def test_multiple_treatments(self):
np.random.seed(123)
# Only applicable to continuous treatments
# Generate data for 2 treatments
TE = np.array(
[[TestOrthoForest._exp_te(x),
TestOrthoForest._const_te(x)] for x in TestOrthoForest.X])
coefs_T = uniform(0, 1, size=(TestOrthoForest.support_size, 2))
T = np.matmul(TestOrthoForest.W[:, TestOrthoForest.support], coefs_T) + \
uniform(-1, 1, size=(TestOrthoForest.n, 2))
delta_Y = np.array(
[np.dot(TE[i], T[i]) for i in range(TestOrthoForest.n)])
Y = delta_Y + np.dot(TestOrthoForest.W[:, TestOrthoForest.support], TestOrthoForest.coefs_Y) + \
TestOrthoForest.epsilon_sample(TestOrthoForest.n)
# Test multiple treatments with controls
est = ContinuousTreatmentOrthoForest(
n_trees=50,
min_leaf_size=10,
max_depth=50,
subsample_ratio=0.30,
bootstrap=False,
n_jobs=4,
model_T=MultiOutputRegressor(Lasso(alpha=0.024)),
model_Y=Lasso(alpha=0.024),
model_T_final=WeightedModelWrapper(MultiOutputRegressor(LassoCV()),
sample_type="weighted"),
model_Y_final=WeightedModelWrapper(LassoCV(),
sample_type="weighted"))
est.fit(Y, T, TestOrthoForest.X, TestOrthoForest.W)
expected_te = np.array([
TestOrthoForest.expected_exp_te, TestOrthoForest.expected_const_te
]).T
self._test_te(est, expected_te, tol=0.5, treatment_type='multi')
def test_continuous_treatments(self):
np.random.seed(123)
# Generate data with continuous treatments
T = np.dot(TestOrthoForest.W[:, TestOrthoForest.support], TestOrthoForest.coefs_T) + \
TestOrthoForest.eta_sample(TestOrthoForest.n)
TE = np.array([self._exp_te(x) for x in TestOrthoForest.X])
Y = np.dot(TestOrthoForest.W[:, TestOrthoForest.support], TestOrthoForest.coefs_Y) + \
T * TE + TestOrthoForest.epsilon_sample(TestOrthoForest.n)
# Instantiate model with most of the default parameters
est = ContinuousTreatmentOrthoForest(
n_jobs=4,
n_trees=10,
model_T=Lasso(),
model_Y=Lasso(),
model_T_final=WeightedModelWrapper(LassoCV(),
sample_type="weighted"),
model_Y_final=WeightedModelWrapper(LassoCV(),
sample_type="weighted"))
# Test inputs for continuous treatments
# --> Check that one can pass in regular lists
est.fit(list(Y), list(T), list(TestOrthoForest.X),
list(TestOrthoForest.W))
# --> Check that it fails correctly if lists of different shape are passed in
self.assertRaises(ValueError, est.fit, Y[:TestOrthoForest.n // 2],
T[:TestOrthoForest.n // 2], TestOrthoForest.X,
TestOrthoForest.W)
# Check that outputs have the correct shape
out_te = est.const_marginal_effect(TestOrthoForest.x_test)
self.assertSequenceEqual((TestOrthoForest.x_test.shape[0], 1),
out_te.shape)
# Test continuous treatments with controls
est = ContinuousTreatmentOrthoForest(
n_trees=50,
min_leaf_size=10,
max_depth=50,
subsample_ratio=0.30,
bootstrap=False,
n_jobs=4,
model_T=Lasso(alpha=0.024),
model_Y=Lasso(alpha=0.024),
model_T_final=WeightedModelWrapper(LassoCV(),
sample_type="weighted"),
model_Y_final=WeightedModelWrapper(LassoCV(),
sample_type="weighted"))
est.fit(Y, T, TestOrthoForest.X, TestOrthoForest.W)
self._test_te(est, TestOrthoForest.expected_exp_te, tol=0.5)
