def pct_rows(net, df, all_views, dist_type, rank_type):
from __init__ import Network
from copy import deepcopy
import numpy as np
import calc_clust, run_filter
copy_net = deepcopy(net)
if len(net.dat['node_info']['col']['cat']) > 0:
cat_key_col = {}
for i in range(len(net.dat['nodes']['col'])):
cat_key_col[net.dat['nodes']['col'][i]] = \
net.dat['node_info']['col']['cat'][i]
all_filt = range(10)
all_filt = [i / float(10) for i in all_filt]
mat = deepcopy(df['mat'])
sum_row = np.sum(mat, axis=1)
max_sum = max(sum_row)
for inst_filt in all_filt:
cutoff = inst_filt * max_sum
copy_net = deepcopy(net)
inst_df = deepcopy(df)
inst_df = run_filter.df_filter_row(inst_df, cutoff, take_abs=False)
tmp_net = deepcopy(Network())
tmp_net.df_to_dat(inst_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net,
dist_type=dist_type,
run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net,
dist_type=dist_type,
run_clustering=False)
inst_view = {}
inst_view['pct_row_' + rank_type] = inst_filt
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
pass
return all_views
def pct_rows(net, df, all_views, dist_type, rank_type):
from __init__ import Network
from copy import deepcopy
import numpy as np
import calc_clust, run_filter
copy_net = deepcopy(net)
if len(net.dat['node_info']['col']['cat']) > 0:
cat_key_col = {}
for i in range(len(net.dat['nodes']['col'])):
cat_key_col[net.dat['nodes']['col'][i]] = \
net.dat['node_info']['col']['cat'][i]
all_filt = range(10)
all_filt = [i / float(10) for i in all_filt]
mat = deepcopy(df['mat'])
sum_row = np.sum(mat, axis=1)
max_sum = max(sum_row)
for inst_filt in all_filt:
cutoff = inst_filt * max_sum
copy_net = deepcopy(net)
inst_df = deepcopy(df)
inst_df = run_filter.df_filter_row(inst_df, cutoff, take_abs=False)
tmp_net = deepcopy(Network())
tmp_net.df_to_dat(inst_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net, dist_type=dist_type,
run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net, dist_type=dist_type,
run_clustering=False)
inst_view = {}
inst_view['pct_row_' + rank_type] = inst_filt
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
pass
return all_views
def make_clust(net,
dist_type='cosine',
run_clustering=True,
dendro=True,
requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average',
sim_mat=False):
''' This will calculate multiple views of a clustergram by filtering the
data and clustering after each filtering. This filtering will keep the top
N rows based on some quantity (sum, num-non-zero, etc). '''
from copy import deepcopy
import calc_clust
import run_filter
import make_views
import scipy
df = net.dat_to_df()
threshold = 0.0001
df = run_filter.df_filter_row(df, threshold)
df = run_filter.df_filter_col(df, threshold)
# calculate initial view with no row filtering
net.df_to_dat(df)
# preparing to make similarity matrices of rows and cols
###########################################################
# tmp_dist_mat = calc_clust.calc_distance_matrix(net.dat['mat'], 'col',
# get_sim=True,
# make_squareform=True,
# filter_sim_below=0.1)
#
# print(tmp_dist_mat)
# print(net.dat['node_info']['row'])
# print('\n')
# print(net.dat['node_info']['col'])
calc_clust.cluster_row_and_col(net,
dist_type=dist_type,
linkage_type=linkage_type,
run_clustering=run_clustering,
dendro=dendro,
ignore_cat=False)
all_views = []
send_df = deepcopy(df)
if 'N_row_sum' in requested_views:
all_views = make_views.N_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='sum')
if 'N_row_var' in requested_views:
all_views = make_views.N_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='var')
if 'pct_row_sum' in requested_views:
all_views = make_views.pct_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='sum')
if 'pct_row_var' in requested_views:
all_views = make_views.pct_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='var')
if sim_mat is True:
print(
'make similarity matrices of rows and columns, add to viz data structure'
)
net.viz['views'] = all_views
def make_clust(net, dist_type='cosine', run_clustering=True,
dendro=True, requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average', sim_mat=False):
''' This will calculate multiple views of a clustergram by filtering the
data and clustering after each filtering. This filtering will keep the top
N rows based on some quantity (sum, num-non-zero, etc). '''
from copy import deepcopy
import calc_clust
import run_filter
import make_views
import scipy
df = net.dat_to_df()
threshold = 0.0001
df = run_filter.df_filter_row(df, threshold)
df = run_filter.df_filter_col(df, threshold)
# calculate initial view with no row filtering
net.df_to_dat(df)
# preparing to make similarity matrices of rows and cols
###########################################################
# tmp_dist_mat = calc_clust.calc_distance_matrix(net.dat['mat'], 'col',
# get_sim=True,
# make_squareform=True,
# filter_sim_below=0.1)
#
# print(tmp_dist_mat)
# print(net.dat['node_info']['row'])
# print('\n')
# print(net.dat['node_info']['col'])
calc_clust.cluster_row_and_col(net, dist_type=dist_type,
linkage_type=linkage_type,
run_clustering=run_clustering,
dendro=dendro, ignore_cat=False)
all_views = []
send_df = deepcopy(df)
if 'N_row_sum' in requested_views:
all_views = make_views.N_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='sum')
if 'N_row_var' in requested_views:
all_views = make_views.N_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='var')
if 'pct_row_sum' in requested_views:
all_views = make_views.pct_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='sum')
if 'pct_row_var' in requested_views:
all_views = make_views.pct_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='var')
if sim_mat is True:
print('make similarity matrices of rows and columns, add to viz data structure')
net.viz['views'] = all_views
