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evaluate.py
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evaluate.py
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import numpy as np
import sys, os
import math
import argparse
import importlib
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import util
from scipy.stats import rankdata
from sklearn.svm import OneClassSVM
from sklearn.ensemble import IsolationForest
from sklearn.metrics import accuracy_score
def create_2d_grid_data(x1_min, x1_max, x2_min, x2_max,n=100):
grid_x1 = np.linspace(x1_min, x1_max, n)
grid_x2 = np.linspace(x2_min, x2_max, n)
v1,v2 = np.meshgrid(grid_x1,grid_x2)
data_grid = np.vstack((v1.ravel(),v2.ravel())).T
return v1, v2, data_grid
def visualization_2d(x1_min, x1_max, x2_min, x2_max, sd_y, scale, n=100):
v1, v2, data_grid = create_2d_grid_data(x1_min, x1_max, x2_min, x2_max,n)
py = RTM.estimate_py_with_IS(data_grid,epoch,sd_y=sd_y,scale=scale,sample_size=sample_size,log=False,save=False)
py = py.reshape((n,n))
plt.figure()
plt.rcParams.update({'font.size': 22})
plt.imshow(py, extent=[v1.min(), v1.max(), v2.min(), v2.max()],
cmap='Blues', alpha=0.9)
plt.colorbar()
plt.savefig('%s/2d_grid_density_pre.png'%path.rstrip('/'))
plt.close()
def odd_evaluate():
X_test = RTM.y_sampler.X_test
X_train = RTM.y_sampler.X_train
label_test = RTM.y_sampler.label_test
#one-class SVM
clf = OneClassSVM(gamma='auto').fit(X_train)
score_svm = clf.decision_function(X_test)#lower, more abnormal
pr_oneclassSVM = precision_at_K(score_svm,label_test)
#Isolation Forest
clf = IsolationForest()
clf.fit(X_train)
score_if = clf.decision_function(X_test)#lower, more abnormal
pr_iso_forest = precision_at_K(score_if,label_test)
#Roundtrip
py = RTM.estimate_py_with_IS(X_test,epoch,sd_y=best_sd,scale=best_scale,sample_size=sample_size,log=True,save=False)
pr_Roundtrip = precision_at_K(py,label_test)
print("The precision at K of Roundtrip model is %.4f"%pr_Roundtrip)
print("The precision at K of One-class SVM is %.4f"%pr_oneclassSVM)
print("The precision at K of Isolation forest is %.4f"%pr_iso_forest)
def precision_at_K(score, label_test):
rank = rankdata(score)
nb_test = np.sum(label_test)
precision = len([1 for item in zip(rank,label_test) if item[0]<=nb_test and item[1]==1])*1.0/nb_test
return precision
def posterior_bayes():
tst_data, tst_label, _ = RTM.y_sampler.load_all()
tst_all = []
tst_one_hot_all = []
eval_idx = []
for i in range(10):
eval_idx += [j for j,item in enumerate(tst_label) if item==i]
for idx in eval_idx:
tst_all.append(np.tile(tst_data[idx],(10,1)))
tst_one_hot_all.append(np.eye(10))
tst_all = np.concatenate(tst_all,axis=0)
tst_one_hot_all = np.concatenate(tst_one_hot_all,axis=0)
#For each test image, we evaluate the conditional density under 10 distinct labels
py = RTM.estimate_py_with_IS(tst_all,tst_one_hot_all,epoch,sd_y=best_sd,scale=best_scale,sample_size=sample_size,log=True,save=False)
py = py.reshape((-1,10))
pre = np.argmax(py,axis=1)
acc = accuracy_score(tst_label[eval_idx],pre)
print('The test accuracy is %.4f.'%acc)
def visualize_img():
if data == "mnist":
for each in os.listdir(path):
if each.startswith('py'):
py = np.load('%s/%s'%(path, each))['arr_0']
data_y_ = np.load('%s/%s'%(path, each))['arr_1']
label_y = np.load('%s/%s'%(path, each))['arr_2']
data_y_ = data_y_.reshape(data_y_.shape[0],28,28)
for j in range(10):
