def test_backends(self):
"""Test that we can use threading or multiprocess backends."""
for backend in ['threading', 'loky']:
with joblib.parallel_backend(backend):
x = np.random.normal(size=(1000, 1))
y = x * 0.5 + np.random.normal(size=(1000, 1))
y = y.flatten()
est = LinearRegression()
est.fit(x, y)
bs = BootstrapEstimator(est, 50, n_jobs=2)
# test that we can fit with the same arguments as the base estimator
bs.fit(x, y)
# test that we can get the same attribute for the bootstrap as the original, with the same shape
self.assertEqual(np.shape(est.coef_), np.shape(bs.coef_))
# 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 = bs.coef__interval()
for bound in [lower, upper]:
self.assertEqual(np.shape(est.coef_), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing once we provide percentile bounds
lower, upper = bs.coef__interval(lower=10, upper=90)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.coef_), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing with the results of a method, rather than an attribute
self.assertEqual(np.shape(est.predict(x)),
np.shape(bs.predict(x)))
# 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 = bs.predict_interval(x)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.predict(x)), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing once we provide percentile bounds
lower, upper = bs.predict_interval(x, lower=10, upper=90)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.predict(x)), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
def test_with_sklearn(self):
"""Test that we can bootstrap sklearn estimators."""
for n_jobs in [None, -1]: # test parallelism
x = np.random.normal(size=(1000, 1))
y = x * 0.5 + np.random.normal(size=(1000, 1))
y = y.flatten()
est = LinearRegression()
est.fit(x, y)
bs = BootstrapEstimator(est, 50, n_jobs=n_jobs)
# test that we can fit with the same arguments as the base estimator
bs.fit(x, y)
# test that we can get the same attribute for the bootstrap as the original, with the same shape
self.assertEqual(np.shape(est.coef_), np.shape(bs.coef_))
# 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 = bs.coef__interval()
for bound in [lower, upper]:
self.assertEqual(np.shape(est.coef_), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing once we provide percentile bounds
lower, upper = bs.coef__interval(lower=10, upper=90)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.coef_), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing with the results of a method, rather than an attribute
self.assertEqual(np.shape(est.predict(x)), np.shape(bs.predict(x)))
# 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 = bs.predict_interval(x)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.predict(x)), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing once we provide percentile bounds
lower, upper = bs.predict_interval(x, lower=10, upper=90)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.predict(x)), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()