load_csv.py

import os.path as osp

import pandas as pd
import torch
from sentence_transformers import SentenceTransformer

from torch_geometric.data import HeteroData, download_url, extract_zip
from torch_geometric.transforms import RandomLinkSplit, ToUndirected

url = 'https://files.grouplens.org/datasets/movielens/ml-latest-small.zip'
root = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens')
extract_zip(download_url(url, root), root)
movie_path = osp.join(root, 'ml-latest-small', 'movies.csv')
rating_path = osp.join(root, 'ml-latest-small', 'ratings.csv')


def load_node_csv(path, index_col, encoders=None, **kwargs):
    df = pd.read_csv(path, index_col=index_col, **kwargs)
    mapping = {index: i for i, index in enumerate(df.index.unique())}

    x = None
    if encoders is not None:
        xs = [encoder(df[col]) for col, encoder in encoders.items()]
        x = torch.cat(xs, dim=-1)

    return x, mapping


def load_edge_csv(path, src_index_col, src_mapping, dst_index_col, dst_mapping,
                  encoders=None, **kwargs):
    df = pd.read_csv(path, **kwargs)

    src = [src_mapping[index] for index in df[src_index_col]]
    dst = [dst_mapping[index] for index in df[dst_index_col]]
    edge_index = torch.tensor([src, dst])

    edge_attr = None
    if encoders is not None:
        edge_attrs = [encoder(df[col]) for col, encoder in encoders.items()]
        edge_attr = torch.cat(edge_attrs, dim=-1)

    return edge_index, edge_attr


class SequenceEncoder:
    # The 'SequenceEncoder' encodes raw column strings into embeddings.
    def __init__(self, model_name='all-MiniLM-L6-v2', device=None):
        self.device = device
        self.model = SentenceTransformer(model_name, device=device)

    @torch.no_grad()
    def __call__(self, df):
        x = self.model.encode(df.values, show_progress_bar=True,
                              convert_to_tensor=True, device=self.device)
        return x.cpu()


class GenresEncoder:
    # The 'GenreEncoder' splits the raw column strings by 'sep' and converts
    # individual elements to categorical labels.
    def __init__(self, sep='|'):
        self.sep = sep

    def __call__(self, df):
        genres = {g for col in df.values for g in col.split(self.sep)}
        mapping = {genre: i for i, genre in enumerate(genres)}

        x = torch.zeros(len(df), len(mapping))
        for i, col in enumerate(df.values):
            for genre in col.split(self.sep):
                x[i, mapping[genre]] = 1
        return x


class IdentityEncoder:
    # The 'IdentityEncoder' takes the raw column values and converts them to
    # PyTorch tensors.
    def __init__(self, dtype=None):
        self.dtype = dtype

    def __call__(self, df):
        return torch.from_numpy(df.values).view(-1, 1).to(self.dtype)


user_x, user_mapping = load_node_csv(rating_path, index_col='userId')

movie_x, movie_mapping = load_node_csv(
    movie_path, index_col='movieId', encoders={
        'title': SequenceEncoder(),
        'genres': GenresEncoder()
    })

edge_index, edge_label = load_edge_csv(
    rating_path,
    src_index_col='userId',
    src_mapping=user_mapping,
    dst_index_col='movieId',
    dst_mapping=movie_mapping,
    encoders={'rating': IdentityEncoder(dtype=torch.long)},
)

data = HeteroData()
data['user'].num_nodes = len(user_mapping)  # Users do not have any features.
data['movie'].x = movie_x
data['user', 'rates', 'movie'].edge_index = edge_index
data['user', 'rates', 'movie'].edge_label = edge_label
print(data)

# We can now convert `data` into an appropriate format for training a
# graph-based machine learning model:

# 1. Add a reverse ('movie', 'rev_rates', 'user') relation for message passing.
data = ToUndirected()(data)
del data['movie', 'rev_rates', 'user'].edge_label  # Remove "reverse" label.

# 2. Perform a link-level split into training, validation, and test edges.
transform = RandomLinkSplit(
    num_val=0.05,
    num_test=0.1,
    neg_sampling_ratio=0.0,
    edge_types=[('user', 'rates', 'movie')],
    rev_edge_types=[('movie', 'rev_rates', 'user')],
)
train_data, val_data, test_data = transform(data)
print(train_data)
print(val_data)
print(test_data)