def run_cc_net_nmf_clusters_worker(network_mat, spreadsheet_mat, lap_dag,
lap_val, run_parameters, sample):
"""Worker to execute net_nmf_clusters in a single process
Args:
network_mat: genes x genes symmetric matrix.
spreadsheet_mat: genes x samples matrix.
lap_dag: laplacian matrix component, L = lap_dag - lap_val.
lap_val: laplacian matrix component, L = lap_dag - lap_val.
run_parameters: dictionay of run-time parameters.
sample: each single loop.
Returns:
None
"""
np.random.seed(sample)
rows_sampling_fraction = run_parameters["rows_sampling_fraction"]
cols_sampling_fraction = run_parameters["cols_sampling_fraction"]
spreadsheet_mat, \
sample_permutation = kn.sample_a_matrix( spreadsheet_mat
, rows_sampling_fraction
, cols_sampling_fraction )
spreadsheet_mat, \
iterations = kn.smooth_matrix_with_rwr(spreadsheet_mat, network_mat, run_parameters)
spreadsheet_mat = kn.get_quantile_norm_matrix(spreadsheet_mat)
h_mat = kn.perform_net_nmf(spreadsheet_mat, lap_val, lap_dag,
run_parameters)
save_a_clustering_to_tmp(h_mat, sample_permutation, run_parameters, sample)
def run_cc_kmeans_clusters_worker(spreadsheet_mat, run_parameters, sample):
#-----------------------------------------------------
"""Worker to execute kmeans in a single process
Args:
spreadsheet_mat: genes x samples matrix.
run_parameters: dictionary of run-time parameters.
sample: each loops.
Returns:
None
"""
import knpackage.toolbox as kn
import numpy as np
np.random.seed(sample)
rows_sampling_fraction = run_parameters["rows_sampling_fraction"]
cols_sampling_fraction = run_parameters["cols_sampling_fraction"]
number_of_clusters = run_parameters["number_of_clusters"]
spreadsheet_mat, sample_permutation = kn.sample_a_matrix(
spreadsheet_mat, rows_sampling_fraction, cols_sampling_fraction)
spreadsheet_mat_T = spreadsheet_mat.T
labels = kn.perform_kmeans(spreadsheet_mat_T, number_of_clusters)
h_mat = labels_to_hmat(labels, number_of_clusters)
kn.save_a_clustering_to_tmp(h_mat, sample_permutation, run_parameters,
sample)
def run_cc_nmf_clusters_worker(spreadsheet_mat, run_parameters, sample):
"""Worker to execute nmf_clusters in a single process
Args:
spreadsheet_mat: genes x samples matrix.
run_parameters: dictionary of run-time parameters.
sample: each loops.
Returns:
None
"""
np.random.seed(sample)
rows_sampling_fraction = run_parameters["rows_sampling_fraction"]
cols_sampling_fraction = run_parameters["cols_sampling_fraction"]
spreadsheet_mat, \
sample_permutation = kn.sample_a_matrix( spreadsheet_mat
, rows_sampling_fraction
, cols_sampling_fraction )
h_mat = kn.perform_nmf(spreadsheet_mat, run_parameters)
save_a_clustering_to_tmp(h_mat, sample_permutation, run_parameters, sample)
def run_cc_hclust_clusters_worker(spreadsheet_mat, run_parameters, sample):
#-----------------------------------------------------
"""Worker to execute hclust in a single process
Args:
spreadsheet_mat: genes x samples matrix.
run_parameters: dictionary of run-time parameters.
sample: each loops.
Returns:
None
"""
import knpackage.toolbox as kn
import numpy as np
np.random.seed(sample)
rows_sampling_fraction = run_parameters["rows_sampling_fraction"]
cols_sampling_fraction = run_parameters["cols_sampling_fraction"]
number_of_clusters = run_parameters["number_of_clusters"]
affinity_metric = run_parameters['affinity_metric']
linkage_criterion = run_parameters['linkage_criterion']
spreadsheet_mat, sample_permutation = kn.sample_a_matrix(
spreadsheet_mat, rows_sampling_fraction, cols_sampling_fraction)
labels, _ = perform_hclust(spreadsheet_mat, number_of_clusters,
affinity_metric, linkage_criterion)
h_mat = labels_to_hmat(labels, number_of_clusters)
kn.save_a_clustering_to_tmp(h_mat, sample_permutation, run_parameters,
sample)
def test_sample_a_matrix(self):
""" assert that the random sample is of the propper size, the
permutation points to the correct columns and that the number of
rows set to zero is correct.
"""
n_test_rows = 11
n_test_cols = 5
pct_smpl = 0.6
n_zero_rows = int(np.round(n_test_rows * (1 - pct_smpl)))
n_smpl_cols = int(np.round(n_test_cols * pct_smpl))
epsilon_sum = max(n_test_rows, n_test_cols) * 1e-15
A = np.random.rand(n_test_rows, n_test_cols) + epsilon_sum
B, P = kn.sample_a_matrix(A, pct_smpl, pct_smpl)
self.assertEqual(B.shape[1],
P.size,
msg='permutation size not equal columns')
self.assertEqual(P.size,
n_smpl_cols,
msg='number of sample columns exception')
perm_err_sum = 0
n_zero_err_sum = 0
B_col = 0
for A_col in P:
n_zeros = (np.int_(B[:, B_col] == 0)).sum()
if n_zeros != n_zero_rows:
n_zero_err_sum += 1
C = A[:, A_col] - B[:, B_col]
C[B[:, B_col] == 0] = 0
B_col += 1
if C.sum() > epsilon_sum:
perm_err_sum += 1
self.assertEqual(n_zero_err_sum,
0,
msg='number of zero columns exception')
self.assertEqual(perm_err_sum, 0, msg='permutation index exception')
