1225 lines
343 KiB
Plaintext
1225 lines
343 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",
|
||
"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"
|
||
],
|
||
"metadata": {
|
||
"id": "6ODl7_-1JoHQ"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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>**"
|
||
],
|
||
"metadata": {
|
||
"id": "H0Lj_c63lrku"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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"
|
||
],
|
||
"metadata": {
|
||
"id": "CTmlafz7W04M"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# !!! Данный блок будет работать только в Google-Colab !!!\n",
|
||
"! gdown 10k8Hwn9kpK9SpK4IEj4-EaWQZqgYT5-Q\n",
|
||
"! pip install -r /content/requirements_2024_25_for_colab_small.txt"
|
||
],
|
||
"metadata": {
|
||
"id": "UodfS2cpXMUd",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:06.668026Z",
|
||
"start_time": "2024-11-15T13:00:58.822586Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Downloading...\n",
|
||
"From: https://drive.google.com/uc?id=10k8Hwn9kpK9SpK4IEj4-EaWQZqgYT5-Q\n",
|
||
"To: C:\\Users\\mozhu\\PycharmProjects\\ML_2024\\Task5\\Base\\requirements_2024_25_for_colab_small.txt\n",
|
||
"\n",
|
||
" 0%| | 0.00/375 [00:00<?, ?B/s]\n",
|
||
"100%|##########| 375/375 [00:00<00:00, 249kB/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"^C\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 2
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"Проверим версию библиотеки:"
|
||
],
|
||
"metadata": {
|
||
"id": "vUCY0KLD7VmA"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import catboost\n",
|
||
"\n",
|
||
"assert (catboost.__version__ == '1.2.7')"
|
||
],
|
||
"metadata": {
|
||
"id": "zyRnTYF5X37Y",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:08.072295Z",
|
||
"start_time": "2024-11-15T13:01:06.746275Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"execution_count": 3
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"Теперь можно приступать к выполнению задания! :)"
|
||
],
|
||
"metadata": {
|
||
"id": "UShU_WkjlqrP"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"-----------\n",
|
||
"<font color=\"white\" style=\"opacity:0.2024\"></font>"
|
||
],
|
||
"metadata": {
|
||
"id": "GLIQiUj6KNYj"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"import warnings\n",
|
||
"\n",
|
||
"warnings.simplefilter(\"ignore\")\n",
|
||
"sns.set(style=\"darkgrid\")\n",
|
||
"%matplotlib inline"
|
||
],
|
||
"metadata": {
|
||
"id": "Gc3xTMopl8c1",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:08.166121Z",
|
||
"start_time": "2024-11-15T13:01:08.119620Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"execution_count": 4
|
||
},
|
||
{
|
||
"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",
|
||
"source": [
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "cbSs0Vyv7siW"
|
||
}
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 731
|
||
},
|
||
"id": "qVGiym5s0a9y",
|
||
"outputId": "ce3215df-d8c5-4371-f796-0e55074f0222",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:08.553787Z",
|
||
"start_time": "2024-11-15T13:01:08.183171Z"
|
||
}
|
||
},
|
||
"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()"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"execution_count": 5
|
||
},
|
||
{
|
||
"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",
|
||
"source": [
|
||
"from sklearn.linear_model import Ridge"
|
||
],
|
||
"metadata": {
|
||
"id": "CA4ewkpWFcBe",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:09.469833Z",
|
||
"start_time": "2024-11-15T13:01:08.571896Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"execution_count": 6
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 655
|
||
},
|
||
"id": "g09w5dAO0a9z",
|
||
"outputId": "1b330e33-5d9e-40af-de18-f42ba332f2a0",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:10.453188Z",
|
||
"start_time": "2024-11-15T13:01:09.488350Z"
|
||
}
|
||
},
|
||
"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()"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1400x700 with 2 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"execution_count": 7
|
||
},
|
||
{
|
||
"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",
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваши выводы тут:**</font>\n",
|
||
"Параметры:\n",
|
||
"1. С увеличением константы веса модели начинают уменьшаться. При очень больших значениях они становятся практически нулевыми\n",
|
||
"2. При больших значениях `alpha` практически весь вклад вносится за счёт коэффициента смещения\n",
|
||
"3. С увеличением значений `alpha` возникает высокий риск недообучения\n",
|
||
"\n",
|
||
"Качество модели:\n",
|
||
"1. При значительном увеличении `alpha` качество модели сильно падает "
|
||
],
|
||
"metadata": {
|
||
"id": "u9XL2W5JKd3J"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "wx3R5ed38SwQ"
|
||
}
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "lgeNX7GW0a90",
|
||
"outputId": "7229cd0e-6a0e-40db-ed5b-78a78a3f151b",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:10.499248Z",
|
||
"start_time": "2024-11-15T13:01:10.484898Z"
|
||
}
|
||
},
|
||
"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])"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"w1: 0.10062207382659173 \tw2: 0.20124414765318352\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 8
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "TwlR5M8l0a90",
|
||
"outputId": "25056e2e-8fe7-4eb7-979a-8504d50b3ef9",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:10.561690Z",
|
||
"start_time": "2024-11-15T13:01:10.541649Z"
|
||
}
|
||
},
|
||
"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])"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"w1: 20443435586638.3 \tw2: -6814478528879.264\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 9
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "zKEqSVP-0a91",
|
||
"outputId": "dac0ffc4-ea77-4665-c89b-91cea4a39ada",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:10.608372Z",
|
||
"start_time": "2024-11-15T13:01:10.597267Z"
|
||
}
|
||
},
|
||
