def test_warm_start(self, ):
n_features = 2
n = 100
random_state = 123
X, y, _ = self._get_regression_data(n, n_features, random_state)
for inference in [True, False]:
forest = RegressionForest(n_estimators=4,
inference=inference,
warm_start=True,
random_state=123).fit(X, y)
forest.n_estimators = 8
forest.fit(X, y)
pred1 = forest.predict(X)
inds1 = forest.get_subsample_inds()
tree_states1 = [t.random_state for t in forest]
forest = RegressionForest(n_estimators=8,
inference=inference,
warm_start=True,
random_state=123).fit(X, y)
pred2 = forest.predict(X)
inds2 = forest.get_subsample_inds()
tree_states2 = [t.random_state for t in forest]
np.testing.assert_allclose(pred1, pred2)
np.testing.assert_allclose(inds1, inds2)
np.testing.assert_allclose(tree_states1, tree_states2)
return
def test_raise_exceptions(self, ):
# test that we raise errors in mishandled situations.
n_features = 2
n = 10
random_state = 123
X, y, truth = self._get_regression_data(n, n_features, random_state)
with np.testing.assert_raises(ValueError):
forest = RegressionForest(n_estimators=20).fit(X, y[:4])
with np.testing.assert_raises(ValueError):
forest = RegressionForest(n_estimators=20,
subforest_size=3).fit(X, y)
with np.testing.assert_raises(ValueError):
forest = RegressionForest(n_estimators=20,
inference=True,
max_samples=.6).fit(X, y)
with np.testing.assert_raises(ValueError):
forest = RegressionForest(n_estimators=20,
max_samples=20).fit(X, y)
with np.testing.assert_raises(ValueError):
forest = RegressionForest(n_estimators=20,
max_samples=1.2).fit(X, y)
with np.testing.assert_raises(ValueError):
forest = RegressionForest(n_estimators=4,
warm_start=True,
inference=True).fit(X, y)
forest.inference = False
forest.n_estimators = 8
forest.fit(X, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4,
criterion='peculiar').fit(X, y, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4, max_depth=-1).fit(X, y, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4,
min_samples_split=-1).fit(X, y, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4,
min_samples_leaf=-1).fit(X, y, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4,
min_weight_fraction_leaf=-1.0).fit(X, y, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4,
min_var_fraction_leaf=-1.0).fit(X, y, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4, max_features=10).fit(X, y, y)
with np.testing.assert_raises(ValueError):
forest = CausalForest(n_estimators=4,
min_balancedness_tol=.55).fit(X, y, y)
return
def test_pickling(self,):
n_features = 2
n = 10
random_state = 123
X, y, _ = self._get_regression_data(n, n_features, random_state)
forest = RegressionForest(n_estimators=4, warm_start=True, random_state=123).fit(X, y)
forest.n_estimators = 8
forest.fit(X, y)
pred1 = forest.predict(X)
joblib.dump(forest, 'forest.jbl')
loaded_forest = joblib.load('forest.jbl')
np.testing.assert_equal(loaded_forest.n_estimators, forest.n_estimators)
np.testing.assert_allclose(loaded_forest.predict(X), pred1)