np.nan, np.nan
    else:
        # otherwise, calculate the variances
        group_mean = [mean(scores) for scores in group_scores_list]
        # sample variance with 1 degree of freedom (denominator n-1); if n=1, return 0 since otherwise throws an error
        group_var = [
            0.0 if nn == 1 else np.var(scores, ddof=1)
            for scores, nn in zip(group_scores_list, group_n)
        ]
        var_total = sum([(nn - 1) * vv for vv, nn in zip(group_var, group_n)])
        pooled_sd = np.sqrt(var_total / (sum(group_n) - 2))

        max_absolute_value = 5
        gmd = float(group_mean[1] - group_mean[0])

        if gmd == 0:
            # if exactly the same, return 0
            g = 0.0
        else:
            try:
                g = gmd / pooled_sd
            except ZeroDivisionError:
                # return a large effect size to avoid explosion if there is zero variance
                g = np.sign(gmd) * max_absolute_value

        n = sum(group_n)
        if 3 < n < 50:
            # small sample adjustment see https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/hedgeg.htm
            # the multiplier is 0 if n <= 3
            g *= ((n - 3) / (n - 2.25)) * np.sqrt((n - 2) / n)
        # clip it at a very large value so it doesn't become infinite if the variance (denominator) is very small or 0
        g = float(np.clip(a=g, a_min=-1 * max_absolute_value, a_max=max_absolute_value))
        norm_g = g / max_absolute_value

    if not interpret:
        return norm_g
    return norm_g, interpret_effect_size(g)


def mean_subgroup_score(
    subgroup_scores_dict: Dict[str, List], subgroup_types: List[str]
):
    """Return the mean instance score for a subset (possibly a single type) of variants (not a comparison).

    Args:
        subgroup_scores_dict: dict where keys are subgroup types and values are lists of instance scores.
        subgroup_types: the keys (subgroup types) for which the average will be computed.

    Returns:
        float score
    """
    subgroup_scores_dict, subgroup_types, _ = validate_subgroup_types(
        subgroup_scores_dict, subgroup_types, []
    )

    # combine all desired subgroup scores
    score_list = np.concatenate(
        [subgroup_scores_dict[subgroup_name] for subgroup_name in subgroup_types]
    )
    if len(score_list) == 0:
        # no scores to use
        return np.nan
    return mean(score_list)


# metrics using mean reduction
class GroupMeanAccuracy(Accuracy):
    reduction_map = {"group_mean": {"agg_func": ["mean", nan_mean, False]}}


class FixedGroupMeanAccuracy(Accuracy):
    # the same as GroupMeanAccuracy, except the groups are fixed and are resampled together
    reduction_map = {"group_mean": {"agg_func": ["mean", nan_mean, True]}}


# same as above, now using StringContainment
class GroupMeanStringContainment(StringContainment):
    reduction_map = {"group_mean": {"agg_func": ["mean", nan_mean, False]}}


class FixedGroupMeanStringContainment(StringContainment):
    # the same as GroupMeanStringContainment, except the groups are fixed and are resampled together
    reduction_map = {"group_mean": {"agg_func": ["mean", nan_mean, True]}}


# take only the (fixed) group mean of baseline or other (paraphrases) scores
class FixedGroupMeanBaselineAccuracy(Accuracy):
    subgroup_column = "variant_type"
    # take mean of "original" variants only
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "mean_baseline",
                lambda scd: mean_subgroup_score(
                    subgroup_scores_dict=scd, subgroup_types=["original"]
                ),
                True,
            ],
        }
    }


class FixedGroupMeanParaphraseAccuracy(Accuracy):
    subgroup_column = "variant_type"
    # take mean of "paraphrase" variants only
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "mean_paraphrase",
                lambda scd: mean_subgroup_score(
                    subgroup_scores_dict=scd, subgroup_types=["paraphrase"]
                ),
                True,
            ],
        }
    }


# same as above but using StringContainment
class FixedGroupMeanBaselineStringContainment(StringContainment):
    subgroup_column = "variant_type"
    # take mean of "original" variants only
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "mean_baseline",
                lambda scd: mean_subgroup_score(
                    subgroup_scores_dict=scd, subgroup_types=["original"]
                ),
                True,
            ],
        }
    }


class FixedGroupMeanParaphraseStringContainment(StringContainment):
    subgroup_column = "variant_type"
    # take mean of "paraphrase" variants only
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "mean_paraphrase",
                lambda scd: mean_subgroup_score(
                    subgroup_scores_dict=scd, subgroup_types=["paraphrase"]
                ),
                True,
            ],
        }
    }


