fix bug

dicrect_compute_metric.py

@@ -3,7 +3,7 @@ from typing import Optional
 import evaluate
 
 
-class DirectComputeMetric(evaluate.Metric):
+class DirectComputeMetric(evaluate.EvaluationModule):
     """
     Base class for metrics that directly compute the score from the predictions and references without add_batch
     """

matching_series.py

@@ -20,8 +20,6 @@ import datasets
 import evaluate
 import numpy as np
 
-from dicrect_compute_metric import DirectComputeMetric
-
 # TODO: Add BibTeX citation
 _CITATION = """\
 @InProceedings{huggingface:module,
@@ -58,7 +56,7 @@ Examples:
 
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class matching_series(DirectComputeMetric):
+class matching_series(evaluate.Metric):
     """TODO: Short description of my evaluation module."""
 
     def _info(self):
@@ -87,6 +85,46 @@ class matching_series(DirectComputeMetric):
         """Optional: download external resources useful to compute the scores"""
         pass
 
+    def compute(self, *, predictions=None, references=None, **kwargs) -> Optional[dict]:
+        """Compute the evaluation module.
+
+        Usage of positional arguments is not allowed to prevent mistakes.
+
+        Args:
+            predictions (`list/array/tensor`, *optional*):
+                Predictions.
+            references (`list/array/tensor`, *optional*):
+                References.
+            **kwargs (optional):
+                Keyword arguments that will be forwarded to the evaluation module [`~evaluate.EvaluationModule.compute`]
+                method (see details in the docstring).
+
+        Return:
+            `dict` or `None`
+
+            - Dictionary with the results if this evaluation module is run on the main process (`process_id == 0`).
+            - `None` if the evaluation module is not run on the main process (`process_id != 0`).
+
+        ```py
+        >>> import evaluate
+        >>> accuracy =  evaluate.load("accuracy")
+        >>> accuracy.compute(predictions=[0, 1, 1, 0], references=[0, 1, 0, 1])
+        ```
+        """
+        all_kwargs = {"predictions": predictions, "references": references, **kwargs}
+        if predictions is None and references is None:
+            missing_kwargs = {k: None for k in self._feature_names() if k not in all_kwargs}
+            all_kwargs.update(missing_kwargs)
+        else:
+            missing_inputs = [k for k in self._feature_names() if k not in all_kwargs]
+            if missing_inputs:
+                raise ValueError(
+                    f"Evaluation module inputs are missing: {missing_inputs}. All required inputs are {list(self._feature_names())}"
+                )
+        inputs = {input_name: all_kwargs[input_name] for input_name in self._feature_names()}
+        compute_kwargs = {k: kwargs[k] for k in kwargs if k not in self._feature_names()}
+        return self._compute(**inputs, **compute_kwargs)
+
     def _compute(
         self,
         predictions: Union[list, np.ndarray],