https://github.com/mirkobunse/qunfold

A unified implementation of quantification and unfolding algorithms
https://github.com/mirkobunse/qunfold

Last synced: about 2 months ago
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A unified implementation of quantification and unfolding algorithms

• Host: GitHub
• URL: https://github.com/mirkobunse/qunfold
• Owner: mirkobunse
• Created: 2022-12-19T17:03:55.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-24T10:21:02.000Z (2 months ago)
• Last Synced: 2024-10-25T06:29:31.473Z (2 months ago)
• Language: Python
• Homepage: https://mirkobunse.github.io/qunfold/
• Size: 5.72 MB
• Stars: 3
• Watchers: 2
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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# qunfold | Quantification & Unfolding

This Python package implements our unified framework of algorithms for quantification and unfolding. It is designed for enabling the composition of novel methods from existing and easily customized loss functions and data representations. Moreover, this package leverages a powerful optimization back-end to yield state-of-the-art performances for all compositions.

## Installation

```

pip install --upgrade pip setuptools wheel

pip install 'qunfold @ git+https://github.com/mirkobunse/qunfold'

```

Moreover, you will need a [JAX](https://jax.readthedocs.io/) backend. Typically, the CPU backend will be ideal:

```

pip install "jax[cpu]"

```

## Quick start

For detailed information, visit [the documentation](https://mirkobunse.github.io/qunfold).

Basically, you use this package as follows:

```python

from qunfold import ACC # Adjusted Classify and Count

from sklearn.ensemble import RandomForestClassifier

acc = ACC( # use OOB predictions for training the quantifier

    RandomForestClassifier(oob_score=True)

)

acc.fit(X_trn, y_trn) # fit to training data

p_hat = acc.predict(X_tst) # estimate a prevalence vector

```