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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()
+
+# --- 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))