def calculate_correlations(table: Table, corr_method: str='spearman',
p_adjustment_method: str='fdr_bh') -> pd.DataFrame:
# TODO: multiprocess this
corr_method_fun = correl_methods[corr_method]
correls = pd.DataFrame(index=['r', 'p'])
for (val_i, id_i, _), (val_j, id_j, _) in table.iter_pairwise(axis='observation'):
r, p = corr_method_fun(val_i, val_j)
correls[id_i, id_j] = r, p
correls = correls.transpose()
correls.index = pd.MultiIndex.from_tuples(correls.index) # Turn tuple index into actual multiindex
if p_adjustment_method is not None:
correls['p_adjusted'] = p_adjust(correls.p, method=p_adjustment_method)
correls = correls.sort_values('p')
return correls
def calculate_correlations(
table: Table,
corr_method=spearmanr,
p_adjustment_method: str = 'fdr_bh') -> pd.DataFrame:
# TODO: multiprocess this
index = list()
data = list()
for (val_i, id_i, _), (val_j, id_j,
_) in table.iter_pairwise(axis='observation'):
r, p = corr_method(val_i, val_j)
index.append((id_i, id_j))
data.append((r, p))
correls = pd.DataFrame(data, index=index, columns=['r', 'p'])
correls.index = pd.MultiIndex.from_tuples(
correls.index) # Turn tuple index into actual multiindex
if p_adjustment_method is not None:
correls['p_adjusted'] = p_adjust(correls.p, method=p_adjustment_method)
return correls
def calculate_correlations(table: Table, corr_method=spearmanr, p_adjust_method: str = 'fdr_bh', nprocs=1) -> \
pd.DataFrame:
if nprocs > multiprocessing.cpu_count():
warnings.warn(
"nprocs greater than CPU count, using all avaliable CPUs")
nprocs = multiprocessing.cpu_count()
pool = multiprocessing.Pool(nprocs)
cor = partial(calculate_correlation, corr_method=corr_method)
results = pool.map(
cor,
pairwise_iter_wo_metadata(table.iter_pairwise(axis='observation')))
index = [i[0] for i in results]
data = [i[1] for i in results]
pool.close()
pool.join()
correls = pd.DataFrame(data, index=index, columns=['r', 'p'])
# Turn tuple index into actual multiindex, now guaranteeing that correls index is sorted
correls.index = pd.MultiIndex.from_tuples(
[sorted(i) for i in correls.index])
if p_adjust_method is not None:
correls['p_adjusted'] = p_adjust(correls.p, method=p_adjust_method)
return correls
