-
Notifications
You must be signed in to change notification settings - Fork 12
/
regression_dev.py
421 lines (330 loc) · 17.3 KB
/
regression_dev.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
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
#!/shares/CIBIO-Storage/CM/scratch/users/paolo.manghi/anaconda3/bin/python
import time
import sys
import argparse as ap
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn import preprocessing as prep
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
class reg_metrics:
mean_absolute_error = []
mean_squared_error = []
r2_score = []
l = []
l_es = []
class reg_params:
learner_type = []
cv_folds = []
cv_grid = []
cv_scoring = []
num_features_input = None
#def compute_reg_metrics(l, l_es, i_tr, i_u):
# rm = reg_metrics()
# n_ts = sum(sum([((i_tr[s]==False) & i_u[s]) for s in range(len(l_es))]))
# l_ = pd.DataFrame([0.0]*n_ts)
# l_es_ = pd.DataFrame([0.0]*n_ts)
# c = -1
# for j in range(len(l_es)):
# h = l[~i_tr[j] & i_u[j]].index
# for i in range(len(h)):
# c = c+1
# l_.loc[c] = l.loc[h[i]].values
# l_es_.loc[c] = l_es[j].loc[i].values
#
# l_ = l_.values.flatten().astype('float')
# l_es_ = l_es_.values.flatten().astype('float')
#
# rm.mean_absolute_error = metrics.mean_absolute_error(l_, l_es_)
# rm.mean_squared_error = metrics.mean_squared_error(l_, l_es_)
# rm.r2_score = metrics.r2_score(l_, l_es_)
# rm.l = l_
# rm.l_es = l_es_
# return rm
def save_results(l, l_es, i_tr, i_u, nf, runs_n, runs_cv_folds, fidout):
n_clts = len(np.unique(l))
rm = reg_metrics()
for j in range(runs_n*runs_cv_folds):
l_ = pd.DataFrame([l.loc[i] for i in l[~i_tr[j] & i_u[j//runs_cv_folds]].index]).values.flatten().astype('float')
l_es_ = l_es[j].values.flatten().astype('float')
for x,y in zip( l_,l_es_ ):
print( x,y )
#print l_, ' Questo e l'
#print l_es_, ' Questo e l predetto'
#
rm.mean_absolute_error.append(metrics.mean_absolute_error(l_, l_es_))
rm.mean_squared_error.append(metrics.mean_squared_error(l_, l_es_))
rm.r2_score.append(metrics.r2_score(l_, l_es_))
rm.l.append(l_)
rm.l_es.append(l_es_)
fidout.write('#samples\t' + str(sum(sum(i_u))//len(i_u)))
fidout.write('#features\t' + str(nf) + '\n')
fidout.write('\n#runs\t' + str(runs_n))
fidout.write('\n#runs_cv_folds\t' + str(runs_cv_folds))
fidout.write('\nmean_absolute_error\t' + str(np.mean(rm.mean_absolute_error)) + '\t' + str(np.std(rm.mean_absolute_error)))
fidout.write('\nmean_squared_error\t' + str(np.mean(rm.mean_squared_error)) + '\t' + str(np.std(rm.mean_squared_error)))
fidout.write('\nr2_square\t' + str(np.mean(rm.r2_score)) + '\t' + str(np.std(rm.r2_score)))
return rm
def plot_estimations(rm, outes):
plt.scatter(rm.l, rm.l_es, c='k', label='estimations (r2 = %0.2f)' % rm.r2_score)
plt.xlabel('True values')
plt.ylabel('Estimated values')
plt.title('')
plt.legend(loc="lower right")
[plt.savefig(outes+('.%s'%fmt), dpi=300) for fmt in ['png', 'svg']]
def read_params(args):
parser = ap.ArgumentParser(description='MicroMetaAnalysis')
arg = parser.add_argument
arg( 'inp_f', metavar='INPUT_FILE', nargs='?', default=sys.stdin, type=str, help="the input dataset file [stdin if not present]")
arg( 'out_f', metavar='OUTPUT_FILE', nargs='?', default=sys.stdout, type=str, help="the output dataset file [stdout if not present]")
arg( '-z','--feature_identifier', type=str, default='k__', help="the feature identifier\n")
arg( '-d','--define', type=str, help="define the regression problem\n")
arg( '-t','--target', type=str, help="define the target domain\n")
arg( '-b','--label_shuffling', action='store_true', help="label shuffling\n")
arg( '-l','--learner_type', choices=['svm', 'rf', 'lgr'], help="the type of learner (regressor)\n")
arg( '-f','--cv_folds', type=int, help="number of cross-validation folds\n")
arg( '-g','--cv_grid', type=str, help="parameter grid for cross-validation\n")
arg( '-s','--cv_scoring', type=str, help="scoring function for cross-validation\n")
arg( '-w','--set_seed', action='store_true', help="setting seed\n")
arg( '-r','--n_runs', default=20, type=int, help="number of runs\n")
arg( '-p', '--runs_cv_folds', default=10, type=int, help="the number of cross-validation folds per run\n")
arg( '-c','--rf_criterion', type=str, choices=['mse', 'mae'], default='mae')
arg( '-mf','--max_number_features'\
, choices=['0.001', '0.01', '0.1', '0.2', '0.3', '0.5', '0.4', '0.6'\
, '100', 'auto', 'sqrt', '0.33', 'log2'], default='0.3')
arg( '-nt','--number_of_trees', type=int, default=1000)
arg( '-nsl','--number_sample_leaf', type=int, default=1)
arg( '-oob','--oob_score', action='store_true', help='Enable out-of-bag choice in random forest')
arg( '-df','--disable_features', action='store_true', help='Doesn\'t perform the features selection' +\
'which follows std random forest (random forest)')
arg( '-cc', '--choose_cut', type=str, default=None, \
help='comma-separated list of numbers which will substitute the std features cuts (10,20,...,150)')
arg( '-wc', '--weight_classes', default=None, type=float, nargs=2)
arg( '-nc', '--ncores', type=int, default=10, help='-1 set to all the available cores.')
