complete_code.py

"""

@author: Hafiz Muhammad Muddassir Shahid
"""


from random import seed
from random import randrange
from random import random
from csv import reader
from math import exp
from sklearn.metrics import confusion_matrix
from sklearn.metrics import cohen_kappa_score
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import inter
import numpy as np
import pandas as pd
import csv
 

def loadCsv(filename):
        trainSet = []
        
        lines = csv.reader(open(filename, 'r'))
        dataset = list(lines)
        for i in range(len(dataset)):
                for j in range(4):
                        #print("DATA {}".format(dataset[i]))
                        dataset[i][j] = float(dataset[i][j])
                trainSet.append(dataset[i])
        return trainSet

def minmax(dataset):
        minmax = list()
        stats = [[min(column), max(column)] for column in zip(*dataset)]
        return stats
 

def normalize(dataset, minmax):
        for row in dataset:
                for i in range(len(row)-1):
                        row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0])
 

def column_to_float(dataset, column):
        for row in dataset:
                try:
                        row[column] = float(row[column])
                except ValueError:
                        print("Error with row",column,":",row[column])
                        pass
 

def column_to_int(dataset, column):
        class_values = [row[column] for row in dataset]
        unique = set(class_values)
        lookup = dict()
        for i, value in enumerate(unique):
                lookup[value] = i
        for row in dataset:
                row[column] = lookup[row[column]]
        return lookup
 
 

def cross_validation_split(dataset, n_folds):
        dataset_split = list()
        dataset_copy = list(dataset)
        fold_size = int(len(dataset) / n_folds)
        for i in range(n_folds):
                fold = list()
                while len(fold) < fold_size:
                        index = randrange(len(dataset_copy))
                        fold.append(dataset_copy.pop(index))
                dataset_split.append(fold)
        return dataset_split
 

def accuracy_met(actual, predicted):
        correct = 0
        for i in range(len(actual)):
                if actual[i] == predicted[i]:
                        correct += 1
        return correct / float(len(actual)) * 100.0
 

def run_algorithm(dataset, algorithm, n_folds, *args):
        
        folds = cross_validation_split(dataset, n_folds)
        #for fold in folds:
                #print("Fold {} \n \n".format(fold))
        scores = list()
        for fold in folds:
                #print("Test Fold {} \n \n".format(fold))
                train_set = list(folds)
                train_set.remove(fold)
                train_set = sum(train_set, [])
                test_set = list()
                for row in fold:
                        row_copy = list(row)
                        test_set.append(row_copy)
                        row_copy[-1] = None
                predicted = algorithm(train_set, test_set, *args)
                actual = [row[-1] for row in fold]
                accuracy = accuracy_met(actual, predicted)
                cm = confusion_matrix(actual, predicted)
                print('\n'.join([''.join(['{:4}'.format(item) for item in row]) for row in cm]))
                #confusionmatrix = np.matrix(cm)
                FP = cm.sum(axis=0) - np.diag(cm)
                FN = cm.sum(axis=1) - np.diag(cm)
                TP = np.diag(cm)
                TN = cm.sum() - (FP + FN + TP)
                print('False Positives\n {}'.format(FP))
                print('False Negetives\n {}'.format(FN))
                print('True Positives\n {}'.format(TP))
                print('True Negetives\n {}'.format(TN))
                TPR = TP/(TP+FN)
                print('Sensitivity \n {}'.format(TPR))
                TNR = TN/(TN+FP)
                print('Specificity \n {}'.format(TNR))
                Precision = TP/(TP+FP)
                print('Precision \n {}'.format(Precision))
                Recall = TP/(TP+FN)
                print('Recall \n {}'.format(Recall))
                Acc = (TP+TN)/(TP+TN+FP+FN)
                print('Áccuracy \n{}'.format(Acc))
                Fscore = 2*(Precision*Recall)/(Precision+Recall)
                print('FScore \n{}'.format(Fscore))
                k=cohen_kappa_score(actual, predicted)
                print('Çohen Kappa \n{}'.format(k))
                scores.append(accuracy)
        return scores

def activate(weights, inputs):
        activation = weights[-1]
        for i in range(len(weights)-1):
                activation += weights[i] * inputs[i]
        return activation
 

def transfer(activation):
        return 1.0 / (1.0 + exp(-activation))
 

def forward_propagate(network, row):
        inputs = row
        for layer in network:
                new_inputs = []
                for neuron in layer:
                        activation = activate(neuron['weights'], inputs)
                        neuron['output'] = transfer(activation)
                        new_inputs.append(neuron['output'])
                inputs = new_inputs
        return inputs

def transfer_derivative(output):
        return output * (1.0 - output)
 

