def test_pca_destructor():
destructor = CompositeDestructor(destructors=[
LinearProjector(
linear_estimator=PCA(),
orthogonal=False,
),
IndependentDestructor(),
], )
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 get_rbig_model(bins="auto", bounds=0.1, alpha=1e-10):
# ================================ #
# Step I - Marginal Uniformization
# ================================ #
# Choose the Histogram estimator that converts the data X to uniform U(0,1)
univariate_estimator = HistogramUnivariateDensity(bounds=bounds,
bins=bins,
alpha=alpha)
# Marginally uses histogram estimator
marginal_uniformization = IndependentDensity(
univariate_estimators=univariate_estimator)
# Creates "Destructor" D_theta_1
uniform_density = IndependentDestructor(marginal_uniformization)
# ================================== #
# Step II - Marginal Gaussianization
# ================================== #
# Choose destructor D_theta_2 that converts data
marginal_gaussianization = IndependentInverseCdf()
# =================== #
# Step III - Rotation
# =================== #
# Choose a linear projection to rotate the features (PCA) "D_theta_3"
rotation = LinearProjector(linear_estimator=PCA())
# ==================== #
# Composite Destructor
# ==================== #
# Composite Destructor
rbig_model = CompositeDestructor([
clone(uniform_density), # Marginal Uniformization
clone(marginal_gaussianization), # Marginal Gaussianization
clone(rotation), # Rotation (PCA)
])
return rbig_model
def _get_toy_destructors_and_names():
# BASELINE SHALLOW DESTRUCTORS
gaussian_full = CompositeDestructor(
destructors=[
LinearProjector(
linear_estimator=PCA(),
orthogonal=False,
),
IndependentDestructor(),
],
)
mixture_20 = AutoregressiveDestructor(
density_estimator=GaussianMixtureDensity(
covariance_type='spherical',
n_components=20,
)
)
random_tree = CompositeDestructor(
destructors=[
IndependentDestructor(),
TreeDestructor(
tree_density=TreeDensity(
tree_estimator=RandomTreeEstimator(min_samples_leaf=20, max_leaf_nodes=50),
node_destructor=IndependentDestructor(
independent_density=IndependentDensity(
univariate_estimators=HistogramUnivariateDensity(
bins=10, alpha=10, bounds=[0, 1]
)
)
)
)
)
]
)
density_tree = CompositeDestructor(
destructors=[
IndependentDestructor(),
TreeDestructor(
tree_density=TreeDensity(
tree_estimator=MlpackDensityTreeEstimator(min_samples_leaf=10),
uniform_weight=0.001,
)
)
]
)
baseline_destructors = [gaussian_full, mixture_20, random_tree, density_tree]
baseline_names = ['Gaussian', 'Mixture', 'SingleRandTree', 'SingleDensityTree']
# LINEAR DESTRUCTORS
alpha_histogram = [10] # [1, 10, 100]
random_linear_projector = LinearProjector(
linear_estimator=RandomOrthogonalEstimator(), orthogonal=True
)
canonical_histogram_destructors = [
IndependentDestructor(
independent_density=IndependentDensity(
univariate_estimators=HistogramUnivariateDensity(bins=20, bounds=[0, 1], alpha=a)
)
)
for a in alpha_histogram
]
linear_destructors = [
DeepDestructorCV(
init_destructor=IndependentDestructor(),
canonical_destructor=CompositeDestructor(destructors=[
IndependentInverseCdf(), # Project to inf real space
random_linear_projector, # Random linear projector
IndependentDestructor(), # Project to canonical space
destructor, # Histogram destructor in canonical space
]),
n_extend=20, # Need to extend since random projections
)
for destructor in canonical_histogram_destructors
]
linear_names = ['RandLin (%g)' % a for a in alpha_histogram]
# MIXTURE DESTRUCTORS
fixed_weight = [0.5] # [0.1, 0.5, 0.9]
mixture_destructors = [
CompositeDestructor(destructors=[
IndependentInverseCdf(),
AutoregressiveDestructor(
density_estimator=FirstFixedGaussianMixtureDensity(
covariance_type='spherical',
n_components=20,
fixed_weight=w,
)
)
])
for w in fixed_weight
]
# Make deep destructors
mixture_destructors = [
DeepDestructorCV(
init_destructor=IndependentDestructor(),
canonical_destructor=destructor,
n_extend=5,
)
for destructor in mixture_destructors
]
mixture_names = ['GausMix (%.2g)' % w for w in fixed_weight]
# TREE DESTRUCTORS
# Random trees
histogram_alpha = [10] # [1, 10, 100]
tree_destructors = [
TreeDestructor(
tree_density=TreeDensity(
tree_estimator=RandomTreeEstimator(
max_leaf_nodes=4
),
node_destructor=IndependentDestructor(
independent_density=IndependentDensity(
univariate_estimators=HistogramUnivariateDensity(
alpha=a, bins=10, bounds=[0, 1]
)
)
),
)
)
for a in histogram_alpha
]
tree_names = ['RandTree (%g)' % a for a in histogram_alpha]
# Density trees using mlpack
tree_uniform_weight = [0.5] # [0.1, 0.5, 0.9]
tree_destructors.extend([
TreeDestructor(
tree_density=TreeDensity(
tree_estimator=MlpackDensityTreeEstimator(min_samples_leaf=10),
uniform_weight=w,
)
)
for w in tree_uniform_weight
])
tree_names.extend(['DensityTree (%.2g)' % w for w in tree_uniform_weight])
# Add random rotation to tree destructors
tree_destructors = [
CompositeDestructor(destructors=[
IndependentInverseCdf(),
LinearProjector(linear_estimator=RandomOrthogonalEstimator()),
IndependentDestructor(),
destructor,
])
for destructor in tree_destructors
]
# Make deep destructors
tree_destructors = [
DeepDestructorCV(
init_destructor=IndependentDestructor(),
canonical_destructor=destructor,
# Density trees don't need to extend as much as random trees
n_extend=50 if 'Rand' in name else 5,
)
for destructor, name in zip(tree_destructors, tree_names)
]
# Collect all destructors and set CV parameter
destructors = baseline_destructors + linear_destructors + mixture_destructors + tree_destructors
destructor_names = baseline_names + linear_names + mixture_names + tree_names
for d in destructors:
if 'cv' in d.get_params():
d.set_params(cv=cv)
# **** Change from notebook to make faster ****
if 'max_canonical_destructors' in d.get_params():
d.set_params(max_canonical_destructors=1)
return destructors, destructor_names