forked from jisungk/RIDDLE
-
Notifications
You must be signed in to change notification settings - Fork 0
/
other_clf.py
298 lines (247 loc) · 12.2 KB
/
other_clf.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
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
"""
other_clf.py
Run various machine learning classifier pipelines.
Requires: Keras, NumPy, scikit-learn, RIDDLE (and their dependencies)
Author: Ji-Sung Kim, Rzhetsky Lab
Copyright: 2016, all rights reserved
"""
from __future__ import print_function
import sys; sys.dont_write_bytecode = True
import os
import pickle
import time
DATA_DIR = '_data'
CACHE_DIR = '_cache'
SEED = 109971161161043253 % 8085
import numpy as np
np.random.seed(SEED) # for reproducibility, must be before Keras imports!
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.metrics import accuracy_score, log_loss
from sklearn.externals import joblib
from riddle import emr
from riddle.parameter_tuning import UniformLogSpace, UniformInteger
from riddle.models import save_test_results
from parameter_search import loss_scorer, get_base_data, preproc_for_sklearn, select_feats
from pipeline import eprint
from kfold_pipeline import print_metrics
# ---------------------------- PUBLIC FUNCTIONS ------------------------------ #
'''
* Run parameter search for various machine learning pipelines.
* Expects:
- data_path = data filepath
- method = method
- which_half = string setting for whether to do first half, last half, or
the complete set of experiments; one of ('first', 'last', 'both')
- prop_missing = float proportion of data simulated to be missing
- k = number of partitions for k-fold cross-validation
- skip_nonlinear_svm = boolean whether to skip nonlinear SVM methods,
only relevant if 'svm' is selected as the method
- nb_searches = number of searches
'''
def run(data_fn, method='lrfc', which_half='both', prop_missing=0.0, k=10,
skip_nonlinear_svm=False, nb_searches=20):
data_path = '{}/{}'.format(DATA_DIR, data_fn)
def get_results_dir(method, k_idx):
base_folder = 'out/more/{}_{}_{}'.format(method, data_fn, prop_missing)
folder = '{}/{}_idx_partition'.format(base_folder, k_idx)
if not os.path.exists('out'): os.makedirs('out')
if not os.path.exists('out/more'): os.makedirs('out/models')
if not os.path.exists(base_folder): os.makedirs(base_folder)
if not os.path.exists(folder): os.makedirs(folder)
return folder
try: # load saved parameters
get_param_fn = lambda x: '{}/{}_{}_{}_param.pkl'.format(CACHE_DIR,
x, data_fn, prop_missing)
if method == 'lrfc':
with open(get_param_fn('logit'), 'r') as f:
logit_params = pickle.load(f)
with open(get_param_fn('rfc'), 'r') as f:
rfc_params = pickle.load(f)
elif method == 'svm':
with open(get_param_fn('linear-svm'), 'r') as f:
linear_svm_params = pickle.load(f)
if not skip_nonlinear_svm:
with open(get_param_fn('poly-svm'), 'r') as f:
poly_svm_params = pickle.load(f)
with open(get_param_fn('rbf-svm'), 'r') as f:
rbf_svm_params = pickle.load(f)
else: raise ValueError('unknown method: {}'.format(method))
except:
eprint('Need to do parameter search!')
