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metric.py

@@ -1,14 +1,15 @@
 from typing import Dict, Iterable, List
 
 import evaluate
-from datasets import Features, Value
 
+from .api import __file__ as _
 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 .dataset_utils import __file__ as _
 from .dict_utils import __file__ as _
 from .file_utils import __file__ as _
 from .formats import __file__ as _
@@ -16,28 +17,23 @@ 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 .metric_utils import UNITXT_METRIC_SCHEMA
+from .metric_utils import __file__ as _
+from .metric_utils import _compute
 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 _
@@ -48,132 +44,6 @@ 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 = {}
-
-        # optimization in to avoid double calculation of metrics
-        # when aggregating results, if there is only one stream.
-        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},
-                )
-            }
-        )
-
-
-# The additional_inputs field in the schema is defined as
-# Sequence({"key": Value(dtype="string"), "value": Value("string")})
-# When receiving instances from this scheme, the keys and values are returned as two separate
-# lists, and are converted to a dictionary.
-
-
-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)
-
-
 # TODO: currently we have two classes with this name. metric.Metric and matrics.Metric...
 # @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class Metric(evaluate.Metric):
@@ -200,16 +70,13 @@ class Metric(evaluate.Metric):
         split_name: str = "all",
     ):
         try:
-            from unitxt.dataset import \
-                get_dataset_artifact as get_dataset_artifact_installed
+            from unitxt.metric_utils import _compute as _compute_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,