import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sn

# Machine Learning libraries

from sklearn.ensemble import RandomForestClassifier

from sklearn.svm import SVC

from sklearn.linear_model import LogisticRegression

from sklearn.neighbors import KNeighborsClassifier

# Model evaluation libraries

from sklearn.model_selection import cross_val_score

from sklearn.metrics import accuracy_score

from sklearn.metrics import confusion_matrix

### Random Forest Classfier

rf = RandomForestClassifier()

### Support Vector Classifier

svc = SVC()

### Logistic Regression 

lr = LogisticRegression(solver='liblinear')

### K Nearest Neighbors

knn = KNeighborsClassifier()

x_data = np.load('featurized_data.npy', allow_pickle = True)

y_data = np.load('labels.npy', allow_pickle = True)

if __name__ == "__main__":

    rf_f_scores = cross_val_score(rf, x_data, y_data, cv=5)

    rf_f_acc = np.mean(rf_f_scores)

    svc_f_scores = cross_val_score(svc, x_data, y_data, cv=5)

    svc_f_acc = np.mean(svc_f_scores)

    lr_f_scores = cross_val_score(lr, x_data, y_data, cv=5)

    lr_f_acc = np.mean(lr_f_scores)

    knn_f_scores = cross_val_score(knn, x_data, y_data, cv=5)

    knn_f_acc = np.mean(knn_f_scores)

    # Visualize performance

    data_r = {'RF':rf_f_acc, 'SVC':svc_f_acc, 'LR':lr_f_acc, 'kNN':knn_f_acc}

    algorithm = list(data_r.keys())

    accuracy = list(data_r.values())

    fig = plt.figure(figsize = (10, 5))

    plt.bar(algorithm, accuracy, color ='red', width = 0.4)

    plt.xlabel("ML models", fontsize = 18)

    plt.ylabel("5 fold accuracy", fontsize = 18)

    plt.title("Result", fontsize = 18)

    plt.xticks(fontsize = 14)

    plt.yticks(fontsize = 14)

    plt.ylim([0, 1])

    plt.show()

    print('Random Forest Accuracy: ', rf_f_acc*100)

    print('Support Vector Classifier Accuracy: ', svc_f_acc*100)

    print('Logistic Regression Accuracy: ', lr_f_acc*100)

    print('K Nearest Neighbours Accuracy: ', knn_f_acc*100)

    ### Retraining RF on shuffeled data

    X_train = []

    X_test = []

    y_train = []

    y_test = []

    for i in range(7):

        current_class_data = x_data[i*20: i*20 + 20]

        X_train.append(current_class_data[0: 16])

        X_test.append(current_class_data[16: ])

        current_class_labels = y_data[i*20: i*20 + 20]

        y_train.append(current_class_labels[0: 16])

        y_test.append(current_class_labels[16: ])

    X_train = np.array(X_train).reshape(-1, 320)

    X_test = np.array(X_test).reshape(-1, 320)

    y_train = np.array(y_train).reshape(-1)

    y_test = np.array(y_test).reshape(-1)

    rf = RandomForestClassifier()

    rf.fit(X_train, y_train)

    predictions = rf.predict(X_test)

    accuracy = accuracy_score(predictions, y_test)

    print('Accuracy: ', accuracy)

    # Confusion Matrix

    conf_matrix = confusion_matrix(y_test, predictions)

    df_cm = pd.DataFrame(conf_matrix, index = [i for i in "0123456"], columns = [i for i in "0123456"])

    plt.figure(figsize = (10,7))

    sn.set(font_scale=1.4)

    sn.heatmap(df_cm, annot=True, annot_kws={"size": 16})

    plt.ylabel('True label')

    plt.xlabel('Predicted label')

    plt.show()

    # Dropping class 4 Datapoints

    idx = (y_data != 4)

    x_data = x_data[idx]

    y_data = np.array([i for i in range(6) for j in range(20)])

    #Retrain shallow ML algorithms without class 4

    rf = RandomForestClassifier()

    rf_f_scores = cross_val_score(rf, x_data, y_data, cv=5)

    rf_f_acc = np.mean(rf_f_scores)

    svc = SVC()

    svc_f_scores = cross_val_score(svc, x_data, y_data, cv=5)

    svc_f_acc = np.mean(svc_f_scores)

    lr = LogisticRegression(solver='liblinear')

    lr_f_scores = cross_val_score(lr, x_data, y_data, cv=5)

    lr_f_acc = np.mean(lr_f_scores)

    knn = KNeighborsClassifier()

    knn_f_scores = cross_val_score(knn, x_data, y_data, cv=5)

    knn_f_acc = np.mean(knn_f_scores)

    data_r = {'RF':rf_f_acc, 'SVC':svc_f_acc, 'LR':lr_f_acc, 'kNN':knn_f_acc}

    algorithm = list(data_r.keys())

    accuracy = list(data_r.values())

    fig = plt.figure(figsize = (10, 5))

    plt.bar(algorithm, accuracy, color ='red', width = 0.4)

    plt.xlabel("ML models", fontsize = 18)

    plt.ylabel("5 fold accuracy", fontsize = 18)

    plt.title("Result", fontsize = 18)

    plt.xticks(fontsize = 14)

    plt.yticks(fontsize = 14)

    plt.ylim([0, 1])

    plt.show()

    print('Random Forest Accuracy: ', rf_f_acc*100)

    print('Support Vector Classifier Accuracy: ', svc_f_acc*100)

    print('Logistic Regression Accuracy: ', lr_f_acc*100)

    print('K Nearest Neighbours Accuracy: ', knn_f_acc*100)

    # Creating train and test set without class 4

    X_train = []

    X_test = []

    y_train = []

    y_test = []

    for i in range(6):

        current_class_data = x_data[i*20: i*20 + 20]

        X_train.append(current_class_data[0: 16])

        X_test.append(current_class_data[16: ])

        current_class_labels = y_data[i*20: i*20 + 20]

        y_train.append(current_class_labels[0: 16])

        y_test.append(current_class_labels[16: ])

    X_train = np.array(X_train).reshape(-1, 320)

    X_test = np.array(X_test).reshape(-1, 320)

    y_train = np.array(y_train).reshape(-1)

    y_test = np.array(y_test).reshape(-1)

    # Training the best model (Random Forest)

    rf = RandomForestClassifier()

    rf.fit(X_train, y_train)

    predictions = rf.predict(X_test)

    accuracy = accuracy_score(predictions, y_test)

    print('Accuracy: ', accuracy)

    # See new confusion matrix of best model without class 4

    conf_matrix = confusion_matrix(y_test, predictions)

    df_cm = pd.DataFrame(conf_matrix, index = [i for i in "012356"], columns = [i for i in "012356"])

    plt.figure(figsize = (10,7))

    sn.set(font_scale=1.4)

    sn.heatmap(df_cm, annot=True, annot_kws={"size": 16})

    plt.ylabel('True label')

    plt.xlabel('Predicted label')

    plt.show()