def oldacc(options, sample):
''' The simpleAcc way of running a trial '''
# Get the final components from the data
print("Building train and test data...")
# Just partition it 80-20 training-testing
trainsize = 0.8 * len(sample)
train = sample[0:trainsize]
test = sample[trainsize:]
trfeat, trlab, _, _ = mldata.data_components(train)
tefeat, telab, _, _ = mldata.data_components(test)
# Check for valid learning algorithm
if (not mlalgos.validate_algo(options.algorithm)):
print("Invalid learning algorithm %s" % (options.algorithm))
sys.exit(1)
# Train
print("Training...")
model = mlalgos.learn(options.algorithm, trfeat, trlab, options.seed)
# Test
print("Testing...")
preds = mlalgos.predict(model, tefeat)
# Analyze -- FScore, acc, learning curves
print("Results:")
printparams(options) # CL options
# Model parameters
pprint(vars(model))
accuracy = mlstat.acc(preds, telab)
print("Accuracy: %f" % (accuracy))
return (['accuracy'], [accuracy])
def oldacc(options, sample):
''' The simpleAcc way of running a trial '''
# Get the final components from the data
print("Building train and test data...")
# Just partition it 80-20 training-testing
trainsize = 0.8 * len(sample)
train = sample[0:trainsize]
test = sample[trainsize:]
trfeat, trlab, _, _ = mldata.data_components(train)
tefeat, telab, _, _ = mldata.data_components(test)
# Check for valid learning algorithm
if(not mlalgos.validate_algo(options.algorithm)):
print("Invalid learning algorithm %s" % (options.algorithm))
sys.exit(1)
# Train
print("Training...")
model = mlalgos.learn(options.algorithm, trfeat, trlab, options.seed)
# Test
print("Testing...")
preds = mlalgos.predict(model, tefeat)
# Analyze -- FScore, acc, learning curves
print("Results:")
printparams(options) # CL options
# Model parameters
pprint(vars(model))
accuracy = mlstat.acc(preds, telab)
print("Accuracy: %f" % (accuracy))
return (['accuracy'], [accuracy])
def atrial(options):
''' Run a single machine learning trial.'''
# TODO: make option for loading intermediate data to skip steps that have
# been done in previous trials
# Select data to read
data = mldata.load_data(options.database)
# Get a sample
if (options.numsamples != None): # Check to see if a sample was requested
if (options.malfrac != None):
sample = mldata.select_sample(int(options.seed), data, \
options.numsamples, options.malfrac)
else: # Only use a percent malware if one was specified
sample = mldata.select_sample(int(options.seed), data,
options.numsamples)
else:
sample = data
# Preprocess data
# TODO: fill in this part
# If specified, output the current database
if (options.newdb != None):
mldata.save_data(sample, options.newdb)
# Original way to run a trial... probably going to be deleted eventually
if (options.simplyAcc):
return oldacc(options, sample)
# Primary way to run a trial
else:
printparams(options)
print(' Measure Average Fold-Scores')
perfmeasures = ['accuracy', 'precision', 'recall', 'f1']
avgs = []
for perfmeasure in perfmeasures:
# Extract the parts of the samples
# Not yet using the filenames and feature names
features, labels, _, featnames = mldata.data_components(sample)
# Split the sample into 10 randomly stratified folds
cvsplits = cross_validation.StratifiedShuffleSplit(labels, \
test_size=0.1, random_state=options.seed)
# Score the folds
est = mlalgos.get_estimator(options.algorithm)
scores = cross_validation.cross_val_score(est, features, y=labels, \
scoring=perfmeasure, cv=cvsplits)
# Print the results
avgs.append(sum(scores) / len(scores))
avgstr = '{:.4}'.format(avgs[-1]).rjust(7)
resultstr = '{} {} '.format(perfmeasure.rjust(9), avgstr)
for score in scores:
resultstr += ' {:.3}'.format(score)
print(resultstr)
# Icing on the cake: draw a decision tree graph
# based on the fold with the best f1 score
if(perfmeasure=='f1' and options.graphfile != None and \
isinstance(est, tree.DecisionTreeClassifier)):
mlalgos.dt_graph(est, cvsplits, scores, features, labels, \
featnames, options.graphfile)
return (perfmeasures, avgs)
def atrial(options):
''' Run a single machine learning trial.'''
# TODO: make option for loading intermediate data to skip steps that have
# been done in previous trials
# Select data to read
data = mldata.load_data(options.database)
# Get a sample
if(options.numsamples != None): # Check to see if a sample was requested
if(options.malfrac != None):
sample = mldata.select_sample(int(options.seed), data, \
options.numsamples, options.malfrac)
else: # Only use a percent malware if one was specified
sample = mldata.select_sample(int(options.seed), data,
options.numsamples)
else:
sample = data
# Preprocess data
# TODO: fill in this part
# If specified, output the current database
if(options.newdb != None):
mldata.save_data(sample, options.newdb)
# Original way to run a trial... probably going to be deleted eventually
if(options.simplyAcc):
return oldacc(options, sample)
# Primary way to run a trial
else:
printparams(options)
print(' Measure Average Fold-Scores')
perfmeasures = ['accuracy', 'precision', 'recall', 'f1']
avgs = []
for perfmeasure in perfmeasures:
# Extract the parts of the samples
# Not yet using the filenames and feature names
features, labels, _, featnames = mldata.data_components(sample)
# Split the sample into 10 randomly stratified folds
cvsplits = cross_validation.StratifiedShuffleSplit(labels, \
test_size=0.1, random_state=options.seed)
# Score the folds
est = mlalgos.get_estimator(options.algorithm)
scores = cross_validation.cross_val_score(est, features, y=labels, \
scoring=perfmeasure, cv=cvsplits)
# Print the results
avgs.append(sum(scores)/len(scores))
avgstr = '{:.4}'.format(avgs[-1]).rjust(7)
resultstr = '{} {} '.format(perfmeasure.rjust(9), avgstr)
for score in scores:
resultstr += ' {:.3}'.format(score)
print(resultstr)
# Icing on the cake: draw a decision tree graph
# based on the fold with the best f1 score
if(perfmeasure=='f1' and options.graphfile != None and \
isinstance(est, tree.DecisionTreeClassifier)):
mlalgos.dt_graph(est, cvsplits, scores, features, labels, \
featnames, options.graphfile)
return (perfmeasures, avgs)