# using PDR
class FixedGroupPDRParaphraseAccuracy(Accuracy):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "pdr_paraphrase",
                lambda scd: performance_drop_rate(
                    subgroup_scores_dict=scd,
                    control_subgroup_types=["original"],
                    comparison_subgroup_types=["paraphrase"],
                ),
                True,
            ],
        }
    }


class FixedGroupPDRParaphraseStringContainment(StringContainment):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "pdr_paraphrase",
                lambda scd: performance_drop_rate(
                    subgroup_scores_dict=scd,
                    control_subgroup_types=["original"],
                    comparison_subgroup_types=["paraphrase"],
                ),
                True,
            ],
        }
    }


class GroupMeanTokenOverlap(TokenOverlap):
    reduction_map = {
        "group_mean": {
            "agg_func": ["mean", nan_mean, False],
            "score_fields": ["f1", "precision", "recall"],
        }
    }


# using Cohens's h for proportions
class FixedGroupNormCohensHParaphraseAccuracy(Accuracy):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "norm_cohens_h_paraphrase",
                lambda scd: normalized_cohens_h(
                    subgroup_scores_dict=scd,
                    control_subgroup_types=["original"],
                    comparison_subgroup_types=["paraphrase"],
                ),
                True,
            ],
        }
    }


class FixedGroupNormCohensHParaphraseStringContainment(StringContainment):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "norm_cohens_h_paraphrase",
                lambda scd: normalized_cohens_h(
                    subgroup_scores_dict=scd,
                    control_subgroup_types=["original"],
                    comparison_subgroup_types=["paraphrase"],
                ),
                True,
            ],
        }
    }


# using Hedges' g (takes into account internal variation in group scores)
class FixedGroupNormHedgesGParaphraseAccuracy(Accuracy):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "norm_hedges_g_paraphrase",
                lambda scd: normalized_hedges_g(
                    subgroup_scores_dict=scd,
                    control_subgroup_types=["original"],
                    comparison_subgroup_types=["paraphrase"],
                ),
                True,
            ],
        }
    }


class FixedGroupNormHedgesGParaphraseStringContainment(StringContainment):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "norm_hedges_g_paraphrase",
                lambda scd: normalized_hedges_g(
                    subgroup_scores_dict=scd,
                    control_subgroup_types=["original"],
                    comparison_subgroup_types=["paraphrase"],
                ),
                True,
            ],
        }
    }


# for above metrics, take absolute value of group score first; this measures variation in either direction
class FixedGroupAbsvalNormCohensHParaphraseAccuracy(Accuracy):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "absval_norm_cohens_h_paraphrase",
                lambda scd: np.abs(
                    normalized_cohens_h(
                        subgroup_scores_dict=scd,
                        control_subgroup_types=["original"],
                        comparison_subgroup_types=["paraphrase"],
                    )
                ),
                True,
            ],
        }
    }


class FixedGroupAbsvalNormCohensHParaphraseStringContainment(StringContainment):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "absval_norm_cohens_h_paraphrase",
                lambda scd: np.abs(
                    normalized_cohens_h(
                        subgroup_scores_dict=scd,
                        control_subgroup_types=["original"],
                        comparison_subgroup_types=["paraphrase"],
                    )
                ),
                True,
            ],
        }
    }


class FixedGroupAbsvalNormHedgesGParaphraseAccuracy(Accuracy):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "absval_norm_hedges_g_paraphrase",
                lambda scd: np.abs(
                    normalized_hedges_g(
                        subgroup_scores_dict=scd,
                        control_subgroup_types=["original"],
                        comparison_subgroup_types=["paraphrase"],
                    )
                ),
                True,
            ],
        }
    }


class FixedGroupAbsvalNormHedgesGParaphraseStringContainment(StringContainment):
    subgroup_column = "variant_type"
    reduction_map = {
        "group_mean": {
            "agg_func": [
                "absval_norm_hedges_g_paraphrase",
                lambda scd: np.abs(
                    normalized_hedges_g(
                        subgroup_scores_dict=scd,
                        control_subgroup_types=["original"],
                        comparison_subgroup_types=["paraphrase"],
                    )
                ),
                True,
            ],
        }
    }