def test_can_use_interpreters(self):
n = 100
for t_shape in [(n, ), (n, 1)]:
for y_shape in [(n, ), (n, 1)]:
X = np.random.normal(size=(n, 4))
T = np.random.binomial(1, 0.5, size=t_shape)
Y = (T.flatten() * (2 * (X[:, 0] > 0) - 1)).reshape(y_shape)
est = LinearDML(model_y=LinearRegression(),
model_t=LogisticRegression(),
discrete_treatment=True)
est.fit(Y, T, X=X)
for intrp in [
SingleTreeCateInterpreter(),
SingleTreePolicyInterpreter()
]:
with self.subTest(t_shape=t_shape,
y_shape=y_shape,
intrp=intrp):
with self.assertRaises(Exception):
# prior to calling interpret, can't plot, render, etc.
intrp.plot()
intrp.interpret(est, X)
intrp.plot()
intrp.render('tmp.pdf', view=False)
intrp.export_graphviz()
def test_cate_uncertainty_needs_inference(self):
n = 100
X = np.random.normal(size=(n, 4))
T = np.random.binomial(1, 0.5, size=(n,))
Y = np.random.normal(size=(n,))
est = LinearDML(discrete_treatment=True)
est.fit(Y, T, X=X, inference=None)
# can interpret without uncertainty
intrp = SingleTreeCateInterpreter()
intrp.interpret(est, X)
intrp = SingleTreeCateInterpreter(include_model_uncertainty=True)
with self.assertRaises(Exception):
# can't interpret with uncertainty if inference wasn't used during fit
intrp.interpret(est, X)
# can interpret with uncertainty if we refit
est.fit(Y, T, X=X)
intrp.interpret(est, X)
def test_can_use_interpreters(self):
n = 100
for t_shape in [(n, ), (n, 1)]:
for y_shape in [(n, ), (n, 1)]:
X = np.random.normal(size=(n, 4))
T = np.random.binomial(1, 0.5, size=t_shape)
Y = np.random.normal(size=y_shape)
est = LinearDMLCateEstimator(discrete_treatment=True)
est.fit(Y, T, X)
for intrp in [
SingleTreeCateInterpreter(),
SingleTreePolicyInterpreter()
]:
with self.subTest(t_shape=t_shape,
y_shape=y_shape,
intrp=intrp):
with self.assertRaises(Exception):
# prior to calling interpret, can't plot, render, etc.
intrp.plot()
intrp.interpret(est, X)
intrp.plot()
intrp.render('tmp.pdf', view=False)
intrp.export_graphviz()
"point_estimate", "pvalue", "stderr"
]]
dml2_summary.index = X_eval.index
dml2_summary["star"] = dml2_summary["pvalue"].apply(stars)
dml2_summary["point_estimate"] = dml2_summary["point_estimate"].apply(
format_float, digits=4)
dml2_summary["point_estimate"] = dml2_summary["point_estimate"].str.cat(
dml2_summary["star"])
dml2_summary["stderr"] = dml2_summary["stderr"].apply(surr_parenthesis,
digits=4)
dml2_summary = dml2_summary[["point_estimate", "stderr"]].stack()
dml2_summary.name = "Ameaça"
# Interpretacao por arvore de decisao para T2
interp = SingleTreeCateInterpreter(include_model_uncertainty=False,
max_depth=3,
min_samples_leaf=10)
interp.interpret(dml, X2)
fig, ax1 = plt.subplots(figsize=(25, 6))
interp.plot(feature_names=X_cols, fontsize=12, ax=ax1)
fig.savefig("Figs/fig_tree_dml.png")
# DML para T3
dml.fit(Y3, T3, X3, inference='auto')
dml3_eff = dml.effect(X3, T0=0, T1=1)
dml3_eff_treat = dml.effect(X3_treat, T0=0, T1=1)
print(
f"ATE T3 por DML: {np.mean(dml3_eff)}\nATT T3 por DML: {np.mean(dml3_eff_treat)}"
)
dml3_inf = dml.effect_inference(X_eval.values)
dml3_summary = dml3_inf.summary_frame(alpha=0.05)[[
def test_random_cate_settings(self):
"""Verify that we can call methods on the CATE interpreter with various combinations of inputs"""
n = 100
for _ in range(100):
t_shape = (n, ) if self.coinflip() else (n, 1)
y_shape = (n, ) if self.coinflip() else (n, 1)
