import re import string import uuid from abc import ABC, abstractmethod from collections import Counter 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 .artifact import Artifact from .dataclass import InternalField, OptionalField from .logging_utils import get_logger from .operator import ( MultiStreamOperator, SingleStreamOperator, StreamingOperator, StreamInstanceOperator, ) from .operators import CopyFields from .random_utils import get_seed from .stream import MultiStream, Stream from .type_utils import isoftype logger = get_logger() # The default number of resamples used to estimate the confidence intervals # global and instances metrics. Use None to disable confidence interval computation by default. _N_RESAMPLES_DEFAULT_FOR_INSTANCE_METRICS = 1000 _N_RESAMPLES_DEFAULT_FOR_GLOBAL_METRICS = 100 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: 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 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): self.n_resamples = None def _can_compute_confidence_intervals(self, num_predictions): return ( self.n_resamples is not None and self.n_resamples > 1 and num_predictions > 1 ) def score_based_confidence_interval(self, instances): """Compute confidence intervals based on existing scores, already computed on the input instances. score_names: List[str] Compute a confidence interval for each score_name from this list. instances: The instances for which the confidence intervals are computed. """ from statistics import mean result = {} if not self._can_compute_confidence_intervals(num_predictions=len(instances)): return result score_names = ( self.ci_scores if self.ci_scores is not None else [self.main_score] ) for score_name in score_names: scores = [ instance["score"]["instance"][score_name] for instance in instances ] ci = bootstrap( (scores,), statistic=mean, n_resamples=self.n_resamples, confidence_level=self.confidence_level, random_state=self.new_random_generator(), ).confidence_interval result[f"{score_name}_ci_low"] = ci.low result[f"{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 compute_global_confidence_intervals( self, references, predictions, additional_inputs, 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_additional_inputs): try: return self._compute( references=sample_refs, predictions=sample_preds, additional_inputs=sample_additional_inputs, )["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 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_additional_inputs=[additional_inputs[i] for i in x], ), axis=axis, arr=arr, ) # when running with bca interval (default), the statistic is called twice: with the # original data and with the resamples. here we want to focus only on the latter. if scores.size > 1: # 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. The question is how to implement this policy. # Options: # 1. skip the errors and return a shorter array => this fails because Scipy demans # 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. error_indices = numpy.isnan(scores) n_errors = sum(error_indices) if n_errors > 0: new_scores = random_gen.choice(scores, n_errors, replace=True) scores = scores[~error_indices] scores = np.concatenate([scores, new_scores]) return 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 = _N_RESAMPLES_DEFAULT_FOR_GLOBAL_METRICS def process(self, stream: Stream, stream_name: Optional[str] = None) -> Generator: references = [] predictions = [] additional_inputs = [] 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_additional_inputs = ( instance["additional_inputs"] if "additional_inputs" in instance else {} ) additional_inputs.append(instance_additional_inputs) try: instance_score = self._compute( [instance_references], [instance_prediction], [instance_additional_inputs], ) except: instance_score = {"score": None, "score_name": self.main_score} if isinstance(self.main_score, str): instance_score[self.main_score] = None instance["score"]["instance"].update(instance_score) result = self._compute(references, predictions, additional_inputs) global_score.update(result) score_name = global_score["score_name"] confidence_interval = self.compute_global_confidence_intervals( references, predictions, additional_inputs, 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], additional_inputs: List[Any], ) -> dict: result = self.compute(references, predictions, additional_inputs) 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], additional_inputs: List[Any], ) -> dict: pass class BulkInstanceMetric(SingleStreamOperator, MetricWithConfidenceInterval): n_resamples = _N_RESAMPLES_DEFAULT_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 ] ), ) additional_inputs = [ instance["additional_inputs"] if "additional_inputs" in instance else {} for instance in stream ] # compute the metric over all refs and preds instance_scores = self.compute( references=references, predictions=predictions, additional_inputs=additional_inputs, ) # 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": from statistics import 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 confidence_interval = self.score_based_confidence_interval( instances=instances ) global_score.update(confidence_interval) for instance in instances: yield instance @abstractmethod def compute( self, references: List[List[Any]], predictions: List[Any], additional_inputs: List[Dict], ) -> List[Dict[str, Any]]: pass class InstanceMetric(SingleStreamOperator, MetricWithConfidenceInterval): n_resamples = _N_RESAMPLES_DEFAULT_FOR_INSTANCE_METRICS implemented_reductions: List[str] = field(default_factory=lambda: ["mean"]) @property @abstractmethod def reduction_map(self) -> dict: pass def process(self, stream: Stream, stream_name: Optional[str] = None) -> Generator: global_score = {} instances = [] for instance in stream: refs, pred = instance["references"], instance["prediction"] additional_inputs = ( instance["additional_inputs"] if "additional_inputs" in instance else {} ) instance_score = self.compute( references=refs, prediction=pred, additional_inputs=additional_inputs ) 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) 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_name in fields: scores = [ instance["score"]["instance"][field_name] for instance in instances ] global_score[field_name] = mean(scores) if field_name == self.main_score: global_score["score"] = global_score[field_name] global_score["score_name"] = self.main_score confidence_interval = self.score_based_confidence_interval( instances=instances ) global_score.update(confidence_interval) for instance in instances: yield instance @abstractmethod def compute( self, references: List[Any], prediction: Any, additional_inputs: 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], additional_inputs: 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" def compute( self, references: List[Any], prediction: Any, additional_inputs: 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" def compute( self, references: List[Any], prediction: Any, additional_inputs: List[Dict] ) -> dict: result = { self.main_score: float( any(str(reference) in 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 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], additional_inputs: List[Dict], ) -> dict: passed_additional_inputs = {} for additional_input_field in self.hf_additional_input_fields: assert ( additional_input_field in additional_inputs[0] ), f"'{additional_input_field}' field required by {__class__.