def get_regression_explainer(xgboost=False, include_y=True):
X_train, y_train, X_test, y_test = titanic_fare()
train_names, test_names = titanic_names()
if xgboost:
model = XGBRegressor().fit(X_train, y_train)
else:
model = RandomForestRegressor(n_estimators=50,
max_depth=10).fit(X_train, y_train)
if include_y:
reg_explainer = RegressionExplainer(model,
X_test,
y_test,
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names,
units="$")
else:
reg_explainer = RegressionExplainer(model,
X_test,
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names,
units="$")
reg_explainer.calculate_properties()
return reg_explainer
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
model = RandomForestRegressor(n_estimators=50,
max_depth=4).fit(X_train, y_train)
X_test.reset_index(drop=True, inplace=True)
X_test.index = X_test.index.astype(str)
X_test1, y_test1 = X_test.iloc[:100], y_test.iloc[:100]
X_test2, y_test2 = X_test.iloc[100:], y_test.iloc[100:]
self.explainer = RegressionExplainer(model,
X_test1,
y_test1,
cats=['Sex', 'Deck'])
def index_exists_func(index):
return index in X_test2.index
def index_list_func():
# only returns first 50 indexes
return list(X_test2.index[:50])
def y_func(index):
idx = X_test2.index.get_loc(index)
return y_test2.iloc[[idx]]
def X_func(index):
idx = X_test2.index.get_loc(index)
return X_test2.iloc[[idx]]
self.explainer.set_index_exists_func(index_exists_func)
self.explainer.set_index_list_func(index_list_func)
self.explainer.set_X_row_func(X_func)
self.explainer.set_y_func(y_func)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
model = RandomForestRegressor(n_estimators=5, max_depth=2).fit(X_train, y_train)
self.explainer = RegressionExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
cv=3)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
model = LinearRegression().fit(X_train, y_train)
self.explainer = RegressionExplainer(model,
X_test.iloc[:20],
y_test.iloc[:20],
shap='kernel',
X_background=shap.sample(
X_train, 5))
def get_catboost_regressor():
X_train, y_train, X_test, y_test = titanic_fare()
model = CatBoostRegressor(iterations=5, verbose=0).fit(X_train, y_train)
explainer = RegressionExplainer(model, X_test, y_test,
cats=["Sex", 'Deck', 'Embarked'])
X_cats, y_cats = explainer.X_merged, explainer.y
model = CatBoostRegressor(iterations=5, verbose=0).fit(X_cats, y_cats, cat_features=[5, 6, 7])
explainer = RegressionExplainer(model, X_cats, y_cats, idxs=X_test.index)
explainer.calculate_properties(include_interactions=False)
return explainer
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = LGBMRegressor()
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model, X_test, y_test, r2_score,
shap='tree',
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names, units="$")
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = XGBRegressor()
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
units="$")
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestRegressor(n_estimators=5, max_depth=2).fit(X_train, y_train)
self.explainer = RegressionExplainer(
model, X_test, y_test, r2_score,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
idxs=test_names, target='Fare', units='$')
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = CatBoostRegressor(iterations=100, learning_rate=0.1, verbose=0)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model, X_test, y_test, r2_score,
shap='tree',
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names, units="$")
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = CatBoostRegressor(iterations=5, learning_rate=0.1, verbose=0)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
idxs=test_names, units="$")
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
train_names, test_names = titanic_names()
model = XGBRegressor(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(
model,
X_test,
y_test,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestRegressor(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model,
X_test,
y_test,
r2_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestRegressor(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(
model,
X_test,
y_test,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
cats_notencoded={'Gender': 'No Gender'},
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = LinearRegression()
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(
model,
X_test,
y_test,
r2_score,
shap='linear',
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
idxs=test_names,
units="$")
