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plot_data_splits

method plot_data_splits(title=None, legend="upper right", figsize=(900, 600), filename=None, display=True)[source]

Visualize the data splits.

Plots the train/test/holdout splits. The x-axis shows the number of rows, where every point corresponds to the n-th sample. Additionally, class labels and groups are plotted when relevant.

Parameters title: str, dict or None, default=None
Title for the plot.

legend: str, dict or None, default="upper right"
Legend for the plot. See the user guide for an extended description of the choices.

  • If None: No legend is shown.
  • If str: Position to display the legend.
  • If dict: Legend configuration.

figsize: tuple, default=(900, 600)
Figure's size in pixels, format as (x, y).

filename: str, Path or None, default=None
Save the plot using this name. Use "auto" for automatic naming. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as html. If None, the plot is not saved.

display: bool or None, default=True
Whether to render the plot. If None, it returns the figure.

Returns{#plot_data_splits-go.Figure or None} go.Figure or None
Plot object. Only returned if display=None.


See Also

plot_cv_splits

Visualize the cross-validation splits.

plot_decomposition

Plot the trend, seasonality and residuals of a time series.

plot_relationships

Plot pairwise relationships in a dataset.


Example

>>> from atom import ATOMClassifier, ATOMForecaster
>>> from random import choices
>>> from sklearn.datasets import load_breast_cancer
>>> from sktime.datasets import load_airline

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> groups = choices(["A", "B", "C", "D"], k=X.shape[0])

>>> atom = ATOMClassifier(
...     X,
...     y=y,
...     metadata={"groups": groups},
...     n_rows=0.2,
...     holdout_size=0.1,
...     random_state=1,
... )
>>> atom.run("LR")
>>> atom.plot_data_splits()