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| import lime | |
| import lime.lime_tabular | |
| import numpy as np | |
| import pandas as pd | |
| class LimeExplainer: | |
| def __init__(self, model, feature_names=None, class_names=None): | |
| self.model = model | |
| self.explainer = None | |
| self.feature_names = feature_names | |
| self.class_names = class_names or ['Denied', 'Approved'] | |
| def fit(self, X_background): | |
| """Fit the explainer with background data""" | |
| # Get feature names | |
| if self.feature_names is None and hasattr(self.model, 'feature_names'): | |
| self.feature_names = self.model.feature_names | |
| # Create prediction function | |
| def predict_fn(x): | |
| if hasattr(self.model, 'predict_proba'): | |
| return self.model.predict_proba(pd.DataFrame(x, columns=self.feature_names)) | |
| else: | |
| return self.model.model.predict_proba(x) | |
| # Create explainer | |
| self.explainer = lime.lime_tabular.LimeTabularExplainer( | |
| X_background.values, | |
| feature_names=self.feature_names, | |
| class_names=self.class_names, | |
| mode='classification' | |
| ) | |
| def explain_instance(self, instance, num_features=5): | |
| """Generate LIME explanation for a single instance""" | |
| if self.explainer is None: | |
| raise ValueError("Explainer not fitted. Call fit() first.") | |
| # Define prediction function | |
| def predict_fn(x): | |
| if hasattr(self.model, 'predict_proba'): | |
| return self.model.predict_proba(pd.DataFrame(x, columns=self.feature_names)) | |
| else: | |
| return self.model.model.predict_proba(x) | |
| # Generate explanation | |
| explanation = self.explainer.explain_instance( | |
| instance.values[0], | |
| predict_fn, | |
| num_features=num_features | |
| ) | |
| return explanation | |
| def generate_explanation(self, instance, num_features=5): | |
| """Generate human-readable explanation""" | |
| explanation = self.explain_instance(instance, num_features) | |
| # Get the explanation as a list of tuples (feature, weight) | |
| features_weights = explanation.as_list() | |
| # Generate text explanation | |
| text_explanation = [] | |
| for feature, weight in features_weights: | |
| direction = "increased" if weight > 0 else "decreased" | |
| text_explanation.append(f"{feature} {direction} the likelihood") | |
| return " and ".join(text_explanation) | |
| def plot_explanation(self, instance, num_features=5): | |
| """Plot the explanation""" | |
| explanation = self.explain_instance(instance, num_features) | |
| return explanation.as_pyplot_figure() |