Loading imgutils/generic/attachment.py +0 −27 Original line number Diff line number Diff line Loading @@ -182,31 +182,6 @@ class Attachment: return retval def _predict_regression(self, embedding: np.ndarray, fmt: Any = 'full'): """ Make regression predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ field_names = [name for name, _, _ in self._meta['problem']['fields']] logits, prediction = self._predict_raw(embedding) retval = [] for logit, pred in zip(logits, prediction): result = dict(zip(field_names, pred.tolist())) retval.append(vreplace(fmt, { 'full': result, 'logit': logit, 'prediction': pred, **{f'field/{key}': value for key, value in result.items()}, })) return retval def predict(self, embedding: np.ndarray, **kwargs): """ Make predictions based on the problem type (classification, tagging, or regression). Loading @@ -231,8 +206,6 @@ class Attachment: result = self._predict_classification(embedding, **kwargs) elif problem_type == 'tagging': result = self._predict_tagging(embedding, **kwargs) elif problem_type == 'regression': result = self._predict_regression(embedding, **kwargs) else: raise ValueError(f'Unknown problem type - {problem_type!r}.') Loading Loading
imgutils/generic/attachment.py +0 −27 Original line number Diff line number Diff line Loading @@ -182,31 +182,6 @@ class Attachment: return retval def _predict_regression(self, embedding: np.ndarray, fmt: Any = 'full'): """ Make regression predictions. :param embedding: Input embedding array :type embedding: np.ndarray :param fmt: Format specification for output :type fmt: Any :return: List of formatted prediction results :rtype: list """ field_names = [name for name, _, _ in self._meta['problem']['fields']] logits, prediction = self._predict_raw(embedding) retval = [] for logit, pred in zip(logits, prediction): result = dict(zip(field_names, pred.tolist())) retval.append(vreplace(fmt, { 'full': result, 'logit': logit, 'prediction': pred, **{f'field/{key}': value for key, value in result.items()}, })) return retval def predict(self, embedding: np.ndarray, **kwargs): """ Make predictions based on the problem type (classification, tagging, or regression). Loading @@ -231,8 +206,6 @@ class Attachment: result = self._predict_classification(embedding, **kwargs) elif problem_type == 'tagging': result = self._predict_tagging(embedding, **kwargs) elif problem_type == 'regression': result = self._predict_regression(embedding, **kwargs) else: raise ValueError(f'Unknown problem type - {problem_type!r}.') Loading