Blame view
LDA/04f-pca.py
2.38 KB
128365a4f
ajout pca
|
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 |
# 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
from sklearn.decomposition import PCA
# 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("{}/pca_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 = []
print
for key in origin_corps[features_key].keys() :
print "#########" + key + "########"
dev_best =[]
test_best = []
test_max = []
pca = PCA(n_components=200, copy=True, whiten=True)
x_train_big = pca.fit_transform(origin_corps[features_key][key]["TRAIN"])
y_train =origin_corps["LABEL"][key]["TRAIN"]
x_dev_big = pca.transform(origin_corps[features_key][key]["DEV"])
y_dev = origin_corps["LABEL"][key]["DEV"]
x_test_big = pca.transform(origin_corps[features_key][key]["TEST"])
y_test = origin_corps["LABEL"][key]["TEST"]
for i in range(1,200):
x_train = x_train_big[:,:i]
x_dev = x_dev_big[:,:i]
x_test = x_test_big[:,:i]
print "xshape",x_train.shape
print "xdev", x_dev.shape
print "xtest",x_test.shape
res=mlp.train_mlp(x_train,y_train,
x_dev,y_dev,
x_test ,y_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 = numpy.argmax(res[1])
dev_best.append(res[1][arg_best])
test_best.append(res[2][arg_best])
test_max.append(numpy.max(res[2]))
print dev_best[-1],test_best[-1]
out_db[key]=(res,(dev_best,test_best,test_max))
ress.append((key,dev_best,test_best,test_max))
out_db.sync()
for el in ress :
print el
out_db.close()
origin_corps.close()
|