def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
_, self.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=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
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="$")
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = LGBMRegressor()
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model, X_test, y_test, r2_score,
shap='tree',
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names, units="$")
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 = 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="$")
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = CatBoostRegressor(iterations=100, learning_rate=0.1, verbose=0)
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(model, X_test, y_test, r2_score,
shap='tree',
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names, units="$")
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 = 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='$')
def get_regression_explainer(xgboost=False, include_y=True):
X_train, y_train, X_test, y_test = titanic_fare()
train_names, test_names = titanic_names()
if xgboost:
model = XGBRegressor().fit(X_train, y_train)
else:
model = RandomForestRegressor(n_estimators=50,
max_depth=10).fit(X_train, y_train)
if include_y:
reg_explainer = RegressionExplainer(model,
X_test,
y_test,
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names,
units="$")
else:
reg_explainer = RegressionExplainer(model,
X_test,
cats=['Sex', 'Deck', 'Embarked'],
idxs=test_names,
units="$")
reg_explainer.calculate_properties()
return reg_explainer
def 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_survive()
train_names, test_names = titanic_names()
self.model = RandomForestClassifier(n_estimators=5, max_depth=2)
self.model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
self.model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
self.dashboard = ExplainerDashboard(self.explainer,
[
ShapDependenceTab(self.explainer, title="Test Tab!"),
ShapDependenceTab,
"importances"
], title="Test Title!")
self.pkl_dir = Path.cwd() / "tests" / "cli_assets"
self.explainer.dump(self.pkl_dir / "explainer.joblib")
self.explainer.to_yaml(self.pkl_dir / "explainer.yaml")
self.dashboard.to_yaml(self.pkl_dir / "dashboard.yaml",
explainerfile=str(self.pkl_dir / "explainer.joblib"))
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_survive()
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = RandomForestClassifierExplainer(
model,
X_test,
y_test,
roc_auc_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
idxs=test_names,
labels=['Not survived', 'Survived'])
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
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, roc_auc_score, n_jobs=-1)
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="$")
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = XGBClassifier()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
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'])
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = LGBMClassifier()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test, roc_auc_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100, learning_rate=0.1, verbose=0)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test, roc_auc_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100, learning_rate=0.1, verbose=0)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = LogisticRegression()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
shap='linear',
cats=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
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_survive()
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', 'Embarked'],
target='Survival',
labels=['Not survived', 'Survived'],
idxs=test_names)
def get_catboost_classifier():
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100, verbose=0).fit(X_train, y_train)
explainer = ClassifierExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
X_cats, y_cats = explainer.X_merged, explainer.y.astype("int")
model = CatBoostClassifier(iterations=5, verbose=0).fit(X_cats, y_cats, cat_features=[5, 6, 7])
explainer = ClassifierExplainer(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_survive()
train_names, test_names = titanic_names()
model = LogisticRegression()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test.iloc[:20],
y_test.iloc[:20],
shap='kernel',
model_output='probability',
X_background=shap.sample(X_train, 5),
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
def get_classification_explainer(include_y=True):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = XGBClassifier().fit(X_train, y_train)
if include_y:
explainer = ClassifierExplainer(
model, X_test, y_test,
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
else:
explainer = ClassifierExplainer(
model, X_test,
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
explainer.calculate_properties()
return explainer
def get_classification_explainer(xgboost=False, include_y=True):
X_train, y_train, X_test, y_test = titanic_survive()
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:
explainer = ClassifierExplainer(model,
X_test,
y_test,
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'])
else:
explainer = ClassifierExplainer(model,
X_test,
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'])
explainer.calculate_properties()
return explainer
def test_int_idx(self):
self.assertEqual(self.explainer.get_idx(titanic_names()[1][0]), 0)
