{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "sTB50uLM0a9o" }, "source": [ "# \n", "\n", "# Машинное обучение. ВМК МГУ" ] }, { "cell_type": "markdown", "metadata": { "id": "0xg3G6bd0a9s" }, "source": [ "# Практическое задание 6: Линейные модели: классификация\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": "PFolkguZym7i" } }, { "cell_type": "markdown", "source": [ "Цель данного задания:\n", "\n", "* Узнать, что такое переобучение и как с ним бороться в линейных моделях;\n", "* Научиться работать с разными типами признаков;\n", "* Понять, чем отличаются разные регуляризаторы;\n", "* Приятно провести осенний вечер, предсказывая дождь.\n", "\n", "---\n", "\n", "**Примерное время выполнения (execution time/время выполнения, если нажать run all) всех ячеек ноутбука при правильной реализации: до 30 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": [ "from pyexpat import features\n", "# !!! 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" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Проверим версию библиотеки:" ], "metadata": { "id": "DJEBgcYnGtWG" } }, { "cell_type": "code", "source": [ "import catboost\n", "\n", "assert (catboost.__version__ == '1.2.7')" ], "metadata": { "id": "zyRnTYF5X37Y", "ExecuteTime": { "end_time": "2024-11-19T17:44:12.388669Z", "start_time": "2024-11-19T17:44:11.076714Z" } }, "outputs": [], "execution_count": null }, { "cell_type": "markdown", "source": [ "Теперь можно приступать к выполнению задания! :)" ], "metadata": { "id": "UShU_WkjlqrP" } }, { "cell_type": "markdown", "source": [ "-----------\n", "" ], "metadata": { "id": "Cw1haZEEzSqI" } }, { "cell_type": "code", "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", "\n", "warnings.simplefilter(\"ignore\")\n", "sns.set(style=\"darkgrid\")\n", "%matplotlib inline" ], "metadata": { "id": "Gc3xTMopl8c1", "ExecuteTime": { "end_time": "2024-11-19T17:44:12.434433Z", "start_time": "2024-11-19T17:44:12.393261Z" } }, "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "06u4EbHT0a97" }, "source": [ "## Часть 1. Классификация" ] }, { "cell_type": "markdown", "metadata": { "id": "B9jGB4uw0a97" }, "source": [ "Напомним, что бинарная линейная классификация с классами $0$ и $1$ — это модель следующего вида:\n", "$a(x)= \\begin{cases}\n", "1, & \\langle w, x \\rangle + b > 0; \\\\\n", "0, & \\text{иначе.}\n", "\\end{cases}$\n", "\n", "где $w \\in \\mathbb{R}^d$, $b \\in \\mathbb{R}$. В логистической регрессии $p(x) = \\frac{1}{1 + e^{-[\\langle w, x \\rangle + b]}}$ интерпретируется как вероятность принадлежности к первому классу. Если объект $x$ принадлежит классу $1$ с вероятностью $p(x)$, то правдоподобие записывается в виде $\\prod_{i=1}^{n} p(x_i)^{y_i} \\cdot \\left( 1 - p(x_i) \\right)^{1 - y_i}$. Обучить логистическую регрессию означает найти параметры $w$ и $b$, которые максимизируют указанное правдоподобие. Что эквивалентно минимизации $- \\sum_{i=1}^n y_i \\log p(x_i) + (1 - y_i) \\log (1 - p(x_i))$. Указанная функция потерь называет логистической (или логлосс)." ] }, { "cell_type": "markdown", "metadata": { "id": "cEu9oLQm0a97" }, "source": [ "По тем же причинам, что и в линейной регрессии, к логистической функции потерь добавляется регуляризация (стандартно это $l_2$)." ] }, { "cell_type": "markdown", "source": [ "### **Задание 1 [1 балл]**\n", "\n", "Можно ли использовать $𝑙_1$ регуляризацию в логистической регрессии?" ], "metadata": { "id": "6S8zXpsk_Uwq" } }, { "cell_type": "markdown", "source": [ "**Ваш ответ тут:**\n", "Да, можно.\n", "\n", "Функция потерь в таком случае будет иметь вид: $J(w, b) = -\\frac{1}{m} \\sum_{i=1}^m \\left[ y_i \\log \\sigma(z_i) + (1 - y_i) \\log (1 - \\sigma(z_i)) \\right] + \\lambda \\sum_{j=1}^n |w_j|$\n", "\n", "Применение $l1$-регуляризации для задачи логической регрессии может быть полезно, если не все признаки полезны для классификации и некоторые из них имеет смысл обнулить." ], "metadata": { "id": "QKRzBdcUaGLV" } }, { "cell_type": "markdown", "source": [ "Давайте рассмотрим модельный пример.\n", "\n", "$x_1 \\sim Uniform(0, 1)$, $x_2 \\sim Uniform(0, 1)$\n", "\n", "$y(x_1, x_2)= \\begin{cases}\n", "0, & x_1 + x_2 < 5; \\\\\n", "1, & \\text{иначе.}\n", "\\end{cases}$\n", "\n", "Сгенерируем данные и выучим логистическую регрессию, визуализировав полученный результат." ], "metadata": { "id": "tw0ZDqf6Dbs_" } }, { "cell_type": "code", "source": [ "np.random.seed(1)\n", "X1 = np.random.uniform(0, 5, 100)\n", "X2 = np.random.uniform(0, 5, 100)\n", "X = np.hstack((X1[:, None], X2[:, None]))\n", "Y = np.where(X1 + X2 < 5, 0, 1)" ], "metadata": { "id": "2n45KivZDisg", "ExecuteTime": { "end_time": "2024-11-19T17:44:15.086725Z", "start_time": "2024-11-19T17:44:15.069115Z" } }, "outputs": [], "execution_count": null }, { "cell_type": "code", "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "clf = LogisticRegression(penalty='l2')\n", "clf.fit(X, Y)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "BPbJwx-6DkET", "outputId": "326a1cde-491c-4a96-f20b-3185a0427c0a", "ExecuteTime": { "end_time": "2024-11-19T17:44:19.978435Z", "start_time": "2024-11-19T17:44:19.959392Z" } }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ], "text/html": [ "
LogisticRegression()
