import os
import pandas as pd
from ROC.get_fpr_tpr import PlotRoc
foo = PlotRoc('/home/yangfang/GFICLEE/test_cel_gficlee/input/',
'/home/yangfang/GFICLEE/test_cel_gficlee/output/',
'/home/yangfang/GFICLEE/test_cel_gficlee/cel.genome_filter.txt',
'/home/yangfang/GFICLEE/test_cel_gficlee/result/')
# threshold
thr = list(reversed([i / 1 for i in range(0, 25)]))
thr2 = [-x / 1 for x in list(range(0, 30, 1))]
thr.extend(thr2)
all_tpr_fpr_precision, all_r = foo.start_roc(6, thr)
foo.write_tpr_fpr(
all_tpr_fpr_precision,
'/home/yangfang/GFICLEE/test_cel_gficlee/cel_tpr_fpr_precision_gficlee.txt'
)
foo.write_all(
all_r,
'/home/yangfang/GFICLEE/test_cel_gficlee/cel_tp_fp_tn_fn_gficlee.txt')
# all_res = list(filter(lambda f: not f.startswith('.'), os.listdir(os.path.join(input_path,i))))
# if all_res != []:
# for j in all_res:
# read_path = os.path.join(input_path,i + '/' + j)
# data = pd.read_csv(read_path,sep='\t')
# df = df.append(data,ignore_index=True)
# df = df.sort_values('score', ascending=False)
# df.to_csv(w_path_name,sep='\t',index=False)
#
# trans_gficlee(input_path='/home/yangfang/GFICLEE/test_corum_gficlee/result/',
# out_path='/home/yangfang/GFICLEE/test_corum_gficlee/result_trans/',)
#Second get fpr and tpr
foo = PlotRoc('/home/yangfang/GFICLEE/test_corum_gficlee/input/',
'/home/yangfang/GFICLEE/test_corum_gficlee/output/',
'/home/yangfang/GFICLEE/test_corum_gficlee/human.corum.txt',
'/home/yangfang/GFICLEE/test_corum_gficlee/result/')
# threshold
thr = list(reversed([i / 1 for i in range(0, 35)]))
thr2 = [-x / 1 for x in list(range(0, 20, 1))]
thr.extend(thr2)
all_tpr_fpr_precision, all_r = foo.start_roc(6, thr)
foo.write_tpr_fpr(
all_tpr_fpr_precision,
'/home/yangfang/GFICLEE/test_corum_gficlee/kegg_tpr_fpr_precision_corum.txt'
)
foo.write_all(
all_r,
# get ecm+ and llr
re = data[[name, 'LLR']][data['ECM/ECM+'].isin(['ECM+'])]
names = ['name', 'score']
re.columns = names
re = re.sort_values('score', ascending=False)
re.to_csv(w_path_name, sep='\t', index=False)
trans_clime('/home/yangfang/GFICLEE/test_ncr_clime/result_all/',
'/home/yangfang/GFICLEE/test_ncr_clime/result_all_trans/',
'Gene Symbol')
#Second get fpr and tpr
foo = PlotRoc('/home/yangfang/GFICLEE/test_ncr_clime/input/',
'/home/yangfang/GFICLEE/test_ncr_clime/output/',
'/home/yangfang/GFICLEE/test_ncr_clime/ncr.genome.txt',
'/home/yangfang/GFICLEE/test_ncr_clime/result_all_trans/')
# threshold
thr = list(reversed([i / 1 for i in range(50)]))
# thr = list(reversed([i / 1 for i in range(0, 20)]))
# thr2 = [-x / 1 for x in list(range(0, 10))]
# thr.extend(thr2)
# thr = list(reversed([i/1 for i in range(0, 25)]))
# thr2 = [-x / 2 for x in list(range(0, 20,1))]
# thr.extend(thr2)
# thr = [-x / 1 for x in list(range(50, 130,2))][-25:]
# thr = list([i / 500 for i in range(60)])[0:30]
all_tpr_fpr_precision, all_r = foo.start_roc(6, thr)
# read_path = os.path.join(input_path,i + '/' + j)
# data = pd.read_csv(read_path,sep='\t')
# df = df.append(data,ignore_index=True)
# df = df.sort_values('score', ascending=False)
# df.to_csv(w_path_name,sep='\t',index=False)
#
# trans_gficlee(input_path='/home/yangfang/GFICLEE/test_ath_gficlee/result/',
# out_path='/home/yangfang/GFICLEE/test_ath_gficlee/result_trans/',)
#Second get fpr and tpr
foo = PlotRoc('/home/yangfang/GFICLEE/test_ath_gficlee/input/',
'/home/yangfang/GFICLEE/test_ath_gficlee/output/',
'/home/yangfang/GFICLEE/test_ath_gficlee/ath00001_5_filter.txt',
'/home/yangfang/GFICLEE/test_ath_gficlee/result_5/')
# threshold
thr = list(reversed([i/1 for i in range(0, 35)]))
thr2 = [-x / 1 for x in list(range(0, 30,1))]
thr.extend(thr2)
all_tpr_fpr_precision, all_r = foo.start_roc(6, thr)
foo.write_tpr_fpr(all_tpr_fpr_precision,
'/home/yangfang/GFICLEE/test_ath_gficlee/ath_tpr_fpr_precision_gficlee5.txt')
foo.write_all(all_r,
'/home/yangfang/GFICLEE/test_ath_gficlee/ath_tp_fp_tn_fn_gficlee5.txt')