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AD.py
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AD.py
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import tensorflow as tf
import numpy as np
import pdb
import json
import model
from mod_core_rnn_cell_impl import LSTMCell # modified to allow initializing bias in lstm
import utils
import eval
import DR_discriminator
import data_utils
# from pyod.utils.utility import *
from sklearn.utils.validation import *
from sklearn.metrics.classification import *
from sklearn.metrics.ranking import *
from time import time
begin = time()
"""
Here, only the discriminator was used to do the anomaly detection
"""
# --- get settings --- #
# parse command line arguments, or use defaults
parser = utils.rgan_options_parser()
settings = vars(parser.parse_args())
# if a settings file is specified, it overrides command line arguments/defaults
if settings['settings_file']: settings = utils.load_settings_from_file(settings)
# --- get data, split --- #
data_path = './experiments/data/' + settings['data_load_from'] + '.data.npy'
print('Loading data from', data_path)
settings["eval_single"] = False
settings["eval_an"] = False
samples, labels, index = data_utils.get_data(settings["data"], settings["seq_length"], settings["seq_step"],
settings["num_signals"], settings["sub_id"], settings["eval_single"],
settings["eval_an"], data_path)
# --- save settings, data --- #
# no need
print('Ready to run with settings:')
for (k, v) in settings.items(): print(v, '\t', k)
# add the settings to local environment
# WARNING: at this point a lot of variables appear
locals().update(settings)
json.dump(settings, open('./experiments/settings/' + identifier + '.txt', 'w'), indent=0)
class myADclass():
def __init__(self, epoch, settings=settings, samples=samples, labels=labels, index=index):
self.epoch = epoch
self.settings = settings
self.samples = samples
self.labels = labels
self.index = index
def ADfunc(self):
num_samples_t = self.samples.shape[0]
print('sample_shape:', self.samples.shape[0])
print('num_samples_t', num_samples_t)
# -- only discriminate one batch for one time -- #
D_test = np.empty([num_samples_t, self.settings['seq_length'], 1])
DL_test = np.empty([num_samples_t, self.settings['seq_length'], 1])
L_mb = np.empty([num_samples_t, self.settings['seq_length'], 1])
I_mb = np.empty([num_samples_t, self.settings['seq_length'], 1])
batch_times = num_samples_t // self.settings['batch_size']
for batch_idx in range(0, num_samples_t // self.settings['batch_size']):
# print('batch_idx:{}
# display batch progress
model.display_batch_progression(batch_idx, batch_times)
start_pos = batch_idx * self.settings['batch_size']
end_pos = start_pos + self.settings['batch_size']
T_mb = self.samples[start_pos:end_pos, :, :]
L_mmb = self.labels[start_pos:end_pos, :, :]
I_mmb = self.index[start_pos:end_pos, :, :]
para_path = './experiments/parameters/' + self.settings['sub_id'] + '_' + str(
self.settings['seq_length']) + '_' + str(self.epoch) + '.npy'
D_t, L_t = DR_discriminator.dis_trained_model(self.settings, T_mb, para_path)
D_test[start_pos:end_pos, :, :] = D_t
DL_test[start_pos:end_pos, :, :] = L_t
L_mb[start_pos:end_pos, :, :] = L_mmb
I_mb[start_pos:end_pos, :, :] = I_mmb
start_pos = (num_samples_t // self.settings['batch_size']) * self.settings['batch_size']
end_pos = start_pos + self.settings['batch_size']
size = samples[start_pos:end_pos, :, :].shape[0]
fill = np.ones([self.settings['batch_size'] - size, samples.shape[1], samples.shape[2]])
batch = np.concatenate([samples[start_pos:end_pos, :, :], fill], axis=0)
para_path = './experiments/parameters/' + self.settings['sub_id'] + '_' + str(
self.settings['seq_length']) + '_' + str(self.epoch) + '.npy'
D_t, L_t = DR_discriminator.dis_trained_model(self.settings, batch, para_path)
L_mmb = self.labels[start_pos:end_pos, :, :]
I_mmb = self.index[start_pos:end_pos, :, :]
D_test[start_pos:end_pos, :, :] = D_t[:size, :, :]
DL_test[start_pos:end_pos, :, :] = L_t[:size, :, :]
L_mb[start_pos:end_pos, :, :] = L_mmb
I_mb[start_pos:end_pos, :, :] = I_mmb
results = np.zeros([18, 4])
for i in range(2, 8):
tao = 0.1 * i
Accu2, Pre2, Rec2, F12 = DR_discriminator.detection_Comb(
DL_test, L_mb, I_mb, self.settings['seq_step'], tao)
print('seq_length:', self.settings['seq_length'])
print('Comb-logits-based-Epoch: {}; tao={:.1}; Accu: {:.4}; Pre: {:.4}; Rec: {:.4}; F1: {:.4}'
.format(self.epoch, tao, Accu2, Pre2, Rec2, F12))
results[i - 2, :] = [Accu2, Pre2, Rec2, F12]
Accu3, Pre3, Rec3, F13 = DR_discriminator.detection_Comb(
D_test, L_mb, I_mb, self.settings['seq_step'], tao)
print('seq_length:', self.settings['seq_length'])
print('Comb-statistic-based-Epoch: {}; tao={:.1}; Accu: {:.4}; Pre: {:.4}; Rec: {:.4}; F1: {:.4}'
.format(self.epoch, tao, Accu3, Pre3, Rec3, F13))
results[i - 2+6, :] = [Accu3, Pre3, Rec3, F13]
Accu5, Pre5, Rec5, F15 = DR_discriminator.sample_detection(D_test, L_mb, tao)
print('seq_length:', self.settings['seq_length'])
print('sample-wise-Epoch: {}; tao={:.1}; Accu: {:.4}; Pre: {:.4}; Rec: {:.4}; F1: {:.4}'
.format(self.epoch, tao, Accu5, Pre5, Rec5, F15))
results[i - 2+12, :] = [Accu5, Pre5, Rec5, F15]
return results
if __name__ == "__main__":
print('Main Starting...')
Results = np.empty([settings['num_epochs'], 18, 4])
for epoch in range(settings['num_epochs']):
# for epoch in range(50, 60):
ob = myADclass(epoch)
Results[epoch, :, :] = ob.ADfunc()
# res_path = './experiments/plots/Results' + '_' + settings['sub_id'] + '_' + str(
# settings['seq_length']) + '.npy'
# np.save(res_path, Results)
print('Main Terminating...')
end = time() - begin
print('Testing terminated | Training time=%d s' % (end))