discrete_t = self.coinflip()
X = np.random.normal(size=(n, 4))
X2 = np.random.normal(size=(10, 4))
T = np.random.binomial(
2, 0.5, size=t_shape) if discrete_t else np.random.normal(
size=t_shape)
Y = ((T.flatten() == 1) * (2 * (X[:, 0] > 0) - 1) +
(T.flatten() == 2) * (2 * (X[:, 1] > 0) - 1)).reshape(y_shape)
if self.coinflip():
y_shape = (n, 2)
Y = np.tile(Y.reshape((-1, 1)), (1, 2))
est = LinearDML(model_y=LinearRegression(),
model_t=LogisticRegression()
if discrete_t else LinearRegression(),
discrete_treatment=discrete_t)
fit_kwargs = {}
cate_init_kwargs = {}
policy_init_kwargs = {}
intrp_kwargs = {}
policy_intrp_kwargs = {}
common_kwargs = {}
plot_kwargs = {}
render_kwargs = {}
export_kwargs = {}
if self.coinflip():
cate_init_kwargs.update(include_model_uncertainty=True)
policy_init_kwargs.update(risk_level=0.1)
else:
fit_kwargs.update(inference=None)
if self.coinflip():
cate_init_kwargs.update(uncertainty_level=0.01)
if self.coinflip():
policy_init_kwargs.update(risk_seeking=True)
if self.coinflip(1 / 3):
policy_intrp_kwargs.update(sample_treatment_costs=0.1)
elif self.coinflip():
if discrete_t:
policy_intrp_kwargs.update(
sample_treatment_costs=np.random.normal(size=(10, 2)))
else:
if self.coinflip():
policy_intrp_kwargs.update(
sample_treatment_costs=np.random.normal(size=(10,
1)))
else:
policy_intrp_kwargs.update(
sample_treatment_costs=np.random.normal(
size=(10, )))
if self.coinflip():
common_kwargs.update(feature_names=['A', 'B', 'C', 'D'])
if self.coinflip():
common_kwargs.update(filled=False)
if self.coinflip():
common_kwargs.update(rounded=False)
if self.coinflip():
common_kwargs.update(precision=1)
if self.coinflip():
render_kwargs.update(rotate=True)
export_kwargs.update(rotate=True)
if self.coinflip():
render_kwargs.update(leaves_parallel=False)
export_kwargs.update(leaves_parallel=False)
if discrete_t:
render_kwargs.update(treatment_names=[
'control gp', 'treated gp', 'more gp'
])
export_kwargs.update(treatment_names=[
'control gp', 'treated gp', 'more gp'
])
else:
render_kwargs.update(
treatment_names=['control gp', 'treated gp'])
export_kwargs.update(
treatment_names=['control gp', 'treated gp'])
if self.coinflip():
render_kwargs.update(format='png')
if self.coinflip():
export_kwargs.update(out_file='out')
if self.coinflip(0.95): # don't launch files most of the time
render_kwargs.update(view=False)
with self.subTest(t_shape=t_shape,
y_shape=y_shape,
discrete_t=discrete_t,
fit_kwargs=fit_kwargs,
cate_init_kwargs=cate_init_kwargs,
policy_init_kwargs=policy_init_kwargs,
policy_intrp_kwargs=policy_intrp_kwargs,
intrp_kwargs=intrp_kwargs,
common_kwargs=common_kwargs,
plot_kwargs=plot_kwargs,
render_kwargs=render_kwargs,
export_kwargs=export_kwargs):
plot_kwargs.update(common_kwargs)
render_kwargs.update(common_kwargs)
export_kwargs.update(common_kwargs)
policy_intrp_kwargs.update(intrp_kwargs)
est.fit(Y, T, X=X, **fit_kwargs)
intrp = SingleTreeCateInterpreter(**cate_init_kwargs)
intrp.interpret(est, X2, **intrp_kwargs)
intrp.plot(**plot_kwargs)
intrp.render('outfile', **render_kwargs)
intrp.export_graphviz(**export_kwargs)
intrp = SingleTreePolicyInterpreter(**policy_init_kwargs)
try:
intrp.interpret(est, X2, **policy_intrp_kwargs)
intrp.plot(**plot_kwargs)
intrp.render('outfile', **render_kwargs)
intrp.export_graphviz(**export_kwargs)
except AttributeError as e:
assert str(e).find("samples should") >= 0