https://github.com/inoueakimitsu/clustermil

clustering based multiple instance learning
https://github.com/inoueakimitsu/clustermil

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clustering based multiple instance learning

• Host: GitHub
• URL: https://github.com/inoueakimitsu/clustermil
• Owner: inoueakimitsu
• License: mit
• Created: 2021-10-23T14:42:24.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-07-23T23:58:11.000Z (over 2 years ago)
• Last Synced: 2024-11-13T12:33:29.736Z (about 2 months ago)
• Language: Python
• Size: 13.7 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# clustermil

[![Build Status](https://app.travis-ci.com/inoueakimitsu/clustermil.svg?branch=main)](https://app.travis-ci.com/inoueakimitsu/clustermil)

 

Python package for multiple instance learning (MIL) for large n_instance dataset.

## Features

- support count-based multiple instance assumptions (see [wikipedia](https://en.wikipedia.org/wiki/Multiple_instance_learning#:~:text=Presence-%2C%20threshold-%2C%20and%20count-based%20assumptions%5Bedit%5D))

- support multi-class setting

- support scikit-learn Clustering algorithms (such as `MiniBatchKMeans`)

- fast even if n_instance is large

## Installation

```bash

pip install clustermil

```

## Usage

```python

# Prepare follwing dataset

#

# - bags ... list of np.ndarray

#            (num_instance_in_the_bag * num_features)

# - lower_threshold ... np.ndarray (num_bags * num_classes)

# - upper_threshold ... np.ndarray (num_bags * num_classes)

#

# bags[i_bag] contains not less than lower_thrshold[i_bag, i_class]

# i_class instances.

# Prepare single-instance clustering algorithms

from sklearn.cluster import MiniBatchKMeans

n_clusters = 100

clustering = MiniBatchKMeans(n_clusters=n_clusters)

clusters = clustering.fit_predict(np.vstack(bags)) # flatten bags into instances

# Prepare one-hot encoder

from sklearn.preprocessing import OneHotEncoder

onehot_encoder = OneHotEncoder()

onehot_encoder.fit(clusters)

# generate ClusterMilClassifier with helper function

from clustermil import generate_mil_classifier

milclassifier = generate_mil_classifier(

            clustering,

            onehot_encoder,

            bags,

            lower_threshold,

            upper_threshold,

            n_clusters)

# after multiple instance learning,

# you can predict instance class

milclassifier.predict([instance_feature])

```

See `tests/test_classification.py` for an example of a fully working test data generation process.

## License

clustermil is available under the MIT License.