Datasets:

pinecone
/

movielens-recent-ratings

	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 dataset streams recent user ratings from the MovieLens 25M dataset and adds poster URLs.
	"""
	_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