def load_condition_stacking_main():
tn_conf = TrainConfigure()
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/term_embed.pkl')
# term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
# 'data/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5',
# dump_path='data/term_embed_ww.pkl')
char_vocab_dict = data_utils.pickle_load(tn_conf.char_dict)
char_embed_matrix = data_utils.load_embedding(char_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/char_embed.pkl')
name = 'model/stack_condition_model.pkl'
model_dir = 'model/stack/'
n_fold = 3
name = 'model/stack_condition_model5.pkl'
model_dir = 'model/stack5/'
n_fold = 5
conf = conditionmodelbase.ModelConfigure()
stk_model = stacking(n_fold, name=name, is_condition=True)
stk_model.add_model(ConditionConvModel, {"conf":conf,"char_embed_matrix": char_embed_matrix,
"term_embed_matrix": term_embed_matrix,
"name": model_dir + 'conditionconvmodel_PE.h5'})
stk_model.add_model(ConditionDPCNNModel, {"conf":conf,"char_embed_matrix": char_embed_matrix,
"term_embed_matrix": term_embed_matrix,
"name": model_dir + 'conditiondpcnnmodel_PE.h5'})
stk_model.add_model(ConditionGatedConvModel, {"conf":conf,"char_embed_matrix": char_embed_matrix,
"term_embed_matrix": term_embed_matrix,
"name": model_dir + 'conditiongatedconvmodel_PE.h5'})
stk_model.add_model(ConditionGatedDeepCNNModel, {"conf":conf,"char_embed_matrix": char_embed_matrix,
"term_embed_matrix": term_embed_matrix,
"name": model_dir + 'conditiongateddeepcnnmodel_PE.h5'})
stk_model.load( )
return stk_model
def encode_main():
lda_file = 'data/lda_vec_val.pkl'
import data_utils100
import data_utils200
tn_conf = TrainConfigure()
val_conf = ValidConfigure()
val_conf200 = data_utils200.ValidConfigure()
val_conf100 = data_utils100.ValidConfigure()
ys_val = predict(tn_conf, lda_file, val_conf, val_conf100, val_conf200)
import data_utils100
import data_utils200
tn_conf = TrainConfigure()
val_conf = TrainConfigure()
val_conf200 = data_utils200.TrainConfigure()
val_conf100 = data_utils100.TrainConfigure()
ys_train = predict(tn_conf, lda_file, val_conf, val_conf100, val_conf200)
data_utils.pickle_dump((ys_train, ys_val), 'data/stack_y.pkl')
def train_main_pe():
print('load data')
import data_utils, training_utils
tn_conf = TrainConfigure()
data_dict = data_utils.pickle_load(tn_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
xterm = data_utils.pickle_load(tn_conf.term_file)
xfeat = data_utils.pickle_load(tn_conf.feat_file)
# normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(xfeat)
data_utils.pickle_dump(scaler, tn_conf.feat_norm)
xfeat = scaler.transform(xfeat)
print('loading embed ...')
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
term_embed_matrix = data_utils.load_embedding(
term_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/term_embed.pkl')
# term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
# 'data/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5',
# dump_path='data/term_embed_ww.pkl')
char_vocab_dict = data_utils.pickle_load(tn_conf.char_dict)
char_embed_matrix = data_utils.load_embedding(
char_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/char_embed.pkl')
print('load embed done.')
xe_char = [[i for i in range(600)] for _ in range(y.shape[0])]
xe_char = np.array(xe_char)
xe_term = [[i for i in range(300)] for _ in range(y.shape[0])]
xe_term = np.array(xe_term)
x_tn, y_tn, x_ts, y_ts = training_utils.split(
[x, xterm, xfeat, xe_char, xe_term], y, shuffle=False)
x_tn, y_tn, x_val, y_val = training_utils.split(x_tn, y_tn, shuffle=False)
print('define model')
model = HybridModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
name='hybridmodel_PE.h5',
PE=True) # +37
print('feat shape', xfeat.shape)
import sys
print('train')
model.train(x_tn, y_tn, x_val, y_val, x_ts, y_ts)
def prepare_tn_data(filename='data/topic_train.txt'):
tn_conf = TrainConfigure()
data_dict = data_utils.pickle_load(tn_conf.char_file)
xterm = data_utils.pickle_load(tn_conf.term_file)
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
reverse_dict = dict()
for k, v in term_vocab_dict.items():
reverse_dict[v] = k
N = xterm.shape[0]
with open(filename, 'w') as fout:
for i in range(N):
xi = xterm[i]
term_list = []
for idx in xi:
if idx != 0 and idx != 1 and idx in reverse_dict:
term_list.append(reverse_dict[idx])
fout.write(' '.join(term_list) + '\n')
print('prepare data done.')
