library.phases.phases_implementation.dataset.split.strategies.noTimeSeries module

class library.phases.phases_implementation.dataset.split.strategies.noTimeSeries.NoTimeSeries(dataset)

Bases: Split

asses_split_classifier(p: float, step: float, upper_bound: float = 0.5, save_plots: bool = False, save_path: str = None) → DataFrame

Assesses the split of the dataframe for classification tasks.

Parameters:

• p (float) – The percentage of the dataframe to split

• step (float) – The step size for the split

• upper_bound (float) – The upper bound for the split

• plot (bool) – If True, the split assessment will be plotted

Returns:

df_split_assesment – A dataframe with the split assessment

Return type:

pd.DataFrame

split_data(y_column: str, otherColumnsToDrop: list[str] = [], train_size: float = 0.8, validation_size: float = 0.1, test_size: float = 0.1, save_plots: bool = False, save_path: str = None) → None

Splits the dataframe into training, validation and test sets

Parameters:

• y_column (str) – The column name of the target variable

• otherColumnsToDrop (list[str]) – The columns to drop from the dataframe (e.g: record identifiers)

• train_size (float) – The size of the training set

• validation_size (float) – The size of the validation set

• test_size (float) – The size of the test set

• plot_distribution (bool) – Whether to plot the distribution of the features

Returns:

• X_train (pd.DataFrame) – The training set

• X_val (pd.DataFrame) – The validation set

• X_test (pd.DataFrame) – The test set

• y_train (pd.Series) – The training set

• y_val (pd.Series) – The validation set

• y_test (pd.Series) – The test set