def _setup_obs_handler(self):
ratings_df = pd.read_csv(rating_csv_path)
obs = ObservationsDF(ratings_df, uid_col=self.user_id_col, iid_col=self.item_id_col)
obs = obs.sample_observations(n_users=1000, n_items=1000)
self.state.train_obs, self.state.test_obs = obs.split_train_test(ratio=0.2, users_ratio=1.0)
# add some fake data for sanity tests
self.state.train_obs.df_obs = self._add_testing_obs_data(self.state.train_obs.df_obs)
def test_splits(self):
from ml_recsys_tools.data_handlers.interaction_handlers_base import ObservationsDF
ratings_df = pd.read_csv(rating_csv_path)
obs = ObservationsDF(ratings_df,
uid_col='userid',
iid_col='itemid',
timestamp_col='timestamp')
obs = obs.sample_observations(n_users=1000, n_items=1000)
ratio = 0.2
# regular split
train_obs, test_obs = obs.split_train_test(ratio=ratio)
self._obs_split_data_check(obs, train_obs, test_obs)
self.state.train_obs, self.state.test_obs = train_obs, test_obs
# split for only some users
user_ratio = 0.2
train_obs, test_obs = obs.split_train_test(ratio=ratio,
users_ratio=user_ratio)
self._obs_split_data_check(obs, train_obs, test_obs)
post_split_ratio = test_obs.df_obs['userid'].nunique(
) / train_obs.df_obs['userid'].nunique()
self.assertAlmostEqual(user_ratio, post_split_ratio, places=1)
# split by timestamp
time_col = obs.timestamp_col
train_obs, test_obs = obs.split_train_test(ratio=ratio,
time_split_column=time_col)
self._obs_split_data_check(obs, train_obs, test_obs)
self.assertGreaterEqual(test_obs.df_obs[time_col].min(),
train_obs.df_obs[time_col].max())
def test_splits(self):
ratings_df = pd.read_csv(rating_csv_path)
obs_params = dict(uid_col='userid',
iid_col='itemid',
timestamp_col='timestamp')
obs = ObservationsDF(ratings_df, **obs_params)
obs = obs.sample_observations(n_users=1000, n_items=1000)
self._split_tester(obs)
items_df = pd.read_csv(movies_csv_path)
obs_feat = ObsWithFeatures(df_obs=ratings_df,
df_items=items_df,
item_id_col='itemid',
**obs_params)
obs_feat = obs_feat.sample_observations(n_users=1000, n_items=1000)
self._split_tester(obs_feat)