import datasets import pandas as pd import re import json _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"] ] # save df.to_json(new_url+"/ratings.jsonl", orient="records", lines=True) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": new_url+"/ratings.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