plot_pipeline

method plot_pipeline(models=None, draw_hyperparameter_tuning=True, color_branches=None, title=None, legend=None, figsize=None, filename=None, display=True)[source]

Plot a diagram of the pipeline.

Warning

This plot uses the schemdraw package, which is incompatible with plotly. The returned plot is therefore a matplotlib figure.

Parameters

models: int, str, Model, slice, sequence or None, default=None

Models for which to draw the pipeline. If None, all pipelines are plotted.

draw_hyperparameter_tuning: bool, default=True

Whether to draw if the models used Hyperparameter Tuning.

color_branches: bool or None, default=None

Whether to draw every branch in a different color. If None, branches are colored when there is more than one.

title: str, dict or None, default=None

Title for the plot.

If None, no title is shown.
If str, text for the title.
If dict, title configuration.

legend: str, dict or None, default=None

Does nothing. Implemented for continuity of the API.

figsize: tuple or None, default=None

Figure's size in pixels, format as (x, y). If None, it adapts the size to the pipeline drawn.

filename: str 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

plt.Figure or None

Plot object. Only returned if display=None.

Example

>>> from atom import ATOMClassifier

>>> X = pd.read_csv("./examples/datasets/weatherAUS.csv")

>>> atom = ATOMClassifier(X, y="RainTomorrow", n_rows=1e4)
>>> atom.impute()
>>> atom.encode()
>>> atom.scale()
>>> atom.run(["GNB", "RNN", "SGD", "MLP"])
>>> atom.voting(models=atom.winners[:2])
>>> atom.plot_pipeline()

plot_pipeline

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer

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

>>> atom = ATOMClassifier(X, y)
>>> atom.scale()
>>> atom.prune()
>>> atom.run("RF", n_trials=30)

>>> atom.branch = "undersample"
>>> atom.balance("nearmiss")
>>> atom.run("RF_undersample")

>>> atom.branch = "oversample_from_master"
>>> atom.balance("smote")
>>> atom.run("RF_oversample")

>>> atom.plot_pipeline()

plot_pipeline