def split(self, dataset):
super(Holdout, self).split(dataset)
URM = sps.csr_matrix(dataset.get_URM())
n_users, n_items = dataset.n_users, dataset.n_items
user_indices = []
URM_train, URM_test, URM_validation = {}, {}, {}
#Select apriori how to randomizely sort every user
users_to_remove = []
for user_id in range(n_users):
assignment = np.random.choice(
3,
URM.indptr[user_id + 1] - URM.indptr[user_id],
replace=True,
p=[self.train_perc, self.validation_perc, self.test_perc])
assignments = [assignment == i for i in range(3)]
#if assignments[2].sum() <= 0:
#No interactions in test
# users_to_remove.append(user_id)
#if self.with_validation and assignments[1].sum() <= 0:
#No interactions in validation
# users_to_remove.append(user_id)
if not self.allow_cold_users and assignments[0].sum() <= 0:
#No interactions in train
users_to_remove.append(user_id)
user_indices.append(assignments)
for URM_name in dataset.get_URM_names():
URM = dataset.get_URM(URM_name)
URM = sps.csr_matrix(URM)
URM_train_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
URM_test_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
if self.with_validation:
URM_validation_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
users_to_remove_index = 0
for user_id in range(n_users):
if users_to_remove_index < len(
users_to_remove
) and user_id == users_to_remove[users_to_remove_index]:
users_to_remove_index += 1
continue
indices = user_indices[user_id]
start_user_position = URM.indptr[user_id]
end_user_position = URM.indptr[user_id + 1]
user_interaction_items = URM.indices[
start_user_position:end_user_position]
user_interaction_data = URM.data[
start_user_position:end_user_position]
# Test interactions
user_interaction_items_test = user_interaction_items[
indices[2]]
user_interaction_data_test = user_interaction_data[indices[2]]
mask = user_interaction_data_test > self.test_rating_threshold
user_interaction_items_test = user_interaction_items_test[mask]
user_interaction_data_test = user_interaction_data_test[mask]
URM_test_builder.add_data_lists(
[user_id] * len(user_interaction_data_test),
user_interaction_items_test, user_interaction_data_test)
# validation interactions
if self.with_validation:
user_interaction_items_validation = user_interaction_items[
indices[1]]
user_interaction_data_validation = user_interaction_data[
indices[1]]
# Remove from validation interactions below a given threshold
mask = user_interaction_data_validation > self.test_rating_threshold
user_interaction_items_validation = user_interaction_items_validation[
mask]
user_interaction_data_validation = user_interaction_data_validation[
mask]
URM_validation_builder.add_data_lists(
[user_id] * len(user_interaction_data_validation),
user_interaction_items_validation,
user_interaction_data_validation)
# Train interactions
user_interaction_items_train = user_interaction_items[
indices[0]]
user_interaction_data_train = user_interaction_data[indices[0]]
URM_train_builder.add_data_lists(
[user_id] * len(user_interaction_items_train),
user_interaction_items_train, user_interaction_data_train)
URM_train[URM_name] = URM_train_builder.get_SparseMatrix()
URM_test[URM_name] = URM_test_builder.get_SparseMatrix()
if self.with_validation:
URM_validation[
URM_name] = URM_validation_builder.get_SparseMatrix()
train = Dataset(dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_train,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
train.remove_users(users_to_remove)
test = Dataset(dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_test,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
test.remove_users(users_to_remove)
if self.with_validation:
validation = Dataset(
dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_validation,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
validation.remove_users(users_to_remove)
return train, test, validation
else:
return train, test
def split(self, dataset):
super(ColdItemsHoldout, self).split(dataset)
n_users, n_items = dataset.n_users, dataset.n_items
URM_train, URM_test, URM_validation = {}, {}, {}
items_split = np.random.choice(
3,
n_items,
replace=True,
p=[self.train_perc, self.validation_perc, self.test_perc])
train_items = np.arange(n_items)[items_split == 0]
validation_items = np.arange(n_items)[items_split == 1]
test_items = np.arange(n_items)[items_split == 2]
