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6.0000000e+00 7.0000000e+00 1.1000000e+01 9.0000000e+00 7.0000000e+00 3.0000000e+00 5.0000000e+00 1.1000000e+01 6.0000000e+00 8.0000000e+00 4.0000000e+00 7.0000000e+00 7.0000000e+00 1.2000000e+01 2.0000000e+00 7.0000000e+00 9.0000000e+00 8.0000000e+00 9.0000000e+00 6.0000000e+00 0.0000000e+00 8.0000000e+00 + 3.0000000e+00 4.0000000e+00 5.0000000e+00 3.0000000e+00 3.0000000e+00 9.0000000e+00 6.0000000e+00 3.0000000e+00 4.0000000e+00 9.0000000e+00 5.0000000e+00 7.0000000e+00 6.0000000e+00 5.0000000e+00 1.0000000e+00 8.0000000e+00 6.0000000e+00 9.0000000e+00 9.0000000e+00 8.0000000e+00 1.1000000e+01 7.0000000e+00 7.0000000e+00 4.0000000e+00 3.0000000e+00 6.0000000e+00 6.0000000e+00 8.0000000e+00 1.0000000e+01 9.0000000e+00 5.0000000e+00 5.0000000e+00 diff --git a/labworks/LW2/out/AE1.h5 b/labworks/LW2/out/AE1.h5 new file mode 100644 index 0000000..6137186 Binary files /dev/null and b/labworks/LW2/out/AE1.h5 differ diff --git a/labworks/LW2/out/AE1_AE2_train_def.png 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+------------Оценка качества AE2 С ПОМОЩЬЮ НОВЫХ МЕТРИК------------ +Approx = 0.36 +Excess = 1.7777777777777777 +Deficit = 0.0 +Coating = 1.0 diff --git a/labworks/LW2/out/train_set.png b/labworks/LW2/out/train_set.png new file mode 100644 index 0000000..9184f9b Binary files /dev/null and b/labworks/LW2/out/train_set.png differ diff --git a/labworks/LW2/report.md b/labworks/LW2/report.md new file mode 100644 index 0000000..f536c1b --- /dev/null +++ b/labworks/LW2/report.md @@ -0,0 +1,471 @@ +# Отчёт по лабораторной работе №2 + +**Кобзев Александр, Кирсанов Егор — А-01-22** + + +## 1.1. В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули. + + +```python +from google.colab import drive +drive.mount('/content/drive') +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/letter_train.txt +!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/letter_test.txt +``` + +``` python +# импорт модулей +import numpy as np +import lab02_lib as lib +``` + +--- + +## 1.2. Сгенерировали индивидуальный набор двумерных данных в пространстве признаков с координатами центра (k, k), где k=10 – номер бригады. Вывели полученные данные на рисунок и в консоль: + +```python +k = 10 +data = lib.datagen(k, k, 1000, 2) +print('Размерность данных:', data.shape) +print('Пример данных:', data) +``` +![Данные](images_for_report/1.png) + +``` +Размерность данных: (1000, 2) +Пример данных: [[ 9.91598657 9.75579359] + [ 9.97948747 9.98708802] + [ 9.99042489 10.01396366] + ... + [ 9.97013826 9.93047258] + [10.00891654 9.94318369] + [10.04614761 9.91592173]] +``` +--- + +## 1.3. Создали и обучили автокодировщик AE1 простой архитектуры, выбрав небольшое количество эпох обучения. + +```python +patience = 300 +ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae( + data, 'out/AE1.h5', 'out/AE1_ire_th.txt', + 1000, False, patience, verbose_every_n_epochs = 100, early_stopping_delta = 0.001 +) +``` + +--- + +## 1.4. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий. + +MSE = 56.8932 + +```python +lib.ire_plot('training', IRE1, IREth1, 'AE1') +print('AE1 IREth =', IREth1) +``` + +![График ошибки реконструкции обучающей выборки](images_for_report/2.png) + +``` +Порог ошибки реконструкции: +AE1 IREth = 10.98 +``` + +--- + +## 1.5. Создали и обучили второй автокодировщик AE2 с усложненной архитектурой, задав большее количество эпох обучения. + +```python +ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae( + data, 'out/AE2.h5', 'out/AE2_ire_th.txt', + 3000, False, patience, verbose_every_n_epochs = 500, early_stopping_delta = 0.001 +) +``` + +--- + +## 1.6. