alik604

# load data and set labels. details admitted
train = pd.read_csv('https://raw.githubusercontent.com/defcom17/NSL_KDD/master/KDDTrain%2B.csv')
test = pd.read_csv('https://raw.githubusercontent.com/defcom17/NSL_KDD/master/KDDTest%2B.csv')
train.columns , test.columns = labels , labels
combined_data = pd.concat([train, test]).drop('difficulty_level', 1)

le = LabelEncoder()
vector = combined_data['attack_type']

print("Attack Vectors:", set(list(vector))) # use print to make it print on single line 

alik604

def EEGNet(nb_classes, Chans = 64, Samples = 128, 
             dropoutRate = 0.5, kernLength = 64, F1 = 8, 
             D = 2, F2 = 16, norm_rate = 0.25, dropoutType = 'Dropout'):
    """ Keras Implementation of EEGNet
    http://iopscience.iop.org/article/10.1088/1741-2552/aace8c/meta

    Inputs:
        
      nb_classes      : int, number of classes to classify
      Chans, Samples  : number of channels and time points in the EEG data

alik604

import pylab as plt
import numpy as np

import matplotlib
import matplotlib.pyplot as plt

import keras
from keras.models import Sequential, Model
from keras.layers import Dense
from keras.optimizers import Adam

alik604

# https://github.com/alik604/Notebooks/blob/master/Data%20Science-Datasets/MNIST/Where_to_add_GaussianNoise_MNIST_.ipynb
# data - MNIST
# note - test_size=0.98... ya...
model = Sequential()
model.add(Conv2D(filters=100, kernel_size=3)) # remove relu
# model.add(GaussianNoise(0.5)) # sandwich between
# GaussianNoise(0.5) here - accuracy: 0.9829 - val_loss: 0.4050 - val_accuracy: 0.9024
model.add(Activation('relu')) # add relu 
# GaussianNoise(0.5) here - accuracy: 0.9900 - val_loss: 1.4703 - val_accuracy: 0.8073
model.add(MaxPooling2D(pool_size=(2, 2)))

alik604

# @credit - kaggle.com/hanzh0420/image-augmentation-with-opencv
import os
print(os.listdir("../input"))
# Input data files are available in the "../input/" directory.
# Any results you write to the current directory are saved as output.

import numpy as np 
#import pandas as pd # pd.read_csv
import cv2
import random

alik604

alik604

%%capture
!pip install scikit-plot
!pip install catboost
!pip install mlxtend
!pip install yfinance
!pip install pyod

import pyod
import yfinance
import xgboost  # xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier

alik604

# imports from matplotlib import pyplot as plt
from matplotlib import pyplot as plt
import pylab
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
%matplotlib inline
%pylab inline

from sklearn.manifold import TSNE
from sklear.decomposition import PCA

alik604

# make dataset to input
def build_dataset(time_series, seq_length):
    dataX = []
    dataY = []
    for i in range(0, len(time_series) - seq_length):
        _x = time_series[i:i + seq_length, :]
        _y = time_series[i + seq_length, [-1]]  # Next close price
        print(_x, "->", _y)
        dataX.append(_x)
        dataY.append(_y)

alik604

import urllib.parse as parse

url = ""
if 'url' not in globals():
    print("Enter URL to parse")
    url = input()


def printDict(data):
    for x in data: