# Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.model_selection import GridSearchCV

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve

# Importing the dataset

dataset = pd.read_csv('../Dataset/diabetes.csv')

X = dataset.iloc[:, :-1].values

y = dataset.iloc[:, 8].values

# Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42)

# Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

# Parameter evaluation

treeclf = DecisionTreeClassifier(random_state=42)

parameters = {'max_depth': [6, 7, 8, 9],

              'min_samples_split': [2, 3, 4, 5],

              'max_features': [1, 2, 3, 4]

}

gridsearch=GridSearchCV(treeclf, parameters, cv=100, scoring='roc_auc')

gridsearch.fit(X,y)

print(gridsearch.best_params_)

print(gridsearch.best_score_)

# Adjusting development threshold

tree = DecisionTreeClassifier(max_depth = 6, max_features = 4, 

                              min_samples_split = 5, 

                              random_state=42)

X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=42)

tree.fit(X_train, y_train)

print("Accuracy on training set: {:.3f}".format(tree.score(X_train, y_train)))

print("Accuracy on test set: {:.3f}".format(tree.score(X_test, y_test)))

# Predicting the Test set results

y_pred = tree.predict(X_test) 

# Making the Confusion Matrix

from sklearn.metrics import classification_report, confusion_matrix

cm = confusion_matrix(y_test, y_pred)

print('TP - True Negative {}'.format(cm[0,0]))

print('FP - False Positive {}'.format(cm[0,1]))

print('FN - False Negative {}'.format(cm[1,0]))

print('TP - True Positive {}'.format(cm[1,1]))

print('Accuracy Rate: {}'.format(np.divide(np.sum([cm[0,0],cm[1,1]]),np.sum(cm))))

print('Misclassification Rate: {}'.format(np.divide(np.sum([cm[0,1],cm[1,0]]),np.sum(cm))))

round(roc_auc_score(y_test,y_pred),5)