import os
import csv
from collections import defaultdict
from glob import glob
from datetime import datetime
from multiprocessing import Manager, freeze_support, Process
import numpy as np
import scipy.stats
from scipy.special import psi, polygamma
from sklearn.metrics import roc_auc_score
from sklearn.svm import OneClassSVM
from sklearn.model_selection import ParameterGrid
from sklearn.externals.joblib import Parallel, delayed
from keras.models import Model, Input, Sequential
from keras.layers import Dense, Dropout
from keras.utils import to_categorical
from utils import load_cifar10, load_cats_vs_dogs, load_fashion_mnist, load_cifar100
from utils import save_roc_pr_curve_data, get_class_name_from_index, get_channels_axis
from transformations import Transformer
from models.wide_residual_network import create_wide_residual_network
from models.encoders_decoders import conv_encoder, conv_decoder
from models import dsebm, dagmm, adgan
import keras.backend as K
RESULTS_DIR = ''
def _transformations_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q):
gpu_to_use = gpu_q.get()
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
if dataset_name in ['cats-vs-dogs']:
transformer = Transformer(16, 16)
n, k = (16, 8)
else:
transformer = Transformer(8, 8)
n, k = (10, 4)
mdl = create_wide_residual_network(x_train.shape[1:], transformer.n_transforms, n, k)
mdl.compile('adam',
'categorical_crossentropy',
['acc'])
x_train_task = x_train[y_train.flatten() == single_class_ind]
transformations_inds = np.tile(np.arange(transformer.n_transforms), len(x_train_task))
x_train_task_transformed = transformer.transform_batch(np.repeat(x_train_task, transformer.n_transforms, axis=0),
transformations_inds)
batch_size = 128
mdl.fit(x=x_train_task_transformed, y=to_categorical(transformations_inds),
batch_size=batch_size, epochs=int(np.ceil(200/transformer.n_transforms))
)
#################################################################################################
# simplified normality score
#################################################################################################
# preds = np.zeros((len(x_test), transformer.n_transforms))
# for t in range(transformer.n_transforms):
# preds[:, t] = mdl.predict(transformer.transform_batch(x_test, [t] * len(x_test)),
# batch_size=batch_size)[:, t]
#
# labels = y_test.flatten() == single_class_ind
# scores = preds.mean(axis=-1)
#################################################################################################
def calc_approx_alpha_sum(observations):
N = len(observations)
f = np.mean(observations, axis=0)
return (N * (len(f) - 1) * (-psi(1))) / (
N * np.sum(f * np.log(f)) - np.sum(f * np.sum(np.log(observations), axis=0)))
def inv_psi(y, iters=5):
# initial estimate
cond = y >= -2.22
x = cond * (np.exp(y) + 0.5) + (1 - cond) * -1 / (y - psi(1))
for _ in range(iters):
x = x - (psi(x) - y) / polygamma(1, x)
return x
def fixed_point_dirichlet_mle(alpha_init, log_p_hat, max_iter=1000):
alpha_new = alpha_old = alpha_init
for _ in range(max_iter):
alpha_new = inv_psi(psi(np.sum(alpha_old)) + log_p_hat)
if np.sqrt(np.sum((alpha_old - alpha_new) ** 2)) < 1e-9:
break
alpha_old = alpha_new
return alpha_new
def dirichlet_normality_score(alpha, p):
return np.sum((alpha - 1) * np.log(p), axis=-1)
scores = np.zeros((len(x_test),))
observed_data = x_train_task
for t_ind in range(transformer.n_transforms):
observed_dirichlet = mdl.predict(transformer.transform_batch(observed_data, [t_ind] * len(observed_data)),
batch_size=1024)
log_p_hat_train = np.log(observed_dirichlet).mean(axis=0)
