import uuid from abc import ABC, abstractmethod from collections import Counter from dataclasses import field from typing import Any, Dict, Generator, List, Optional import evaluate import nltk import numpy from editdistance import eval from .dataclass import InternalField from .operator import ( MultiStreamOperator, SingleStreamOperator, StreamingOperator, StreamInstanceOperator, ) from .operators import CopyFields from .stream import MultiStream, Stream nltk.download("punkt") def abstract_factory(): return {} def abstract_field(): return field(default_factory=abstract_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, stream_name: str = None) -> Generator: 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"] try: instance_score = self._compute([refs], [pred]) except: instance_score = {"score": None} if isinstance(self.main_score, str) and self.main_score is not None: instance_score[self.main_score] = None 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=references, predictions=predictions) result["score"] = result[self.main_score] return result @abstractmethod def compute(self, references: List[str], prediction: str) -> dict: pass class Squad(GlobalMetric): _metric = None reduction_map = {"mean": ["f1"]} main_score = "f1" metric = "squad" def prepare(self): super(Squad, self).prepare() self._metric = evaluate.load(self.metric) def compute(self, references: List[List[str]], predictions: List[str]) -> 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 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))} 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 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) multi_stream = self.prepare_score(multi_stream) return multi_stream class HuggingfaceMetric(GlobalMetric): metric_name: str = None main_score: str = None scale: float = 1.0 def prepare(self): super().prepare() self.metric = evaluate.load(self.metric_name) def compute(self, references: List[List[str]], predictions: List[str]) -> dict: result = self.metric.compute(predictions=predictions, references=references) if self.scale != 1.0: for key in result: if isinstance(result[key], float): result[key] /= self.scale return result class F1(GlobalMetric): _metric = None main_score = "f1_macro" average = None # Report per class then aggregate by mean metric = "f1" def prepare(self): super(F1, self).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]) -> 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] unique_labels = 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): from statistics import mean 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 F1MultiLabel(GlobalMetric): _metric = None main_score = "f1_macro" average = None # Report per class then aggregate by mean def prepare(self): super(F1MultiLabel, self).prepare() self._metric = evaluate.load("f1", "multilabel") def add_str_to_id(self, str): if not str 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[str]) -> dict: self.str_to_id = {} self.id_to_str = {} assert all( len(reference) == 1 for reference in references ), "Only a single reference per prediction is allowed in F1 metric" references = [reference[0] for reference in references] labels = list(set([label for reference in references for label in reference])) 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] result = self._metric.compute( predictions=formatted_predictions, references=formatted_references, average=self.average ) if isinstance(result["f1"], numpy.ndarray): from statistics import mean final_result = {self.main_score: mean(result["f1"])} for i, label in enumerate(labels): final_result["f1_" + label] = result["f1"][i] else: final_result = {self.main_score: result["f1"]} return final_result class F1MicroMultiLabel(F1MultiLabel): main_score = "f1_micro" average = "micro" class F1MacroMultiLabel(F1MultiLabel): main_score = "f1_macro" average = None class Rouge(HuggingfaceMetric): metric_name = "rouge" main_score = "rougeL" scale = 1.0 def compute(self, references, predictions): predictions = ["\n".join(nltk.sent_tokenize(prediction.strip())) for prediction in predictions] references = [["\n".join(nltk.sent_tokenize(r.strip())) for r in reference] for reference in references] return super().compute(references, predictions) # Computes chat edit distance, ignoring repeating whitespace class CharEditDistanceAccuracy(SingleReferenceInstanceMetric): reduction_map = {"mean": ["char_edit_dist_accuracy"]} main_score = "char_edit_dist_accuracy" def compute(self, reference, prediction: str) -> dict: formatted_prediction = " ".join(prediction.split()) formatted_reference = " ".join(reference.split()) max_length = max(len(formatted_reference), len(formatted_prediction)) if max_length == 0: return 0 edit_dist = eval(formatted_reference, formatted_prediction) return {"char_edit_dist_accuracy": (1 - edit_dist / max_length)} class Wer(HuggingfaceMetric): metric_name = "wer" main_score = "wer" def prepare(self): super().prepare() self.metric = evaluate.load(self.metric_name) def compute(self, references: List[List[str]], predictions: List[str]) -> 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 Bleu(HuggingfaceMetric): metric_name = "bleu" main_score = "bleu" scale = 1.0 class MatthewsCorrelation(HuggingfaceMetric): 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]) -> dict: formatted_references = [self.get_str_id(reference[0]) for reference in references] formatted_predictions = [self.get_str_id(prediction) for prediction in predictions] result = self.metric.compute(predictions=formatted_predictions, references=formatted_references) return result class CustomF1(GlobalMetric): main_score = "f1_micro" classes = None @abstractmethod def get_element_group(self, element): pass @abstractmethod def get_element_representation(self, element): pass def group_elements(self, l): return { k: Counter([self.get_element_representation(value) for value in l if self.get_element_group(value) == k]) for k in set([self.get_element_group(e) for e in l]) } 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 f1(self, pn, pd, rn, rd): precision = 1.0 if pn == 0 and pd == 0 else pn / pd recall = 1.0 if rn == 0 and rd == 0 else rn / rd try: return 2 * precision * recall / (precision + recall) except ZeroDivisionError: return 0.0 def compute(self, references: List[Any], predictions: List[Any]) -> dict: # in case reference are List[List[List[Any]]] and predictions are List[List[Any]]: if isinstance(references[0], list) 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.classes is None: classes = set([self.get_element_group(e) for sublist in references for e in sublist]) else: classes = self.classes groups_statistics = dict() for references_batch, predictions_batch in zip(references, predictions): grouped_references = self.group_elements(references_batch) grouped_predictions = self.group_elements(predictions_batch) 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 result = {} num_of_unknown_class_predictions = 0 pn_total = pd_total = rn_total = rd_total = 0 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 classes: result[f"f1_{group}"] = self.f1(pn, pd, rn, rd) else: num_of_unknown_class_predictions += pd try: result["f1_macro"] = sum(result.values()) / len(result.keys()) except ZeroDivisionError: result["f1_macro"] = 1.0 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[f"f1_micro"] = self.f1(pn_total, pd_total, rn_total, rd_total) return result class NER(CustomF1): def get_element_group(self, element): return element[1] def get_element_representation(self, element): return str(element)