1105 lines
328 KiB
Plaintext
1105 lines
328 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "sTB50uLM0a9o"
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},
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"source": [
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"# <img 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\" width=\"50\"/>\n",
|
||
"\n",
|
||
"# Машинное обучение. ВМК МГУ"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "0xg3G6bd0a9s"
|
||
},
|
||
"source": [
|
||
"\n",
|
||
"# Практическое задание 5: Линейные модели: регрессия\n",
|
||
"\n",
|
||
"## Уровень: <font color='SkyBlue'>**Базовый (Base)**</font>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "6ODl7_-1JoHQ"
|
||
},
|
||
"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:0.2025\"></font>\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "H0Lj_c63lrku"
|
||
},
|
||
"source": [
|
||
"Цель данного задания:\n",
|
||
"\n",
|
||
"* Узнать, что такое регуляризация, зачем она нужна, и чем отличаются разные регуляризаторы.\n",
|
||
"* Научиться решать задачу регрессии линейными моделями.\n",
|
||
"-------\n",
|
||
"\n",
|
||
"<font color=DarkOrange>**Примерное время выполнения (execution time/время выполнения, если нажать run all) всех ячеек ноутбука при правильной реализации: 5 минут </font>**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "CTmlafz7W04M"
|
||
},
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "UodfS2cpXMUd"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
|
||
]
|
||
}
|
||
],
|
||
"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",
|
||
"metadata": {
|
||
"id": "vUCY0KLD7VmA"
|
||
},
|
||
"source": [
|
||
"Проверим версию библиотеки:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"id": "zyRnTYF5X37Y"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import catboost\n",
|
||
"assert(catboost.__version__ == '1.2.8')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "UShU_WkjlqrP"
|
||
},
|
||
"source": [
|
||
"Теперь можно приступать к выполнению задания! :)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "GLIQiUj6KNYj"
|
||
},
|
||
"source": [
|
||
"-----------\n",
|
||
"<font color=\"white\" style=\"opacity:0.2025\"></font>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"id": "Gc3xTMopl8c1"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"import warnings\n",
|
||
"warnings.simplefilter(\"ignore\")\n",
|
||
"sns.set(style=\"darkgrid\")\n",
|
||
"%matplotlib inline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "cLTHFUz40a9w"
|
||
},
|
||
"source": [
|
||
"## Линейная регрессия и регуляризация"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "PGuTcL0H0a9w"
|
||
},
|
||
"source": [
|
||
"Напомним, что <font color='CornflowerBlue'>**линейная регрессия**</font> — это модель следующего вида: $$a(x) = \\langle w, x \\rangle + b$$ где $w \\in \\mathbb{R}^d$, $b \\in \\mathbb{R}$. Обучить линейную регрессию — значит найти $w$ и $b$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "1oms7GV80a9x"
|
||
},
|
||
"source": [
|
||
"Для обучения линейной регрессии, равно как и для обучения остальных простых моделей (линейные модели, решающие деревья, knn и т.д.) отлично подходит библиотека `scikit-learn` (sklearn): в ней очень понятный и простой интерфейс.\n",
|
||
"\n",
|
||
"Однако для обучения более сложных моделей вроде бустинга и нейронных сетей всё же нужно пользоваться специализированными библиотеками: XGBoost, LightGBM, CatBoost и пр. для градиентного бустинга над деревьями, PyTorch, Tensorflow и пр. для нейронных сетей.\n",
|
||
"\n",
|
||
"---\n",
|
||
"Напомним, что линейная регрессия — это модель вида\n",
|
||
"\n",
|
||
