1820 lines
298 KiB
Plaintext
1820 lines
298 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "VFxYDW1SM-r0",
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"metadata": {
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"id": "VFxYDW1SM-r0"
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},
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"source": [
|
||
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\" width=\"50\"/>\n",
|
||
"\n",
|
||
"# Машинное обучение. ВМК МГУ"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "myHxcHVTNDfn",
|
||
"metadata": {
|
||
"id": "myHxcHVTNDfn"
|
||
},
|
||
"source": [
|
||
"# Практическое задание 7: Кластеризация. Методы снижения размерности.\n",
|
||
"## Уровень: <font color='MediumSeaGreen'>**Исследовательский (Research)**</font>\n",
|
||
"\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fKpuBwx27X2n",
|
||
"metadata": {
|
||
"id": "fKpuBwx27X2n"
|
||
},
|
||
"source": [
|
||
"# О формате сдачи\n",
|
||
"\n",
|
||
"🔷 **<font color='plum'>При решении ноутбука используйте данный шаблон</font>**\n",
|
||
"\n",
|
||
" ✅ Можно добавлять новые ячейки любых типов\n",
|
||
" ❌ Не нужно удалять текстовые ячейки c разметкой частей ноутбука и формулировками заданий\n",
|
||
"\n",
|
||
"\n",
|
||
"🔷 **<font color='plum'>При оценивании задач учитывается код</font>**\n",
|
||
"\n",
|
||
" ✅ Задания, в которых необходим код, обычно помечаются фразами \"Your code here\"/\"Ваш код\" и аналогичными\n",
|
||
" ❌ Ответы на вопросы без сопутствующего кода оцениваются в 0 баллов\n",
|
||
" ❌ Наличе работоспособного кода в ноутбуке, если на сказано иного, обязательно\n",
|
||
"\n",
|
||
"🔷 **<font color='plum'>При оценивании задач учитываются выводы</font>**\n",
|
||
"\n",
|
||
" ✅ Задания, в которых необходимы выводы, обычно помечаются фразами Вывод\"/\"Ответ на вопрос\"/\"Ваш текст\" и аналогичными\n",
|
||
" ✅ Обычно выводы подразумевают под собой текстовый ответ (можно писать markdown, latex).\n",
|
||
" ✅ Сопутствующие изображения, графики, таблички - приветствуются!\n",
|
||
" ❌ При отсутствии выводов задание не засчитается на полный балл\n",
|
||
"\n",
|
||
"-----------\n",
|
||
"<font color=\"white\" style=\"opacity:0.2024\"></font>\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "baEqASDy7X2o",
|
||
"metadata": {
|
||
"id": "baEqASDy7X2o"
|
||
},
|
||
"source": [
|
||
"__В этом задании вы..:__\n",
|
||
"\n",
|
||
"* Познакомитесь с одним способом визуализации процесса обучения\n",
|
||
"* Сравните между собой результаты разных способов кластеризации\n",
|
||
"* Посмотрите и реализуете несколько метрик качества кластеризации\n",
|
||
"* Попробуете разные методы снижения размерности\n",
|
||
"\n",
|
||
"----\n",
|
||
"\n",
|
||
"<font color=\"white\" style=\"opacity:0.2024\"></font>\n",
|
||
"<font color=DarkOrange>**Примерное время выполнения (execution time/время выполнения, если нажать run all) всех ячеек ноутбука при правильной реализации: 60 минут </font>**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "Fl6R5pHXNWVc",
|
||
"metadata": {
|
||
"id": "Fl6R5pHXNWVc"
|
||
},
|
||
"source": [
|
||
"----------------------------------------------\n",
|
||
"<font color=\"white\" style=\"opacity:0.2023\"></font>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "df956837",
|
||
"metadata": {
|
||
"id": "df956837"
|
||
},
|
||
"source": [
|
||
"Перед началом выполнения переведите ноутбук в `Доверенный режим` (`Trusted`) для корректного отображения изображений:\n",
|
||
"\n",
|
||
"<img alt=\"\" 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\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d0ef4020",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T13:59:34.820566Z",
|
||
"start_time": "2023-03-01T13:59:34.788142Z"
|
||
},
|
||
"id": "d0ef4020"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%config Completer.use_jedi = False\n",
|
||
"%load_ext autoreload\n",
|
||
"%autoreload 2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "VEIxThGGNcxw",
|
||
"metadata": {
|
||
"id": "VEIxThGGNcxw"
|
||
},
|
||
"source": [
|
||
"----------------------------------------------\n",
|
||
"<font color=\"white\" style=\"opacity:0.2023\"></font>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dGuTHcED7i4j",
|
||
"metadata": {
|
||
"id": "dGuTHcED7i4j"
|
||
},
|
||
"source": [
|
||
"# Подготовка рабочей среды\n",
|
||
"\n",
|
||
"Сначала установим нужные нам версии библиотек. Мы гарантируем, что в данных версиях задание будет корректно отрабатывать.\n",
|
||
"\n",
|
||
"После установки нужных версий, **возможно,** нужно перезагрузить среду (runtime), но скорее всего вам это не понадобится\n",
|
||
"\n",
|
||
"\n",
|
||
"На скачивание файла и установку понадобится не более 5 минут.\n",
|
||
"\n",
|
||
"<font color='OrangeRed'>**Важно!**</font>\n",
|
||
"\n",
|
||
"Устанавливать нужные версии нужно каждый раз, когда создается новый рантайм. Например, если вы 2 часа подряд делаете это задание, то подготовить библиотеки достаточно 1 раз. Но если вы, например, начали в понедельник, затем закрыли/выключили ноутбук, то при продолжении в среду, вам нужно будет запустить рантайм заново и следовательно заново установить библиотеки.\n",
|
||
"\n",
|
||
"<font color='OrangeRed'>**Важно!**</font>\n",
|
||
"Если вы предпочитаете делать практические задания на своем личном ноутбуке, то проверьте, что вы установили рабочее окружение в [соответствии с гайдом](https://github.com/MSU-ML-COURSE/ML-COURSE-24-25/blob/main/tutorials/%D0%A2%D1%83%D1%82%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%20%D0%BF%D0%BE%20%D1%83%D1%81%D1%82%D0%B0%D0%BD%D0%BE%D0%B2%D0%BA%D0%B5%20%D1%80%D0%B0%D0%B1%D0%BE%D1%87%D0%B5%D0%B3%D0%BE%20%D0%BE%D0%BA%D1%80%D1%83%D0%B6%D0%B5%D0%BD%D0%B8%D1%8F%20%D0%B2%20Python%20%D0%B4%D0%BB%D1%8F%20%D1%80%D0%B5%D1%88%D0%B5%D0%BD%D0%B8%D1%8F%20%D0%B7%D0%B0%D0%B4%D0%B0%D1%87%20(2).pdf)\n",
|
||
"\n",
|
||
"-----\n",
|
||
"\n",
|
||
"<font color='OrangeRed'>**Важно!**</font> В этом задании мы будем использовать полное виртуальное окружение, так как понадобятся библиотеки `torch` и `tensorflow`\n",
|
||
"\n",
|
||
"Обратите внимание, что установка `torch` и `tensorflow` через `pip `может сломать ваше окружение, особенно если вы используете GPU. Выполняйте их установку в соответствии с Вашей конфигурацией системы или в отдельном виртуальном окружении"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "-gSWrzAD7iIq",
|
||
"metadata": {
|
||
"id": "-gSWrzAD7iIq"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"! curl https://raw.githubusercontent.com/MSU-ML-COURSE/ML-COURSE-25-26/refs/heads/master/requirements/requirements.txt -o ./requirements_2025_26_for_colab_small.txt\n",
|
||
"! pip install -q -r ./requirements_2025_26_for_colab_small.txt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "92056c3a",
|
||
"metadata": {},
|
||
"source": [
|
||
"i use arch btw"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "Th8Xxd4aA-vC",
|
||
"metadata": {
|
||
"id": "Th8Xxd4aA-vC"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import catboost"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "34062f10",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "KVECRShlPiMo",
|
||
"metadata": {
|
||
"id": "KVECRShlPiMo"
|
||
},
|
||
"source": [
|
||
"Теперь можно приступать к выполнению задания! :)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "GDOxRUyMPl6I",
|
||
"metadata": {
|
||
"id": "GDOxRUyMPl6I"
|
||
},
|
||
"source": [
|
||
"-----------\n",
|
||
"<font color=\"white\" style=\"opacity:0.2024\"></font>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c431261c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2021-08-27T20:30:10.688162Z",
|
||
"start_time": "2021-08-27T20:30:10.590165Z"
|
||
},
|
||
"id": "c431261c"
|
||
},
|
||
"source": [
|
||
"# 1 О задании"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "18f2f660",
|
||
"metadata": {
|
||
"id": "18f2f660"
|
||
},
|
||
"source": [
|
||
