pdpbox.info_plots.InteractTargetPlot

class pdpbox.info_plots.InteractTargetPlot(df, features, feature_names, target, num_grid_points=10, grid_types='percentile', percentile_ranges=None, grid_ranges=None, cust_grid_points=None, show_outliers=False, endpoints=True)

TargetPlot for interaction between two features.

Attributes:

dfpd.DataFrame

A processed DataFrame that includes feature and target (for target plot) or predict (for predict plot) columns, feature buckets, along with the count of samples within each bucket.

feature_infoslist of FeatureInfo

A list of FeatureInfo objects.

feature_colslist of str

List of feature columns.

targetlist of int

List of target indices. For binary and regression problems, the list will be just [0]. For multi-class targets, the list is the class indices.

n_classesint

The number of classes inferred from the target columns.

plot_typestr

The type of the plot to be generated.

plot_enginesdict

A dictionary that maps plot types to their plotting engines.

summary_dfpd.DataFrame

A DataFrame that contains the summary statistics of target (for target plot) or predict (for predict plot) values for each feature bucket.

plot_dfpd.DataFrame

A DataFrame that contains the aggregated target (for target plot) or predict (for predict plot) values for each unique pair of feature values.

Methods

plot(**kwargs)

Generates the plot.

Both pdpbox.info_plots.InteractTargetPlot and pdpbox.info_plots.InteractPredictPlot inherit from pdpbox.info_plots._InteractInfoPlot class and share the same plot method.

class pdpbox.info_plots._InteractInfoPlot(df, features, feature_names, target=None, model=None, model_features=None, pred_func=None, n_classes=None, predict_kwds=None, chunk_size=-1, plot_type='interact_target', **kwargs)

Methods

plot([which_classes, show_percentile, ...])

Generate the interaction plot.

plot(which_classes=None, show_percentile=False, figsize=None, dpi=300, ncols=2, annotate=False, plot_params=None, engine='plotly', template='plotly_white')

Generate the interaction plot.

Parameters:

which_classeslist of int, optional

List of class indices to plot. If None, all classes will be plotted. Default is None.

show_percentilebool, optional

If True, percentiles are shown in the plot. Default is False.

figsizetuple or None, optional

The figure size for matplotlib or plotly figure. If None, the default figure size is used. Default is None.

dpiint, optional

The resolution of the plot, measured in dots per inch. Only applicable when engine is ‘matplotlib’. Default is 300.

ncolsint, optional

The number of columns of subplots in the figure. Default is 2.

annotatebool, optional

If it is True, the circles on the plot will be annotated with detailed information. Default is False.

plot_paramsdict or None, optional

Custom plot parameters that control the style and aesthetics of the plot. Default is None.

engine{‘matplotlib’, ‘plotly’}, optional

The plotting engine to use. Default is plotly.

templatestr, optional

The template to use for plotly plots. Only applicable when engine is ‘plotly’. Reference: https://plotly.com/python/templates/ Default is plotly_white.

Returns:

matplotlib.figure.Figure or plotly.graph_objects.Figure

A Matplotlib or Plotly figure object depending on the plot engine being used.

dict of matplotlib.axes.Axes or None

A dictionary of Matplotlib axes objects. The keys are the names of the axes. The values are the axes objects. If engine is ‘ploltly’, it is None.

pd.DataFrame

A DataFrame that contains the summary statistics of target (for target plot) or predict (for predict plot) values for each feature bucket.