"""
=========================================
Predict FR from DC-Resistivity data  
=========================================

shows some steps for predicting flow rate(FR) from 
DC-ERP and VES data 
"""
# Author: L.Kouadio 
# Licence: BSD-3-clause

#%% 
# Import the required modules 
from watex.datasets import fetch_data 
from watex.datasets import load_gbalo, load_tankesse, load_bagoue
from watex.methods import ResistivityProfiling, VerticalSounding
from watex.methods import DCProfiling, DCSounding
from watex.view.plot import QuickPlot 
from watex.models import pModels
from watex.exlib import train_test_split, accuracy_score

#%%
# The raw DC data is recorded in zenodo   during the drinking water supply 
# campaign (DWSC) in 2012-2014 in `Cote d'Ivoire <https://en.wikipedia.org/wiki/Ivory_Coast>`_ 
# in partnership with global organizations. The objective was 
# to supply 2000 villages with potable water. The geophysical companies 
# were associated with drilling ventures to locate the best position to carry 
# out the drilling operations. The data is free of charge and can be distributed 
# to a third party provided that it cites the authors as a reference. 
# First, I randomly fetch the raw DC profiling and sounding data of one of 
# these localities (Gbalo) during the DWSC project as:

gdata= load_gbalo () 
gdata.head(2)

#%%
# Secondly, I will compute the DC prediction parameters by calling the 
# appropriate methods. For demonstration, I assume that the drilling is 
# performed at station 5(S05) on the survey line, i.e the DC parameters are 
# computed at that station. However, if the station is not specified, 
# the algorithm will find the best conductive zone based on the resistivity 
# values and will store the value in attribute :attr:`sves_` (position to locate a drill). 
# The auto-detection can be used when users need to propose a place to make a 
# drill.  Note that for a real case study, it is recommended to specify the 
# station where the drilling operation was performed through the parameter 
# station. For instance, automatic drilling location detection can predict a 
# station located in a marsh area that is not a suitable place for making a 
# drill. Therefore, to avoid any misinterpretation due to the complexity of 
# the geological area, it is useful to provide the station position. The 
# code snippets are:

erpo = ResistivityProfiling (station = 'S05').fit(gdata )
erpo.conductive_zone_  #(1) 

#%%
erpo.summary(keep_params=True,return_table= True) #(2) 

#%%
# `#(1)` shows the resistivity of the best conductive zone and `#(2)` returns 
# the main prediction parameters.   For reading multiple ERP data, it is 
# suggested to use the :class:`~watex.methods.DCProfiling` method. It performs 
# the same task but each parameter is stored in a line object. Let's go ahead  
# by fetching another locality of ERP data (**Tankesse**) for demonstration:
    
dcpo= DCProfiling (stations =['S05', 'S07'] ).fit(gdata, load_tankesse() ) 
dcpo.nlines_ #(3)

#%%
dcpo.line1.conductive_zone_ #(4)
 
#%%
dcpo.line1.sfi_ 

#%%
# `#(3)` shows the number of the given lines (line 1 for ERP **Gbalo** and 
# line 2 for ERP **Tankesse**. The line 1 value in `#(4)` computed from 
# multiple DC-profiling is similar to the individual computation of the 
# same line in (1) although the first approach gives multiple other features 
# such as the conductive zone visualization with the plotAnomaly method/function. 
# The same scheme for sounding parameter computation can be done with 
# the :class:`~watex.methods.VerticalSounding` and :class:`~watex.methods.electrical.DCSounding`. 
# Note that the latter saves data into a site object and not in line. For instance:
    

gvdata= load_gbalo (kind ='ves') 
veso= VerticalSounding (search= 45 ).fit(gvdata)
dcvo = DCSounding(search=45).fit(gvdata)
veso.ohmic_area_

