def test_sample_fraction(self, boston_X, boston_y, sample_fraction):
forest = GRFForestQuantileRegressor(sample_fraction=sample_fraction)
forest.quantiles = [0.2, 0.5, 0.8]
if sample_fraction <= 0 or sample_fraction > 1:
with pytest.raises(ValueError):
forest.fit(boston_X, boston_y)
else:
forest.fit(boston_X, boston_y)
def test_mtry(self, boston_X, boston_y, mtry):
forest = GRFForestQuantileRegressor(mtry=mtry)
forest.quantiles = [0.2, 0.5, 0.8]
forest.fit(boston_X, boston_y)
if mtry is not None:
assert forest.mtry_ == mtry
else:
assert forest.mtry_ == 6
def test_alpha(self, boston_X, boston_y, alpha):
forest = GRFForestQuantileRegressor(alpha=alpha)
forest.quantiles = [0.2, 0.5, 0.8]
if alpha <= 0 or alpha >= 0.25:
with pytest.raises(ValueError):
forest.fit(boston_X, boston_y)
else:
forest.fit(boston_X, boston_y)
def test_honesty_fraction(self, boston_X, boston_y, honesty_fraction):
forest = GRFForestQuantileRegressor(honesty=True,
honesty_fraction=honesty_fraction,
honesty_prune_leaves=True)
forest.quantiles = [0.2, 0.5, 0.8]
if honesty_fraction <= 0 or honesty_fraction >= 1:
with pytest.raises(RuntimeError):
forest.fit(boston_X, boston_y)
else:
forest.fit(boston_X, boston_y)
def test_fit(self, boston_X, boston_y):
forest = GRFForestQuantileRegressor()
with pytest.raises(NotFittedError):
check_is_fitted(forest)
with pytest.raises(ValueError):
forest.fit(boston_X, boston_y)
forest.quantiles = [0.2, 0.5, 0.8]
forest.fit(boston_X, boston_y)
check_is_fitted(forest)
assert hasattr(forest, "grf_forest_")
assert hasattr(forest, "mtry_")
assert forest.criterion == "gini"
def test_equalize_cluster_weights(self, boston_X, boston_y, boston_cluster,
equalize_cluster_weights):
forest = GRFForestQuantileRegressor(
equalize_cluster_weights=equalize_cluster_weights)
forest.quantiles = [0.2, 0.5, 0.8]
forest.fit(boston_X, boston_y, cluster=boston_cluster)
if equalize_cluster_weights:
assert forest.samples_per_cluster_ == 20
else:
assert forest.samples_per_cluster_ == boston_y.shape[0] - 20
forest.fit(boston_X, boston_y, cluster=boston_cluster)
forest.fit(boston_X, boston_y, cluster=None)
assert forest.samples_per_cluster_ == 0
def test_serialize(self, boston_X, boston_y):
forest = GRFForestQuantileRegressor()
forest.quantiles = [0.2, 0.5, 0.8]
# not fitted
tf = tempfile.TemporaryFile()
pickle.dump(forest, tf)
tf.seek(0)
forest = pickle.load(tf)
forest.fit(boston_X, boston_y)
# fitted
tf = tempfile.TemporaryFile()
pickle.dump(forest, tf)
tf.seek(0)
new_forest = pickle.load(tf)
pred = new_forest.predict(boston_X)
assert len(pred) == boston_X.shape[0]
def test_clone(self, boston_X, boston_y):
forest = GRFForestQuantileRegressor()
forest.quantiles = [0.2, 0.5, 0.8]
forest.fit(boston_X, boston_y)
clone(forest)
def test_predict(self, boston_X, boston_y):
forest = GRFForestQuantileRegressor()
forest.quantiles = [0.2, 0.5, 0.8]
forest.fit(boston_X, boston_y)
pred = forest.predict(boston_X)
assert len(pred) == boston_X.shape[0]
def test_honesty_prune_leaves(self, boston_X, boston_y,
honesty_prune_leaves):
forest = GRFForestQuantileRegressor(
honesty=True, honesty_prune_leaves=honesty_prune_leaves)
forest.quantiles = [0.2, 0.5, 0.8]
forest.fit(boston_X, boston_y)
def test_honesty(self, boston_X, boston_y, honesty):
forest = GRFForestQuantileRegressor(honesty=honesty)
forest.quantiles = [0.2, 0.5, 0.8]
forest.fit(boston_X, boston_y)