idx = [i for i,item in enumerate(label_y) if item==j]
data_y_class_ = data_y_[idx]
combine = sorted(zip(data_y_class_[:25],py[25*j:25*(j+1)]),key=lambda x:x[1],reverse=True)
fig, ax = plt.subplots(nrows=5, ncols=5, sharex='all', sharey='all',figsize=(10,10))
ax = ax.flatten()
for i in range(25):
ax[i].imshow(combine[i][0],plt.cm.gray)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout(pad=1.08, h_pad=0.2, w_pad=0.2)
plt.savefig('%s/mnist_pre_class_%d.png'%(path,j))
plt.show()
else:
for each in os.listdir(path):
if each.startswith('py'):
py = np.load('%s/%s'%(path, each))['arr_0']
data_y_ = np.load('%s/%s'%(path, each))['arr_1']
label_y = np.load('%s/%s'%(path, each))['arr_2']
data_y_ = data_y_.reshape(data_y_.shape[0],32,32,3)
for j in range(10):
idx = [i for i,item in enumerate(label_y) if item==j]
data_y_class_ = data_y_[idx]
combine = sorted(zip(data_y_class_[:25],py[25*j:25*(j+1)]),key=lambda x:x[1],reverse=True)
fig, ax = plt.subplots(nrows=5, ncols=5, sharex='all', sharey='all',figsize=(10,10))
ax = ax.flatten()
for i in range(25):
ax[i].imshow(combine[i][0],plt.cm.gray)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout(pad=1.08, h_pad=0.2, w_pad=0.2)
plt.savefig('%s/cifar10_pre_class_%d.png'%(path,j))
plt.show()
def parse_params(path):
exp_info = path.strip('/').split('/')[-1]
timestamp = exp_info[12:27]
x_dim = int(exp_info.split('=')[1].split('_')[0])
y_dim = int(exp_info.split('=')[2].split('_')[0])
return x_dim, y_dim, timestamp
def find_y_sampler():
global best_sd, best_scale
if data == "indep_gmm":
best_sd, best_scale = 0.05, 0.5
ys = util.GMM_indep_sampler(N=20000, sd=0.1, dim=2, n_components=3, bound=1)
elif data == "eight_octagon_gmm":
best_sd, best_scale = 0.1, 0.5
n_components = 8
def cal_cov(theta,sx=1,sy=0.4**2):
Scale = np.array([[sx, 0], [0, sy]])
c, s = np.cos(theta), np.sin(theta)
Rot = np.array([[c, -s], [s, c]])
T = Rot.dot(Scale)
Cov = T.dot(T.T)
return Cov
radius = 3
mean = np.array([[radius*math.cos(2*np.pi*idx/float(n_components)),radius*math.sin(2*np.pi*idx/float(n_components))] for idx in range(n_components)])
cov = np.array([cal_cov(2*np.pi*idx/float(n_components)) for idx in range(n_components)])
ys = util.GMM_sampler(N=20000,mean=mean,cov=cov)
elif data == "involute":
best_sd, best_scale = 0.4, 0.5
ys = util.Swiss_roll_sampler(N=20000)
elif data == "uci_AReM":
best_sd, best_scale = 0.1, 0.1
ys = util.UCI_sampler('datasets/AReM/data.npy')
elif data == "uci_CASP":
best_sd, best_scale = 0.1, 0.1
ys = util.UCI_sampler('datasets/Protein/data.npy')
elif data == "uci_HEPMASS":
best_sd, best_scale = 0.1, 0.1
ys = util.hepmass_sampler()
elif data == "uci_BANK":
best_sd, best_scale = 0.1, 0.1
ys = util.UCI_sampler('datasets/BANK/data.npy')
elif data == "uci_YPMSD":
best_sd, best_scale = 0.1, 0.1
ys = util.UCI_sampler('datasets/YearPredictionMSD/data.npy')
elif data == "odds_Shuttle":
best_sd, best_scale = 0.05, 1
ys = util.Outlier_sampler('datasets/ODDS/Shuttle/data.npz')
elif data == "odds_Mammography":
best_sd, best_scale = 0.05, 0.5
ys = util.Outlier_sampler('datasets/ODDS/Mammography/data.npz')
elif data == "odds_ForestCover":
best_sd, best_scale = 0.1, 0.2
ys = util.Outlier_sampler('datasets/ODDS/ForestCover/data.npz')
elif data == "mnist":
best_sd, best_scale = 0.1, 0.01
ys = util.mnist_sampler()
elif data == "cifar10":
best_sd, best_scale = 0.1, 0.01
ys = util.cifar10_sampler()
else:
print("Wrong data name!")
