def test_amazon_office(self):
random.seed(time.time())
if random.random() > 0.8:
ratings = office.load_rating()
contexts = office.load_context()
self.assertEqual(len(ratings), 53282)
self.assertEqual(len(contexts), 108466)
# ============================================================================
"""Fit to and evaluate PCRL [1] on the Office Amazon dataset.
[1] Salah, Aghiles, and Hady W. Lauw. Probabilistic Collaborative Representation Learning\
for Personalized Item Recommendation. In UAI 2018.
"""
from cornac.data import GraphModule
from cornac.eval_methods import RatioSplit
from cornac.experiment import Experiment
from cornac import metrics
from cornac.models import PCRL
from cornac.datasets import amazon_office as office
# Load office ratings and item contexts
ratings = office.load_rating()
contexts = office.load_context()
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)