"В данной работе вам предстоит познакомится с методами машинного обучения без учителя — кластеризацией и алгоритмами снижения размерности."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e2a6a58",
|
||
"metadata": {
|
||
"id": "6e2a6a58"
|
||
},
|
||
"source": [
|
||
"Рекомендуется использовать Kaggle так как в нём корректно работают интерактивные визуализации."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4f1d3166",
|
||
"metadata": {
|
||
"id": "4f1d3166"
|
||
},
|
||
"source": [
|
||
"Здесь перечислены основные функции и библиотеки, которые могут понадобиться Вам в процессе выполнения задания. Подключение других библиотек возможно, но нежелательно. **Работа каких-либо других библиотек не гарантируется.**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "454c2b3b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T22:39:16.136071Z",
|
||
"start_time": "2023-03-01T22:39:02.734480Z"
|
||
},
|
||
"id": "454c2b3b"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import os\n",
|
||
"\n",
|
||
"import gdown\n",
|
||
"\n",
|
||
"import scipy\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"import tqdm.auto as tqdm\n",
|
||
"\n",
|
||
"import matplotlib\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
|
||
"\n",
|
||
"from ipywidgets import interactive, fixed, interact_manual, IntSlider, FloatLogSlider, FloatSlider\n",
|
||
"\n",
|
||
"import torch\n",
|
||
"from torchvision.datasets import CIFAR10\n",
|
||
"\n",
|
||
"# Необходима преварительная установка tensorflow\n",
|
||
"from keras.applications.inception_v3 import InceptionV3, preprocess_input\n",
|
||
"\n",
|
||
"import sklearn\n",
|
||
"\n",
|
||
"from sklearn.decomposition import KernelPCA\n",
|
||
"from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
|
||
"\n",
|
||
"# Библиотека umap-learn, а не umap\n",
|
||
"from umap import UMAP\n",
|
||
"from sklearn.manifold import TSNE, Isomap\n",
|
||
"\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.datasets import make_classification, make_moons, make_blobs\n",
|
||
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
|
||
"\n",
|
||
"from warnings import simplefilter\n",
|
||
"from sklearn.exceptions import ConvergenceWarning\n",
|
||
"simplefilter(\"ignore\", category=ConvergenceWarning)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "664fdc07",
|
||
"metadata": {
|
||
"id": "664fdc07"
|
||
},
|
||
"source": [
|
||
"Определим вспомогательную функцию для отрисовки двумерных кластеризованных данных. При выполенении задания желательно пользоваться этой функцией для визуализации. При необходимости можете менять сигнатуру и поведение функции как вам удобно, _оставляя стиль отрисовки в целом неизменным_."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "edcc67b5",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T14:05:19.377145Z",
|
||
"start_time": "2023-03-01T14:05:19.292571Z"
|
||
},
|
||
"code_folding": [],
|
||
"id": "edcc67b5"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_2d_data(data, labels, title='Исходные данные', cmap='tab20', ax=None):\n",
|
||
" '''\n",
|
||
" Отрисовка 2d scatter plot.\n",
|
||
" :param np.ndarray data: 2d массив точек\n",
|
||
" :param Union[list, np.ndarray] labels: список меток для каждой точки выборки\n",
|
||
" :param str title: Заголовок графика\n",
|
||
" :param str cmap: Цветовая палитра\n",
|
||
" :param ax Optional[matplotlib.axes.Axes]: Оси для отрисовки графика.\n",
|
||
" Если оси не заданы, то создаётся новая фигура и сразу же происходит её отрисовка\n",
|
||
" Иначе, график добавляется на существуюущие оси. Отрисовки фигуры не происходит\n",
|
||
" '''\n",
|
||
" n_clusters = len(np.unique(labels))\n",
|
||
"\n",
|
||
" if ax is None:\n",
|
||
" fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
|
||
" else:\n",
|
||
" fig = None\n",
|
||
"\n",
|
||
" scatter = ax.scatter(\n",
|
||
" data[:, 0], data[:, 1], c=labels,\n",
|
||
" cmap=plt.get_cmap(cmap, n_clusters)\n",
|
||
" )\n",
|
||
"\n",
|
||
" cbar = plt.colorbar(scatter, label='Номер кластера', ax=ax)\n",
|
||
" cbar.set_ticks(np.min(labels) + (np.arange(n_clusters) + 0.5) * (n_clusters - 1) / n_clusters)\n",
|
||
" cbar.set_ticklabels(np.unique(labels))\n",
|
||
"\n",
|
||
" ax.set_title(title)\n",
|
||
" ax.grid(True)\n",
|
||
"\n",
|
||
" if fig is not None:\n",
|
||
" fig.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "EgriKAd1PP1p",
|
||
"metadata": {
|
||
"id": "EgriKAd1PP1p"
|
||
},
|
||
"source": [
|
||
"Также используйте написанные реализации из [Base] задания:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "9smT4T9jPUc9",
|
||
"metadata": {
|
||
"id": "9smT4T9jPUc9"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def silhouette_score(x, labels):\n",
|
||
" '''\n",
|
||
" :param np.ndarray x: Непустой двумерный массив векторов-признаков\n",
|
||
" :param np.ndarray labels: Непустой одномерный массив меток объектов\n",
|
||
" :return float: Коэффициент силуэта для выборки x с метками labels\n",
|
||
" '''\n",
|
||
"\n",
|
||
" # Ваш код здесь:＼(º □ º l|l)/\n",
|
||
"\n",
|
||
" return sil_score\n",
|
||
"\n",
|
||
"def bcubed_score(true_labels, predicted_labels):\n",
|
||
" '''\n",
|
||
" :param np.ndarray true_labels: Непустой одномерный массив меток объектов\n",
|
||
" :param np.ndarray predicted_labels: Непустой одномерный массив меток объектов\n",
|
||
" :return float: B-Cubed для объектов с истинными метками true_labels и предсказанными метками predicted_labels\n",
|
||
" '''\n",
|
||
"\n",
|
||
" # Ваш код здесь:＼(º □ º l|l)/\n",
|
||
"\n",
|
||
" return score"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c5b91971",
|
||
"metadata": {
|
||
"id": "c5b91971"
|
||
},
|
||
"source": [
|
||
"## 1.1 Ещё несколько важных замечаний"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ec58d95f",
|
||
"metadata": {
|
||
"id": "ec58d95f"
|
||
},
|
||
"source": [
|
||
"При выполнении задания запрещено:\n",
|
||
"1. Менять те seed, которые явно указаны в коде\n",
|
||
"2. Менять прототипы функций, классов, методов классов\n",
|
||
"3. Менять константы, используемые для генерации выборок"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "80e5b836",
|
||
"metadata": {
|
||
"id": "80e5b836"
|
||
},
|
||
"source": [
|
||
"При оформлении задания обратите внимание на форматирование кода и на оформление графиков:\n",
|
||
"\n",
|
||
"* Весь код должен быть оформлен в строгом соответствии с [PEP8](https://pep8.org/)\n",
|
||
"\n",
|
||
"Графики должны быть с одной стороны понятными и информативными, а с другой стороны *красивыми*. Вот несколько пунктов, которые помогут удовлетворить этим требования:\n",
|
||
"1. Все графики должны быть отрисованы в **векторном формате**. Обратите внимание, что смена режима графиков с динамического на статический и обратно может приводить к сбросу параметров отрисовки графиков. Переход в векторный режим можно выполнить с помощью команды `matplotlib_inline.backend_inline.set_matplotlib_formats('pdf', 'svg')`. Если изображения в векторном формате приводят к слишком большому размеру Jupyter Notebook можете использовать растровые изображения с **высоким dpi**. Напирмер, можно установить глобальный dpi в matplotlib: `matplotlib.rcParams['figure.dpi'] = 300`\n",
|
||
"2. На всех графиках без исключения должна быть нарисована сетка\n",
|
||
"3. Все графики и группы графиков должны иметь заголовок (`title`)\n",
|
||
"4. При необходимости оси должны быть подписаны\n",
|
||
"5. Если на графике отображено несколько сущностей (линии/точки/bar разных цветов, формы и так далее), то необходима исчерпывающая легенда\n",
|
||
"6. Все линии на графиках должны быть чётко видны (нет похожих цветов или цветов, сливающихся с фоном и так далее)\n",
|
||
"7. Масштаб по каждой оси на графике должен быть выбран правильно. Используйте масштабы `log`, `symlog` по необходимости\n",
|
||
"8. Если отображена величина, имеющая очевидный диапазон значений (например, проценты могут быть от 0 до 100), то желательно масштабировать ось на весь диапазон значений (исключением является случай, когда вам необходимо показать малое отличие, которое незаметно в таких масштабах)\n",
