"""
=======================
K-Means Featurization
=======================
Featurize data for boosting the prediction and 
force model to generalization 

"""
# License: BSD-3-clause
# Author: L.Kouadio 
#%% 
# KMeans Featurisation ( KMF) is a surrogate booster to predict permeability 
# coefficient (k) before any drilling construction. Indeed, KMF creates a 
# compressed spatial index of the data which can be fed into the model for ease 
# of learning and enforce the model capability of generalization. A new 
# predictor based on model stacking technique is built with full target k 
# which balances the spatial distribution of k-labels by clustering the original data. 

#%% 
# We start by importing the required modules 
import matplotlib.pyplot as plt

from sklearn.datasets import make_moons
from watex.transformers import featurize_X,  KMeansFeaturizer
from watex.exlib import train_test_split 
from watex.utils import plot_voronoi 


# %% 
# * Build models with a common data sets. (e.g. Moons dataset) 
# We generate 8000 samples of dataset where we divided as 50% training and 50% testing 
# For reproducing the same samples of data, we fixed the `seed`. 
seed = 1 # to reproduce the dataset
X0 , y0 = make_moons(n_samples = 8000, noise= 0.2 , random_state= seed ) 
X0_train, X0_test, y0_train, y0_test = train_test_split (
    X0, y0 , test_size =.5, random_state = seed )

# %% 
# Here, there is a shortcut way to featurize data at once by calling  the 
# transformer :func:`watex.transformers.featurize_X` to transform X data. It 
# could also returns  KMF_model (kmf_hint) if the parameter `return_model` is set 
# to ``True``.  
X0_train_kmf, y0_train_kmf  = featurize_X(
    X0_train, y0_train , n_clusters =200, target_scale =10) 

#%% 
# * Voronoi plot 2D 
# Veronoi plot can be used to visualize the model using hint ( target associated) and without. 
# For a human representation ( 2D), we used the most two features importances 
# of the consistent data set.
 
# Xpca = nPCA( X0_train, n_components= 2 ) # reduce the data set for two most feature components 
# X0_test, y0_test = make_moons(n_samples=2000, noise=0.3)
fig, ax = plt.subplots(2,1, figsize =(7, 7)) 
kmf_hint = KMeansFeaturizer(n_clusters=200, target_scale=10).fit(X0_train,y0_train)
kmf_no_hint = KMeansFeaturizer(n_clusters=200, target_scale=0).fit(X0_train, y0_train)
plot_voronoi ( X0_train, y0_train ,cluster_centers=kmf_hint.cluster_centers_, 
                  fig_title ='KMF with hint', ax = ax [0] )
plot_voronoi ( X0_train, y0_train,cluster_centers=kmf_no_hint.cluster_centers_, 
                  fig_title ='KMF No hint' , ax = ax[1])

# %%
# As shown in the figure above. The number of clusters when target information is 
# missed span too much of the space between the two classes. Commonly KMF demonstrates 
# its usefulness when cluster boundaries align with class boundaries much more closely.