# Test continuous treatments without controls
T = TestOrthoForest.eta_sample(TestOrthoForest.n)
Y = T * TE + TestOrthoForest.epsilon_sample(TestOrthoForest.n)
est.fit(Y, T, TestOrthoForest.X)
self._test_te(est, TestOrthoForest.expected_exp_te, tol=0.5)
def test_binary_treatments(self):
np.random.seed(123)
# Generate data with binary treatments
log_odds = np.dot(TestOrthoForest.W[:, TestOrthoForest.support], TestOrthoForest.coefs_T) + \
TestOrthoForest.eta_sample(TestOrthoForest.n)
T_sigmoid = 1 / (1 + np.exp(-log_odds))
T = np.array([np.random.binomial(1, p) for p in T_sigmoid])
TE = np.array([self._exp_te(x) for x in TestOrthoForest.X])
Y = np.dot(TestOrthoForest.W[:, TestOrthoForest.support], TestOrthoForest.coefs_Y) + \
T * TE + TestOrthoForest.epsilon_sample(TestOrthoForest.n)
# Instantiate model with default params
est = DiscreteTreatmentOrthoForest(
n_trees=10,
n_jobs=4,
propensity_model=LogisticRegression(),
model_Y=Lasso(),
propensity_model_final=LogisticRegressionCV(penalty='l1',
solver='saga'),
model_Y_final=WeightedModelWrapper(LassoCV(),
sample_type="weighted"))
# Test inputs for binary treatments
# --> Check that one can pass in regular lists
est.fit(list(Y), list(T), list(TestOrthoForest.X),
list(TestOrthoForest.W))
# --> Check that it fails correctly if lists of different shape are passed in
self.assertRaises(ValueError, est.fit, Y[:TestOrthoForest.n // 2],
T[:TestOrthoForest.n // 2], TestOrthoForest.X,
TestOrthoForest.W)
# --> Check that it works when T, Y have shape (n, 1)
est.fit(Y.reshape(-1, 1), T.reshape(-1, 1), TestOrthoForest.X,
TestOrthoForest.W)
# --> Check that it fails correctly when T has shape (n, 2)
self.assertRaises(ValueError, est.fit, Y,
np.ones((TestOrthoForest.n, 2)), TestOrthoForest.X,
TestOrthoForest.W)
# --> Check that it fails correctly when the treatments are not numeric
self.assertRaises(ValueError, est.fit, Y,
np.array(["a"] * TestOrthoForest.n),
TestOrthoForest.X, TestOrthoForest.W)
# Check that outputs have the correct shape
out_te = est.const_marginal_effect(TestOrthoForest.x_test)
self.assertSequenceEqual((TestOrthoForest.x_test.shape[0], 1),
out_te.shape)
# Test binary treatments with controls
est = DiscreteTreatmentOrthoForest(
n_trees=100,
min_leaf_size=10,
max_depth=30,
subsample_ratio=0.30,
bootstrap=False,
n_jobs=4,
propensity_model=LogisticRegression(C=1 / 0.024, penalty='l1'),
model_Y=Lasso(alpha=0.024),
propensity_model_final=LogisticRegressionCV(penalty='l1',
solver='saga'),
model_Y_final=WeightedModelWrapper(LassoCV(),
sample_type="weighted"))
est.fit(Y, T, TestOrthoForest.X, TestOrthoForest.W)
self._test_te(est,
TestOrthoForest.expected_exp_te,
tol=0.7,
treatment_type='discrete')
# Test binary treatments without controls
log_odds = TestOrthoForest.eta_sample(TestOrthoForest.n)
T_sigmoid = 1 / (1 + np.exp(-log_odds))
T = np.array([np.random.binomial(1, p) for p in T_sigmoid])
Y = T * TE + TestOrthoForest.epsilon_sample(TestOrthoForest.n)
est.fit(Y, T, TestOrthoForest.X)
self._test_te(est,
TestOrthoForest.expected_exp_te,
tol=0.5,
treatment_type='discrete')