def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(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 = ClassifierExplainer(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 generate_assets():
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.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'])
dashboard = ExplainerDashboard(explainer,
[
ShapDependenceComposite(explainer, title="Test Tab!"),
ShapDependenceComposite,
"importances"
], title="Test Title!")
pkl_dir = Path.cwd() / "tests" / "cli_assets"
explainer.dump(pkl_dir / "explainer.joblib")
explainer.to_yaml(pkl_dir / "explainer.yaml")
dashboard.to_yaml(pkl_dir / "dashboard.yaml",
explainerfile=str(pkl_dir / "explainer.joblib"))
return None
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_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_survive()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_train.iloc[:50], y_train.iloc[:50],
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
cv=3)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=50, max_depth=5)
model.fit(X_train, y_train)
self.explainer = RandomForestClassifierBunch(
model, X_test, y_test, roc_auc_score,
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 = 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_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_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 = 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,
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 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 = 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 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()
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', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
else:
explainer = ClassifierExplainer(
model, X_test,
cats=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
explainer.calculate_properties()
return explainer
# Have to run on http://127.0.0.1:8050/ to work, to figure out if can work with streamlit or not.
# Import libraries here
from sklearn.ensemble import RandomForestClassifier
from explainerdashboard import ClassifierExplainer, ExplainerDashboard
from explainerdashboard.datasets import titanic_survive, feature_descriptions
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=50,
max_depth=10).fit(X_train, y_train)
explainer = ClassifierExplainer(model,
X_test,
y_test,
cats=['Sex', 'Deck', 'Embarked'],
descriptions=feature_descriptions,
labels=['Not survived', 'Survived'])
ExplainerDashboard(explainer).run()
def test_explainer_len(self):
self.assertEqual(len(self.explainer), len(titanic_survive()[2]))