Feature_selection.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep  7 19:02:25 2018

@author: alex
"""

import numpy as np
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('data.csv', sep = '|')
X = dataset.drop(['Name', 'md5', 'legitimate'], axis = 1).values
y = dataset['legitimate'].values

# Tree-based feature selection:
from sklearn.feature_selection import SelectFromModel
import sklearn.ensemble as ske
fsel = ske.ExtraTreesClassifier().fit(X, y)
model = SelectFromModel(fsel, prefit=True)
X_new = model.transform(X)
nb_features = X_new.shape[1]
indices = np.argsort(fsel.feature_importances_)[::-1][:nb_features]
for f in range(nb_features):
    print("%d. feature %s (%f)" % (f + 1, dataset.columns[2+indices[f]], fsel.feature_importances_[indices[f]]))
features = []
for f in sorted(np.argsort(fsel.feature_importances_)[::-1][:nb_features]):
    features.append(dataset.columns[2+f])
    
    
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size = 0.20, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#------------------------K-NN--------------------------------------
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 3, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
#------------------------------------------------------------------

#-----------------Random-Forest------------------------------------
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 50, criterion = 'entropy')
classifier.fit(X_train, y_train)

#predict the test results
y_pred = classifier.predict(X_test)

#Makeing the confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
#------------------------------------------------------------------

#-------------------XGBoost----------------------------------------
from xgboost import XGBClassifier
classifier = XGBClassifier(max_depth=10, learning_rate=0.1, n_estimators=50)
classifier.fit(X_train, y_train)

#predict the test results
y_pred = classifier.predict(X_test)

#Makeing the confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
#-----------------------------------------------------------------