#Select apriori how to randomizely sort every user
users_to_remove = []
for URM_name in dataset.get_URM_names():
URM = dataset.get_URM(URM_name)
URM = sps.csr_matrix(URM)
URM_train_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
URM_test_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
if self.with_validation:
URM_validation_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
for user_id in range(n_users):
start_user_position = URM.indptr[user_id]
end_user_position = URM.indptr[user_id + 1]
user_interaction_items = URM.indices[
start_user_position:end_user_position]
user_interaction_data = URM.data[
start_user_position:end_user_position]
# Test interactions
indices = np.in1d(user_interaction_items,
test_items,
assume_unique=True)
user_interaction_items_test = user_interaction_items[indices]
user_interaction_data_test = user_interaction_data[indices]
# Remove from test interactions below a given threshold
mask = user_interaction_data_test > self.test_rating_threshold
user_interaction_items_test = user_interaction_items_test[mask]
user_interaction_data_test = user_interaction_data_test[mask]
URM_test_builder.add_data_lists(
[user_id] * len(user_interaction_data_test),
user_interaction_items_test, user_interaction_data_test)
# validation interactions
if self.with_validation:
indices = np.in1d(user_interaction_items,
validation_items,
assume_unique=True)
user_interaction_items_validation = user_interaction_items[
indices]
user_interaction_data_validation = user_interaction_data[
indices]
# Remove from validation interactions below a given threshold
mask = user_interaction_data_validation > self.test_rating_threshold
user_interaction_items_validation = user_interaction_items_validation[
mask]
user_interaction_data_validation = user_interaction_data_validation[
mask]
URM_validation_builder.add_data_lists(
[user_id] * len(user_interaction_data_validation),
user_interaction_items_validation,
user_interaction_data_validation)
#if len(user_interaction_items_validation) <= 0:
# users_to_remove.append(user_id)
# Train interactions
indices = np.in1d(user_interaction_items,
train_items,
assume_unique=True)
user_interaction_items_train = user_interaction_items[indices]
user_interaction_data_train = user_interaction_data[indices]
URM_train_builder.add_data_lists(
[user_id] * len(user_interaction_items_train),
user_interaction_items_train, user_interaction_data_train)
#if len(user_interaction_items_test) <= 0:
# users_to_remove.append(user_id)
if not self.allow_cold_users and len(
user_interaction_items_train) <= 0:
users_to_remove.append(user_id)
URM_train[URM_name] = URM_train_builder.get_SparseMatrix()
URM_test[URM_name] = URM_test_builder.get_SparseMatrix()
if self.with_validation:
URM_validation[
URM_name] = URM_validation_builder.get_SparseMatrix()
train = Dataset(dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_train,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
train.remove_users(users_to_remove)
test = Dataset(dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_test,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
test.remove_users(users_to_remove)
if self.with_validation:
validation = Dataset(
dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_validation,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
validation.remove_users(users_to_remove)
return train, test, validation
else:
return train, test
def split(self, dataset):
super(WarmItemsKFold, self).split(dataset)
# I can do the kfold of a slice of the initial URM!
if self.percentage_initial_data_to_split < 1.0:
h = Holdout(train_perc=self.percentage_initial_data_to_split, test_perc=1-self.percentage_initial_data_to_split)
dataset = h.split(dataset)[0]
folds = []
URM = dataset.get_URM().tocoo()
split_belonging = np.random.choice(self.n_folds, URM.data.size, replace=True)
for i in range(self.n_folds):
urm = {}
urm_mappers = {}
mask = split_belonging == i
for URM_name in dataset.get_URM_names():
URM = dataset.get_URM(URM_name).tocoo()
# Sort nnz values by row and column indices, in order to remain consistent in the splits of different URMs
row, col, data = zip(*sorted(zip(URM.row, URM.col, URM.data), key=lambda x: (x[0], x[1])))
urm[URM_name] = sps.csr_matrix((np.array(data)[mask], (np.array(row)[mask], np.array(col)[mask])),
shape=URM.shape)
urm_mappers[URM_name] = dataset.get_URM_mapper(URM_name)
folds.append(
