qbiocode.utils.qc_winner_finder module#

Summary#

Functions:

qml_winner

This function finds data sets where QML was beneficial (higher F1 scores than CML) and create new .csv files with the relevant evaluation and performance for these specific datasets, for further analysis.

Reference#

qml_winner(results_df, rawevals_df, output_dir, tag)[source]#

This function finds data sets where QML was beneficial (higher F1 scores than CML) and create new .csv files with the relevant evaluation and performance for these specific datasets, for further analysis. It also computes the best results per method across all splits and the best results per dataset. It returns two DataFrames: one with the datasets where QML methods outperformed CML methods, and another with the evaluation scores for the best QML method for each of these datasets. It also saves these DataFrames as .csv files in the specified output directory.

Parameters:

  • results_df (pandas.DataFrame) – Dataset in pandas corresponding to ‘ModelResults.csv’

  • rawevals_df (pandas.DataFrame) – Dataset in pandas corresponding to ‘RawDataEvaluation.csv’

Returns:

contais the input datasets for which at least one QML method

performed better than CML. DataFrame contains the scores of all the methods.

winner_eval_score (pandas.DataFrame): contains the input datasets, their evaluation, and scores for the

specific qml method that yielded the best score.

Return type:

qml_winners (pandas.DataFrame)