metric / metrics.py
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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 .dataclass import InternalField
from .operator import (
MultiStreamOperator,
SingleStreamOperator,
StreamingOperator,
StreamInstanceOperator,
)
from .operators import CopyFields
from .stream import MultiStream, Stream
nltk.download("punkt")
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, 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
), "One single reference per predictition are 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
seperator = ","
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 = {}
labels = list(set([label for reference in references for label in reference]))
for label in labels:
assert (
not self.seperator in label
), "Reference label (f{label}) can not contain multi label seperator (f{self.seperator}) "
self.add_str_to_id(label)
formatted_references = [self.get_one_hot_vector(reference) for reference in references]
split_predictions = [
[label.strip() for label in prediction.split(self.seperator)] for prediction in predictions
]
formatted_predictions = [self.get_one_hot_vector(prediction) for prediction in split_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)
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)