Dataset(dataset.get_name(), base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=urm, URM_mappers_dict=urm_mappers,
ICM_dict=dataset.get_ICM_dict(), ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(), UCM_mappers_dict=dataset.get_UCM_mappers_dict()
)
)
r = []
for i in range(self.n_folds):
urm = {}
urm_mappers = {}
for URM_name in folds[i].get_URM_names():
# Keep i-th fold as test and merge the others as train
urm[URM_name] = folds[(i + 1) % self.n_folds].get_URM(URM_name)
urm_mappers[URM_name] = folds[(i + 1) % self.n_folds].get_URM_mapper(URM_name)
for j in range(2, self.n_folds):
urm[URM_name] += folds[(i + j) % self.n_folds].get_URM(URM_name)
train = Dataset(folds[i].get_name(), base_folder=folds[i].get_base_folder(),
postprocessings=folds[i].get_postprocessings(),
URM_dict=urm, URM_mappers_dict=urm_mappers,
ICM_dict=folds[i].get_ICM_dict(), ICM_mappers_dict=folds[i].get_ICM_mappers_dict(),
UCM_dict=folds[i].get_UCM_dict(), UCM_mappers_dict=folds[i].get_UCM_mappers_dict())
urm = {}
test_urm = folds[i].get_URM()
test_urm.sort_indices()
mask = test_urm.data <= self.test_rating_threshold
for URM_name in folds[i].get_URM_names():
urm[URM_name] = folds[i].get_URM(URM_name)
urm[URM_name].sort_indices()
urm[URM_name].data[mask] = 0.0
urm[URM_name].eliminate_zeros()
test = Dataset(folds[i].get_name(), base_folder=folds[i].get_base_folder(),
postprocessings=folds[i].get_postprocessings(),
URM_dict=urm, URM_mappers_dict=folds[i].get_URM_mappers_dict(),
ICM_dict=folds[i].get_ICM_dict(), ICM_mappers_dict=folds[i].get_ICM_mappers_dict(),
UCM_dict=folds[i].get_UCM_dict(), UCM_mappers_dict=folds[i].get_UCM_mappers_dict())
if not self.allow_cold_users:
users_to_remove = np.arange(train.n_users)[np.ediff1d(train.get_URM().tocsr().indptr) <= 0]
train.remove_users(users_to_remove)
test.remove_users(users_to_remove)
r.append((train, test))
return r
def split(self, dataset):
super(ColdItemsKFold, self).split(dataset)
folds = []
split_belonging = np.random.choice(self.n_folds, dataset.n_items, replace=True)
for i in range(self.n_folds):
urm = {}
urm_mappers = {}
mask = split_belonging != i
for URM_name in dataset.get_URM_names():
URM = dataset.get_URM(URM_name).tocsc(copy=True)
# Sort nnz values by row and column indices, in order to remain consistent in the splits of different URMs
for j in np.arange(URM.shape[1])[mask].tolist():
URM.data[URM.indptr[j]:URM.indptr[j + 1]] = 0.0
URM.eliminate_zeros()
urm[URM_name] = URM.tocsr()
urm_mappers[URM_name] = dataset.get_URM_mapper(URM_name)
folds.append(
Dataset(dataset.get_name(), base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=urm, URM_mappers_dict=urm_mappers,
ICM_dict=dataset.get_ICM_dict(), ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(), UCM_mappers_dict=dataset.get_UCM_mappers_dict()
)
)
r = []
for i in range(self.n_folds):
urm = {}
urm_mappers = {}
for URM_name in folds[i].get_URM_names():
# Keep i-th fold as test and merge the others as train
urm[URM_name] = folds[(i + 1) % self.n_folds].get_URM(URM_name)
urm_mappers[URM_name] = folds[(i + 1) % self.n_folds].get_URM_mapper(URM_name)
for j in range(2, self.n_folds):
urm[URM_name] += folds[(i + j) % self.n_folds].get_URM(URM_name)
train = Dataset(folds[i].get_name(), base_folder=folds[i].get_base_folder(),
postprocessings=folds[i].get_postprocessings(),
URM_dict=urm, URM_mappers_dict=urm_mappers,
ICM_dict=folds[i].get_ICM_dict(), ICM_mappers_dict=folds[i].get_ICM_mappers_dict(),
UCM_dict=folds[i].get_UCM_dict(), UCM_mappers_dict=folds[i].get_UCM_mappers_dict())
urm = {}
test_urm = folds[i].get_URM()
test_urm.sort_indices()
mask = test_urm.data <= self.test_rating_threshold
for URM_name in folds[i].get_URM_names():
urm[URM_name] = folds[i].get_URM(URM_name)
urm[URM_name].sort_indices()
urm[URM_name].data[mask] = 0.0
urm[URM_name].eliminate_zeros()
test = Dataset(folds[i].get_name(), base_folder=folds[i].get_base_folder(),
postprocessings=folds[i].get_postprocessings(),
URM_dict=urm, URM_mappers_dict=folds[i].get_URM_mappers_dict(),
ICM_dict=folds[i].get_ICM_dict(), ICM_mappers_dict=folds[i].get_ICM_mappers_dict(),
UCM_dict=folds[i].get_UCM_dict(), UCM_mappers_dict=folds[i].get_UCM_mappers_dict())
if not self.allow_cold_users:
users_to_remove = np.arange(train.n_users)[np.ediff1d(train.get_URM().tocsr().indptr) <= 0]
train.remove_users(users_to_remove)
