metrics.py

from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Dict, List, Generator

from .operator import SingleStreamOperator, StreamInstanceOperator
from .stream import Stream


def absrtact_factory():
    return {}


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


class UpdateStream(StreamInstanceOperator):
    update: dict

    def process(self, instance: Dict[str, Any], stream_name: 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(ABC):
    @property
    @abstractmethod
    def main_score(self):
        pass


class GlobalMetric(SingleStreamOperator, Metric):
    def process(self, stream: Stream):
        references = []
        predictions = []
        global_score = {}

        instances = []

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

            refs, pred = instance["references"], instance["prediction"]

            instance_score = self._compute([refs], [pred])
            instance["score"]["instance"].update(instance_score)

            references.append(refs)
            predictions.append(pred)
            instances.append(instance)

        result = self._compute(references, predictions)

        global_score.update(result)

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

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

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


class InstanceMetric(SingleStreamOperator, Metric):
    implemented_reductions: List[str] = field(default_factory=lambda: ["mean"])

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

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

        for instance in stream:
            refs, pred = instance["references"], instance["prediction"]

            instance_score = self._compute(refs, pred)

            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)

        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":
                from statistics import mean

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

        for instance in instances:
            yield instance

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

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


class SingleReferenceInstanceMetric(InstanceMetric):
    def _compute(self, references: List[str], prediction: str) -> dict:
        result = self.compute(references[0], prediction)
        result["score"] = result[self.main_score]
        return result

    @abstractmethod
    def compute(self, reference, prediction: str) -> dict:
        pass


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

    def compute(self, reference, prediction: str) -> dict:
        return {"accuracy": float(str(reference) == str(prediction))}