model = xDeepFM(features.feature_size(),
features.field_size(),
dropout_deep=[0, 0, 0],
deep_layer_sizes=[400, 400],
cin_layer_sizes=[100, 100, 50],
embedding_size=6).to(device)
optimizer = torch.optim.Adam(model.parameters())
model_path = os.path.join(pwd_path, 'xdeepfm_model.pt')
model, loss_history = train_model(model=model,
model_path=model_path,
dataset=X,
loss_func=nn.BCELoss(),
optimizer=optimizer,
device=device,
val_size=0.2,
batch_size=32,
epochs=40,
patience=10)
print(loss_history)
if __name__ == '__main__':
# load criteo sample dataset
dataset = Criteo(n_samples=-1)
features, X_idx, X_value, y, categorical_index, continuous_value = dataset.get_features(
)
print("X_idx[0], X_value[0], y[0] :\n", X_idx[0], X_value[0], y[0])
train(X_idx, X_value, y, features)
def train(x_idx, x_value, label, features, out_type='binary'):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
X_idx_tensor = torch.LongTensor(x_idx).to(device)
X_value_tensor = torch.Tensor(x_value).to(device)
y_tensor = torch.Tensor(label).to(device)
y_tensor = y_tensor.reshape(-1, 1)
X = TensorDataset(X_idx_tensor, y_tensor)
model = FLEN(features.feature_size(), features.field_size(), features.feature_size(), field_ranges=features.field_range(),
out_type=out_type).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
model_path = os.path.join(pwd_path, 'flen_model.pt')
model, loss_history = train_model(model=model, model_path=model_path, dataset=X, loss_func=nn.BCELoss(),
optimizer=optimizer, device=device, val_size=0.2, batch_size=32, epochs=10)
print(loss_history)
if __name__ == '__main__':
# load criteo sample dataset
dataset = Criteo(n_samples=100)
features, X_idx, X_value, y, category_index, continuous_value = dataset.get_features(use_continuous_columns=True,
use_category_columns=True)
print(features.feature_size(), features.field_size())
print("X_idx[0], X_value[0], y[0] :\n", X_idx[0], X_value[0], y[0])
train(X_idx, X_value, y, features)
def setUp(self):
self.choices = list(range(1, 10))
self.movielens_data = Movielens()
self.criteo_data = Criteo()
X = TensorDataset(X_idx_tensor, y_tensor)
model = AutoInt(feature_size=features.feature_size(),
field_size=features.field_size(),
out_type=out_type).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
model_path = os.path.join(pwd_path, 'autoint_model.pt')
model, loss_history = train_model(model=model,
model_path=model_path,
dataset=X,
loss_func=nn.BCELoss(),
optimizer=optimizer,
device=device,
val_size=0.2,
batch_size=32,
epochs=10,
shuffle=True)
print(loss_history)
if __name__ == '__main__':
# load criteo sample dataset
dataset = Criteo()
features, X_idx, X_value, y, category_index, continuous_value = dataset.get_features(
)
print(features.feature_size(), features.field_size())
print("X_idx[0], X_value[0], y[0] :\n", X_idx[0], X_value[0], y[0])
train(X_idx, X_value, y, features)