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metric.py

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+#####
+# imports for hf system:
+#####
+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 .common import __file__ as _
+from .file_utils import __file__ as _
+
+# from .fusion import __file__
+from .generator_utils import __file__ as _
+from .instructions import __file__ as _
+from .loaders import __file__ as _
+from .load import __file__ as _
+from .metrics import __file__ as _
+from .normalizers import __file__ as _
+from .operator import __file__ as _
+from .operators import __file__ as _
+from .processors import __file__ as _
+from .recipe import __file__ as _
+from .register import __file__ as _
+from .splitters import __file__ as _
+from .split_utils import __file__ as _
+from .stream import __file__ as _
+from .task import __file__ as _
+from .templates import __file__ as _
+from .text_utils import __file__ as _
+from .schema import __file__ as _
+
+# from .utilize import __file__ as _
+# from .validate import __file__ as _
+#############
+
+from .stream import MultiStream, Stream
+
+from .operator import SequntialOperator, SequntialOperatorInitilizer, MultiStreamOperator, StreamInitializerOperator
+
+from .operators import (
+    ApplyValueOperatorsField,
+    ApplyStreamOperatorsField,
+    SplitByValue,
+    MergeStreams,
+    FlattenInstances,
+)
+
+import evaluate
+import datasets
+
+from datasets import (
+    Features,
+    Value,
+    Sequence,
+)
+
+from dataclasses import field
+from typing import List, Union, Dict, Optional, Generator, Any, Iterable
+
+
+class MultiStreamScoreMean(MultiStreamOperator):
+    def aggegate_results(self, multi_stream: MultiStream):
+        scores = []
+        for stream in multi_stream.values():
+            instance = stream.peak()
+            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 process(self, multi_stream: MultiStream) -> MultiStream:
+        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})}
+        )
+
+
+from .schema import UNITXT_DATASET_SCHEMA
+
+
+class MetricRecipe(SequntialOperatorInitilizer):
+    def prepare(self):
+        self.steps = [
+            FromPredictionsAndOriginalData(),
+            ApplyValueOperatorsField(
+                value_field="prediction", operators_field="processors", default_operators=["to_string"]
+            ),
+            SplitByValue(["group"]),
+            ApplyStreamOperatorsField(
+                "metrics",
+                reversed=True,
+            ),
+            MultiStreamScoreMean(),
+            MergeStreams(),
+        ]
+
+
+UNITXT_METRIC_SCHEMA = Features({"predictions": Value("string"), "references": dict(UNITXT_DATASET_SCHEMA)})
+
+
+# @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class UnitextMetric(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description="_DESCRIPTION",
+            citation="_CITATION",
+            # inputs_description=_KWARGS_DESCRIPTION,
+            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"):
+        recipe = MetricRecipe()
+
+        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)