"$$a(x) = \\langle w, x \\rangle + b$$ где $w \\in \\mathbb{R}^d$, $b \\in \\mathbb{R}$. Для обучения параметров $w$ решается оптимизационная задача следующего вида:\n",
|
||
"\n",
|
||
"$$\\frac{1}{M} ∑_{i=1}^M (w_1 \\cdot x_{i1} + \\dots + w_n \\cdot x_{in} + b - y_i)^2 + \\alpha \\cdot R(w) \\rightarrow \\min_{w_1, \\dots, w_n, b}$$\n",
|
||
"\n",
|
||
"Здесь $R(w)$ — это регуляризация параметров модели, $\\alpha$ — коэффициент регуляризации, задаваемый перед началом обучения.\n",
|
||
"\n",
|
||
"Для обучения линейной регрессии, нам подойдет реализация из sklearn. В sklearn есть несколько классов, реализующих линейную регрессию. Основные это:\n",
|
||
"\n",
|
||
"- `LinearRegression` — линейная регрессия без регуляризации $R(w) = 0$ (метод наименьших квадратов)\n",
|
||
"- `Ridge` — линейная регрессия с оптимизацией MSE и $\\ell_2$-регуляризацией $R(w) = \\frac{1}{2} \\cdot \\left( w_1^2 + \\dots + w_n^2 \\right)$\n",
|
||
"- `Lasso` — линейная регрессия с оптимизацией MSE и $\\ell_1$-регуляризацией $R(w) = |w_1| + \\dots + |w_n|$\n",
|
||
"\n",
|
||
"Также есть SVR, ElasticNet и пр., но не будем сегодня о них\n",
|
||
"\n",
|
||
"У моделей из sklearn есть методы fit и predict. Первый принимает на вход обучающую выборку и вектор целевых переменных и обучает модель, второй, будучи вызванным после обучения модели, возвращает предсказание на выборке."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "cbSs0Vyv7siW"
|
||
},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "X7WovweT0a9x"
|
||
},
|
||
"source": [
|
||
"Рассмотрим, модельные данные для задачи регрессии. Пусть $x$ будет обычным числом из равномерного распределения, а $y = 0.5 \\cdot x + 0.1$ — целевая переменная. При этом наблюдаем мы $\\overline{y} = y + \\varepsilon,~\\varepsilon \\sim N(0, 0.01)$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 731
|
||
},
|
||
"id": "qVGiym5s0a9y",
|
||
"outputId": "ce3215df-d8c5-4371-f796-0e55074f0222"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.random.seed(1)\n",
|
||
"X = np.random.uniform(0, 1, 100)\n",
|
||
"Y = X * 0.5 + 0.1 + np.random.randn(100) * 0.1\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8, 8))\n",
|
||
"plt.scatter(X, Y)\n",
|
||
"plt.title(\"Обучающая выборка зависимости y от x\", size=15)\n",
|
||
"plt.xlabel(\"x\", size=15)\n",
|
||
"plt.ylabel(r'$\\overline{y}$', size=15)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "t0cYZCLC0a9z"
|
||
},
|
||
"source": [
|
||
"<font color='DarkSeaGreen'>**Обучим**</font> линейную регрессию с $l_2$ регуляризацией, и посмотрим как регуляризация влияет на качество модели. В реализации библиотеки `sklearn` (класс Ridge) коэффициент регуляризации задаётся параметром `alpha`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {
|
||
"id": "CA4ewkpWFcBe"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.linear_model import Ridge"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 655
|
||
},
|
||
"id": "g09w5dAO0a9z",
|
||
"outputId": "1b330e33-5d9e-40af-de18-f42ba332f2a0"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x700 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"x_axis = np.linspace(0, 1, 200)\n",
|
||
"fig, axs = plt.subplots(figsize=(14, 7), ncols=2)\n",
|
||
"axs[0].scatter(X, Y)\n",
|
||
"\n",
|
||
"w = []\n",
|
||
"b = []\n",
|
||
"\n",
|
||
"alphas = [0.0, 0.1, 1.0, 10.0, 100.0, 1000.0]\n",
|
||
"\n",
|
||
"for alpha in alphas:\n",
|
||
" reg = Ridge(alpha=alpha) # Задаем параметр alpha\n",
|
||
" reg.fit(X[:, None], Y)\n",
|
||
" pred = reg.predict(x_axis[:, None])\n",
|
||
" w.append(reg.coef_[0])\n",
|
||
" b.append(reg.intercept_)\n",
|
||
" axs[0].plot(x_axis, pred, label=\"alpha=\" + str(alpha))\n",
|
||
"\n",
|
||
"axs[0].legend()\n",
|
||
"axs[0].set_xlabel(\"x\", size=15)\n",
|
||
