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Interpretable Dynamic Customer Segmentation

with RFM, KMeans, and LightGBM

patcharanat p.

Local Usage

# Example for local execution
python main.py --env local --method filesystem --input_path '../../data/ecomm_invoice_transaction.parquet' --output_path output --exec_date 2024-10-29

Usage via Docker Container

# workdir code/models

# docker build -t <image_name:tag> .
docker build -t ecomm/interpretable-dynamic-rfm-service:v1 .

# docker run --name <container_name> <image_name:tag> <app_args1> ...

# --rm: to remove container after runtime

# copy data/ecomm_invoice_transaction.parquet to code/models

# with python main.py as an entrypoint
docker run --rm \
    ecomm/interpretable-dynamic-rfm-service:v1 \
    --env local --method filesystem \
    --input_path 'ecomm_invoice_transaction.parquet' \
    --output_path output --exec_date 2024-10-29

# with bash as en entrypoint (cmd)
docker run --rm -it ecomm/interpretable-dynamic-rfm-service:v1
python main.py ...

Cloud Authentication

# first time
gcloud auth login

# using ADC (Application Default Credentials) method
gcloud auth application-default login

# for impersonated account
gcloud auth application-default login --impersonate-service-account <SERVICE_ACCT_EMAIL>

gcloud config set project <project_id>

Terraform

# workdir: code/models/terraform

terraform init
terraform plan
terraform apply
terraform destroy

# For Workload Identity Federation
# workdir: code/models/terraform/auth

terraform init
terraform plan
terraform apply
# terraform destroy # be careful with work identity pools deletion

Usgae via Cloud (GCP)

# apply terraform to create bucket or bigquery dataset
# workdir: code/models/terraform

# workdir: code/models
# ./test/initial_gcp.sh
cp ../../data/ecomm_invoice_transaction.parquet .
gsutil cp ecomm_invoice_transaction.parquet gs://<landing/uri>
rm ecomm_invoice_transaction.parquet

# ./test/input_sample.sh
# local - gcp
python main.py --env gcp --project_id <project_id> --method filesystem --input_path 'gs://<landing_bucket_name>/input/data/2024-11-03/ecomm_invoice_transaction.parquet' --output_path 'gs://<staging_bucket_name>/output' --exec_date 2024-11-03

docker build -t ecomm/interpretable-dynamic-rfm-service:v2 .

# with python main.py as an entrypoint
# docker - gcp
docker run --rm \
    -v $HOME/.config/gcloud:/root/.config/gcloud \
    ecomm/interpretable-dynamic-rfm-service:v2 \
    --env gcp \
    --method filesystem \
    --input_path 'gs://<landing_bucket_name>/input/data/2024-11-03/ecomm_invoice_transaction.parquet' \
    --output_path 'gs://<staging_bucket_name>/output' \
    --exec_date 2024-11-03

GAR Manual Deployment

# workdir: code/models
docker build -t <any-image-name> .

docker tag <previous-image-name> $REGION-docker.pkg.dev/$PROJECT_ID/$REPOSITORY/$IMAGE_NAME:latest

docker push $REGION-docker.pkg.dev/$PROJECT_ID/$REPOSITORY/$IMAGE_NAME:latest