gaitmap.evaluation_utils.precision_score#

gaitmap.evaluation_utils.precision_score(matches_df: dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame], *, zero_division: typing_extensions.Literal[warn, 0, 1] = 'warn') → dict[Union[collections.abc.Hashable, str], float][source]#

gaitmap.evaluation_utils.precision_score(matches_df: DataFrame, *, zero_division: typing_extensions.Literal[warn, 0, 1] = 'warn') → float

Compute the precision.

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 best value is 1 and the worst value is 0.

Parameters:

matches_df: A 3 column dataframe with the column names s_id{stride_list_postfix}, s_id{ground_truth_postfix} and match_type or a dictionary of such dataframes. Each row is a match containing the index value of the left and the corresponding right one. The match_type column 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:

precision_score: This is a float, if the input is just a single dataframe or a dictionary, if the input is a dictionary of dataframes.