-
Notifications
You must be signed in to change notification settings - Fork 0
/
svm_tests.py
164 lines (126 loc) · 4.7 KB
/
svm_tests.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
import os
from DataLoader import DataLoader
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import cross_validate
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
def single_svm_test(X, y):
dl = DataLoader()
X = dl.preprocess_bow(X)
vectorizer = TfidfVectorizer(max_features=1000, ngram_range=(1, 2))
classifier = SVC()
model = Pipeline([('vectorizer', vectorizer), ('classifier', classifier)])
scores = cross_validate(model,
X,
y,
scoring=['accuracy', 'precision', 'recall', 'f1'],
return_train_score=True,
verbose=1000,
n_jobs=-1)
return scores['test_accuracy'].mean()
def single_svm_consistency_test(X_orig, y_orig, X_aug, y_aug):
dl = DataLoader()
X_orig = dl.preprocess_bow(X_orig)
X_aug = dl.preprocess_bow(X_aug)
vectorizer = TfidfVectorizer(max_features=1000, ngram_range=(1, 2))
classifier = SVC()
model = Pipeline([('vectorizer', vectorizer), ('classifier', classifier)])
model.fit(X_orig, y_orig)
return model.score(X_aug, y_aug)
def run_svm_tests():
dl = DataLoader()
sizes = [50, 100, 500, 1000, 5000]
file_name = 'svm_scores.csv'
da_methods = {
'eda': dl.import_from_eda,
'unaltered': dl.import_unaltered_reddit,
'mt': dl.import_from_mt
}
if os.path.exists(file_name):
df = pd.read_csv(file_name, index_col=0)
else:
df = pd.DataFrame(columns=da_methods.keys())
for size in sizes:
if size not in df.index:
df.loc[size] = np.nan
for method_name in da_methods:
da_method = da_methods[method_name]
if method_name not in df.columns:
df.insert(loc=0, column=method_name, value=np.nan)
if np.isnan(df.loc[size][method_name]):
X, y = da_method(size=size)
df.loc[size][method_name] = single_svm_test(X, y)
print(df)
df.to_csv(file_name)
def run_svm_tests_dir():
dl = DataLoader()
sizes = [50, 100, 500, 1000]
file_name = 'svm_scores_many.csv'
da_methods = {'eda': dl.import_from_eda_dir, 'unaltered': dl.import_unaltered_reddit_dir}
dat = []
for size in sizes:
row = []
for method_name in da_methods:
da_method = da_methods[method_name]
col = []
for X, y in da_method(size=size):
col.append(single_svm_test(X, y))
row.append(col)
dat.append(row)
df = pd.DataFrame(dat, columns=["eda_means", "unaltered_means"])
df.index = sizes
df.to_csv(file_name)
return df
def run_svm_consistency_tests():
dl = DataLoader()
sizes = [50, 100, 500, 1000, 5000]
file_name = 'svm_consistency.csv'
da_methods = {
'unaltered': dl.import_unaltered_reddit,
'eda': dl.import_from_eda,
'mt': dl.import_from_mt
}
if os.path.exists(file_name):
df = pd.read_csv(file_name, index_col=0)
else:
df = pd.DataFrame(columns=da_methods.keys())
for size in sizes:
X_orig, y_orig = dl.import_unaltered_reddit(size=size)
if size not in df.index:
df.loc[size] = np.nan
for method_name in da_methods:
da_method = da_methods[method_name]
if method_name not in df.columns:
df.insert(loc=0, column=method_name, value=np.nan)
if np.isnan(df.loc[size][method_name]):
X_aug, y_aug = da_method(size=size)
df.loc[size][method_name] = single_svm_consistency_test(
X_orig, y_orig, X_aug, y_aug)
print(df)
df.to_csv(file_name)
def run_svm_consistency_tests_dir():
dl = DataLoader()
sizes = [50, 100, 500, 1000]
file_name = 'svm_consistency_many.csv'
da_methods = {'eda': dl.import_from_eda_dir, 'unaltered': dl.import_unaltered_reddit_dir}
dat = []
for size in sizes:
origs = list(dl.import_unaltered_reddit_dir(size))
row = []
for method_name in da_methods:
da_method = da_methods[method_name]
augs = list(da_method(size=size))
col = [
single_svm_consistency_test(orig[0], orig[1], aug[0], aug[1])
for orig, aug in zip(origs, augs)
]
row.append(col)
dat.append(row)
df = pd.DataFrame(dat, columns=["eda_consistencies", "unaltered_consistencies"])
df.index = sizes
df.to_csv(file_name)
return df
if __name__ == '__main__':
run_svm_consistency_tests()