def test_mtry(self, iris_X, iris_y, mtry):
forest = GRFForestClassifier(mtry=mtry)
forest.fit(iris_X, iris_y)
if mtry is not None:
assert forest.mtry_ == mtry
else:
assert forest.mtry_ == 4
def test_with_X_nan(self, iris_X, iris_y):
iris_X_nan = iris_X.copy()
index = np.random.choice(iris_X_nan.size, 100, replace=False)
iris_X_nan.ravel()[index] = np.nan
assert np.sum(np.isnan(iris_X_nan)) == 100
forest = GRFForestClassifier()
forest.fit(iris_X_nan, iris_y)
pred = forest.predict(iris_X_nan)
assert len(pred) == iris_X_nan.shape[0]
def test_fit(self, iris_X, iris_y):
forest = GRFForestClassifier()
with pytest.raises(NotFittedError):
check_is_fitted(forest)
forest.fit(iris_X, iris_y)
check_is_fitted(forest)
assert hasattr(forest, "grf_forest_")
assert hasattr(forest, "mtry_")
assert forest.criterion == "gini"
def test_check_estimator(self):
# using honesty here means that the test
# `check_classifiers_predictions` will fail, because
# the test dataset is very small. the failure occurs
# when comparing y == y_pred on binary classification
check_estimator(GRFForestClassifier(honesty=False))
with pytest.raises(AssertionError) as exc:
check_estimator(GRFForestClassifier(honesty=True))
assert "Arrays are not equal" in exc
def test_shap_classifier(iris_X, iris_y):
from shap import TreeExplainer
forest = GRFForestClassifier(enable_tree_details=True)
forest.fit(iris_X, iris_y)
with shap_patch():
explainer = TreeExplainer(model=forest, data=iris_X)
shap_values = explainer.shap_values(iris_X, check_additivity=False)
print(shap_values)
def test_serialize(self, iris_X, iris_y):
forest = GRFForestClassifier()
# not fitted
tf = tempfile.TemporaryFile()
pickle.dump(forest, tf)
tf.seek(0)
forest = pickle.load(tf)
forest.fit(iris_X, iris_y)
# fitted
tf = tempfile.TemporaryFile()
pickle.dump(forest, tf)
tf.seek(0)
new_forest = pickle.load(tf)
pred = new_forest.predict(iris_X)
assert len(pred) == iris_X.shape[0]
def test_estimators_(self, iris_X, iris_y):
forest = GRFForestClassifier(n_estimators=10)
with pytest.raises(AttributeError):
_ = forest.estimators_
forest.fit(iris_X, iris_y)
with pytest.raises(ValueError):
_ = forest.estimators_
forest = GRFForestClassifier(n_estimators=10, enable_tree_details=True)
forest.fit(iris_X, iris_y)
estimators = forest.estimators_
assert len(estimators) == 10
assert isinstance(estimators[0], GRFTreeClassifier)
check_is_fitted(estimators[0])
def test_accuracy(self, iris_X, iris_y):
X_train, X_test, y_train, y_test = train_test_split(iris_X,
iris_y,
test_size=0.33,
random_state=42)
# train and test a random forest classifier
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred_rf = rf.predict(X_test)
rf_acc = accuracy_score(y_test, y_pred_rf)
# train and test a ranger classifier
ra = GRFForestClassifier()
ra.fit(X_train, y_train)
y_pred_ra = ra.predict(X_test)
ranger_acc = accuracy_score(y_test, y_pred_ra)
# the accuracy should be good
assert rf_acc > 0.9
assert ranger_acc > 0.9
def test_alpha(self, iris_X, iris_y, alpha):
forest = GRFForestClassifier(alpha=alpha)
if alpha <= 0 or alpha >= 0.25:
with pytest.raises(ValueError):
forest.fit(iris_X, iris_y)
else:
forest.fit(iris_X, iris_y)
def test_honesty_fraction(self, iris_X, iris_y, honesty_fraction):
forest = GRFForestClassifier(honesty=True,
honesty_fraction=honesty_fraction,
honesty_prune_leaves=True)
if honesty_fraction <= 0 or honesty_fraction >= 1:
with pytest.raises(RuntimeError):
forest.fit(iris_X, iris_y)
else:
forest.fit(iris_X, iris_y)
def test_equalize_cluster_weights(self, iris_X, iris_y, iris_cluster,