"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))"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MSE loss: 0.0197\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 10
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "K2_S39zx0a91",
|
||
"outputId": "e87fdd60-16cc-4ee4-b575-57a96a5c40b3",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:10.672147Z",
|
||
"start_time": "2024-11-15T13:01:10.652994Z"
|
||
}
|
||
},
|
||
"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 объекта с шумом: \", Y3_test_pred_noisy[0])\n",
|
||
"print(\"Предсказание для первого объекта без шума: \", Y3_test_pred[0])"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MSE loss: 4641.811179703668\n",
|
||
"Предсказание для первого объекта с шумом: -681.03515625\n",
|
||
"Предсказание для первого объекта без шума: 0.4140625\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 11
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"id": "4fYvzntu0a92",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:10.735212Z",
|
||
"start_time": "2024-11-15T13:01:10.718340Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"np.random.seed(2024)\n",
|
||
"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], \"\\tw2:\", reg.coef_[1])\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], \"\\tw2:\", reg.coef_[1])"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"w1: -78898433254335.61 \tw2: 5259895550289.071\n",
|
||
"w1: 5865168356578.415 \tw2: 4013009928184.904\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 12
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "markdown",
|
||
"source": "`Ответ:` да, в случае линейно зависимости признаков, мы получаем весьма значительные по модулю веса"
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"-------------"
|
||
],
|
||
"metadata": {
|
||
"id": "RayRFAUQ8_im"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Масштабирование данных"
|
||
],
|
||
"metadata": {
|
||
"id": "QntGTsze_FPB"
|
||
}
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"id": "6Wz-2yw70a95",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:10.999419Z",
|
||
"start_time": "2024-11-15T13:01:10.778796Z"
|
||
}
|
||
},
|
||
"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)"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 13
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"Взглянем немножко на данные. Выведем средние значения каждого признака"
|
||
],
|
||
"metadata": {
|
||
"id": "H_nI4pA4so5t"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"with np.printoptions(formatter={'float': '{: 0.3f}'.format}):\n",
|
||
" print(X_train.mean(axis=0))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "VJxbObHelbzz",
|
||
"outputId": "fd0843bc-44f8-42bf-9618-5f70d6784947",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.046371Z",
|
||
"start_time": "2024-11-15T13:01:11.033174Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[ 3.868 28.712 5.411 1.093 1416.185 3.109 35.637 -119.583]\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 14
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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."
|
||
],
|
||
"metadata": {
|
||
"id": "0FqB2pnvtRAG"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.preprocessing import StandardScaler"
|
||
],
|
||
"metadata": {
|
||
"id": "xDqH8v2BmF4G",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.092921Z",
|
||
"start_time": "2024-11-15T13:01:11.079596Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"execution_count": 15
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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"
|
||
],
|
||
"metadata": {
|
||
"id": "keUMYxF5tjXH"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"scaler = StandardScaler()\n",
|
||
"# scaler.fit(X_train)\n",
|
||
"X_train_scaled = scaler.fit_transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"print(f\"Для X_train_scaled:\\nСреднее значение = {X_train_scaled.mean()}, а дисперсия = {X_train_scaled.var()}\")\n",
|
||
"print(f\"Для X_train_scaled:\\nСреднее значение = {X_test_scaled.mean()}, а дисперсия = {X_test_scaled.var()}\")"
|
||
],
|
||
"metadata": {
|
||
"id": "m0j2RZhmmgPY",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.138559Z",
|
||
"start_time": "2024-11-15T13:01:11.126312Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Для X_train_scaled:\n",
|
||
"Среднее значение = -1.52766934084994e-14, а дисперсия = 1.0000000000000036\n",
|
||
"Для X_train_scaled:\n",
|
||
"Среднее значение = 0.009713241824084505, а дисперсия = 1.2685666097336878\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 16
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"Измерим качество прогнозатора. Будем использовать метрику RMSE."
|
||
],
|
||
"metadata": {
|
||
"id": "76eKB65uuK0i"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.metrics import mean_squared_error\n",
|
||
"from sklearn.linear_model import Lasso"
|
||
],
|
||
"metadata": {
|
||
"id": "t6yAIUIFujHO",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.184761Z",
|
||
"start_time": "2024-11-15T13:01:11.171787Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"execution_count": 17
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ObS-2QbA0a95",
|
||
"outputId": "a8fe0587-c74b-4575-d697-e5dd1a6b8e9c",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.309085Z",
|
||
"start_time": "2024-11-15T13:01:11.217909Z"
|
||
}
|
||
},
|
||
"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\" % mean_squared_error(y_test, y_pred, squared=False))"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Test RMSE = 0.9798\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 18
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.357169Z",
|
||
"start_time": "2024-11-15T13:01:11.343073Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "print(\"Test MSE = %.4f\" % mean_squared_error(y_test, y_pred))",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Test MSE = 0.9601\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 19
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### <font color='DarkOrange'>**Задание 4 [2 баллa]**</font>\n",
|
||
"\n",
|
||
"В чем плюсы RMSE по сравнению с MSE?"
