def test_sparse_ICE_normalization():
n = 100
X = np.random.random((n, n))
thres = (np.random.random((n, n)) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.csr_matrix(X)
true_normed_X = ICE_normalization(X, eps=1e-10, max_iter=10)
normed_X = ICE_normalization(sparse_X, eps=1e-10, max_iter=10)
assert_array_almost_equal(X, sparse_X.todense())
assert_array_almost_equal(true_normed_X, np.array(normed_X.todense()))
def test_sparse_ICE_normalization():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.csr_matrix(X)
true_normed_X = ICE_normalization(X, eps=1e-10, max_iter=10)
normed_X = ICE_normalization(sparse_X, eps=1e-10, max_iter=10)
assert_array_almost_equal(X, sparse_X.todense())
assert_array_almost_equal(true_normed_X, np.array(normed_X.todense()))
def test_sparse_ICE_normalization_triu():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.triu(X)
true_normed_X = ICE_normalization(X, eps=1e-10, max_iter=10)
true_normed_X = np.triu(true_normed_X)
X = np.triu(X)
normed_X = ICE_normalization(sparse_X, eps=1e-10, max_iter=10)
assert_array_almost_equal(X, sparse_X.todense())
# The sparse and dense version are going to be equal up to a constant
# factor
normed_X *= true_normed_X.mean() / normed_X.mean()
assert_array_almost_equal(true_normed_X, np.array(normed_X.todense()))
def test_sparse_ICE_normalization_triu():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.triu(X)
true_normed_X, true_biases = ICE_normalization(X,
eps=1e-10,
max_iter=10,
output_bias=True)
true_normed_X = np.triu(true_normed_X)
normed_X_sparse, biases_sparse = ICE_normalization(sparse_X,
eps=1e-10,
max_iter=100,
output_bias=True)
normed_X_dense, biases_dense = ICE_normalization(np.triu(X),
eps=1e-10,
max_iter=100,
output_bias=True)
# The sparse and dense version are going to be equal up to a constant
# factor
assert_array_almost_equal(normed_X_dense,
np.array(normed_X_sparse.toarray()))
normed_X_sparse *= true_normed_X.mean() / normed_X_sparse.mean()
normed_X_dense *= true_normed_X.mean() / normed_X_dense.mean()
assert_array_almost_equal(true_normed_X,
np.array(normed_X_sparse.todense()))
assert_array_almost_equal(true_normed_X, normed_X_dense)
total_counts = 5000
normed_X = ICE_normalization(sparse_X,
eps=1e-10,
total_counts=total_counts)
assert pytest.approx(normed_X.sum(), total_counts)
def test_sparse_ICE_normalization_triu():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.triu(X)
true_normed_X, true_biases = ICE_normalization(
X, eps=1e-10, max_iter=10, output_bias=True)
true_normed_X = np.triu(true_normed_X)
normed_X_sparse, biases_sparse = ICE_normalization(
sparse_X, eps=1e-10, max_iter=100,
output_bias=True)
normed_X_dense, biases_dense = ICE_normalization(
np.triu(X), eps=1e-10, max_iter=100,
output_bias=True)
# The sparse and dense version are going to be equal up to a constant
# factor
assert_array_almost_equal(normed_X_dense,
np.array(normed_X_sparse.toarray()))
normed_X_sparse *= true_normed_X.mean() / normed_X_sparse.mean()
normed_X_dense *= true_normed_X.mean() / normed_X_dense.mean()
assert_array_almost_equal(true_normed_X,
np.array(normed_X_sparse.todense()))
assert_array_almost_equal(true_normed_X, normed_X_dense)
total_counts = 5000
normed_X = ICE_normalization(sparse_X, eps=1e-10,
total_counts=total_counts)
assert_almost_equal(normed_X.sum(), total_counts)