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" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "code", "source": [ "from matplotlib.colors import ListedColormap\n", "\n", "\n", "def plot_separating_surface(X, y, cls, view_support=False):\n", " x_min = min(X[:, 0]) - 0.1\n", " x_max = max(X[:, 0]) + 0.1\n", " y_min = min(X[:, 1]) - 0.1\n", " y_max = max(X[:, 1]) + 0.1\n", " h = 0.005\n", " cm = plt.cm.RdBu\n", " cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n", "\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", " Z = cls.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", " plt.figure(figsize=(8, 8))\n", " plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', s=40, cmap=cm_bright)\n", " if view_support:\n", " plt.scatter(X[cls.support_, 0], X[cls.support_, 1],\n", " c=y[cls.support_], edgecolors='k', s=150, cmap=cm_bright)\n", " Z = Z.reshape(xx.shape)\n", " plt.contourf(xx, yy, Z, cmap=cm, alpha=.3)\n", " plt.title(\"Визуализация прогнозатора\", size=15)\n", " plt.xlabel(r'$x_1$', size=15)\n", " plt.ylabel(r'$x_2$', size=15)" ], "metadata": { "id": "PO_roVlXA_28", "ExecuteTime": { "end_time": "2024-11-19T17:44:25.411993Z", "start_time": "2024-11-19T17:44:25.392173Z" } }, "outputs": [], "execution_count": null }, { "cell_type": "code", "source": [ "plot_separating_surface(X, Y, clf)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 731 }, "id": "QT3WEAR_BExz", "outputId": "4dba3e33-297c-4d2f-c4e4-f7e478edd573", "ExecuteTime": { "end_time": "2024-11-19T17:44:28.395400Z", "start_time": "2024-11-19T17:44:27.853242Z" } }, "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": null }, { "cell_type": "markdown", "source": [ "### **Задание 2 [2 баллa]**\n", "Придумайте, сгенерируйте и визуализируйте пример (рекомендуется использовать написанную выше функцию plot_separating_surface), в котором логистическая регрессия будет плохо классифицировать данные.\n" ], "metadata": { "id": "O0uhSD2iEbjk" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.datasets import make_circles\n", "\n", "X, y = make_circles(n_samples=500, factor=0.7, random_state=1024)\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=50)\n", "sc = StandardScaler()\n", "X_train_scaled = sc.fit_transform(X_train)\n", "X_test_scaled = sc.transform(X_test)\n", "\n", "lg = LogisticRegression()\n", "lg.fit(X_train_scaled, y_train)\n", "plot_separating_surface(X_train_scaled, y_train, lg)" ], "metadata": { "id": "3AvOOGN5Ee0-", "ExecuteTime": { "end_time": "2024-11-19T17:44:33.469314Z", "start_time": "2024-11-19T17:44:33.041973Z" }, "outputId": "faa7a66a-2061-4aeb-908e-5cc856f66fb1" }, "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": null }, { "cell_type": "markdown", "source": [ "**Ваш ответ тут (для доп. комментариев):**\n", "Логистическая регрессия плохо классифицирует круговые данные, так как она не может это сделать, ибо строит линейную разделяющую поверхность" ], "metadata": { "id": "yBvvvClpHlxJ" } }, { "cell_type": "markdown", "metadata": { "id": "ITcQZe4I0a99" }, "source": [ "## Обучение на реальных данных" ] }, { "cell_type": "markdown", "metadata": { "id": "bHuEPdZQ0a99" }, "source": [ "Рассмотрим набор данных от метеорологической службы одной страны. В нём требуется предсказать, будет ли дождь на следующий день." ] }, { "cell_type": "markdown", "source": [ "Для начала, скачаем данные" ], "metadata": { "id": "hRUp-L79VhLc" } }, { "cell_type": "code", "source": [ "!gdown 1AgUMxgMK-eRjzthevCk9g-J_s2vpBFpe" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CeEPldEfVelW", "outputId": "7388709f-2259-4099-bbc4-5b26dda10adf", "ExecuteTime": { "end_time": "2024-11-19T13:09:57.561715Z", "start_time": "2024-11-19T13:09:35.797974Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading...\n", "From: https://drive.google.com/uc?id=1AgUMxgMK-eRjzthevCk9g-J_s2vpBFpe\n", "To: C:\\Users\\mozhu\\PycharmProjects\\ML_2024\\Task6\\Base\\weatherAUS.csv\n", "\n", " 0%| | 0.00/14.1M [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9am...Humidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
02008-12-01Albury13.422.90.6NaNNaNW44.0W...71.022.01007.71007.18.0NaN16.921.8NoNo
12008-12-02Albury7.425.10.0NaNNaNWNW44.0NNW...44.025.01010.61007.8NaNNaN17.224.3NoNo
22008-12-03Albury12.925.70.0NaNNaNWSW46.0W...38.030.01007.61008.7NaN2.021.023.2NoNo
32008-12-04Albury9.228.00.0NaNNaNNE24.0SE...45.016.01017.61012.8NaNNaN18.126.5NoNo
42008-12-05Albury17.532.31.0NaNNaNW41.0ENE...82.033.01010.81006.07.08.017.829.7NoNo
\n", "

5 rows × 23 columns

\n", "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "ypwFkAVM0a99" }, "source": [ "### **Задание 3 [1 балл]**\n", "\n", "Что это за страна? Подсказка: жители этой страны воспользовались бы методом tail вместо head :)" ] }, { "metadata": { "ExecuteTime": { "end_time": "2024-11-19T17:44:48.201033Z", "start_time": "2024-11-19T17:44:48.187365Z" }, "id": "ToRkclNg8X00", "outputId": "5fdbbf64-9fa4-4c7b-bf61-221685427c9a" }, "cell_type": "code", "source": [ "(df[\"Location\"].value_counts())" ], "outputs": [ { "data": { "text/plain": [ "Location\n", "Canberra 3436\n", "Sydney 3344\n", "Darwin 3193\n", "Melbourne 3193\n", "Brisbane 3193\n", "Adelaide 3193\n", "Perth 3193\n", "Hobart 3193\n", "Albany 3040\n", "MountGambier 3040\n", "Ballarat 3040\n", "Townsville 3040\n", "GoldCoast 3040\n", "Cairns 3040\n", "Launceston 3040\n", "AliceSprings 3040\n", "Bendigo 3040\n", "Albury 3040\n", "MountGinini 3040\n", "Wollongong 3040\n", "Newcastle 3039\n", "Tuggeranong 3039\n", "Penrith 3039\n", "Woomera 3009\n", "Nuriootpa 3009\n", "Cobar 3009\n", "CoffsHarbour 3009\n", "Moree 3009\n", "Sale 3009\n", "PerthAirport 3009\n", "PearceRAAF 3009\n", "Witchcliffe 3009\n", "BadgerysCreek 3009\n", "Mildura 3009\n", "NorfolkIsland 3009\n", "MelbourneAirport 3009\n", "Richmond 3009\n", "SydneyAirport 3009\n", "WaggaWagga 3009\n", "Williamtown 3009\n", "Dartmoor 3009\n", "Watsonia 3009\n", "Portland 3009\n", "Walpole 3006\n", "NorahHead 3004\n", "SalmonGums 3001\n", "Katherine 1578\n", "Nhil 1578\n", "Uluru 1578\n", "Name: count, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "0ilGH1BA0a99" }, "source": [ "**Ваш ответ тут:**\n", "Это австралия, потому что Сидней и Мельбурн находятся там" ] }, { "cell_type": "markdown", "metadata": { "id": "gccjEINl0a99" }, "source": [ "Извлечём немного информации из набора данных" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0zJ3rbE70a99", "outputId": "5c9063c0-83a0-450f-f006-259672459384", "ExecuteTime": { "end_time": "2024-11-19T17:44:53.149713Z", "start_time": "2024-11-19T17:44:53.144378Z" } }, "source": [ "df.shape" ], "outputs": [ { "data": { "text/plain": [ "(145460, 23)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6kkngRpk0a99", "outputId": "7cca3dcd-4578-4cfa-c96f-a6fe87237028", "ExecuteTime": { "end_time": "2024-11-19T17:44:54.394704Z", "start_time": "2024-11-19T17:44:54.387534Z" } }, "source": [ "df.columns" ], "outputs": [ { "data": { "text/plain": [ "Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation',\n", " 'Sunshine', 'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',\n", " 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',\n", " 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',\n", " 'Temp3pm', 'RainToday', 'RainTomorrow'],\n", " dtype='object')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "WzaHxpDm0a9-" }, "source": [ "Внимательно приглядимся к столбцам. Напомним, что мы предсказываем значение RainTomorrow. Давайте посмотрим, на этот столбец" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qaHrRtR40a9-", "outputId": "da8667f7-46db-471e-d06d-5ad957be064e", "ExecuteTime": { "end_time": "2024-11-19T17:44:56.574757Z", "start_time": "2024-11-19T17:44:56.545865Z" } }, "source": [ "df['RainTomorrow'].unique()" ], "outputs": [ { "data": { "text/plain": [ "array(['No', 'Yes', nan], dtype=object)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "yS_8SmiD0a9-" }, "source": [ "Целевая переменная содержит неопределённые значения! Их нужно удалить из всей выборки. Также, переименуем 'Yes' и 'No' в $1$ и $0$." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JLWybXWH0a9-", "outputId": "fac23d75-bb85-4f30-ed5e-3179581fbf0b", "ExecuteTime": { "end_time": "2024-11-19T17:44:58.688278Z", "start_time": "2024-11-19T17:44:58.644163Z" } }, "source": [ "df = df[df['RainTomorrow'] == df['RainTomorrow']]\n", "df['RainTomorrow'].unique()" ], "outputs": [ { "data": { "text/plain": [ "array(['No', 'Yes'], dtype=object)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "code", "metadata": { "id": "ghVk5gFm0a9-", "ExecuteTime": { "end_time": "2024-11-19T17:45:00.339868Z", "start_time": "2024-11-19T17:45:00.293458Z" } }, "source": [ "df['RainTomorrow'] = df['RainTomorrow'].map({'Yes': 1., 'No': 0.})\n", "df['RainToday'] = df['RainToday'].map({'Yes': 1., 'No': 0.})" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gc6O6hfu0a9-", "outputId": "7195c513-0c7d-460c-f208-f6acb5890485", "ExecuteTime": { "end_time": "2024-11-19T17:45:01.820254Z", "start_time": "2024-11-19T17:45:01.812704Z" } }, "source": [ "print(df.shape)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(142193, 23)\n" ] } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "eAfDxOsR0a9-" }, "source": [ "Объектов стало чуть-чуть поменьше. Давайте выведем немного информации о них" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cu9SkdCq0a9-", "outputId": "7796b3c5-6f14-4d8e-869c-1a5a944c520f", "ExecuteTime": { "end_time": "2024-11-19T17:45:03.716003Z", "start_time": "2024-11-19T17:45:03.655064Z" } }, "source": [ "df.info()" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 142193 entries, 0 to 145458\n", "Data columns (total 23 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Date 142193 non-null object \n", " 1 Location 142193 non-null object \n", " 2 MinTemp 141556 non-null float64\n", " 3 MaxTemp 141871 non-null float64\n", " 4 Rainfall 140787 non-null float64\n", " 5 Evaporation 81350 non-null float64\n", " 6 Sunshine 74377 non-null float64\n", " 7 WindGustDir 132863 non-null object \n", " 8 WindGustSpeed 132923 non-null float64\n", " 9 WindDir9am 132180 non-null object \n", " 10 WindDir3pm 138415 non-null object \n", " 11 WindSpeed9am 140845 non-null float64\n", " 12 WindSpeed3pm 139563 non-null float64\n", " 13 Humidity9am 140419 non-null float64\n", " 14 Humidity3pm 138583 non-null float64\n", " 15 Pressure9am 128179 non-null float64\n", " 16 Pressure3pm 128212 non-null float64\n", " 17 Cloud9am 88536 non-null float64\n", " 18 Cloud3pm 85099 non-null float64\n", " 19 Temp9am 141289 non-null float64\n", " 20 Temp3pm 139467 non-null float64\n", " 21 RainToday 140787 non-null float64\n", " 22 RainTomorrow 142193 non-null float64\n", "dtypes: float64(18), object(5)\n", "memory usage: 26.0+ MB\n" ] } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "GodNwUb60a9-" }, "source": [ "Как видим, у нас есть 17 признаков имеющих вещественные значения (вещественные признаки), и 5 признаков типа object (категориальные признаки). Для них требуется отдельная предобработка. Пока разобьём выборку на обучающую и тестовую." ] }, { "cell_type": "code", "metadata": { "id": "IWopL7eQ0a9_", "ExecuteTime": { "end_time": "2024-11-19T17:45:05.654977Z", "start_time": "2024-11-19T17:45:05.648977Z" } }, "source": [ "from sklearn.model_selection import train_test_split" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "q_7emyJ80a9_" }, "source": [ "" ] }, { "cell_type": "code", "metadata": { "id": "sSzuvkLg0a9_", "ExecuteTime": { "end_time": "2024-11-19T17:45:08.376582Z", "start_time": "2024-11-19T17:45:08.316458Z" } }, "source": [ "y = df.RainTomorrow\n", "X = df.drop(columns=[\"RainTomorrow\"])\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2024)" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2gitqG7z0a9_", "outputId": "441ade66-771d-4a1e-e193-c862c3c80760", "ExecuteTime": { "end_time": "2024-11-19T17:45:09.644612Z", "start_time": "2024-11-19T17:45:09.635613Z" } }, "source": [ "X_train.shape" ], "outputs": [ { "data": { "text/plain": [ "(99535, 22)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "CxIhynaO0a9_" }, "source": [ "#### Вещественные признаки" ] }, { "cell_type": "markdown", "metadata": { "id": "_TxDU8TB0a9_" }, "source": [ "Как вы могли заметить, среди вещественных и категориальных признаков есть пропущенные значения. В случае с вещественными признаками, пропущенные значения заполняют средним, медианой, нулём или даже пытаются предсказывать по другим признакам. Мы заполним медианой" ] }, { "cell_type": "code", "metadata": { "id": "rqTVbP4m0a9_", "ExecuteTime": { "end_time": "2024-11-19T17:45:11.500075Z", "start_time": "2024-11-19T17:45:11.413613Z" } }, "source": [ "numeric_data = X_train.select_dtypes([np.number])\n", "numeric_data_median = numeric_data.median()\n", "numeric_features = numeric_data.columns\n", "X_train = X_train.fillna(numeric_data_median)\n", "X_test = X_test.fillna(numeric_data_median)" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "P8ZIa_E20a9_", "outputId": "8a4d7a76-e601-4a52-d250-18e5cc297ccc", "ExecuteTime": { "end_time": "2024-11-19T17:45:13.459199Z", "start_time": "2024-11-19T17:45:13.454200Z" } }, "source": [ "len(numeric_features)" ], "outputs": [ { "data": { "text/plain": [ "17" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 860 }, "id": "j8P-jkLw0a9_", "outputId": "9a7a31bb-30a7-46e1-9728-3f8c31f7a818", "ExecuteTime": { "end_time": "2024-11-19T17:45:15.082455Z", "start_time": "2024-11-19T17:45:14.718921Z" } }, "source": [ "correlations = X_train[numeric_features].corrwith(y_train).sort_values(ascending=False)\n", "plot = sns.barplot(y=correlations.index, x=correlations)\n", "plot.set_title(\"Корреляции между вещественными признаками и целевой переменной\", size=15)\n", "plot.figure.set_size_inches(17, 10)" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "QX-G7RM00a-A" }, "source": [ "### **Задание 4 [2 баллa]**\n", "\n", "Попробуйте объяснить для каких-нибудь признаков получившиеся значения корреляции (почему для одних эти значения высокие, а для других низкие)?" ] }, { "cell_type": "markdown", "metadata": { "id": "9X95a2GI0a-A" }, "source": [ "**Ваш ответ тут:**\n", "Так как корреляция показывает, насколько одна переменная линейно зависит от другой (если корреляция $\\approx +1$, то это указывает на сильную положительную зависимость переменных, т.е. одна линейно растёт с ростом другой. Аналогично с $-1$)\n", "\n", "Рассмотрим несколько параметров:\n", "1. **Humidity3pm.** Его значение корреляции сильно положительно (относительно остальных признаков). Это указывает на то, что при высокой влажности в 3 дня на следующий день с большей вероятностью будет дождь.\n", "2. **Temp9am.** Его значение корреляции близко к $0$, значит будет завтра дождь или не будет, практически не зависит от этого признака.\n", "\n", "Аналогичные рассуждения можно провести для остальных признаков, опираясь на значения корреляции для них" ] }, { "cell_type": "markdown", "source": [ "----" ], "metadata": { "id": "ZlqVllUzH1jM" } }, { "cell_type": "markdown", "metadata": { "id": "rk8ESuAF0a-A" }, "source": [ "Дополнительно визуализируем признаки Sunshine и Humidity3pm. Библиотека seaborn предоставляет график swarmplot, который в отличие от scatterplot старается разместить на графике как можно больше точек, так чтобы они не пересекались, уложившись при этом в заданную ширину." ] }, { "cell_type": "code", "metadata": { "scrolled": false, "colab": { "base_uri": "https://localhost:8080/", "height": 473 }, "id": "DciOA0Jd0a-A", "outputId": "3d0af40d-5ae7-4df1-ad52-0b92d9b6cf08", "ExecuteTime": { "end_time": "2024-11-19T17:45:32.565078Z", "start_time": "2024-11-19T17:45:22.490701Z" } }, "source": [ "fig, axs = plt.subplots(figsize=(16, 5), nrows=1, ncols=2)\n", "_ = sns.swarmplot(x=\"RainTomorrow\", y=\"Sunshine\", data=df.head(10000), ax=axs[0])\n", "_ = sns.swarmplot(x=\"RainTomorrow\", y=\"Humidity3pm\", data=df.head(1000), ax=axs[1])" ], "outputs": [ { "data": { "text/plain": [ "
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}, "metadata": {}, "output_type": "display_data" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "itkZ46lt0a-A" }, "source": [ "Для оценки качества классификации воспользуемся реализованными в sklearn logloss и ROC AUC. ROC AUC является метрикой по умолчанию для бинарной классификации, поскольку очень устойчива к несбалансированности классов. Подробнее про неё можно прочитать https://alexanderdyakonov.wordpress.com/2017/07/28/auc-roc-площадь-под-кривой-ошибок/ . Обучим логистическую регрессию на вещественных признаках, не подбирая константу регуляризации" ] }, { "cell_type": "code", "metadata": { "id": "5V5BKCkg0a-A", "ExecuteTime": { "end_time": "2024-11-19T17:45:32.579951Z", "start_time": "2024-11-19T17:45:32.569833Z" } }, "source": [ "from sklearn.metrics import log_loss, roc_auc_score" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "J4_pfAdT0a-A", "outputId": "e733cf95-3307-4d87-b9d6-4de9c06c4d23", "ExecuteTime": { "end_time": "2024-11-19T17:45:36.677463Z", "start_time": "2024-11-19T17:45:32.628071Z" } }, "source": [ "model = LogisticRegression(solver='lbfgs', max_iter=1000)\n", "model.fit(X_train[numeric_features], y_train)" ], "outputs": [ { "data": { "text/plain": [ "LogisticRegression(max_iter=1000)" ], "text/html": [ "
LogisticRegression(max_iter=1000)
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" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "467XLKKt0a-A", "outputId": "1b5676cb-a080-48c3-ab36-843debe41898", "ExecuteTime": { "end_time": "2024-11-19T17:45:36.817506Z", "start_time": "2024-11-19T17:45:36.725400Z" } }, "source": [ "y_pred = model.predict_proba(X_test[numeric_features])[:, 1]\n", "y_train_pred = model.predict_proba(X_train[numeric_features])[:, 1]\n", "\n", "print(\"Test logloss = %.4f\" % log_loss(y_test, y_pred))\n", "print(\"Train logloss = %.4f\" % log_loss(y_train, y_train_pred))\n", "print(\"Test roc auc score = %.4f\" % roc_auc_score(y_test, y_pred))\n", "print(\"Train roc auc score = %.4f\" % roc_auc_score(y_train, y_train_pred))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test logloss = 0.3635\n", "Train logloss = 0.3695\n", "Test roc auc score = 0.8603\n", "Train roc auc score = 0.8564\n" ] } ], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 628 }, "id": "wqo_xxf80a-B", "outputId": "74bbb985-30c8-43d5-fbd4-5aae52daca4f", "ExecuteTime": { "end_time": "2024-11-19T17:45:37.129140Z", "start_time": "2024-11-19T17:45:36.865935Z" } }, "source": [ "plt.figure(figsize=(7, 7))\n", "sorted_weights = sorted(zip(model.coef_[0], numeric_features), reverse=True)\n", "weights = [x[0] for x in sorted_weights]\n", "features = [x[1] for x in sorted_weights]\n", "_ = sns.barplot(y=features, x=weights).set_title(\"Гистограмма весов\", size=15)" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "KChgMVgO0a-B" }, "source": [ "Если приглядеться к весам, то можно увидеть, что между корреляциями признаков с целевой переменной и значением соответствующих весов мало