Method add_batch

src/evaluate/module.py:488–546

Add a batch of predictions and references for the evaluation module's stack. Args: predictions (`list/array/tensor`, *optional*): Predictions. references (`list/array/tensor`, *optional*): References. Example: ```py

(self, *, predictions=None, references=None, **kwargs)

            return None

    def add_batch(self, *, predictions=None, references=None, **kwargs):
        """Add a batch of predictions and references for the evaluation module's stack.

        Args:
            predictions (`list/array/tensor`, *optional*):
                Predictions.
            references (`list/array/tensor`, *optional*):
                References.

        Example:

        ```py
        >>> import evaluate
        >>> accuracy = evaluate.load("accuracy")
        >>> for refs, preds in zip([[0,1],[0,1]], [[1,0],[0,1]]):
        ...     accuracy.add_batch(references=refs, predictions=preds)
        ```
        """
        bad_inputs = [input_name for input_name in kwargs if input_name not in self._feature_names()]
        if bad_inputs:
            raise ValueError(
                f"Bad inputs for evaluation module: {bad_inputs}. All required inputs are {list(self._feature_names())}"
            )
        batch = {"predictions": predictions, "references": references, **kwargs}
        batch = {input_name: batch[input_name] for input_name in self._feature_names()}
        if self.writer is None:
            self.selected_feature_format = self._infer_feature_from_batch(batch)
            self._init_writer()
        try:
            for key, column in batch.items():
                if len(column) > 0:
                    self._enforce_nested_string_type(self.selected_feature_format[key], column[0])
            batch = self.selected_feature_format.encode_batch(batch)
            self.writer.write_batch(batch)
        except (pa.ArrowInvalid, TypeError):
            if any(len(batch[c]) != len(next(iter(batch.values()))) for c in batch):
                col0 = next(iter(batch))
                bad_col = [c for c in batch if len(batch[c]) != len(batch[col0])][0]
                error_msg = (
                    f"Mismatch in the number of {col0} ({len(batch[col0])}) and {bad_col} ({len(batch[bad_col])})"
                )
            elif set(self.selected_feature_format) != {"references", "predictions"}:
                error_msg = (
                    f"Module inputs don't match the expected format.\n"
                    f"Expected format: {self.selected_feature_format },\n"
                )
                error_msg_inputs = ",\n".join(
                    f"Input {input_name}: {summarize_if_long_list(batch[input_name])}"
                    for input_name in self.selected_feature_format
                )
                error_msg += error_msg_inputs
            else:
                error_msg = (
                    f"Predictions and/or references don't match the expected format.\n"
                    f"Expected format: {self.selected_feature_format },\n"
                    f"Input predictions: {summarize_if_long_list(predictions)},\n"
                    f"Input references: {summarize_if_long_list(references)}"
                )