def test_internal_options(self):
"""Test that the internal use of bootstrap within an estimator using custom options works."""
x = np.random.normal(size=(1000, 2))
z = np.random.normal(size=(1000, 1))
t = np.random.normal(size=(1000, 1))
t2 = np.random.normal(size=(1000, 1))
y = x[:, 0] * 0.5 + t + np.random.normal(size=(1000, 1))
opts = BootstrapOptions(50, 2)
est = NonparametricTwoStageLeastSquares(PolynomialFeatures(2),
PolynomialFeatures(2),
PolynomialFeatures(2),
None,
inference=opts)
est.fit(y, t, x, None, z)
# test that we can get an interval for the same attribute for the bootstrap as the original,
# with the same shape for the lower and upper bounds
lower, upper = est.effect_interval(x, t, t2)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.effect(x, t, t2)), np.shape(bound))
self.assertFalse(np.allclose(lower, upper))
# test that we can do the same thing once we provide percentile bounds
lower, upper = est.effect_interval(x, t, t2, lower=10, upper=90)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.effect(x, t, t2)), np.shape(bound))
self.assertFalse(np.allclose(lower, upper))
def test_internal_options(self):
"""Test that the internal use of bootstrap within an estimator using custom options works."""
x = np.random.normal(size=(1000, 2))
z = np.random.normal(size=(1000, 1))
t = np.random.normal(size=(1000, 1))
t2 = np.random.normal(size=(1000, 1))
y = x[:, 0:1] * 0.5 + t + np.random.normal(size=(1000, 1))
opts = BootstrapInference(50, 2)
est = NonparametricTwoStageLeastSquares(PolynomialFeatures(2),
PolynomialFeatures(2),
PolynomialFeatures(2), None)
est.fit(y, t, x, None, z, inference=opts)
# test that we can get an interval for the same attribute for the bootstrap as the original,
# with the same shape for the lower and upper bounds
eff = est.effect(x, t, t2)
lower, upper = est.effect_interval(x, T0=t, T1=t2)
for bound in [lower, upper]:
self.assertEqual(np.shape(eff), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that the estimated effect is usually within the bounds
assert np.mean(np.logical_and(lower <= eff, eff <= upper)) >= 0.7
# test that we can do the same thing once we provide percentile bounds
lower, upper = est.effect_interval(x, T0=t, T1=t2, alpha=0.2)
for bound in [lower, upper]:
self.assertEqual(np.shape(eff), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that the estimated effect is usually within the bounds
assert np.mean(np.logical_and(lower <= eff, eff <= upper)) >= 0.65