arg( '-hv','--how_verbose', default=1, type=int, choices=[0,1,2])
#arg( '-p','--p_test', default=0.1, type=float, help="dataset proportion to include in the test split\n")
arg( '-u','--unique', type=str, help="unique samples to select\n")
return vars(parser.parse_args())
def compute_feature_importance(el, feat, feat_sel, ltype):
fi = feature_importance(feat, 0.0)
if ltype == 'rf':
t = el.feature_importances_
elif (ltype == 'lasso') | (ltype == 'enet'):
t = abs(el.coef_)/sum(abs(el.coef_))
else:
t = [1.0/len(feat_sel)]*len(feat_sel)
ti = [feat.index(s) for s in feat_sel]
fi.imp[ti] = t
t = sorted(range(len(t)), key=lambda s: t[s], reverse=True)
fi.feat_sel = [feat[ti[s]] for s in t if fi.imp[ti[s]] != 0]
return fi
def save_average_feature_importance(fi, feat):
fi_ave = feature_importance(feat, 0.0)
t = [s.imp for s in fi]
fi_ave_std = np.std(t, axis=0)
t = np.mean(t, axis=0)
fi_ave.imp = t
t = sorted(range(len(t)), key=lambda s: t[s], reverse=True)
fi_ave.feat_sel = [feat[s] for s in t if fi_ave.imp[s] != 0]
fidout.write('Feature importance (ranking, name, average, std)\n')
[fidout.write(str(s) + '\t' + feat[t[s]] + '\t' + str(fi_ave.imp[t[s]]) \
+ '\t' + str(fi_ave_std[t[s]]) + '\n') for s in range(len(t))]
return fi_ave
#def save_results(rm, f, i_u):
# pd.DataFrame([sum(sum(i_u))/len(i_u)], index=['#samples']).to_csv(par['out_f'], sep='\t', header=False, index=True, line_terminator='\n')
# pd.DataFrame([len(f.iloc[0,:])], index=['#features']).to_csv(par['out_f'], sep='\t', header=False, index=True, line_terminator='\n', mode='a')
# pd.DataFrame([rm.mean_absolute_error], index=['mean_absolute_error']).to_csv(par['out_f'], sep='\t', header=False, index=True, line_terminator='\n', mode='a')
# pd.DataFrame([rm.mean_squared_error], index=['mean_squared_error']).to_csv(par['out_f'], sep='\t', header=False, index=True, line_terminator='\n', mode='a')
# pd.DataFrame([rm.r2_score], index=['r2_score']).to_csv(par['out_f'], sep='\t', header=False, index=True, line_terminator='\n', mode='a')
def set_reg_params(args):
lp = reg_params()
if par['learner_type']:
lp.learner_type = par['learner_type']
else:
lp.learner_type = 'svm'
if par['cv_folds']:
lp.cv_folds = int(par['cv_folds'])
elif lp.learner_type == 'svm':
lp.cv_folds = 5
if par['cv_grid']:
lp.learner_type = par['cv_grid']
elif lp.learner_type == 'svm':
# lp.cv_grid = [ {'C': [2**s for s in range(-5,16,2)], 'epsilon': [0.001, 0.01, 0.1], 'kernel': ['linear']},
##{'C': [2**s for s in range(-5,16,2)], 'gamma': [2**s for s in range(3,-15,-2)], 'epsilon': [0.001, 0.01, 0.1], 'kernel': ['rbf']} ]
lp.cv_grid = [{'C':[1, 10, 100, 1000], 'epsilon':[0.001, 0.01, 0.1], 'kernel':['linear']}, \
