ml_grid.pipeline.data_plot_split

Functions

plot_pie_chart_with_counts(→ None)	Plots a pie chart showing the relative sizes of datasets.
plot_dict_values(→ None)	Creates a horizontal bar chart representing boolean values in a dictionary.
create_bar_chart(→ None)	Creates a horizontal bar chart from a dictionary of data.
plot_candidate_feature_category_lists(→ None)	Plots a bar chart for candidate feature category counts.

Module Contents

ml_grid.pipeline.data_plot_split.plot_pie_chart_with_counts(X_train: Sized, X_test: Sized, X_test_orig: Sized) → None

Plots a pie chart showing the relative sizes of datasets.

Parameters:

• X_train (Sized) – The training dataset.

• X_test (Sized) – The test dataset.

• X_test_orig (Sized) – The original, unsplit test dataset.

ml_grid.pipeline.data_plot_split.plot_dict_values(data_dict: Dict[str, bool]) → None

Creates a horizontal bar chart representing boolean values in a dictionary.

Parameters:

data_dict (Dict[str, bool]) – A dictionary with string keys and boolean values.

ml_grid.pipeline.data_plot_split.create_bar_chart(data_dict: Dict[str, int | float], title: str = '', x_label: str = '', y_label: str = '') → None

Creates a horizontal bar chart from a dictionary of data.

Parameters:

• data_dict (Dict[str, Union[int, float]]) – Dictionary with category names as keys and their corresponding values.

• title (str, optional) – The title of the chart. Defaults to “”.

• x_label (str, optional) – The label for the x-axis. Defaults to “”.

• y_label (str, optional) – The label for the y-axis. Defaults to “”.

ml_grid.pipeline.data_plot_split.plot_candidate_feature_category_lists(data: Dict[str, int]) → None

Plots a bar chart for candidate feature category counts.

Parameters:

data (Dict[str, int]) – A dictionary where keys are feature category names and values are the counts of features in that category.