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import os
import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (
HeteroData,
InMemoryDataset,
download_url,
extract_zip,
)
class MovieLens(InMemoryDataset):
r"""A heterogeneous rating dataset, assembled by GroupLens Research from
the `MovieLens web site <https://movielens.org>`_, consisting of nodes of
type :obj:`"movie"` and :obj:`"user"`.
User ratings for movies are available as ground truth labels for the edges
between the users and the movies :obj:`("user", "rates", "movie")`.
Args:
root (str): Root directory where the dataset should be saved.
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.HeteroData` object and returns a
transformed version. The data object will be transformed before
every access. (default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.HeteroData` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
model_name (str): Name of model used to transform movie titles to node
features. The model comes from the`Huggingface SentenceTransformer
<https://huggingface.co/sentence-transformers>`_.
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
url = 'https://files.grouplens.org/datasets/movielens/ml-latest-small.zip'
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
model_name: Optional[str] = 'all-MiniLM-L6-v2',
force_reload: bool = False,
) -> None:
self.model_name = model_name
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0], data_cls=HeteroData)
@property
def raw_file_names(self) -> List[str]:
return [
osp.join('ml-latest-small', 'movies.csv'),
osp.join('ml-latest-small', 'ratings.csv'),
]
@property
def processed_file_names(self) -> str:
return f'data_{self.model_name}.pt'
def download(self) -> None:
path = download_url(self.url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.remove(path)
def process(self) -> None:
import pandas as pd
from sentence_transformers import SentenceTransformer
data = HeteroData()
df = pd.read_csv(self.raw_paths[0], index_col='movieId')
movie_mapping = {idx: i for i, idx in enumerate(df.index)}
genres = df['genres'].str.get_dummies('|').values
genres = torch.from_numpy(genres).to(torch.float)
model = SentenceTransformer(self.model_name)
with torch.no_grad():
emb = model.encode(df['title'].values, show_progress_bar=True,
convert_to_tensor=True).cpu()
data['movie'].x = torch.cat([emb, genres], dim=-1)
df = pd.read_csv(self.raw_paths[1])
user_mapping = {idx: i for i, idx in enumerate(df['userId'].unique())}
data['user'].num_nodes = len(user_mapping)
src = [user_mapping[idx] for idx in df['userId']]
dst = [movie_mapping[idx] for idx in df['movieId']]
edge_index = torch.tensor([src, dst])
rating = torch.from_numpy(df['rating'].values).to(torch.long)
time = torch.from_numpy(df['timestamp'].values).to(torch.long)
data['user', 'rates', 'movie'].edge_index = edge_index
data['user', 'rates', 'movie'].edge_label = rating
data['user', 'rates', 'movie'].time = time
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
|
