"""
=================================================
Plot Receiving Operating Characteristic (ROC)
=================================================

visualizes the ROC classifier(s) performance. 
"""
# Author: L.Kouadio 
# Licence: BSD-3-clause

#%% 
# it can plot multiple classifiers at once. If multiple classifiers are 
# given, each classifier must be a tuple of  
# ``( <name>, classifier>, <method>)``. Refer to 
# :meth:`~watex.view.mlplot.EvalPlot.plotROC`

# (1) Plot ROC for single classifier 
# note that plot can be customize 

from watex.exlib.sklearn import SGDClassifier, RandomForestClassifier
from watex.datasets.dload import load_bagoue 
from watex.utils import cattarget 
from watex.view.mlplot import EvalPlot 
X , y = load_bagoue(as_frame =True )
sgd_clf = SGDClassifier(random_state= 42) # our estimator 
b= EvalPlot(scale = True , encode_labels=True)
b.lc='b'
b.lw=7
b.font_size =7. 
b.fit_transform(X, y)
# binarize the label b.y 
ybin = cattarget(b.y, labels= 2 ) # can also use labels =[0, 1]
b.y = ybin 
# plot ROC 
b.plotROC(sgd_clf , label =1) # class=1

#%%
# (2)-> Plot ROC for multiple classifiers 
# examples of RandomForest and SDG 

b= EvalPlot(scale = True , encode_labels=True, 
                lw =3., lc=(.9, 0, .8), font_size=7 )
sgd_clf = SGDClassifier(random_state= 42)
forest_clf =RandomForestClassifier(random_state=42)
b.fit_transform(X, y)
# binarize the label b.y 
ybin = cattarget(b.y, labels= 2 ) # can also use labels =[0, 1]
b.y = ybin 
clfs =[('sgd', sgd_clf, "decision_function" ), 
        ('forest', forest_clf, "predict_proba")]
b.plotROC (clfs =clfs , label =1 )