|
||
"9. Частота отметок по каждой оси должна быть тщательно подобрана, по необходимости задавайте `[xy]ticks`, `[xy]ticklabels` вручную. Подписи тиков на осях не должны сливаться как на одной оси, так и между ними\n",
|
||
"10. Помните, что matplotlib умеет выполнять [рендеринг Latex](https://matplotlib.org/stable/gallery/text_labels_and_annotations/tex_demo.html). Используйте эту возможность для написания формул в заголовках, легенде и в подписях осей\n",
|
||
"11. Используйте *красивую* цветовую палитру с хорошо различимыми цветами. Примеры цветовых палитр можно посмотреть [здесь](https://matplotlib.org/stable/gallery/color/colormap_reference.html). При наличи особенностей восприятия цвета можно использовать специальные палитры:\n",
|
||
"```python\n",
|
||
"plt.style.use('seaborn-colorblind')\n",
|
||
"# Или\n",
|
||
"plt.style.use('tableau-colorblind10')\n",
|
||
"# Затем, при отрисовке графиков не используйте параметр cmap\n",
|
||
"```\n",
|
||
"12. Графики должны быть не супер-микро и не супер-макро по размерам, так, чтобы можно было увидеть все, что нужно"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "IsIaogfeNZMr",
|
||
"metadata": {
|
||
"id": "IsIaogfeNZMr"
|
||
},
|
||
"source": [
|
||
"----------------------------------------------\n",
|
||
"<font color=\"white\" style=\"opacity:0.2023\"></font>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6a004841",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2021-08-15T20:20:07.191101Z",
|
||
"start_time": "2021-08-15T20:20:07.173102Z"
|
||
},
|
||
"id": "6a004841"
|
||
},
|
||
"source": [
|
||
"# <font color='DarkOrange'>2. Кластеризация \"естественных\" данных.</font>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "80d78c9c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2021-08-16T13:40:56.472421Z",
|
||
"start_time": "2021-08-16T13:40:53.607422Z"
|
||
},
|
||
"id": "80d78c9c"
|
||
},
|
||
"source": [
|
||
"Синтетические данные имеют достаточно простую структуру, поэтому методы снижения размерности позволяют получать хорошее низкоразмерное представление с достаточно выраженными кластерами. Однако, реальные данные могут быть устроены существенно сложнее. Посмотрим как поведут себя методы снижения размерности на датасете с картинками CIFAR10."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d6959566",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2021-08-18T18:04:08.749254Z",
|
||
"start_time": "2021-08-18T18:04:08.682253Z"
|
||
},
|
||
"id": "d6959566"
|
||
},
|
||
"source": [
|
||
"Загрузим датасет. Будем использовать только часть обучающей выборки, чтобы ускорить вычисления на высокоразмерных данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "6290c0b5",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T14:14:34.544651Z",
|
||
"start_time": "2023-03-01T14:14:33.016499Z"
|
||
},
|
||
"id": "6290c0b5"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"cifar10_test_dataset = CIFAR10('./cifar10', train=False, download=True)\n",
|
||
"cifar10_train_dataset = CIFAR10('./cifar10', train=True, download=False)\n",
|
||
"\n",
|
||
"cifar10_labels_test = np.array(cifar10_test_dataset.targets)\n",
|
||
"cifar10_labels_train = np.array(cifar10_train_dataset.targets)\n",
|
||
"\n",
|
||
"cifar10_images_test = cifar10_test_dataset.data\n",
|
||
"cifar10_images_train = cifar10_train_dataset.data\n",
|
||
"\n",
|
||
"cifar10_images_train, _, cifar10_labels_train, _ = train_test_split(\n",
|
||
" cifar10_images_train, cifar10_labels_train,\n",
|
||
" train_size=cifar10_images_test.shape[0], stratify=cifar10_labels_train, random_state=6886\n",
|
||
")\n",
|
||
"\n",
|
||
"cifar10_data_test = (cifar10_images_test.astype(np.float32) / 255.0).reshape([cifar10_images_test.shape[0], -1])\n",
|
||
"cifar10_data_train = (cifar10_images_train.astype(np.float32) / 255.0).reshape([cifar10_images_train.shape[0], -1])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f02a6a78",
|
||
"metadata": {
|
||
"id": "f02a6a78"
|
||
},
|
||
"source": [
|
||
"Отобразим данные в проекции на две случайные оси. Для удобства воспользуемся здесь ещё одним вариантом динамического контента в jupyter notebook — при наведении на точку на графике будем отображать исходную картинку."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "dfd15f60",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T14:55:46.519715Z",
|
||
"start_time": "2023-03-01T14:55:46.428281Z"
|
||
},
|
||
"code_folding": [
|
||
0
|
||
],
|
||
"id": "dfd15f60"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_interactive(lowd_data, images, labels, names, n_dots=1000, image_scale=1.0):\n",
|
||
" with matplotlib.rc_context(rc={\n",
|
||
" 'font.size': image_scale * matplotlib.rcParams['font.size'],\n",
|
||
" 'xtick.major.size': image_scale * matplotlib.rcParams['xtick.major.size'],\n",
|
||
" 'xtick.minor.size': image_scale * matplotlib.rcParams['xtick.minor.size'],\n",
|
||
" 'ytick.major.size': image_scale * matplotlib.rcParams['ytick.major.size'],\n",
|
||
" 'ytick.minor.size': image_scale * matplotlib.rcParams['ytick.minor.size'],\n",
|
||
"\n",
|
||
" 'axes.linewidth': image_scale * matplotlib.rcParams['axes.linewidth'],\n",
|
||
" 'grid.linewidth': image_scale * matplotlib.rcParams['grid.linewidth'],\n",
|
||
" 'patch.linewidth': image_scale * matplotlib.rcParams['patch.linewidth'],\n",
|
||
" 'xtick.major.width': image_scale * matplotlib.rcParams['xtick.major.width'],\n",
|
||
" 'xtick.minor.width': image_scale * matplotlib.rcParams['xtick.minor.width'],\n",
|
||
" 'ytick.major.width': image_scale * matplotlib.rcParams['ytick.major.width'],\n",
|
||
" 'ytick.minor.width': image_scale * matplotlib.rcParams['ytick.minor.width'],\n",
|
||
"\n",
|
||
" 'lines.markeredgewidth': image_scale * matplotlib.rcParams['lines.markeredgewidth'],\n",
|
||
" }):\n",
|
||
" fig, ax = plt.subplots(1, 1, figsize=(image_scale * 10, image_scale * 5))\n",
|
||
" fig.set_dpi(300)\n",
|
||
" ax.grid(True)\n",
|
||
"\n",
|
||
" n_clusters = len(np.unique(labels))\n",
|
||
"\n",
|
||
" scatter = plt.scatter(\n",
|
||
" lowd_data[:n_dots, 0], lowd_data[:n_dots, 1], s=image_scale * 10,\n",
|
||
" c=labels[:n_dots], cmap=plt.get_cmap('tab20', n_clusters), edgecolors='none'\n",
|
||
" )\n",
|
||
"\n",
|
||
" cbar = plt.colorbar(scatter, ax=ax, label='Название кластера')\n",
|
||
" cbar.set_ticks(np.min(labels[:n_dots]) + (np.arange(n_clusters) + 0.5) * (n_clusters - 1) / n_clusters)\n",
|
||
" cbar.set_ticklabels(names)\n",
|
||
"\n",
|
||
" offset_image = OffsetImage(images[0], zoom=image_scale * 2.0)\n",
|
||
" ann_bbox = AnnotationBbox(\n",
|
||
" offset_image, (0,0), xybox=(image_scale * 50., image_scale * 50.), xycoords='data',\n",
|
||
" boxcoords=\"offset points\", pad=0.3, arrowprops=dict(\n",
|
||
" arrowstyle='->, head_length={0:.2f}, head_width={1:.2f}'.format(\n",
|
||
" image_scale * 0.4, image_scale * 0.2\n",
|
||
" )\n",
|
||
" )\n",
|
||
" )\n",
|
||
" ax.add_artist(ann_bbox)\n",
|
||
" ax.set_title('Распределение данных CIFAR10 в проекции на 2 случайные оси')\n",
|
||
" ann_bbox.set_visible(False)\n",
|
||
"\n",
|
||
" def image_hover(event):\n",
|
||
" if scatter.contains(event)[0]:\n",
|
||
" ind, *_ = scatter.contains(event)[1][\"ind\"]\n",
|
||
" w, h = fig.get_size_inches() * fig.dpi\n",
|
||
" ws = (event.x > w / 2.) * -1 + (event.x <= w / 2.)\n",
|
||
" hs = (event.y > h / 2.) * -1 + (event.y <= h / 2.)\n",
|
||
" ann_bbox.xybox = (image_scale * 50.0 * ws, image_scale * 50.0 * hs)\n",
|
||
" ann_bbox.set_visible(True)\n",
|
||
" ann_bbox.xy =(lowd_data[ind, 0], lowd_data[ind, 1])\n",
|
||
" offset_image.set_data(images[ind])\n",
|
||
" else:\n",
|
||
" ann_bbox.set_visible(False)\n",
|
||
" fig.canvas.draw_idle()\n",
|
||
"\n",
|
||
" fig.canvas.mpl_connect('motion_notify_event', image_hover)\n",
|
||
"\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d47026c4",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T14:55:47.324878Z",
|
||
"start_time": "2023-03-01T14:55:47.221576Z"
|
||
},
|
||