"axs[0].set_ylabel(\"y\", size=15)\n",
|
||
"axs[0].set_title(\"Ridge регрессия с разными коэффициентами регуляризации\")\n",
|
||
"axs[1].plot(alphas, w, label=\"w\")\n",
|
||
"axs[1].plot(alphas, b, label=\"b\")\n",
|
||
"axs[1].set_xlabel(\"alpha\", size=15)\n",
|
||
"axs[1].set_ylabel(\"Значение параметров\", size=15)\n",
|
||
"axs[1].set_title(\"Значение параметров w и b при разных значениях регуляризации\")\n",
|
||
"axs[1].set_xscale(\"symlog\", linthresh=0.01)\n",
|
||
"axs[1].legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "351BHRAS0a9z"
|
||
},
|
||
"source": [
|
||
"### <font color='DarkOrange'>**Задание 1 [2 баллa]**</font>\n",
|
||
"\n",
|
||
"Как зависят параметры модели от константы регуляризации? А качество?\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "u9XL2W5JKd3J"
|
||
},
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваши выводы тут:**</font>\n",
|
||
"\n",
|
||
"Зависимость параметров:\n",
|
||
"- С увеличением константы веса модели начинают уменьшаться. При очень больших значениях они становятся практически нулевыми\n",
|
||
"- При больших значениях alpha практически весь вклад вносится за счёт коэффициента смещения\n",
|
||
"- С увеличением значений alpha возникает высокий риск недообучения\n",
|
||
"\n",
|
||
"Качество модели:\n",
|
||
"- При значительном увеличении alpha качество модели сильно падает "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "wx3R5ed38SwQ"
|
||
},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Y7z1NWrn0a90"
|
||
},
|
||
"source": [
|
||
"Казалось бы, зачем нам <font color='CornflowerBlue'>**регуляризация**</font>?\n",
|
||
"\n",
|
||
"Давайте рассмотрим ещё один модельный пример. Представим, что кто-то добавил в данные к переменной $x$ её же умноженную на $2$. То есть, теперь у нас два признака $x_1$ и $x_2 = 2 \\cdot x_1$. Тогда, $y = c \\cdot 0.5 \\cdot x_1 + \\frac{1 - c}{4} \\cdot x_2 + 0.1$, где $c$ любое сколь угодно большое вещественное число. Это может привести к тому, что без регуляризации мы рискуем выучить очень большие веса!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "lgeNX7GW0a90",
|
||
"outputId": "7229cd0e-6a0e-40db-ed5b-78a78a3f151b"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"w1: 0.003110369132959204 \tw2: 0.24999999999999994\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"np.random.seed(1)\n",
|
||
"X2 = np.hstack((X[:, None], 2 * X[:, None]))\n",
|
||
"Y2 = X2[:, 0] * 0.5 + 0.1 + np.random.randn(100) * 0.1\n",
|
||
"\n",
|
||
"reg = Ridge(alpha=0.0)\n",
|
||
"reg.fit(X2, Y2)\n",
|
||
"print(\"w1:\", reg.coef_[0], \"\\tw2:\", reg.coef_[1])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "fMvEEVdd0a90"
|
||
},
|
||
"source": [
|
||
"Коэффициенты адекватные, хотя и не похожи на изначальную зависимость. Но что, если $x_2$ будет равняться $3 \\cdot x_1$?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "TwlR5M8l0a90",
|
||
"outputId": "25056e2e-8fe7-4eb7-979a-8504d50b3ef9"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"w1: 9734611262837.348 \tw2: -3244870420945.615\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"np.random.seed(1)\n",
|
||
"X3 = np.hstack((X[:, None], 3 * X[:, None]))\n",
|
||
"Y3 = X3[:, 0] * 0.5 + 0.1 + np.random.randn(100) * 0.1\n",
|
||
"\n",
|
||
"reg = Ridge(alpha=0.0)\n",
|
||
"reg.fit(X3, Y3)\n",
|
||
"print(\"w1:\", reg.coef_[0], \"\\tw2:\", reg.coef_[1])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "aESDtjM40a91"
|
||
},
|
||
"source": [
|
||
"Тут вот уже не повезло. Коэффициенты случайно выучились неадекватно большими.\n",
|
||
"\n",
|
||
"Создадим обучающую выборку из того же распределения и посмотрим на качество:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "zKEqSVP-0a91",
|
||
