OOD Detection

This library is used for OOD Detection where a model encounters new classes at test time that were not seen during training. The goal is to detect that such inputs do not belong to any of the training classes.

Install

pip install ood_detection

or

conda install -c yashkhandelwal ood_detection

Example Usage

import numpy as np
from sklearn.datasets import make_blobs
from ood_detection.core import *

# example dataset
n_samples = 1000
n_centers = 10
n_features = 1024

x, y = make_blobs(n_samples=n_samples, n_features=n_features, centers=n_centers, random_state=0)

# using the last 5 cluster as the test and rest as train
train_embedding = x[np.where(y < (n_centers - 5))]
train_labels = y[np.where(y < (n_centers - 5))]

test_embedding = x[np.where(y >= (n_centers - 5))]
test_labels = y[np.where(y >= (n_centers - 5))]

ood = OODMetric(train_embedding, train_labels)

in_distribution_rmd = ood.compute_rmd(train_embedding)
ood_rmd = ood.compute_rmd(test_embedding)

plt.hist([in_distribution_rmd, ood_rmd], label=["In Distribution", "OOD"])
plt.legend()
plt.show()

Built using NBDev

This OOD Detection library was built in a jupyter notebook with proper documentation and test cases. These test cases are verified before they are published to Github Pages, PyPi, Conda etc.

I’ve written down a NBDev Tutorial explaining the thought process of Jeremy Howard and folks at FastAI behind building it. The tutorial covers about how to get started, important functions and description of those I used with the issues I faced while exploring the tool for the first time.

Acknowledgements

Special thanks to Yugam Tiwari for explaining the code he has written for the OODMetric and helping me packaging in the library.

Thanks to Soma Dhavala for coming up with the idea to prepare NBDev Tutorial and helping with the initial reading and exploration material.