def numpy2csv(csv_out, X, y, file_names=None):
'''
Creates a CSV file from the given data points (X, scipy matrix) and labels
(y, numpy.array). The CSV file has a header. The first column is named
'class' and the others after PDFrate features. All features are written
in their respective type format (e.g., True/False for booleans).
If 'csv_out' is an open Python file, it will not be reopened. If
it is a string, a file will be created with that name.
'''
we_opened_csvfile = type(csv_out) == str
csvfile = open(csv_out, 'wb+') if we_opened_csvfile else csv_out
# Write header
csvfile.write('class')
if file_names:
csvfile.write(',filename')
names = FeatureDescriptor.get_feature_names()
for name in names:
csvfile.write(',{}'.format(name))
csvfile.write('\n')
descs = FeatureDescriptor.get_feature_descriptions()
# Write data
for i in range(0, X.shape[0]):
csvfile.write('{}'.format('TRUE' if bool(y[i]) else 'FALSE'))
if file_names:
csvfile.write(',{}'.format(file_names[i]))
for j in range(0, X.shape[1]):
feat_type = descs[names[j]]['type']
feat_val = X[i, j]
if feat_type == bool:
feat_val = 'TRUE' if feat_val >= 0.5 else 'FALSE'
elif feat_type == int:
feat_val = int(round(feat_val))
csvfile.write(',{}'.format(feat_val))
csvfile.write('\n')
if we_opened_csvfile:
csvfile.close()
Implementation of the mimicry attack.
Created on July 1, 2013.
'''
import os
import random
import sys
import numpy
from mimicus.tools.featureedit import FeatureEdit
from mimicus.tools.datasets import numpy2csv, csv2numpy
from mimicus.tools.featureedit import FeatureDescriptor
descs = FeatureDescriptor.get_feature_descriptions()
names = FeatureDescriptor.get_feature_names()
def binarize(X_old):
X_new = numpy.copy(X_old)
for i in range(0, X_new.shape[0]):
for j in range(0, X_new.shape[1]):
feat_type = descs[names[j]]['type']
if feat_type != bool:
if X_new[i][j] != 0:
X_new[i][j] = 1
return X_new
def validate(X_old):