"outputId": "dac0ffc4-ea77-4665-c89b-91cea4a39ada"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MSE loss: 0.0219\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"np.random.seed(2)\n",
|
||
"X3_test = np.random.uniform(0, 1, 100)\n",
|
||
"X3_test = np.hstack((X3_test[:, None], 3 * X3_test[:, None]))\n",
|
||
"Y3_test = X3_test[:, 0] * 0.5 + 0.1 + np.random.randn(100) * 0.1\n",
|
||
"\n",
|
||
"Y3_test_pred = np.sum(reg.coef_[None] * X3_test + reg.intercept_, axis=1)\n",
|
||
"print(\"MSE loss: %.4f\" % np.mean((Y3_test_pred - Y3_test) ** 2))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "o3h2jGLt0a91"
|
||
},
|
||
"source": [
|
||
"Вроде бы неплохое, но что если мы добавим ко второму признаку одного из объектов небольшой шум?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "K2_S39zx0a91",
|
||
"outputId": "e87fdd60-16cc-4ee4-b575-57a96a5c40b3"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MSE loss: 1051.9490520394384\n",
|
||
"Предсказание для первого с шумом: -324.0625\n",
|
||
"Предсказание для первого без шума: 0.4248046875\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X3_test[0, 1] = X3_test[0, 1] + 1e-10\n",
|
||
"Y3_test_pred_noisy = np.sum(reg.coef_[None] * X3_test + reg.intercept_, axis=1)\n",
|
||
"print(\"MSE loss:\", np.mean((Y3_test_pred_noisy - Y3_test) ** 2))\n",
|
||
"print(\"Предсказание для первого с шумом: \", Y3_test_pred_noisy[0])\n",
|
||
"print(\"Предсказание для первого без шума: \", Y3_test_pred[0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "mb8h28NH0a92"
|
||
},
|
||
"source": [
|
||
"Как видим, даже небольшое изменение в данных, приводит к резкому падению качества."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "JO03NCnf0a92"
|
||
},
|
||
"source": [
|
||
"### <font color='DarkOrange'>**Задание 2 [2 баллa]**</font>\n",
|
||
"Рассмотрите больше примеров (хотя бы два) с двумя признаками $x_1$ и $x_2$, где $x_2$ линейно зависит от $x_1$. Убедитесь, что линейная модель без регуляризации крайне неустойчива."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {
|
||
"id": "4fYvzntu0a92"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"w1: 1696626244189.3145 \n",
|
||
"w2: -113108416279.25912\n",
|
||
"-----\n",
|
||
"w1: -0.24676691369166734 \n",
|
||
"w2: -0.5\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X_4 = np.random.uniform(0, 1, 100)\n",
|
||
"X_4 = np.hstack((X_4[:, None], 15 * X_4[:, None]))\n",
|
||
"Y_4 = X_4[:, 0] * 0.5 + 0.1 + np.random.randn(100) * 0.1\n",
|
||
"\n",
|
||
"reg = Ridge(alpha=0.0)\n",
|
||
"reg.fit(X_4, Y_4)\n",
|
||
"print(\"w1:\", reg.coef_[0], \"\\nw2:\", reg.coef_[1])\n",
|
||
"print(\"-----\")\n",
|
||
"\n",
|
||
"X_5 = np.random.uniform(0, 1, 100)\n",
|
||
"X_5 = np.hstack((X_5[:, None], -19 / 13 * X_5[:, None]))\n",
|
||
"Y_5 = X_5[:, 0] * 0.5 + 0.1 + np.random.randn(100) * 0.1\n",
|
||
"\n",
|
||
"reg = Ridge(alpha=0.0)\n",
|
||
"reg.fit(X_5, Y_5)\n",
|
||
"print(\"w1:\", reg.coef_[0], \"\\nw2:\", reg.coef_[1])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "RayRFAUQ8_im"
|
||
},
|
||
"source": [
|
||
"-------------"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "QntGTsze_FPB"
|
||
},
|
||
"source": [
|
||
"## Масштабирование данных"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "qFrLJkwU0a95"
|
||
},
|
||
"source": [
|
||
"Попробуем обучить линейную регрессию уже c $l_1$ регуляризацией (Lasso) на специальном датасете из sklearn"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {
|
||
"id": "6Wz-2yw70a95"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.datasets import fetch_california_housing\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"X, y = fetch_california_housing(return_X_y=True)\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2024)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "H_nI4pA4so5t"
