Upload data.py

data.py

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+# -*- 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
+