uqregressors 1.0.5

PyTorch-based regressors with uncertainty quantification and scikit-learn interface

UQRegressors

UQRegressors is a Python library for regression models that provide prediction intervals, in addition to point estimates. It is meant for machine learning applications where quantifying uncertainty is important.

It features highly customizable parameters for each model, an easy to use interface with built-in dataset validation, GPU compatibility with a PyTorch backend, validated implementations with comparisons to published results, easy saving and loading of created models, and a wide variety of metrics and visualization tools available to assess model quality.

Please direct any questions or suggestions to the email [e-mail].

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Key Capabilities

1. Dataset Loading & Validation — Utility functions to clean and validate your input data.
2. Uncertainty‑Aware Regressors
   • Conformal: CQR, K‑Fold CQR, Ensemble‑based CQR
   • Bayesian: Deep Ensembles, MC Dropout, Gaussian Processes (GP, BBMM GP)
3. Hyperparameter Tuning — Optuna‑based tuning with support for custom interval‑width objective functions.
4. Uncertainty Metrics — RMSE, coverage, interval width, interval score, NLL, correlation diagnostics, conditional coverage, RMSCD variants, and more.
5. Visualization Tools — Calibration curves, prediction-vs-true plots, model comparison bar charts.

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Installation

To install the core features of UQRegressors:

pip install uqregressors

UQRegressors requires PyTorch, which you should install according to your setup:

• CPU only:

  pip install torch torchvision torchaudio
  

• CUDA GPU (choose the version matching your GPU):

  pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
  

• For other versions, see PyTorch Install Guide.

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Getting Started (More detail in full docs)

from uqregressors.bayesian.dropout import MCDropoutRegressor
from uqregressors.tuning.tuning import tune_hyperparams, interval_width
from uqregressors.plotting.plotting import plot_pred_vs_true

# Define a dataset
X_train = np.linspace(0, 1, 5)
X_test = np.linspace(0, 1, 40)
y_train = np.sin(2 * np.pi * X_train)
y_test = np.sin(2 * np.pi * X_test)

# Train an MC‑Dropout regressor
reg = MCDropoutRegressor(epochs=50, random_seed=42)
reg.fit(X_train, y_train)
mean, lower, upper = reg.predict(X_test)

# Visualize results
plot_pred_vs_true(mean, lower, upper, y_test, show=True, title="MC‑Dropout")

# Hyperparameter tuning example (e.g., tuning CQR)
from uqregressors.conformal.cqr import ConformalQuantileRegressor
cqr = ConformalQuantileRegressor(alpha=0.1, epochs=20, random_seed=42)

opt_cqr, best_score, study = tune_hyperparams(
    regressor=cqr,
    param_space={"tau_lo": lambda t: t.suggest_float("tau_lo", 0.01, 0.1),
                 "tau_hi": lambda t: t.suggest_float("tau_hi", 0.9, 0.99)},
    X=X_train, y=y_train,
    score_fn=interval_width,
    greater_is_better=False,
    n_trials=10,
    n_splits=3
)
mean_t, lo_t, hi_t = opt_cqr.predict(X_test)
plot_pred_vs_true(mean_t, lo_t, hi_t, y_test, show=True, title="Tuned CQR")

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Documentation

See the complete API Documentation with complete examples:
https://arjunrs3.github.io/UQRegressors/

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Contributing

Contributions, issues, and feature requests are welcome! Please:

1. Fork the repo
2. Create a feature branch (git checkout -b my-feature)
3. Commit your changes and push
4. Open a Pull Request
5. Email [e-mail] with any questions

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License

MIT License

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