Выполненная лабораторная работа №2

labworks/LW2/Lab02/data_test.txt

@@ -0,0 +1,4 @@
+8.1 8.5
+7.2 8.0
+9.0 8.0
+8.5 9.5

labworks/LW2/cardio_test.txt

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+-3.125388000000000055e-01 -9.199884400000000184e-01 -1.843164300000000033e-01 6.081039499999999487e-01 1.103934399999999982e+00 -6.140064399999999728e-02 4.280468599999999846e+00 1.281003399999999903e+00 1.569757499999999917e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 5.328240400000000543e-01 -1.439841399999999938e+00 -1.115347799999999889e+00 6.226310299999999742e-01 -4.882788900000000210e-01 -1.862696399999999919e+00 -3.063139000000000056e+00 -1.837519700000000089e+00 -3.231860799999999867e-01 -4.932939700000000260e-01
+-3.125388000000000055e-01 -9.199884400000000184e-01 -1.817479100000000125e-01 5.431914500000000201e-01 1.341529600000000100e+00 -6.140064399999999728e-02 3.164841700000000202e+00 1.281003399999999903e+00 2.033006199999999986e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 5.857767600000000074e-01 -1.439841399999999938e+00 -1.005344999999999933e+00 1.310643999999999920e+00 -4.882788900000000210e-01 -1.862696399999999919e+00 -3.254683299999999946e+00 -1.975454199999999938e+00 -3.565864100000000203e-01 -4.932939700000000260e-01
+-3.125388000000000055e-01 -9.199884400000000184e-01 -1.799788099999999891e-01 7.192773000000000083e-01 1.140380500000000019e+00 -6.140064399999999728e-02 3.443074999999999886e+00 1.340828799999999932e+00 2.033006199999999986e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 1.621550400000000003e-01 -8.696503300000000269e-01 -1.005344999999999933e+00 9.666375000000000384e-01 -4.882788900000000210e-01 -1.862696399999999919e+00 -3.382379400000000036e+00 -2.044421400000000055e+00 -3.899867499999999931e-01 -4.932939700000000260e-01
+-3.125388000000000055e-01 -9.199884400000000184e-01 -1.810197299999999898e-01 6.156709799999999788e-01 1.060141100000000058e+00 -6.140064399999999728e-02 3.279366599999999909e+00 1.340828799999999932e+00 2.148818299999999848e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 1.459496600000000033e+00 -9.765611499999999889e-01 1.524719200000000052e+00 9.666375000000000384e-01 -4.882788900000000210e-01 -1.862696399999999919e+00 -3.382379400000000036e+00 -2.044421400000000055e+00 -2.229850800000000022e-01 -2.126605500000000148e+00
+-4.183558299999999841e-01 -9.199884400000000184e-01 -1.805742399999999970e-01 1.042659299999999956e+00 1.094481700000000002e+00 -6.140064399999999728e-02 3.349429999999999907e+00 1.281003399999999903e+00 1.685569700000000060e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 1.459496600000000033e+00 -9.765611499999999889e-01 1.524719200000000052e+00 6.226310299999999742e-01 9.630825400000000425e-01 -1.982927499999999954e+00 -3.126987100000000019e+00 -1.837519700000000089e+00 -2.258307600000000054e-02 -2.126605500000000148e+00
+-4.183558299999999841e-01 -9.199884400000000184e-01 -1.611781499999999923e-01 8.295637099999999542e-01 9.530231700000000306e-01 -6.140064399999999728e-02 3.060819500000000026e+00 1.221177900000000038e+00 1.338133199999999912e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 1.459496600000000033e+00 -9.765611499999999889e-01 1.524719200000000052e+00 6.226310299999999742e-01 -4.882788900000000210e-01 -1.982927499999999954e+00 -2.616202400000000150e+00 -1.561650699999999947e+00 2.446195899999999979e-01 -4.932939700000000260e-01
+-4.183558299999999841e-01 -9.199884400000000184e-01 -1.710553999999999963e-01 7.966295399999999693e-01 8.235100100000000145e-01 -6.140064399999999728e-02 3.565299299999999949e+00 1.281003399999999903e+00 1.453945400000000054e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 1.459496600000000033e+00 -9.765611499999999889e-01 1.524719200000000052e+00 -6.538189499999999543e-02 -4.882788900000000210e-01 -1.862696399999999919e+00 -2.807746700000000040e+00 -1.699585200000000018e+00 1.081725700000000026e-02 -4.932939700000000260e-01
+-4.183558299999999841e-01 -9.199884400000000184e-01 -1.646348499999999992e-01 9.523956000000000088e-01 1.120470300000000030e+00 -6.140064399999999728e-02 2.788995200000000008e+00 1.221177900000000038e+00 1.222321100000000049e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 1.459496600000000033e+00 -9.765611499999999889e-01 1.524719200000000052e+00 6.226310299999999742e-01 -4.882788900000000210e-01 -1.922811999999999966e+00 -2.488506199999999779e+00 -1.492683399999999994e+00 2.446195899999999979e-01 -4.932939700000000260e-01
+-4.183558299999999841e-01 -9.199884400000000184e-01 -1.509341100000000102e-01 6.957258400000000398e-01 1.212427700000000108e+00 -6.140064399999999728e-02 2.484822299999999817e+00 1.221177900000000038e+00 1.222321100000000049e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 1.459496600000000033e+00 -9.765611499999999889e-01 1.524719200000000052e+00 2.786245700000000158e-01 -4.882788900000000210e-01 -1.321656200000000059e+00 -2.296962000000000170e+00 -1.423716200000000098e+00 1.444185900000000133e-01 -4.932939700000000260e-01
+-4.183558299999999841e-01 -9.199884400000000184e-01 -2.036404900000000073e-01 1.173281800000000041e+00 2.579785699999999959e+00 -6.140064399999999728e-02 -2.782949499999999854e-01 1.101526999999999923e+00 3.191127700000000011e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 4.269186099999999762e-01 -8.696503300000000269e-01 -4.553310600000000097e-01 2.786245700000000158e-01 -4.882788900000000210e-01 -4.026857099999999967e+00 -3.829316099999999778e+00 -1.285781599999999969e+00 3.584652900000000031e+00 -4.932939700000000260e-01

