Update movielens-recent-ratings.py

movielens-recent-ratings.py

@@ -33,7 +33,6 @@ class MovieLens(datasets.GeneratorBasedBuilder):
                     "user_id": datasets.Value("int32"),
                     "rating": datasets.Value("float32"),
                     "title": datasets.Value("string"),
-                    "year": datasets.Value("int32"),
                 }
             ),
             supervised_keys=None,
@@ -74,22 +73,9 @@ class MovieLens(datasets.GeneratorBasedBuilder):
         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"]
+            ["imdb_id", "movie_id", "user_id", "rating", "title"]
         ]
         # save
         df.to_json(new_url+"/ratings.jsonl", orient="records", lines=True)