def evaluate(self, category_projection):
assert type(category_projection) == CategoryProjection
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley_estimator = RipleysKEstimator(area=1., x_max=1., y_max=1., x_min=0., y_min=0.)
proj = category_projection.projection[:, [category_projection.x_dim, category_projection.y_dim]]
scaled_proj = np.array([stretch_0_to_1(proj.T[0]), stretch_0_to_1(proj.T[1])]).T
radii = np.linspace(0, self.max_distance, 1000)
deviances = np.abs(ripley_estimator(scaled_proj, radii, mode='ripley') - ripley_estimator.poisson(radii))
return np.trapz(deviances, x=radii)
def get_optimal_category_projection(
corpus,
n_dims=3,
n_steps=10,
projector=lambda n_terms, n_dims: CategoryProjector(
AssociationCompactor(n_terms, scorer=RankDifference),
projector=PCA(n_dims)),
verbose=False):
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley = RipleysKEstimator(area=1., x_max=1., y_max=1., x_min=0., y_min=0.)
min_dev = None
best_k = None
best_x = None
best_y = None
best_projector = None
for k in np.power(
2,
np.linspace(
np.log(corpus.get_num_categories()) / np.log(2),
np.log(corpus.get_num_terms()) / np.log(2),
n_steps)).astype(int):
r = np.linspace(0, np.sqrt(2), 100)
category_projector = projector(k, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
dev = np.sum(
np.abs(
ripley(scaled_proj, r, mode='ripley') -
ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_k = k
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print(k, dim_1, dim_2, dev, best_k, best_x, best_y,
min_dev)
if verbose:
print(best_k, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def get_optimal_category_projection_by_rank(
corpus,
n_dims=2,
n_steps=20,
projector=lambda rank, n_dims: CategoryProjector(
AssociationCompactorByRank(rank), projector=PCA(n_dims)),
verbose=False):
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley = RipleysKEstimator(area=1., x_max=1., y_max=1., x_min=0., y_min=0.)
min_dev = None
best_rank = None
best_x = None
best_y = None
best_projector = None
for rank in np.linspace(1,
TermCategoryRanker().get_max_rank(corpus),
n_steps):
r = np.linspace(0, np.sqrt(2), 100)
category_projector = projector(rank, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
dev = np.sum(
np.abs(
ripley(scaled_proj, r, mode='ripley') -
ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_rank = rank
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print('rank', rank, 'dims', dim_1, dim_2, 'K', dev)
print(' best rank', best_rank, 'dims', best_x, best_y,
'K', min_dev)
if verbose:
print(best_rank, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def evaluate(self, category_projection):
assert type(category_projection) == CategoryProjection
proj = category_projection.projection[:, [
category_projection.x_dim, category_projection.y_dim
]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
morista_sum = 0
N = scaled_proj.shape[0]
for i in range(self.num_bin_range[0], self.num_bin_range[1]):
bins, _, _ = np.histogram2d(scaled_proj.T[0], scaled_proj.T[1], i)
# I_M = Q * (\sum_{k=1}^{Q}{n_k * (n_k - 1)})/(N * (N _ 1))
Q = len(bins) # num_quadrants
# Eqn 1.
morista_sum += Q * np.sum(np.ravel(bins) *
(np.ravel(bins) - 1)) / (N * (N - 1))
return morista_sum / (self.num_bin_range[1] - self.num_bin_range[0])
def get_optimal_category_projection(
corpus,
n_dims=3,
n_steps=10,
projector=lambda n_terms, n_dims: CategoryProjector(
selector=AssociationCompactor(n_terms, scorer=RankDifference),
projector=PCA(n_dims)),
optimizer=ripley_poisson_difference,
verbose=False):
min_dev = None
best_k = None
best_x = None
best_y = None
best_projector = None
for k in np.power(
2,
np.linspace(
np.log(corpus.get_num_categories()) / np.log(2),
np.log(corpus.get_num_terms()) / np.log(2),
n_steps)).astype(int):
category_projector = projector(k, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
dev = optimizer(scaled_proj)
#dev = np.sum(np.abs(ripley(scaled_proj, r, mode='ripley') - ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_k = k
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print(k, dim_1, dim_2, dev, best_k, best_x, best_y,
min_dev)
if verbose:
print(best_k, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def get_optimal_category_projection_by_rank(
corpus,
n_dims=2,
n_steps=20,
projector=lambda rank, n_dims: CategoryProjector(AssociationCompactorByRank(rank),
projector=PCA(n_dims)),
verbose=False
):
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley = RipleysKEstimator(area=1., x_max=1., y_max=1., x_min=0., y_min=0.)
min_dev = None
best_rank = None
best_x = None
best_y = None
best_projector = None
for rank in np.linspace(1, TermCategoryRanker().get_max_rank(corpus), n_steps):
r = np.linspace(0, np.sqrt(2), 100)
category_projector = projector(rank, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array([stretch_0_to_1(proj.T[0]), stretch_0_to_1(proj.T[1])]).T
dev = np.sum(np.abs(ripley(scaled_proj, r, mode='ripley') - ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_rank = rank
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print('rank', rank, 'dims', dim_1, dim_2, 'K', dev)
print(' best rank', best_rank, 'dims', best_x, best_y, 'K', min_dev)
if verbose:
print(best_rank, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def get_optimal_category_projection(
corpus,
n_dims=3,
n_steps=10,
projector=lambda n_terms, n_dims: CategoryProjector(AssociationCompactor(n_terms, scorer=RankDifference),
projector=PCA(n_dims)),
verbose=False
):
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley = RipleysKEstimator(area=1., x_max=1., y_max=1., x_min=0., y_min=0.)
min_dev = None
best_k = None
best_x = None
best_y = None
best_projector = None
for k in np.power(2, np.linspace(np.log(corpus.get_num_categories()) / np.log(2),
np.log(corpus.get_num_terms()) / np.log(2), n_steps)).astype(int):
r = np.linspace(0, np.sqrt(2), 100)
category_projector = projector(k, n_dims)
category_projection = category_projector.project(corpus)
for dim_1 in range(0, n_dims):
for dim_2 in range(dim_1 + 1, n_dims):
proj = category_projection.projection[:, [dim_1, dim_2]]
scaled_proj = np.array([stretch_0_to_1(proj.T[0]), stretch_0_to_1(proj.T[1])]).T
dev = np.sum(np.abs(ripley(scaled_proj, r, mode='ripley') - ripley.poisson(r)))
if min_dev is None or dev < min_dev:
min_dev = dev
best_k = k
best_projector = category_projector
best_x, best_y = (dim_1, dim_2)
if verbose:
print(k, dim_1, dim_2, dev, best_k, best_x, best_y, min_dev)
if verbose:
print(best_k, best_x, best_y)
return best_projector.project(corpus, best_x, best_y)
def evaluate(self, category_projection):
assert type(category_projection) == CategoryProjection
try:
from astropy.stats import RipleysKEstimator
except:
raise Exception("Please install astropy")
ripley_estimator = RipleysKEstimator(area=1.,
x_max=1.,
y_max=1.,
x_min=0.,
y_min=0.)
proj = category_projection.projection[:, [
category_projection.x_dim, category_projection.y_dim
]]
scaled_proj = np.array(
[stretch_0_to_1(proj.T[0]),
stretch_0_to_1(proj.T[1])]).T
radii = np.linspace(0, self.max_distance, 1000)
deviances = np.abs(
ripley_estimator(scaled_proj, radii, mode='ripley') -
ripley_estimator.poisson(radii))
return np.trapz(deviances, x=radii)
