def KNN():
print("\n--------------------------------")
print("KNN")
model = KNeighborsClassifier(n_neighbors=3)
model.max_iter = 20000
model.fit(train_x, train_y.values.ravel())
# Report how successful model was
print("Test : %s" % model.score(test_x, test_y.values.ravel()))
print("Train: %s" % model.score(train_x, train_y.values.ravel()))
predictions = model.predict(test_x)
# Confusion Matrix testing data
print("\nCM - Testing Model")
cm = metrics.confusion_matrix(test_y, predictions)
print(cm)
# Confusion Matrix training data
trainpred = model.predict(train_x)
tcm = metrics.confusion_matrix(train_y, trainpred)
print("\nCM - Training Model")
print(tcm)
# Do report writing
# Write Preditions to CSV file
report_csv('Result-KNN.csv', predictions)
report_matrix('KNN', cm, model.score(train_x, train_y.values.ravel()), tcm, model.score(test_x, test_y.values.ravel()))
#import seaborn as sb
import matplotlib.pyplot as plt
import sklearn
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import scale
from sklearn import metrics
#from collections import Counter
training = pd.read_csv('UNSW_NB15_training-set.csv')
testing = pd.read_csv('UNSW_NB15_testing-set.csv')
#print(training.head())
#print(training.corr())
train_x = training.loc[:, [
'sttl', 'dmean', 'rate', 'dwin', 'ct_src_dport_ltm', 'ct_dst_sport_ltm',
'swin', 'dload'
]]
train_y = training.loc[:, ['label']]
test_x = testing.loc[:, [
'sttl', 'dmean', 'rate', 'dwin', 'ct_src_dport_ltm', 'ct_dst_sport_ltm',
'swin', 'dload'
]]
test_y = testing.loc[:, ['label']]
model = KNeighborsClassifier(n_neighbors=3)
model.max_iter = 20000
model.fit(train_x, train_y.values.ravel())
print(model.score(test_x, test_y.values.ravel()))
predictions = model.predict(test_x)
cm = metrics.confusion_matrix(test_y, predictions)
print(cm)