labworks/LW2/lab02_lib.py

@@ -29,12 +29,14 @@ from pandas import DataFrame
 from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Activation
+from tensorflow.keras.callbacks import Callback
 
 
 
 visual = True
 verbose_show = False
 
+
 # generate 2d classification dataset
 def datagen(x_c, y_c, n_samples, n_features):
 
@@ -91,8 +93,27 @@ class EarlyStoppingOnValue(tensorflow.keras.callbacks.Callback):
             )
         return monitor_value
 
+
+class VerboseEveryNEpochs(Callback):
+    def __init__(self, every_n_epochs=1000, verbose=1):
+        super().__init__()
+        self.every_n_epochs = every_n_epochs
+        self.verbose = verbose
+
+    def on_epoch_end(self, epoch, logs=None):
+        if (epoch + 1) % self.every_n_epochs == 0:
+            if self.verbose:
+                print(f"\nEpoch {epoch + 1}/{self.params['epochs']}")
+                if logs:
+                    log_str = ", ".join([f"{k}: {v:.4f}" for k, v in logs.items()])
+                    print(f" - {log_str}")
+
+
 #создание и обучение модели автокодировщика
-def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience):
+def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience, **kwargs):
+    verbose_every_n_epochs = kwargs.get('verbose_every_n_epochs', 1000)
+    early_stopping_delta = kwargs.get('early_stopping_delta', 0.01)
+    early_stopping_value = kwargs.get('early_stopping_value', 0.0001)
 
     size = cl_train.shape[1]
     #ans = '2'
@@ -140,22 +161,28 @@ def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience
 
     optimizer = tensorflow.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
     ae.compile(loss='mean_squared_error', optimizer=optimizer)
-    error_stop = 0.0001
     epo = epohs
 
-    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=error_stop)
+
+    verbose = 1 if verbose_show else 0
+
+    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=early_stopping_value)
     early_stopping_callback_on_improving = tensorflow.keras.callbacks.EarlyStopping(monitor='loss',
-                                                                                           min_delta=0.0001, patience = patience,
+                                                                                           min_delta=early_stopping_delta, patience = patience,
-                                                                                           verbose=1, mode='auto',
+                                                                                           verbose=verbose, mode='min',
                                                                                            baseline=None,
-                                                                                           restore_best_weights=False)
+                                                                                           restore_best_weights=True)
     history_callback = tensorflow.keras.callbacks.History()
-    verbose = 1 if verbose_show else 0
+
     history_object = ae.fit(cl_train, cl_train,
                                 batch_size=cl_train.shape[0],
                                 epochs=epo,
-                                callbacks=[early_stopping_callback_on_error, history_callback,
+                                callbacks=[
-                                early_stopping_callback_on_improving],
+                                  early_stopping_callback_on_error, 
+                                  history_callback,
+                                  early_stopping_callback_on_improving,
+                                  VerboseEveryNEpochs(every_n_epochs=verbose_every_n_epochs),
+                                ],
                                 verbose=verbose)
     ae_trainned = ae
     ae_pred = ae_trainned.predict(cl_train)
@@ -538,4 +565,4 @@ def ire_plot(title, IRE_test, IREth, ae_name):
     plt.gcf().savefig('out/IRE_' + title + ae_name + '.png')
     plt.show()
 