def get_vec(out_file, mode="tn"):
index = 0
vec_len = 20
dictionary = corpora.Dictionary.load('./data/lda.dict')
lda = gensim.models.LdaModel.load('data/LDA20.model')
if mode == "train":
print('train')
tn_conf = TrainConfigure()
elif mode == "val":
print('val')
tn_conf = data_utils.ValidConfigure()
else:
print("test")
tn_conf = data_utils.TestConfigure()
# data_dict = data_utils.pickle_load(tn_conf.char_file)
xterm = data_utils.pickle_load(tn_conf.term_file)
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
reverse_dict = dict()
for k, v in term_vocab_dict.items():
reverse_dict[v] = k
all_lda = []
for xi in xterm:
doc = []
for idx in xi:
if idx != 0 and idx != 1 and idx in reverse_dict:
doc.append(reverse_dict[idx])
doc_bow = dictionary.doc2bow(doc)
lda_vec_tmp = lda[doc_bow]
lda_vec = np.zeros(vec_len)
for (index, p) in lda_vec_tmp:
lda_vec[index] = p
index += 1
all_lda.append(lda_vec)
data_utils.pickle_dump(np.array(all_lda), out_file)
print('done.')
def predict():
"""
根据概率集成
:return:
"""
print('load data')
tn_conf = TrainConfigure()
data_dict = data_utils.pickle_load(tn_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
xterm = data_utils.pickle_load(tn_conf.term_file)
xfeat = data_utils.pickle_load(tn_conf.feat_file)
# normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(xfeat)
data_utils.pickle_dump(scaler, tn_conf.feat_norm)
xfeat = scaler.transform(xfeat)
print('loading embed ...')
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
term_embed_matrix = data_utils.load_embedding(
term_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/term_embed.pkl')
# term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
# 'data/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5',
# dump_path='data/term_embed_ww.pkl')
char_vocab_dict = data_utils.pickle_load(tn_conf.char_dict)
char_embed_matrix = data_utils.load_embedding(
char_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/char_embed.pkl')
print('load embed done.')
val_conf = ValidConfigure()
data_dict = data_utils.pickle_load(val_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
ids = data_dict['id']
xterm = data_utils.pickle_load(val_conf.term_file)
xfeat = data_utils.pickle_load(val_conf.feat_file)
xfeat = scaler.transform(xfeat)
print('feat shape', xfeat.shape)
xtopic = data_utils.pickle_load('data/lda_vec_val.pkl')
xe = [[i for i in range(600)] for _ in range(y.shape[0])]
xe = np.array(xe)
xe_term = [[i for i in range(300)] for _ in range(y.shape[0])]
xe_term = np.array(xe_term)
import data_utils100
val_conf100 = data_utils100.ValidConfigure()
data_dict100 = data_utils.pickle_load(val_conf100.char_file)
x100 = data_dict100['x']
xterm100 = data_utils.pickle_load(val_conf100.term_file)
xe100 = [[i for i in range(100)] for _ in range(y.shape[0])]
xe100 = np.array(xe100)
xe_term100 = [[i for i in range(100)] for _ in range(y.shape[0])]
xe_term100 = np.array(xe_term100)
import data_utils200
val_conf200 = data_utils200.ValidConfigure()
data_dict200 = data_utils.pickle_load(val_conf200.char_file)
x200 = data_dict200['x']
xterm200 = data_utils.pickle_load(val_conf200.term_file)
xe200 = [[i for i in range(200)] for _ in range(y.shape[0])]
xe200 = np.array(xe200)
xe_term200 = [[i for i in range(200)] for _ in range(y.shape[0])]
xe_term200 = np.array(xe_term200)
ys = []
print('define model')
model = HybridDenseModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybriddensemodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
model = HybridDenseMAModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybriddensemodelma_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('dense model done.')
model = HybridSEModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybridsemodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('se model done.')
