def test_negative_binomial_obj_sparse_dispersion_biased():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis ** alpha
_, mean, variance, _ = dispersion.compute_mean_variance(
counts**2,
np.array([counts.shape[0]]))
mean, variance = mean[:-1], variance[:-1]
d = dispersion.ExponentialDispersion()
d.fit(mean, variance)
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts)
obj_sparse = negative_binomial_structure.negative_binomial_obj(
X, counts, dispersion=d, alpha=alpha, beta=beta)
obj_dense = negative_binomial_structure.negative_binomial_obj(
X, counts.toarray(), dispersion=d, alpha=alpha, beta=beta)
obj_ = negative_binomial_structure.negative_binomial_obj(
random_state.rand(*X.shape),
counts, dispersion=d, alpha=alpha, beta=beta)
assert(obj_sparse < obj_)
assert_almost_equal(obj_sparse, obj_dense, 6)
def test_negative_binomial_obj_sparse():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis ** alpha
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts)
obj = negative_binomial_structure.negative_binomial_obj(
X, counts, alpha=alpha, beta=beta)
# Create a random structure
rand_X = random_state.rand(*X.shape)
# Compute the objective function for both sparse and dense
obj_sparse = negative_binomial_structure.negative_binomial_obj(
rand_X,
counts, alpha=alpha, beta=beta)
obj_dense = negative_binomial_structure.negative_binomial_obj(
rand_X,
counts.toarray(), alpha=alpha, beta=beta)
# The objective for a random point should be larger than for the solution
assert(obj < obj_sparse)
# The objective in sparse and dense should be equal
assert_almost_equal(obj_sparse, obj_dense)
def test_negative_binomial_obj_dense():
lengths = np.array([5, 5])
n = lengths.sum()
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis ** alpha
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
obj = negative_binomial_structure.negative_binomial_obj(
X, counts, alpha=alpha, beta=beta)
obj_ = negative_binomial_structure.negative_binomial_obj(
random_state.rand(*X.shape),
counts, alpha=alpha, beta=beta)
assert(obj < obj_)
obj = negative_binomial_structure.negative_binomial_obj(
X, counts, alpha=alpha, beta=beta, use_zero_counts=True)
obj_ = negative_binomial_structure.negative_binomial_obj(
random_state.rand(*X.shape),
counts, alpha=alpha, beta=beta, use_zero_counts=True)
assert(obj < obj_)
def test_negative_binomial_obj_sparse_dispersion_biased():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis ** alpha
return True
from minorswing import dispersion
mean, variance = dispersion.compute_mean_variance(
counts**2,
np.array([counts.shape[0]]))
mean, variance = mean[:-1], variance[:-1]
d = dispersion.Dispersion()
d.fit(mean, variance)
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts)
obj = negative_binomial_structure.negative_binomial_obj(
X, counts, dispersion=d, alpha=alpha, beta=beta)
obj_ = negative_binomial_structure.negative_binomial_obj(
random_state.rand(*X.shape),
counts, dispersion=d, alpha=alpha, beta=beta)
assert(obj < obj_)
def test_negative_binomial_obj_sparse():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis ** alpha
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts)
obj = negative_binomial_structure.negative_binomial_obj(
X, counts, alpha=alpha, beta=beta)
obj_ = negative_binomial_structure.negative_binomial_obj(
random_state.rand(*X.shape),
counts, alpha=alpha, beta=beta)
assert(obj < obj_)
