UncertaintyPlayground 0.1.2

A Python library for uncertainty estimation in supervised learning tasks

UncertaintyPlayground

Installation

Requirements:

• Python >= 3.8
• PyTorch == 2.0.1
• GPyTorch == 1.10
• Numpy == 1.24.0
• Seaborn == 0.12.2

Use PyPI to install the package:

pip install uncertaintyplayground

or alterntively, to use the development version, install directly from GitHub:

pip install git+https://github.com/unco3892/UncertaintyPlayground.git

Usage

You can train and visualize the results of the models in the following way (this example uses the California Housing dataset from Sklearn):

from uncertaintyplayground.trainers.svgp_trainer import SparseGPTrainer
from uncertaintyplayground.trainers.mdn_trainer import MDNTrainer
from uncertaintyplayground.predplot.svgp_predplot import compare_distributions_svgpr
from uncertaintyplayground.predplot.mdn_predplot import compare_distributions_mdn
from uncertaintyplayground.predplot.grid_predplot import plot_results_grid
import torch
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split

# Load the California Housing dataset
california = fetch_california_housing()

# Convert X and y to numpy arrays of float32
X = np.array(california.data, dtype=np.float32)
y = np.array(california.target, dtype=np.float32)

# Set random seed for reproducibility
np.random.seed(42)
torch.manual_seed(1)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# SVGPR: Initialize and train a SVGPR model with 100 inducing points
california_trainer_svgp = SparseGPTrainer(X_train, y_train, num_inducing_points=100, num_epochs=30, batch_size=512, lr=0.1, patience=3)
california_trainer_svgp.train()

# MDN: Initialize and train an MDN model
california_trainer_mdn = MDNTrainer(X_train, y_train, num_epochs=100, lr=0.001, dense1_units=50, n_gaussians=10)
california_trainer_mdn.train()

# SVPGR: Visualize the SVGPR's predictions for multiple instances
plot_results_grid(trainer=california_trainer_svgp, compare_func=compare_distributions_svgpr, X_test=X_test, Y_test=y_test, indices=[900, 500], ncols=2)

# MDN: Visualize the MDN's predictions for multiple instances
plot_results_grid(trainer=california_trainer_mdn, compare_func=compare_distributions_mdn, X_test=X_test, Y_test=y_test, indices=[900, 500], ncols=2)

You can find another example for MDN in the examples folder.

Contributors

This library is maintained by Ilia Azizi (University of Lausanne). Any other contributors are welcome to join! Feel free to get in touch with (contact links on my website).

Citation

If you use this package in your research, please cite our work:

UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation , Ilia Azizi, arXiv:2310.15281

@misc{azizi2023uncertaintyplayground,
      title={UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation}, 
      author={Ilia Azizi},
      year={2023},
      eprint={2310.15281},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

License

Please see the project MIT licensed here.