def split_train_test_userwise_random(
df_: pd.DataFrame,
user_colname: str,
item_colname: str,
item_ids: List[Any],
heldout_ratio: float,
n_heldout: Optional[int],
rns: np.random.RandomState,
rating_column: Optional[str] = None,
) -> UserTrainTestInteractionPair:
"""Split the user x item data frame into a pair of sparse matrix (represented as a UserDataSet).
Parameters
----------
df_:
user x item interaction matrix.
user_colname:
The column name for the users.
item_colname:
The column name for the items.
item_id_to_iid:
The mapper from item id to item index. If not supplied, create own mapping from df_.
heldout_ratio:
The percentage of items (per-user) to be held out as a test(validation) ones.
n_heldout:
The maximal number of items (per-user) to be held out as a test(validation) ones.
rns:
The random state
rating_column:
The column for the rating values. If None, the rating values will be all equal (1), by default None
Returns
-------
UserDataSet
Resulting train-test split dataset.
"""
df_ = df_[df_[item_colname].isin(item_ids)]
item_indices = pd.Categorical(df_[item_colname], categories=item_ids).codes
user_ids, user_indices = np.unique(df_[user_colname], return_inverse=True)
if rating_column is not None:
data = df_[rating_column].values
else:
data = np.ones(df_.shape[0], dtype=np.int32)
X_all = sps.csr_matrix(
(data, (user_indices, item_indices)),
shape=(len(user_ids), len(item_ids)),
)
X_learn, X_predict = rowwise_train_test_split(
X_all,
heldout_ratio,
n_heldout,
random_seed=rns.randint(-(2**31), 2**31 - 1),
)
return UserTrainTestInteractionPair(user_ids, X_learn.tocsr(),
X_predict.tocsr(), item_ids)
def test_split() -> None:
warnings.simplefilter("always")
X_1, X_2 = rowwise_train_test_split(X, test_ratio=0.5, random_seed=1)
assert np.all((X - X_1 - X_2).toarray() == 0)
# should have no overwrap
assert np.all(X_1.multiply(X_2).toarray() == 0)
def split_train_test_userwise_random(
df_: pd.DataFrame,
user_colname: str,
item_colname: str,
item_ids: Union[List[Any], np.ndarray],
heldout_ratio: float,
n_heldout: Optional[int],
rns: np.random.RandomState,
rating_column: Optional[str] = None,
ceil_n_heldout: bool = False,
) -> UserTrainTestInteractionPair:
r"""Split the user x item data frame into a pair of sparse matrix (represented as a UserDataSet).
Args
----------
df_:
user x item interaction matrix.
user_colname:
The column name for the users.
item_colname:
The column name for the items.
item_id_to_iid:
The mapper from item id to item index. If not supplied, create own mapping from df_.
heldout_ratio:
The percentage of items (per-user) to be held out as a test(validation) ones.
n_heldout:
The maximal number of items (per-user) to be held out as a test(validation) ones.
rns:
The random state
rating_column:
The column for the rating values. If None, the rating values will be all equal (1), by default None
ceil_n_heldout:
If this is `True` and `n_heldout` is `None`, the number of test interaction for a given user `u` will be
`ceil(N_u * heldout_ratio)` where `N_u` is the number of interactions fo `u`.
If this is `False`, `floor(N_u * heldout_ratio)` will be used instead. Defaults to `False`.
Returns
-------
UserDataSet
Resulting train-test split dataset.
"""
X_all, user_ids, _ = df_to_sparse(
df_,
user_colname=user_colname,
item_colname=item_colname,
item_ids=item_ids,
rating_colname=rating_column,
)
X_learn, X_predict = rowwise_train_test_split(
X_all,
heldout_ratio,
n_heldout,
random_state=rns,
ceil_n_heldout=ceil_n_heldout)
return UserTrainTestInteractionPair(user_ids, X_learn.tocsr(),
X_predict.tocsr(), item_ids)
def test_split_fixed_n() -> None:
X_1, X_2 = rowwise_train_test_split(X,
test_ratio=0.5,
n_test=1,
random_seed=1)
np.testing.assert_allclose(X.toarray(), (X_1 + X_2).toarray())
# should have no overwrap
assert np.all(X_1.multiply(X_2).toarray() == 0)
X_2.data[:] = 1
assert X_2.sum(axis=1).max() <= 1
def test_split() -> None:
X_1, X_2 = rowwise_train_test_split(X, test_ratio=0.5, random_seed=1)
np.testing.assert_allclose(X.toarray(), (X_1 + X_2).toarray())
# should have no overwrap
assert np.all(X_1.multiply(X_2).toarray() == 0)
nnzs = X.indptr[1:] - X.indptr[:-1]
X_2_bin = X_2.copy()
X_2_bin.data[:] = 1.0
X_2_nnzs = X_2_bin.sum(axis=1).A1
assert np.all((nnzs * 0.5) >= X_2_nnzs)