modeva.TestSuite.explain_lime#
- TestSuite.explain_lime(dataset: str = 'test', sample_index: int = 0, centered: bool = True, random_state: int = 0)#
Generate a LIME (Local Interpretable Model-agnostic Explanations) explanation for a specific sample.
This method provides local feature importance and contribution analysis for a single prediction using the LIME algorithm. It supports both regression and classification tasks.
- Parameters:
dataset ({"main", "train", "test"}, default="test") – The data set used for calculating the explanation results.
sample_index (int, default=0) – The index of the sample in the selected dataset to be explained.
centered (bool, default=True) – Whether to center the feature values by subtracting the mean of each feature.
random_state (int, default=0) – Random seed for LIME’s perturbation sampling process. Use the same value for reproducible results.
- Returns:
A result object containing:
key: “explain_lime”
data: Name of the dataset used
model: Name of the model used
inputs: Input parameters used for the analysis
value: Dictionary containing
”Name”: List of feature names;
”Value”: List of feature values;
”Effect”: List of feature contributions measured by LIME;
”Coefficient”: List of feature coefficients measured by LIME.
value: Dictionary with feature names, values, contributions, and coefficients
table: DataFrame representation of the explanation
options: Dictionary of visualizations configuration for a horizontal bar plot where x-axis is LIME value, and y-axis is the feature name. Run results.plot() to show this plot.
- Return type:
Notes
The explanation includes both feature coefficients (importance) and feature contributions (coefficient x feature value). Results are visualized using a stem-and-bar plot showing both the feature values and their contributions.
Examples