|
||
},
|
||
"source": [
|
||
"Взглянем немножко на данные. Выведем средние значения каждого признака"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "VJxbObHelbzz",
|
||
"outputId": "fd0843bc-44f8-42bf-9618-5f70d6784947"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[ 3.868 28.712 5.411 1.093 1416.185 3.109 35.637 -119.583]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"with np.printoptions(formatter={'float': '{: 0.3f}'.format}):\n",
|
||
" print(X_train.mean(axis=0))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "0FqB2pnvtRAG"
|
||
},
|
||
"source": [
|
||
"Нетрудно видеть, что масштаб у разных признаков сильно отличается. Это может приводить к разным неприятным эффектам. Подробнее эту проблему мы разберём в следующем задании."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {
|
||
"id": "xDqH8v2BmF4G"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.preprocessing import StandardScaler"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "keUMYxF5tjXH"
|
||
},
|
||
"source": [
|
||
"### <font color='DarkOrange'>**Задание 3 [1 балл]**</font>\n",
|
||
"\n",
|
||
"Отмасштабируйте данные при помощи класса `StandardScaler`. Выведите средние значения и дисперсии признаков на обучающей и тестовой выборках.\n",
|
||
"\n",
|
||
"<font color='OrangeRed'>**Примечание**</font> Результат положите в переменные X_train_scaled и X_test_scaled , чтобы последующий код был рабочим"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {
|
||
"id": "m0j2RZhmmgPY"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"X_train_scaled:\n",
|
||
"Среднее значение = -1.5276078667068268e-14\n",
|
||
"дисперсия = 1.0000000000000033\n",
|
||
"\n",
|
||
"X_train_scaled:\n",
|
||
"Среднее значение = 0.009713241824084506\n",
|
||
"дисперсия = 1.2685666097336883\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"scaler = StandardScaler()\n",
|
||
"\n",
|
||
"X_train_scaled = scaler.fit_transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"print(f\"X_train_scaled:\\nСреднее значение = {X_train_scaled.mean()}\\nдисперсия = {X_train_scaled.var()}\\n\")\n",
|
||
"print(f\"X_train_scaled:\\nСреднее значение = {X_test_scaled.mean()}\\nдисперсия = {X_test_scaled.var()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "76eKB65uuK0i"
|
||
},
|
||
"source": [
|
||
"Измерим качество прогнозатора. Будем использовать метрику RMSE."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {
|
||
"id": "t6yAIUIFujHO"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.metrics import mean_squared_error, root_mean_squared_error\n",
|
||
"from sklearn.linear_model import Lasso"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ObS-2QbA0a95",
|
||
"outputId": "a8fe0587-c74b-4575-d697-e5dd1a6b8e9c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Test RMSE = 0.9798\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"reg = Lasso(alpha=0.5)\n",
|
||
"reg.fit(X_train_scaled, y_train)\n",
|
||
"y_pred = reg.predict(X_test_scaled)\n",
|
||
"print(\"Test RMSE = %.4f\" % root_mean_squared_error(y_test, y_pred))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "WOczQNKBvAY-"
|
||
},
|
||
"source": [
|
||
"### <font color='DarkOrange'>**Задание 4 [2 баллa]**</font>\n",
|
||
"\n",
|
||
"В чем плюсы RMSE по сравнению с MSE?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "qOHAhn1S9K8i"
|
||
},
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваши выводы тут:**</font>\n",
|
||
"\n",
|
||
"Плюсы:\n",
|
||
"- `RMSE`, за счёт извлечения квадратного корня, выражается в тех же единицах измерения, что и оригинальные данные.\n",
|
||
" - `MSE` же, напротив, имеет в качестве единиц измерения квадрат исходных е.и.\n",
