pyplt.gui.experiment.results package

Submodules

pyplt.gui.experiment.results.resultsscreen module

class pyplt.gui.experiment.results.resultsscreen.ResultsWindow(parent, parent_window, experiment, time_info, data_info, preproc_info, pl_algo_info, eval_metrics, shuffle_info, fs_info=None, fs_algo_info=None, fs_eval_info=None, pl_eval_info=None, fold_metrics=None)

Bases: tkinter.Toplevel

GUI window displaying the results of an experiment.

The window widget extends the class tkinter.Toplevel.

Initializes the window widget with all of the information about and results obtained from the given experiment.

Parameters:
  • parent (tkinter widget) – the parent widget of this window widget.
  • parent_window (tkinter.Toplevel) – the window which this window widget will be stacked on top of.
  • experiment (pyplt.experiment.Experiment) – the given experiment.
  • time_info (list of float (size 3)) – a list containing meta-data about the experiment related to time (the start timestamp (UTC), the end timestamp (UTC), and the duration).
  • data_info (list (size 3)) – a list containing the number of objects, the number of ranks, and the list of data file paths.
  • preproc_info (list of dict (size 2)) – a list containing the include settings dict and the normalization settings dict.
  • pl_algo_info (list (size 2)) – a list containing the algorithm type (pyplt.util.enums.PLAlgo) and the string representation of its parameters.
  • eval_metrics (dict) –

    the evaluation/training results in the form of a dict with keys:

    • ’Training Accuracy’
    • ’Test Accuracy’ (if applicable)
  • shuffle_info (list (size 2)) –

    list containing the chosen settings related to shuffling the dataset:

    • shuffle – bool specifying whether or not the dataset was shuffled at the start of the experiment execution.
    • random_seed – optional seed (int or None) used to shuffle the dataset.
  • fs_info (list (size 3) or None, optional) – a list containing the chosen feature selection method type (pyplt.util.enums.FSMethod), the string representation of its parameters, and the list of features selected by the feature selection method.
  • fs_algo_info (list (size 2) or None, optional) – a list containing the chosen algorithm type (pyplt.util.enums.PLAlgo) for the feature selection stage and the string representation of its parameters.
  • fs_eval_info (list (size 2) or None, optional) – a list containing the evaluation method type (pyplt.util.enums.EvaluatorType) for the feature selection stage and the string representation of its parameters.
  • pl_eval_info (list (size 2) or None, optional) – a list containing the evaluation method type (pyplt.util.enums.EvaluatorType) and the string representation of its parameters.
  • fold_metrics (list of tuple, optional) –

    optional fold-specific information (default None) in the form of list of tuples, each containing the start timestamp, end timestamp, evaluation metrics, and a pandas.DataFrame representation of the trained model as follows:

    • start_time – datetime timestamp (UTC timezone)
    • end_time – datetime timestamp (UTC timezone)
    • eval_metrics – dict with keys:
      • ’Training Accuracy’
      • ’Test Accuracy’ (if applicable)
    • model – pandas.DataFrame

Module contents

This package contains GUI-based modules that manage the display of results of an experiment.