общего. Чтобы это предотвратить, будем масштабировать наши признаки перед обучением модели. Это, среди, прочего, сделает нашу регуляризацию более честной: теперь все признаки будут регуляризоваться в равной степени." ] }, { "cell_type": "code", "metadata": { "id": "BfA-lalG0a-B", "ExecuteTime": { "end_time": "2024-11-19T17:45:37.238677Z", "start_time": "2024-11-19T17:45:37.178698Z" } }, "source": [ "from sklearn.preprocessing import StandardScaler\n", "\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train[numeric_features])\n", "X_test_scaled = scaler.transform(X_test[numeric_features])" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "fFHztY2r0a-C", "outputId": "59f7fd89-0d88-48ac-cddb-41dc3d002bb1", "ExecuteTime": { "end_time": "2024-11-19T17:45:37.425730Z", "start_time": "2024-11-19T17:45:37.287185Z" } }, "source": [ "model = LogisticRegression(solver='lbfgs', max_iter=1000)\n", "model.fit(X_train_scaled, y_train)" ], "outputs": [ { "data": { "text/plain": [ "LogisticRegression(max_iter=1000)" ], "text/html": [ "
LogisticRegression(max_iter=1000)
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" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yav1SGHn0a-C", "outputId": "e246583d-4f80-4f56-a678-b839466ed340", "ExecuteTime": { "end_time": "2024-11-19T17:45:37.550973Z", "start_time": "2024-11-19T17:45:37.474484Z" } }, "source": [ "y_pred = model.predict_proba(X_test_scaled)[:, 1]\n", "y_train_pred = model.predict_proba(X_train_scaled)[:, 1]\n", "\n", "print(\"Test logloss = %.4f\" % log_loss(y_test, y_pred))\n", "print(\"Train logloss = %.4f\" % log_loss(y_train, y_train_pred))\n", "print(\"Test roc auc score = %.4f\" % roc_auc_score(y_test, y_pred))\n", "print(\"Train roc auc score = %.4f\" % roc_auc_score(y_train, y_train_pred))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test logloss = 0.3589\n", "Train logloss = 0.3637\n", "Test roc auc score = 0.8652\n", "Train roc auc score = 0.8623\n" ] } ], "execution_count": null }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 628 }, "id": "Y_eApqhn0a-D", "outputId": "fc7de43e-6aaa-4e95-ad80-db8a3d038362", "ExecuteTime": { "end_time": "2024-11-19T17:45:37.849293Z", "start_time": "2024-11-19T17:45:37.598561Z" } }, "source": [ "plt.figure(figsize=(7, 7))\n", "sorted_weights = sorted(zip(model.coef_[0], numeric_features), reverse=True)\n", "weights = [x[0] for x in sorted_weights]\n", "features = [x[1] for x in sorted_weights]\n", "_ = sns.barplot(y=features, x=weights).set_title(\"Гистограмма весов\", size=15)" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": null }, { "cell_type": "markdown", "source": [ "### **Задание 5 [1 балл]**\n", "\n", "Почему даже после нормализации график не до конца похож на гистограмму корреляций?" ], "metadata": { "id": "h5FV_Nw2Ma7r" } }, { "metadata": { "id": "Z7KGhIKE8X07" }, "cell_type": "markdown", "source": [ "**Ваш ответ тут:**\n", "1. Корреляция показывает зависимость лишь между двумя конкретными признаками, она не учитывает те зависимости, в которые включены большее число признаков. Именно такие зависимости и могут вызывать отличия в графиках\n", "2. Корреляция демонстрирует лишь линейную зависимость, однако если зависимость имеет нелинейный характер, то корреляция её не отобразит." ] }, { "cell_type": "markdown", "metadata": { "id": "0J_rl4B20a-E" }, "source": [ "Рассмотрим теперь категориальные признаки. Сразу отметим, что признак \"Date\" очень опасен, и лучше пока его выкинуть. Это связано с тем, что мы можем получить прямую информацию о том, будет ли завтра дождь, если текущее место и завтрашняя дата встречались где-то в обучающей выборке. Очень часто также встречаются признак наподобие \"ID\", которые могут содержать аналогичные утечки информации. С такими признаками всегда нужно обращаться осторожно!" ] }, { "cell_type": "code", "metadata": { "id": "kvogjlYT0a-E", "ExecuteTime": { "end_time": "2024-11-19T17:45:43.153949Z", "start_time": "2024-11-19T17:45:43.099705Z" } }, "source": [ "categorical = list(X_train.drop(columns=[\"Date\"]).dtypes[X_train.dtypes == \"object\"].index)\n", "X_train[categorical] = X_train[categorical].fillna(\"NotGiven\")\n", "X_test[categorical] = X_test[categorical].fillna(\"NotGiven\")" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "9eXbLX1_0a-E" }, "source": [ "Для работы с категориальными признаками нужно их как-то закодировать числами. Для этого воспользуемся реализацией one-hot кодирования из библиотеки sklearn" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SrJFeJ2o0a-E", "outputId": "3010091e-4a13-4d14-fab9-9d7b90bf21a6", "ExecuteTime": { "end_time": "2024-11-19T17:45:50.535108Z", "start_time": "2024-11-19T17:45:49.974726Z" } }, "source": [ "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.pipeline import Pipeline\n", "\n", "column_transformer = ColumnTransformer([\n", " ('ohe', OneHotEncoder(), categorical),\n", " ('scaling', StandardScaler(), numeric_features)\n", "])\n", "\n", "pipeline = Pipeline(steps=[\n", " ('ohe', column_transformer),\n", " ('classification', LogisticRegression(solver='lbfgs', max_iter=200))\n", "])\n", "\n", "model = pipeline.fit(X_train.drop(columns=[\"Date\"]), y_train)\n", "y_pred = model.predict_proba(X_test.drop(columns=[\"Date\"]))[:, 1]\n", "print(\"Test logloss = %.4f\" % log_loss(y_test, y_pred))\n", "print(\"Test roc auc score = %.4f\" % roc_auc_score(y_test, y_pred))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test logloss = 0.3498\n", "Test roc auc score = 0.8721\n" ] } ], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "_BWQWC0C0a-F" }, "source": [ "Качество немного выросло, если смотреть на ROC AUC!" ] }, { "cell_type": "markdown", "source": [ "### **Задание 6 [1 балл]**\n", "\n", "Почему итоговое качество выросло?" ], "metadata": { "id": "0w0vvXUWGl6m" } }, { "cell_type": "markdown", "source": [ "**Ваш ответ тут:**\n", "\n", "Потому что до этого мы проводили обучение исключительно на численных данных, не учитывая возможные зависимости от категориальных признаков. Однако теперь, после применения one-hot encoder'a, мы можем учесть эти дополнительные зависимости. В связи с этими новыми данным для пердсказания качество выросло." ], "metadata": { "id": "dxQOX8UAHayY" } }, { "cell_type": "markdown", "source": [ "### **Задание 7 [2 баллa]**\n", "\n", "Попробуйте улучшить качество модели, попробовав другие гиперпараметры (например, число итераций, метод оптимизации, константу регуляризации и т.