def __init__(self, field_dims, embed_dim, mlp_dims, dropouts):
super().__init__()
self.embedding = FeaturesEmbedding(field_dims, embed_dim)
self.linear = LogisticRegressionModel(field_dims)
self.fm = torch.nn.Sequential(FactorizationMachine(reduce_sum=False),
torch.nn.BatchNorm1d(embed_dim),
torch.nn.Dropout(dropouts[0]))
self.mlp = MultiLayerPerceptron(embed_dim, mlp_dims, dropouts[1])
def get_model(name, dataset):
"""
Hyperparameters are empirically determined, not opitmized.
"""
field_dims = dataset.field_dims
print('field_dims={}, {}'.format(type(field_dims), field_dims.tolist()))
#1/0
if name == 'lr':
return LogisticRegressionModel(field_dims)
elif name == 'fm':
return FactorizationMachineModel(field_dims, embed_dim=16)
elif name == 'hofm':
return HighOrderFactorizationMachineModel(field_dims, order=3, embed_dim=16)
elif name == 'ffm':
return FieldAwareFactorizationMachineModel(field_dims, embed_dim=4)
elif name == 'fnn':
return FactorizationSupportedNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)
elif name == 'wd':
return WideAndDeepModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)
elif name == 'ipnn':
#return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='inner', dropout=0.2)
#return ProductNeuralNetworkModel(field_dims, embed_dim=64, mlp_dims=(16,16), method='inner', dropout=0.2)
return ProductNeuralNetworkModel(field_dims, embed_dim=64, mlp_dims=(128,64,128,128,128,), method='inner', dropout=0.2)
elif name == 'opnn':
return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='outer', dropout=0.2)
elif name == 'dcn':
return DeepCrossNetworkModel(field_dims, embed_dim=16, num_layers=3, mlp_dims=(16, 16), dropout=0.2)
elif name == 'nfm':
return NeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,), dropouts=(0.2, 0.2))
elif name == 'ncf':
# only supports MovieLens dataset because for other datasets user/item colums are indistinguishable
assert isinstance(dataset, MovieLens20MDataset) or isinstance(dataset, MovieLens1MDataset)
return NeuralCollaborativeFiltering(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2,
user_field_idx=dataset.user_field_idx,
item_field_idx=dataset.item_field_idx)
elif name == 'fnfm':
return FieldAwareNeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,32,), dropouts=(0.2, 0.2))
elif name == 'dfm':
#return DeepFactorizationMachineModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2)
#return DeepFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(256, 256, 256, 256, 256,256), dropout=0.2)
#return DeepFactorizationMachineModel(field_dims, embed_dim=128, mlp_dims=(400, 400, 400, 400), dropout=0.2)
return DeepFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(128, 128, 128), dropout=0.2)
elif name == 'xdfm':
return ExtremeDeepFactorizationMachineModel(
field_dims, embed_dim=16, cross_layer_sizes=(16, 16), split_half=False, mlp_dims=(16, 16), dropout=0.2)
elif name == 'afm':
return AttentionalFactorizationMachineModel(field_dims, embed_dim=16, attn_size=16, dropouts=(0.2, 0.2))
elif name == 'afi':
return AutomaticFeatureInteractionModel(
field_dims, embed_dim=16, atten_embed_dim=64, num_heads=2, num_layers=3, mlp_dims=(400, 400), dropouts=(0, 0, 0))
elif name == 'afn':
print("Model:AFN")
return AdaptiveFactorizationNetwork(
field_dims, embed_dim=64, LNN_dim=1500, mlp_dims=(400, 400, 400), dropouts=(0, 0, 0))
else:
raise ValueError('unknown model name: ' + name)
def __init__(self, field_dims, embed_dim, mlp_dims, dropouts):
super().__init__()
self.linear = LogisticRegressionModel(field_dims)
self.ffm = torch.nn.Sequential(
FieldAwareFactorizationMachine(field_dims, embed_dim),
torch.nn.BatchNorm1d(embed_dim), torch.nn.Dropout(dropouts[0]))
self.ffm_output_dim = len(field_dims) * (len(field_dims) -
1) // 2 * embed_dim
self.mlp = MultiLayerPerceptron(self.ffm_output_dim, mlp_dims,
dropouts[1])
def get_model(name, field_dims):
"""
Hyperparameters are empirically determined, not opitmized.
"""
if name == 'lr':
return LogisticRegressionModel(field_dims)
elif name == 'fm':
return FactorizationMachineModel(field_dims, embed_dim=16)
elif name == 'ffm':
return FieldAwareFactorizationMachineModel(field_dims, embed_dim=4)
elif name == 'fnn':
return FactorizationSupportedNeuralNetworkModel(field_dims,
embed_dim=16,
mlp_dims=(16, 16),
dropout=0.2)
elif name == 'wd':
return WideAndDeepModel(field_dims,
embed_dim=16,
mlp_dims=(16, 16),
dropout=0.2)
elif name == 'ipnn':
return ProductNeuralNetworkModel(field_dims,
embed_dim=16,
mlp_dims=(16, ),
method='inner',
dropout=0.2)
elif name == 'opnn':
return ProductNeuralNetworkModel(field_dims,
embed_dim=16,
mlp_dims=(16, ),
method='outer',
dropout=0.2)
elif name == 'dcn':
return DeepCrossNetworkModel(field_dims,
embed_dim=16,
num_layers=3,
mlp_dims=(16, 16),
dropout=0.2)
elif name == 'nfm':
return NeuralFactorizationMachineModel(field_dims,
embed_dim=64,
mlp_dims=(64, ),
dropouts=(0.2, 0.2))
elif name == 'fnfm':
return FieldAwareNeuralFactorizationMachineModel(field_dims,
embed_dim=4,
mlp_dims=(64, ),
dropouts=(0.2, 0.2))
elif name == 'dfm':
return DeepFactorizationMachineModel(field_dims,
embed_dim=16,
mlp_dims=(16, 16),
dropout=0.2)
elif name == 'xdfm':
return ExtremeDeepFactorizationMachineModel(field_dims,
embed_dim=16,
cross_layer_sizes=(16, 16),
split_half=False,
mlp_dims=(16, 16),
dropout=0.2)
elif name == 'afm':
return AttentionalFactorizationMachineModel(field_dims,
embed_dim=16,
attn_size=16,
dropouts=(0.2, 0.2))
elif name == 'afi':
return AutomaticFeatureInteractionModel(field_dims,
embed_dim=32,
num_heads=4,
num_layers=2,
mlp_dims=(16, 16),
dropouts=(0.2, 0.2))
else:
raise ValueError('unknown model name: ' + name)