gaitmap.evaluation_utils.calculate_parameter_errors#

gaitmap.evaluation_utils.calculate_parameter_errors(*, reference_parameter: DataFrame | dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame], predicted_parameter: DataFrame | dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame], id_column: str = 's_id') → tuple[Union[pandas.core.frame.DataFrame, dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame]], Union[pandas.core.frame.DataFrame, dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame]]][source]#

Calculate the error per row between a parameter predicted and a given ground truth.

We calculate four different groups of errors:

  • The error between the predicted and the reference value (predicted - reference)

  • The relative error between the predicted and the reference value ((predicted - reference) / reference)

  • The absolute error between the predicted and the reference value (abs(predicted - reference))

  • The absolute relative error between the predicted and the reference value (abs(predicted - reference) / abs(reference))

The output dataframe also contains the reference and predicted values, so that all information is available in one place.

All errors are calculated by first aligning the reference and predicted values based on the id_column (i.e. we drop all values that are not present in both dataframes). The same happens for the columns (i.e. parameters) of the dataframes. Errors are only calculated for parameters that are present in both dataframes.

If the input is a multi-sensor parameter list (i.e. a dict of dataframes) the alignment and error calculation is done for each sensor and the output has the same structure as the input.

Parameters:
reference_parameter

The reference the predicted values should be compared against. This must be the same type (i.e. single/multi sensor) as the predicted input. Further, sensor names, column names, and unique ids must match with the predicted_parameters.

predicted_parameter

The predicted parameter values. Usually, this is the output of the temporal or spatial parameter calculation. But, you can also pass a custom calculation/aggregation. Make sure you adjust the id_column parameter accordingly. This can be a Dataframe or a dict of such Dataframes.

id_column

The name of the column/index that contains unique entry ids. This will be used to align the predicted and reference parameters. For a normal output of the temporal or spatial parameter calculation, use id_column="s_id" (default) column. If you are using the “pretty” output of these calculations, use id_column="stride id". For custom calculations/aggregations, make sure you have a column/index that contains unique ids.

Returns:
error_df

A Dataframe/Dict of Dataframes with a mult-columns. The first level represents the parameter name, the second level the error type (see above).

common_rows_stats

A Dataframe/Dict of Dataframes representing the statistics of the alignment. I.e. how many datapoints were present in both the reference and the predicted parameters. This can be different per parameter.Parameters