д.). Измерьте получившийся результат и напишите, благодаря чему удалось или не удалось улучшить текущие метрики" ], "metadata": { "id": "8h5nDJDkHTx6" } }, { "metadata": { "ExecuteTime": { "end_time": "2024-11-19T17:53:51.952970Z", "start_time": "2024-11-19T17:53:51.947970Z" }, "id": "Dn5ib2Kv8X08", "outputId": "2c89d4ca-d485-46cf-8b8a-4ec899d10eb2" }, "cell_type": "code", "source": [ "list(np.logspace(2, 6, 5))" ], "outputs": [ { "data": { "text/plain": [ "[100.0, 1000.0, 10000.0, 100000.0, 1000000.0]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "code", "source": [ "from sklearn.model_selection import GridSearchCV\n", "import numpy as np\n", "\n", "column_transformer = ColumnTransformer([\n", " ('ohe', OneHotEncoder(), categorical),\n", " ('scaling', StandardScaler(), numeric_features)\n", "])\n", "\n", "pipeline = Pipeline(steps=[\n", " ('ohe', column_transformer),\n", " ('classification', LogisticRegression(tol=0.00000001))\n", "])\n", "\n", "param_grid = {\n", " \"classification__max_iter\": list(map(int,np.logspace(3, 6, 5))),\n", " \"classification__solver\": ['lbfgs', 'newton-cg'],\n", " \"classification__C\": list(np.arange(0.1, 0.6, 0.1)),\n", " # \"classification__penalty\": ['none','l2'],\n", "}\n", "gr = GridSearchCV(pipeline, param_grid, cv=5, n_jobs=-1, scoring='roc_auc')\n", "model = gr.fit(X_train.drop(columns=[\"Date\"]), y_train)\n", "results=pd.DataFrame(model.cv_results_)\n", "# y_pred = model.predict(X_test.drop(columns=[\"Date\"]))\n", "# print(y_pred)" ], "metadata": { "id": "-5HDVQGnH3T6", "ExecuteTime": { "end_time": "2024-11-20T06:56:55.080346Z", "start_time": "2024-11-20T06:55:10.928653Z" } }, "outputs": [], "execution_count": null }, { "metadata": { "ExecuteTime": { "end_time": "2024-11-20T06:54:51.590035Z", "start_time": "2024-11-20T06:54:51.566799Z" }, "id": "pu_GFJ8O8X08", "outputId": "9e8cdd9f-bf0b-4946-a9d6-8dddf0e80047" }, "cell_type": "code", "source": [ "print(model.best_params_)\n", "print(\"Test logloss = %.4f\" % log_loss(y_test, y_pred))\n", "print(\"Test roc auc score = %.4f\" % roc_auc_score(y_test, y_pred))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'classification__C': 0.1, 'classification__max_iter': 1000, 'classification__penalty': 'l2', 'classification__solver': 'newton-cg'}\n", "Test logloss = 0.3498\n", "Test roc auc score = 0.8721\n" ] } ], "execution_count": null }, { "metadata": { "ExecuteTime": { "end_time": "2024-11-20T07:00:13.951384Z", "start_time": "2024-11-20T07:00:13.889411Z" }, "id": "jRUun7xK8X09", "outputId": "9ae3d425-dca8-4132-fc26-fa4191f01163" }, "cell_type": "code", "source": [ "results.sort_values(by=\"mean_test_score\", ascending=False)" ], "outputs": [ { "data": { "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", "3 3.594939 0.458304 0.164868 0.032512 \n", "5 3.245754 0.311483 0.150112 0.026728 \n", "7 3.501111 0.420182 0.165937 0.013190 \n", "9 3.312821 0.203438 0.172997 0.015427 \n", "1 3.268429 0.150067 0.163901 0.009364 \n", "0 4.508291 0.146109 0.156594 0.008510 \n", "2 5.074120 0.172969 0.159811 0.012056 \n", "4 5.477219 0.660987 0.166482 0.013652 \n", "6 5.327319 0.474824 0.155696 0.013844 \n", "8 5.082660 0.601089 0.157559 0.009131 \n", "15 3.058160 0.294788 0.166503 0.028733 \n", "19 3.102210 0.275121 0.156448 0.015349 \n", "17 3.268719 0.340584 0.159141 0.013200 \n", "13 3.059704 0.338614 0.167431 0.026594 \n", "11 2.999901 0.314353 0.161564 0.008450 \n", "14 6.327667 0.418129 0.164756 0.014605 \n", "16 5.988765 0.347370 0.175798 0.017905 \n", "12 5.780409 0.733486 0.153095 0.020617 \n", "18 6.218963 0.550002 0.160398 0.004238 \n", "10 6.204335 0.856147 0.143904 0.010592 \n", "20 6.188158 0.343305 0.177847 0.023823 \n", "22 6.493763 0.905100 0.157970 0.020397 \n", "28 6.516454 0.640200 0.171326 0.016684 \n", "24 6.123650 0.455116 0.176829 0.025590 \n", "26 5.676543 0.353486 0.147139 0.010447 \n", "27 3.277087 0.317038 0.164307 0.025108 \n", "29 2.859274 0.344828 0.156385 0.019292 \n", "25 3.066653 0.257113 0.144786 0.010282 \n", "23 3.088915 0.269694 0.163284 0.023207 \n", "21 3.247651 0.513697 0.162852 0.009673 \n", "33 3.380329 0.152825 0.155778 0.011507 \n", "37 3.225115 0.318642 0.161296 0.013668 \n", "35 3.225217 0.173962 0.155751 0.014079 \n", "39 3.278247 0.366075 0.156586 0.027581 \n", "31 3.062422 0.192526 0.158300 0.026043 \n", "34 5.746976 0.193833 0.168369 0.020759 \n", "32 5.957838 0.268363 0.147518 0.006013 \n", "36 6.434315 0.863096 0.166765 0.042122 \n", "38 5.852002 0.451367 0.163169 0.012807 \n", "30 6.087838 0.178105 0.150718 0.005899 \n", "40 6.492460 0.909012 0.159141 0.027942 \n", "42 6.452951 0.596886 0.169076 0.024734 \n", "44 6.243939 0.637473 0.168383 0.021860 \n", "46 6.395725 0.439159 0.177232 0.021989 \n", "48 5.119398 0.558995 0.064428 0.041514 \n", "41 3.143163 0.220217 0.184511 0.027615 \n", "43 3.118131 0.242155 0.159907 0.012866 \n", "45 3.053760 0.182356 0.148105 0.018770 \n", "47 3.023546 0.147036 0.162190 0.016610 \n", "49 2.658327 0.225790 0.097124 0.020557 \n", "\n", " param_classification__C param_classification__max_iter \\\n", "3 0.1 5623 \n", "5 0.1 31622 \n", "7 0.1 177827 \n", "9 0.1 1000000 \n", "1 0.1 1000 \n", "0 0.1 1000 \n", "2 0.1 5623 \n", "4 0.1 31622 \n", "6 0.1 177827 \n", "8 0.1 1000000 \n", "15 0.2 31622 \n", "19 0.2 1000000 \n", "17 0.2 177827 \n", "13 0.2 5623 \n", "11 0.2 1000 \n", "14 0.2 31622 \n", "16 0.2 177827 \n", "12 0.2 5623 \n", "18 0.2 1000000 \n", "10 0.2 1000 \n", "20 0.3 1000 \n", "22 0.3 5623 \n", "28 0.3 1000000 \n", "24 0.3 31622 \n", "26 0.3 177827 \n", "27 0.3 177827 \n", "29 0.3 1000000 \n", "25 0.3 31622 \n", "23 0.3 5623 \n", "21 0.3 1000 \n", "33 0.4 5623 \n", "37 0.4 177827 \n", "35 0.4 31622 \n", "39 0.4 1000000 \n", "31 0.4 1000 \n", "34 0.4 31622 \n", "32 0.4 5623 \n", "36 0.4 177827 \n", "38 0.4 1000000 \n", "30 0.4 1000 \n", "40 0.5 1000 \n", "42 0.5 5623 \n", "44 0.5 31622 \n", "46 0.5 177827 \n", "48 0.5 1000000 \n", "41 0.5 1000 \n", "43 0.5 5623 \n", "45 0.5 31622 \n", "47 0.5 177827 \n", "49 0.5 1000000 \n", "\n", " param_classification__solver \\\n", "3 newton-cg \n", "5 newton-cg \n", "7 newton-cg \n", "9 newton-cg \n", "1 newton-cg \n", "0 lbfgs \n", "2 lbfgs \n", "4 lbfgs \n", "6 lbfgs \n", "8 lbfgs \n", "15 newton-cg \n", "19 newton-cg \n", "17 newton-cg \n", "13 newton-cg \n", "11 newton-cg \n", "14 lbfgs \n", "16 lbfgs \n", "12 lbfgs \n", "18 lbfgs \n", "10 lbfgs \n", "20 lbfgs \n", "22 lbfgs \n", "28 lbfgs \n", "24 lbfgs \n", "26 lbfgs \n", "27 newton-cg \n", "29 newton-cg \n", "25 newton-cg \n", "23 newton-cg \n", "21 newton-cg \n", "33 newton-cg \n", "37 newton-cg \n", "35 newton-cg \n", "39 newton-cg \n", "31 newton-cg \n", "34 lbfgs \n", "32 lbfgs \n", "36 lbfgs \n", "38 lbfgs \n", "30 lbfgs \n", "40 lbfgs \n", "42 lbfgs \n", "44 lbfgs \n", "46 lbfgs \n", "48 lbfgs \n", "41 newton-cg \n", "43 newton-cg \n", "45 newton-cg \n", "47 newton-cg \n", "49 newton-cg \n", "\n", " params \\\n", "3 {'classification__C': 0.1, 'classification__max_iter': 5623, 'classification__solver': 'newton-cg'} \n", "5 {'classification__C': 0.1, 'classification__max_iter': 31622, 'classification__solver': 'newton-cg'} \n", "7 {'classification__C': 0.1, 'classification__max_iter': 177827, 'classification__solver': 'newton-cg'} \n", "9 {'classification__C': 0.1, 'classification__max_iter': 1000000, 'classification__solver': 'newton-cg'} \n", "1 {'classification__C': 0.1, 'classification__max_iter': 1000, 'classification__solver': 'newton-cg'} \n", "0 {'classification__C': 0.1, 'classification__max_iter': 1000, 'classification__solver': 'lbfgs'} \n", "2 {'classification__C': 0.1, 'classification__max_iter': 5623, 'classification__solver': 'lbfgs'} \n", "4 {'classification__C': 0.1, 'classification__max_iter': 31622, 'classification__solver': 'lbfgs'} \n", "6 {'classification__C': 0.1, 'classification__max_iter': 177827, 'classification__solver': 'lbfgs'} \n", "8 {'classification__C': 0.1, 'classification__max_iter': 1000000, 'classification__solver': 'lbfgs'} \n", "15 {'classification__C': 0.2, 'classification__max_iter': 31622, 'classification__solver': 'newton-cg'} \n", "19 {'classification__C': 0.2, 'classification__max_iter': 1000000, 'classification__solver': 'newton-cg'} \n", "17 {'classification__C': 0.2, 'classification__max_iter': 177827, 'classification__solver': 'newton-cg'} \n", "13 {'classification__C': 0.2, 'classification__max_iter': 5623, 'classification__solver': 'newton-cg'} \n", "11 {'classification__C': 0.2, 'classification__max_iter': 1000, 'classification__solver': 'newton-cg'} \n", "14 {'classification__C': 0.2, 'classification__max_iter': 31622, 'classification__solver': 'lbfgs'} \n", "16 {'classification__C': 0.2, 'classification__max_iter': 177827, 'classification__solver': 'lbfgs'} \n", "12 {'classification__C': 0.2, 'classification__max_iter': 5623, 'classification__solver': 'lbfgs'} \n", "18 {'classification__C': 0.2, 'classification__max_iter': 1000000, 'classification__solver': 'lbfgs'} \n", "10 {'classification__C': 0.2, 'classification__max_iter': 1000, 'classification__solver': 'lbfgs'} \n", "20 {'classification__C': 0.30000000000000004, 'classification__max_iter': 1000, 'classification__solver': 'lbfgs'} \n", "22 {'classification__C': 0.30000000000000004, 'classification__max_iter': 5623, 'classification__solver': 'lbfgs'} \n", "28 {'classification__C': 0.30000000000000004, 'classification__max_iter': 1000000, 'classification__solver': 'lbfgs'} \n", "24 {'classification__C': 0.30000000000000004, 'classification__max_iter': 31622, 'classification__solver': 'lbfgs'} \n", "26 {'classification__C': 0.30000000000000004, 'classification__max_iter': 177827, 'classification__solver': 'lbfgs'} \n", "27 {'classification__C': 0.30000000000000004, 'classification__max_iter': 177827, 'classification__solver': 'newton-cg'} \n", "29 {'classification__C': 0.30000000000000004, 'classification__max_iter': 1000000, 'classification__solver': 'newton-cg'} \n", "25 {'classification__C': 0.30000000000000004, 'classification__max_iter': 31622, 'classification__solver': 'newton-cg'} \n", "23 {'classification__C': 0.30000000000000004, 'classification__max_iter': 5623, 'classification__solver': 'newton-cg'} \n", "21 {'classification__C': 0.30000000000000004, 'classification__max_iter': 1000, 'classification__solver': 'newton-cg'} \n", "33 {'classification__C': 0.4, 'classification__max_iter': 5623, 'classification__solver': 'newton-cg'} \n", "37 {'classification__C': 0.4, 'classification__max_iter': 177827, 'classification__solver': 'newton-cg'} \n", "35 {'classification__C': 0.4, 'classification__max_iter': 31622, 'classification__solver': 'newton-cg'} \n", "39 {'classification__C': 0.4, 'classification__max_iter': 1000000, 'classification__solver': 'newton-cg'} \n", "31 {'classification__C': 0.4, 'classification__max_iter': 1000, 'classification__solver': 'newton-cg'} \n", "34 {'classification__C': 0.4, 'classification__max_iter': 31622, 'classification__solver': 'lbfgs'} \n", "32 {'classification__C': 0.4, 'classification__max_iter': 5623, 'classification__solver': 'lbfgs'} \n", "36 {'classification__C': 0.4, 'classification__max_iter': 177827, 'classification__solver': 'lbfgs'} \n", "38 {'classification__C': 0.4, 'classification__max_iter': 1000000, 'classification__solver': 'lbfgs'} \n", "30 {'classification__C': 0.4, 'classification__max_iter': 1000, 'classification__solver': 'lbfgs'} \n", "40 {'classification__C': 0.5, 'classification__max_iter': 1000, 'classification__solver': 'lbfgs'} \n", "42 {'classification__C': 0.5, 'classification__max_iter': 5623, 'classification__solver': 'lbfgs'} \n", "44 {'classification__C': 0.5, 'classification__max_iter': 31622, 'classification__solver': 'lbfgs'} \n", "46 {'classification__C': 0.5, 'classification__max_iter': 177827, 'classification__solver': 'lbfgs'} \n", "48 {'classification__C': 0.5, 'classification__max_iter': 1000000, 'classification__solver': 'lbfgs'} \n", "41 {'classification__C': 0.5, 'classification__max_iter': 1000, 'classification__solver': 'newton-cg'} \n", "43 {'classification__C': 0.5, 'classification__max_iter': 5623, 'classification__solver': 'newton-cg'} \n", "45 {'classification__C': 0.5, 'classification__max_iter': 31622, 'classification__solver': 'newton-cg'} \n", "47 {'classification__C': 0.5, 'classification__max_iter': 177827, 'classification__solver': 'newton-cg'} \n", "49 {'classification__C': 0.5, 'classification__max_iter': 1000000, 'classification__solver': 'newton-cg'} \n", "\n", " split0_test_score split1_test_score split2_test_score \\\n", "3 0.867347 0.868983 0.870583 \n", "5 0.867347 0.868983 0.870583 \n", "7 0.867347 0.868983 0.870583 \n", "9 0.867347 0.868983 0.870583 \n", "1 0.867347 0.868983 0.870583 \n", "0 0.867347 0.868983 0.870583 \n", "2 0.867347 0.868983 0.870583 \n", "4 0.867347 0.868983 0.870583 \n", "6 0.867347 0.868983 0.870583 \n", "8 0.867347 0.868983 0.870583 \n", "15 0.867362 0.868928 0.870573 \n", "19 0.867362 0.868928 0.870573 \n", "17 0.867362 0.868928 0.870573 \n", "13 0.867362 0.868928 0.870573 \n", "11 0.867362 0.868928 0.870573 \n", "14 0.867362 0.868928 0.870573 \n", "16 0.867362 0.868928 0.870573 \n", "12 0.867362 0.868928 0.870573 \n", "18 0.867362 0.868928 0.870573 \n", "10 0.867362 0.868928 0.870573 \n", "20 0.867369 0.868903 0.870569 \n", "22 0.867369 0.868903 0.870569 \n", "28 0.867369 0.868903 0.870569 \n", "24 0.867369 0.868903 0.870569 \n", "26 0.867369 0.868903 0.870569 \n", "27 0.867369 0.868903 0.870569 \n", "29 0.867369 0.868903 0.870569 \n", "25 0.867369 0.868903 0.870569 \n", "23 0.867369 0.868903 0.870569 \n", "21 0.867369 0.868903 0.870569 \n", "33 0.867369 0.868890 0.870562 \n", "37 0.867369 0.868890 0.870562 \n", "35 0.867369 0.868890 0.870562 \n", "39 0.867369 0.868890 0.870562 \n", "31 0.867369 0.868890 0.870562 \n", "34 0.867369 0.868889 0.870562 \n", "32 0.867369 0.868889 0.870562 \n", "36 0.867369 0.868889 0.870562 \n", "38 0.867369 0.868889 0.870562 \n", "30 0.867369 0.868889 0.870562 \n", "40 0.867368 0.868881 0.870559 \n", "42 0.867368 0.868881 0.870559 \n", "44 0.867368 0.868881 0.870559 \n", "46 0.867368 0.868881 0.870559 \n", "48 0.867368 0.868881 0.870559 \n", "41 0.867368 0.868881 0.870559 \n", "43 0.867368 0.868881 0.870559 \n", "45 0.867368 0.868881 0.870559 \n", "47 0.867368 0.868881 0.870559 \n", "49 0.867368 0.868881 0.870559 \n", "\n", " split3_test_score split4_test_score mean_test_score std_test_score \\\n", "3 0.868644 0.873923 0.869896 0.002262 \n", "5 0.868644 0.873923 0.869896 0.002262 \n", "7 0.868644 0.873923 0.869896 0.002262 \n", "9 0.868644 0.873923 0.869896 0.002262 \n", "1 0.868644 0.873923 0.869896 0.002262 \n", "0 0.868644 0.873923 0.869896 0.002262 \n", "2 0.868644 0.873923 0.869896 0.002262 \n", "4 0.868644 0.873923 0.869896 0.002262 \n", "6 0.868644 0.873923 0.869896 0.002262 \n", "8 0.868644 0.873923 0.869896 0.002262 \n", "15 0.868631 0.873878 0.869874 0.002248 \n", "19 0.868631 0.873878 0.869874 0.002248 \n", "17 0.868631 0.873878 0.869874 0.002248 \n", "13 0.868631 0.873878 0.869874 0.002248 \n", "11 0.868631 0.873878 0.869874 0.002248 \n", "14 0.868631 0.873878 0.869874 0.002248 \n", "16 0.868631 0.873878 0.869874 0.002248 \n", "12 0.868631 0.873878 0.869874 0.002248 \n", "18 0.868631 0.873878 0.869874 0.002248 \n", "10 0.868631 0.873878 0.869874 0.002248 \n", "20 0.868627 0.873857 0.869865 0.002242 \n", "22 0.868627 0.873857 0.869865 0.002242 \n", "28 0.868627 0.873857 0.869865 0.002242 \n", "24 0.868627 0.873857 0.869865 0.002242 \n", "26 0.868627 0.873857 0.869865 0.002242 \n", "27 0.868627 0.873857 0.869865 0.002242 \n", "29 0.868627 0.873857 0.869865 0.002242 \n", "25 0.868627 0.873857 0.869865 0.002242 \n", "23 0.868627 0.873857 0.869865 0.002242 \n", "21 0.868627 0.873857 0.869865 0.002242 \n", "33 0.868622 0.873849 0.869858 0.002240 \n", "37 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\n", "
" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "execution_count": null }, { "cell_type": "markdown", "source": [ "**Ваш ответ тут:**\n", "\n", "Нет, текущие метрики улучшить не удалось, так как любые изменения гиперпараметров не значительно их изменяют (разница < $10^4$).\n", "\n", "Это может происходить по следующим причинам:\n", "1. Наша модель и так хорошо интерпритирует полученные данные. К примеру, данные получились линейно разделимыми и мы сразу получаем высокую оценку точности\n", "2. Данные хорошо нормализованы. Так как мы уже применили нормализацию к выборкам, то l2-регуляризация не оказывает существенного влияния на итоговую оценку, так как данные и так масштабированы и скачки весов маловероятны.\n", "3. Перебор методов оптимизации также может не дать значительного улучшения, если модель и так находит достаточно простое решение" ], "metadata": { "id": "mv_o8A1gG2Th" } }, { "cell_type": "markdown", "metadata": { "id": "licDx4i_0a-G" }, "source": [ "**Выводы** Во второй части задания по линейным моделям мы должны были узнать:\n", ".\n", "\n", "1. Зачем нужно нормализовать данные.\n", "2. Как работать с вещественными и категориальными признаками.\n", "3. Как интерпретировать результат обучения линейной модели, опираясь на описание обучающих данных" ] }, { "cell_type": "markdown", "source": [ "-----------\n", "" ], "metadata": { "id": "XisHvraS0L1Q" } }, { "cell_type": "markdown", "source": [ "## **Задание [Bonus][2 балла]** " ], "metadata": { "id": "up2qsAwStN1i" } }, { "cell_type": "markdown", "source": [ "Вставьте мем или красивую картинку связанные с чем-то из:\n", "\n", "\n", "1. Страна, данные о которой мы анализировали\n", "2. Линейные модели\n", "3. Погода\n", "4. Динозавры" ], "metadata": { "id": "Tj0UE-KAtO_k" } }, { "cell_type": "markdown", "source": [ "![Безымянный-1_Монтажная область 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