def test_ICE_normalization_cancer():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
X = X + X.T
profile = np.ones(n)
profile[:10] = 0
profile[50:] = 2
normed_X, bias = ICE_normalization(X,
eps=1e-10,
counts_profile=profile,
output_bias=True)
assert not np.all(np.isnan(normed_X))
normed_X[np.isnan(normed_X)] = 0
mask = np.isnan(bias).flatten()
bias[np.isnan(bias)] = 1
normed_from_bias_X = X / (bias.T * bias)
normed_from_bias_X[mask] = 0
normed_from_bias_X[:, mask] = 0
assert_array_almost_equal(normed_X, normed_from_bias_X, 6)
inferred_profile = normed_X.sum(axis=0)
inferred_profile /= inferred_profile.max()
assert_array_almost_equal(inferred_profile, profile / profile.max())
# Do the same for sparse matriecs
normed_X = ICE_normalization(sparse.coo_matrix(X),
eps=1e-10,
counts_profile=profile)
def test_ICE_normalization():
n = 100
X = np.random.random((n, n))
X = X + X.T
normed_X = ICE_normalization(X, eps=1e-10, max_iter=1000000)
normed = normed_X.sum(axis=1)
assert_array_almost_equal(normed / normed.mean(), np.ones((len(X), )),
decimal=0)
def test_ICE_normalization():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
X = X + X.T
normed_X = ICE_normalization(X, eps=1e-10, max_iter=1000000)
normed = normed_X.sum(axis=1)
assert_array_almost_equal(normed / normed.mean(), np.ones((len(X), )),
decimal=0)
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)