|
||
" - В целом `RMSE` чаще используется для прикладных задач за счёт удобства оценки данных в тех же единицах измерения, в то время как `MSE` применяется в математических задачах, так как более явно учитывает влияние больших выбросов\n",
|
||
"- Несмотря на то, что обе приведённые метрики весьма чувствительны к большим значениям ошибки (так как все ошибки вносят квадратичный вклад), `RMSE` несколько сглаживает их влияние за счёт извлечения корня\n",
|
||
" - Это может быть полезно, когда влияние подобных ошибок не столь важно для модели\n",
|
||
"- При равномерном распределении ошибок `RMSE` можно использовать как среднеквадратичное отклонения"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "zJ1M29rY89m5"
|
||
},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "0NVSmCoL0a96"
|
||
},
|
||
"source": [
|
||
"Обратим внимание на веса модели. Почти все из них занулились! Это большое преимущество, так как разреживание весов позволяет отбирать нужные признаки, делая модель более лёгкой."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {
|
||
"id": "pVFBOLvf0a96"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 0.28811554, 0. , 0. , -0. , -0. ,\n",
|
||
" -0. , -0. , -0. ])"
|
||
]
|
||
},
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"reg.coef_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "RuP2oVgo0a96"
|
||
},
|
||
"source": [
|
||
"А теперь обучим с $l_2$ регуляризацией."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {
|
||
"id": "3TJN4CGY0a96"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[ 8.50854581e-01 1.25545440e-01 -2.78892640e-01 3.08812622e-01\n",
|
||
" -1.99686054e-04 -4.12942247e-02 -8.88296917e-01 -8.60046905e-01]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"reg = Ridge(alpha=0.5)\n",
|
||
"reg.fit(X_train_scaled, y_train)\n",
|
||
"print(reg.coef_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "OImrNXyD0a97"
|
||
},
|
||
"source": [
|
||
"Как видим, веса не разрежены, хотя и есть очень маленькие значения"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "TLMmhppM9AVY"
|
||
},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "3Np7mCO7_iwz"
|
||
},
|
||
"source": [
|
||
"## Подбор гиперпараметра при регуляризации"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "QSvWWyfWeBaz"
|
||
},
|
||
"source": [
|
||
"При обучении мы заранее не знаем, какое значение параметра регуляризации даст наилучшие результаты. Подобрать оптимальные параметры можно с помощью <font color='CornflowerBlue'>**кросс-валидации**</font>. В sklearn есть несколько классов со встроенной кросс-валидацией"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {
|
||
"id": "pdsMzMTvd_6K"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"from sklearn.pipeline import Pipeline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "m9ZE2NxwfOJo"
|
||
},
|
||
"source": [
|
||
"Воспользуемся классом GridSearch для перебора параметров по сетке.\n",
|
||
"\n",
|
||
"* Для линейных регрессий перебирается параметр $\\alpha$ - сила регуляризации. Обычно важнее перебирать порядок этого параметра, а не точное его значение. В силу этого сетку перебора будет удобно сделать через функцию np.logspace, например np.logspace(-3, 3, 10).\n",
|
||
"\n",
|
||
"### <font color='DarkOrange'>**Задание 5 [3 баллa]**</font>\n",
|
||
"\n",
|
||
"Воспользуйтесь классом [GridSearch](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) и подберите константы регуляризации для Lasso и Ridge регрессий. Измерьте качество обученных моделей. Сетка перебора должна быть логарифмической, из хотя бы 10 значений\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "6VHlyE8vLWrc"
|
||
},
|
||
"source": [
|
||
