def test_lightgcn_component_definition(resource_path):
yaml_file = os.path.join(
resource_path,
"..",
"..",
"reco_utils",
"recommender",
"deeprec",
"config",
"lightgcn.yaml",
)
df = movielens.load_pandas_df(size="100k")
train, test = python_stratified_split(df, ratio=0.75)
data = ImplicitCF(train=train, test=test)
embed_size = 64
hparams = prepare_hparams(yaml_file, embed_size=embed_size)
model = LightGCN(hparams, data)
assert model.norm_adj is not None
assert model.ua_embeddings.shape == [data.n_users, embed_size]
assert model.ia_embeddings.shape == [data.n_items, embed_size]
assert model.u_g_embeddings is not None
assert model.pos_i_g_embeddings is not None
assert model.neg_i_g_embeddings is not None
assert model.batch_ratings is not None
assert model.loss is not None
assert model.opt is not None
def _wrap_dataset(self, dataset: RecommendationDataset) -> ImplicitCF:
return ImplicitCF(train=dataset.data,
seed=42,
col_user=dataset.user_col,
col_item=dataset.item_col,
col_rating=dataset.score_col,
col_prediction=self.prediction_col)
def test_model_lightgcn(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec",
"dkn")
yaml_file = os.path.join(
resource_path,
"..",
"..",
"reco_utils",
"recommender",
"deeprec",
"config",
"lightgcn.yaml",
)
user_file = os.path.join(data_path, r"user_embeddings.csv")
item_file = os.path.join(data_path, r"item_embeddings.csv")
df = movielens.load_pandas_df(size="100k")
train, test = python_stratified_split(df, ratio=0.75)
data = ImplicitCF(train=train, test=test)
hparams = prepare_hparams(yaml_file, epochs=1)
model = LightGCN(hparams, data)
assert model.run_eval() is not None
model.fit()
assert model.recommend_k_items(test) is not None
model.infer_embedding(user_file, item_file)
assert os.path.getsize(user_file) != 0
assert os.path.getsize(item_file) != 0
def prepare_training_lightgcn(train, test):
return ImplicitCF(train=train, test=test)