akinbezatoglu / pyspark-recommendation-system

Anime recommendation system with pyspark

pyspark-recommendation-system

My teammate on this project Akın Bezatoğlu

Anime recommendation system with pyspark
Data taken from kaggle. See:

• MyAnimeList Dataset

You can skip preprocessing part and run program from Recommandaiton Engine with preprocessed data

Contents

1. • Preprocessing
2. • Visualization
3. • Recommendation System with ALS
4. • Cosine Similarity

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Update : 17/03/2024

To create a S3 bucket and upload preprocessed data files the S3 bucket run the following:

$ cd sagemaker
$ python upload_data.py -n 'anime-recommendation-system' -r 'eu-central-1' -f '../preprocessed_data'

../preprocessed_data\user.csv  4579798 / 4579798.0  (100.00%)00%)

Create cloud infrastructure using Terraform

$ terraform init
Initializing the backend...

Initializing provider plugins...
- Finding latest version of hashicorp/aws...
- Installing hashicorp/aws v5.41.0...
- Installed hashicorp/aws v5.41.0 (signed by HashiCorp)
...
$ terraform plan
...
Plan: 4 to add, 0 to change, 0 to destroy.
$ terraform apply --auto-approve
aws_iam_policy.sagemaker_s3_full_access: Creating...
aws_iam_role.sagemaker_role: Creating...
aws_iam_policy.sagemaker_s3_full_access: Creation complete after 1s [id=arn:aws:iam::749270828329:policy/SageMaker_S3FullAccessPoliciy]
aws_iam_role.sagemaker_role: Creation complete after 1s [id=AnimeRecommendation_SageMakerRole]
aws_iam_role_policy_attachment.sagemaker_s3_policy_attachment: Creating...
aws_sagemaker_notebook_instance.notebookinstance: Creating...
aws_iam_role_policy_attachment.sagemaker_s3_policy_attachment: Creation complete after 1s [id=AnimeRecommendation_SageMakerRole-20240317120654686300000001]
aws_sagemaker_notebook_instance.notebookinstance: Still creating... [10s elapsed]
...
Apply complete! Resources: 4 added, 0 changed, 0 destroyed.

All resources have been created. for details, you can look at terraform.tfstate file.

Open Jupyter in the Notebook Instance that created in Amazon SageMaker

Create a new conda_python3 notebook and Run sagemaker-anime-recommendation-system.ipynb step by step on the Notebook Instance

You can look at the sagemaker-anime-recommendation-system-test.html file to see the output.

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In this code, model data is saved locally and then uploaded properly to an s3 bucket.

model.save(SparkContext.getOrCreate(), 'model')

import boto3
import os

# Initialize S3 client
s3 = boto3.client('s3')

# Upload files to the created bucket
bucketname = 'anime-recommendation-system'
local_directory = './model'
destination = 'model/'
for root, dirs, files in os.walk(local_directory):
    for filename in files:
        # construct the full local path
        local_path = os.path.join(root, filename)

        relative_path = os.path.relpath(local_path, local_directory)
        s3_path = os.path.join(destination, relative_path)
        
        s3.upload_file(local_path, bucketname, s3_path)

---

Destroy the all resources

$ terraform destroy
...
Destroy complete! Resources: 4 destroyed.

To load and test the model, run the test_another_instance.ipynb jupyter notebook

You can look at the test_another_instance.html file to see the output.

In this code, model data is retrieved from the s3 bucket and loaded using the Matrix Factorization Model

import boto3
import os 

s3_resource = boto3.resource('s3')
bucket = s3_resource.Bucket('anime-recommendation-system') 
for obj in bucket.objects.filter(Prefix = 'model'):
    if not os.path.exists(os.path.dirname(obj.key)):
        os.makedirs(os.path.dirname(obj.key))
    bucket.download_file(obj.key, obj.key)

from pyspark import SparkContext
from pyspark.mllib.recommendation import MatrixFactorizationModel

m = MatrixFactorizationModel.load(SparkContext.getOrCreate(), 'model')