equalize_cluster_weights):
forest = GRFForestClassifier(
equalize_cluster_weights=equalize_cluster_weights)
forest.fit(iris_X, iris_y, cluster=iris_cluster)
if equalize_cluster_weights:
assert forest.samples_per_cluster_ == 20
else:
assert forest.samples_per_cluster_ == iris_y.shape[0] - 20
if equalize_cluster_weights:
with pytest.raises(ValueError):
forest.fit(iris_X,
iris_y,
cluster=iris_cluster,
sample_weight=iris_y)
forest.fit(iris_X, iris_y, cluster=None)
assert forest.samples_per_cluster_ == 0
def test_get_kernel_weights(self, iris_X, iris_y):
X_train, X_test, y_train, y_test = train_test_split(iris_X,
iris_y,
test_size=0.33,
random_state=42)
forest = GRFForestClassifier()
forest.fit(X_train, y_train)
weights = forest.get_kernel_weights(X_test)
assert weights.shape[0] == X_test.shape[0]
assert weights.shape[1] == X_train.shape[0]
oob_weights = forest.get_kernel_weights(X_train, True)
assert oob_weights.shape[0] == X_train.shape[0]
assert oob_weights.shape[1] == X_train.shape[0]
def test_get_split_frequencies(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y)
sf = forest.get_split_frequencies()
assert sf.shape[1] == iris_X.shape[1]
def test_clone(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y)
clone(forest)
def test_sample_fraction(self, iris_X, iris_y,
sample_fraction): # and ci_group_size
forest = GRFForestClassifier(sample_fraction=sample_fraction,
ci_group_size=1)
if sample_fraction <= 0 or sample_fraction > 1:
with pytest.raises(ValueError):
forest.fit(iris_X, iris_y)
else:
forest.fit(iris_X, iris_y)
forest = GRFForestClassifier(sample_fraction=sample_fraction,
ci_group_size=2)
if sample_fraction <= 0 or sample_fraction > 0.5:
with pytest.raises(ValueError):
forest.fit(iris_X, iris_y)
else:
forest.fit(iris_X, iris_y)
def test_predict_log_proba(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y)
pred_log_proba = forest.predict_log_proba(iris_X)
assert pred_log_proba.shape == (iris_X.shape[0], forest.n_classes_)
def test_predict_oob(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y, compute_oob_predictions=True)
pred = np.atleast_1d(
np.squeeze(np.array(forest.grf_forest_["predictions"])))
assert len(pred) == iris_X.shape[0]
def test_honesty(self, iris_X, iris_y, honesty):
forest = GRFForestClassifier(honesty=honesty)
forest.fit(iris_X, iris_y)
def test_predict(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y)
pred = forest.predict(iris_X)
assert len(pred) == iris_X.shape[0]
def test_honesty_prune_leaves(self, iris_X, iris_y, honesty_prune_leaves):
forest = GRFForestClassifier(honesty=True,
honesty_prune_leaves=honesty_prune_leaves)
forest.fit(iris_X, iris_y)
def test_init(self):
_ = GRFForestClassifier()
def test_get_feature_importances(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y)
fi = forest.get_feature_importances()
assert len(fi) == iris_X.shape[1]
def test_from_forest(self, iris_X, iris_y):
forest = GRFForestClassifier()
forest.fit(iris_X, iris_y)
tree = GRFTreeClassifier.from_forest(forest=forest, idx=0)
tree.predict(iris_X)
def test_get_estimator(self, iris_X, iris_y):
forest = GRFForestClassifier(n_estimators=10)
with pytest.raises(NotFittedError):
_ = forest.get_estimator(idx=0)
forest.fit(iris_X, iris_y)
with pytest.raises(ValueError):
_ = forest.get_estimator(idx=0)
forest = GRFForestClassifier(n_estimators=10, enable_tree_details=True)
forest.fit(iris_X, iris_y)
estimator = forest.get_estimator(idx=0)
check_is_fitted(estimator)
assert isinstance(estimator, GRFTreeClassifier)
with pytest.raises(IndexError):
_ = forest.get_estimator(idx=20)
