gaitmap.evaluation_utils.precision_recall_f1_score#
- gaitmap.evaluation_utils.precision_recall_f1_score(matches_df: dict[str, pandas.core.frame.DataFrame], *, zero_division: typing_extensions.Literal[warn, 0, 1] = 'warn') dict[str, gaitmap.evaluation_utils.scores._ScoresDict][source]#
- gaitmap.evaluation_utils.precision_recall_f1_score(matches_df: DataFrame, *, zero_division: typing_extensions.Literal[warn, 0, 1] = 'warn') _ScoresDict
Compute precision, recall and F1-score.
The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.
The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.
The F1 score can be interpreted as the harmonic mean of precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
- Parameters:
- matches_df
A 3 column dataframe with the column names
s_id{stride_list_postfix},s_id{ground_truth_postfix}andmatch_typeor a dictionary of such dataframes. Each row is a match containing the index value of the left and the corresponding right one. Thematch_typecolumn indicates the type of match: “tp” (true positive), “fp” (false positives) or “fn” (false negative) if no segmented counterpart exists.- zero_division“warn”, 0 or 1, default=”warn”
Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised.
- Returns:
- score_metrics{“precision”: precision, “recall”: recall, “f1_score”: f1_score} or
- {“s_1”: {“precision”: precision, … }, “s_2”: {“recall”: recall, … }, “s_3”: {“f1_score”: f1_score, … }}
Dictionary of precision scores or a dictionary with the keys as the sensor names and the values as dictionaries with the scores.
See also
gaitmap.evaluation_utils.evaluate_segmented_stride_listGenerate matched_df from stride lists
