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)
def monte_carlo():
n = 5000
d = 5
x_grid = np.linspace(-1, 1, 1000)
X_test = np.hstack(
[x_grid.reshape(-1, 1),
np.random.normal(size=(1000, d - 1))])
coverage = []
exp_dict = {'point': [], 'low': [], 'up': []}
for it in range(100):
print(it)
X = np.random.normal(0, 1, size=(n, d))
y = X[:, 0] + np.random.normal(size=(n, ))
est = RegressionForest(n_estimators=1000, verbose=1)
est.fit(X, y)
point = est.predict(X_test)
low, up = est.predict_interval(X_test, alpha=0.05)
coverage.append((low <= x_grid) & (x_grid <= up))
exp_dict['point'].append(point)
exp_dict['low'].append(low)
exp_dict['up'].append(up)
if not os.path.exists('figures'):
os.makedirs('figures')
if not os.path.exists(os.path.join("figures", 'honestforest')):
os.makedirs(os.path.join("figures", 'honestforest'))
plt.figure()
plt.plot(x_grid, np.mean(coverage, axis=0))
plt.savefig('figures/honestforest/coverage.png')
plt.figure()
plt.plot(x_grid,
np.sqrt(np.mean((np.array(exp_dict['point']) - x_grid)**2,
axis=0)),
label='RMSE')
plt.savefig('figures/honestforest/rmse.png')
plt.figure()
plt.plot(x_grid,
np.mean(np.array(exp_dict['up']) - np.array(exp_dict['low']),
axis=0),
label='length')
plt.savefig('figures/honestforest/length.png')