#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep  2 11:30:18 2022

@author: tanu
"""
#https://github.com/yuneeham/PCA-and-feature-selection_sklearn/blob/main/Report%20-%20PCA%20and%20Feature%20Selection.pdf

#Load Libraries 
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn import datasets
from sklearn.preprocessing import scale
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
from sklearn.feature_selection import SelectFromModel

#Load Data 
df = pd.read_csv("/home/tanu/Downloads/data.csv")
X = df.loc[:, ' ROA(C) before interest and depreciation before interest':' Equity to Liability'].values
y = df.loc[:,['Bankrupt?']].values

fn = df.loc[:, ' ROA(C) before interest and depreciation before interest':' Equity to Liability'].keys()

#Scaler/normalize 
scaler = StandardScaler()
Xn = scaler.fit_transform(X)

#PCA 
pca_prep = PCA().fit(Xn)
pca_prep.n_components_


#PCA Explained Variance 
pca_prep.explained_variance_
plt.plot(pca_prep.explained_variance_ratio_)

#Graph plot - PCA components 
plt.plot(pca_prep.explained_variance_ratio_)
plt.xlabel('k number of components')
plt.ylabel('Explained variance')
plt.grid(True)
plt.show()

#Number of components 
n_pc = 17
pca = PCA(n_components = n_pc).fit(Xn)
Xp = pca.transform(Xn)
print(f'After PCA, we use {pca.n_components_} components. \n')


# Split the data into training and testing subsets.
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size =.2,random_state=1234,stratify=y)
Xp_train, Xp_test, yp_train, yp_test = train_test_split(Xp,y,test_size =.2,random_state=1234,stratify=y)


#Random Forest Model
rfcm = RandomForestClassifier().fit(X_train, y_train) #Original Data
rfcm_p = RandomForestClassifier().fit(Xp_train, yp_train) #Reduced Data


#Prediction
y_pred = rfcm.predict(X_test) 
y_pred_p = rfcm_p.predict(Xp_test) 


# Compare the performance of each mode
report_original = classification_report(y_test, y_pred)
report_pca = classification_report(yp_test, y_pred_p)
print(f'Classification Report - original\n{report_original}')
print(f'Classification Report - pca\n{report_pca}')



## Feature selection and performance comparison 

# Draw a bar chart to see the sorted importance values with feature names.
# Horizontal Bar Chart 
# %matplotlib auto
# %matplotlib inline

importances = rfcm.feature_importances_
np.sum(importances)
plt.barh(fn,importances)

df_importances = pd.DataFrame(data=importances, index=fn, 
                              columns=['importance_value'])
df_importances.sort_values(by = 'importance_value', ascending=True, 
                            inplace=True)

plt.barh(df_importances.index,df_importances.importance_value)


# Build a model with a subset of those features.  
selector = SelectFromModel(estimator=RandomForestClassifier(),threshold=0.015)
X_reduced = selector.fit_transform(X,y)
selector.threshold_ 
selected_TF = selector.get_support()
print(f'\n** {selected_TF.sum()} features are selected.')  
X_reduced.shape


# Show those selected features.
selected_features = []
for i,j in zip(selected_TF, fn):
    if i: selected_features.append(j)
print(f'Selected Features: {selected_features}') 


# First 5 features
print(selected_features[0:5])


# Build a model using those reduced number of features.
X_reduced_train, X_reduced_test, y_reduced_train, y_reduced_test \
       = train_test_split(X_reduced,y,test_size =.3, stratify=y)


# Build a model with the reduced number of features.
rfcm2 = RandomForestClassifier().fit(X_reduced_train, y_reduced_train)
y_reduced_pred = rfcm2.predict(X_reduced_test)


#Classification for Reduced Data 
print('\nClassification Report after feature reduction\n')
print(metrics.classification_report(y_reduced_test,y_reduced_pred))