-    return
+    return

labworks/LW2/out/AE1_ire_th.txt

@@ -0,0 +1 @@
+3.64

labworks/LW2/out/AE2_ire_th.txt

@@ -0,0 +1 @@
+0.4

labworks/LW2/out/AE3_ire_th.txt

@@ -0,0 +1 @@
+3.07

labworks/LW2/out/result.txt

@@ -0,0 +1,5 @@
+------------Оценка качества AE2 С ПОМОЩЬЮ НОВЫХ МЕТРИК------------
+Approx = 0.7333333333333333
+Excess = 0.36363636363636365
+Deficit = 0.0
+Coating = 1.0

labworks/LW2/report.md

@@ -0,0 +1,282 @@
+# Отчет по лабораторной работе №2
+Юсуфов Юнус,Романов Мирон , А-01-22
+## 1. Определили свой набор данных по таблице.
+Бригада №9 -> k = 9 mod 3 = 0 -> Cardio
+## 2. Подготовили  программную  среду Google Colaboratory для выполнения лабораторной работы.
+```
+import os
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2/')
+```
+
+```
+!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/lab02_lib.py
+!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_train.txt
+!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_test.txt
+```
+
+### Задание 1.
+
+## 1. В среде GoogleColab создалиновый блокнот(notebook). Импортировали необходимые для работы библиотеки и модули.
+
+```
+import numpy as np
+import lab02_lib as lib
+```
+
+## 2. Сгенерировали индивидуальный набор двумерных данныхв пространстве признаковс координатами центра (k, k), где k–номер бригады. Вывели полученные данные на рисунок и в консоль.
+
+```
+# генерация датасета
+# data=lib.datagen(9, 9, 1000, 2)
+data = np.loadtxt('data.txt', dtype=float)
+# вывод данных и размерности
+print('Исходные данные:')
+print(data)
+print('Размерность данных:')
+print(data.shape)
+```
+>Исходные данные:  
+>[[9.19152143 8.99994525]  
+ >[9.04581739 9.01557436]  
+ >[9.15556184 9.0895709 ]  
+ >...  
+ >[8.95859907 8.92197639]  
+ >[9.00243424 8.96375469]  
+ >[8.86636465 9.13183014]]  
+>Размерность данных:  
+>(1000, 2)  
+
+## 3. Создали и обучили автокодировщик AE1 простой архитектуры, выбрав небольшое количество эпох обучения. Зафиксировали значения, для будущего внесения в таблицу.
+
+```
+# обучение AE1
+patience = 300
+ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt', 1000, True, patience)
+
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE1, IREth1, 'AE1')
+```
+*   5 Скрытых слоев
+*   5 3 1 3 5
+
+## 4. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий.
+
+![ae1_training](ae1_training.png)
+
+*   MSE_stop = 5.3137
+*   IREth1 = 3.6
+
+## 5. Создали и обучили второй автокодировщик AE2 с усложненной архитектурой, задав большее количество эпох обучения.
+
+```
+# обучение AE2
+ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt', 3000, True, patience)
+lib.ire_plot('training', IRE2, IREth2, 'AE2')
+```
+*   7 скрытых слоев
+*   5 3 2 1 2 3 5
+
+## 6. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали второй порог ошибки реконструкции – порог обнаружения аномалий.
+
+![ae2_training](ae2_training.png)
+
+*   MSE_stop = 0.0103
+*   IREth1 = 0.4
+
+## 7. Рассчитали характеристики качества обучения EDCA для AE1 и AE2. Визуализировали и сравнили области пространства признаков, распознаваемые автокодировщиками AE1 и AE2. Сделали вывод о пригодности AE1 и AE2 для качественного обнаружения аномалий.
+
+* AE1
+
+```
+# построение областей покрытия и границ классов
+# расчет характеристик качества обучения
+numb_square = 20
+xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)
+```
+
+![ae1_class](ae1_class.png)
+
+* EDCA AE1  
+
+![ae1_EDCA](ae1_EDCA.png)
+![ae1_EDCA](ae1_EDCA1.png)
+
+* Оценка качества AE1  
+* IDEAL = 0. Excess:  12.636363636363637  
+* IDEAL = 0. Deficit:  0.0  
+* IDEAL = 1. Coating:  1.0  
+* summa:  1.0  
+* IDEAL = 1. Extrapolation precision (Approx):  0.07333333333333332  
+
+* AE2
+
+```
+# построение областей покрытия и границ классов
+# расчет характеристик качества обучения
+numb_square = 20
+xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)
