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
"""
from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP
from cornac.eval_methods import PropensityStratifiedEvaluation
from cornac.experiment import Experiment
# Load the MovieLens 1M dataset
ml_dataset = cornac.datasets.movielens.load_feedback(variant="1M")
# Instantiate an instance of PropensityStratifiedEvaluation method
stra_eval_method = PropensityStratifiedEvaluation(
data=ml_dataset,
n_strata=2, # number of strata
rating_threshold=4.0,
verbose=True)
# define the examined models
models = [
WMF(k=10, seed=123),
BPR(k=10, seed=123),
]
# define the metrics
metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(), AUC(), MAP()]
# run an experiment
exp_stra = Experiment(eval_method=stra_eval_method,
models=models,
metrics=metrics)
exp_stra.run()