from fastai.tabular import *
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit
import os
import sys
import glob
from sklearn.utils import shuffle
dep_var = 'Label'
cat_names = ['Dst Port', 'Protocol']
cont_names = ['Timestamp', 'Flow Duration', 'Tot Fwd Pkts',
'Tot Bwd Pkts', 'TotLen Fwd Pkts', 'TotLen Bwd Pkts', 'Fwd Pkt Len Max',
'Fwd Pkt Len Min', 'Fwd Pkt Len Mean', 'Fwd Pkt Len Std',
'Bwd Pkt Len Max', 'Bwd Pkt Len Min', 'Bwd Pkt Len Mean',
'Bwd Pkt Len Std', 'Flow Byts/s', 'Flow Pkts/s', 'Flow IAT Mean',
'Flow IAT Std', 'Flow IAT Max', 'Flow IAT Min', 'Fwd IAT Tot',
'Fwd IAT Mean', 'Fwd IAT Std', 'Fwd IAT Max', 'Fwd IAT Min',
'Bwd IAT Tot', 'Bwd IAT Mean', 'Bwd IAT Std', 'Bwd IAT Max',
'Bwd IAT Min', 'Fwd PSH Flags', 'Bwd PSH Flags', 'Fwd URG Flags',
'Bwd URG Flags', 'Fwd Header Len', 'Bwd Header Len', 'Fwd Pkts/s',
'Bwd Pkts/s', 'Pkt Len Min', 'Pkt Len Max', 'Pkt Len Mean',
'Pkt Len Std', 'Pkt Len Var', 'FIN Flag Cnt', 'SYN Flag Cnt',
'RST Flag Cnt', 'PSH Flag Cnt', 'ACK Flag Cnt', 'URG Flag Cnt',
'CWE Flag Count', 'ECE Flag Cnt', 'Down/Up Ratio', 'Pkt Size Avg',
'Fwd Seg Size Avg', 'Bwd Seg Size Avg', 'Fwd Byts/b Avg',
'Fwd Pkts/b Avg', 'Fwd Blk Rate Avg', 'Bwd Byts/b Avg',
'Bwd Pkts/b Avg', 'Bwd Blk Rate Avg', 'Subflow Fwd Pkts',
'Subflow Fwd Byts', 'Subflow Bwd Pkts', 'Subflow Bwd Byts',
'Init Fwd Win Byts', 'Init Bwd Win Byts', 'Fwd Act Data Pkts',
'Fwd Seg Size Min', 'Active Mean', 'Active Std', 'Active Max',
'Active Min', 'Idle Mean', 'Idle Std', 'Idle Max', 'Idle Min']
dataPath = 'CleanedTrafficData' # use your path
resultPath = 'results/fastai'
def loadData(fileName):
dataFile = os.path.join(dataPath, fileName)
pickleDump = '{}.pickle'.format(dataFile)
if os.path.exists(pickleDump):
df = pd.read_pickle(pickleDump)
else:
df = pd.read_csv(dataFile)
df = df.dropna()
df = shuffle(df)
df.to_pickle(pickleDump)
return df
def experimentIndividual(dataFile, epochs=5, normalize=False):
# procs = [FillMissing, Categorify, Normalize]
procs = [FillMissing, Categorify]
if normalize:
procs.append(Normalize)
seed = 7
np.random.seed(seed)
# load data
data = loadData(dataFile)
# define 10-fold cross validation test harness
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)
cvscores = []
fold = 1
for train_idx, test_idx in kfold.split(data.index, data[dep_var]):
print('running fold = ', fold)
fold += 1
# create model
data_fold = (TabularList.from_df(data, path=dataPath, cat_names=cat_names, cont_names=cont_names, procs=procs)
.split_by_idxs(train_idx, test_idx)
.label_from_df(cols=dep_var)
.databunch())
# create model and learn
model = tabular_learner(
data_fold, layers=[200, 100], metrics=accuracy, callback_fns=ShowGraph)
model.fit(epochs, 1e-2)
model.save('{}.model'.format(os.path.basename(dataFile)))
# train the model, iterating on the data in batches of batch_size
# evaluate the model
loss, acc = model.validate()
print('loss {}: accuracy: {:.2f}%'.format(loss, acc*100))
cvscores.append(acc*100)
resultFile = os.path.join(resultPath, dataFile)
with open('{}.result'.format(resultFile), 'a') as fout:
fout.write(
'accuracy: {:.2f} std-dev: {:.2f}\n'.format(np.mean(cvscores), np.std(cvscores)))
if __name__ == '__main__':
if len(sys.argv) < 2:
print('Usage: python fastai-experiments.py inputDataFile.csv')
else:
experimentIndividual(sys.argv[1])