+```
+
+![ae2_class](ae2_class.png)
+
+* EDCA AE2  
+
+![ae2_EDCA](ae2_EDCA.png)
+![ae2_EDCA](ae2_EDCA1.png)
+
+* Оценка качества AE2  
+* IDEAL = 0. Excess:  0.36363636363636365  
+* IDEAL = 0. Deficit:  0.0  
+* IDEAL = 1. Coating:  1.0  
+* summa:  1.0  
+* IDEAL = 1. Extrapolation precision (Approx):  0.7333333333333333  
+
+### Сравним области пространства признаков AE1 и AE2
+
+```
+# сравнение характеристик качества обучения и областей аппроксимации
+lib.plot2in1(data, xx, yy, Z1, Z2)
+```
+![ae1_ae2](ae1_ae2.png)
+
+### Вывод по пункту.
+AE1 не достаточно хорошо аппроксимирует область обучающих данных, что может значительно повлиять на результаты тестов. По сравнению с AE2, AE1 плохо ограничивает нашу область признаков с "левой" стороны.
+
+## 8. Изучили сохраненный набор данных и пространство признаков. Создали тестовую выборку, состоящую из 4-ёх элементов, не входящих в обучающую выборку. Элементы выбраны так, чтобы AE1 распознавал их как норму, а AE2 детектировал как аномалии.
+
+```
+# загрузка тестового набора
+data_test = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/is_lab2/Lab02/data_test.txt', dtype=float)
+print(data_test)
+```
+>[[8.1 8.5]  
+ >[7.2 8. ]  
+ >[9.  8. ]  
+ >[8.5 9.5]]  
+
+## 9. Применили обученные автокодировщики AE1 и AE2 к тестовым данным и вывели значения ошибки реконструкции для каждого элемента тестовой выборки относительно порога.
+
+```
+# тестирование АE1
+predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)
+lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)
+lib.ire_plot('test', ire1, IREth1, 'AE1')
+```
+>Аномалий не обнаружено
+![ae1_test](ae1_test.png)
+
+```
+# тестирование АE2
+predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)
+lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)
+lib.ire_plot('test', ire2, IREth2, 'AE1')
+```
+>i         Labels    IRE       IREth     
+>0         [1.]      [1.02]    0.4       
+>1         [1.]      [2.05]    0.4       
+>2         [1.]      [1.]      0.4       
+>3         [1.]      [0.69]    0.4       
+>Обнаружено  4.0  аномалий  
+![ae2_test](ae2_test.png)
+
+## 10. Визуализировали элементы обучающей и тестовой выборки в областях пространства признаков, распознаваемых автокодировщиками AE1 и AE2.
+
+```
+# построение областей аппроксимации и точек тестового набора
+lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)
+```
+![ae1_ae2_test](ae1_ae2_test.png)
+
+## 11. Результаты исследования занесли в таблицу:
+
+| |Количество скрытых слоев|Количество нейронов в скрытых слоях|Количество эпох обучения|Ошибка MSE_stop|Порог ошибки реконструкции|Значение показателя Excess|Значение показателя Approx|Количество обнаруженных аномалий|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|AE1|5|5 3 1 3 5|1000|5.3137|3.6|12.636363636363637|0.07333333333333332|0|
+|AE2|7|5 3 2 1 2 3 5|3000|0.0103|0.4|0.36363636363636365|0.7333333333333333|4|
+
+## 12. Сделали выводы о требованиях к:
+
+* Данным обучения - необходима плотная выборка нормальных состояний без выбросов, покрывающая рабочую область (в эксперименте — 1000 точек вокруг (9, 9)), чтобы модель корректно восстанавливала типичные объекты.
+* Архитектуре кодировщика - оптимальна глубокая симметричная структура с узким горлышком (5-3-2-1-2-3-5), обеспечивающая точную реконструкцию и устойчивые границы нормы.
+* Количеству эпох обучения - требуется не менее 3000 эпох для данного набора данных; обучение в 1000 эпох оставляет высокую ошибку и не формирует надежный порог аномалий.
+* Ошибке MSE_stop, приемлемой для останова обучения - ориентир на значения порядка 0.001 или 0.01 (0.0103 для AE2); более крупные значения, как 5.3137 у AE1, свидетельствуют о недообучении.
+* Ошибке реконструкции обучающей выборки - целесообразно удерживать порог ниже 0.5 (IREth = 0.4 у AE2), тогда как порог 3.6 у AE1 приводит к пропуску аномалий.
