AtrCheema/AI4Water: AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods

This package provides a python framework for building and testing machine learning models for time series and tabular data.

• updated master with dev by @AtrCheema in https://github.com/AtrCheema/AI4Water/pull/12
• Dev by @AtrCheema in https://github.com/AtrCheema/AI4Water/pull/13

Full Changelog: https://github.com/AtrCheema/AI4Water/compare/v1.0-beta...v1.0-beta.1

• predict method returns only predicted array by default. If the user wants true array as well, return_true should be set to True.
• changed name of sub-modules pre_processing => preprocessing, post_processing => postprocessing, hyper_opt => hyperopt and ETUtil => et
• improved docs
• removed bugs
• transformations classes have config and from_config methods
• integration of nbeats
• added fit_with_tpot method for experiments
• added autocorrelation and partial autocorrelation in eda
• predict method does not receive prefix method
• added examples
• datahandler can read xlsx, csv, mat, npz, netcdf pqrquet and feather file types.
• added regplot function as part of utils
• predict method can calculate variety of errors. By default now calculates minimal errors
• improved speed of tests and made them less verbose
• unified ShapExplainer class for both ml and dl models
• unified LimeExplainer class for both ml and dl models
• Experiment class can handle verbosity argument better
• improved interdependency of packages in different sub-modules i.e. shapfile not required if datasets sub-module is not used
• pin the versions with which ai4water is tested
• predict can take user defined arguments just as keras model or sklearn model
• added conditionalize layer which is part of ConditionalRNN