from typing import Optional
from functools import partial
from .base import BaseData
from dhg.datapipe import load_from_txt
[docs]class MovieLens1M(BaseData):
r"""The MovieLens1M dataset is collected for user-item recommendation task. Wherein, movies are viewed as items.
`Released 2/2003 <https://grouplens.org/datasets/movielens/1m/>`_. The dataset contains 1 million ratings from 6022 users on 3043 items.
The content of the MovieLens-1M dataset includes the following:
- ``num_users``: The number of users: :math:`6,022`.
- ``num_items``: The number of items: :math:`3,043`.
- ``num_interactions``: The number of interactions: :math:`995,154`.
- ``train_adj_list``: The train adjacency list.
- ``test_adj_list``: The test adjacency list.
.. note::
The first item of each line in the ``adj_list`` is the user id, and the rest is the item id.
Args:
``data_root`` (``str``, optional): The ``data_root`` has stored the data. If set to ``None``, this function will auto-download from server and save into the default direction ``~/.dhg/datasets/``. Defaults to ``None``.
"""
def __init__(self, data_root: Optional[str] = None) -> None:
super().__init__("movielens_1m", data_root)
self._content = {
"num_users": 6022,
"num_items": 3043,
"num_interactions": 995154,
"train_adj_list": {
"upon": [
{
"filename": "train.txt",
"md5": "db93f671bc5d1b1544ce4c29664f6778",
}
],
"loader": partial(load_from_txt, dtype="int", sep=" "),
},
"test_adj_list": {
"upon": [
{
"filename": "test.txt",
"md5": "5e55bcbb6372ad4c6fafe79989e2f956",
}
],
"loader": partial(load_from_txt, dtype="int", sep=" "),
},
}