__name__} is not in passed in additional inputs: {additional_inputs[0]}" passed_additional_inputs[additional_input_field] = [ additional_input[additional_input_field] for additional_input in additional_inputs ] for additional_input_field in self.hf_additional_input_fields_pass_one_value: assert ( additional_input_field in additional_inputs[0] ), f"'{additional_input_field}' field required by {__class__.__name__} is not in passed in additional inputs: {additional_inputs[0]}" values = { additional_input[additional_input_field] for additional_input in additional_inputs } 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_additional_inputs[additional_input_field] = next(iter(values)) # add check that all required fields in self.metrics are in passed_additional_inputs print(passed_additional_inputs) result = self.metric.compute( predictions=predictions, references=references, **passed_additional_inputs, **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], additional_inputs: List[Any], ) -> List[Dict[str, Any]]: passed_additional_inputs = {} for additional_input_field in self.hf_additional_input_fields: assert ( additional_input_field in additional_inputs[0] ), f"'{additional_input_field}' field required by {__class__.__name__} is not in passed in additional inputs: {additional_inputs[0]}" passed_additional_inputs[additional_input_field] = [ additional_input[additional_input_field] for additional_input in additional_inputs ] # add check that all required fields in self.metrics are in passed_additional_inputs scores = self.metric.compute( predictions=predictions, references=references, **passed_additional_inputs, **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], additional_inputs: 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): 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 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 classes_to_ignore = ["none"] 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]], additional_inputs: 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 = [ lbl for lbl in {label for reference in references for label in reference} if lbl not in self.classes_to_ignore ] # if no classes are left then F1 is not defined # (e.g. only "none" in references) 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): from statistics import mean 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 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, additional_inputs: 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, additional_inputs) # Computes char edit distance, ignoring whitespace class CharEditDistanceAccuracy(InstanceMetric): reduction_map = {"mean": ["char_edit_dist_accuracy"]} main_score = "char_edit_dist_accuracy" def prepare(self): super().prepare() import editdistance self.eval = editdistance.eval def compute( self, references, prediction: str, additional_inputs: 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" def compute( self, references: List[List[str]], predictions: List[str], additional_inputs: 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 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], additional_inputs: 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 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, additional_inputs): groups = set() for sublist, additional_input in zip(elements, additional_inputs): 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], additional_inputs: 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, additional_inputs) else: groups = self.groups groups_statistics = {} for references_batch, predictions_batch, additional_input in zip( references, predictions, additional_inputs ): 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, additional_inputs: List[Dict] ) -> dict: results = [ self._compute_single_ref(reference, 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(prediction).split() reference_tokens = normalize_answer(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 def prepare(self): super().prepare() self.hf_compute_args = {"model_type": self.model_name} class SentenceBert(BulkInstanceMetric): reduction_map = {"mean": ["score"]} main_score = "score" batch_size: int = 32 model_name: str def prepare(self): super().prepare() from sentence_transformers import SentenceTransformer from sentence_transformers import util as sbert_util self.model = SentenceTransformer(self.model_name) self.util = sbert_util def compute( self, references: List[List[Any]], predictions: List[Any], additional_inputs: 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) refs_emb = self.model.encode( [ref for ref_group in references for ref in ref_group] ) # 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 def prepare(self): super().prepare() from transformers import pipeline self.pipe = pipeline("text-classification", model=self.model_name) def compute( self, references: List[List[Any]], predictions: List[Any], additional_inputs: 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 def compute( self, references: List[List[Any]], predictions: List[Any], additional_inputs: List[Dict], ) -> List[Dict[str, Any]]: """Computes the likelihood of generating text Y after text X - P(Y|X). :param references: the list of Y texts as a list of singletons. :param predictions: the list of X texts as a plain list of strings :return: the likelihood of generating text Y_i after text X_i = P(Y_i|X_i) 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} {prediction}") targets.append(instance_reference) 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 instance_scores[self.main_score] = mean(instance_scores_list) instance_scores[self.main_score] = mean(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.tokenizer = AutoTokenizer.from_pretrained(self.model_name) self.model = self.model_class().from_pretrained(self.model_name) self.is_cuda = torch.cuda.is_available() 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 ) # the model returns mean over all batch. We run the CE again without reduction # and extarct the mean for each document loss_fct = torch.nn.CrossEntropyLoss( ignore_index=-100, reduction="none" ) 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 ) # append the batch scores to the list of all scores scores.append(batch_loss) 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"] attention = tokens_source["attention_mask"] labels = tokens_target["input_ids"] if self.is_cuda: tokens_docs_ids, attention, labels = ( tokens_docs_ids.cuda(), attention.cuda(), labels.cuda(), ) 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 if self.is_cuda: tokens, attention, labels = ( tokens.cuda(), attention.cuda(), labels.cuda(), ) # 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" 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], additional_inputs: List[Any], ) -> dict: from collections import defaultdict from statistics import mean query_to_predictions_and_references = defaultdict(lambda: [[], []]) for reference, pred, inputs_dict in zip( references, predictions, additional_inputs ): 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, additional_inputs: 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" 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" 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"