# print('start len 100 model')
# model = HybridConvModel(char_embed_matrix=char_embed_matrix,
# term_embed_matrix=term_embed_matrix, MAX_LEN=100, MAX_LEN_TERM=100,NUM_FEAT=8,
# PE=True, name='hybridconvmodel_n100.h5')
# model.load_weights()
# y = model.predict([x100, xe100, xterm100, xe_term100, xfeat, xtopic])
# ys.append(y)
# del model
# print('hybrid conv model done.')
#
# model = HybridGatedDeepCNNModel(char_embed_matrix=char_embed_matrix,
# term_embed_matrix=term_embed_matrix, MAX_LEN=100, MAX_LEN_TERM=100,NUM_FEAT=8,
# PE=True, name='hybridgateddeepcnnmodel_n100.h5')
# model.load_weights()
# y = model.predict([x100, xe100, xterm100, xe_term100, xfeat, xtopic])
# ys.append(y)
# del model
# print('hybrid gated deep cnn model done.')
#
# model = HybridRCNNModel(char_embed_matrix=char_embed_matrix,
# term_embed_matrix=term_embed_matrix, MAX_LEN=100, MAX_LEN_TERM=100,NUM_FEAT=8,
# PE=True, name='hybridrcnnmodel_n100.h5')
# model.load_weights()
# y = model.predict([x100, xe100, xterm100, xe_term100, xfeat, xtopic])
# ys.append(y)
# del model
# print('hybrid RCNN model done.')
print('start len 200 model')
model = HybridConvModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
MAX_LEN=200,
MAX_LEN_TERM=200,
NUM_FEAT=8,
PE=True,
name='hybridconvmodel_n200.h5')
model.load_weights()
y = model.predict([x200, xe200, xterm200, xe_term200, xfeat, xtopic])
ys.append(y)
del model
print('hybrid conv model done.')
model = HybridDPCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
MAX_LEN=200,
MAX_LEN_TERM=200,
NUM_FEAT=8,
PE=True,
name='hybriddpcnnmodel_n200.h5')
model.load_weights()
y = model.predict([x200, xe200, xterm200, xe_term200, xfeat, xtopic])
ys.append(y)
del model
print('hybrid dpcnn model done.')
model = HybridGatedConvTopicModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
MAX_LEN=200,
MAX_LEN_TERM=200,
NUM_FEAT=8,
PE=True,
name='hybridgatedconvtopicmodel_n200.h5')
model.load_weights()
y = model.predict([x200, xe200, xterm200, xe_term200, xfeat, xtopic])
ys.append(y)
del model
print('hybrid dpcnn model done.')
model = HybridGatedDeepCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
MAX_LEN=200,
MAX_LEN_TERM=200,
NUM_FEAT=8,
PE=True,
name='hybridgateddeepcnnmodel_n200.h5')
model.load_weights()
y = model.predict([x200, xe200, xterm200, xe_term200, xfeat, xtopic])
ys.append(y)
del model
print('hybrid gated deep cnn model done.')
model = HybridRCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
MAX_LEN=200,
MAX_LEN_TERM=200,
NUM_FEAT=8,
PE=True,
name='hybridrcnnmodel_n200.h5')
model.load_weights()
y = model.predict([x200, xe200, xterm200, xe_term200, xfeat, xtopic])
ys.append(y)
del model
#这个模型太慢
model = ConditionAttModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='conditionattmodel_PE.h5',
lr=0.001)
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
print('condition att model done.')
model = ConditionConvModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='conditionconvmodel_PE.h5',
lr=0.001)
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('condition conv model done.')
model = ConditionDPCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='conditiondpcnnmodel_PE.h5',
lr=0.001)
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('condition dpcnn model done.')
model = ConditionGatedConvModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='conditiongatedconvmodel_PE.h5',
lr=0.001)
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('condition gated conv model done.')
model = ConditionGatedDeepCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='conditiongateddeepcnnmodel_PE.h5',
lr=0.001)
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('condition gated deepcnn model done.')
model = HybridAttModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybridattmodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
print('hybrid att model done.')
model = HybridConvModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybridconvmodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('hybrid conv model done.')
model = HybridDPCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybriddpcnnmodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('hybrid dpcnn model done.')
model = HybridGatedDeepCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybridgateddeepcnnmodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('hybrid gated deep cnn model done.')
model = HybridRCNNModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybridrcnnmodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
del model
print('hybrid rcnn model done.')
model = HybridGatedConvTopicModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8,
PE=True,
name='hybridgatedconvtopicmodel_PE.h5')
model.load_weights()
y = model.predict([x, xe, xterm, xe_term, xfeat, xtopic])
ys.append(y)
print('hybrid gated conv topic done.')
y = fasttextmodel.predict_char()
ys.append(y)
y = fasttextmodel.predict_term()
ys.append(y)
print(y.shape)
print('fast text done.')
#hybrid model
# model = HybridModel(char_embed_matrix=char_embed_matrix, term_embed_matrix=term_embed_matrix, NUM_FEAT=8)# + 37
# model.load_weights()
# y = model.predict([x, xterm, xfeat])
# ys.append( y )
# print(y.shape)
# print('hybrid model done.')
labels = ['人类作者', '自动摘要', '机器作者', '机器翻译']
y_pred = np.mean(ys, axis=0)
y_pred = convert_y(y_pred)
out_file = 'result.csv'
with open(out_file, 'w', encoding='utf-8') as fout:
for id, yi in zip(ids, y_pred):
label = labels[yi]
fout.write('{},{}\n'.format(id, label))
print('done.')
def predict2():
"""
根据分类结果集成
:return:
"""
print('load data')
tn_conf = TrainConfigure()
data_dict = data_utils.pickle_load(tn_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
xterm = data_utils.pickle_load(tn_conf.term_file)
xfeat = data_utils.pickle_load(tn_conf.feat_file)
# normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(xfeat)
data_utils.pickle_dump(scaler, tn_conf.feat_norm)
xfeat = scaler.transform(xfeat)
print('loading embed ...')
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
term_embed_matrix = data_utils.load_embedding(
term_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/term_embed.pkl')
# term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
# 'data/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5',
# dump_path='data/term_embed_ww.pkl')
char_vocab_dict = data_utils.pickle_load(tn_conf.char_dict)
char_embed_matrix = data_utils.load_embedding(
char_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/char_embed.pkl')
print('load embed done.')
val_conf = ValidConfigure()
data_dict = data_utils.pickle_load(val_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
ids = data_dict['id']
xterm = data_utils.pickle_load(val_conf.term_file)
xfeat = data_utils.pickle_load(val_conf.feat_file)
xfeat = scaler.transform(xfeat)
xe = [[i for i in range(600)] for _ in range(y.shape[0])]
xe = np.array(xe)
ys = []
print('define model')
#hybrid model
model = HybridModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8) # + 37
print('feat shape', xfeat.shape)
model.load_weights()
y = model.predict([x, xterm, xfeat])
ys.append(convert_onehot(y))
print('hybrid model done.')
#CNN model (char)
model = CharModel(embed_matrix=char_embed_matrix)
model.load_weights()
y = model.predict(x)
ys.append(convert_onehot(y))
print('char model done.')
model = CharModel(embed_matrix=char_embed_matrix,
name='charmodel_PE.h5',
PE=True)
model.load_weights()
y = model.predict([x, xe])
ys.append(convert_onehot(y))
print('char model done.')
model = CharModel(embed_matrix=char_embed_matrix,
name='charmodel_PE_OE.h5',
PE=True)
model.load_weights()
y = model.predict([x, xe])
ys.append(convert_onehot(y))
print('char model done.')
#CNN (term)
model = TermModel(embed_matrix=term_embed_matrix)
model.load_weights()
y = model.predict(xterm)
ys.append(convert_onehot(y))
print('term model done.')
model = DeepCNNModel(embed_matrix=char_embed_matrix)
model.load_weights()
y = model.predict(x)
ys.append(convert_onehot(y))
print('deep cnn done.')
