def run(params):
evaluation = []
# params=dataset.classification.process_embedding(reader,params)
qdnn = models.setup(params)
model = qdnn.getModel()
model.summary()
if hasattr(loss.pairwise_loss, params.loss):
loss_func = getattr(loss.pairwise_loss, params.loss)
else:
loss_func = params.loss
optimizer = units.getOptimizer(name=params.optimizer, lr=params.lr)
#
# test_data = [to_array(i,params.max_sequence_length) for i in test_data]
if hasattr(loss.pairwise_loss, params.metric_type):
metric_func = getattr(loss.pairwise_loss, params.metric_type)
else:
metric_func = params.metric_type
model.compile(
loss=loss_func, #""
optimizer=optimizer,
metrics=[metric_func])
# pairwise:
# loss = identity_loss
# metric = precision_batch
# pointwise:
# loss = categorical_hinge or mean_squared_error
# metric = acc or mean_squared_error
# classification:
# loss = mean_squared_error
# matrix = acc
if params.dataset_type == 'qa':
test_x, test_y = params.reader.get_test_2(
iterable=False
) # train_x,train_y = params.reader.get_train_2(iterable = False, sampling_per_question = True)
# model.fit(x=train_x, y = train_y, batch_size = params.batch_size, epochs= params.epochs,validation_data= (test_x, test_y))
# print(model.evaluate(x = test_x, y =test_y))
#
for i in range(params.epochs):
model.fit_generator(params.reader.get_train_2(
iterable=True, sampling_per_question=True).__iter__(),
epochs=1,
steps_per_epoch=int(reader.num_samples /
reader.batch_size),
verbose=True)
y_pred = model.predict(x=test_x)
score = batch_softmax_with_first_item(
y_pred)[:, 1] if params.onehot else y_pred
metric = params.reader.evaluate(score, mode="test")
evaluation.append(metric)
print(metric)
logger.info(metric)
df = pd.DataFrame(evaluation, columns=["map", "mrr", "p1"])
# generator = params.reader.get_train_2(iterable = True, sampling_per_question = False,need_balanced=True,always=True,balance_temperature=0.5)
# model.fit_generator(generator, epochs= params.epochs,validation_data= (test_x, test_y),steps_per_epoch=100)
#
#
# test_data = params.reader.get_test(iterable = False)
# y_pred = model.predict(x = test_data)
# score = batch_softmax_with_first_item(y_pred)[:,1] if params.onehot else y_pred
#
# metric = params.reader.evaluate(score, mode = "test",acc=True)
# evaluation.append(metric)
# print(metric)
#
# model.evaluate(x = test_x, y = test_y)
# for i in range(params.epochs):
# model.fit_generator(params.reader.get_train_2(iterable = True,sampling_per_question = False).__iter__(),epochs = 1,steps_per_epoch = int(reader.num_samples/reader.batch_size),verbose = True)
#
# print(model.evaluate(x = test_x, y =test_y))
# from models.match import keras as models
# for i in range(params.epochs):
## model.fit_generator(params.reader.batch_gen(params.reader.get_train(iterable = True)),epochs = 1,steps_per_epoch=int(len(reader.datas["train"])/reader.batch_size),verbose = True)
# model.fit_generator(params.reader.get_train_2(iterable = True),epochs = 1)
# y_pred = model.predict(x = test_x)
# score = batch_softmax_with_first_item(y_pred)[:,1] if params.onehot else y_pred
#
# metric = params.reader.evaluate(score, mode = "test")
# evaluation.append(metric)
# print(metric)
# logger.info(metric)
elif params.dataset_type == 'classification':
# from models import representation as models
# model.summary()
# train_data = params.reader.get_train(iterable = False)
# test_data = params.reader.get_test(iterable = False)
# val_data =params.reader.get_val(iterable = False)
# # (train_x, train_y),(test_x, test_y),(val_x, val_y) = reader.get_processed_data()
# train_x, train_y = train_data
# test_x, test_y = test_data
# val_x, val_y = val_data
train_x, train_y = params.reader.get_train(iterable=False)
test_x, test_y = params.reader.get_test(iterable=False)
val_x, val_y = params.reader.get_val(iterable=False)
history = model.fit(x=train_x,
y=train_y,
batch_size=params.batch_size,
epochs=params.epochs,
validation_data=(test_x, test_y))