alpha_sum_approx = calc_approx_alpha_sum(observed_dirichlet)
alpha_0 = observed_dirichlet.mean(axis=0) * alpha_sum_approx
mle_alpha_t = fixed_point_dirichlet_mle(alpha_0, log_p_hat_train)
x_test_p = mdl.predict(transformer.transform_batch(x_test, [t_ind] * len(x_test)),
batch_size=1024)
scores += dirichlet_normality_score(mle_alpha_t, x_test_p)
scores /= transformer.n_transforms
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_transformations_{}_{}.npz'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
mdl_weights_name = '{}_transformations_{}_{}_weights.h5'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
mdl_weights_path = os.path.join(RESULTS_DIR, dataset_name, mdl_weights_name)
mdl.save_weights(mdl_weights_path)
gpu_q.put(gpu_to_use)
def _train_ocsvm_and_score(params, xtrain, test_labels, xtest):
return roc_auc_score(test_labels, OneClassSVM(**params).fit(xtrain).decision_function(xtest))
def _raw_ocsvm_experiment(dataset_load_fn, dataset_name, single_class_ind):
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
x_train = x_train.reshape((len(x_train), -1))
x_test = x_test.reshape((len(x_test), -1))
x_train_task = x_train[y_train.flatten() == single_class_ind]
if dataset_name in ['cats-vs-dogs']: # OC-SVM is quadratic on the number of examples, so subsample training set
subsample_inds = np.random.choice(len(x_train_task), 5000, replace=False)
x_train_task = x_train_task[subsample_inds]
pg = ParameterGrid({'nu': np.linspace(0.1, 0.9, num=9),
'gamma': np.logspace(-7, 2, num=10, base=2)})
results = Parallel(n_jobs=6)(
delayed(_train_ocsvm_and_score)(d, x_train_task, y_test.flatten() == single_class_ind, x_test)
for d in pg)
best_params, best_auc_score = max(zip(pg, results), key=lambda t: t[-1])
best_ocsvm = OneClassSVM(**best_params).fit(x_train_task)
scores = best_ocsvm.decision_function(x_test)
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_raw-oc-svm_{}_{}.npz'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
def _cae_ocsvm_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q):
gpu_to_use = gpu_q.get()
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
n_channels = x_train.shape[get_channels_axis()]
input_side = x_train.shape[2] # channel side will always be at shape[2]
enc = conv_encoder(input_side, n_channels)
dec = conv_decoder(input_side, n_channels)
x_in = Input(shape=x_train.shape[1:])
x_rec = dec(enc(x_in))
cae = Model(x_in, x_rec)
cae.compile('adam', 'mse')
x_train_task = x_train[y_train.flatten() == single_class_ind]
x_test_task = x_test[y_test.flatten() == single_class_ind] # This is just for visual monitoring
cae.fit(x=x_train_task, y=x_train_task, batch_size=128, epochs=200, validation_data=(x_test_task, x_test_task))
x_train_task_rep = enc.predict(x_train_task, batch_size=128)
if dataset_name in ['cats-vs-dogs']: # OC-SVM is quadratic on the number of examples, so subsample training set
subsample_inds = np.random.choice(len(x_train_task_rep), 2500, replace=False)
x_train_task_rep = x_train_task_rep[subsample_inds]
x_test_rep = enc.predict(x_test, batch_size=128)
pg = ParameterGrid({'nu': np.linspace(0.1, 0.9, num=9),
'gamma': np.logspace(-7, 2, num=10, base=2)})
results = Parallel(n_jobs=6)(
delayed(_train_ocsvm_and_score)(d, x_train_task_rep, y_test.flatten() == single_class_ind, x_test_rep)
for d in pg)
best_params, best_auc_score = max(zip(pg, results), key=lambda t: t[-1])
print(best_params)
best_ocsvm = OneClassSVM(**best_params).fit(x_train_task_rep)
scores = best_ocsvm.decision_function(x_test_rep)
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_cae-oc-svm_{}_{}.npz'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
