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""" |
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Created on Fri Apr 26 16:31:20 2019 |
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@author: ELİF NUR |
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""" |
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import pandas as pd |
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import numpy as np |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import LabelEncoder |
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from sklearn import preprocessing |
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def loadData(fromPath,LabelColumnName,labelCount): |
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data_=pd.read_csv(fromPath) |
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if labelCount==2: |
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dataset=data_ |
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dataset[LabelColumnName]=dataset[LabelColumnName].apply({'DoS':'Anormal','BENIGN':'Normal' ,'DDoS':'Anormal', 'PortScan':'Anormal'}.get) |
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else: |
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dataset=data_ |
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data=dataset[LabelColumnName].value_counts() |
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data.plot(kind='pie') |
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featureList= dataset.drop([LabelColumnName],axis=1).columns |
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return dataset,featureList |
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def datasetSplit(df,LabelColumnName): |
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labelencoder = LabelEncoder() |
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df.iloc[:, -1] = labelencoder.fit_transform(df.iloc[:, -1]) |
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X = df.drop([LabelColumnName],axis=1) |
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X = np.array(X) |
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X = X.T |
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for column in X: |
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median = np.nanmedian(column) |
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column[np.isnan(column)] = median |
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column[column == np.inf] = 0 |
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column[column == -np.inf] = 0 |
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X = X.T |
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scaler = preprocessing.MinMaxScaler() |
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X= scaler.fit_transform(X) |
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y=df[[LabelColumnName]] |
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return X,y |
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def train_test_dataset(df): |
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labelencoder = LabelEncoder() |
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df.iloc[:, -1] = labelencoder.fit_transform(df.iloc[:, -1]) |
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X = df.drop([LabelColumnName],axis=1) |
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y=df[[LabelColumnName]] |
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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) |
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X_train = np.array(X_train) |
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X_train = X_train.T |
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for column in X_train: |
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median = np.nanmedian(column) |
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column[np.isnan(column)] = median |
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column[column == np.inf] = 0 |
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column[column == -np.inf] = 0 |
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X_train = X_train.T |
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y_train = np.array(y_train) |
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y_train = y_train.T |
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for column in y_train: |
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median = np.nanmedian(column) |
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column[np.isnan(column)] = median |
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column[column == np.inf] = 0 |
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column[column == -np.inf] = 0 |
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y_train = y_train.T |
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X_test = np.array(X_test) |
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X_test = X_test.T |
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for column in X_test: |
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median = np.nanmedian(column) |
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column[np.isnan(column)] = median |
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column[column == np.inf] = 0 |
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column[column == -np.inf] = 0 |
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X_test = X_test.T |
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y_test = np.array(y_test) |
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y_test = y_test.T |
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for column in y_test: |
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median = np.nanmedian(column) |
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column[np.isnan(column)] = median |
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column[column == np.inf] = 0 |
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column[column == -np.inf] = 0 |
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y_test = y_test.T |
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return X_train, X_test, y_train, y_test |
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