# # attention model (char)
# model = AttModel(MAX_LEN=600, name='charattmodel.h5', embed_matrix=char_embed_matrix)
# model.load_weights()
# y = model.predict(x)
# ys.append(convert_onehot(y))
# # attention model (term)
# model = AttModel(MAX_LEN=300, embed_matrix=term_embed_matrix)
# model.load_weights()
# y = model.predict(xterm)
# ys.append(convert_onehot(y))
#
# model = ConditionModel(embed_matrix=char_embed_matrix)
# model.load_weights()
# y = model.predict(x)
# ys.append(convert_onehot(y))
model = SSCharModel(embed_matrix=char_embed_matrix,
name='sscharmodel_PE.h5',
PE=True,
train_embed=True)
model.load_weights()
y = model.predict([x, xe])
ys.append(convert_onehot(y))
model = SSCharModel(embed_matrix=char_embed_matrix, train_embed=True)
model.load_weights()
y = model.predict(x)
ys.append(convert_onehot(y))
model = GatedConvModel(embed_matrix=char_embed_matrix,
name='gatedconvmodel_PE.h5',
PE=True)
model.load_weights()
y = model.predict([x, xe])
ys.append(convert_onehot(y))
model = GatedConvModel(embed_matrix=char_embed_matrix, train_embed=True)
model.load_weights()
y = model.predict(x)
ys.append(convert_onehot(y))
model = GatedDeepCNNModel(embed_matrix=char_embed_matrix,
name='gateddeepcnnmodel_PE.h5',
PE=True,
train_embed=True)
model.load_weights()
y = model.predict([x, xe])
ys.append(convert_onehot(y))
model = GatedDeepCNNModel(embed_matrix=char_embed_matrix, train_embed=True)
model.load_weights()
y = model.predict(x)
ys.append(convert_onehot(y))
labels = ['人类作者', '自动摘要', '机器作者', '机器翻译']
y_pred = np.mean(ys, axis=0)
y_pred = convert_y(y_pred)
out_file = 'result.csv'
with open(out_file, 'w', encoding='utf-8') as fout:
for id, yi in zip(ids, y_pred):
label = labels[yi]
fout.write('{},{}\n'.format(id, label))
print('done.')
def load_weights(self, name=None):
if name is None:
save_path = self.name
else:
save_path = name
self.model.load_weights(save_path)
def predict(self, x):
y_pred = self.model.predict(x, batch_size=512)
return y_pred
if __name__ == '__main__':
import sys
tn_conf = TrainConfigure()
if len(sys.argv) > 1 and sys.argv[1] == 'char':
if len(sys.argv) > 2 and sys.argv[2] == 'pe':
print('define char model with position embedding')
print('load data')
import data_utils, training_utils
data_dict = data_utils.pickle_load(tn_conf.char_file)
y = to_categorical(data_dict['y'])
char_vocab_dict = data_utils.pickle_load(tn_conf.char_dict)
char_embed_matrix = data_utils.load_embedding(
char_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/char_embed.pkl')
xe = [[i for i in range(600)] for _ in range(y.shape[0])]
xe = np.array(xe)
def train_main():
print('load data')
import data_utils, training_utils
tn_conf = TrainConfigure()
data_dict = data_utils.pickle_load(tn_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
xterm = data_utils.pickle_load(tn_conf.term_file)
xfeat = data_utils.pickle_load(tn_conf.feat_file)
# normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(xfeat)
data_utils.pickle_dump(scaler, tn_conf.feat_norm)
xfeat = scaler.transform(xfeat)
print('loading embed ...')
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
term_embed_matrix = data_utils.load_embedding(
term_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/term_embed.pkl')
# term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
# 'data/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5',
# dump_path='data/term_embed_ww.pkl')
char_vocab_dict = data_utils.pickle_load(tn_conf.char_dict)
char_embed_matrix = data_utils.load_embedding(
char_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/char_embed.pkl')
print('load embed done.')
