Metadata-Version: 2.1
Name: milwrap
Version: 0.1.3
Summary: milwrap - multiple instane meta-learner that can use any supervised-learning algorithms.
Home-page: https://github.com/inoueakimitsu/milwrap
Author: Akimitsu Inoue
Author-email: akimitsu.inoue@gmail.com
License: MIT
Description: # milwrap
        
        [![Build Status](https://app.travis-ci.com/inoueakimitsu/milwrap.svg?branch=main)](https://app.travis-ci.com/inoueakimitsu/milwrap)
        <a href="https://github.com/inoueakimitsu/milwrap/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/inoueakimitsu/milwrap"></a> 
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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
        
        ```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.
        
Keywords: machine learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7.*
Description-Content-Type: text/markdown