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий. + +MSE = 0.0225 + +```python +lib.ire_plot('training', IRE2, IREth2, 'AE2') +print('AE2 IREth =', IREth2) +``` + +![График ошибки реконструкции обучающей выборки](images_for_report/3.png) + +``` +Порог ошибки реконструкции: +AE2 IREth = 0.55 +``` +--- + +## 1.7. Рассчитали характеристики качества обучения EDCA для AE1 и AE2. Визуализировали и сравнить области пространства признаков, распознаваемые автокодировщиками AE1 и AE2. Сделать вывод о пригодности AE1 и AE2 для качественного обнаружения аномалий. + +```python +# AE1 +numb_square = 20 +xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True) +``` + +![Грфик](images_for_report/4.png) + +``` +amount: 18 +amount_ae: 293 +``` + +![Грфик](images_for_report/5.png) +![Грфик](images_for_report/6.png) + +``` +Оценка качества AE1 +IDEAL = 0. Excess: 15.277777777777779 +IDEAL = 0. Deficit: 0.0 +IDEAL = 1. Coating: 1.0 +summa: 1.0 +IDEAL = 1. Extrapolation precision (Approx): 0.06143344709897611 +``` + +```python +# AE2 +numb_square = 20 +xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True) +``` + +![Грфик](images_for_report/7.png) + +``` +amount: 18 +amount_ae: 50 +``` + +![Грфик](images_for_report/8.png) +![Грфик](images_for_report/9.png) + +``` +Оценка качества AE2 +IDEAL = 0. Excess: 1.7777777777777777 +IDEAL = 0. Deficit: 0.0 +IDEAL = 1. Coating: 1.0 +summa: 1.0 +IDEAL = 1. Extrapolation precision (Approx): 0.36 +``` + +```python +# Сравнение +lib.plot2in1(data, xx, yy, Z1, Z2) +``` + +![Грфик](images_for_report/10.png) + + +--- + +## 1.8. Если автокодировщик AE2 недостаточно точно аппроксимирует область обучающих данных, то подобрать подходящие параметры автокодировщика и повторить шаги (6) – (8). + +Немного изменили параметры автокодировщика AE2 для более качесвенной аппроксимации(использовались выше) + +--- + +## 1.9. Изучили сохраненный набор данных и пространство признаков. Создали тестовую выборку, состоящую, как минимум, из 4ёх элементов, не входящих в обучающую выборку. Элементы должны быть такими, чтобы AE1 распознавал их как норму, а AE2 детектировал как аномалии. + +```python +with open('data_test.txt', 'w') as file: + file.write("10.5 9.5\n") + file.write("9.0 10.2\n") + file.write("9.6 10.5\n") + file.write("9.0 9.0\n") +data_test = np.loadtxt('data_test.txt', dtype=float) +print(data_test) +``` + +``` +[[10.5 9.5] + [ 9. 10.2] + [ 9.6 10.5] + [ 9. 9. ]] + ``` + + +--- + +## 1.10. Применили обученные автокодировщики AE1 и AE2 к тестовым данным и вывели значения ошибки реконструкции для каждого элемента тестовой выборки относительно порога на график и в консоль. + +```python +# AE1 +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 +``` + +![Грфик](images_for_report/11.png) + +```python +# AE2 +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, 'AE2') +``` + +``` +i Labels IRE IREth +0 [1.] [0.62] 0.55 +1 [1.] [1.05] 0.55 +2 [1.] [0.77] 0.55 +3 [1.] [1.29] 0.55 +Обнаружено 4.0 аномалий +``` + +![Грфик](images_for_report/12.png) + +--- +## 1.11. Визуализирывали