def test_lime_stability_indices_good_behaviour(self):
np.random.seed(1)
rf = RandomForestClassifier(n_estimators=500)
rf.fit(self.train, self.labels_train)
i = np.random.randint(0, self.test.shape[0])
explainer = LimeTabularExplainerOvr(self.train,
mode="classification",
feature_names=self.feature_names,
class_names=self.target_names,
discretize_continuous=True)
indices = explainer.check_stability(data_row=self.test[i],
predict_fn=rf.predict_proba,
num_features=2,
n_calls=10,
index_verbose=True,
verbose=True,
model_regressor=None)
self.assertIsNotNone(indices)
csi, vsi = indices
self.assertTrue((csi >= 0) & (csi <= 100),
"CSI Index value is not in the range [0,100]")
self.assertTrue((vsi >= 0) & (vsi <= 100),
"VSI Index value is not in the range [0,100]")
def test_lime_stability_indices_model_error(self):
np.random.seed(1)
rf = RandomForestClassifier(n_estimators=500)
rf.fit(self.train, self.labels_train)
lin_regr = LinearRegression(fit_intercept=True)
i = np.random.randint(0, self.test.shape[0])
explainer = LimeTabularExplainerOvr(self.train,
mode="classification",
feature_names=self.feature_names,
class_names=self.target_names,
discretize_continuous=True)
with self.assertRaises(LocalModelError):
exp = explainer.check_stability(self.test[i],
rf.predict_proba,
num_features=2,
n_calls=10,
model_regressor=lin_regr)