"id": "d47026c4"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib ipympl\n",
|
||
"matplotlib.rcParams['figure.dpi'] = 300\n",
|
||
"\n",
|
||
"# Для работы в Google Colab нужно выполнить специфичную магию\n",
|
||
"# Обычно, она не срабатывает с первого раза, поэтому может потребоваться\n",
|
||
"# несколько раз выполнить ячейку и несколько раз попробовать нарисовать график\n",
|
||
"try:\n",
|
||
" from google.colab import output\n",
|
||
" output.enable_custom_widget_manager()\n",
|
||
"except:\n",
|
||
" pass"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "157a31c3",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T14:56:11.717485Z",
|
||
"start_time": "2023-03-01T14:56:11.439758Z"
|
||
},
|
||
"id": "157a31c3"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Если картинка окажется слишком маленькой/большой, то поменяйте image_scale на подходящее значение\n",
|
||
"plot_interactive(\n",
|
||
" cifar10_data_train[:, [17, 64]], cifar10_images_train, cifar10_labels_train,\n",
|
||
" cifar10_test_dataset.classes, n_dots=2000, image_scale=0.35\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4534b090",
|
||
"metadata": {
|
||
"id": "4534b090"
|
||
},
|
||
"source": [
|
||
"Вернёмся в статичный режим отрисовки изображений:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "7068db47",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T14:57:40.572601Z",
|
||
"start_time": "2023-03-01T14:57:40.493455Z"
|
||
},
|
||
"id": "7068db47"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"matplotlib.rcParams['figure.dpi'] = 300"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fc40ed1a",
|
||
"metadata": {
|
||
"id": "fc40ed1a"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.1 [кросспроверка, 1 балл][код]**\n",
|
||
"<a id='task_2.1'></a></font>\n",
|
||
"Воспользуйтесь алгоритмами снижения размерности `TSNE`, `UMAP`, `Isomap`, `KernelPCA` для визуализации картинок.\n",
|
||
"\n",
|
||
"Постройте визуализацию низкоразмерного представления, полученного с помощью этих моделей — изобразите четыре графика в одной строке. Во второй строке отобразите результат применения обученных моделей на тестовой выборке. Если для данного алгоритма невозможно сделать предсказания на тестовой выборке — оставьте соответствующий график пустым. Обозначьте разными цветами разные классы объектов. Для повышения производительности можете отобразить только часть выборки на графике ($1000\\text{-}2000$ объектов).\n",
|
||
"\n",
|
||
"<font color='OrangeRed'>**Замечание:**</font> обратите внимание, что все алгоритмы снижения размерности также требуют правильного масштабирования признаков, для корректной работы и интерпретируемых результатов."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "8cd1716b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T14:57:42.465402Z",
|
||
"start_time": "2023-03-01T14:57:42.385992Z"
|
||
},
|
||
"id": "8cd1716b"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Ваш код здесь:＼(º □ º l|l)/\n",
|
||
"n_samples = 2000\n",
|
||
"train_indices = np.random.choice(cifar10_data_train.shape[0], n_samples, replace=False)\n",
|
||
"test_indices = np.random.choice(cifar10_data_test.shape[0], n_samples, replace=False)\n",
|
||
"cifar10_data_train_scaled = StandardScaler().fit_transform(cifar10_data_train)\n",
|
||
"cifar10_data_test_scaled = StandardScaler().fit_transform(cifar10_data_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "60ac0430",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_train_sample = cifar10_data_train_scaled[train_indices]\n",
|
||
"y_train_sample = cifar10_labels_train[train_indices]\n",
|
||
"X_test_sample = cifar10_data_test_scaled[test_indices]\n",
|
||
"y_test_sample = cifar10_labels_test[test_indices]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "08a720b5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tsne = TSNE(n_components=2, perplexity=30, n_iter=1000, n_jobs=-1)\n",
|
||
"umap_reducer = UMAP(n_components=2, n_neighbors=15, min_dist=0.1, metric='euclidean')\n",
|
||
"isomap = Isomap(n_components=2, n_neighbors=10, n_jobs=-1)\n",
|
||
"kpca = KernelPCA(n_components=2, kernel='rbf', gamma=None,\n",
|
||
" n_jobs=-1)\n",
|
||
"models = {\n",
|
||
" \"t-SNE\": tsne,\n",
|
||
" \"UMAP\": umap_reducer,\n",
|
||
" \"Isomap\": isomap,\n",
|
||
" \"KernelPCA (RBF)\": kpca\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "6cef4b2c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"fig, axes = plt.subplots(2, 4, figsize=(20, 10))\n",
|
||
"fig.suptitle(\n",
|
||
" \"Визуализация понижения размерности CIFAR-10 ({} сэмплов)\".format(n_samples),\n",
|
||
" fontsize=16,\n",
|
||
")\n",
|
||
"for i, (name, model) in enumerate(models.items()):\n",
|
||
"\n",
|
||
" if name == \"t-SNE\":\n",
|
||
" X_train_reduced = model.fit_transform(X_train_sample)\n",
|
||
" else:\n",
|
||
" model.fit(X_train_sample)\n",
|
||
" X_train_reduced = model.transform(X_train_sample)\n",
|
||
"\n",
|
||
" plot_2d_data(\n",
|
||
" X_train_reduced, y_train_sample, title=f\"{name} (Train)\", ax=axes[0, i]\n",
|
||
" )\n",
|
||
"\n",
|
||
" if name == \"t-SNE\":\n",
|
||
" axes[1, i].set_title(f\"{name} (Test - N/A)\")\n",
|
||
" axes[1, i].set_xticks([])\n",
|
||
" axes[1, i].set_yticks([])\n",
|
||
" axes[1, i].text(\n",
|
||
" 0.5,\n",
|
||
" 0.5,\n",
|
||
" \"N/A for sklearn t-SNE\",\n",
|
||
" horizontalalignment=\"center\",\n",
|
||
" verticalalignment=\"center\",\n",
|
||
" transform=axes[1, i].transAxes,\n",
|
||
" color=\"gray\",\n",
|
||
" )\n",
|
||
" else:\n",
|
||
" X_test_reduced = model.transform(X_test_sample)\n",
|
||
" plot_2d_data(\n",
|
||
" X_test_reduced, y_test_sample, title=f\"{name} (Test)\", ax=axes[1, i]\n",
|
||
" )\n",
|
||
"\n",
|
||
"plt.tight_layout(rect=(0, 0.03, 1, 0.95))\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fa21247b",
|
||
"metadata": {
|
||
"id": "fa21247b"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.2 [кросспроверка, 1 балл][вопрос]**</font>\n",
|
||
"Опишите увиденное. Почему алгоритмы могли отработать не так, как вы ожидали?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8bac15da",
|
||
"metadata": {
|
||
"id": "8bac15da"
|
||
},
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваш ответ здесь:**</font> (ｏ・_・)ノ”(ノ_<、):\n",
|
||
"\n",
|
||
"Кластеры на графиках получились слабо разделёнными. График t-SNE для тестовых данных пуст, потому что стандартная реализация TSNE в scikit-learn не умеет отдельно применять уже обученное преобразование к новым данным.\n",
|
||
"\n",
|
||
"Основные причины плохого результата: данные CIFAR-10 имеют очень высокую размерность — 3072 признака, и при сжатии до 2 измерений теряется много важной информации. Кроме того, качество визуализации сильно зависит от выбранных гиперпараметров, которые могли быть неудачными для этой задачи.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "59a23068",
|
||
"metadata": {
|
||
"id": "59a23068"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.3 [кросспроверка, 1 балл][вопрос]**</font>\n",
|
||
"Методы снижения размерности, как и другие метрические методы испытывают трудности при работе с данными высокой размерности. Напишите как минимум две причины, почему."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cb1d9444",
|
||
"metadata": {
|
||
"id": "cb1d9444"
|
||
},
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваш ответ здесь:**</font> (ｏ・_・)ノ”(ノ_<、):\n",
|
||
"\n",
|
||
"В высоких размерностях метрические методы работают хуже из-за «проклятия размерности». Расстояния между точками становятся почти одинаковыми, поэтому понятие ближайшего соседа теряет смысл.\n",
|
||
"\n",
|
||
"Кроме того, большое число шумовых или нерелевантных признаков может скрывать влияние действительно важных признаков. При росте размерности пространство быстро становится разреженным, точки оказываются далеко друг от друга, а локальные окрестности перестают быть информативными.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c059cf2",
|
||
"metadata": {
|
||
"id": "6c059cf2"
|
||
},
