uci-heart-failure

A minimal ML model training and deployment example with FastAPI, Uvicorn and Docker based on Heart failure clinical records dataset.

Running a service

In order to run service locally, make run-local command can be used. It'll install necessary dependencies and start a service. You'll need to have Python 3.6+ for running this command.

Alternatively, make run-docker command will build Docker image and start service within a Docker container. You'll need to have Docker installed for running this command.

Service and API Description

Once service is started, a simple ensemble baseline model will be trained automatically which takes several seconds. There are two API endpoints: /train and /predict. Both should be called with a POST request.

• /train endpoint retrains the ensemble model by using new or default params. The resulting Out of Fold validation metrics will be returned.
• /predict endpoint makes model prediction on a new patient data that should be provided in JSON in the POST request (see test commands below). It returns death_event_probability and death_event_prediction values.

The FastAPI generated docs can be accessed at http://127.0.0.1:8000/docs

Test curl commands

Negative example

curl -X 'POST' \
  'http://127.0.0.1:8000/predict' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "age": 32,
  "anaemia": false,
  "creatinine_phosphokinase": 580.3,
  "diabetes": false,
  "ejection_fraction": 39,
  "high_blood_pressure": 1,
  "platelets": 263359,
  "serum_creatinine": 1.4,
  "serum_sodium": 137,
  "sex": 0,
  "smoking": false,
  "time": 132
  }'

Expected result:

{"message":"Successful prediction.","death_event_probability":0.05885180085897446,"death_event_prediction":false}

Positive example

curl -X 'POST' \
  'http://127.0.0.1:8000/predict' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "age": 70,
  "anaemia": true,
  "creatinine_phosphokinase": 580.3,
  "diabetes": true,
  "ejection_fraction": 62.3,
  "high_blood_pressure": true,
  "platelets": 263359,
  "serum_creatinine": 9.0,
  "serum_sodium": 137,
  "sex": 1,
  "smoking": true,
  "time": 132
}'

Expected result:

{"message":"Successful prediction.","death_event_probability":0.6206271052360535,"death_event_prediction":true}

Re-training model

curl -X 'POST' 'http://127.0.0.1:8000/train?num_folds=5&seed=82'

Expected result:

{"message":"Model was trained successfully with the following Out of Fold validation scores: MCC=0.5386 F1=0.6931 ROC_AUC=0.8739 ","mcc_score":0.5385856891631564,"f1_score":0.693069306930693,"roc_auc_score":0.8738711001642037}

Running tests locally

In order to run tests locally, make run-unit-tests command can be used. It'll install necessary dependencies and start a service. You'll need to have Python 3.6+ for running this command.