eprint('Please run `parameter_search.py` with the relevant' +
'command line arguments')
raise
X, y, perm_indices, nb_features, nb_classes = get_base_data(data_path,
prop_missing)
losses = {'logit':[], 'rfc':[], 'linear-svm':[], 'poly-svm':[], 'rbf-svm':[]}
accs = {'logit':[], 'rfc':[], 'linear-svm':[], 'poly-svm':[], 'rbf-svm':[]}
runtimes = {'logit':[], 'rfc':[], 'linear-svm':[], 'poly-svm':[], 'rbf-svm':[]}
if which_half == 'first': loop_seq = range(0, k / 2)
elif which_half == 'last': loop_seq = range(k / 2, k)
elif which_half == 'both': loop_seq = range(0, k)
else: raise ValueError('`which_half` must be \'first\', \'last\' or \'both\'')
for k_idx in loop_seq:
print('-' * 72)
print('Partition k = {}'.format(k_idx))
data_partition_dict = emr.get_k_fold_partition(X, y, k_idx=k_idx, k=k,
perm_indices=perm_indices)
X_train = data_partition_dict['X_train']
y_train = data_partition_dict['y_train']
X_val = data_partition_dict['X_val']
y_val = data_partition_dict['y_val']
X_test = data_partition_dict['X_test']
y_test = data_partition_dict['y_test']
selected_feat_indices = select_feats(X_train + X_val, y_train + y_val,
nb_features=nb_features)
X_train, y_train = preproc_for_sklearn(X_train, y_train, nb_features)
X_test, y_test = preproc_for_sklearn(X_test, y_test, nb_features)
old_nb_features = len(X_train[0])
X_train = X_train[:, selected_feat_indices]
X_test = X_test[:, selected_feat_indices]
nb_features = len(X_train[0]) # extraneous but for future utility
print('Reduced features from {} to {}'.format(old_nb_features, nb_features))
if method == 'lrfc':
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
# logistic regression
start = time.time()
logit = LogisticRegression(multi_class='multinomial', solver='lbfgs',
**logit_params[k_idx])
logit.fit(X_train, y_train)
logit_acc = accuracy_score(y_test, logit.predict(X_test))
logit_y_test_proba = logit.predict_proba(X_test)
logit_loss = log_loss(y_test, logit_y_test_proba)
logit_time = time.time() - start
print('Logistic regression / loss: {:.3f} / accuracy: {:.3f} / time: {:.3f} s'
.format(logit_loss, logit_acc, logit_time))
# random forest classifier
start = time.time()
rfc = RandomForestClassifier(**rfc_params[k_idx])
rfc.fit(X_train, y_train)
rfc_acc = accuracy_score(y_test, rfc.predict(X_test))
rfc_y_test_proba = rfc.predict_proba(X_test)
rfc_loss = log_loss(y_test, rfc_y_test_proba)
rfc_time = time.time() - start
print('Random forest / loss: {:.3f} / accuracy: {:.3f} / time: {:.3f} s'
.format(rfc_loss, rfc_acc, rfc_time))
save_test_results(logit_y_test_proba, y_test,
'{}/test_results.txt'.format(get_results_dir('logit', k_idx)))
save_test_results(rfc_y_test_proba, y_test,
'{}/test_results.txt'.format(get_results_dir('rfc', k_idx)))
# joblib.dump(logit, get_results_dir('logit', k_idx) + '/clf.pkl')
# joblib.dump(rfc, get_results_dir('rfc', k_idx) + '/clf.pkl')
losses['logit'].append(logit_loss)
accs['logit'].append(logit_acc)
runtimes['logit'].append(logit_time)
losses['rfc'].append(rfc_loss)
accs['rfc'].append(rfc_acc)
runtimes['rfc'].append(rfc_time)
elif method == 'svm':
from sklearn.svm import SVC
# linear SVM
start = time.time()
linear_svm = SVC(kernel='linear', probability=True,
**linear_svm_params[k_idx])
linear_svm.fit(X_train, y_train)
linear_svm_acc = accuracy_score(y_test, linear_svm.predict(X_test))
linear_svm_y_test_proba = linear_svm.predict_proba(X_test)
linear_svm_loss = log_loss(y_test, linear_svm_y_test_proba)
linear_svm_time = time.time() - start
print('Linear SVM / accuracy: {:.3f} / loss: {:.3f} / time: {:.3f} s'
.format(linear_svm_acc, linear_svm_loss, linear_svm_time))
save_test_results(linear_svm_y_test_proba, y_test,
'{}/test_results.txt'.format(get_results_dir('linear-svm', k_idx)))
