# Importing the libraries

import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split, cross_val_score

from sklearn.model_selection import GridSearchCV

from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve

from sklearn.svm import SVC

import matplotlib.pyplot as plt

from sklearn.metrics import classification_report

# 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)

#svm with grid search

svm = SVC(random_state = 42)

parameters = {'kernel':('linear', 'rbf'), 'C':(1,0.25,0.5,0.75),

              'gamma': (1,2,3,'auto'),'decision_function_shape':('ovo','ovr'),

              'shrinking':(True,False)}

scores = ['precision', 'recall']

for score in scores:

    print("# Tuning hyper-parameters for %s" % score)

    print()

    svm = GridSearchCV(SVC(), parameters, cv=5,

                       scoring='%s_macro' % score)

    svm.fit(X_train, y_train)

    print("Best parameters set found on development set:")

    print()

    print(svm.best_params_)

    print()

    print("Grid scores on development set:")

    print()

    means = svm.cv_results_['mean_test_score']

    stds = svm.cv_results_['std_test_score']

    for mean, std, params in zip(means, stds, svm.cv_results_['params']):

        print("%0.3f (+/-%0.03f) for %r"

              % (mean, std * 2, params))

    print()

    print("Detailed classification report:")

    print()

    print("The model is trained on the full development set.")

    print("The scores are computed on the full evaluation set.")

    print()

    y_true, y_pred = y_test, svm.predict(X_test)

    print(classification_report(y_true, y_pred))

    print()

svm_model = SVC(kernel='rbf', C=100, gamma = 0.0001, random_state=42)

svm_model.fit(X_train, y_train)

spred = svm_model.predict(X_test)

print ('Accuracy with SVM {0}'.format(accuracy_score(spred, y_test) * 100))

# 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)