|
||
"source": [
|
||
"Один из способов решения этих проблем — перейти в другое, более репрезентативное пространство признаков, где объекты будут расположены в многообразии, которое легче представить в двумерном пространстве. Чтобы выполнить такое преобразование воспользуемся типичным подходом **Transfer Learning** — предобученными нейронными сетями. С помощью глубокой сети обученной на другом наборе изображений (`ImageNet`) мы перейдём в новое векторное пространство и затем применим методы снижения размерности."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a4811aa2",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T23:11:12.954110Z",
|
||
"start_time": "2023-03-01T23:11:12.949227Z"
|
||
},
|
||
"id": "a4811aa2"
|
||
},
|
||
"source": [
|
||
"Так как локальный подсчёт эмбеддингов изображений может занять много времени, Вы можете попробовать скачать их c помощью `gdown`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "99c42afa",
|
||
"metadata": {
|
||
"deletable": false,
|
||
"editable": false,
|
||
"id": "99c42afa",
|
||
"run_control": {
|
||
"frozen": true
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"gdown.download(id='16UgWo1Emt9ar1O4h2Xxed0ZpJZ0OG5V-', output='cifar10_deep_features.npy')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "14d31486",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:03:18.550553Z",
|
||
"start_time": "2023-03-01T15:03:18.414991Z"
|
||
},
|
||
"id": "14d31486"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"FEATURES_PATH = './cifar10_deep_features.npy'\n",
|
||
"\n",
|
||
"if not os.path.exists(FEATURES_PATH):\n",
|
||
" deep_cnn = InceptionV3(weights='imagenet', include_top=False, input_shape=(139, 139, 3))\n",
|
||
"\n",
|
||
" cifar10_tensors_test = torch.nn.functional.interpolate(torch.tensor(\n",
|
||
" cifar10_images_test.transpose(0, 3, 1, 2)\n",
|
||
" ), size=139).numpy().transpose(0, 2, 3, 1).astype(np.float32)\n",
|
||
" cifar10_tensors_train = torch.nn.functional.interpolate(torch.tensor(\n",
|
||
" cifar10_images_train.transpose(0, 3, 1, 2)\n",
|
||
" ), size=139).numpy().transpose(0, 2, 3, 1).astype(np.float32)\n",
|
||
"\n",
|
||
" cifar10_deep_features_test = deep_cnn.predict(\n",
|
||
" preprocess_input(cifar10_tensors_test)\n",
|
||
" ).mean(axis=(1, 2)).reshape([cifar10_tensors_test.shape[0], -1])\n",
|
||
" cifar10_deep_features_train = deep_cnn.predict(\n",
|
||
" preprocess_input(cifar10_tensors_train)\n",
|
||
" ).mean(axis=(1, 2)).reshape([cifar10_tensors_train.shape[0], -1])\n",
|
||
"\n",
|
||
" np.save(FEATURES_PATH, [cifar10_deep_features_test, cifar10_deep_features_train])\n",
|
||
"else:\n",
|
||
" cifar10_deep_features_test, cifar10_deep_features_train = np.load(FEATURES_PATH, allow_pickle=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4da40a6c",
|
||
"metadata": {
|
||
"id": "4da40a6c"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.4 [кросспроверка, 2 баллa][код]**</font>\n",
|
||
"Используйте выделенные признаки для обучения алгоритмов из предыдущего пункта. Постройте графики. Замечание из пункта [**2.1**](#task_2.1) остаётся в силе."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "52f01d2e",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:03:24.502561Z",
|
||
"start_time": "2023-03-01T15:03:24.438443Z"
|
||
},
|
||
"id": "52f01d2e"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Ваш код здесь:＼(º □ º l|l)/\n",
|
||
"X_train_deep_sample = cifar10_deep_features_train[train_indices]\n",
|
||
"y_train_sample = cifar10_labels_train[train_indices]\n",
|
||
"X_test_deep_sample = cifar10_deep_features_test[test_indices]\n",
|
||
"y_test_sample = cifar10_labels_test[test_indices]\n",
|
||
"\n",
|
||
"scaler_deep = StandardScaler()\n",
|
||
"X_train_deep_scaled = scaler_deep.fit_transform(X_train_deep_sample)\n",
|
||
"X_test_deep_scaled = scaler_deep.transform(X_test_deep_sample)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ccbed545",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"fig_deep, axes_deep = plt.subplots(2, 4, figsize=(20, 10))\n",
|
||
"fig_deep.suptitle(\n",
|
||
" \"Визуализация понижения размерности CIFAR-10 (Глубокие признаки, {} сэмплов)\".format(\n",
|
||
" n_samples\n",
|
||
" ),\n",
|
||
" fontsize=16,\n",
|
||
")\n",
|
||
"\n",
|
||
"for i, (name, model) in enumerate(models.items()):\n",
|
||
" if name == \"t-SNE\":\n",
|
||
" X_train_deep_reduced = model.fit_transform(X_train_deep_scaled)\n",
|
||
" else:\n",
|
||
" model.fit(X_train_deep_scaled)\n",
|
||
" X_train_deep_reduced = model.transform(X_train_deep_scaled)\n",
|
||
"\n",
|
||
" plot_2d_data(\n",
|
||
" X_train_deep_reduced,\n",
|
||
" y_train_sample,\n",
|
||
" title=f\"{name} (Train - Deep Feat.)\",\n",
|
||
" ax=axes_deep[0, i],\n",
|
||
" )\n",
|
||
"\n",
|
||
" if name == \"t-SNE\":\n",
|
||
" axes_deep[1, i].set_title(f\"{name} (Test - N/A)\")\n",
|
||
" axes_deep[1, i].set_xticks([])\n",
|
||
" axes_deep[1, i].set_yticks([])\n",
|
||
" axes_deep[1, i].text(\n",
|
||
" 0.5,\n",
|
||
" 0.5,\n",
|
||
" \"N/A for sklearn t-SNE\",\n",
|
||
" horizontalalignment=\"center\",\n",
|
||
" verticalalignment=\"center\",\n",
|
||
" transform=axes_deep[1, i].transAxes,\n",
|
||
" color=\"gray\",\n",
|
||
" )\n",
|
||
" else:\n",
|
||
" X_test_deep_reduced = model.transform(X_test_deep_scaled)\n",
|
||
" plot_2d_data(\n",
|
||
" X_test_deep_reduced,\n",
|
||
" y_test_sample,\n",
|
||
" title=f\"{name} (Test - Deep Feat.)\",\n",
|
||
" ax=axes_deep[1, i],\n",
|
||
" )\n",
|
||
"\n",
|
||
"plt.tight_layout(rect=(0, 0.03, 1, 0.95))\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f47774e2",
|
||
"metadata": {
|
||
"id": "f47774e2"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.5 [кросспроверка, 1 балл][вопрос]**</font>\n",
|
||
"1. Есть ли какие-то изменения по сравнению с использованием исходных признаков?\n",
|
||
"2. Как вы думаете, почему использование глубоких признаков помогло/не помогло в задаче снижения размерности?\n",
|
||
"3. Какой алгоритм показал себя лучше на ваш взгляд?\n",
|
||
"4. Согласованы ли преобразования на обучающей и тестовых выборках? Какие недостатки есть в том, что преобразование на тестовой выборке выглядит отлично от низкоразмерного представления обучающей выборки?\n",
|
||
"5. Какие из алгоритмов можно использовать в качестве первого шага по снижению размерности в задачах машинного обучения? Какой из них использовали бы вы?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d5c7c172",
|
||
"metadata": {
|
||
"id": "d5c7c172"
|
||
},
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваш ответ здесь:**</font> (ｏ・_・)ノ”(ノ_<、):\n",
|
||
"\n",
|
||
"Да, различия стали заметны: на глубоких признаках кластеры, особенно у t-SNE и UMAP, выглядят намного более чёткими. Точки одного класса группируются плотнее и лучше отделяются от остальных.\n",
|
||
"\n",
|
||
"Глубокие признаки помогли, потому что нейросети выделяют не сырые пиксели, а более содержательные характеристики изображений: формы, текстуры и части объектов. Поэтому изображения одного класса оказываются ближе друг к другу. Также такие признаки устойчивее к сдвигам, масштабу и изменению освещения.\n",
|
||
"\n",
|
||
"Лучше всего показал себя UMAP: его кластеры выглядят наиболее компактными и хорошо разделёнными.\n",
|
||
"\n",
|
||
"Преобразования для обучающей и тестовой выборок выглядят согласованными: структура кластеров и их расположение похожи. Это важно, потому что при сильных различиях нельзя было бы доверять положению новых точек на 2D-графике, а сама модель могла бы быть переобучена.\n",
|
||
"\n",
|
||
"Из всех алгоритмов я бы выбрал UMAP, так как он даёт хорошее разделение классов и работает достаточно быстро. Его удобно использовать для предварительной обработки данных перед применением ML-алгоритмов.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c8a1410e",
|
||
"metadata": {
|
||
"id": "c8a1410e"
|
||
},
|
||
"source": [
|
||
