https://github.com/inoueakimitsu/milwrap
Wrapping single instance learning algorithms for fitting them to data for multiple instance learning
https://github.com/inoueakimitsu/milwrap
large-data machine-learning multi-class-classification multiple-instance-learning python sklearn
Last synced: 9 months ago
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Wrapping single instance learning algorithms for fitting them to data for multiple instance learning
- Host: GitHub
- URL: https://github.com/inoueakimitsu/milwrap
- Owner: inoueakimitsu
- License: mit
- Created: 2021-09-29T11:06:11.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-01-24T06:21:14.000Z (about 1 year ago)
- Last Synced: 2025-05-07T23:43:46.856Z (9 months ago)
- Topics: large-data, machine-learning, multi-class-classification, multiple-instance-learning, python, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 634 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# milwrap
[](https://github.com/inoueakimitsu/milwrap/actions/workflows/python-tests.yml)
[](https://colab.research.google.com/github/inoueakimitsu/milwrap/blob/master/introduction.ipynb)
Python package for multiple instance learning (MIL).
This wraps single instance learning algorithms so that they can be fitted to data for MIL.
## 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 algorithms (such as `RandomForestClassifier`, `SVC`, `LogisticRegression`)
## Installation
```bash
pip install milwrap
```
## Usage
For more information, see [Use scikit-learn models in multiple instance learning based on the count-based assumption](https://github.com/inoueakimitsu/milwrap/blob/main/intro-en.md).
```python
# Prepare single-instance supervised-learning algorithm
# Note: only supports models with predict_proba() method.
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
# Wrap it with MilCountBasedMultiClassLearner
from milwrap import MilCountBasedMultiClassLearner
mil_learner = MilCountBasedMultiClassLearner(clf)
# 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.
# run multiple instance learning
clf_mil, y_mil = learner.fit(
bags,
lower_threshold,
upper_threshold,
n_classes,
max_iter=10)
# after multiple instance learning,
# you can predict instance class
clf_mil.predict([instance_feature])
```
See `tests/test_countbased.py` for an example of a fully working test data generation process.
## License
milwrap is available under the MIT License.