def _prepare(self):
self.kg_train = self.model.stage == 1 and self.phase == 'train'
if self.kg_train:
self.data = utils.df_to_dict(self.corpus.relation_df)
self.neg_heads = np.zeros(len(self), dtype=int)
self.neg_tails = np.zeros(len(self), dtype=int)
super()._prepare()
def __init__(self, model, corpus, phase: str):
self.model = model # model object reference
self.corpus = corpus # reader object reference
self.phase = phase
self.data = utils.df_to_dict(corpus.data_df[phase])
# ↑ DataFrame is not compatible with multi-thread operations
self.buffer_dict = dict()
self.buffer = self.model.buffer and self.phase != 'train'
self._prepare()
def _prepare(self):
if self.phase == 'train':
interaction_df = pd.DataFrame({
'head': self.data['user_id'],
'tail': self.data['item_id'],
'relation': np.zeros_like(self.data['user_id'])
})
self.data = utils.df_to_dict(pd.concat((self.corpus.relation_df, interaction_df), axis=0))
self.neg_heads = np.zeros(len(self), dtype=int)
self.neg_tails = np.zeros(len(self), dtype=int)
super()._prepare()
def _prepare(self):
self.kg_train = self.model.stage == 1 and self.phase == 'train'
if self.kg_train:
self.data = utils.df_to_dict(self.corpus.relation_df)
self.neg_heads = np.zeros(len(self), dtype=int)
self.neg_tails = np.zeros(len(self), dtype=int)
else:
col_name = self.model.category_col
items = self.corpus.item_meta_df['item_id']
categories = self.corpus.item_meta_df[col_name] if col_name is not None else np.zeros_like(items)
self.item2cate = dict(zip(items, categories))
super()._prepare()
def __init__(self, model, corpus, phase):
self.model = model
self.corpus = corpus
self.phase = phase
self.data = utils.df_to_dict(corpus.data_df[phase])
# ↑ DataFrame is not compatible with multi-thread operations
self.neg_items = None if phase == 'train' else self.data[
'neg_items']
# ↑ Sample negative items before each epoch during training
self.buffer_dict = dict()
self.buffer = self.model.buffer and self.phase != 'train'
self._prepare()
