item_graph_module = GraphModule(data=contexts)
ratio_split = RatioSplit(data=ratings,
test_size=0.2,
rating_threshold=3.5,
shuffle=True,
exclude_unknowns=True,
verbose=True,
item_graph=item_graph_module)
pcrl = PCRL(k=100, z_dims=[300], max_iter=300, learning_rate=0.001)
# Evaluation metrics
nDgc = metrics.NDCG(k=-1)
rec = metrics.Recall(k=20)
pre = metrics.Precision(k=20)
# Instantiate and run your experiment
exp = Experiment(eval_method=ratio_split,
models=[pcrl],
metrics=[nDgc, rec, pre])
exp.run()
"""
Output:
| NDCG@-1 | Recall@20 | Precision@20 | Train (s) | Test (s)
---- + ------- + --------- + ------------ + --------- + --------
pcrl | 0.1922 | 0.0862 | 0.0148 | 2591.4878 | 4.0957
*Results may change slightly from one run to another due to different random initial parameters
"""
exclude_unknowns=True,
verbose=True,
user_graph=user_graph_modality,
seed=123,
)
# Instantiate CVAECF model
cvaecf = CVAECF(z_dim=20,
h_dim=20,
autoencoder_structure=[40],
learning_rate=0.001,
n_epochs=70,
batch_size=128,
verbose=True,
seed=123)
# Evaluation metrics
ndcg = metrics.NDCG(k=50)
rec = metrics.Recall(k=50)
pre = metrics.Precision(k=50)
# Put everything together into an experiment and run it
Experiment(eval_method=ratio_split, models=[cvaecf], metrics=[ndcg, pre,
rec]).run()
"""
Output:
| NDCG@50 | Precision@50 | Recall@50 | Train (s) | Test (s)
------ + ------- + ------------ + --------- + --------- + --------
CVAECF | 0.4171 | 0.0781 | 0.8689 | 13.0752 | 1.4574
"""