+* Характеристикам EDCA, для качественного обнаружения аномалий в данных - требуется минимальный Excess (<1) и высокая Approx (≈0.7); такие значения достигаются AE2, тогда как AE1 с Excess = 12.6 непригоден.
+
+### Задание 2.
+
+## 1. Изучили описание своего набора реальных данных, что он из себя представляет.
+
+## 2. Загрузили многомерную обучающую выборку реальных данных cardio_train.txt. Изучили и загрузили тестовую выборку cardio_test.txt.
+
+```
+train = np.loadtxt('cardio_train.txt', dtype=float)
+test = np.loadtxt('cardio_test.txt', dtype=float)
+
+print('Исходные данные:')
+print(train)
+print('Размерность данных:')
+print(train.shape)
+print('Исходные данные:')
+print(test)
+print('Размерность данных:')
+print(test.shape)
+```
+Исходные данные:
+>[[ 0.00491231  0.69319077 -0.20364049 ...  0.23149795 -0.28978574 -0.49329397]
+>[ 0.11072935 -0.07990259 -0.20364049 ...  0.09356344 -0.25638541 -0.49329397]
+>[ 0.21654639 -0.27244466 -0.20364049 ...  0.02459619 -0.25638541 1.1400175 ]
+>...
+>[ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.65718941 1.1400175 ]
+>[ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.62378908 -0.49329397]
+>[ 1.0630827  -0.51148142 -0.16958144 ...  0.57633422 -0.65718941 -0.49329397]]
+>Размерность данных:
+>(1654, 21)
+
+## 3. Создали и обучили автокодировщик с подходящей для данных
+архитектурой. Выбрали необходимое количество эпох обучения.
+
+```
+# обучение AE3
+patience = 7500
+from time import time
+start = time()
+ae3_trained, IRE3, IREth3 = lib.create_fit_save_ae(train,'out/AE3.h5','out/AE3_ire_th.txt', 120000, False, 7500, early_stopping_delta = 0.001)
+print("Время на обучение: ", time() - start)
+
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE3, IREth3, 'AE3')
+```
+*   15 скрытых слоев
+*   21 19 17 15 13 11 9 7 9 11 13 15 17 19 21
+
+## 4. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий.
+
+![ae3_training](ae3_training.png)
+
+*   MSE_stop = 0.0319
+*   IREth1 = 3.07
+
+## 5. Подали тестовую выборку на вход обученного автокодировщика для обнаружения аномалий. Вывели график ошибки реконструкции элементов тестовой выборки относительно порога.
+
+```
+# тестирование АE3
+predicted_labels3, ire3 = lib.predict_ae(ae3_trained, test, IREth3)
+lib.anomaly_detection_ae(predicted_labels3, ire3, IREth3)
+lib.ire_plot('test', ire3, IREth3, 'AE3')
+```
+>Обнаружено  94.0  аномалий
+>Accuracy: 86.23%
+![ae3_test](ae3_test.png)
+
+|Dataset name|Количество скрытых слоев|Количество нейронов в скрытых слоях|Количество эпох обучения|Ошибка MSE_stop|Порог ошибки реконструкции|% Обнаруженных аномалий|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|Cardio|15|21 19 17 15 13 11 9 7 9 11 13 15 17 19 21|85000|0.0319|3.07|86.23|
+
+## 6. Сделали выводы о требованиях к:
+
+* Данным обучения - необходима масштабированная и очищенная выборка, охватывающая все состояния нормы; 1654 нормализованных записей cardio_train дают устойчивую оценку распределения.
+* Архитектуре кодировщика - эффективна глубокая симметричная сеть, которая сжимает 21 признаковое измерение до компактного латентного пространства и поддерживает высокую точность восстановления.
+* Количеству эпох обучения - требуется длительное обучение (100к+ эпох) с ранней остановкой; меньшая длительность не успевает снизить ошибку реконструкции до приемлемого уровня.
+* Ошибке MSE_stop, приемлемой для останова обучения - ориентир на значения порядка 0.01 (0.0319), после чего дальнейшее обучение дает минимальный прирост качества при заметном росте времени.
+* Ошибке реконструкции обучающей выборки, для качественного обнаружения аномалий в случае, когда размерность пространства признаков высока - порог около 3.0 (IREth = 3.07) позволяет выделять ~86% аномалий без чрезмерного количества ложных срабатываний; важно калибровать порог по распределению ошибок.