params.yaml

base:
  project_name: mlflow-testing
  target: quality

load_dataset:
  drive_link: https://drive.google.com/file/d/1FCVWcNyBX6tFYjfORhnAvIwS8I8zV92j/view?usp=sharing
  raw_data: /data/raw
  file_name: wineq

make_dataset:
  test_split: 0.25
  seed: 41
  processed_data: /data/processed
  res_seed: 42

build_features:
  extended_data: /data/extended

mlflow_config:
  # artifacts_dir: artifacts
  trainingExpName: modeltraining
  tunningExpName: modeltunning
  bestModelExpName: bestModel 
  remote_server_uri: http://localhost:5000     # 127.0.0.1
  mlflow_tracking_uri: sqlite:///mlflow.db
  cmd: mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifacts --host localhost -p 5000
  reg_model_name: 'outperforming models'
  stage: production

train_model:
  n_estimators: 50
  criterion: gini
  max_depth: 50
  min_samples_leaf: 30 
  random_state: 42
  min_samples_split: 60
  model_dir: /models

hyperopt:
  max_eval: 1
  model_name: best_model