элементы обучающей и тестовой выборки в областях пространства признаков, распознаваемых автокодировщиками AE1 и AE2. + +```python +lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test) +``` + +![Грфик](images_for_report/13.png) + +--- + +## 1.12. Результаты исследования занесли в таблицу: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Количество скрытых слоёвКоличество нейронов в скрытых слояхКоличество эпох обученияОшибка MSE_stopПорог ошибки реконструкцииЗначение показателя ExcessЗначение показателя ApproxКоличество обнаруженных аномалий
АЕ111100056.8910.9815.280.060
АЕ255 3 2 3 530000.020.551.780.364
+ +--- + +## 1.13. Сделали выводы о требованиях к: +- данным для обучения, +- архитектуре автокодировщика, +- количеству эпох обучения, +- ошибке MSE_stop, приемлемой для останова обучения, +- ошибке реконструкции обучающей выборки (порогу обнаружения аномалий), +- характеристикам качества обучения EDCA одноклассового классификатора + +для качественного обнаружения аномалий в данных. + +- Данные для обучения: должны содержать только нормальные примеры без выбросов и полно описывать область нормы. +- Архитектура автокодировщика: симметричная, с узким «бутылочным горлышком» +- Количество эпох: достаточно большое, чтобы сеть обучилась, но без переобучения, в для нашего набора оптимально окло 3000. +- Ошибка MSE_stop: должна быть небольшой, но не меньше 0.01 , дабы избежать переобучение. +- Порог ошибки реконструкции: выбирается по максимуму IRE обучающей выборки; высокий порог → пропуски аномалий. +- Характеристики EDCA: хорошая модель имеет низкий Excess, высокий Approx ≈ 1; такие модели точнее очерчивают область нормы и лучше детектируют аномалии. + +--- + +## 2.1. Изучили описание своего набора реальных данных + +Набор Letter Recognition предназначен для распознавания букв английского алфавита по 16 числовым признакам, описывающим изображение буквы. +Для задачи обнаружения аномалий из исходных данных выделены три буквы, формирующие нормальный класс, а для аномалий — случайные буквы, не входящие в нормальный класс. +Чтобы усложнить задачу, данные объединены попарно, в результате чего размерность увеличена до 32 признаков. Таким образом, каждый аномальный пример частично содержит признаки нормального класса, что делает задачу более сложной для автокодировщика. +| Количество признаков | Количество примеров | Количество нормальных примеров | Количество аномальных примеров | +| ----- | ----- | ------ | ----- | +| **32** | **1600** | **1500** | **100** | + + +--- + +## 2.2. Загрузили многомерную обучающую выборку реальных данных name_train.txt. + +```python +train = np.loadtxt('letter_train.txt', dtype=float) +``` + +--- + +## 2.3. Вывели полученные данные и их размерность в консоли. + +```python +print('train.shape =', train.shape) +print('Пример train:', train) +``` + +``` +train.shape = (1500, 32) +Пример train: [[ 6. 10. 5. ... 10. 2. 7.] + [ 0. 6. 0. ... 8. 1. 7.] + [ 4. 7. 5. ... 8. 2. 8.] + ... + [ 7. 10. 10. ... 8. 5. 6.] + [ 7. 7. 10. ... 6. 0. 8.] + [ 3. 4. 5. ... 9. 5. 