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import datasets
import pandas as pd
import re
import json
from sklearn.model_selection import train_test_split
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {MovieLens Ratings},
author={Ismail Ashraq, James Briggs},
year={2022}
}
"""
_DESCRIPTION = """\
This is a dataset that streams user ratings from the MovieLens 25M dataset from the MovieLens servers.
"""
_HOMEPAGE = "https://grouplens.org/datasets/movielens/"
_LICENSE = ""
_URL = "https://files.grouplens.org/datasets/movielens/ml-25m.zip"
class MovieLens(datasets.GeneratorBasedBuilder):
"""The MovieLens 25M dataset for ratings"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"imdb_id": datasets.Value("string"),
"movie_id": datasets.Value("int32"),
"user_id": datasets.Value("int32"),
"rating": datasets.Value("float32"),
"title": datasets.Value("string"),
"poster": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://grouplens.org/datasets/movielens/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
new_url = dl_manager.download_and_extract(_URL)
# PREPROCESS
# load all files
movie_ids = pd.read_csv(new_url+"/ml-25m/links.csv")
movie_meta = pd.read_csv(new_url+"/ml-25m/movies.csv")
movie_ratings = pd.read_csv(new_url+"/ml-25m/ratings.csv")
# merge to create movies dataframe
movies = movie_meta.merge(movie_ids, on="movieId")
# keep only subset of recent movies
recent_movies = movies[movies["imdbId"].astype(int) >= 2000000].fillna("None")
# mask movie ratings for movies that exist in movies
mask = movie_ratings['movieId'].isin(recent_movies["movieId"])
filtered_movie_ratings = movie_ratings[mask]
# merge with movies
df = filtered_movie_ratings.merge(
recent_movies, on="movieId"
).astype(
{"movieId": int, "userId": int, "rating": float}
)
# remove user and movies which occurs only once in the dataset
df = df.groupby("movieId").filter(lambda x: len(x) > 2)
df = df.groupby("userId").filter(lambda x: len(x) > 2)
# convert unique movie IDs to sequential index values
unique_movieids = sorted(df["movieId"].unique())
mapping = {unique_movieids[i]: i for i in range(len(unique_movieids))}
df["movie_id"] = df["movieId"].map(lambda x: mapping[x])
# get unique user sequential index values
unique_userids = sorted(df["userId"].unique())
mapping = {unique_userids[i]: i for i in range(len(unique_userids))}
df["user_id"] = df["userId"].map(lambda x: mapping[x])
# add "tt" prefix to align with IMDB URL IDs
df["imdb_id"] = df["imdbId"].apply(lambda x: "tt" + str(x))
# now add the movie posters
posters = datasets.load_dataset("pinecone/movie-posters", split='train').to_pandas()
df = df.merge(posters, left_on='imdb_id', right_on='imdbId')
# we also don't need all columns
df = df[
["imdb_id", "movie_id", "user_id", "rating", "title", "poster"]
]
# create train-test split
train, test = train_test_split(
df, test_size=0.1, shuffle=True, stratify=df["movie_id"], random_state=0
)
# save
train.to_json(new_url+"/train.jsonl", orient="records", lines=True)
test.to_json(new_url+"/test.jsonl", orient="records", lines=True)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": new_url+"/train.jsonl"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": new_url+"/test.jsonl"}
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
with open(filepath, "r") as f:
id_ = 0
for line in f:
yield id_, json.loads(line)
id_ += 1 |