Labeler Utils¶
- dataprofiler.labelers.labeler_utils.f1_report_dict_to_str(f1_report, label_names)¶
Returns the report string from the f1_report dict.
- Example Output:
precision recall f1-score support
class 0 0.00 0.00 0.00 1 class 1 1.00 0.67 0.80 3
micro avg 0.67 0.50 0.57 4 macro avg 0.50 0.33 0.40 4
weighted avg 0.75 0.50 0.60 4
Note: this is generally taken from the classification_report function inside sklearn. :param f1_report: f1 report dictionary from sklearn :type f1_report: dict :param label_names: names of labels included in the report :type label_names: list(str) :return: string representing f1_report printout :rtype: str
- dataprofiler.labelers.labeler_utils.evaluate_accuracy(predicted_entities_in_index, true_entities_in_index, num_labels, entity_rev_dict, verbose=True, omitted_labels=('PAD', 'UNKNOWN'), confusion_matrix_file=None)¶
Evaluate the accuracy from comparing the predicted labels with true labels
- Parameters
predicted_entities_in_index (list(array(int))) – predicted encoded labels for input sentences
true_entities_in_index (list(array(int))) – true encoded labels for input sentences
entity_rev_dict (dict([index, entity])) – dictionary to convert indices to entities
verbose (boolean) – print additional information for debugging
omitted_labels (list() of text labels) – labels to omit from the accuracy evaluation
confusion_matrix_file (str) – File name (and dir) for confusion matrix
:return : f1-score :rtype: float