Metadata-Version: 2.1
Name: agmodeling
Version: 0.9
Summary: Statistical modeling tools, to unify model creation and scoring based on python
Home-page: https://github.com/agriscope/agmodeling/
Author: Agriscope
Author-email: team@agriscope.fr
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Development Status :: 1 - Planning
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Natural Language :: French
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Database :: Database Engines/Servers
License-File: LICENSE


Agmodeling
===========
Statistical modeling tools, to unify model creation and scoring based on python

package agmodeling.setscoring implements a part of the SET method for comparing
sensor output as described by :

An Evaluation Tool Kit of Air Quality 1 Micro-Sensing Units 
(Barak Fishbain1,Uri Lerner, Nuria Castell-Balaguer)



What's New
===========
- (2022/10) change the way to calculate NRMSE
- (2022/09) logging configuration added  (v 0.8)
- (2022/09) introducing get_detailed_score() returning all coef  (v 0.7)
            + move to python logger
- (2022/09) correction of warning after pandas version evolution (v 0.6)
- (2019/08) python 3 support (v 0.4)
- (2018/11) First version (v 0.3)



Dependencies
=============

Agmodeling is written to be use with python 2.7 and python 3.6
It requires Pandas, numpy and  scipy
It requires `Pandas`::

    pip install pandas
    pip install numpy
    pip install scipy
    
    
Installations
=============

    pip install agmodeling
    

Uses cases
========== 

	
You can run the whole demo inside the package   

	cd demo
	python .\demo_SET_scoring.py
	Read excel data file : sample_data.xlsx
	containing 2568 data
	

	Score IPI for PM25_RAW
	 Match     : RMSE      : Pearson   : Kendall   : Spearman  : LFE        :: IPI       
	 0.492835  : 0.869434  : 0.639916  : 0.417968  : 0.575632  : 0.980010   :: 0.539488  
	
	Score IPI for PM25_MOD_QUAD
	 Match     : RMSE      : Pearson   : Kendall   : Spearman  : LFE        :: IPI       
	 0.687539  : 0.374117  : 0.747821  : 0.524258  : 0.695786  : 0.980010   :: 0.710216  
	
	Score IPI for PM25_MOD_EARTH
	 Match     : RMSE      : Pearson   : Kendall   : Spearman  : LFE        :: IPI       
	 0.648910  : 0.337527  : 0.800773  : 0.537126  : 0.713852  : 0.980010   :: 0.723857  
	
	Score IPI for PM10_RAW
	 Match     : RMSE      : Pearson   : Kendall   : Spearman  : LFE        :: IPI       
	 0.486604  : 0.786641  : 0.454199  : 0.269705  : 0.393423  : 0.990388   :: 0.467946  
	
	Score IPI for PM10_MOD_QUAD
	 Match     : RMSE      : Pearson   : Kendall   : Spearman  : LFE        :: IPI       
	 0.742056  : 0.221220  : 0.866073  : 0.612143  : 0.789426  : 0.990388   :: 0.796478  
	
	Score IPI for PM10_MOD_EARTH
	 Match     : RMSE      : Pearson   : Kendall   : Spearman  : LFE        :: IPI       
	 0.763240  : 0.184250  : 0.909195  : 0.657553  : 0.832455  : 0.990388   :: 0.828097  
		
	Results :
	"		RAW  MOD_QUAD  MOD_EARTH
	PM10  0.467946  0.796478   0.828097
	PM25  0.539488  0.710216   0.723857"
	
	Fin du programme



