generate_feasible_attack.py

from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals  

import os
import sys
import argparse
import json
import shutil
from collections import defaultdict

import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster, metrics, svm, model_selection

import matplotlib.pyplot as plt
import seaborn as sns
import scipy.linalg as slin
import scipy.sparse.linalg as sparselin
import scipy.sparse as sparse
import scipy.io as sio

import IPython

import data_utils as data
import datasets
import defenses
import defense_testers
import upper_bounds
from upper_bounds import hinge_loss, hinge_grad

### This just thresholds and rounds IMDB
### Not guaranteed to actually be feasible

dataset_name = 'imdb'

weight_decay = datasets.DATASET_WEIGHT_DECAYS[dataset_name]
weight_decay = 0.17 ### HACK, need to rerun on proper weight_decay

epsilons = datasets.DATASET_EPSILONS[dataset_name]
norm_sq_constraint = datasets.DATASET_NORM_SQ_CONSTRAINTS[dataset_name]

for epsilon in epsilons:
    if epsilon == 0: continue

    attack_npz_path = datasets.get_attack_npz_path(dataset_name, weight_decay, epsilon, norm_sq_constraint)
    X_modified, Y_modified, X_test, Y_test, idx_train, idx_poison = datasets.load_attack_npz(dataset_name, attack_npz_path)

    X_modified = sparse.csr_matrix(data.rround(data.threshold(X_modified)))
    IPython.embed()

    break