def optimize(model):
logger.info("Checkpoint2")
X = model.predictor_src #+ self.predictor_tgt
y = model.predictor_tgt
# y = model.config.sentence_level
print(X)
print(y)
#Hyperparameter Tuning with Random Search
net = NeuralNetRegressor(
model,
max_epochs=10,
lr=0.1,
# Shuffle training data on each epoch
iterator_train__shuffle=True,
)
net.fit(X, y)
y_proba = net.predict_proba(X)
# deactivate skorch-internal train-valid split and verbose logging
net.set_params(train_split=False, verbose=0)
params = {
'epochs': [7],
'hidden_LSTM': [32, 64, 128],
'learning_rate_batch': [(32, '1e-3'), (64, '2e-3')],
'dropout': [0.5],
}
gs = RandomizedSearchCV(net,
params,
refit=False,
cv=3,
scoring='accuracy',
verbose=2)
gs.fit(X, y)
print("best score: {:.3f}, best params: {}".format(
gs.best_score_, gs.best_params_))
return
print("Fitting")
net.fit(train0df, y=None)
print("Fit completed")
history = net.history
train_loss0 = history[:, 'train_loss']
valid_loss0 = history[:, 'valid_loss']
ax1.plot(train_loss0)
ax1.plot(valid_loss0)
ax1.legend(['train_loss', 'valid_loss'])
net.save_params(f_params='dcs0_0005.pkl',
f_optimizer='dcs0_0005_optimizer.pkl',
f_history='dcs0_0005_history.json')
pred = net.predict_proba(valid0)
label = valid0.get_label()
accuracy = concordance_index(pred, label)
print(accuracy)
net1 = NeuralNetRegressor(model,
criterion=NegativeLogLikelihood,
lr=0.00001,
batch_size=512,
max_epochs=100,
optimizer=SGD,
optimizer__momentum=0.9,
optimizer__weight_decay=0.001,
iterator_train__shuffle=True,
iterator_train__num_workers=10,
iterator_valid__shuffle=True,