5.]] +``` + +--- + +## 2.4. Cоздали и обучили автокодировщик с подходящей для данных архитектурой. + +```python +patience = 5000 +ae3_trained, IRE3, IREth3 = lib.create_fit_save_ae( + train, 'out/AE3.h5', 'out/AE3_ire_th.txt', + 100000, False, patience, verbose_every_n_epochs = 5000, early_stopping_delta = 0.001 +) +``` + +--- + +## 2.5. Зафиксирывали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксирывали порог ошибки реконструкции – порог обнаружения аномалий. + +MSE = 0.4862 + +```python +lib.ire_plot('training', IRE3, IREth3, 'AE3') +print('AE3 IREth =', IREth3) +``` + +![Грфик](images_for_report/14.png) + +``` +Порог ошибки реконструкции: +AE3 IREth = 10.75 +``` + +--- + +## 2.6. Сделать вывод о пригодности обученного автокодировщика для качественного обнаружения аномалий. Если порог ошибки реконструкции слишком велик, то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (6). + +### Очент высокий порог, так что попробуем другю структуру: + + +MSE = 0.3363 + +```python +lib.ire_plot('training', IRE3, IREth3, 'AE3') +print('AE3 IREth =', IREth3) +``` + +![Грфик](images_for_report/15.png) + +``` +Порог ошибки реконструкции: +AE3 IREth = 6.75 +``` + +Отсановимся на этой модели + +--- + +## 2.7. Изучили и загрузили тестовую выборку letter_test.txt. + +```python +test = np.loadtxt('letter_test.txt', dtype=float) +print('test.shape =', test.shape) +print('Пример train:', test) +``` + +``` +test.shape = (100, 32) +Пример train: [[ 8. 11. 8. ... 7. 4. 9.] + [ 4. 5. 4. ... 13. 8. 8.] + [ 3. 3. 5. ... 8. 3. 8.] + ... + [ 4. 9. 4. ... 8. 3. 8.] + [ 6. 10. 6. ... 9. 8. 8.] + [ 3. 1. 3. ... 9. 1. 7.]] +``` + +--- + +## 2.8. Подали тестовую выборку на вход обученного автокодировщика для обнаружения аномалий. Вывели график ошибки реконструкции элементов тестовой выборки относительно порога. + +```python +predicted_labels3_v1, ire3_v1 = lib.predict_ae(ae3_v1_trained, test, IREth3_v1) +lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1') +``` + +``` +i Labels IRE IREth +0 [1.] [9.78] 6.75 +1 [1.] [8.92] 6.75 +2 [1.] [14.27] 6.75 +... +99 [1.] [15.66] 6.75 +Обнаружено 91.0 аномалий +``` + +![Грфик](images_for_report/16.png) + +--- + +## 2.9. Если результаты обнаружения аномалий не удовлетворительные (обнаружено менее 70% аномалий), то подобрать подходящие параметры автокодировщика и повторить шаги (4) – (9). + +Результаты приемлемы + +--- + +## 2.10. Параметры наилучшего автокодировщика и результаты обнаружения аномалий занести в таблицу: + +| Dataset name | Кол-во скрытых слоёв | Кол-во нейронов в скрытых слоях | Кол-во эпох обучения | Ошибка MSE_stop | Порог ошибки реконструкции |% обнаруженных аномалий | +| --- | --- | --- | --- | --- | --- | --- | +| Letter | 9 | 35 28 21 14 7 14 21 28 35 | 100000 | 0.3363 | 6.75 | 91% | + + +--- + +## 2.11. Сделать выводы о требованиях к: +- данным для обучения, +- архитектуре автокодировщика, +- количеству эпох обучения, +- ошибке MSE_stop, приемлемой для останова обучения, +- ошибке реконструкции обучающей выборки (порогу обнаруженияаномалий) + +для качественного обнаружения аномалий в случае, когда размерность +пространства признаков высока. + +- Данные для обучения: должны содержать только нормальные объекты(отсутсвие аномалий). +- Архитектура автокодировщика: симметричная, 9 скрытых слоёв с наличием бутылочного горлышка +- Количество эпох: около 100 000 достаточно для стабилизации ошибки и точного восстановления данных. +- Ошибка MSE_stop: практика показала, что 0.34 — приемлемое значение для высокоразмерного набора Letter. +- Качество обнаружения: 91 % аномалий — модель адекватно аппроксимирует область нормы и надёжно выделяет отклонения. +