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Merlin Jupyter Notebook Examples

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NVIDIA

Merlin Jupyter Notebook Examples

This resource is a collection of jupyter notebook examples to provide end-to-end examples for NVIDIA Merlin.

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01-Download-Convert.ipynb

In [ ]:

# Copyright 2021 NVIDIA Corporation. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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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

In [2]:

# 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.

In [3]:

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

We will download and unzip the data.

In [4]:

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

Convert the dataset

First, we take a look on the movie metadata.

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

Out[5]:

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.

In [6]:

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

Out[6]:

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

In [7]:

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

Splitting into train and validation dataset

We load the movie ratings.

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

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

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ratings = ratings.drop("timestamp", axis=1)
# convert ratings to pandas df to use sklearn train_test_split func
ratings = ratings.to_pandas()
train, valid = train_test_split(ratings, test_size=0.2, random_state=42)

We save the dataset to disk.

In [10]:

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