{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "sTB50uLM0a9o" }, "source": [ "# \n", "\n", "# Машинное обучение. ВМК МГУ" ] }, { "cell_type": "markdown", "metadata": { "id": "0xg3G6bd0a9s" }, "source": [ "\n", "# Практическое задание 5: Линейные модели: регрессия\n", "\n", "## Уровень: **Базовый (Base)**" ] }, { "cell_type": "markdown", "source": [ "# О формате сдачи\n", "\n", "🔷 **При решении ноутбука используйте данный шаблон**\n", "\n", " ✅ Можно добавлять новые ячейки любых типов\n", " ❌ Не нужно удалять текстовые ячейки c разметкой частей ноутбука и формулировками заданий\n", "\n", "\n", "🔷 **При оценивании задач учитывается код**\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", "🔷 **При оценивании задач учитываются выводы**\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", "\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", "**Примерное время выполнения (execution time/время выполнения, если нажать run all) всех ячеек ноутбука при правильной реализации: 5 минут **" ], "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", "**Важно!**\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", "**Важно!**\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" ], "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 **линейная регрессия** — 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": [ "
" ], "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 5 }, { "cell_type": "markdown", "metadata": { "id": "t0cYZCLC0a9z" }, "source": [ "**Обучим** 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": [ "
" ], "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 7 }, { "cell_type": "markdown", "metadata": { "id": "351BHRAS0a9z" }, "source": [ "### **Задание 1 [2 баллa]**\n", "\n", "Как зависят параметры модели от константы регуляризации? А качество?\n", "\n" ] }, { "cell_type": "markdown", "source": [ "**Ваши выводы тут:**\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 **регуляризация**?\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": [ "### **Задание 2 [2 баллa]**\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": [ "### **Задание 3 [1 балл]**\n", "\n", "Отмасштабируйте данные при помощи класса `StandardScaler`. Выведите средние значения и дисперсии признаков на обучающей и тестовой выборках.\n", "\n", "**Примечание** 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": [ "### **Задание 4 [2 баллa]**\n", "\n", "В чем плюсы RMSE по сравнению с MSE?" ], "metadata": { "id": "WOczQNKBvAY-" } }, { "cell_type": "markdown", "source": [ "**Ваши выводы тут:**\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 **кросс-валидации**. 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", "### **Задание 5 [3 баллa]**\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": [ "**Подсказка**\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", "**Примечание** 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", "\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 }