heatmap_plot

method heatmap_plot(models=None, index=None, show=None, target=1, title=None, figsize=(8, 6), filename=None, display=True, **kwargs) [source]

Plot SHAP's heatmap plot. This plot is designed to show the population substructure of a dataset using supervised clustering and a heatmap. Supervised clustering involves clustering data points not by their original feature values but by their explanations. Read more about SHAP plots in the user guide.

Parameters:

models: str, sequence or None, optional (default=None)
Name of the model to plot. If None, all models in the pipeline are selected. Note that leaving the default option could raise an exception if there are multiple models in the pipeline. To avoid this, call the plot from a model, e.g. atom.xgb.heatmap_plot().

index: tuple, slice or None, optional (default=None)
Indices of the rows in the dataset to plot. If tuple (n, m), it selects rows n until m. If None, it selects all rows in the test set. The heatmap plot does not support plotting a single sample.

show: int or None, optional (default=None)
Number of features (ordered by importance) to show. None to show all.

target: int or str, optional (default=1)
Index or name of the class in the target column to look at. Only for multi-class classification tasks.

title: str or None, optional (default=None)
Plot's title. If None, the title is left empty.

figsize: tuple, optional (default=(8, 6)))
Figure's size, format as (x, y).

filename: str or None, optional (default=None)
Name of the file. Use "auto" for automatic naming. If None, the figure is not saved.

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

**kwargs
Additional keyword arguments for SHAP's heatmap plot.

Returns: fig: matplotlib.figure.Figure
Plot object. Only returned if display=None.

Example

from atom import ATOMRegressor

atom = ATOMRegressor(X, y)
atom.run("RF")
atom.heatmap_plot()