test.remove_users(users_to_remove)
r.append((train, test))
return r
def split(self, dataset):
super(LeaveKOut, self).split(dataset)
URM = sps.csr_matrix(dataset.get_URM())
URM.sort_indices()
split_number = 2
if self.with_validation:
split_number += 1
# Min interactions at least self.k_value for each split +1 for train
min_user_interactions = split_number * (self.k_value - 1) + 1
users_to_preserve = np.arange(URM.shape[0])
if not self.allow_cold_users:
urm_threshold = URM.copy()
urm_threshold.data[
urm_threshold.data <= self.test_rating_threshold] = 0
urm_threshold.eliminate_zeros()
user_interactions = np.ediff1d(urm_threshold.tocsr().indptr)
users_to_preserve = users_to_preserve[
user_interactions >= min_user_interactions]
print(
"DataSplitterLeaveKOut: Removing {} of {} users because they have less than the {} interactions required for {} splits"
.format(URM.shape[0] - len(users_to_preserve), URM.shape[0],
min_user_interactions, split_number))
users_to_remove = np.setdiff1d(np.arange(URM.shape[0]),
users_to_preserve)
n_users, n_items = URM.shape
user_indices = []
URM_train, URM_test, URM_validation = {}, {}, {}
#Select apriori how to randomizely sort every user
for user_id in users_to_preserve.tolist():
user_profile = URM.data[URM.indptr[user_id]:URM.
indptr[user_id +
1]] > self.test_rating_threshold
test_and_val = np.random.permutation(
np.arange(URM.indptr[user_id + 1] -
URM.indptr[user_id])[user_profile])
limit = self.k_value
if self.with_validation:
limit = self.k_value * 2
# Train, Test and Validation
user_indices.append((np.setdiff1d(np.arange(len(user_profile)),
test_and_val[:limit]),
test_and_val[:self.k_value],
test_and_val[self.k_value:limit]))
for URM_name in dataset.get_URM_names():
URM = dataset.get_URM(URM_name).tocsr()
URM.sort_indices()
URM_train_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
URM_test_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
if self.with_validation:
URM_validation_builder = IncrementalSparseMatrix(
auto_create_row_mapper=False,
n_rows=n_users,
auto_create_col_mapper=False,
n_cols=n_items)
for i, user_id in enumerate(users_to_preserve.tolist()):
start_user_position = URM.indptr[user_id]
end_user_position = URM.indptr[user_id + 1]
indices = user_indices[i]
user_interaction_items = URM.indices[
start_user_position:end_user_position]
user_interaction_data = URM.data[
start_user_position:end_user_position]
# Test interactions
user_interaction_items_test = user_interaction_items[
indices[1]]
user_interaction_data_test = user_interaction_data[indices[1]]
URM_test_builder.add_data_lists([user_id] * self.k_value,
user_interaction_items_test,
user_interaction_data_test)
train_start = self.k_value
# validation interactions
if self.with_validation:
user_interaction_items_validation = user_interaction_items[
indices[2]]
user_interaction_data_validation = user_interaction_data[
indices[2]]
URM_validation_builder.add_data_lists(
[user_id] * self.k_value,
user_interaction_items_validation,
user_interaction_data_validation)
train_start = self.k_value * 2
# Train interactions
user_interaction_items_train = user_interaction_items[
indices[0]]
user_interaction_data_train = user_interaction_data[indices[0]]
URM_train_builder.add_data_lists(
[user_id] * len(user_interaction_items_train),
user_interaction_items_train, user_interaction_data_train)
URM_train[URM_name] = URM_train_builder.get_SparseMatrix()
URM_test[URM_name] = URM_test_builder.get_SparseMatrix()
if self.with_validation:
URM_validation[
URM_name] = URM_validation_builder.get_SparseMatrix()
train = Dataset(dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_train,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
train.remove_users(users_to_remove)
test = Dataset(dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_test,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
test.remove_users(users_to_remove)
if self.with_validation:
validation = Dataset(
dataset.get_name(),
base_folder=dataset.get_base_folder(),
postprocessings=dataset.get_postprocessings(),
URM_dict=URM_validation,
URM_mappers_dict=dataset.get_URM_mappers_dict(),
ICM_dict=dataset.get_ICM_dict(),
ICM_mappers_dict=dataset.get_ICM_mappers_dict(),
UCM_dict=dataset.get_UCM_dict(),
UCM_mappers_dict=dataset.get_UCM_mappers_dict())
validation.remove_users(users_to_remove)
return train, test, validation
else:
return train, test