예제 #1
0
파일: test_orf.py 프로젝트: sijeong/EconML
 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')
예제 #2
0
파일: test_orf.py 프로젝트: sijeong/EconML
 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)
예제 #3
0
파일: test_orf.py 프로젝트: sijeong/EconML
 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')