Metadata-Version: 2.1
Name: MD-MTL
Version: 0.0.9
Summary: An Ensemble Med-Multi-Task Learning Package
Home-page: https://github.com/Interactive-Media-Lab-Data-Science-Team/Vampyr-MTL
Author: Max Jiang
Author-email: haoyanhy.jiang@mail.utoronto.ca
License: UNKNOWN
Description: # MD-MTL: An Ensemble Med-Multi-Task Learning Package
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        ---
        ![Vampire Squid](/package_info/logo_large.png "Vampyr Inspiration")
        
        **MD-MTL** is a machine learning python package inspired by [MALSAR](https://github.com/jiayuzhou/MALSAR) multi-task learning Matlab algorithm, combined with up-to-date multi-task learning researches and algorithm for public research purposes.
        
        ## [Demo](https://colab.research.google.com/drive/1SVMAEnu1Jk4ldvUqI5iuA7D1zlxXrLrr?usp=sharing)
        Demo for runing Clustered Multitask Learning algorithm with risk factor analysis, pls copy to your playground and do not ask for change authorizations
        
        ## Functionality
        * Algorithms:
          - Multitask Binary Logistic Regression
            + Hinge Loss 
            + L21 normalization
          - Multitask Linear Regression
            + Mean Square Error
            + L21 normalization
          - Cluster Multitask Least Square Regression
            + L21 Normalization
        * Util Functions:
          - MTL_data_split
            + Split data set inside each task with predefined proportions, build on sklearn train_test_split
          - MTL_data_extract
            + Extract data from pandas.DataFrame to desired data matrix format, with desired target and task
          - Cross Validation with k Folds:
            + Cross validation with predefined k folds and scoring methods
          - RFA
            + Risk Factor Analysis with Plotly fig returned
            
        more see [*Documentation*](https://vampyr-mtl.readthedocs.io/en/latest/)
        
        ## Related Reseaches
        [Accelerated Gredient Method](https://arxiv.org/pdf/1310.3787.pdf)
        
        [Clustered Multi-Task Learning: a Convex Formulation](https://papers.nips.cc/paper/3499-clustered-multi-task-learning-a-convex-formulation.pdf)
        
        [Regularized Multi-task Learning](https://dl.acm.org/doi/pdf/10.1145/1014052.1014067)
        
        ## Installation (test version)
        ``pip install -i https://test.pypi.org/simple/ MD_MTL==0.0.9``
        
        ## Dependency
        Auto generated by [pigar](https://github.com/damnever/pigar)
        - scikit_learn == 0.22.1
        
        - setuptools == 45.2.0
        
        - tqdm == 4.46.1
        
        - plotly == 4.8.1
        
        - numpy == 1.18.1
        
        - pandas == 1.0.4
        
        - pytest == 5.3.5
        
        - scipy == 1.4.1
        
        ## Package Update
        
        * Manual Deployment:
        
          - [test-pypi manual](https://realpython.com/pypi-publish-python-package/)
        
          - ``python setup.py sdist bdist_wheel``
        
          - ``twine check dist/*``
        
          - ``twine upload --repository-url https://test.pypi.org/legacy/ dist/*``
        
          or rewrite .pypirc file with credencials and 
        
          - ``python3 twine upload -r pypi dist/*``
        
          - ``python3 setup.py dist bdist_wheel``
        
        * Automation(Linux):
          - deploy: ``./build_deploy.sh``
          - test: ``./build_deploy.sh --test``
        
        ## Development
        
        *Windows*
        ```$ git clone https://github.com/Interactive-Media-Lab-Data-Science-Team/Vampyr-MTL.git
        
        $ cd Vampyr_MTL
        
        $ python3 -m venv myenv
        
        $ myenv/Scripts/activate
        
        $ pip3 install -r requirements.txt
        ```
        
        ## Doc
        
        https://test.pypi.org/project/MD-MTL/0.0.9/
        
        powered by Sphinx with Google comment style, compile with napoleon:
        ```
        sphinx-apidoc -f -o docs/source Vampyr_MTL
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
