| from typing import Dict, Iterable, List |
|
|
| import evaluate |
| from datasets import Features, Value |
|
|
| from .artifact import __file__ as _ |
| from .blocks import __file__ as _ |
| from .card import __file__ as _ |
| from .catalog import __file__ as _ |
| from .collections import __file__ as _ |
| from .dataclass import __file__ as _ |
| from .dict_utils import __file__ as _ |
| from .file_utils import __file__ as _ |
| from .formats import __file__ as _ |
| from .fusion import __file__ as _ |
| from .generator_utils import __file__ as _ |
| from .hf_utils import __file__ as _ |
| from .instructions import __file__ as _ |
| from .load import __file__ as _ |
| from .loaders import __file__ as _ |
| from .logging_utils import __file__ as _ |
| from .metrics import __file__ as _ |
| from .normalizers import __file__ as _ |
| from .operator import (MultiStreamOperator, SequentialOperator, |
| SequentialOperatorInitilizer, StreamInitializerOperator) |
| from .operator import __file__ as _ |
| from .operators import (Apply, ApplyMetric, ApplyOperatorsField, |
| FlattenInstances, MergeStreams, SplitByValue) |
| from .operators import __file__ as _ |
| from .processors import __file__ as _ |
| from .random_utils import __file__ as _ |
| from .recipe import __file__ as _ |
| from .register import __file__ as _ |
| from .register import _reset_env_local_catalogs, register_all_artifacts |
| from .schema import UNITXT_DATASET_SCHEMA |
| from .schema import __file__ as _ |
| from .split_utils import __file__ as _ |
| from .splitters import __file__ as _ |
| from .standard import __file__ as _ |
| from .stream import MultiStream, Stream |
| from .stream import __file__ as _ |
| from .task import __file__ as _ |
| from .templates import __file__ as _ |
| from .text_utils import __file__ as _ |
| from .type_utils import __file__ as _ |
| from .utils import __file__ as _ |
| from .validate import __file__ as _ |
| from .version import __file__ as _ |
|
|
|
|
| class MultiStreamScoreMean(MultiStreamOperator): |
| def aggegate_results(self, multi_stream: MultiStream): |
| scores = [] |
| for stream in multi_stream.values(): |
| instance = stream.peek() |
| scores.append(instance["score"]["global"]["score"]) |
|
|
| from statistics import mean |
|
|
| return mean(scores) |
|
|
| def spread_results(self, stream: Stream, score: float): |
| for instance in stream: |
| instance["score"]["global"]["groups_mean_score"] = score |
| yield instance |
|
|
| def spread_results_one_stream(self, stream: Stream): |
| for instance in stream: |
| instance["score"]["global"]["groups_mean_score"] = instance["score"][ |
| "global" |
| ]["score"] |
| yield instance |
|
|
| def process(self, multi_stream: MultiStream) -> MultiStream: |
| result = {} |
|
|
| |
| |
| if len(multi_stream) == 1: |
| for stream_name, stream in multi_stream.items(): |
| result[stream_name] = Stream( |
| self.spread_results_one_stream, gen_kwargs={"stream": stream} |
| ) |
| return MultiStream(result) |
|
|
| mean_score = self.aggegate_results(multi_stream) |
| result = {} |
| for stream_name, stream in multi_stream.items(): |
| result[stream_name] = Stream( |
| self.spread_results, gen_kwargs={"stream": stream, "score": mean_score} |
| ) |
|
|
| return MultiStream(result) |
|
|
|
|
| class FromPredictionsAndOriginalData(StreamInitializerOperator): |
| def zip(self, predictions, references): |
| for prediction, original in zip(predictions, references): |
| yield {**original, "prediction": prediction} |
|
|
| def process( |
| self, predictions: List[str], references: Iterable, split_name: str = "all" |
| ) -> MultiStream: |
| return MultiStream( |
| { |
| split_name: Stream( |
| self.zip, |
| gen_kwargs={"predictions": predictions, "references": references}, |
| ) |
| } |
| ) |
|
|
|
|
| |
| |
| |
| |
|
|
|
|
| def _from_key_value_pairs(key_value_list: Dict[str, list]) -> Dict[str, str]: |
| return dict(zip(key_value_list["key"], key_value_list["value"])) |
|
|
|
|
| class MetricRecipe(SequentialOperatorInitilizer): |
| calc_confidence_intervals: bool = True |
|
|
| def prepare(self): |
| register_all_artifacts() |
| self.steps = [ |
| FromPredictionsAndOriginalData(), |
| Apply( |
| "additional_inputs", |
| function=_from_key_value_pairs, |
| to_field="additional_inputs", |
| ), |
| ApplyOperatorsField( |
| operators_field="postprocessors", |
| ), |
| SplitByValue(["group"]), |
| ApplyMetric( |
| "metrics", |
| calc_confidence_intervals=self.calc_confidence_intervals, |
| ), |
| MultiStreamScoreMean(), |
| MergeStreams(), |
| ] |
|
|
|
|
| UNITXT_METRIC_SCHEMA = Features( |
| {"predictions": Value("string"), "references": dict(UNITXT_DATASET_SCHEMA)} |
| ) |
|
|
|
|
| def _compute( |
| predictions: List[str], |
| references: Iterable, |
| flatten: bool = False, |
| split_name: str = "all", |
| calc_confidence_intervals: bool = True, |
| ): |
| _reset_env_local_catalogs() |
| register_all_artifacts() |
| recipe = MetricRecipe(calc_confidence_intervals=calc_confidence_intervals) |
|
|
| multi_stream = recipe( |
| predictions=predictions, references=references, split_name=split_name |
| ) |
|
|
| if flatten: |
| operator = FlattenInstances() |
| multi_stream = operator(multi_stream) |
|
|
| stream = multi_stream[split_name] |
| return list(stream) |
|
|
|
|
| |
| |
| class Metric(evaluate.Metric): |
| calc_confidence_intervals: bool = True |
|
|
| def _info(self): |
| return evaluate.MetricInfo( |
| description="_DESCRIPTION", |
| citation="_CITATION", |
| |
| features=UNITXT_METRIC_SCHEMA, |
| codebase_urls=["https://"], |
| reference_urls=[ |
| "https://", |
| "https://", |
| ], |
| ) |
|
|
| def _compute( |
| self, |
| predictions: List[str], |
| references: Iterable, |
| flatten: bool = False, |
| split_name: str = "all", |
| ): |
| try: |
| from unitxt.dataset import \ |
| get_dataset_artifact as get_dataset_artifact_installed |
|
|
| unitxt_installed = True |
| except ImportError: |
| unitxt_installed = False |
|
|
| if unitxt_installed: |
| from unitxt.metric import _compute as _compute_installed |
|
|
| return _compute_installed( |
| predictions=predictions, |
| references=references, |
| flatten=flatten, |
| split_name=split_name, |
| calc_confidence_intervals=self.calc_confidence_intervals, |
| ) |
|
|
| return _compute( |
| predictions=predictions, |
| references=references, |
| flatten=flatten, |
| split_name=split_name, |
| calc_confidence_intervals=self.calc_confidence_intervals, |
| ) |
|
|