def build(self,
task: Task,
base_dataset: Dataset,
epochs=50,
components=200,
gmm_clusters=10,
logloss_weight=2.0,
lr=5e-3,
batch_size=5000):
task.set_user_properties(
**to_clear_ml_params(locals(), ["task", "self"]))
self.item_ids = list(range(len(base_dataset.id_to_item)))
self.avg_ratings_per_user = base_dataset.n_ratings / base_dataset.n_users
if self.user_vectors is None:
self._build_mf(base_dataset, task, components)
self._build_torch_model(base_dataset, task, components)
self._train_torch_model(base_dataset,
task,
epochs,
logloss_weight=logloss_weight,
lr=lr,
batch_size=batch_size)
self._build_gmm(task, gmm_clusters)
def build(self,
task: Task,
base_dataset: Dataset,
epochs=50,
components=200,
gmm_clusters=10,
logloss_weight=2.0,
lr=5e-3,
epochs_logloss_only=25):
task.set_user_properties(
**to_clear_ml_params(locals(), ["task", "self"]))
s_coo = base_dataset.rating_matrix.tocoo()
self._implicit_coef = 1.0
self.user_map = {}
self.user_ids = []
self.avg_rating = np.mean(s_coo.data)
for u, i, r in zip(s_coo.row, s_coo.col, s_coo.data):
self.user_ids.append(u)
if u not in self.user_map:
self.user_map[u] = {}
self.user_map[u][i] = r - self.avg_rating
self.user_map_list = {
k: list(v.keys())
for k, v in self.user_map.items()
}
self.user_ids = list(set(self.user_ids))
self.item_ids = list(range(len(base_dataset.id_to_item)))
self.avg_ratings_per_user = base_dataset.n_ratings / base_dataset.n_users
if self.user_vectors is None:
self._build_mf(base_dataset, task, components)
self._build_torch_model(base_dataset, task, components)
self._train_torch_model(base_dataset,
task,
epochs,
logloss_weight=logloss_weight,
lr=lr,
epochs_logloss_only=epochs_logloss_only)
self._build_gmm(task, gmm_clusters)