gpu_q.put(gpu_to_use)
def _dsebm_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q):
gpu_to_use = gpu_q.get()
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
n_channels = x_train.shape[get_channels_axis()]
input_side = x_train.shape[2] # image side will always be at shape[2]
encoder_mdl = conv_encoder(input_side, n_channels, representation_activation='relu')
energy_mdl = dsebm.create_energy_model(encoder_mdl)
reconstruction_mdl = dsebm.create_reconstruction_model(energy_mdl)
# optimization parameters
batch_size = 128
epochs = 200
reconstruction_mdl.compile('adam', 'mse')
x_train_task = x_train[y_train.flatten() == single_class_ind]
x_test_task = x_test[y_test.flatten() == single_class_ind] # This is just for visual monitoring
reconstruction_mdl.fit(x=x_train_task, y=x_train_task,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test_task, x_test_task))
scores = -energy_mdl.predict(x_test, batch_size)
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_dsebm_{}_{}.npz'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
gpu_q.put(gpu_to_use)
def _dagmm_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q):
gpu_to_use = gpu_q.get()
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
n_channels = x_train.shape[get_channels_axis()]
input_side = x_train.shape[2] # image side will always be at shape[2]
enc = conv_encoder(input_side, n_channels, representation_dim=5,
representation_activation='linear')
dec = conv_decoder(input_side, n_channels=n_channels, representation_dim=enc.output_shape[-1])
n_components = 3
estimation = Sequential([Dense(64, activation='tanh', input_dim=enc.output_shape[-1] + 2), Dropout(0.5),
Dense(10, activation='tanh'), Dropout(0.5),
Dense(n_components, activation='softmax')]
)
batch_size = 256
epochs = 200
lambda_diag = 0.0005
lambda_energy = 0.01
dagmm_mdl = dagmm.create_dagmm_model(enc, dec, estimation, lambda_diag)
dagmm_mdl.compile('adam', ['mse', lambda y_true, y_pred: lambda_energy*y_pred])
x_train_task = x_train[y_train.flatten() == single_class_ind]
x_test_task = x_test[y_test.flatten() == single_class_ind] # This is just for visual monitoring
dagmm_mdl.fit(x=x_train_task, y=[x_train_task, np.zeros((len(x_train_task), 1))], # second y is dummy
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test_task, [x_test_task, np.zeros((len(x_test_task), 1))]),
# verbose=0
)
energy_mdl = Model(dagmm_mdl.input, dagmm_mdl.output[-1])
scores = -energy_mdl.predict(x_test, batch_size)
scores = scores.flatten()
if not np.all(np.isfinite(scores)):
min_finite = np.min(scores[np.isfinite(scores)])
scores[~np.isfinite(scores)] = min_finite - 1
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_dagmm_{}_{}.npz'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
gpu_q.put(gpu_to_use)
def _adgan_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q):
gpu_to_use = gpu_q.get()
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
if len(x_test) > 5000:
# subsample x_test due to runtime complexity
chosen_inds = np.random.choice(len(x_test), 5000, replace=False)
x_test = x_test[chosen_inds]
y_test = y_test[chosen_inds]
n_channels = x_train.shape[get_channels_axis()]
input_side = x_train.shape[2] # image side will always be at shape[2]
critic = conv_encoder(input_side, n_channels, representation_dim=1,
representation_activation='linear')
noise_size = 256
generator = conv_decoder(input_side, n_channels=n_channels, representation_dim=noise_size)
def prior_gen(b_size):
return np.random.normal(size=(b_size, noise_size))
batch_size = 128
epochs = 100
x_train_task = x_train[y_train.flatten() == single_class_ind]
