def test_histogram_univariate_destructor():
destructor = IndependentDestructor(independent_density=IndependentDensity(
# Note only one univariate estimator
univariate_estimators=[
HistogramUnivariateDensity(bins=4, alpha=10, bounds=[0, 1])
]))
assert check_destructor(destructor)
def test_deep_destructor_cv_with_tree_destructor():
destructor = DeepDestructorCV(
canonical_destructor=TreeDestructor(),
max_canonical_destructors=2,
cv=2,
)
assert check_destructor(destructor)
def test_pca_destructor():
destructor = CompositeDestructor(destructors=[
LinearProjector(
linear_estimator=PCA(),
orthogonal=False,
),
IndependentDestructor(),
], )
assert check_destructor(destructor, is_canonical=False)
def test_histogram_multivariate_destructor():
destructor = IndependentDestructor(
independent_density=IndependentDensity(
univariate_estimators=HistogramUnivariateDensity(
bins=4, alpha=10, bounds=[0, 1]
)
)
)
assert check_destructor(destructor)
def test_autoregressive_gaussian_destructor():
"""Currently takes too long for regular testing."""
destructor = AutoregressiveDestructor(
density_estimator=GaussianDensity(
covariance_type='spherical',
reg_covar=0.1
)
)
assert check_destructor(destructor, is_canonical=False)
def test_autoregressive_sklearn_mixture_destructor():
"""Test autoregressive destructor and sklearn's Gaussian mixture."""
destructor = AutoregressiveDestructor(
density_estimator=GaussianMixtureDensity(
covariance_type='spherical',
max_iter=1,
n_components=2,
random_state=0,
))
assert check_destructor(destructor, is_canonical=False)
def test_random_linear_householder_destructor():
destructor = CompositeDestructor(
destructors=[
LinearProjector(
# Since n_components=1, a Householder matrix will be used
linear_estimator=RandomOrthogonalEstimator(n_components=1),
orthogonal=False,
),
IndependentDestructor(),
], )
assert check_destructor(destructor, is_canonical=False)
def test_inverse_canonical_destructor():
rng = check_random_state(0)
fitted_canonical_destructor = IdentityDestructor().fit(rng.rand(10, 2))
destructor = get_inverse_canonical_destructor(fitted_canonical_destructor)
assert check_destructor(destructor)
# Alpha must be high to pass the identity test
fitted_canonical_destructor = get_inverse_canonical_destructor(
TreeDestructor(TreeDensity(uniform_weight=0.99)).fit(rng.rand(10, 2))
)
destructor = get_inverse_canonical_destructor(fitted_canonical_destructor)
assert check_destructor(destructor)
# Alpha must be high to pass the identity test
fitted_canonical_destructor = IndependentDestructor(
independent_density=IndependentDensity(
univariate_estimators=HistogramUnivariateDensity(bins=10, alpha=1000, bounds=[0, 1])
)
).fit(rng.rand(10, 2))
destructor = get_inverse_canonical_destructor(fitted_canonical_destructor)
assert check_destructor(destructor)
def test_tree_destructor_with_node_destructor():
node_tree_destructor = IndependentDestructor(
independent_density=IndependentDensity(
univariate_estimators=HistogramUnivariateDensity(
bins=10, alpha=100, bounds=[0, 1])))
for node_destructor in [IdentityDestructor(), node_tree_destructor]:
destructor = TreeDestructor(tree_density=TreeDensity(
tree_estimator=RandomTreeEstimator(max_leaf_nodes=3,
random_state=0),
node_destructor=node_destructor,
uniform_weight=0.9,
))
assert check_destructor(destructor)
def test_normal_independent_destructor():
destructor = IndependentDestructor()
assert check_destructor(destructor, is_canonical=False)
def test_tree_destructor():
assert check_destructor(TreeDestructor())
def test_independent_destructor():
assert check_destructor(IndependentDestructor(), is_canonical=False)
def test_identity_destructor():
assert check_destructor(IdentityDestructor())
def test_deep_destructor_with_tree_destructor():
destructor = DeepDestructor(
canonical_destructor=TreeDestructor(),
n_canonical_destructors=2,
)
assert check_destructor(destructor)
def test_mlpack_density_tree_destructor():
destructor = TreeDestructor(
tree_density=TreeDensity(tree_estimator=MlpackDensityTreeEstimator(
max_leaf_nodes=10), ))
assert check_destructor(destructor)
