04a-mmdf.py
2.99 KB
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
# coding: utf-8
# In[29]:
# Import
import itertools
import shelve
import pickle
import numpy
import scipy
from scipy import sparse
import scipy.sparse
import scipy.io
from mlp import *
import mlp
import sys
import utils
import dill
from collections import Counter
from gensim.models import LdaModel
# In[3]:
#30_50_50_150_0.0001
# In[4]:
#db=shelve.open("SPELIKE_MLP_DB.shelve",writeback=True)
origin_corps=shelve.open("{}".format(sys.argv[2]))
in_dir = sys.argv[1]
if len(sys.argv) > 3 :
features_key = sys.argv[3]
else :
features_key = "LDA"
out_db=shelve.open("{}/mlp_scores.shelve".format(in_dir),writeback=True)
mlp_h = [ 250, 250 ]
mlp_loss = "categorical_crossentropy"
mlp_dropouts = [0.25]* len(mlp_h)
mlp_sgd = Adam(lr=0.0001)
mlp_epochs = 3000
mlp_batch_size = 5
mlp_input_activation = "relu"
mlp_output_activation="softmax"
ress = []
for key in origin_corps["features_key"].keys() :
res=mlp.train_mlp(origin_corps[features_key][key]["TRAIN"],origin_corps["LABEL"][key]["TRAIN"],
origin_corps[features_key][key]["DEV"],origin_corps["LABEL"][key]["DEV"],
origin_corps[features_key][key]["TEST"],origin_corps["LABEL"][key]["TEST"],
mlp_h,dropouts=mlp_dropouts,sgd=mlp_sgd,
epochs=mlp_epochs,
batch_size=mlp_batch_size,
save_pred=False,keep_histo=False,
loss="categorical_crossentropy",fit_verbose=0)
arg_best=[]
dev_best=[]
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
arg_best.append(numpy.argmax(res[1]))
dev_best.append(res[1][arg_best[-1]])
res[1][arg_best[-1]]=0
test_best =[ res[2][x] for x in arg_best ]
test_max = numpy.max(res[2])
out_db[key]=(res,(dev_best,test_best,test_max))
ress.append((key,dev_best,test_best,test_max))
print sys.argv[2]
for el in ress :
print el
out_db.close()
origin_corps.close()