x_tn, y_tn, x_ts, y_ts = training_utils.split([x, xterm, xfeat],
y,
shuffle=False)
x_tn, y_tn, x_val, y_val = training_utils.split(x_tn, y_tn, shuffle=False)
print('define model')
model = HybridModel(char_embed_matrix=char_embed_matrix,
term_embed_matrix=term_embed_matrix,
NUM_FEAT=8) # +37
print('feat shape', xfeat.shape)
import sys
if len(sys.argv) <= 1 or sys.argv[1] == 'train':
print('train')
model.train(x_tn, y_tn, x_val, y_val, x_ts, y_ts)
if len(sys.argv) > 1 and sys.argv[1] == 'val':
val_conf = ValidConfigure()
data_dict = data_utils.pickle_load(val_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
ids = data_dict['id']
xterm = data_utils.pickle_load(val_conf.term_file)
xfeat = data_utils.pickle_load(val_conf.feat_file)
xfeat = scaler.transform(xfeat)
model.load_weights()
model.test([x, xterm, xfeat], ids, val_conf.out_file)
if len(sys.argv) > 1 and sys.argv[1] == 'error':
start_index = y_tn.shape[0] + y_val.shape[0]
texts = data_utils.load_all_text(tn_conf)
model.load_weights()
model.error_analysis(x_ts, y_ts, texts, start_index)
def stacking_main_condition():
print('load data')
tn_conf = TrainConfigure()
data_dict = data_utils.pickle_load(tn_conf.char_file)
y = to_categorical(data_dict['y'])
x = data_dict['x']
xterm = data_utils.pickle_load(tn_conf.term_file)
xfeat = data_utils.pickle_load(tn_conf.feat_file)
# normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(xfeat)
data_utils.pickle_dump(scaler, tn_conf.feat_norm)
xfeat = scaler.transform(xfeat)
xe = [[i for i in range(600)] for _ in range(y.shape[0])]
xe = np.array(xe)
xe_term = [[i for i in range(300)] for _ in range(y.shape[0])]
xe_term = np.array(xe_term)
xtopic = data_utils.pickle_load('data/lda_vec.pkl')
print('loading embed ...')
term_vocab_dict = data_utils.pickle_load(tn_conf.term_dict)
term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/term_embed.pkl')
# term_embed_matrix = data_utils.load_embedding(term_vocab_dict,
# 'data/sgns.target.word-word.dynwin5.thr10.neg5.dim300.iter5',
# dump_path='data/term_embed_ww.pkl')
char_vocab_dict = data_utils.pickle_load(tn_conf.char_dict)
char_embed_matrix = data_utils.load_embedding(char_vocab_dict,
'data/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim300.iter5',
dump_path='data/char_embed.pkl')
print('load embed done.')
name = 'model/stack_condition_model.pkl'
model_dir = 'model/stack/'
n_fold = 3
name = 'model/stack_condition_model5.pkl'
model_dir = 'model/stack5/'
n_fold = 5
stk_model = stacking(n_fold, name=name, is_condition=True)
conf = conditionmodelbase.ModelConfigure()
conf.PE = True
stk_model.add_model(ConditionConvModel, {"conf":conf,"char_embed_matrix":char_embed_matrix,
"term_embed_matrix":term_embed_matrix,
"name":model_dir+'conditionconvmodel_PE.h5'})
stk_model.add_model(ConditionGatedConvModel, {"conf":conf,"char_embed_matrix": char_embed_matrix,
"term_embed_matrix": term_embed_matrix,
"name": model_dir+'conditiongatedconvmodel_PE.h5'})
stk_model.add_model(ConditionGatedDeepCNNModel, {"conf":conf,"char_embed_matrix": char_embed_matrix,
"term_embed_matrix": term_embed_matrix,
"name": model_dir+'conditiongateddeepcnnmodel_PE.h5'})
conf.lr = 0.0005
stk_model.add_model(ConditionDPCNNModel, {"conf": conf, "char_embed_matrix": char_embed_matrix,
"term_embed_matrix": term_embed_matrix,
"name": model_dir + 'conditiondpcnnmodel_PE.h5'})
#采样0.1用于测试
# x_tn, y_tn, x_ts, y_ts = training_utils.split([x, xe, xterm, xe_term, xfeat, xtopic], y, split_ratio=0.005, shuffle=False)
# x_tn, y_tn, x_ts, y_ts = training_utils.split(x_tn, y_tn, shuffle=False)
x_tn, y_tn, x_ts, y_ts = training_utils.split([x, xe, xterm, xe_term, xfeat, xtopic], y, split_ratio=0.95)
stk_model.fit(x_tn, y_tn)
# joblib.dump(stk_model, 'model/stack_model_3.pkl')
y_pred = stk_model.predict(x_ts)
acc = accuracy_score(training_utils.convert_y(y_pred), training_utils.convert_y(y_ts) )
print(acc)
cnf_matrix = confusion_matrix(training_utils.convert_y(y_pred), training_utils.convert_y(y_ts) )
print(cnf_matrix)
stk_model.save( )