{'C':[1, 10, 100, 1000], 'gamma':[0.001, 0.0001], 'epsilon':[0.001, 0.01, 0.1], 'kernel':['rbf']}]
if par['cv_scoring']:
lp.learner_type = par['cv_scoring']
elif lp.learner_type == 'svm':
lp.cv_scoring = 'mean_absolute_error'
try:
lp.num_features_input = float(par['max_number_features'])
except:
lp.num_features_input = str(par['max_number_features'])
#lp.num_cores = par['ncores'] if par['ncores'] > 0 else 'all'
return lp
if __name__ == "__main__":
par = read_params(sys.argv)
if par['rf_max_features'] in ['0.001', '0.01', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.33','100', '1.0']:
par['rf_max_features'] = float(par['rf_max_features'])
f = pd.read_csv(par['inp_f'], sep='\t', header=None, index_col=0) #, dtype=unicode)
f = f.T
if par['out_f']:
fidout = open(par['out_f'] + '.txt','w')
fidoutes = par['out_f'] + '_estimations'
else:
fidout = sys.stdout
if par['unique']:
pf = pd.DataFrame([s.split(':') for s in par['unique'].split(',')])
if par['define']:
l = pd.DataFrame(f[par['define']])
else:
l = pd.DataFrame(f.iloc[:,0])
if par['label_shuffling']:
np.random.shuffle(l.values)
n_runs = par['n_runs']
if par ['target']:
runs_cv_folds = 1
else:
runs_cv_folds = par['runs_cv_folds'] ## to be added
i_tr = pd.DataFrame(True, index=range(len(f.index)), columns=range(n_runs*runs_cv_folds))## classifier
if par['target']:
i_u = pd.DataFrame(True, index=range(len(f.index)), columns=range(n_runs))
#t = pd.DataFrame([s.split(':') for s in par['target'].split(',')])
#for i in range(len(t)):
# i_tr[(f[t.iloc[i,0]].isin(t.iloc[i,1:])).tolist()] = False
else:
if par['unique']:
i_u = pd.DataFrame(False, index=range(len(f.index)), columns=range(n_runs))
meta_u = [s for s in f.columns if s in pf.iloc[0,0:].tolist()]
else:
i_u = pd.DataFrame(True, index=range(len(f.index)), columns=range(n_runs))
for j in range(n_runs):
if par['set_seed']:
np.random.seed(j)
if par['target']:
t = pd.DataFrame([s.split(':') for s in par['target'].split(',')])
for i in range(len(t)):
i_tr[j][(f[t.iloc[i,0]].isin(t.iloc[i,1:])).tolist()] = False
else:
if par['unique']:
ii_u = [s-1 for s in (f.loc[np.random.permutation(f.index),:].drop_duplicates(meta_u)).index]
i_u[j][ii_u] = True
else:
ii_u = range(len(f.index))
#if par['set_seed']:
# skf = StratifiedKFold(l.iloc[i_u.values.T[j],0], runs_cv_folds, shuffle=True, random_state=j)
#else:
# skf = StratifiedKFold(l.iloc[i_u.values.T[j],0], runs_cv_folds, shuffle=True)
###for i in range(runs_cv_folds):
### for s in np.where(skf.test_folds == i)[0]:
### i_tr[j*runs_cv_folds+i][ii_u[s]] = False
## COMMENTED PART IS AN OLD IMPLEMENTATION; I M COPYING THE NEW
#rs = [s[1] for s in ShuffleSplit(len(ii_u), n_iter=1, test_size=float(par['runs_cv_folds'])/100.)]
