fatf.vis.lime.plot_lime

fatf.vis.lime.plot_lime(surrogate_explanation: Union[Dict[str, float], Dict[str, Dict[str, float]]]) → matplotlib.figure.Figure

Plots an importance-based surrogate explanation, e.g., LIME.

This plotting function is intended for the fatf.transparency.predictions.surrogate_explainers.TabularBlimeyLime and fatf.transparency.predictions.surrogate_explainers.TabularBlimeyTree explainers. When multiple explanations are provided, they will share a common y-axis if they use the same set of interpretable features.

Parameters

surrogate_explanationDictionary[string, float] or Dictionary[string, Dictionary[string, float]]

An explanation returned by the explain_instance method of a surrogate explainer. For a classifier this will be a dictionary where the keys are class names and the values are dictionaries where the key is an interpretable feature name and the value is the importance of this interpretable feature. For regressor explanations this will be a dictionary where the key is an interpretable feature name and the value is the importance of this interpretable feature.

Changed in version 0.1.0: Dropped support for LIME explanation format: List[Tuple[string, float]] for regressors and Dictionary[string, List[Tuple[string, float]]] for probabilistic classifiers.

Changed in version 0.0.2: Support for surrogate explainer explanations of the form: Dictionary[string, Dictionary[string, float]].

Returns

figurematplotlib.pyplot.Figure

A matplotlib figure with subplots explaining every label in a surrogate explanation.

Raises

TypeError

The surrogate_explanation parameter is not a dictionary. One of the class names is not a string. One of the interpretable feature names is not a string. One of the importance values is not a number.

ValueError

One of the explanations is an empty dictionary.

Examples using fatf.vis.lime.plot_lime

Using a Surrogate Tree Explainer

Using LIME Explainer

Using LIME Image Explainer