# Copyright 2023-2025 MTS (Mobile Telesystems)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""LastNSplitter."""
import typing as tp
import numpy as np
import pandas as pd
from rectools import Columns
from rectools.dataset import Interactions
from rectools.model_selection.splitter import Splitter
[docs]class LastNSplitter(Splitter):
"""
Splitter for cross-validation by leave-one-out / leave-k-out scheme (recent activity).
Generate train and test putting last n interactions for each user in test
and all of his previous interactions in train.
Cross-validation is achieved with sliding window over each users interactions history.
This technique may be used for sequential recommendation scenarios.
It is common in research papers on sequential recommendations.
But it doesn't fully prevent data leak from the future.
It is also possible to exclude cold users and items and already seen items.
Parameters
----------
n : int
Number of interactions for each user that will be included in test.
n_splits : int, default 1
Number of test folds.
filter_cold_users : bool, default ``True``
If `True`, users that are not present in train will be excluded from test.
WARNING: both cold and warm users will be excluded from test.
filter_cold_items : bool, default ``True``
If `True`, items that are not present in train will be excluded from test.
WARNING: both cold and warm items will be excluded from test.
filter_already_seen : bool, default ``True``
If ``True``, pairs (user, item) that are present in train will be excluded from test.
Examples
--------
>>> from rectools import Columns
>>> df = pd.DataFrame(
... [
... [1, 1, 1, "2021-09-01"], # 0
... [1, 2, 1, "2021-09-02"], # 1
... [1, 1, 1, "2021-09-03"], # 2
... [1, 2, 1, "2021-09-04"], # 3
... [1, 2, 1, "2021-09-05"], # 4
... [2, 1, 1, "2021-08-20"], # 5
... [2, 2, 1, "2021-08-21"], # 6
... [2, 2, 1, "2021-08-22"], # 7
... ],
... columns=[Columns.User, Columns.Item, Columns.Weight, Columns.Datetime],
... ).astype({Columns.Datetime: "datetime64[ns]"})
>>> interactions = Interactions(df)
>>>
>>> splitter = LastNSplitter(2, 2, False, False, False)
>>> for train_ids, test_ids, _ in splitter.split(interactions):
... print(train_ids, test_ids)
[0] [1 2 5]
[0 1 2 5] [3 4 6 7]
>>>
>>> splitter = LastNSplitter(2, 2, True, False, False)
>>> for train_ids, test_ids, _ in splitter.split(interactions):
... print(train_ids, test_ids)
[0] [1 2]
[0 1 2 5] [3 4 6 7]
"""
def __init__(
self,
n: int,
n_splits: int = 1,
filter_cold_users: bool = True,
filter_cold_items: bool = True,
filter_already_seen: bool = True,
) -> None:
super().__init__(filter_cold_users, filter_cold_items, filter_already_seen)
self.n = n
self.n_splits = n_splits
def _split_without_filter(
self,
interactions: Interactions,
collect_fold_stats: bool = False,
) -> tp.Iterator[tp.Tuple[np.ndarray, np.ndarray, tp.Dict[str, tp.Any]]]:
df = interactions.df
idx = pd.RangeIndex(0, len(df))
# Here we guarantee that last appeared interaction in df will have lowest rank when datetime is not unique
grouped = df.groupby(Columns.User)
time_order = grouped[Columns.Datetime].rank(method="first", ascending=True).astype(int)
n_interactions = grouped[Columns.User].transform("size").astype(int)
inv_ranks = n_interactions - time_order + 1
for i_split in range(self.n_splits)[::-1]:
min_rank = i_split * self.n # excluded
max_rank = min_rank + self.n # included
test_mask = (inv_ranks > min_rank) & (inv_ranks <= max_rank)
train_mask = inv_ranks > max_rank
test_idx = idx[test_mask].values
train_idx = idx[train_mask].values
split_info = {"i_split": self.n_splits - i_split - 1}
yield train_idx, test_idx, split_info