"<font color='LightSteelBlue'>**Подсказка**</font>\n",
|
||
"\n",
|
||
"* Пример, как можно перебирать параметры в GridSearch у вложенных [Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline) можно [найти вот тут](https://www.kaggle.com/code/ilnazsalimov/gridsearch-with-pipeline)\n",
|
||
"* Обратите внимание, что сейчас мы сразу заносим масштабирование в Pipeline - чтобы иметь возможность сразу вызываться от оригинальных X_train, а также чтобы не было утечки данных при использовании GridSearch\n",
|
||
"* В GridSearch в качестве скоринговой функции можно подавать строковое описание функции из sklearn, которое [можно посмотреть вот тут](https://scikit-learn.org/stable/modules/model_evaluation.html), а также саму скоринговую функцию из sklearn или собственную функцию, сделанную [через make_scorer](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer)\n",
|
||
"\n",
|
||
"<font color='OrangeRed'>**Примечание**</font> Итоговое качество должно быть не больше 0.75 RMSE. За меньшее качество балл будет снижаться"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {
|
||
"id": "AcwCrIunwoxK"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2 0.72237\n",
|
||
"Name: mean_test_score, dtype: float64\n",
|
||
"5 0.722382\n",
|
||
"Name: mean_test_score, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model_lasso = Pipeline([\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"regr\", Lasso())\n",
|
||
"])\n",
|
||
"\n",
|
||
"model_ridge = Pipeline([\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"regr\", Ridge())\n",
|
||
"])\n",
|
||
"\n",
|
||
"parametrs = {\n",
|
||
" 'regr__alpha': list(np.logspace(-5, 6, 12)),\n",
|
||
"}\n",
|
||
"\n",
|
||
"lasso_cv = GridSearchCV(\n",
|
||
" model_lasso,\n",
|
||
" cv=5,\n",
|
||
" scoring=\"neg_root_mean_squared_error\",\n",
|
||
" param_grid = parametrs\n",
|
||
")\n",
|
||
"\n",
|
||
"ridge_cv = GridSearchCV(\n",
|
||
" model_ridge,\n",
|
||
" cv=5,\n",
|
||
" scoring=\"neg_root_mean_squared_error\",\n",
|
||
" param_grid = parametrs\n",
|
||
")\n",
|
||
"\n",
|
||
"lasso_cv.fit(X_train, y_train)\n",
|
||
"ridge_cv.fit(X_train, y_train)\n",
|
||
"lasso_res = pd.DataFrame(lasso_cv.cv_results_)\n",
|
||
"ridge_res = pd.DataFrame(ridge_cv.cv_results_)\n",
|
||
"\n",
|
||
"print(-lasso_res['mean_test_score'].sort_values(ascending=False).head(1))\n",
|
||
"print(-ridge_res['mean_test_score'].sort_values(ascending=False).head(1))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "AjG4DjsE5VOd"
|
||
},
|
||
"source": [
|
||
"Убедимся, что Lasso всё ещё зануляет признаки (скорее всего модель Lasso занулила хотя бы один)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"metadata": {
|
||
"id": "NIvVQE2bQfid"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 0.84604739, 0.12597833, -0.26816238, 0.29792527, 0. ,\n",
|
||
" -0.04027369, -0.87740579, -0.84850035])"
|
||
]
|
||
},
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"lasso_cv.best_estimator_.named_steps[\"regr\"].coef_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "QU7Z9Ku8ycY_"
|
||
},
|
||
"source": [
|
||
"**Выводы** В первой части задания по линейным моделям мы должны были узнать:\n",
|
||
".\n",
|
||
"\n",
|
||
"1. Зачем нужна регуляризация.\n",
|
||
"2. Как отбирать значащие признаки.\n",
|
||
"3. Кaк подбирать параметры линейной модели.\n",
|
||
"\n",
|
||
"-----\n",
|
||
"<font color=\"white\" style=\"opacity:0.2025\"></font>\n",
|
||
"\n",
|
||
"Во **второй части** мы будем применять линейные модели для классификации реальных данных, где мы сможем проверить наши выводы, полученные на искуственных примерах. А также убедимся в полезности нормировки и научимся работать с разными видами данных.\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|