Metadata-Version: 2.1
Name: howiml
Version: 1.0.0
Summary: A top-level machine learning framework
Home-page: https://github.com/hermanwh/howi-ml
Author: Herman Wika Horn
Author-email: hermanwh@hotmail.com
License: UNKNOWN
Description: # howi-ml
        HoWi ML is a top-level machine learning package for prototyping and comparison between different scikit-learn, MLP, LSTM and GRU model architectures. It originates from a master thesis focusing on the use of Machine Learning regression models for the oil and gas domain.
        
        The package is published on PyPi. To install, do the following:
        - Install Python 3.6
        - Create a new virtual environment
        - pip install howiml
        
        Additional packages like Tensorflow, Keras etc. are automatically installed.
        
        # Usage
        Code documentation is available in the "doc" folder
        
        Two examples using the stateless (default) and stateful module are seen in the top-level repository ("example_stateful.ipynb" and "example_stateless.ipynb", respectively).
        
        Some features of the package are:
        - Stateless top-level module with most required functionality to define and compare machine learning regression models
        - Similar, stateful top-level module for inexperienced users
        - A lot of underlying functionality for more advanced users, available from howiml.utils
        
        Usage is as follows:
        - Make sure your dataset is available in .csv format, with column names in the first row and each data row in subsequent rows
        - Define the required metadata for your dataset. It is suggested that you implement a local config file and import this in your project, e.g. configs.py with methods to extract all the same metadata as seen defined in the notebook examples
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6, <3.7
Description-Content-Type: text/markdown
