fix: extend xlim in plot_ml_vs_dl to prevent clipping of value labels

fix: update calibration and precision-recall plotting functions to load data from files

fix: update confusion matrix and score distribution functions to load pre-computed CV predictions

feat: add script to generate publication-ready figures for AURIS paper

fix: update evaluation to use optimal threshold and adjust BCEWithLogitsLoss for class imbalance

fix: adjust BCEWithLogitsLoss to handle class imbalance with pos_weight

fix: add missing roc_curve metric import for evaluation

feat: define families dictionary for candidate classifiers in _build_candidate_families function

feat: enhance candidate selection with class ratio and calibrated SVC for improved model performance

feat: implement optimal threshold calculation for binary predictions in train function

feat: add CalibratedClassifierCV and roc_curve imports for enhanced model evaluation

feat: add TorchSklearnWrapper for PyTorch model compatibility with sklearn and enhance final model training process

Add Gradio dependency to requirements.txt with version constraints

feat: train all models on full data and save their paths

feat: implement mixed precision training and gradient accumulation in training loop

feat: adjust confidence range in analysis result and optimize parallel processing for feature extraction, vocal analysis, and CLAP detection

feat: update model parameters for Random Forest, Gradient Boosting, XGBoost, and LightGBM to enhance performance

feat: adjust Random Forest parameters for improved model performance

feat: optimize Gradient Boosting and XGBoost parameters for improved model performance

feat: refactor training history figure generation to use GradientBoostingClassifier and improve metric tracking

feat: update model parameters and fix data leakage by removing duration_sec and sample_rate from features

feat: add deep learning classifier comparison script for AURIS with multiple architectures

feat: update excluded columns in feature statistics computation to prevent data leakage

feat: exclude additional metadata columns from feature loading to prevent data leakage

feat: update output directory for generated figures to docs/academic/figures

feat: add deep-learning figure generation script for enhanced model evaluation

feat: enhance calibration figure with Brier score and update prediction functions

feat: update ROC and Precision-Recall curve functions to include best model visualization

feat: implement cross-validated predictions caching and update confusion matrix function

feat: add additional imports for model evaluation and cross-validation

feat: add figure generation script for training results visualization

fix: rename variable for clarity in compute_stats function

feat: add feature statistics computation from training CSV for enhanced analysis

feat: implement resume functionality in batch feature extraction to skip processed samples

feat: enhance visualization pipeline with additional figures and manifest support

feat: add dataset statistics and class balance visualizations to enhance analysis capabilities

feat: refactor worker function for batch feature extraction to support multiprocessing

feat: implement parallel processing for batch feature extraction to improve performance

feat: add full training and evaluation pipeline for AURIS

feat: enhance training pipeline with multi-model evaluation and detailed metrics

feat: enhance feature extraction with additional spectral and temporal metrics

fix: gitignore models/data and add logging to feature extraction

fix: add debug logging for vocal extraction failures in extract_sample_features

fix: add return type annotations and docstrings for clarity in Wav2Vec2MusicClassifier and AudioDataset

feat: add wav2vec2 classifier for AI music detection with training loop and dataset handling

feat: add AURIS classifier training module with model evaluation and feature importance

feat: implement batch feature extraction for AURIS training pipeline

feat: add evaluation framework for AURIS models with metrics computation

feat: add SONICS dataset loader for AURIS training pipeline

feat: add AURIS training pipeline module for dataset loading and model training