Metadata-Version: 2.1
Name: misso
Version: 0.1
Summary: Python package to compute mutual information matrix
Home-page: https://github.com/AntixK/MISSO
Author: Anand K Subramanian
Author-email: anandkrish894@gmail.com
License: MIT
Description: MISSO
        
        
        ### Installation
         - Using `pip`
        ```
        pip install misso
        ```
        **Note:** In our benchmarks, multi-core version was always faster than the GPU accelerated version. So, we
        highly recommend installing just the CPU version and using multi-core computation.
        
        - Installing from source
        ```
        
        ```
        
        ### Usage
        ```
        from misso import MISSO
        
        
        ```
        For a more detailed usage, check out the Tutorials folder.
        
        ### Benchmarks
        
        ** Benchmarks were run on a machine with the following configuration
        ```
        CPU:       6 core Intel Core i7-8750H (-MT-MCP-) [12 core with Hyperthreading]
                   arch: Skylake rev.10 cache: 9216 KB
                   flags: (lm nx sse sse2 sse3 sse4_1 sse4_2 ssse3 vmx) bmips: 26399
                   clock speeds: max: 4100 MHz 1: 2479 MHz 2: 3013 MHz 3: 3211 MHz
                   4: 3098 MHz 5: 3362 MHz 6: 3769 MHz 7: 3082 MHz 8: 3290 MHz
                   9: 3090 MHz 10: 3141 MHz 11: 3055 MHz 12: 3650 MHz
        Graphics:  Card-1: Intel Device 3e9b bus-ID: 00:02.0
                   Card-2: NVIDIA Device 1f10 bus-ID: 01:00.0
                   Display Server: x11 (X.Org 1.19.6 )
                   drivers: modesetting,nvidia (unloaded: fbdev,vesa,nouveau)
                   Resolution: 3840x1600@59.99hz
                   OpenGL: renderer: GeForce RTX 2070 with Max-Q Design/PCIe/SSE2
                   version: 4.6.0 NVIDIA 440.100 Direct Render: Yes
        ```
        
        ### License
        
        ### TODO
        - [ ] Try gradient-based solvers 
            - [ ] Conjugate-gradient descent
        - [x] Multi-processing for `lsmi` computation
            - [x] Reduce interprocess overhead
            - [x] Try other methods to parallelize the code
        - [ ] Benchmarks
            - [x] Multiprocessing
            - [x] GPU benchmarks
            - [ ] Solver Benchmarks
            - [ ] Run benchmarks on multiple machines and put in benchmark reports
        - [ ] Detailed comparison with graphical Lasso (Tutorials)
            - [ ] Toy Example
            - [ ] Time Series: Stationary & Dynamic [link](https://academic.oup.com/cercor/article-pdf/24/3/663/14099596/bhs352.pdf)
            - [ ] Comparison of MISSO and GLASSO on indirect coupling [link](https://academic.oup.com/bioinformatics/article-pdf/28/2/184/16908913/btr638.pdf)
        - [ ] GPU Acceleration    
            - [x] Use Cupy for solving
            - [x] Reduce GPU overhead 
            - [ ] Verify correctness (Still an issue)
            - [x] Try torch for GPU acceleration
        - [x] tqdm for Notebook and Script
        - [ ] Pandas DataFrame support
        - [ ] Packaging
            - [ ] pip package
            - [ ] Travis CI
        - [ ] Readme
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Requires-Python: >=3.6
Description-Content-Type: text/markdown