# joblib.dump(linear_svm, get_results_dir('linear-svm', k_idx) + '/clf.pkl')
losses['linear-svm'].append(linear_svm_loss)
accs['linear-svm'].append(linear_svm_acc)
runtimes['linear-svm'].append(linear_svm_time)
if skip_nonlinear_svm: continue # skip
# polynomial SVM
start = time.time()
poly_svm = SVC(kernel='poly', probability=True,
**poly_svm_params[k_idx])
poly_svm.fit(X_train, y_train)
poly_svm_acc = accuracy_score(y_test, poly_svm.predict(X_test))
poly_svm_y_test_proba = poly_svm.predict_proba(X_test)
poly_svm_loss = log_loss(y_test, poly_svm_y_test_proba)
poly_svm_time = time.time() - start
print('Polynomial SVM / accuracy: {:.3f} / loss: {:.3f} / time: {:.3f} s'
.format(poly_svm_acc, poly_svm_loss, poly_svm_time))
# RBF SVM
start = time.time()
rbf_svm = SVC(kernel='rbf', probability=True,
**rbf_svm_params[k_idx])
rbf_svm.fit(X_train, y_train)
rbf_svm_acc = accuracy_score(y_test, rbf_svm.predict(X_test))
rbf_svm_y_test_proba = rbf_svm.predict_proba(X_test)
rbf_svm_loss = log_loss(y_test, rbf_svm_y_test_proba)
rbf_svm_time = time.time() - start
print('RBF SVM / accuracy: {:.3f} / loss: {:.3f} / time: {:.3f} s'
.format(rbf_svm_acc, rbf_svm_loss, rbf_svm_time))
save_test_results(poly_svm_y_test_proba, y_test,
'{}/test_results.txt'.format(get_results_dir('poly-svm', k_idx)))
save_test_results(rbf_svm_y_test_proba, y_test,
'{}/test_results.txt'.format(get_results_dir('rbf-svm', k_idx)))
# joblib.dump(poly_svm, get_results_dir('poly-svm', k_idx) + '/clf.pkl')
# joblib.dump(rbf_svm, get_results_dir('rbf-svm', k_idx) + '/clf.pkl')
losses['poly-svm'].append(poly_svm_loss)
accs['poly-svm'].append(poly_svm_acc)
runtimes['poly-svm'].append(poly_svm_time)
losses['rbf-svm'].append(rbf_svm_loss)
accs['rbf-svm'].append(rbf_svm_acc)
runtimes['rbf-svm'].append(rbf_svm_time)
else: raise ValueError('unknown method: {}'.format(method))
print()
print('#' * 72)
if method == 'lrfc':
print_metrics(losses['logit'], accs['logit'], runtimes['logit'],
'Logistic regression')
print_metrics(losses['rfc'], accs['rfc'], runtimes['rfc'],
'Random forest')
elif method == 'svm':
print_metrics(losses['linear-svm'], accs['linear-svm'],
runtimes['linear-svm'], 'Linear SVM')
if not skip_nonlinear_svm:
print_metrics(losses['poly-svm'], accs['poly-svm'],
runtimes['poly-svm'], 'Polynomial SVM')
print_metrics(losses['rbf-svm'], accs['rbf-svm'],
runtimes['rbf-svm'], 'RBF SVM')
else: raise ValueError('unknown method: {}'.format(method))
print('#' * 72)
'''
* Runs parameter searches for various machine learning pipelines.
> Command line arguments:
+ method = one of ('lrfc', 'svm')
+ data_fn = string data file name
+ which_half = string setting for whether to do first half, last half, or
the complete set of experiments; one of ('first', 'last', 'both')
+ prop_missing = float proportion of data to randomly simulate as missing
'''
def main(args):
try: method = args[1].lower()
except:
method = 'lrfc'
eprint('Using default method = \'{}\''.format(method))
try: data_fn = args[2]
except:
data_fn = 'dummy.txt'
eprint('Using default data_fn = \'{}\''.format(data_fn))
try: which_half = args[3].lower()
except:
which_half = 'both'
eprint('Using default which_half = {}'.format(which_half))
try: prop_missing = float(args[4])
except:
prop_missing = 0.0
eprint('Using default prop_missing = {}'.format(prop_missing))
# not going to finish in time, so skip nonlinear svm if using full dataset
skip_nonlinear_svm = 'final-100.txt' in data_fn
if skip_nonlinear_svm:
eprint('Skipping SVMs with non-linear kernels')
run(data_fn, method=method, which_half=which_half,
prop_missing=prop_missing, skip_nonlinear_svm=skip_nonlinear_svm)
# if run as script, execute main
if __name__ == '__main__':
import sys
main(sys.argv)