def evaluate_cnn_model(hyperparameters, train, test, validation, random_state):
h = hyperparameters
net = CNNNet(train.num_items,
embedding_dim=h['embedding_dim'],
kernel_width=h['kernel_width'],
dilation=h['dilation'],
num_layers=h['num_layers'],
nonlinearity=h['nonlinearity'],
residual_connections=h['residual'])
model = ImplicitSequenceModel(loss=h['loss'],
representation=net,
batch_size=h['batch_size'],
learning_rate=h['learning_rate'],
l2=h['l2'],
n_iter=h['n_iter'],
use_cuda=CUDA,
random_state=random_state)
model.fit(train, verbose=True)
test_mrr = sequence_mrr_score(model, test)
val_mrr = sequence_mrr_score(model, validation)
return test_mrr, val_mrr
def _evaluate(model, test):
test_mrr = sequence_mrr_score(model, test)
print('Test MRR {}'.format(test_mrr.mean()))
return test_mrr
def evaluate_model(model, train, test, validation):
start_time = time.time()
model.fit(train, verbose=True)
elapsed = time.time() - start_time
print('Elapsed {}'.format(elapsed))
print(model)
if hasattr(test, 'sequences'):
test_mrr = sequence_mrr_score(model, test)
val_mrr = sequence_mrr_score(model, validation)
else:
test_mrr = mrr_score(model, test)
val_mrr = mrr_score(model, test.tocsr() + validation.tocsr())
return test_mrr, val_mrr, elapsed
def evaluate_pooling_model(hyperparameters, train, test, validation,
random_state):
h = hyperparameters
model = ImplicitSequenceModel(loss=h['loss'],
representation='pooling',
batch_size=h['batch_size'],
learning_rate=h['learning_rate'],
l2=h['l2'],
n_iter=h['n_iter'],
use_cuda=CUDA,
random_state=random_state)
model.fit(train, verbose=True)
test_mrr = sequence_mrr_score(model, test)
val_mrr = sequence_mrr_score(model, validation)
return test_mrr, val_mrr
def test_implicit_sequence_serialization(data):
train, test = data
train = train.to_sequence(max_sequence_length=128)
test = test.to_sequence(max_sequence_length=128)
model = ImplicitSequenceModel(loss='bpr',
representation=CNNNet(train.num_items,
embedding_dim=32,
kernel_width=3,
dilation=(1, ),
num_layers=1),
batch_size=128,
learning_rate=1e-1,
l2=0.0,
n_iter=5,
random_state=RANDOM_STATE,
use_cuda=CUDA)
model.fit(train)
mrr_original = sequence_mrr_score(model, test).mean()
mrr_recovered = sequence_mrr_score(_reload(model), test).mean()
assert mrr_original == mrr_recovered
