update ACC.py

ACC.py

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+
+
+import datasets
+import evaluate
+
+
+_DESCRIPTION = """
+The Accuracy (ACC) metric is used to measure the proportion of correctly predicted sequences compared to the total number of sequences.
+This metric can handle both integer and string inputs by converting them to strings for comparison.
+The ACC ranges from 0 to 1, where 1 indicates perfect accuracy (all predictions are correct) and 0 indicates complete failure (no predictions are correct).
+It is particularly useful in tasks such as OCR, digit recognition, sequence prediction, and any task where exact matches are required.
+The accuracy can be calculated using the formula:
+
+ACC = (Number of Correct Predictions) / (Total Number of Predictions)
+
+Where a prediction is considered correct if it exactly matches the ground truth sequence after converting both to strings.
+"""
+
+_KWARGS_DESCRIPTION = """
+Args:
+    predictions (`list` of `str` or `int`): Predicted labels (can be strings or integers).
+    references (`list` of `str` or `int`): Ground truth labels (can be strings or integers).
+Returns:
+    acc (`float`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0.
+Examples:
+    Example 1 - String inputs:
+        >>> acc_metric = evaluate.load("Bekhouche/ACC")
+        >>> results = acc_metric.compute(references=['123', '456', '789'], predictions=['123', '456', '789'])
+        >>> print(results)
+        {'acc': 1.0}
+    
+    Example 2 - Integer inputs:
+        >>> results = acc_metric.compute(references=[123, 456, 789], predictions=[123, 456, 789])
+        >>> print(results)
+        {'acc': 1.0}
+    
+    Example 3 - Mixed inputs:
+        >>> results = acc_metric.compute(references=['123', 456, '789'], predictions=[123, '456', 789])
+        >>> print(results)
+        {'acc': 1.0}
+"""
+
+_CITATION = """
+@inproceedings{accuracy_metric,
+  title={Accuracy as a fundamental metric for sequence prediction tasks},
+  author={Various Authors},
+  booktitle={Proceedings of various conferences},
+  pages={1--10},
+  year={2023},
+  organization={Various}
+}
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class ACC(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Sequence(datasets.Value("string")),
+                    "references": datasets.Sequence(datasets.Value("string")),
+                }
+                if self.config_name == "multilabel"
+                else {
+                    "predictions": datasets.Value("string"),
+                    "references": datasets.Value("string"),
+                }
+            ),
+            reference_urls=["https://huggingface.co/spaces/Bekhouche/ACC"],
+        )
+
+    def _compute(self, predictions, references):
+        acc = 0.0
+        if isinstance(predictions, list) and isinstance(references, list):
+            correct_predictions = sum([self._compute_acc(prediction, reference) for prediction, reference in zip(predictions, references)])
+            acc = correct_predictions / len(predictions)
+        elif (isinstance(predictions, (str, int)) and isinstance(references, (str, int))):
+            acc = self._compute_acc(predictions, references)
+        else:
+            raise ValueError("Predictions and references must be either a list[str/int] or str/int")
+        return {
+            "acc": float(
+                acc
+            )
+        }
+    
+    @staticmethod
+    def _compute_acc(prediction, reference):
+        # Convert both prediction and reference to strings for comparison
+        pred_str = str(prediction)
+        ref_str = str(reference)
+        return 1.0 if pred_str == ref_str else 0.0