-
Notifications
You must be signed in to change notification settings - Fork 0
/
get_results_mean_std_multiple_complete.py
140 lines (127 loc) · 9.82 KB
/
get_results_mean_std_multiple_complete.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
import json
import numpy as np
# Use this script if you want to obtain the mean and std of models trained and tested with incomplete data
"""
root_name = './results/'
MODEL_ROOT = [
"{}", "{}_withDropout0.1",
"embrace{}",
"embrace{}_withDropout0.1"
]
"""
#root_name = '/media/ceslea/DATA/EmbraceBERT-results-backup/'
root_name = './results/'
MODEL_ROOT = [
#"{}",
#"{}withatt", "{}withattclsprojection",
#"{}withprojection", "{}withprojectionatt",
#"embrace{}_attention_p_multinomial",
#"embrace{}_attention_p_attention_clsquery_weights",
#"embrace{}_projection_p_multinomial",
#"embrace{}_projection_p_attention_clsquery_weights",
#"embrace{}concatatt_attention_p_multinomial",
#"embrace{}concatatt_attention_p_attention_clsquery_weights",
#"embrace{}concatatt_projection_p_multinomial",
#"embrace{}concatatt_projection_p_attention_clsquery_weights",
"embrace{}withkeyvaluequery_attention_p_multinomial",
"embrace{}withkeyvaluequery_attention_p_attention_clsquery_weights",
"embrace{}withkeyvaluequery_projection_p_multinomial",
"embrace{}withkeyvaluequery_projection_p_attention_clsquery_weights",
"embrace{}withkeyvaluequeryconcatatt_attention_p_multinomial",
"embrace{}withkeyvaluequeryconcatatt_attention_p_attention_clsquery_weights",
"embrace{}withkeyvaluequeryconcatatt_projection_p_multinomial",
"embrace{}withkeyvaluequeryconcatatt_projection_p_attention_clsquery_weights",
]
MODEL_BERT = []
for M in MODEL_ROOT:
MODEL_BERT.append(M.format('bert'))
#MODEL_ROBERTA = []
#for M in MODEL_ROOT:
# MODEL_ROBERTA.append(M.format('roberta'))
MODEL_NAME = {"bert": " BERT-bs{} ",
"bert_withDropout0.1": " BERT-bs{}+Dropout0.1 ",
"bertplustransformerlayer": " BERT+Transf_1HiddenLayer ",
"bertplustransformerlayer_12hiddenlayers": " BERT+Transf_12HiddenLayers ",
"bertwithatt": " BERTwithAtt-bs{} ",
"bertwithprojection": " BERTwithProjection-bs{} ",
"bertwithprojectionatt": " BERTwithProjectionAtt-bs{} ",
"bertwithattprojection": " BERTwithAttProjection-bs{} ",
"bertwithattclsprojection": " BERTwithAttClsProjection-bs{} ",
"embracebert": " EmbraceBERT-bs{} ",
"embracebert_withDropout0.1": " EmbraceBERT-bs{}+Dropout0.1 ",
"embracebert_attention_p_multinomial": " EmbraceBERT-bs{} ",
"embracebert_attention_p_attention_clsquery_weights": " EmbraceBERT-bs{}-p_att_clsquery_weights ",
"embracebert_projection_p_multinomial": " EmbraceBERTwithProj-bs{} ",
"embracebert_projection_p_attention_clsquery_weights":" EmbraceBERTwithProj-bs{}-p_attclsqw ",
"embracebertwithkeyvaluequery_p_multinomial": " EmbraceBERT-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracebertwithkeyvaluequery_p_selfattention": " EmbraceBERT-bs{}-p_multiheadatt_bertKeyValQuery_selfatt ",
"embracebertwithkeyvaluequery_attention_p_multinomial": " EmbraceBERT-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracebertwithkeyvaluequery_attention_p_attention_clsquery_weights": " EmbraceBERT-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
"embracebertwithkeyvaluequery_projection_p_multinomial": " EmbraceBERTwithProj-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracebertwithkeyvaluequery_projection_p_attention_clsquery_weights": " EmbraceBERTwithProj-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
"embracebertconcatatt_attention_p_multinomial": " EBERTconcatAtt-bs{} ",
"embracebertconcatatt_attention_p_attention_clsquery_weights": " EBERTconcatAtt-bs{}_attclsqw ",
"embracebertconcatatt_projection_p_multinomial": " EBERTconcatAttwithProj-bs{} ",
"embracebertconcatatt_projection_p_attention_clsquery_weights": " EBERTconcatAttwithProj-bs{}_attclsqw ",
