def test_inference_with_none_stderr(self):
Y, T, X, W = TestInference.Y, TestInference.T, TestInference.X, TestInference.W
est = DML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=Lasso(alpha=0.1, fit_intercept=False),
featurizer=PolynomialFeatures(degree=1, include_bias=False),
random_state=123)
est.fit(Y, T, X=X, W=W)
est.summary()
est.coef__inference().summary_frame()
est.intercept__inference().summary_frame()
est.effect_inference(X).summary_frame()
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
est.marginal_effect_inference(T, X).summary_frame()
est = NonParamDML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=LinearRegression(fit_intercept=False),
featurizer=PolynomialFeatures(degree=1, include_bias=False),
random_state=123)
est.fit(Y, T, X=X, W=W)
est.effect_inference(X).summary_frame()
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
est.marginal_effect_inference(T, X).summary_frame()
est = DRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
model_final=LinearRegression())
est.fit(Y, T, X=X, W=W)
est.effect_inference(X).summary_frame()
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
est.marginal_effect_inference(T, X).summary_frame()
def test_pickle_inferenceresult(self):
Y, T, X, W = TestInference.Y, TestInference.T, TestInference.X, TestInference.W
est = DML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=Lasso(alpha=0.1, fit_intercept=False),
featurizer=PolynomialFeatures(degree=1, include_bias=False),
random_state=123)
est.fit(Y, T, X=X, W=W)
effect_inf = est.effect_inference(X)
s = pickle.dumps(effect_inf)
def test_auto_inference(self):
Y, T, X, W = TestInference.Y, TestInference.T, TestInference.X, TestInference.W
est = DRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
model_final=StatsModelsLinearRegression())
est.fit(Y, T, X=X, W=W)
est.effect_inference(X).summary_frame()
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
est.marginal_effect_inference(T, X).summary_frame()
est = DRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
model_final=LinearRegression(),
multitask_model_final=True)
est.fit(Y, T, X=X, W=W)
with pytest.raises(AttributeError):
est.effect_inference(X)
est = DML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=StatsModelsLinearRegression(fit_intercept=False),
random_state=123)
est.fit(Y, T, X=X, W=W)
est.summary()
est.coef__inference().summary_frame()
assert est.coef__inference().stderr is not None
est.intercept__inference().summary_frame()
assert est.intercept__inference().stderr is not None
est.effect_inference(X).summary_frame()
assert est.effect_inference(X).stderr is not None
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
assert est.const_marginal_effect_inference(X).stderr is not None
est.marginal_effect_inference(T, X).summary_frame()
assert est.marginal_effect_inference(T, X).stderr is not None
est = NonParamDML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=DebiasedLasso(),
random_state=123)
est.fit(Y, T, X=X, W=W)
est.effect_inference(X).summary_frame()
assert est.effect_inference(X).stderr is not None
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
assert est.const_marginal_effect_inference(X).stderr is not None
est.marginal_effect_inference(T, X).summary_frame()
assert est.marginal_effect_inference(T, X).stderr is not None
def test_rlearner_residuals(self):
y, T, X, W = self._get_data()
dml = DML(model_y=LinearRegression(),
model_t=LinearRegression(),
cv=1,
model_final=StatsModelsLinearRegression(fit_intercept=False),
linear_first_stages=False,
random_state=123)
with pytest.raises(AttributeError):
y_res, T_res, X_res, W_res = dml.residuals_
dml.fit(y, T, X=X, W=W)
with pytest.raises(AttributeError):
y_res, T_res, X_res, W_res = dml.residuals_
dml.fit(y, T, X=X, W=W, cache_values=True)
y_res, T_res, X_res, W_res = dml.residuals_
np.testing.assert_array_equal(X, X_res)
np.testing.assert_array_equal(W, W_res)
XW = np.hstack([X, W])
np.testing.assert_array_equal(y_res, y - LinearRegression().fit(XW, y).predict(XW))
np.testing.assert_array_equal(T_res, T - LinearRegression().fit(XW, T).predict(XW))
def test_dml(self):
"""Test setting attributes and refitting"""
y, T, X, W = self._get_data()
dml = DML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=StatsModelsLinearRegression(fit_intercept=False),
linear_first_stages=False,
random_state=123)
dml.fit(y, T, X=X, W=W)
with pytest.raises(Exception):
dml.refit_final()
dml.fit(y, T, X=X, W=W, cache_values=True)
dml.model_final = StatsModelsRLM(fit_intercept=False)
dml.refit_final()
assert isinstance(dml.model_cate, StatsModelsRLM)
np.testing.assert_array_equal(dml.model_cate.coef_[1:].flatten(), dml.coef_.flatten())
lb, ub = dml.model_cate.coef__interval(alpha=0.01)
lbt, ubt = dml.coef__interval(alpha=0.01)
np.testing.assert_array_equal(lb[1:].flatten(), lbt.flatten())
np.testing.assert_array_equal(ub[1:].flatten(), ubt.flatten())
