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from abc import ABC, abstractmethod |
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from dataclasses import dataclass, field |
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from typing import Any, Dict, List |
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from .operator import SingleStreamOperator, StreamInstanceOperator |
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from .stream import Stream |
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def absrtact_factory(): |
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return {} |
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def abstract_field(): |
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return field(default_factory=absrtact_factory) |
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class UpdateStream(StreamInstanceOperator): |
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update: dict |
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def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]: |
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instance.update(self.update) |
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return instance |
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class Metric(ABC): |
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@property |
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@abstractmethod |
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def main_score(self): |
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pass |
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class GlobalMetric(SingleStreamOperator, Metric): |
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def process(self, stream: Stream): |
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references = [] |
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predictions = [] |
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global_score = {} |
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instances = [] |
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for instance in stream: |
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if "score" not in instance: |
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instance["score"] = {"global": global_score, "instance": {}} |
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else: |
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global_score = instance["score"]["global"] |
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refs, pred = instance["references"], instance["prediction"] |
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instance_score = self._compute([refs], [pred]) |
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instance["score"]["instance"].update(instance_score) |
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references.append(refs) |
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predictions.append(pred) |
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instances.append(instance) |
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result = self._compute(references, predictions) |
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global_score.update(result) |
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for instance in instances: |
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instance["score"]["global"] = global_score |
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yield instance |
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def _compute(self, references: List[List[str]], predictions: List[str]) -> dict: |
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result = self.compute(references, predictions) |
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result["score"] = result[self.main_score] |
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return result |
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@abstractmethod |
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def compute(self, references: List[List[str]], predictions: List[str]) -> dict: |
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pass |
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class InstanceMetric(SingleStreamOperator, Metric): |
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implemented_reductions: List[str] = field(default_factory=lambda: ["mean"]) |
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@property |
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@abstractmethod |
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def reduction_map(self) -> dict: |
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pass |
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def process(self, stream: Stream): |
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global_score = {} |
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instances = [] |
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for instance in stream: |
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refs, pred = instance["references"], instance["prediction"] |
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instance_score = self._compute(refs, pred) |
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if "score" not in instance: |
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instance["score"] = {"global": global_score, "instance": {}} |
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else: |
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global_score = instance["score"]["global"] |
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instance["score"]["instance"].update(instance_score) |
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instances.append(instance) |
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for reduction, fields in self.reduction_map.items(): |
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assert ( |
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reduction in self.implemented_reductions |
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), f"Reduction {reduction} is not implemented, use one of {self.implemented_reductions}" |
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if reduction == "mean": |
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from statistics import mean |
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for field in fields: |
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global_score[field] = mean([instance["score"]["instance"][field] for instance in instances]) |
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if field == self.main_score: |
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global_score["score"] = global_score[field] |
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for instance in instances: |
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yield instance |
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def _compute(self, references: List[List[str]], predictions: List[str]) -> dict: |
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result = self.compute(references, predictions) |
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result["score"] = result[self.main_score] |
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return result |
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@abstractmethod |
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def compute(self, references: List[str], prediction: str) -> dict: |
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pass |
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class SingleReferenceInstanceMetric(InstanceMetric): |
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def _compute(self, references: List[str], prediction: str) -> dict: |
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result = self.compute(references[0], prediction) |
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result["score"] = result[self.main_score] |
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return result |
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@abstractmethod |
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def compute(self, reference, prediction: str) -> dict: |
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pass |
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class Accuracy(SingleReferenceInstanceMetric): |
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reduction_map = {"mean": ["accuracy"]} |
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main_score = "accuracy" |
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def compute(self, reference, prediction: str) -> dict: |
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return {"accuracy": float(str(reference) == str(prediction))} |
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