"Далее, для визуализации кластеризации используйте один из методов снижения размерности на ваш выбор и то векторное представление, которое лучше всего себя проявило (исходное или полученное с помощью глубокой сети). Кластеризацию обучайте также на наиболее подходящем высокоразмерном векторном представлении."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "63e9c886",
|
||
"metadata": {
|
||
"id": "63e9c886"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.6 [кросспроверка, 1 балл][код, вопрос]**</font>\n",
|
||
"Изобразите выборку CIFAR10 с помощью выбранного алгоритма снижения размерности.\n",
|
||
"\n",
|
||
"<font color='LightSteelBlue'>**Совет**</font> Изобразите результат с помощью `plot_interactive`, чтобы изучить особенности кластеризации в соответствии с исходными изображениями. Если вы нашли интересные особенности — напишите про это."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "3ea28424",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:07:48.895654Z",
|
||
"start_time": "2023-03-01T15:07:48.812018Z"
|
||
},
|
||
"id": "3ea28424"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib ipympl\n",
|
||
"matplotlib.rcParams['figure.dpi'] = 300\n",
|
||
"\n",
|
||
"# Для работы в Google Colab нужно выполнить специфичную магию\n",
|
||
"# Обычно, она не срабатывает с первого раза, поэтому может потребоваться\n",
|
||
"# несколько раз выполнить ячейку и несколько раз попробовать нарисовать график\n",
|
||
"try:\n",
|
||
" from google.colab import output\n",
|
||
" output.enable_custom_widget_manager()\n",
|
||
"except:\n",
|
||
" pass"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ffb1d8c8",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:07:52.786201Z",
|
||
"start_time": "2023-03-01T15:07:52.703184Z"
|
||
},
|
||
"id": "ffb1d8c8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Ваш код здесь:＼(º □ º l|l)/\n",
|
||
"plot_interactive(\n",
|
||
" umap_reducer.transform(X_train_deep_scaled),\n",
|
||
" cifar10_images_train,\n",
|
||
" y_train_sample,\n",
|
||
" cifar10_test_dataset.classes,\n",
|
||
" n_dots=2000,\n",
|
||
" image_scale=0.35,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0c0917b7",
|
||
"metadata": {
|
||
"id": "0c0917b7"
|
||
},
|
||
"source": [
|
||
"Вернёмся в статичный режим отрисовки изображений:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2f18f1bf",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:09:28.554080Z",
|
||
"start_time": "2023-03-01T15:09:28.469693Z"
|
||
},
|
||
"id": "2f18f1bf"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"matplotlib.rcParams['figure.dpi'] = 300"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2568e573",
|
||
"metadata": {
|
||
"id": "2568e573"
|
||
},
|
||
"source": [
|
||
"Теперь, когда мы можем визуализировать кластеризацию, можно сравнить алгоритмы из первой части на естественных данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ccd94f6a",
|
||
"metadata": {
|
||
"id": "ccd94f6a"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.7 [кросспроверка, 1.5 балла][код]**</font>\n",
|
||
"Подберите параметры `KMeans`, `DBSCAN`, `AgglomerativeClustering` используя силуэт и B-Cubed. Визуализируйте получившиеся кластеризации также, как и в задании **1.с.4** в ноутбуке [Base] Clusterizartion. Для ускорения перебора можете производить его на небольшой доле от всех объектов ($1000\\text{-}2000$ объектов).\n",
|
||
"\n",
|
||
"*Замечание:* Алгоритмы кластеризации нужно применять к исходному векторному представлению. Снижение размерности используется только для визуализации."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "cc34b6a9",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:09:31.321473Z",
|
||
"start_time": "2023-03-01T15:09:31.238779Z"
|
||
},
|
||
"id": "cc34b6a9"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"n_objects = 2000"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e1debbc8",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:09:32.490742Z",
|
||
"start_time": "2023-03-01T15:09:32.411260Z"
|
||
},
|
||
"id": "e1debbc8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Ваш код здесь:＼(º □ º l|l)/\n",
|
||
"from matplotlib.ticker import MaxNLocator\n",
|
||
"\n",
|
||
"\n",
|
||
"def heatmap(\n",
|
||
" data, row_labels, col_labels, ax=None, cbar_kw=None, cbarlabel=\"\", **kwargs\n",
|
||
"):\n",
|
||
" if ax is None:\n",
|
||
" ax = plt.gca()\n",
|
||
" if cbar_kw is None:\n",
|
||
" cbar_kw = {}\n",
|
||
" im = ax.imshow(data, **kwargs)\n",
|
||
" cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n",
|
||
" cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n",
|
||
" ax.set_xticks(np.arange(data.shape[1]), labels=col_labels)\n",
|
||
" ax.set_yticks(np.arange(data.shape[0]), labels=row_labels)\n",
|
||
" ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)\n",
|
||
" plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\", rotation_mode=\"anchor\")\n",
|
||
" for edge, spine in ax.spines.items():\n",
|
||
" spine.set_visible(False)\n",
|
||
" ax.set_xticks(np.arange(data.shape[1] + 1) - 0.5, minor=True)\n",
|
||
" ax.set_yticks(np.arange(data.shape[0] + 1) - 0.5, minor=True)\n",
|
||
" ax.grid(which=\"minor\", color=\"w\", linestyle=\"-\", linewidth=3)\n",
|
||
" ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
|
||
" return im, cbar\n",
|
||
"\n",
|
||
"\n",
|
||
"def annotate_heatmap(\n",
|
||
" im,\n",
|
||
" data=None,\n",
|
||
" valfmt=\"{x:.2f}\",\n",
|
||
" textcolors=(\"black\", \"white\"),\n",
|
||
" threshold=None,\n",
|
||
" **textkw\n",
|
||
"):\n",
|
||
" if not isinstance(data, (list, np.ndarray)):\n",
|
||
" data = im.get_array()\n",
|
||
" if threshold is not None:\n",
|
||
" threshold = im.norm(threshold)\n",
|
||
" else:\n",
|
||
" threshold = im.norm(data.max()) / 2.0\n",
|
||
" kw = dict(horizontalalignment=\"center\", verticalalignment=\"center\")\n",
|
||
" kw.update(textkw)\n",
|
||
" if isinstance(valfmt, str):\n",
|
||
" valfmt = plt.matplotlib.ticker.StrMethodFormatter(valfmt)\n",
|
||
" texts = []\n",
|
||
" for i in range(data.shape[0]):\n",
|
||
" for j in range(data.shape[1]):\n",
|
||
" kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n",
|
||
" text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)\n",
|
||
" texts.append(text)\n",
|
||
" return texts"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "effd3bcf",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_sample = cifar10_deep_features_train[train_indices]\n",
|
||
"y_sample = cifar10_labels_train[train_indices]\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_sample_scaled = scaler.fit_transform(X_sample)\n",
|
||
"X_sample_umap = umap_reducer.fit_transform(X_sample_scaled)\n",
|
||
"\n",
|
||
"k_range = list(range(1, 21))\n",
|
||
"kmeans_sil_scores = []\n",
|
||
"kmeans_bcubed_scores = []\n",
|
||
"\n",
|
||
"for k in k_range:\n",
|
||
" kmeans = KMeans(n_clusters=k, n_init=\"auto\")\n",
|
||
" labels = kmeans.fit_predict(X_sample_scaled)\n",
|
||
" sil = silhouette_score(X_sample_scaled, labels)\n",
|
||
" bcubed = bcubed_score(y_sample, labels)\n",
|
||
" kmeans_sil_scores.append(sil)\n",
|
||
" kmeans_bcubed_scores.append(bcubed)\n",
|
||
"\n",
|
||
"best_k_sil = k_range[np.argmax(kmeans_sil_scores)]\n",
|
||
"best_k_bcubed = k_range[np.argmax(kmeans_bcubed_scores)]\n",
|
||
"print(f\"Best k (Silhouette): {best_k_sil} (Score: {max(kmeans_sil_scores):.4f})\")\n",
|
||
"print(f\"Best k (B-Cubed): {best_k_bcubed} (Score: {max(kmeans_bcubed_scores):.4f})\")\n",
|
||
"\n",
|
||
"kmeans_best_sil = KMeans(n_clusters=best_k_sil, n_init=\"auto\")\n",
|
||
"labels_best_sil_km = kmeans_best_sil.fit_predict(X_sample_scaled)\n",
|
||
"\n",
|
||
"kmeans_best_bcubed = KMeans(n_clusters=best_k_bcubed, n_init=\"auto\")\n",
|
||
"labels_best_bcubed_km = kmeans_best_bcubed.fit_predict(X_sample_scaled)\n",
|
||
"\n",
|
||
"fig_km, axes_km = plt.subplots(2, 2, figsize=(12, 10)) # Увеличим размер фигуры\n",
|
||
"fig_km.suptitle(\"KMeans Clustering Performance (Deep Features)\", fontsize=14)\n",
|
||
"\n",
|
||
"axes_km[0, 0].plot(k_range, kmeans_sil_scores, marker=\"o\")\n",
|
||
