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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() |