sys.exit()
return ys
def load_model(path, epoch, pretrain):
pool = util.DataPool()
x_dim, y_dim, timestamp = parse_params(path)
xs = util.Gaussian_sampler(mean=np.zeros(x_dim),sd=1.0)
ys = find_y_sampler()
if data == 'mnist' or data == 'cifar10':
from main_density_est_img import RoundtripModel
g_net = model.Generator_img(input_dim=x_dim,output_dim = y_dim,name='g_net',nb_layers=2,nb_units=256,dataset=data,is_training=False)
h_net = model.Encoder_img(input_dim=y_dim,output_dim = x_dim,name='h_net',nb_layers=2,nb_units=256,dataset=data)
dx_net = model.Discriminator(input_dim=x_dim,name='dx_net',nb_layers=2,nb_units=128)
dy_net = model.Discriminator_img(input_dim=y_dim,name='dy_net',nb_layers=2,nb_units=128,dataset=data)
RTM = RoundtripModel(g_net, h_net, dx_net, dy_net, xs, ys, data, pool, batch_size=64, nb_classes=10, alpha=10.0, beta=10.0, df=1, is_train=False)
else:
from main_density_est import RoundtripModel
g_net = model.Generator(input_dim=x_dim,output_dim = y_dim,name='g_net',nb_layers=10,nb_units=512)
h_net = model.Generator(input_dim=y_dim,output_dim = x_dim,name='h_net',nb_layers=10,nb_units=256)
dx_net = model.Discriminator(input_dim=x_dim,name='dx_net',nb_layers=2,nb_units=128)
dy_net = model.Discriminator(input_dim=y_dim,name='dy_net',nb_layers=4,nb_units=256)
RTM = RoundtripModel(g_net, h_net, dx_net, dy_net, xs, ys, data, pool, batch_size=64, alpha=10.0, beta=10.0, df=1, is_train=False)
RTM.load(pre_trained=pretrain, timestamp = timestamp, epoch = epoch)
return RTM
if __name__=="__main__":
parser = argparse.ArgumentParser('')
parser.add_argument('--data', type=str, default='indep_gmm',help='name of data type')
parser.add_argument('--epoch', type=int, default=200,help='which epoch to be loaded')
parser.add_argument('--path', type=str, default='',help='path to ODDS predicted data')
parser.add_argument('--ss', type=int, default=10000,help='importance sampling size')
parser.add_argument('--pretrain', type=bool, default=False)
args = parser.parse_args()
data = args.data
epoch = args.epoch
path = args.path
sample_size = args.ss
model = importlib.import_module('model_img') if data=="mnist" or data=='cifar10' else importlib.import_module('model')
RTM = load_model(path,epoch,pretrain=args.pretrain)
if data == "indep_gmm":
visualization_2d(-1.5, 1.5, -1.5, 1.5, 0.05, 0.5)
elif data == "eight_octagon_gmm":
visualization_2d(-5, 5, -5, 5, 0.1, 0.5)
elif data == "involute":
visualization_2d(-6, 5, -5, 5, 0.4, 0.5)
elif data.startswith("odds"):
odd_evaluate()
elif data == "mnist":
posterior_bayes()
elif data == "cifar10":
visualize_img()
else:
print("Wrong data name!")