File size: 2,873 Bytes
9650bca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 26 16:31:20 2019
@author: ELİF NUR
"""
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing
def loadData(fromPath,LabelColumnName,labelCount):#This method to read the csv file and change the label feature
data_=pd.read_csv(fromPath)
if labelCount==2:
dataset=data_
dataset[LabelColumnName]=dataset[LabelColumnName].apply({'DoS':'Anormal','BENIGN':'Normal' ,'DDoS':'Anormal', 'PortScan':'Anormal'}.get)
else:
dataset=data_
data=dataset[LabelColumnName].value_counts()
data.plot(kind='pie')
featureList= dataset.drop([LabelColumnName],axis=1).columns
return dataset,featureList
def datasetSplit(df,LabelColumnName):#This method is to separate the dataset as X and y.
labelencoder = LabelEncoder()
df.iloc[:, -1] = labelencoder.fit_transform(df.iloc[:, -1])
X = df.drop([LabelColumnName],axis=1)
X = np.array(X)
X = X.T
for column in X: #Control of values in X
median = np.nanmedian(column)
column[np.isnan(column)] = median
column[column == np.inf] = 0
column[column == -np.inf] = 0
X = X.T
scaler = preprocessing.MinMaxScaler()
X= scaler.fit_transform(X)
y=df[[LabelColumnName]]
return X,y
def train_test_dataset(df): #This method is to separate the dataset as X_train,X_test,y_train and y_test.
labelencoder = LabelEncoder()
df.iloc[:, -1] = labelencoder.fit_transform(df.iloc[:, -1])
X = df.drop([LabelColumnName],axis=1)
y=df[[LabelColumnName]]
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size = 0.7, test_size = 0.3, random_state = 0, stratify = y)
X_train = np.array(X_train)
X_train = X_train.T
for column in X_train:
median = np.nanmedian(column)
column[np.isnan(column)] = median
column[column == np.inf] = 0
column[column == -np.inf] = 0
X_train = X_train.T
y_train = np.array(y_train)
y_train = y_train.T
for column in y_train:
median = np.nanmedian(column)
column[np.isnan(column)] = median
column[column == np.inf] = 0
column[column == -np.inf] = 0
y_train = y_train.T
X_test = np.array(X_test)
X_test = X_test.T
for column in X_test:
median = np.nanmedian(column)
column[np.isnan(column)] = median
column[column == np.inf] = 0
column[column == -np.inf] = 0
X_test = X_test.T
y_test = np.array(y_test)
y_test = y_test.T
for column in y_test:
median = np.nanmedian(column)
column[np.isnan(column)] = median
column[column == np.inf] = 0
column[column == -np.inf] = 0
y_test = y_test.T
return X_train, X_test, y_train, y_test
|