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Getting Started MovieLens: Download and Convert#
This notebook is created using the latest stable merlin-hugectr, merlin-tensorflow, or merlin-pytorch container.
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 merlin.core.utils import download_file
# Get dataframe library - cudf or pandas
from merlin.core.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 [00:06, 42.1MB/s]
unzipping files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:04<00:00, 1.74files/s]
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 datasets. We use a simple random split.
ratings = ratings.drop("timestamp", axis=1)
# shuffle the dataset
ratings = ratings.sample(len(ratings), replace=False)
# split the train_df as training and validation data sets.
num_valid = int(len(ratings) * 0.2)
train = ratings[:-num_valid]
valid = ratings[-num_valid:]
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"))