# coding: utf-8

import numpy as np  
import pandas as pd  
from sklearn import utils  
import matplotlib


data = pd.read_csv('kddcup_data_10_percent.csv', low_memory=False)


data = data[data['service'] == "http"]  
data = data[data["logged_in"] == 1]

relevant_features = [  
    "duration",
    "src_bytes",
    "dst_bytes",
    "label"
]

data = data[relevant_features ]


data["duration"] = np.log((data["duration"] + 0.1).astype(float))  
data["src_bytes"] = np.log((data["src_bytes"] + 0.1).astype(float))  
data["dst_bytes"] = np.log((data["dst_bytes"] + 0.1).astype(float)) 

# we're using a one-class SVM, so we need.. a single class. the dataset 'label'
# column contains multiple different categories of attacks, so to make use of 
# this data in a one-class system we need to convert the attacks into
# class 1 (normal) and class -1 (attack)
data.loc[data['label'] == "normal.", "attack"] = 1  
data.loc[data['label'] != "normal.", "attack"] = -1

# grab out the attack value as the target for training and testing. since we're
# only selecting a single column from the `data` dataframe, we'll just get a
# series, not a new dataframe
target = data['attack']
# find the proportion of outliers we expect (aka where `attack == -1`). because 
# target is a series, we just compare against itself rather than a column.
outliers = target[target == -1]  
print("outliers.shape", outliers.shape)  
print("outlier fraction", outliers.shape[0]/target.shape[0])

# drop label columns from the dataframe. we're doing this so we can do 
# unsupervised training with unlabelled data. we've already copied the label
# out into the target series so we can compare against it later.
data.drop(["label", "attack"], axis=1, inplace=True)

# check the shape for sanity checking.
data.shape  



from sklearn.model_selection import train_test_split  
train_data, test_data, train_target, test_target = train_test_split(data, target, train_size = 0.8)  
train_data.shape  



from sklearn import svm

# set nu (which should be the proportion of outliers in our dataset)
nu = outliers.shape[0] / target.shape[0]  
print("nu", nu)

model = svm.OneClassSVM(nu=nu, kernel='rbf', gamma=0.00005)  
model.fit(train_data) 



from sklearn import metrics  
preds = model.predict(train_data)  
targs = train_target

print("accuracy: ", metrics.accuracy_score(targs, preds))  
print("precision: ", metrics.precision_score(targs, preds))  
print("recall: ", metrics.recall_score(targs, preds))  
print("f1: ", metrics.f1_score(targs, preds))  
print("area under curve (auc): ", metrics.roc_auc_score(targs, preds))



preds = model.predict(test_data)  
targs = test_target

print("accuracy: ", metrics.accuracy_score(targs, preds))  
print("precision: ", metrics.precision_score(targs, preds))  
print("recall: ", metrics.recall_score(targs, preds))  
print("f1: ", metrics.f1_score(targs, preds))  
print("area under curve (auc): ", metrics.roc_auc_score(targs, preds))