-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathworkers.py
195 lines (167 loc) · 7.46 KB
/
workers.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
#########################################################################################
# workers of calculate jaccard index
#
# Copyright 2025 Sony Corporation
#########################################################################################
import os
import pandas as pd
import argparse
import numpy as np
import copy
def calc_jaccard_meta(Outdir, workdir, fold_num, div_num, sample_name):
os.chdir(workdir)
print(sample_name)
base_path = os.path.join(Outdir,"FSOM",sample_name)
df = [None] * fold_num
df_cl = [None] * div_num * fold_num
df_merge = [None] * div_num
df_outer = [None] * div_num
df_rate = [None] * div_num
for fold in range(fold_num):
fname = "fsom_%s_jaccard_%d_meta.csv"%(sample_name,fold+1)
fpath = os.path.join(base_path, fname)
df[fold] = pd.read_csv(fpath)
for fold in range(fold_num):
for clust in range(div_num):
df_cl[fold * div_num + clust] = pd.DataFrame(df[fold][str(clust + 1)]).dropna()
result_list = []
for fold in range(fold_num):
print(fold)
for n in range(div_num):
compare = df_cl[fold * div_num + n]
for fold2 in range(fold_num):
if fold != fold2:
df_rate = [None] * div_num
for m in range(div_num):
new = df_cl[fold2 * div_num + m].rename(columns={str(m + 1):str(n + 1)})
df_merge[m] = pd.merge(new, compare).dropna()
df_outer[m] = pd.merge(new, compare, how='outer').dropna()
df_rate[m] = len(df_merge[m]) / len(df_outer[m])
res = [fold,n,fold2,df_rate.index(max(df_rate)),max(df_rate)]
result_list.append(res)
result_df = pd.DataFrame(result_list)
result_df.to_csv("%s_jaccard_fsom_meta.csv"%sample_name)
result_df.describe()
def calc_jaccard_clust(Outdir, workdir, fold_num, div_num, sample_name):
os.chdir(workdir)
print(sample_name)
base_path = os.path.join(Outdir,"FSOM",sample_name)
df = [None] * fold_num
df_cl = [None] * div_num * fold_num
df_merge = [None] * div_num
df_outer = [None] * div_num
df_rate = [None] * div_num
for fold in range(fold_num):
fname = "fsom_%s_jaccard_%d_clust.csv"%(sample_name,fold+1)
fpath = os.path.join(base_path, fname)
df[fold] = pd.read_csv(fpath)
for fold in range(fold_num):
for clust in range(div_num):
df_cl[fold * div_num + clust] = pd.DataFrame(df[fold][str(clust + 1)]).dropna()
result_list = []
for fold in range(fold_num):
print("top_fold")
print(fold)
for n in range(div_num):
compare = df_cl[fold * div_num + n]
for fold2 in range(fold_num):
if fold != fold2:
df_rate = [None] * div_num
for m in range(div_num):
new = df_cl[fold2 * div_num + m].rename(columns={str(m + 1):str(n + 1)})
df_merge[m] = pd.merge(new, compare).dropna()
df_outer[m] = pd.merge(new, compare, how='outer').dropna()
df_rate[m] = len(df_merge[m]) / len(df_outer[m])
res = [fold,n,fold2,df_rate.index(max(df_rate)),max(df_rate)]
result_list.append(res)
result_df = pd.DataFrame(result_list)
result_df.to_csv("%s_jaccard_fsom_clust.csv"%sample_name)
result_df.describe()
def calc_jaccard_meta_table4(Outdir, workdir, fold_num, div_num):
os.chdir(workdir)
base_path = os.path.join(Outdir,)
df = [None] * fold_num
df_cl = [None] * div_num * fold_num
df_merge = [None] * div_num
df_outer = [None] * div_num
df_rate = [None] * div_num
for fold in range(fold_num):
fname = "fsom_samsik_all_head%d_jaccard_meta.csv"%(fold+1)
#fname = "fsom_%s_jaccard_%d_meta.csv"%(sample_name,fold+1)
fpath = os.path.join(base_path, fname)
df[fold] = pd.read_csv(fpath)
for fold in range(fold_num):
for clust in range(div_num):
df_cl[fold * div_num + clust] = pd.DataFrame(df[fold][str(clust + 1)]).dropna()
result_list = []
for fold in range(fold_num):
print(fold)
for n in range(div_num):
compare = df_cl[fold * div_num + n]
for fold2 in range(fold_num):
if fold != fold2:
df_rate = [None] * div_num
for m in range(div_num):
new = df_cl[fold2 * div_num + m].rename(columns={str(m + 1):str(n + 1)})
df_merge[m] = pd.merge(new, compare).dropna()
df_outer[m] = pd.merge(new, compare, how='outer').dropna()
df_rate[m] = len(df_merge[m]) / len(df_outer[m])
res = [fold,n,fold2,df_rate.index(max(df_rate)),max(df_rate)]
result_list.append(res)
result_df = pd.DataFrame(result_list)
result_df.to_csv("samsik_reorder_jaccard_fsom_meta_table4.csv")
result_df.describe()
def calc_jaccard_clust_table4(Outdir, workdir, fold_num, div_num):
os.chdir(workdir)
base_path = os.path.join(Outdir)
df = [None] * fold_num
df_cl = [None] * div_num * fold_num
df_merge = [None] * div_num
df_outer = [None] * div_num
df_rate = [None] * div_num
for fold in range(fold_num):
fname = "fsom_samsik_all_head%d_jaccard_clust.csv"%(fold+1)
fpath = os.path.join(base_path, fname)
df[fold] = pd.read_csv(fpath)
for fold in range(fold_num):
for clust in range(div_num):
df_cl[fold * div_num + clust] = pd.DataFrame(df[fold][str(clust + 1)]).dropna()
result_list = []
for fold in range(fold_num):
print("top_fold")
print(fold)
for n in range(div_num):
compare = df_cl[fold * div_num + n]
for fold2 in range(fold_num):
if fold != fold2:
df_rate = [None] * div_num
for m in range(div_num):
new = df_cl[fold2 * div_num + m].rename(columns={str(m + 1):str(n + 1)})
df_merge[m] = pd.merge(new, compare).dropna()
df_outer[m] = pd.merge(new, compare, how='outer').dropna()
df_rate[m] = len(df_merge[m]) / len(df_outer[m])
res = [fold,n,fold2,df_rate.index(max(df_rate)),max(df_rate)]
result_list.append(res)
result_df = pd.DataFrame(result_list)
result_df.to_csv("samsik_reorder_jaccard_fsom_clust_table4.csv")
result_df.describe()
def get_rawnum(sample_id, event, df):
d = df[df["sample"] == sample_id]
#print(d)
c = d[d["event"] == event]["Unnamed: 0"].iloc[0]
return c
def save_trans_result(ref_df, clust_fname, table_fname, out_fname):
cdf = pd.read_csv(clust_fname)
dest = copy.deepcopy(cdf)
table_df = pd.read_csv(table_fname)
idx = 0
for row in table_df.itertuples():
if idx %100000 == 0:
print(idx)
sample_id = row.sample
event = row.event
row = get_rawnum(sample_id,event, ref_df) -1
label_id = cdf["label"][idx]
dest["label"][row] = label_id
idx = idx +1
dest.to_csv(out_fname, index=False)