Output Structure

Output Structure

All outputs are written under:

{output_dir}/{network}.{station}.{location}.{channel}/

For example, with network="VG", station="OJN", location="00", channel="EHZ":

output/VG.OJN.00.EHZ/

---

Full Directory Tree

output/
└── VG.OJN.00.EHZ/
    │
    ├── tremor/
    │   ├── daily/                            # Per-day CSV files (removed if cleanup_daily_dir=True)
    │   ├── figures/                          # Daily tremor plots (created if plot_daily=True)
    │   └── tremor_*.csv                      # Final merged tremor data
    │
    ├── features/
    │   ├── tremor_matrix_*.csv               # Aligned tremor matrix (all columns)
    │   ├── tremor_matrix_per_method/         # Per-column tremor matrices (optional)
    │   ├── all_extracted_features_*.csv      # tsfresh output per tremor column
    │   └── label_features_*.csv             # Labels aligned with features
    │
    ├── trainings/
    │   │
    │   ├── model-with-evaluation/            # Output of train_and_evaluate()
    │   │   └── {classifier-slug}/            # e.g., random-forest-classifier
    │   │       └── {cv-slug}/                # e.g., stratified-shuffle-split
    │   │           ├── features/
    │   │           │   ├── significant_features/     # Per-seed top-N features
    │   │           │   │   ├── 00000.csv
    │   │           │   │   └── ...
    │   │           │   ├── all_features/             # All ranked features (optional)
    │   │           │   │   ├── 00000.csv
    │   │           │   │   └── ...
    │   │           │   ├── figures/significant/      # Feature importance plots (optional)
    │   │           │   │   └── 00000.jpg
    │   │           │   ├── tests/                    # Per-seed held-out test splits
    │   │           │   │   ├── 00000_X_test.csv
    │   │           │   │   ├── 00000_y_test.csv
    │   │           │   │   └── ...
    │   │           │   ├── significant_features.csv  # Aggregated top features (all seeds)
    │   │           │   └── top_{n}_significant_features.csv
    │   │           ├── models/
    │   │           │   ├── 00000.pkl                 # Trained model — seed 0
    │   │           │   ├── 00001.pkl
    │   │           │   └── ...
    │   │           ├── metrics/
    │   │           │   ├── 00000.json                # Per-seed metrics JSON
    │   │           │   └── ...
    │   │           ├── figures/                      # Aggregate evaluation plots
    │   │           │   ├── aggregate_roc_curve.png / .csv
    │   │           │   ├── aggregate_pr_curve.png / .csv
    │   │           │   ├── aggregate_calibration.png / .csv
    │   │           │   ├── aggregate_prediction_distribution.png / .csv
    │   │           │   ├── aggregate_confusion_matrix.png / .csv
    │   │           │   ├── aggregate_threshold_analysis.png / .csv
    │   │           │   ├── aggregate_feature_importance.png / .csv
    │   │           │   └── aggregate_metrics.csv
    │   │           ├── trained_model_{suffix}.csv    # Registry of all trained models
    │   │           ├── all_metrics_{suffix}.csv      # All per-seed metrics
    │   │           └── metrics_summary_{suffix}.csv  # Mean ± std summary
    │   │
    │   └── model-only/                       # Output of train()
    │       └── {classifier-slug}/
    │           └── {cv-slug}/
    │               ├── features/
    │               │   ├── significant_features/
    │               │   │   ├── 00000.csv
    │               │   │   └── ...
    │               │   ├── all_features/ (optional)
    │               │   ├── figures/significant/ (optional)
    │               │   ├── significant_features.csv
    │               │   └── top_{n}_significant_features.csv
    │               ├── models/
    │               │   ├── 00000.pkl
    │               │   └── ...
    │               └── trained_model_{suffix}.csv    # Registry used by ModelPredictor
    │
    ├── forecast/
    │   ├── predictions.csv                   # Forecast output (predict_proba)
    │   └── figures/
    │       └── eruption_forecast.png
    │
    ├── config.yaml                           # Saved pipeline config (written by save_config())
    └── forecast_model.pkl                    # Serialised ForecastModel (written by save_model())

---

File Name Suffixes

Training output files follow this naming pattern:

{ClassifierName}-{CVName}_rs-{random_state}_ts-{total_seed}_top-{n}

Example:

XGBClassifier-StratifiedShuffleSplit_rs-0_ts-500_top-20

So the model registry is:

trained_model_XGBClassifier-StratifiedShuffleSplit_rs-0_ts-500_top-20.csv

---

Classifier and CV Slugs

Classifier key	Folder slug
rf	random-forest-classifier
xgb	xgb-classifier
gb	gradient-boosting-classifier
svm	svm-classifier
lr	logistic-regression-classifier
nn	mlp-classifier
dt	decision-tree-classifier
knn	knn-classifier
nb	gaussian-nb-classifier
voting	voting-classifier

CV strategy key	Folder slug
shuffle	stratified-shuffle-split
stratified	stratified-k-fold
timeseries	time-series-split

---

ModelPredictor Output

Evaluation mode (predict() / predict_best())

{output_dir}/
├── metrics/
│   ├── all_metrics.csv
│   └── metrics_summary.csv
└── seed_00000/                 # Only created when plot=True
    ├── seed_00000_confusion_matrix.png
    ├── seed_00000_roc_curve.png
    ├── seed_00000_pr_curve.png
    ├── seed_00000_threshold_analysis.png
    ├── seed_00000_feature_importance.png
    ├── seed_00000_calibration.png
    └── seed_00000_prediction_distribution.png

Forecast mode (predict_proba())

{output_dir}/
├── predictions.csv             # eruption_probability, uncertainty, confidence, prediction
└── figures/
    └── eruption_forecast.png