def test_exclude_unknowns_empty_error(self):
try:
Dataset.build(self.triplet_data, exclude_unknowns=True)
except ValueError:
assert True
def _build_stratified_datasets(self, train_data, test_data, val_data):
if train_data is None or len(train_data) == 0:
raise ValueError("train_data is required but None or empty!")
if test_data is None or len(test_data) == 0:
raise ValueError("test_data is required but None or empty!")
self.global_uid_map.clear()
self.global_iid_map.clear()
# build training set
self.train_set = Dataset.build(
data=train_data,
fmt=self.fmt,
global_uid_map=self.global_uid_map,
global_iid_map=self.global_iid_map,
seed=self.seed,
exclude_unknowns=False,
)
if self.verbose:
print("---")
print("Training data:")
print("Number of users = {}".format(self.train_set.num_users))
print("Number of items = {}".format(self.train_set.num_items))
print("Number of ratings = {}".format(self.train_set.num_ratings))
print("Max rating = {:.1f}".format(self.train_set.max_rating))
print("Min rating = {:.1f}".format(self.train_set.min_rating))
print("Global mean = {:.1f}".format(self.train_set.global_mean))
# build test set
self.test_set = Dataset.build(
data=test_data,
fmt=self.fmt,
global_uid_map=self.global_uid_map,
global_iid_map=self.global_iid_map,
seed=self.seed,
exclude_unknowns=self.exclude_unknowns,
)
if self.verbose:
print("---")
print("Test data (Q0):")
print("Number of users = {}".format(len(self.test_set.uid_map)))
print("Number of items = {}".format(len(self.test_set.iid_map)))
print("Number of ratings = {}".format(self.test_set.num_ratings))
print("Max rating = {:.1f}".format(self.test_set.max_rating))
print("Min rating = {:.1f}".format(self.test_set.min_rating))
print("Global mean = {:.1f}".format(self.test_set.global_mean))
print(
"Number of unknown users = {}".format(
self.test_set.num_users - self.train_set.num_users
)
)
print(
"Number of unknown items = {}".format(
self.test_set.num_items - self.train_set.num_items
)
)
# build stratified datasets
self.stratified_sets = {}
# match the corresponding propensity score for each feedback
test_props = np.array([self.props[i]
for u, i, r in test_data], dtype=np.float64)
# stratify
strata, bins = pd.cut(x=test_props,
bins=self.n_strata,
labels=['Q%d' %
i for i in range(1, self.n_strata+1)],
retbins=True)
for stratum in sorted(np.unique(strata)):
# sample the corresponding sub-population
qtest_data = []
for (u, i, r), q in zip(test_data, strata):
if q == stratum:
qtest_data.append((u, i, r))
# build a dataset
qtest_set = Dataset.build(
data=qtest_data,
fmt=self.fmt,
global_uid_map=self.global_uid_map,
global_iid_map=self.global_iid_map,
seed=self.seed,
exclude_unknowns=self.exclude_unknowns,
)
if self.verbose:
print("---")
print("Test data ({}):".format(stratum))
print("Number of users = {}".format(
len(qtest_set.uid_map)))
print("Number of items = {}".format(
len(qtest_set.iid_map)))
print("Number of ratings = {}".format(
qtest_set.num_ratings))
print("Max rating = {:.1f}".format(qtest_set.max_rating))
print("Min rating = {:.1f}".format(qtest_set.min_rating))
print("Global mean = {:.1f}".format(qtest_set.global_mean))
print(
"Number of unknown users = {}".format(
qtest_set.num_users - self.train_set.num_users
)
)
print(
"Number of unknown items = {}".format(
self.test_set.num_items - self.train_set.num_items
)
)
self.stratified_sets[stratum] = qtest_set
if val_data is not None and len(val_data) > 0:
self.val_set = Dataset.build(
data=val_data,
fmt=self.fmt,
global_uid_map=self.global_uid_map,
global_iid_map=self.global_iid_map,
seed=self.seed,
exclude_unknowns=self.exclude_unknowns,
)
if self.verbose:
print("---")
print("Validation data:")
print("Number of users = {}".format(len(self.val_set.uid_map)))
print("Number of items = {}".format(len(self.val_set.iid_map)))
print("Number of ratings = {}".format(self.val_set.num_ratings))
if self.verbose:
print("---")
print("Total users = {}".format(self.total_users))
print("Total items = {}".format(self.total_items))
self.train_set.total_users = self.total_users
self.train_set.total_items = self.total_items
self._build_modalities()
return self