"embracebertwithkeyvaluequeryconcatatt_attention_p_multinomial": " EmbraceBERTconcatatt-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracebertwithkeyvaluequeryconcatatt_attention_p_attention_clsquery_weights": " EmbraceBERTconcatatt-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
"embracebertwithkeyvaluequeryconcatatt_projection_p_multinomial": " EmbraceBERTconcatattWithProj-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracebertwithkeyvaluequeryconcatatt_projection_p_attention_clsquery_weights": " EmbraceBERTconcatattWithProj-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
"roberta": " RoBERTa-bs{} ",
"robertawithatt": " RoBERTawithAtt-bs{} ",
"robertawithprojection": " RoBERTawithProjection-bs{} ",
"robertawithprojectionatt": " RoBERTawithProjectionAtt-bs{} ",
"robertawithattclsprojection": " RoBERTawithAttClsProjection-bs{} ",
"embraceroberta": " EmbraceRoBERTa-bs{} ",
"embraceroberta_attention_p_multinomial": " EmbraceRoBERTa-bs{} ",
"embraceroberta_attention_p_attention_clsquery_weights": " EmbraceRoBERTa-bs{}-p_att_clsquery_weights ",
"embraceroberta_projection_p_multinomial": " EmbraceRoBERTawithProj-bs{} ",
"embraceroberta_projection_p_attention_clsquery_weights": " EmbraceRoBERTawithProj-bs{}-p_attclsqw ",
"embracerobertawithkeyvaluequery_attention_p_multinomial": " EmbraceRoBERTa-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracerobertawithkeyvaluequery_attention_p_attention_clsquery_weights": " EmbraceRoBERTa-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
"embracerobertawithkeyvaluequery_projection_p_multinomial": " EmbraceRoBERTawithProj-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracerobertawithkeyvaluequery_projection_p_attention_clsquery_weights": " EmbraceRoBERTawithProj-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
"embracerobertaconcatatt_attention_p_multinomial": " ERoBERTaconcatAtt-bs8 ",
"embracerobertaconcatatt_attention_p_attention_clsquery_weights": " ERoBERTaTconcatAtt-bs8_attclsqw ",
"embracerobertaconcatatt_projection_p_multinomial": " ERoBERTaconcatAttwithProj-bs8 ",
"embracerobertaconcatatt_projection_p_attention_clsquery_weights": " ERoBERTaconcatAttwithProj-bs8_attclsqw ",
"embracerobertawithkeyvaluequeryconcatatt_attention_p_multinomial": " ERoBERTaconcatatt-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracerobertawithkeyvaluequeryconcatatt_attention_p_attention_clsquery_weights": " ERoBERTaconcatatt-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
"embracerobertawithkeyvaluequeryconcatatt_projection_p_multinomial": " ERoBERTaconcatattWithProj-bs{}-p_multiheadatt_bertKeyValQuery ",
"embracerobertawithkeyvaluequeryconcatatt_projection_p_attention_clsquery_weights": " ERoBERTaconcatattWithProj-bs{}-p_multiheadatt_bertKeyValQuery_attclsqw ",
}
is_comp_inc = False
for dataname in ["snips"]: #["askubuntu", "chatbot", "webapplications", "snips"]:
if dataname == "snips":
bs_array = [48] # [32]
epoch_array = [100]
else:
bs_array = [8] #, 16]
epoch_array = [100]
for epoch in epoch_array:
print("- {}, ep {}".format(dataname.upper(), epoch))
for model_type in [MODEL_BERT]:
for bs in bs_array:
print("| ------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |")
for model in model_type:
model_name = MODEL_NAME[model]
root_dir = '{root_name}{model}/{dataname}/complete/{dataname}_ep{epoch}_bs{bs}_'. \
format(root_name=root_name, model=model, dataname=dataname, epoch=epoch, bs=bs)
f1_micro_str_all = ""
for perc in [0.1]:
f1_micro_arr = []
if bs == 4:
f1_micro_str_all += "|{} ".format(model_name.format(bs))
else:
f1_micro_str_all += "|{}".format(model_name.format(bs))
for i in range(1, 10 + 1):
tmp_dir = "{}seed{}/".format(root_dir, i)
tmp_dir += "eval_results.json"
# Load json file
with open(tmp_dir, 'r') as f:
datastore = json.load(f)
f1_score = datastore['f1']
f1_micro_arr.append(f1_score)
f1_micro_str_all += "|{:.2f}".format(f1_score*100)
f1_micro_str_all += "|{:.2f}|{:.2f}|".format(np.mean(f1_micro_arr)*100, np.std(f1_micro_arr)*100)
print(f1_micro_str_all)