Python Bits and Pieces with Cyber Security: June 2022

Monday, June 20, 2022

Titanic Dataset: Predict who survived and who died in the disaster

The Titanic Dataset found in kaggle is a good way to use my python program I posted earlier which you can read it here. The basic problem is to predict who among the passengers died and survived. And surpringly I got 100% accuracy.

The dataset link: Titanic Kaggle Dataset

The output:

The code:

import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.metrics import ConfusionMatrixDisplay, classification_report, confusion_matrix
from matplotlib import style
style.use('classic')

df = pd.read_csv('tested.csv')

df['Sex']  = df['Sex'].apply(lambda x: 1 if x=='female' else 0)
df['Embarked'] = df['Embarked'].apply(lambda x : 1 if x=='Q' else (0 if x=='S' else 2))
df['Age'] = df['Age'].fillna(0)
df['Fare'] = df['Fare'].fillna(0)
X = df.drop(['PassengerId', 's', 'Name', 'Ticket', 'Cabin'], axis=1)
y = df['s'].apply(lambda x: 1 if x==1 else 0)


y = np.array([[y]])  
y =np.reshape(y, (-1, 1))
Xxx = X.to_numpy()

alphas = [0.001]
hiddenSize = 80

batches = 128
# compute sigmoid nonlinearity
def sigmoid(x):
  if x.any() < 0.0:
    output = 0.0
  elif x.any() > 20.0:
    output = 1.0
  else:
    #return 1.0 / (1.0 + np.exp(-x))
    output = 1/(1+np.exp(-x))
  
  return output
# convert output of sigmoid function to its derivative
def sigmoid_output_to_derivative(output):
    return output*(1-output)
    
# rectified linear function
def relu(x):
	return max(0.0, x.all())

for alpha in alphas:
    print("\nTraining With Alpha:" + str(alpha))
    np.random.seed(1)

    # randomly initialize our weights with mean 0
    synapse_0 = 2*np.random.random((7,hiddenSize)) - 1
    #print(synapse_0.shape)
    synapse_1 = 2*np.random.random((hiddenSize,hiddenSize)) - 1
    #print(synapse_1.shape)
    synapse_2 = 2*np.random.random((hiddenSize,1)) - 1
    #print(synapse_2.shape)
    
    for j in range(10000):

        # Feed forward through layers 0, 1, and 2
        layer_0 = Xxx
        #print(layer_0.shape)
        layer_1 = sigmoid(np.dot(layer_0,synapse_0))
        #print(layer_1.shape)
        layer_2 = sigmoid(np.dot(layer_1,synapse_1))
        #print(layer_2.shape)
        layer_3 = sigmoid(np.dot(layer_2,synapse_2))
        #print(layer_3.shape)
        # how much did we miss the target value?
        layer_3_error = layer_3 - y
        #print(layer_3.shape)
        
        if (j% 1000) == 0:
            print( "Error after "+str(j)+" iterations:" + str(np.mean(np.abs(layer_3_error))))

        # in what direction is the target value?
        # were we really sure? if so, don't change too much.
        layer_3_delta = layer_3_error*sigmoid_output_to_derivative(layer_3)
       
        # how much did each l1 value contribute to the l2 error (according to the weights)?
        layer_2_error = layer_3_delta.dot(synapse_2.T)

        # in what direction is the target l1?
        # were we really sure? if so, don't change too much.
        layer_2_delta = layer_2_error * sigmoid_output_to_derivative(layer_2)
        
        layer_1_error = layer_2_delta.dot(synapse_1.T)
        layer_1_delta = layer_1_error * sigmoid_output_to_derivative(layer_1)
        synapse_2 -= alpha * (layer_1.T.dot(layer_3_delta))
        synapse_1 -= alpha * (layer_2.T.dot(layer_2_delta))
        synapse_0 -= alpha * (layer_0.T.dot(layer_1_delta))

y_hat = [0 if val < 0.5 else 1 for val in layer_3]


print(accuracy_score(y, y_hat))

y_hat = pd.DataFrame(y_hat)
y = pd.DataFrame(y)
cm = confusion_matrix(y, y_hat)
print(cm)

Monday, June 6, 2022

PyQt6: Open only one MDI Child Window at a time with subclassing

This demo program shows how to create a custom event(close event for an MDI Child Window) by using the subclassing approach.

The output:

The code:

import sys
from PyQt6.QtCore import *
from PyQt6.QtGui import *
from PyQt6.QtWidgets import *



sub_open = False


class MainW(QMainWindow):
    count = 0
    def __init__(self, parent=None):
        super(MainW, self).__init__(parent)
        self.mdi = QMdiArea()
        self.setCentralWidget(self.mdi)
        bar = self.menuBar()
        nsub = bar.addMenu("New")
        nsub.addAction("New")
        nsub.triggered[QAction].connect(self.waction)
        self.setWindowTitle("Post 40")

 
    #@f.pyqtSlot(str)
    def wclosed(self, text):
        global sub_open
        sub_open = False
        
        
    def waction(self, q):
        global sub_open
        if sub_open == True:
           return None
        sub_open = True   
        if q.text() == "New":
            MainW.count = MainW.count + 1
            sub = CustomSubWindow() # not QMdiSubWindow
            sub.setWidget(QTextEdit())
            #sub.setAttribute(Qt.WA_DeleteOnClose)
            sub.setWindowTitle("subwindow" + str(MainW.count))
            sub.subClosed.connect(self.wclosed)
            self.mdi.addSubWindow(sub)
            sub.show()

class CustomSubWindow(QMdiSubWindow):    
    subClosed = pyqtSignal(str)
    def closeEvent(self, event):
        self.subClosed.emit("")
        QMdiSubWindow.closeEvent(self, event)
        
        
def main():
   app = QApplication(sys.argv)
   ex = MainW()
   ex.show()
   sys.exit(app.exec())
    
if __name__ == '__main__':
   main()

Sunday, June 5, 2022

Upgrade 05: PyQt6 Desktop App Template

I have added CRUD(create/read/update/delete) for users with search function which is similar to SAP's search help. It can be accessed from the Admin pull-down menu.

The latest version can be downloaded in this github repository: python_desktop_app_template

The Output: