def get_multiclass_explainer(xgboost=False, include_y=True):
X_train, y_train, X_test, y_test = titanic_embarked()
train_names, test_names = titanic_names()
if xgboost:
model = XGBClassifier().fit(X_train, y_train)
else:
model = RandomForestClassifier(n_estimators=50, max_depth=10).fit(X_train, y_train)
if include_y:
if xgboost:
multi_explainer = ClassifierExplainer(model, X_test, y_test,
model_output='logodds',
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
else:
multi_explainer = ClassifierExplainer(model, X_test, y_test,
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
else:
if xgboost:
multi_explainer = ClassifierExplainer(model, X_test,
model_output='logodds',
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
else:
multi_explainer = ClassifierExplainer(model, X_test,
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
multi_explainer.calculate_properties()
return multi_explainer
def setUp(self):
X_train, y_train, X_test, y_test = titanic_embarked()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck'],
idxs=test_names,
labels=['Queenstown', 'Southampton', 'Cherbourg'])