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Getting Started MovieLens: Download and Convert

MovieLens25M

The MovieLens25M is a popular dataset for recommender systems and is used in academic publications. The dataset contains 25M movie ratings for 62,000 movies given by 162,000 users. Many projects use only the user/item/rating information of MovieLens, but the original dataset provides metadata for the movies, as well. For example, which genres a movie has. Although we may not improve state-of-the-art results with our neural network architecture in this example, we will use the metadata to show how to multi-hot encode the categorical features.

Download the dataset

# External dependencies
import os

from sklearn.model_selection import train_test_split

from nvtabular.utils import download_file

# Get dataframe library - cudf or pandas
from nvtabular.dispatch import get_lib
df_lib = get_lib()

We define our base input directory, containing the data.

INPUT_DATA_DIR = os.environ.get(
    "INPUT_DATA_DIR", os.path.expanduser("~/nvt-examples/movielens/data/")
)

We will download and unzip the data.

download_file(
    "http://files.grouplens.org/datasets/movielens/ml-25m.zip",
    os.path.join(INPUT_DATA_DIR, "ml-25m.zip"),
)

downloading ml-25m.zip: 262MB [00:20, 12.5MB/s]
unzipping files: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:09<00:00,  1.20s/files]

Convert the dataset

First, we take a look on the movie metadata.

movies = df_lib.read_csv(os.path.join(INPUT_DATA_DIR, "ml-25m/movies.csv"))
movies.head()

	movieId	title	genres
0	1	Toy Story (1995)	Adventure|Animation|Children|Comedy|Fantasy
1	2	Jumanji (1995)	Adventure|Children|Fantasy
2	3	Grumpier Old Men (1995)	Comedy|Romance
3	4	Waiting to Exhale (1995)	Comedy|Drama|Romance
4	5	Father of the Bride Part II (1995)	Comedy

We can see, that genres are a multi-hot categorical features with different number of genres per movie. Currently, genres is a String and we want split the String into a list of Strings. In addition, we drop the title.

movies["genres"] = movies["genres"].str.split("|")
movies = movies.drop("title", axis=1)
movies.head()

	movieId	genres
0	1	[Adventure, Animation, Children, Comedy, Fantasy]
1	2	[Adventure, Children, Fantasy]
2	3	[Comedy, Romance]
3	4	[Comedy, Drama, Romance]
4	5	[Comedy]

We save movies genres in parquet format, so that they can be used by NVTabular in the next notebook.

movies.to_parquet(os.path.join(INPUT_DATA_DIR, "movies_converted.parquet"))

Splitting into train and validation dataset

We load the movie ratings.

ratings = df_lib.read_csv(os.path.join(INPUT_DATA_DIR, "ml-25m", "ratings.csv"))
ratings.head()

	userId	movieId	rating	timestamp
0	1	296	5.0	1147880044
1	1	306	3.5	1147868817
2	1	307	5.0	1147868828
3	1	665	5.0	1147878820
4	1	899	3.5	1147868510

We drop the timestamp column and split the ratings into training and test dataset. We use a simple random split.

ratings = ratings.drop("timestamp", axis=1)
train, valid = train_test_split(ratings, test_size=0.2, random_state=42)

We save the dataset to disk.

train.to_parquet(os.path.join(INPUT_DATA_DIR, "train.parquet"))
valid.to_parquet(os.path.join(INPUT_DATA_DIR, "valid.parquet"))