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Trainer Module

The Trainer module simplifies and automates the process of feature reduction, model training, and evaluation for various machine learning tasks, ensuring flexibility and efficiency.

Key Features

  1. Feature Reduction:

    • Supports methods such as mrmr, variance_threshold, corr, and chi2 to identify and retain relevant features.

  2. Automated Model Training:

    • Integrates with AutoGluon for model training, selection, and optimization.
    • Handles tasks such as binary classification, multiclass classification, regression, and survival.

Example Usage

from jarvais.trainer import TrainerSupervised

trainer = TrainerSupervised(task='binary', output_dir='./trainer_outputs')
trainer.run(data=data, target_variable='target', save_data=True)

Example Output

Training fold 1/5...  
Fold 1 score: `0.8467207586933614`

Training fold 2/5...  
Fold 2 score: `0.8487846136306914`
...

Model Leaderboard

Displays values in mean [min, max] format across training folds.

Model Score Test Score Val Score Train
WeightedEnsemble_L2 AUROC: 0.82 [0.82, 0.83] AUROC: 0.85 [0.85, 0.85] AUROC: 1.0 [1.0, 1.0]
F1: 0.13 [0.11, 0.14] F1: 0.09 [0.07, 0.12] F1: 0.95 [0.9, 1.0]
AUPRC: 0.48 [0.45, 0.52] AUPRC: 0.47 [0.44, 0.49] AUPRC: 0.96 [0.91, 1.0]
ExtraTreesGini AUROC: 0.82 [0.82, 0.82] AUROC: 0.84 [0.84, 0.84] AUROC: 1.0 [1.0, 1.0]
F1: 0.21 [0.19, 0.22] F1: 0.16 [0.14, 0.18] F1: 1.0 [1.0, 1.0]
AUPRC: 0.45 [0.45, 0.45] AUPRC: 0.43 [0.41, 0.45] AUPRC: 1.0 [1.0, 1.0]
...

Output Files

Binary/Regression/Multiclass:

├── autogluon_models
│   ├── autogluon_models_best_fold
│   │   ├── learner.pkl
│   │   ├── models
│   │   ├── ...
│   ├── autogluon_models_fold_1
│   │   ├── learner.pkl
│   │   ├── models
│   │   ├── ...
│   ├── autogluon_models_fold_2
│   │   ├── learner.pkl
│   │   ├── models
│   │   ├── ...
│   ├── autogluon_models_fold_3
│   │   ├── learner.pkl
│   │   ├── models
│   │   ├── ...
│   ├── autogluon_models_fold_4
│   │   ├── learner.pkl
│   │   ├── models
│   │   ├── ...
│   └── autogluon_models_fold_5
│   │   ├── learner.pkl
│   │   ├── models
│   │   ├── ...

Survival:

└── survival_models
    ├── CoxPH.pkl
    ├── GradientBoosting.pkl
    ├── RandomForest.pkl
    └── SVM.pkl
    ├── lightning_logs
    │   ├── version_0
    │   ├── ...
    ├── DeepSurv.ckpt
    ├── MTLR.ckpt