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main3_partition_search_seeds.py
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main3_partition_search_seeds.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Mar 3 17:36:07 2020
@author: Meg_94
"""
from time import time as time_time
start_time = time_time()
from matplotlib import use as mpl_use
mpl_use('Agg') # Issues warning on spyder - don't worry abt it
from os import path as os_path, mkdir as os_mkdir, chdir as os_chdir
os_chdir(os_path.dirname(os_path.abspath(__file__)))
from sys import path as sys_path
# insert at 1, 0 is the script path (or '' in REPL)
sys_path.insert(1, './functions_py3/')
from yaml import load as yaml_load, dump as yaml_dump, Loader as yaml_Loader
from argparse import ArgumentParser as argparse_ArgumentParser
from pickle import load as pickle_load, dump as pickle_dump
from logging import basicConfig as logging_basicConfig, INFO as logging_INFO, DEBUG as logging_DEBUG
from numpy.random import permutation as rand_perm
from networkx import find_cliques as nx_find_cliques
from os import listdir as os_listdir, path as os_path
from numpy import percentile as np_percentile
from math import ceil as math_ceil
def main():
parser = argparse_ArgumentParser("Input parameters")
parser.add_argument("--input_file_name", default="input_toy.yaml", help="Input parameters file name")
parser.add_argument("--out_dir_name", default="/results", help="Output directory name")
parser.add_argument("--train_test_files_dir", default="", help="Train test file path")
parser.add_argument("--graph_files_dir", default="", help="Graph files' folder path")
parser.add_argument("--seed_mode", help="Seed mode - specify 'cliques' for the cliques algo")
parser.add_argument("--max_size_thres", help="Max size threshold")
parser.add_argument("--n_pts", default=1, help="number of partitions (computers)")
args = parser.parse_args()
with open(args.input_file_name, 'r') as f:
inputs = yaml_load(f, yaml_Loader)
if args.seed_mode:
inputs['seed_mode'] = args.seed_mode
if args.max_size_thres:
inputs['max_size_thres'] = int(args.max_size_thres)
# Override output directory name if same as gen
if args.out_dir_name or inputs['out_comp_nm'] == "/results/res":
if not os_path.exists(inputs['dir_nm'] + args.out_dir_name):
os_mkdir(inputs['dir_nm'] + args.out_dir_name)
inputs['out_comp_nm'] = args.out_dir_name + "/res"
inputs['train_test_files_dir'] = ''
if args.train_test_files_dir:
if not os_path.exists(inputs['dir_nm'] + args.train_test_files_dir):
os_mkdir(inputs['dir_nm'] + args.train_test_files_dir)
inputs['train_test_files_dir'] = args.train_test_files_dir
inputs['graph_files_dir'] = ''
if args.graph_files_dir:
if not os_path.exists(inputs['dir_nm'] + args.graph_files_dir):
os_mkdir(inputs['dir_nm'] + args.graph_files_dir)
inputs['graph_files_dir'] = args.graph_files_dir
with open(inputs['dir_nm'] + inputs['out_comp_nm'] + "_input_sample_partition.yaml", 'w') as outfile:
yaml_dump(inputs, outfile, default_flow_style=False)
logging_basicConfig(filename=inputs['dir_nm'] + inputs['out_comp_nm'] + "_logs.yaml", level=logging_INFO)
neig_dicts_folder = inputs['dir_nm'] +inputs['graph_files_dir']+ "/neig_dicts"
num_comp = inputs['num_comp']
max_size_thres = inputs['max_size_thres']
max_size_trainF = inputs['dir_nm'] + inputs['train_test_files_dir']+ "/res_max_size_train"
with open(max_size_trainF, 'rb') as f:
max_size_train = pickle_load(f)
max_size = max_size_train
max_sizeF_feat = inputs['dir_nm'] + inputs['train_test_files_dir']+ "/res_max_size_search"
if os_path.exists(max_sizeF_feat):
with open(max_sizeF_feat, 'rb') as f:
max_size = pickle_load(f)
else:
with open(inputs['dir_nm'] + inputs['comf_nm']) as f:
sizes = [len(line.rstrip().split()) for line in f.readlines()]
max_size = max(sizes)
q1 = np_percentile(sizes, 25)
q3 = np_percentile(sizes, 75)
max_wo_outliers = math_ceil(q3 + 4.5*(q3-q1)) # Maximum after removing outliers
max_size = min(max_size,max_wo_outliers)
if max_size >= max_size_thres:
max_size = max_size_thres
out_comp_nm = inputs['dir_nm'] + inputs['out_comp_nm']
with open(out_comp_nm + '_metrics.out', "a") as fid:
print("Max number of steps for complex growth = ", max_size, file=fid) # NOT actual max size since you merge later
max_sizeF = inputs['dir_nm'] + inputs['train_test_files_dir']+ "/res_max_size_search_par"
with open(max_sizeF, 'wb') as f:
pickle_dump(max_size, f)
seed_mode = inputs['seed_mode']
if seed_mode == "all_nodes":
#graph_nodes = list(myGraph.nodes())
seed_nodes = rand_perm(os_listdir(neig_dicts_folder))
elif seed_mode == "n_nodes":
seed_nodes = rand_perm(os_listdir(neig_dicts_folder))[:num_comp]
elif seed_mode == "all_nodes_known_comp":
protlistfname = inputs['dir_nm']+ inputs['train_test_files_dir'] + "/res_protlist"
with open(protlistfname, 'rb') as f:
prot_list = pickle_load(f)
seed_nodes = list(prot_list)
elif seed_mode == "cliques":
myGraphName = inputs['dir_nm'] + inputs['graph_files_dir']+ "/res_myGraph"
with open(myGraphName, 'rb') as f:
myGraph = pickle_load(f)
clique_list = list(nx_find_cliques(myGraph))
to_rem = []
# Removing 2 node and big complexes
for comp in clique_list:
if len(comp) <= 2 or len(comp) >= max_size:
to_rem.append(comp)
for comp in to_rem:
clique_list.remove(comp)
seed_nodes = clique_list # Remove duplicates later.
# partition
ptns = int(args.n_pts)
nc = len(seed_nodes)
if seed_mode == 'n_nodes':
seed_nodes_F = out_comp_nm + "_seed_nodes"
each_ptn = nc // ptns
for i in range(ptns - 1):
with open(seed_nodes_F + str(i), 'wb') as f:
pickle_dump(seed_nodes[i * each_ptn:(i + 1) * each_ptn], f)
with open(seed_nodes_F + str(ptns - 1), 'wb') as f:
pickle_dump(seed_nodes[(ptns - 1) * each_ptn:], f)
else:
seed_nodes_dir = inputs['dir_nm'] + inputs['graph_files_dir']+ "/" + seed_mode + "_n_pts_" + str(ptns)
if not os_path.exists(seed_nodes_dir):
os_mkdir(seed_nodes_dir)
seed_nodes_F = seed_nodes_dir + "/res_seed_nodes"
each_ptn = nc // ptns
for i in range(ptns - 1):
with open(seed_nodes_F + str(i), 'wb') as f:
pickle_dump(seed_nodes[i * each_ptn:(i + 1) * each_ptn], f)
with open(seed_nodes_F + str(ptns - 1), 'wb') as f:
pickle_dump(seed_nodes[(ptns - 1) * each_ptn:], f)
if __name__ == '__main__':
main()