#%%

dcvo.site1.ohmic_area_ 
#%%
# The `search` parameter passed to the above class is useful to find water
# outside of the pollution. Usually, when the VES  is performed, we are 
# expecting groundwater in the fractured rock in deeper that is outside of 
# any anthropic pollution (Biemi, 1992). Thus, the search parameter indicates 
# where the search of the fracture zone in deeper must start. For instance, 
# ``search=45`` tells the algorithm to start detecting the fracture zone from 45m 
# to deeper (Figure below). In addition, when computing the prediction parameter 
# like ohmic-area (ohmS) of multiple sounding data for prediction purposes, 
# it is strongly recommended to settle the search argument unchangeable 
# for all sounding sites. 

veso.plotOhmicArea(fbtw=True , style ='dark_background')

#%%
# Furthermore, the DC parameters from the :meth:`~watex.methods.VerticalSounding.summary` 
# methods are combined with the geological data of the survey area to 
# compose the predictor :math:`X`. One of the interesting features of computing the 
# DC parameters for prediction purposes is to discuss the features. 
# This is possible with the :meth:`~watex.view.QuickPlot.discussingfeatures` 
# method of :class:`~watex.view.QuickPlot`. An example of code snippets for 
# discussing plot is given below using the complete Bagoue DC parameters  
# computed from 431 boreholes:: 

data = load_bagoue ().frame 
qkObj = QuickPlot(  leg_kws={'loc':'upper right'},
          fig_title = '`sfi` vs`ohmS|`geol`',
            ) 
qkObj.tname='flow' # target the DC-flow rate prediction dataset
qkObj.mapflow=True  # to hold category FR0, FR1 etc..
qkObj.fit(data) 
sns_pkws={'aspect':2 , 
          "height": 2, 
                  }
map_kws={'edgecolor':"w"}   
qkObj.discussingfeatures(features =['ohmS', 'sfi','geol', 'flow'],
                           map_kws=map_kws,  **sns_pkws
                         ) 
#%%
# As a comment of discussing features above. Figure shows  at a glance  
# that most of the drillings carried out on granites have an `FR` of around 
# :math:`1 m^3/hr` (FR1: 0< FR <=1). With these kinds of 
# flows, it is obvious that the boreholes will be unproductive (unsustainable) 
# within a few years.  However, the volcano-sedimentary schists seem the most 
# suitable geological structure with an `FR` greater than :math:`3m^3/hr`. 
# However, the wide fractures on these formations (explained by `ohmS > 1500`) 
# do not mean that they should be more productive since all of the drillings performed 
# on the wide fracture do not always give a productive FR ( :math:`FR>3m^3/hr`) 
# contrary to the narrow fractures (around `1000 ohmS`). As a result, it is reliable to 
# consider this approach during a future DWSC such as the geology of the area 
# and also the rock fracturing ratio computed thanks to the parameters 
# sfi and ohmS.  
# The following examples demonstrate how to predict FR with a complete 
# preprocessed dataset of DC parameters. The pre-trained models (optimal 
# model with acceptable variance and bias) of :class:`~watex.models.pModels` 
# can be used to predict FR as: 
    

X, y = fetch_data ('bagoue prepared data')
X_train, X_test, y_train, y_test = train_test_split (
    X, y, test_size =0.2 ) 
pmo = pModels (model='svm', kernel ='poly').fit (X_train, y_train ) 
y_pred =  pmo.predict (X_test ) 
accuracy_score (y_test, y_pred )

#%%
# Note the pre-trained estimator is stored in an attribute :attr:`estimator_`. 
# For instance, the pre-trained SVM model can be retrieved using 
pmo.estimator_ 

#%%
# or 

pmo.SVM.poly.best_estimator_

#%%
# If the model is not a kernel machine, the kernel attribute is discarded instead. For example:
pmo=pModels(model ='xgboost').fit (X_train, y_train )  

# where xgboost stands for extreme gradient boosting machine(Friedman, 2001) 
# and  the pre-trained estimator could be retrieved as 
pmo.XGB.best_estimator_
 
#%%
# Let make a new prediction with XGB 
y_pred =  pmo.predict (X_test ) 
accuracy_score (y_test, y_pred )