#for i in range(runs_cv_folds):
# for s in np.nditer(rs):
# #i_tr[j][ii_u[s]] = False
# i_tr[j*runs_cv_folds+i][ii_u[s]] = False
skf = ShuffleSplit(n_splits = n_runs, test_size = runs_cv_folds/100., \
random_state = (j if par['set_seed'] else None))
allowed_values = np.array(l.iloc[i_u.values.T[j], 0].values, dtype=np.float64)
skf_split = skf.split(np.array([[0, 0] for q in range(len(allowed_values))], dtype=np.float64), allowed_values)
test_folds = [tf[1] for tf in skf_split] # [train_index,test_index in skf_split]]
for i in range(runs_cv_folds):
for s in test_folds[i]:
i_tr[j*runs_cv_folds + i][ii_u[s]] = False
print( i_tr )
#else:
# i_u = pd.DataFrame(True, index=range(len(f.index)), columns=range(n_runs))
# rs = [s[1] for s in ShuffleSplit(len(l), n_iter=n_runs, test_size=par['p_test'])]
# for j in range(n_runs):
# i_tr[j][rs[j]] = False
i_tr = i_tr.values.T
i_u = i_u.values.T
if par['label_shuffling']:
np.random.shuffle(l.values)
feat = [s for s in f.columns if sum([s2 in s for s2 in par['feature_identifier'].split(':')])>0]
if 'unclassified' in f.columns: feat.append('unclassified')
f = f.loc[:,feat].astype('float')
if not par['no_norm']:
f = (f-f.min())/(f.max()-f.min())
lp = set_reg_params(sys.argv)
clf = []
l_es = []
global_time = time.time()
for j in range(n_runs*runs_cv_folds):
start_run_time = time.time()
fi.append(feature_importance(feat, 1.0/len(feat)))
#if lp.learner_type == 'svm':
# clf.append(GridSearchCV(SVR(C=1), lp.cv_grid, \
# cv=StratifiedKFold(l.iloc[i_tr[j] & i_u[j//runs_cv_folds],0], lp.cv_folds), scoring=lp.cv_scoring\
# ).fit(f[i_tr[j] & i_u[j]].values, l[i_tr[j] & i_u[j]].values.flatten().astype('float')))
if lp.learner_type == 'rf':
clf.append(RandomForestRegressor(\
n_estimators=par['number_of_trees'], \
max_features=par["max_features"]\
, min_samples_leaf=par['number_sample_leaf'], verbose=1, \
n_jobs=par['ncores'], \
criterion=par['rf_criterion']).fit(\
f.loc[i_tr[j] & i_u[j//runs_cv_folds]].values\
, l[i_tr[j] & i_u[j//runs_cv_folds]].values.flatten().astype('float')))
elif lp.learner_type == 'lgr':
clf.append(LogisticRegression(verbose=1, n_jobs=par['ncores'], \
solver='liblinear').fit(f.loc[i_tr[j] & i_u[j//runs_cv_folds]].values\
, l[i_tr[j] & i_u[j//runs_cv_folds]].values.flatten().astype('float')))
elif lp.learner_type == 'lm':
clf.append()
# p_es.append(pd.DataFrame(clf[j].predict_proba(f.loc[~i_tr[j] & i_u[j//runs_cv_folds], fi[j].feat_sel].values)))
# l_es.append(pd.DataFrame([list(p_es[j].iloc[i,:]).index(max(p_es[j].iloc[i,:])) for i in range(len(p_es[j]))]))
#print(f.loc[~i_tr[j] & i_u[j//runs_cv_folds]].values)
l_es.append(pd.DataFrame(clf[-1].predict(f.loc[~i_tr[j] & i_u[j//runs_cv_folds]].values)))
elapsed_run_time = time.time() - start_run_time
if par['how_verbose'] > 0:
print('%i run-time: %.4f sec.' %(j, float(elapsed_run_time)))
reg_met = save_results(l, l_es, i_tr, i_u, len(feat), n_runs, runs_cv_folds, fidout)
#plot_estimations(reg_met, fidoutes)
global_elapsed = time.time() - global_time
if par['how_verbose'] > 0:
print ('global-time: %.4f sec.' %(global_elapsed))
if lp.learner_type == 'rf':
if not par['disable_features']:
fi_f = []
for j in range(runs_n*runs_cv_folds):
fi_f.append(compute_feature_importance(clf[j], feat, fi[j].feat_sel, lp.learner_type))
if not par['choose_cut']:
if par['feature_identifier'] != 'UniRef90':
steps = [1,2,4,8,16,32,64]
elif par['feature_identifier'] == 'UniRef90':
steps = [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
else:
steps = list(map(int, par['choose_cut'].split(',')))
if lp.learner_type == 'rf':
for k in steps:
clf_f = []
p_es_f = []
l_es_f = []
p_es_f_co = []
l_es_f_co = []
for j in range(runs_n*runs_cv_folds):
clf_f.append(RandomForestRegressor(\
n_estimators=par['number_of_trees'], \
max_features=(k if par['feature_identifier'] != 'UniRef90' else (k if k<=128 else 0.3))\
, min_samples_leaf=par['number_sample_leaf'], verbose=par['how_verbose'], \
n_jobs=par['ncores'], class_weight='balanced'), \
criterion=par['rf_criterion']).fit(\
f.loc[i_tr[j] & i_u[j//runs_cv_folds], fi_f[j].feat_sel[:k] ].values\
, l[i_tr[j] & i_u[j//runs_cv_folds]].values.flatten().astype('float')))
l_es_f.append(pd.DataFrame(clf_f[-1].predict(f.loc[~i_tr[j] & i_u[j//runs_cv_folds], fi_f[j].feat_sel[:k]].values)))
reg_met_f = save_results(l, l_es_f, i_tr, i_u, k, n_runs, runs_cv_folds, fidout)
fi_ave = save_average_feature_importance(fi_f, feat)
if par['out_f']:
fidout.close()
###