-
Notifications
You must be signed in to change notification settings - Fork 1
/
evaluate_on_flips.py
154 lines (108 loc) · 3.48 KB
/
evaluate_on_flips.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.optim as optim
import os, sys
import numpy as np
import pandas as pd
import argparse
import json
import logging
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
import datetime
import sys
date_time = str(datetime.date.today()) + "_" + ":".join(str(datetime.datetime.now()).split()[1].split(":")[:2])
parser = argparse.ArgumentParser()
parser.add_argument(
"-dataset",
type = str,
help = "select dataset / task",
default = "sst",
choices = ["sst", "agnews", "evinf", "adr", "multirc", "subj", "semeval"]
)
parser.add_argument(
"-data_dir",
type = str,
help = "directory of saved processed data",
default = "datasets/"
)
parser.add_argument(
"-model_dir",
type = str,
help = "directory to save models",
default = "full_text_models/"
)
parser.add_argument(
"-extracted_rationale_dir",
type = str,
help = "directory to save extracted_rationales",
default = "extracted_rationales/"
)
parser.add_argument(
"-evaluation_dir",
type = str,
help = "directory to save decision flips",
default = "decision_flip/"
)
parser.add_argument(
"--saliency_scorer",
type = str,
help = "saliency_scorer for loss",
default = None,
choices = [
"textrank", "tfidf","chisquared", None,
"textgraph", "random_alloc", "uniform_alloc"
]
)
parser.add_argument(
"--devel_stage",
help = "run evaluation on devel set",
action = "store_true"
)
parser.add_argument(
"--extract_importance_scores",
help = "run evaluation on devel set",
action = "store_true"
)
user_args = vars(parser.parse_args())
log_dir = "experiment_logs/evaluate_" + user_args["dataset"] + "_" + date_time + "/"
config_dir = "experiment_config/evaluate_" + user_args["dataset"] + "_" + date_time + "/"
os.makedirs(log_dir, exist_ok = True)
os.makedirs(config_dir, exist_ok = True)
import config.cfg
config.cfg.config_directory = config_dir
logging.basicConfig(
filename= log_dir + "/out.log",
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S'
)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
logging.info("Running on cuda ? {}".format(torch.cuda.is_available()))
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from src.utils.prep import initial_preparations, checks_on_local_args
import datetime
import sys
# creating unique config from stage_config.json file and model_config.json file
args = initial_preparations(user_args, stage = "evaluate")
args = checks_on_local_args("evaluate", args)
logging.info("config : \n ----------------------")
[logging.info(k + " : " + str(v)) for k,v in args.items()]
logging.info("\n ----------------------")
# re-importing module to reset args if needed
from src.utils import dataholder
from src.utils.dataholder import classification_dataholder
data = classification_dataholder(
args["data_dir"],
b_size = args["batch_size"]
)
from src.evaluation import fraction_of_tokens
evaluator = fraction_of_tokens.evaluate(data.nu_of_labels)
logging.info("-- conducting flip experiments")
evaluator.fraction_of_experiments_(data)
logging.info("-- finished flip experiments")
# delete full data not needed anymore
del data
torch.cuda.empty_cache()