"axes_km[0, 0].set_xlabel(\"Number of clusters (k)\")\n",
|
||
"axes_km[0, 0].set_ylabel(\"Silhouette Score\")\n",
|
||
"axes_km[0, 0].set_title(\"Silhouette vs. k\")\n",
|
||
"axes_km[0, 0].axvline(\n",
|
||
" best_k_sil, color=\"r\", linestyle=\"--\", label=f\"Best k = {best_k_sil}\"\n",
|
||
")\n",
|
||
"axes_km[0, 0].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
|
||
"axes_km[0, 0].grid(True)\n",
|
||
"axes_km[0, 0].legend()\n",
|
||
"\n",
|
||
"axes_km[0, 1].plot(k_range, kmeans_bcubed_scores, marker=\"o\")\n",
|
||
"axes_km[0, 1].set_xlabel(\"Number of clusters (k)\")\n",
|
||
"axes_km[0, 1].set_ylabel(\"B-Cubed F1 Score\")\n",
|
||
"axes_km[0, 1].set_title(\"B-Cubed F1 vs. k\")\n",
|
||
"axes_km[0, 1].axvline(\n",
|
||
" best_k_bcubed, color=\"r\", linestyle=\"--\", label=f\"Best k = {best_k_bcubed}\"\n",
|
||
")\n",
|
||
"axes_km[0, 1].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
|
||
"axes_km[0, 1].grid(True)\n",
|
||
"axes_km[0, 1].legend()\n",
|
||
"\n",
|
||
"plot_2d_data(\n",
|
||
" X_sample_umap,\n",
|
||
" labels_best_sil_km,\n",
|
||
" title=f\"KMeans (k={best_k_sil}, Best Silhouette)\",\n",
|
||
" ax=axes_km[1, 0],\n",
|
||
")\n",
|
||
"plot_2d_data(\n",
|
||
" X_sample_umap,\n",
|
||
" labels_best_bcubed_km,\n",
|
||
" title=f\"KMeans (k={best_k_bcubed}, Best B-Cubed)\",\n",
|
||
" ax=axes_km[1, 1],\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "0d7af531",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_2d_data_new(data, labels, title=\"Исходные данные\", cmap=\"tab20\", ax=None):\n",
|
||
" unique_labels = np.unique(labels)\n",
|
||
" n_unique_labels_total = len(unique_labels)\n",
|
||
" do_show = False\n",
|
||
" if ax is None:\n",
|
||
" fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
|
||
" do_show = True\n",
|
||
"\n",
|
||
" is_noise = -1 in unique_labels\n",
|
||
" valid_labels = unique_labels[unique_labels != -1]\n",
|
||
" n_clusters = len(valid_labels)\n",
|
||
"\n",
|
||
" if is_noise:\n",
|
||
" noise_mask = labels == -1\n",
|
||
" cluster_mask = ~noise_mask\n",
|
||
" else:\n",
|
||
" cluster_mask = np.ones(len(labels), dtype=bool)\n",
|
||
" noise_mask = np.zeros(len(labels), dtype=bool)\n",
|
||
"\n",
|
||
" if n_clusters > 0:\n",
|
||
" cmap_instance = plt.get_cmap(cmap, n_clusters if n_clusters > 1 else 2)\n",
|
||
" else:\n",
|
||
" cmap_instance = None\n",
|
||
"\n",
|
||
" if np.sum(cluster_mask) > 0 and cmap_instance is not None:\n",
|
||
" scatter = ax.scatter(\n",
|
||
" data[cluster_mask, 0],\n",
|
||
" data[cluster_mask, 1],\n",
|
||
" c=labels[cluster_mask],\n",
|
||
" cmap=cmap_instance,\n",
|
||
" s=20,\n",
|
||
" alpha=0.9,\n",
|
||
" )\n",
|
||
"\n",
|
||
" if n_clusters > 0:\n",
|
||
" cbar = plt.colorbar(scatter, label=\"Номер кластера\", ax=ax)\n",
|
||
" max_ticks = 15\n",
|
||
" if n_clusters > max_ticks:\n",
|
||
" cbar.ax.yaxis.set_major_locator(\n",
|
||
" MaxNLocator(integer=True, nbins=max_ticks)\n",
|
||
" )\n",
|
||
" else:\n",
|
||
" if len(valid_labels) > 0:\n",
|
||
" ticks = np.arange(min(valid_labels), max(valid_labels) + 1, 1)\n",
|
||
" cbar.set_ticks(ticks)\n",
|
||
"\n",
|
||
" if np.sum(noise_mask) > 0:\n",
|
||
" ax.scatter(\n",
|
||
" data[noise_mask, 0],\n",
|
||
" data[noise_mask, 1],\n",
|
||
" c=\"gray\",\n",
|
||
" marker=\"x\",\n",
|
||
" s=15,\n",
|
||
" alpha=0.5,\n",
|
||
" label=f\"Шум ({np.sum(noise_mask)} точек)\",\n",
|
||
" )\n",
|
||
" ax.legend(loc=\"best\", fontsize=\"small\")\n",
|
||
"\n",
|
||
" ax.set_title(f\"{title}\\n(Найдено кластеров: {n_clusters})\", fontsize=10)\n",
|
||
" ax.set_xticks([])\n",
|
||
" ax.set_yticks([])\n",
|
||
" ax.grid(True, linestyle=\"--\", alpha=0.5)\n",
|
||
"\n",
|
||
" if do_show:\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "aab17212",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"eps_range = np.linspace(15, 60, 5)\n",
|
||
"min_samples_range = list(range(1, 101, 10))\n",
|
||
"\n",
|
||
"dbscan_sil_scores = np.zeros((len(eps_range), len(min_samples_range)))\n",
|
||
"dbscan_bcubed_scores = np.zeros((len(eps_range), len(min_samples_range)))\n",
|
||
"dbscan_n_clusters = np.zeros((len(eps_range), len(min_samples_range)), dtype=int)\n",
|
||
"\n",
|
||
"for i, eps in enumerate(eps_range):\n",
|
||
" for j, ms in enumerate(min_samples_range):\n",
|
||
" dbscan = DBSCAN(eps=eps, min_samples=ms, n_jobs=-1)\n",
|
||
" labels = dbscan.fit_predict(X_sample_scaled)\n",
|
||
"\n",
|
||
" unique_labels = np.unique(labels)\n",
|
||
" n_clusters = len(unique_labels[unique_labels != -1])\n",
|
||
" dbscan_n_clusters[i, j] = n_clusters\n",
|
||
"\n",
|
||
" mask_non_noise = labels != -1\n",
|
||
" if np.sum(mask_non_noise) > 1 and len(np.unique(labels[mask_non_noise])) > 1:\n",
|
||
" sil = silhouette_score(\n",
|
||
" X_sample_scaled[mask_non_noise], labels[mask_non_noise]\n",
|
||
" )\n",
|
||
" else:\n",
|
||
" sil = 0.0\n",
|
||
"\n",
|
||
" bcubed = bcubed_score(y_sample, labels)\n",
|
||
"\n",
|
||
" dbscan_sil_scores[i, j] = sil\n",
|
||
" dbscan_bcubed_scores[i, j] = bcubed\n",
|
||
"\n",
|
||
"sil_scores_valid = np.copy(dbscan_sil_scores)\n",
|
||
"sil_scores_valid[sil_scores_valid <= 0] = -np.inf\n",
|
||
"best_idx_sil_db = np.unravel_index(\n",
|
||
" np.argmax(sil_scores_valid, axis=None), sil_scores_valid.shape\n",
|
||
")\n",
|
||
"\n",
|
||
"best_idx_bcubed_db = np.unravel_index(\n",
|
||
" np.argmax(dbscan_bcubed_scores, axis=None), dbscan_bcubed_scores.shape\n",
|
||
")\n",
|
||
"\n",
|
||
"best_eps_sil = eps_range[best_idx_sil_db[0]]\n",
|
||
"best_ms_sil = min_samples_range[best_idx_sil_db[1]]\n",
|
||
"print(\n",
|
||
" f\"Best params (Silhouette): eps={best_eps_sil:.2f}, ms={best_ms_sil} (Score: {dbscan_sil_scores[best_idx_sil_db]:.4f})\"\n",
|
||
")\n",
|
||
"\n",
|
||
"best_eps_bcubed = eps_range[best_idx_bcubed_db[0]]\n",
|
||
"best_ms_bcubed = min_samples_range[best_idx_bcubed_db[1]]\n",
|
||
"print(\n",
|
||
" f\"Best params (B-Cubed): eps={best_eps_bcubed:.2f}, ms={best_ms_bcubed} (Score: {dbscan_bcubed_scores[best_idx_bcubed_db]:.4f})\"\n",
|
||
")\n",
|
||
"\n",
|
||
"dbscan_best_sil = DBSCAN(eps=best_eps_sil, min_samples=best_ms_sil, n_jobs=-1)\n",
|
||
"labels_best_sil_db = dbscan_best_sil.fit_predict(X_sample_scaled)\n",
|
||
"\n",
|
||
"dbscan_best_bcubed = DBSCAN(eps=best_eps_bcubed, min_samples=best_ms_bcubed, n_jobs=-1)\n",
|
||
"labels_best_bcubed_db = dbscan_best_bcubed.fit_predict(X_sample_scaled)\n",
|
||
"\n",
|
||
"fig_db, axes_db = plt.subplots(2, 2, figsize=(14, 12))\n",
|
||
"fig_db.suptitle(\"DBSCAN Clustering Performance (Deep Features)\", fontsize=14)\n",
|
||
"\n",
|
||
"im_sil, _ = heatmap(\n",
|
||
" dbscan_sil_scores,\n",
|
||
" [f\"{e:.1f}\" for e in eps_range],\n",
|
||
" [str(ms) for ms in min_samples_range],\n",
|
||
" ax=axes_db[0, 0],\n",
|
||
" cmap=\"viridis\",\n",
|
||
" cbarlabel=\"Silhouette Score\",\n",
|
||
")\n",
|
||
"annotate_heatmap(im_sil, valfmt=\"{x:.2f}\")\n",
|
||
"axes_db[0, 0].set_title(\"Silhouette Score\")\n",
|
||
"axes_db[0, 0].set_ylabel(\"eps\")\n",
|
||
"axes_db[0, 0].add_patch(\n",
|
||
" plt.Rectangle(\n",
|
||
" (best_idx_sil_db[1] - 0.5, best_idx_sil_db[0] - 0.5),\n",
|
||
" 1,\n",
|
||
" 1,\n",
|
||
" fill=False,\n",
|
||
" edgecolor=\"red\",\n",
|
||
" lw=2,\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"im_bc, _ = heatmap(\n",
|
||
" dbscan_bcubed_scores,\n",
|
||
" [f\"{e:.1f}\" for e in eps_range],\n",
|
||
" [str(ms) for ms in min_samples_range],\n",
|
||
" ax=axes_db[0, 1],\n",
|
||
" cmap=\"viridis\",\n",
|
||
" cbarlabel=\"B-Cubed F1 Score\",\n",
|
||
")\n",
|
||
"annotate_heatmap(im_bc, valfmt=\"{x:.2f}\")\n",
|
||
"axes_db[0, 1].set_title(\"B-Cubed F1 Score\")\n",
|
||
"axes_db[0, 1].set_ylabel(\"eps\")\n",
|
||
"axes_db[0, 1].add_patch(\n",
|
||
" plt.Rectangle(\n",
|
||
" (best_idx_bcubed_db[1] - 0.5, best_idx_bcubed_db[0] - 0.5),\n",
|
||
" 1,\n",
|
||
" 1,\n",
|
||
" fill=False,\n",
|
||
" edgecolor=\"red\",\n",
|
||
" lw=2,\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"plot_2d_data_new(\n",
|
||
