Metadata-Version: 1.2
Name: mllearn
Version: 1.2.3
Summary: a package for multi-label classify
Home-page: https://github.com/Lxinyuelxy/multi-label-learn
Author: Lxinyuelxy
Author-email: Lxinyuelxy@gmail.com
License: UNKNOWN
Description-Content-Type: text/markdown
Description: # multi-label-learn
        
        mlleran is a python library for multi-label classification bulti on scikit-learn and numpy.
        
        ## Implementation
        The implementation is based on the paper [A Review on Multi-Label Learning Algorithms](https://ieeexplore.ieee.org/document/6471714/), and the implementated algorithms include:
        
        **Problem Transformation**
        
        - [x] Binary Relevance
        - [x] Classifier Chains
        - [x] Calibrated Label Ranking
        - [x] Random k-Labelsets
        
        **Algorithm Adaptation**
        
        - [x] Multi-Label k-Nearest Neighbor
        - [x] Multi-Label Decision Tree
        - [ ] Ranking Support Vector Machine
        - [ ] Collective Multi-Label Classifier
        
        ## Installation
        ```bash
        pip install mllearn
        ```
        **Note: Support Python3 only.** 
        
        ## Data Format
        All data type should be `ndarray`, especially y should be the binary format. For example, if your dataset totally have 5 labels and one of your samples has only first and last labels, then the corresponding output should be `[1, 0, 0, 0, 1]`.
        ```python
        samples, features = X_train.shape
        samples, labels = y_train.shape
        samples_test, features = X_test.shape
        samples_test, labels = y_test.shape
        ```
        You can also find multi-label dataset provided by Mulan [here](http://mulan.sourceforge.net/datasets-mlc.html).
        
        ## Example Usage
        This library includes 2 parts, algorithms and metrics.
        ```python
        from mllearn.problem_transform import BinaryRelevance
        
        classif = BinaryRelevance()
        classif.fit(X_train, y_train)
        predictions = classif.predict(X_test)
        ```
        
        ```python
        from mllearn.metrics import subset_acc
        acc = subset_acc(y_test, predictions)
        ```
        
Keywords: multi label classification
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: ~=3.5