metric = model.evaluate(
x=val_x, y=val_y) # !!!!!! change the order to val and test
evaluation.append(metric)
logger.info(metric)
print(history)
print(metric)
df = pd.DataFrame(evaluation, columns=["map", "mrr", "p1"])
logger.info("\n".join(
[params.to_string(), "score: " + str(df.max().to_dict())]))
K.clear_session()
def run(params):
if "bert" in params.network_type.lower() :
params.max_sequence_length = 512
reader.max_sequence_length = 512
evaluation=[]
# params=dataset.classification.process_embedding(reader,params)
qdnn = models.setup(params)
model = qdnn.getModel()
model.summary()
if hasattr(loss.pairwise_loss, params.loss):
loss_func = getattr(loss.pairwise_loss, params.loss)
else:
loss_func = params.loss
optimizer = units.getOptimizer(name=params.optimizer,lr=params.lr)
test_data = params.reader.get_test(iterable = False)
test_data = [to_array(i,reader.max_sequence_length) for i in test_data]
if hasattr(loss.pairwise_loss, params.metric_type):
metric_func = getattr(loss.pairwise_loss, params.metric_type)
else:
metric_func = params.metric_type
model.compile(loss = loss_func, #""
optimizer = optimizer,
metrics=[metric_func])
# pairwise:
# loss = identity_loss
# metric = precision_batch
# pointwise:
# loss = categorical_hinge or mean_squared_error
# metric = acc or mean_squared_error
# classification:
# loss = mean_squared_error
# matrix = acc
if params.dataset_type == 'qa':
# from models.match import keras as models
for i in range(params.epochs):
model.fit_generator(reader.batch_gen(reader.get_train(iterable = True)),epochs = 1,steps_per_epoch=int(len(reader.datas["train"])/reader.batch_size),verbose = True)
y_pred = model.predict(x = test_data)
score = batch_softmax_with_first_item(y_pred)[:,1] if params.onehot else y_pred
metric = reader.evaluate(score, mode = "test")
evaluation.append(metric)
print(metric)
logger.info(metric)
df=pd.DataFrame(evaluation,columns=["map","mrr","p1"])
elif params.dataset_type == 'classification':
# from models import representation as models
# model.summary()
train_data = params.reader.get_train(iterable = False)
test_data = params.reader.get_test(iterable = False)
val_data =params.reader.get_val(iterable = False)
# (train_x, train_y),(test_x, test_y),(val_x, val_y) = reader.get_processed_data()
train_x, train_y = train_data
test_x, test_y = test_data
val_x, val_y = val_data
if "bert" in params.network_type.lower() :
train_x, train_x_mask = to_array(train_x,reader.max_sequence_length,use_mask=True)
test_x,test_x_mask = to_array(test_x,reader.max_sequence_length,use_mask=True)
val_x,val_x_mask = to_array(val_x,reader.max_sequence_length,use_mask=True)
#pretrain_x, pretrain_y = dataset.get_sentiment_dic_training_data(reader,params)
#model.fit(x=pretrain_x, y = pretrain_y, batch_size = params.batch_size, epochs= 3,validation_data= (test_x, test_y))
history = model.fit(x=[train_x,train_x_mask], y = train_y, batch_size = params.batch_size, epochs= params.epochs,validation_data= ([test_x,test_x_mask], test_y))
metric = model.evaluate(x = [val_x,val_x_mask], y = val_y) # !!!!!! change the order to val and test myzip(
else:
train_x = to_array(train_x,reader.max_sequence_length,use_mask=False)
test_x = to_array(test_x,reader.max_sequence_length,use_mask=False)
val_x = to_array(val_x,reader.max_sequence_length,use_mask=False)
#pretrain_x, pretrain_y = dataset.get_sentiment_dic_training_data(reader,params)
#model.fit(x=pretrain_x, y = pretrain_y, batch_size = params.batch_size, epochs= 3,validation_data= (test_x, test_y))
history = model.fit(x=train_x, y = train_y, batch_size = params.batch_size, epochs= params.epochs,validation_data= (test_x, test_y))
metric = model.evaluate(x = val_x, y = val_y) # !!!!!! change the order to val and test
evaluation.append(metric)
logger.info(metric)
print(metric)
df=pd.DataFrame(evaluation,columns=["map","mrr","p1"])
logger.info("\n".join([params.to_string(),"score: "+str(df.max().to_dict())]))
K.clear_session()