def backward_propagate_error(network, expected):
        for i in reversed(range(len(network))):
                layer = network[i]
                errors = list()
                if i != len(network)-1:
                        for j in range(len(layer)):
                                error = 0.0
                                for neuron in network[i + 1]:
                                        error += (neuron['weights'][j] * neuron['delta'])
                                errors.append(error)
                else:
                        for j in range(len(layer)):
                                neuron = layer[j]
                                errors.append(expected[j] - neuron['output'])
                for j in range(len(layer)):
                        neuron = layer[j]
                        neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])
 

def update_weights(network, row, l_rate):
        for i in range(len(network)):
                inputs = row[:-1]                
                if i != 0:
                        inputs = [neuron['output'] for neuron in network[i - 1]]
                for neuron in network[i]:
                        for j in range(len(inputs)):
                                temp = l_rate * neuron['delta'] * inputs[j] + mu * neuron['prev'][j]
                                
                                neuron['weights'][j] += temp
                                #print("neuron weight{} \n".format(neuron['weights'][j]))
                                neuron['prev'][j] = temp
                        temp = l_rate * neuron['delta'] + mu * neuron['prev'][-1]
                        neuron['weights'][-1] += temp
                        neuron['prev'][-1] = temp
                                
 
# Train a network for a fixed number of epochs
def train_network(network, train, l_rate, n_epoch, n_outputs):
        for epoch in range(n_epoch):
                for row in train:
                        outputs = forward_propagate(network, row)
                        #print(network)
                        expected = [0 for i in range(n_outputs)]
                        expected[row[-1]] = 1
                        #print("expected row{}\n".format(expected))
                        backward_propagate_error(network, expected)
                        update_weights(network, row, l_rate)
 

def initialize_network(n_inputs, n_hidden, n_outputs):
        network = list()
        hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)], 'prev':[0 for i in range(n_inputs+1)]} for i in range(n_hidden)]        
        network.append(hidden_layer)
        hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)], 'prev':[0 for i in range(n_inputs+1)]} for i in range(n_hidden)]        
        network.append(hidden_layer)
        output_layer = [{'weights':[random() for i in range(n_hidden + 1)],'prev':[0 for i in range(n_hidden+1)]} for i in range(n_outputs)]
        network.append(output_layer)
        #print(network)
        return network
 

def predict(network, row):
        outputs = forward_propagate(network, row)
        return outputs.index(max(outputs))
 

def back_propagation(train, test, l_rate, n_epoch, n_hidden):
        n_inputs = len(train[0]) - 1
        n_outputs = len(set([row[-1] for row in train]))
        network = initialize_network(n_inputs, n_hidden, n_outputs)
        train_network(network, train, l_rate, n_epoch, n_outputs)
        #print("network {}\n".format(network))
        predictions = list()
        for row in test:
                prediction = predict(network, row)
                predictions.append(prediction)
        return(predictions)
 

seed(1)

filename = 'filename'
dataset = loadCsv(filename)
for i in range(len(dataset[0])-1):
        column_to_float(dataset, i)
# convert class column to integers
column_to_int(dataset, len(dataset[0])-1)
# normalize input variables
minmax = minmax(dataset)
normalize(dataset, minmax)
# evaluate algorithm
n_folds = 5 # 10
l_rate = 0.1
mu=0.001
n_epoch = 15000
n_hidden = 6
scores = run_algorithm(dataset, back_propagation, n_folds, l_rate, n_epoch, n_hidden)

print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))



datum = pd.read_csv('file_name.csv')
X= datum.iloc[:,0:4].values
y = datum.iloc[:,-1].values

y = label_binarize(y, classes=[1,2,3,4,5,6])
n_classes = 6

# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# classifier
clf = OneVsRestClassifier(LinearSVC(random_state=0))
y_score = clf.fit(X_train, y_train).decision_function(X_test)

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# Plot of a ROC curve for a specific class
for i in range(n_classes):
    plt.figure()
    plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()
    
print(__doc__)

""" P;otting ROC Curves"""

data_set = pd.read_csv('data_review_1.csv')
X= data_set.iloc[:,0:4].values
y = data_set.iloc[:,-1].values

#y = label_binarize(y, classes=[1,2,3,4,5,6])
n_classes = 6
# Binarize the output
y = label_binarize(y, classes=[1, 2, 3, 4, 5, 6])
n_classes = y.shape[1]

random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2,
                                                    random_state=0)

classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                 random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)

#  ROC curve and AUC for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

#  micro-average ROC curve and AUC 
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

plt.figure()
lw = 2
plt.plot(fpr[2], tpr[2], color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()




all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))


mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()

all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))


mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])


mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()