|
||
],
|
||
"metadata": {
|
||
"id": "WOczQNKBvAY-"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"<font color='MediumOrchid'>**Ваши выводы тут:**</font>\n",
|
||
"Плюсы:\n",
|
||
"1. `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",
|
||
"2. 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",
|
||
"3. При равномерном распределении ошибок `RMSE` можно использовать как среднеквадратичное отклонения (или нечто весьма близкое)\n"
|
||
],
|
||
"metadata": {
|
||
"id": "qOHAhn1S9K8i"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "zJ1M29rY89m5"
|
||
}
|
||
},
|
||
{
|
||
"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",
|
||
"metadata": {
|
||
"id": "pVFBOLvf0a96",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.419617Z",
|
||
"start_time": "2024-11-15T13:01:11.404768Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"reg.coef_"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 0.28811554, 0. , 0. , -0. , -0. ,\n",
|
||
" -0. , -0. , -0. ])"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 20
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "RuP2oVgo0a96"
|
||
},
|
||
"source": [
|
||
"А теперь обучим с $l_2$ регуляризацией."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "3TJN4CGY0a96",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.481441Z",
|
||
"start_time": "2024-11-15T13:01:11.468152Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"reg = Ridge(alpha=0.5)\n",
|
||
"reg.fit(X_train_scaled, y_train)\n",
|
||
"print(reg.coef_)"
|
||
],
|
||
"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"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 21
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "OImrNXyD0a97"
|
||
},
|
||
"source": [
|
||
"Как видим, веса не разрежены, хотя и есть очень маленькие значения"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "TLMmhppM9AVY"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Подбор гиперпараметра при регуляризации"
|
||
],
|
||
"metadata": {
|
||
"id": "3Np7mCO7_iwz"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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"
|
||
],
|
||
"metadata": {
|
||
"id": "QSvWWyfWeBaz"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"from sklearn.pipeline import Pipeline"
|
||
],
|
||
"metadata": {
|
||
"id": "pdsMzMTvd_6K",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:01:11.528328Z",
|
||
"start_time": "2024-11-15T13:01:11.513766Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"execution_count": 22
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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"
|
||
],
|
||
"metadata": {
|
||
"id": "m9ZE2NxwfOJo"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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"
|
||
],
|
||
"metadata": {
|
||
"id": "6VHlyE8vLWrc"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"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, 5, 100)),\n",
|
||
"}\n",
|
||
"lasso_cv = GridSearchCV(\n",
|
||
" model_lasso,\n",
|
||
" parametrs,\n",
|
||
" cv=5,\n",
|
||
" scoring=\"neg_root_mean_squared_error\"\n",
|
||
")\n",
|
||
"ridge_cv = GridSearchCV(\n",
|
||
" model_ridge,\n",
|
||
" parametrs,\n",
|
||
" cv=5,\n",
|
||
" scoring=\"neg_root_mean_squared_error\"\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))\n",
|
||
"# Ваш код: о модели и измеряем качество на тесте\n",
|
||
"# Можно вызывать predict прямо от обученных lasso_cv и ridge_cv"
|
||
],
|
||
"metadata": {
|
||
"id": "AcwCrIunwoxK",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:29:30.008245Z",
|
||
"start_time": "2024-11-15T13:29:24.157245Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"18 0.722366\n",
|
||
"Name: mean_test_score, dtype: float64\n",
|
||
"56 0.722381\n",
|
||
"Name: mean_test_score, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 36
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"Убедимся, что Lasso всё ещё зануляет признаки (скорее всего модель Lasso занулила хотя бы один)."
|
||
],
|
||
"metadata": {
|
||
"id": "AjG4DjsE5VOd"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"lasso_cv.best_estimator_.named_steps[\"regr\"].coef_"
|
||
],
|
||
"metadata": {
|
||
"id": "NIvVQE2bQfid",
|
||
"ExecuteTime": {
|
||
"end_time": "2024-11-15T13:29:33.060178Z",
|
||
"start_time": "2024-11-15T13:29:33.053933Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 0.84770117, 0.12584016, -0.27185324, 0.30168796, -0. ,\n",
|
||
" -0.04062777, -0.88125938, -0.85258618])"
|
||
]
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 37
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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:0.2023\"></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": {
|
||
"id": "QU7Z9Ku8ycY_"
|
||
}
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.7.3"
|
||
},
|
||
"colab": {
|
||
"provenance": [],
|
||
"toc_visible": true
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|