def data_gen(b_size):
chosen_inds = np.random.choice(len(x_train_task), b_size, replace=False)
return x_train_task[chosen_inds]
adgan.train_wgan_with_grad_penalty(prior_gen, generator, data_gen, critic, batch_size, epochs, grad_pen_coef=20)
scores = adgan.scores_from_adgan_generator(x_test, prior_gen, generator)
labels = y_test.flatten() == single_class_ind
res_file_name = '{}_adgan_{}_{}.npz'.format(dataset_name,
get_class_name_from_index(single_class_ind, dataset_name),
datetime.now().strftime('%Y-%m-%d-%H%M'))
res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name)
save_roc_pr_curve_data(scores, labels, res_file_path)
gpu_q.put(gpu_to_use)
def run_experiments(load_dataset_fn, dataset_name, q, n_classes):
# CAE OC-SVM
processes = [Process(target=_cae_ocsvm_experiment,
args=(load_dataset_fn, dataset_name, c, q)) for c in range(n_classes)]
for p in processes:
p.start()
p.join()
# Raw OC-SVM
for c in range(n_classes):
_raw_ocsvm_experiment(load_dataset_fn, dataset_name, c)
n_runs = 5
# Transformations
for _ in range(n_runs):
processes = [Process(target=_transformations_experiment,
args=(load_dataset_fn, dataset_name, c, q)) for c in range(n_classes)]
if dataset_name in ['cats-vs-dogs']: # Self-labeled set is memory consuming
for p in processes:
p.start()
p.join()
else:
for p in processes:
p.start()
for p in processes:
p.join()
# DSEBM
for _ in range(n_runs):
processes = [Process(target=_dsebm_experiment,
args=(load_dataset_fn, dataset_name, c, q)) for c in range(n_classes)]
for p in processes:
p.start()
for p in processes:
p.join()
# DAGMM
for _ in range(n_runs):
processes = [Process(target=_dagmm_experiment,
args=(load_dataset_fn, dataset_name, c, q)) for c in range(n_classes)]
for p in processes:
p.start()
for p in processes:
p.join()
# ADGAN
processes = [Process(target=_adgan_experiment,
args=(load_dataset_fn, dataset_name, c, q)) for c in range(n_classes)]
for p in processes:
p.start()
for p in processes:
p.join()
def create_auc_table(metric='roc_auc'):
file_path = glob(os.path.join(RESULTS_DIR, '*', '*.npz'))
results = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
methods = set()
for p in file_path:
_, f_name = os.path.split(p)
dataset_name, method, single_class_name = f_name.split(sep='_')[:3]
methods.add(method)
npz = np.load(p)
roc_auc = npz[metric]
results[dataset_name][single_class_name][method].append(roc_auc)
for ds_name in results:
for sc_name in results[ds_name]:
for method_name in results[ds_name][sc_name]:
roc_aucs = results[ds_name][sc_name][method_name]
results[ds_name][sc_name][method_name] = [np.mean(roc_aucs),
0 if len(roc_aucs) == 1 else scipy.stats.sem(np.array(roc_aucs))
]
with open('results-{}.csv'.format(metric), 'w') as csvfile:
fieldnames = ['dataset', 'single class name'] + sorted(list(methods))
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for ds_name in sorted(results.keys()):
for sc_name in sorted(results[ds_name].keys()):
row_dict = {'dataset': ds_name, 'single class name': sc_name}
row_dict.update({method_name: '{:.3f} ({:.3f})'.format(*results[ds_name][sc_name][method_name])
for method_name in results[ds_name][sc_name]})
writer.writerow(row_dict)
if __name__ == '__main__':
freeze_support()
N_GPUS = 2
man = Manager()
q = man.Queue(N_GPUS)
for g in range(N_GPUS):
q.put(str(g))
experiments_list = [
(load_cifar10, 'cifar10', 10),
(load_cifar100, 'cifar100', 20),
(load_fashion_mnist, 'fashion-mnist', 10),
(load_cats_vs_dogs, 'cats-vs-dogs', 2),
]
for data_load_fn, dataset_name, n_classes in experiments_list:
run_experiments(data_load_fn, dataset_name, q, n_classes)