intcpt = dml.intercept_
dml.fit_cate_intercept = False
np.testing.assert_equal(dml.intercept_, intcpt)
dml.refit_final()
np.testing.assert_array_equal(dml.model_cate.coef_.flatten(), dml.coef_.flatten())
lb, ub = dml.model_cate.coef__interval(alpha=0.01)
lbt, ubt = dml.coef__interval(alpha=0.01)
np.testing.assert_array_equal(lb.flatten(), lbt.flatten())
np.testing.assert_array_equal(ub.flatten(), ubt.flatten())
with pytest.raises(AttributeError):
dml.intercept_
with pytest.raises(AttributeError):
dml.intercept__interval()
dml.model_final = DebiasedLasso(fit_intercept=False)
dml.refit_final()
assert isinstance(dml.model_cate, DebiasedLasso)
dml.featurizer = PolynomialFeatures(degree=2, include_bias=False)
dml.model_final = StatsModelsLinearRegression(fit_intercept=False)
dml.refit_final()
assert isinstance(dml.featurizer_, PolynomialFeatures)
dml.fit_cate_intercept = True
dml.refit_final()
assert isinstance(dml.featurizer_, Pipeline)
np.testing.assert_array_equal(dml.coef_.shape, (X.shape[1]**2))
np.testing.assert_array_equal(dml.coef__interval()[0].shape, (X.shape[1]**2))
coefpre = dml.coef_
coefpreint = dml.coef__interval()
dml.fit(y, T, X=X, W=W)
np.testing.assert_array_equal(coefpre, dml.coef_)
np.testing.assert_array_equal(coefpreint[0], dml.coef__interval()[0])
dml.discrete_treatment = True
dml.featurizer = None
dml.linear_first_stages = True
dml.model_t = LogisticRegression()
dml.fit(y, T, X=X, W=W)
newdml = DML(model_y=LinearRegression(),
model_t=LogisticRegression(),
model_final=StatsModelsLinearRegression(fit_intercept=False),
discrete_treatment=True,
linear_first_stages=True,
random_state=123).fit(y, T, X=X, W=W)
np.testing.assert_array_equal(dml.coef_, newdml.coef_)
np.testing.assert_array_equal(dml.coef__interval()[0], newdml.coef__interval()[0])
ldml = LinearDML(model_y=LinearRegression(),
model_t=LinearRegression(),
linear_first_stages=False)
ldml.fit(y, T, X=X, W=W, cache_values=True)
# can set final model for plain DML, but can't for LinearDML (hardcoded to StatsModelsRegression)
with pytest.raises(ValueError):
ldml.model_final = StatsModelsRLM()
ldml = SparseLinearDML(model_y=LinearRegression(),
model_t=LinearRegression(),
linear_first_stages=False)
ldml.fit(y, T, X=X, W=W, cache_values=True)
# can set final model for plain DML, but can't for LinearDML (hardcoded to StatsModelsRegression)
with pytest.raises(ValueError):
ldml.model_final = StatsModelsRLM()
ldml.alpha = 0.01
ldml.max_iter = 10
ldml.tol = 0.01
ldml.refit_final()
np.testing.assert_equal(ldml.model_cate.estimators_[0].alpha, 0.01)
np.testing.assert_equal(ldml.model_cate.estimators_[0].max_iter, 10)
np.testing.assert_equal(ldml.model_cate.estimators_[0].tol, 0.01)
def test_comparison(self):
def reg():
return LinearRegression()
def clf():
return LogisticRegression()
y, T, X, true_eff = self._get_data()
(X_train, X_val, T_train, T_val, Y_train, Y_val, _,
true_eff_val) = train_test_split(X, T, y, true_eff, test_size=.4)
models = [
('ldml',
LinearDML(model_y=reg(),
model_t=clf(),
discrete_treatment=True,
linear_first_stages=False,
cv=3)),
('sldml',
SparseLinearDML(model_y=reg(),
model_t=clf(),
discrete_treatment=True,
featurizer=PolynomialFeatures(degree=2,
include_bias=False),
linear_first_stages=False,
cv=3)),
('xlearner',
XLearner(models=reg(), cate_models=reg(),
propensity_model=clf())),
('dalearner',
DomainAdaptationLearner(models=reg(),
final_models=reg(),
propensity_model=clf())),
('slearner', SLearner(overall_model=reg())),
('tlearner', TLearner(models=reg())),
('drlearner',
DRLearner(model_propensity=clf(),
model_regression=reg(),
model_final=reg(),
cv=3)),
('rlearner',
NonParamDML(model_y=reg(),
model_t=clf(),
model_final=reg(),
discrete_treatment=True,
cv=3)),
('dml3dlasso',
DML(model_y=reg(),
model_t=clf(),
model_final=reg(),
discrete_treatment=True,
featurizer=PolynomialFeatures(degree=3),
linear_first_stages=False,
cv=3))
]
models = Parallel(n_jobs=-1, verbose=1)(
delayed(_fit_model)(name, mdl, Y_train, T_train, X_train)
for name, mdl in models)
scorer = RScorer(model_y=reg(),
model_t=clf(),
discrete_treatment=True,
cv=3,
mc_iters=2,
mc_agg='median')
scorer.fit(Y_val, T_val, X=X_val)
rscore = [scorer.score(mdl) for _, mdl in models]
rootpehe_score = [
np.sqrt(
np.mean(
(true_eff_val.flatten() - mdl.effect(X_val).flatten())**2))
for _, mdl in models
]
assert LinearRegression().fit(
np.array(rscore).reshape(-1, 1),
np.array(rootpehe_score)).coef_ < 0.5
mdl, _ = scorer.best_model([mdl for _, mdl in models])
rootpehe_best = np.sqrt(
np.mean((true_eff_val.flatten() - mdl.effect(X_val).flatten())**2))
assert rootpehe_best < 1.2 * np.min(rootpehe_score)
mdl, _ = scorer.ensemble([mdl for _, mdl in models])
rootpehe_ensemble = np.sqrt(
np.mean((true_eff_val.flatten() - mdl.effect(X_val).flatten())**2))
assert rootpehe_ensemble < 1.2 * np.min(rootpehe_score)