def implicit_model(train_val_implicit_data, gpu_count):
train, val = train_val_implicit_data
model = MatrixFactorizationModel(train=train,
val=val,
embedding_dim=10,
lr=1e-1)
model_trainer = CollieTrainer(model=model,
gpus=gpu_count,
max_epochs=10,
deterministic=True,
logger=False,
checkpoint_callback=False)
model_trainer.fit(model)
model.freeze()
return model
def models_trained_for_one_step(request,
train_val_implicit_data,
movielens_metadata_df,
movielens_implicit_df,
train_val_implicit_pandas_data,
gpu_count):
train, val = train_val_implicit_data
if request.param == 'mf_hdf5':
# create, fit, and return the model all at once so we can close the HDF5 file
train_pandas_df, val_pandas_df = train_val_implicit_pandas_data
with tempfile.TemporaryDirectory() as temp_dir:
pandas_df_to_hdf5(df=train_pandas_df,
out_path=os.path.join(temp_dir, 'train.h5'),
key='interactions')
pandas_df_to_hdf5(df=val_pandas_df,
out_path=os.path.join(temp_dir, 'val.h5'),
key='interactions')
train_loader = HDF5InteractionsDataLoader(hdf5_path=os.path.join(temp_dir, 'train.h5'),
user_col='user_id',
item_col='item_id',
num_users=train.num_users,
num_items=train.num_items,
batch_size=1024,
shuffle=True)
val_loader = HDF5InteractionsDataLoader(hdf5_path=os.path.join(temp_dir, 'val.h5'),
user_col='user_id',
item_col='item_id',
num_users=val.num_users,
num_items=val.num_items,
batch_size=1024,
shuffle=False)
model = MatrixFactorizationModel(train=train_loader,
val=val_loader,
embedding_dim=15,
dropout_p=0.1,
lr=1e-1,
bias_lr=1e-2,
optimizer='adam',
bias_optimizer='sgd',
weight_decay=1e-7,
loss='bpr',
sparse=False)
model_trainer = CollieTrainer(model=model,
gpus=gpu_count,
max_steps=1,
deterministic=True,
logger=False,
checkpoint_callback=False)
model_trainer.fit(model)
model.freeze()
return model
elif request.param == 'sparse_mf':
model = MatrixFactorizationModel(train=train,
val=val,
embedding_dim=15,
dropout_p=0.1,
lr=1e-1,
bias_lr=1e-2,
optimizer='sparse_adam',
bias_optimizer='sgd',
weight_decay=0,
loss='hinge',
sparse=True)
elif request.param == 'mf_no_val':
model = MatrixFactorizationModel(train=train, val=None)
elif request.param == 'mf_non_approximate' or request.param == 'mf_approximate':
if request.param == 'mf_non_approximate':
train_loader = InteractionsDataLoader(interactions=train, batch_size=1024, shuffle=True)
val_loader = InteractionsDataLoader(interactions=val, batch_size=1024, shuffle=False)
else:
train_loader = ApproximateNegativeSamplingInteractionsDataLoader(interactions=train,
batch_size=1024,
shuffle=True)
val_loader = ApproximateNegativeSamplingInteractionsDataLoader(interactions=val,
batch_size=1024,
shuffle=False)
model = MatrixFactorizationModel(train=train_loader,
val=val_loader,
embedding_dim=15,
dropout_p=0.1,
lr=1e-1,
bias_lr=1e-2,
optimizer='adam',
bias_optimizer='sgd',
weight_decay=1e-7,
loss='bpr',
sparse=False)
elif request.param == 'mf_with_y_range':
model = MatrixFactorizationModel(train=train,
val=val,
y_range=(0, 4))
elif request.param == 'nonlinear_mf':
model = NonlinearMatrixFactorizationModel(train=train,
val=val,
user_embedding_dim=15,
item_embedding_dim=15,
user_dense_layers_dims=[15, 10],
item_dense_layers_dims=[15, 10],
embedding_dropout_p=0.05,
dense_dropout_p=0.1,
lr=1e-1,
bias_lr=1e-2,
optimizer='adam',
bias_optimizer='sgd',
weight_decay=1e-7,
loss='bpr')
elif request.param == 'nonlinear_mf_with_y_range':
model = NonlinearMatrixFactorizationModel(train=train,
val=val,
y_range=(0, 4))
elif request.param == 'neucf':
model = NeuralCollaborativeFiltering(train=train,
val=val,
embedding_dim=10,
num_layers=1,
dropout_p=0.1,
lr=1e-3,
weight_decay=0.,
optimizer='adam',
loss='adaptive')
elif request.param == 'neucf_sigmoid':
model = NeuralCollaborativeFiltering(train=train,
val=val,
final_layer='sigmoid')
elif request.param == 'neucf_relu':
model = NeuralCollaborativeFiltering(train=train,
val=val,
final_layer='relu')
elif request.param == 'neucf_leaky_rulu':
model = NeuralCollaborativeFiltering(train=train,
val=val,
final_layer='leaky_relu')
elif request.param == 'neucf_custom':
model = NeuralCollaborativeFiltering(train=train,
val=val,
final_layer=torch.tanh)
elif (
request.param == 'hybrid_pretrained' or request.param == 'hybrid_pretrained_metadata_layers'
):
implicit_model = MatrixFactorizationModel(train=train,
val=val,
embedding_dim=10,
lr=1e-1,
optimizer='adam')
implicit_model_trainer = CollieTrainer(model=implicit_model,
gpus=gpu_count,
max_steps=1,
deterministic=True,
logger=False,
checkpoint_callback=False)
implicit_model_trainer.fit(implicit_model)
implicit_model.freeze()
genres = (
torch.tensor(movielens_metadata_df[
[c for c in movielens_metadata_df.columns if 'genre' in c]
].values)
.topk(1)
.indices
.view(-1)
)
if request.param == 'hybrid_pretrained_metadata_layers':
metadata_layers_dims = [16, 12]
else:
metadata_layers_dims = None
model_frozen = HybridPretrainedModel(train=train,
val=val,
item_metadata=movielens_metadata_df,
trained_model=implicit_model,
metadata_layers_dims=metadata_layers_dims,
freeze_embeddings=True,
dropout_p=0.15,
loss='warp',
lr=.01,
optimizer=torch.optim.Adam,
metadata_for_loss={'genre': genres},
metadata_for_loss_weights={'genre': .4},
weight_decay=0.0)
model_frozen_trainer = CollieTrainer(model=model_frozen,
gpus=gpu_count,
max_steps=1,
deterministic=True,
logger=False,
checkpoint_callback=False)
model_frozen_trainer.fit(model_frozen)
model = HybridPretrainedModel(train=train,
val=val,
item_metadata=movielens_metadata_df,
trained_model=implicit_model,
metadata_layers_dims=metadata_layers_dims,
freeze_embeddings=False,
dropout_p=0.15,
loss='bpr',
lr=1e-4,
optimizer=torch.optim.Adam,
metadata_for_loss={'genre': genres},
metadata_for_loss_weights={'genre': .4},
weight_decay=0.0)
model.load_from_hybrid_model(model_frozen)
model_trainer = CollieTrainer(model=model,
gpus=gpu_count,
max_steps=1,
deterministic=True,
logger=False,
checkpoint_callback=False)
if request.param == 'mf_no_val':
with pytest.warns(UserWarning):
model_trainer.fit(model)
else:
model_trainer.fit(model)
model.freeze()
return model