" X_sample_umap,\n",
|
||
" labels_best_sil_db,\n",
|
||
" title=f\"DBSCAN (eps={best_eps_sil:.1f}, ms={best_ms_sil}, Best Sil.)\",\n",
|
||
" ax=axes_db[1, 0],\n",
|
||
")\n",
|
||
"plot_2d_data_new(\n",
|
||
" X_sample_umap,\n",
|
||
" labels_best_bcubed_db,\n",
|
||
" title=f\"DBSCAN (eps={best_eps_bcubed:.1f}, ms={best_ms_bcubed}, Best B-Cubed)\",\n",
|
||
" ax=axes_db[1, 1],\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.tight_layout(rect=(0, 0.03, 1, 0.95))\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7bb549d8",
|
||
"metadata": {
|
||
"id": "7bb549d8"
|
||
},
|
||
"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.8 [кросспроверка, 1 балл][вопрос]** </font>\n",
|
||
"1. Какие алгоритмы справились с кластеризацией естественных данных?\n",
|
||
"2. Получилось ли подобрать оптимальное число кластеров с помощью BCubed и коэффициента силуэта?\n",
|
||
"3. Объясните почему коэффициент силуэта не позволил выполнить подбор оптимальных гиперпараметров."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0f00d2ec",
|
||
"metadata": {
|
||
"id": "0f00d2ec"
|
||
},
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваш ответ здесь:**</font> (ｏ・_・)ノ”(ノ_<、):\n",
|
||
"\n",
|
||
"KMeans и Agglomerative Clustering показали адекватные результаты по метрике B-Cubed: оба алгоритма смогли частично выявить структуру данных. На визуализациях для выбранных значений k видно более осмысленное разбиение на несколько групп, чем при k=2, хотя кластеры всё ещё заметно перекрываются.\n",
|
||
"\n",
|
||
"Подобрать число кластеров удалось по метрике B-Cubed. Коэффициент силуэта, наоборот, не дал корректного результата для разбиения на реальные классы.\n",
|
||
"\n",
|
||
"Силуэт плохо подходит для этой задачи, потому что это внутренняя метрика, основанная на евклидовых расстояниях. Она лучше работает для плотных, хорошо разделённых и примерно сферических кластеров. В случае CIFAR-10 кластеры на UMAP-визуализациях пересекаются, имеют сложную форму и не являются идеально отделёнными, поэтому силуэт даёт менее полезную оценку.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6f8bd1c1",
|
||
"metadata": {
|
||
"id": "6f8bd1c1"
|
||
},
|
||
"source": [
|
||
"Интересный способ визуализации Иерархической кластеризации — построение дендрограммы. Такой способ визуализации позволяет анализировать, как именно связаны между собой объекты, подбирать оптимальное число кластеров, а также определять, какие классы отделяются \"хорошо\" от других классов, а какие классы перемешаны в одном кластере."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "13af91a1",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:43:08.779419Z",
|
||
"start_time": "2023-03-01T15:43:08.693099Z"
|
||
},
|
||
"id": "13af91a1"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_dendrogram(model, labels, classes, ax):\n",
|
||
" n_classes = len(classes)\n",
|
||
" n_samples = len(model.labels_)\n",
|
||
" n_u_connections = model.children_.shape[0]\n",
|
||
" colors = plt.get_cmap('tab20', n_classes).colors\n",
|
||
"\n",
|
||
" bin_counts = np.zeros([n_u_connections, n_classes])\n",
|
||
" for i, merge in enumerate(model.children_):\n",
|
||
" current_bin_count = np.zeros(n_classes)\n",
|
||
" for child_idx in merge:\n",
|
||
" if child_idx < n_samples:\n",
|
||
" current_bin_count[labels[child_idx]] += 1\n",
|
||
" else:\n",
|
||
" current_bin_count += bin_counts[child_idx - n_samples]\n",
|
||
"\n",
|
||
" bin_counts[i] = current_bin_count\n",
|
||
"\n",
|
||
" linkage_matrix = np.column_stack(\n",
|
||
" [model.children_, model.distances_, np.sum(bin_counts, axis=1)]\n",
|
||
" ).astype(float)\n",
|
||
"\n",
|
||
" def leaf_label_func(idx):\n",
|
||
" if idx < len(labels):\n",
|
||
" return None\n",
|
||
" else:\n",
|
||
" ratio = 100 * np.max(bin_counts[idx - n_samples]) / np.sum(bin_counts[idx - n_samples])\n",
|
||
" if ratio < 100:\n",
|
||
" return '{0:.0f}%'.format(ratio)\n",
|
||
" else:\n",
|
||
" return None\n",
|
||
"\n",
|
||
" def link_color_func(idx):\n",
|
||
" mode_class = np.argmax(bin_counts[idx - n_samples])\n",
|
||
" return matplotlib.colors.to_hex(colors[mode_class], keep_alpha=True)\n",
|
||
"\n",
|
||
" scipy.cluster.hierarchy.dendrogram(\n",
|
||
" linkage_matrix, ax=ax, link_color_func=link_color_func, leaf_label_func=leaf_label_func,\n",
|
||
" orientation='right', truncate_mode=\"level\", p=9\n",
|
||
" )\n",
|
||
"\n",
|
||
" for idx, class_name in enumerate(classes):\n",
|
||
" ax.plot([], [], c=matplotlib.colors.to_hex(colors[idx], keep_alpha=True), label=class_name)\n",
|
||
" ax.legend()\n",
|
||
"\n",
|
||
" # Удалим накладывающиеся метки\n",
|
||
" threshold = 55\n",
|
||
" prev_position = -(threshold + 1)\n",
|
||
"\n",
|
||
" y_labels = ax.get_yaxis().get_ticklabels()\n",
|
||
" for label in y_labels:\n",
|
||
" if label.get_text() == '':\n",
|
||
" continue\n",
|
||
"\n",
|
||
" _, position = label.get_position()\n",
|
||
" if position - prev_position < threshold:\n",
|
||
" label.set_text('')\n",
|
||
" else:\n",
|
||
" prev_position = position\n",
|
||
" ax.get_yaxis().set_ticklabels(y_labels)\n",
|
||
"\n",
|
||
" ax.set_xlabel('Расстояние между кластерами')\n",
|
||
" ax.set_ylabel('Доля объектов наибольшего класса в данном кластере')\n",
|
||
"\n",
|
||
" ax.set_title('Дендрограмма Иерархической Кластеризации')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4ac4622c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2023-03-01T15:44:13.974036Z",
|
||
"start_time": "2023-03-01T15:44:07.940015Z"
|
||
},
|
||
"id": "4ac4622c",
|
||
"scrolled": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"n_objects = 2000\n",
|
||
"model = AgglomerativeClustering(\n",
|
||
" n_clusters=None, distance_threshold=0.0, compute_distances=True, compute_full_tree=True\n",
|
||
")\n",
|
||
"model = model.fit(cifar10_deep_features_train[:n_objects])\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
|
||
"\n",
|
||
"plot_dendrogram(\n",
|
||
" model,\n",
|
||
" labels=cifar10_labels_train[:n_objects],\n",
|
||
" classes=cifar10_train_dataset.classes,\n",
|
||
" ax=ax\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
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},
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{
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"cell_type": "markdown",
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"id": "36866d6f",
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"metadata": {
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"id": "36866d6f"
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|
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"source": [
|
||
"#### <font color='DarkOrange'>**Задание 2.9 [кросспроверка, 0.5 балла][вопрос]** </font>\n",
|
||
"\n",
|
||
"Проанализируйте получившуюся дендрограмму. Напишите свои наблюдения ниже."
|
||
]
|
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "07e76533"
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},
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"source": [
|
||
"<font color='MediumOrchid'>**Ваш ответ здесь:**</font> (ｏ・_・)ノ”(ノ_<、):\n",
|
||
"\n",
|
||
"На малых расстояниях происходит много ранних слияний, причём часто объединяются объекты одного истинного класса. Это видно по множеству одноцветных линий в левой части дендрограммы.\n",
|
||
"\n",
|
||
"Некоторые классы образуют крупные и относительно чистые ветви, например automobile, bird, truck и airplane. Это говорит о том, что они достаточно хорошо отделяются в пространстве признаков.\n",
|
||
"\n",
|
||
"Дендрограмма показывает иерархическую структуру: сначала объединяются наиболее похожие объекты, а затем небольшие чистые группы постепенно сливаются в более крупные, но менее однородные кластеры.\n",
|
||
"\n",
|
||
"При этом даже в доминирующих по цвету ветвях встречаются линии других классов, поэтому кластеры нельзя считать полностью чистыми: присутствует смешение объектов разных категорий.\n"
|
||
]
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