def run_experiments(isn1=True,
isn2=True,
isn3=True,
issigmoid=True,
iscnn=True,
nfolds=10):
"""Runs all experiments"""
n1_results = None
n2_results = None
n3_results = None
sigmoid_results = None
cnn_results = None
if isn1:
n1_results = unigram.run(nfolds=nfolds)
if isn3:
n3_results = trigram.run(nfolds=nfolds)
if issigmoid:
sigmoid_results = sigmoid.run(nfolds=nfolds)
if isn2:
n2_results = bigram.run(nfolds=nfolds)
if iscnn:
cnn_results = cnn.run(nfolds=nfolds)
return n1_results, n2_results, n3_results, sigmoid_results, cnn_results
def run_experiments(isbigram=True, islstm=True, nfolds=10):
"""Runs all experiments"""
bigram_results = None
lstm_results = None
if isbigram:
bigram_results = bigram.run(nfolds=nfolds)
if islstm:
lstm_results = lstm.run(nfolds=nfolds)
return bigram_results, lstm_results
def run_experiments(isbigram=True, islstm=True, nfolds=10):
"""Runs all experiments"""
bigram_results = None
lstm_results = None
if isbigram:
bigram_results = bigram.run(nfolds=nfolds)
if islstm:
lstm_results = lstm.run(nfolds=nfolds)
return bigram_results, lstm_results
def run_experiments(isbigram=True, islstm=True, nfolds=10, savemodel=False):
"""Runs all experiments"""
bigram_results = None
lstm_results = None
if isbigram:
bigram_results = bigram.run(nfolds=nfolds, savemodel=savemodel)
if islstm:
lstm_results = lstm.run(nfolds=nfolds, savemodel=savemodel)
return bigram_results, lstm_results
def run_experiments(isbigram=True, islstm=True, nfolds=10):
"""Runs all experiments"""
bigram_results = None
lstm_results = None
if isbigram:
max_bigram_epoch = int(os.environ.get('MAX_BIGRAM_EPOCH', 50))
bigram_results = bigram.run(nfolds=nfolds, max_epoch=max_bigram_epoch)
if islstm:
max_lstm_epoch = int(os.environ.get('MAX_LSTM_EPOCH', 25))
lstm_results = lstm.run(nfolds=nfolds, max_epoch=max_lstm_epoch)
return bigram_results, lstm_results
def run_experiments(nfolds=10):
options = {
'nfolds': nfolds,
# enable for quick functional testing
# 'max_epoch':2
}
"""Runs all experiments"""
print '========== aloha_cnn_lstm =========='
aloha_cnn_lstm_results = aloha_cnn_lstm.run(**options)
print '========== aloha_cnn =========='
aloha_cnn_results = aloha_cnn.run(**options)
print '========== aloha_bigram =========='
aloha_bigram_results = aloha_bigram.run(**options)
print '========== aloha_lstm =========='
aloha_lstm_results = aloha_lstm.run(**options)
print '========== cnn_lstm =========='
cnn_lstm_results = cnn_lstm.run(**options)
print '========== cnn =========='
cnn_results = cnn.run(**options)
print '========== bigram =========='
bigram_results = bigram.run(**options)
print '========== lstm =========='
lstm_results = lstm.run(**options)
return {
'options': options,
'model_results': {
'aloha_bigram': aloha_bigram_results,
'aloha_lstm': aloha_lstm_results,
'aloha_cnn': aloha_cnn_results,
'aloha_cnn_lstm': aloha_cnn_lstm_results,
'bigram': bigram_results,
'lstm': lstm_results,
'cnn': cnn_results,
'cnn_lstm': cnn_lstm_results,
}
}
"""Run experiments and create figs"""
# -*- coding: utf-8 -*-
import itertools
import os
import pickle
import matplotlib
matplotlib.use('Agg')
import numpy as np
import dga_classifier.unigram as unigram
import dga_classifier.bigram as bigram
import dga_classifier.trigram as trigram
import dga_classifier.sigmoid as sigmoid
import dga_classifier.lstm as lstm
import dga_classifier.cnn as cnn
from scipy import interp
from sklearn.metrics import roc_curve, auc
if __name__ == "__main__":
bigram.run(nfolds=2)
"""Run experiments and create figs"""
# -*- coding: utf-8 -*-
import itertools
import os
import pickle
import matplotlib
matplotlib.use('Agg')
import numpy as np
import dga_classifier.unigram as unigram
import dga_classifier.bigram as bigram
import dga_classifier.trigram as trigram
import dga_classifier.sigmoid as sigmoid
import dga_classifier.lstm as lstm
import dga_classifier.cnn as cnn
from scipy import interp
from sklearn.metrics import roc_curve, auc
if __name__ == "__main__":
bigram.run(nfolds=1)
