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movielens-recent-ratings.py

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+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"),
+                    "year": datasets.Value("int32"),
+                }
+            ),
+            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))
+        # extract year from title where possible
+        year_list = []
+        find_year = re.compile(r"(?<=\()\d{4}(?=\))")
+        for title in df['title']:
+            match = find_year.search(title)
+            if match:
+                year_list.append(int(match[0]))
+            else:
+                year_list.append(None)
+        # add year to df
+        df['year'] = year_list
+        # drop rows where no year is found
+        df = df[~df["year"].isna()]
+        # we also don't need all columns
+        df = df[
+            ["imdb_id", "movie_id", "user_id", "rating", "title", "year"]
+        ]
+        # 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