Skip to content

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, segment, 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.

legend: str, dict or None, default=None
Do 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, 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 png. 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_pipeline-plt.Figure or None} plt.Figure or None
Plot object. Only returned if display=None.


See Also

plot_wordcloud

Plot a wordcloud from the corpus.


Example

>>> 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, random_state=1)
>>> atom.run(["GNB", "RNN", "SGD", "MLP"])
>>> atom.voting(models=atom.winners[:2])
>>> atom.plot_pipeline()
atom.plot_pipeline
>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.scale()
>>> atom.prune()
>>> atom.run("RF", n_trials=30)

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

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

>>> atom.plot_pipeline()
atom.plot_pipeline