From ee242d01af3609b471f2d52db21f1e0474543fc5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=94=D0=BC=D0=B8=D1=82=D1=80=D0=B8=D0=B9?= Date: Wed, 14 May 2025 16:58:38 +0300 Subject: [PATCH] =?UTF-8?q?=D0=BD=D0=BE=D1=83=D1=82=D0=B1=D1=83=D0=BA=20?= =?UTF-8?q?=D1=81=20kaggle=20-=20MNIST=20in=20CSV?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- week4_scikit_learn-Copy1.ipynb | 337 +++++++++++++++++++++++++++++++++ 1 file changed, 337 insertions(+) create mode 100644 week4_scikit_learn-Copy1.ipynb diff --git a/week4_scikit_learn-Copy1.ipynb b/week4_scikit_learn-Copy1.ipynb new file mode 100644 index 0000000..57af9e7 --- /dev/null +++ b/week4_scikit_learn-Copy1.ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "176dffdb-49ec-48ab-8d73-13ffb4db75b6", + "metadata": {}, + "source": [ + "## 1. Цель задачи\n", + " \n", + "Задача: Разработка модели машинного обучения для распознавания рукописных цифр от 0 до 9.\n", + "\n", + "Датасеты:\n", + "\n", + "Встроенный датасет load_digits из scikit-learn (8x8 пикселей)\n", + "https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py\n", + "\n", + "Внешний датасет MNIST (28x28 пикселей), загруженный через CSV-файл\n", + "внешний датасет - MNIST в CSV: https://www.kaggle.com/datasets/oddrationale/mnist-in-csv?resource=download\n", + "Цель:\n", + "\n", + "Реализовать классификацию цифр на обоих датасетах\n", + "\n", + "Сравнить эффективность модели SVM на данных разного масштаба\n", + "\n", + "Проанализировать характерные ошибки распознавания\n", + "\n", + "\n", + "## 2. Препроцессинг\n", + " \n", + "Для load_digits:\n", + "\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "digits = load_digits()\n", + "X = digits.data\n", + "y = digits.target\n", + "\n", + "# Разделение на train/test\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "\n", + "Для MNIST (CSV):\n", + "\n", + "import pandas as pd\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Загрузка данных\n", + "data = pd.read_csv('mnist_train.csv')\n", + "X = data.drop('label', axis=1).values\n", + "y = data['label'].values\n", + "\n", + "# Нормализация (приведение к диапазону [0, 1])\n", + "scaler = MinMaxScaler()\n", + "X = scaler.fit_transform(X)\n", + "\n", + "# Разделение данных\n", + "X_train_mnist, X_test_mnist, y_train_mnist, y_test_mnist = train_test_split(\n", + " X, y, test_size=0.2, random_state=42)\n", + "\n", + " \n", + "Особенности препроцессинга:\n", + "\n", + "MNIST требует нормализации (значения пикселей 0-255 → 0-1)\n", + "\n", + "load_digits уже имеет предобработанные данныеДля load_digits:\n", + "\n", + "## 3. Код реализации:\n", + "\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Модель для load_digits\n", + "model_digits = SVC(kernel='rbf', C=10)\n", + "model_digits.fit(X_train, y_train)\n", + "\n", + "# Модель для MNIST\n", + "model_mnist = SVC(kernel='rbf', C=10)\n", + "model_mnist.fit(X_train_mnist, y_train_mnist)\n", + "\n", + "# Оценка качества\n", + "acc_digits = model_digits.score(X_test, y_test)\n", + "acc_mnist = model_mnist.score(X_test_mnist, y_test_mnist)\n", + "\n", + "print(f\"Точность на load_digits: {acc_digits:.4f}\")\n", + "print(f\"Точность на MNIST: {acc_mnist:.4f}\")\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5b1b0aa5-9c86-4931-8977-d8ab20d11384", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Точность модели: 0.967\n", + "\n", + "Примеры ошибок (всего 67):\n" + ] + }, + { + "data": { + 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "\n", + "# Загрузка данных\n", + "data = pd.read_csv('mnist_test.csv')\n", + "X = data.drop('label', axis=1).values # Пиксели изображений\n", + "y = data['label'].values # Метки цифр\n", + "\n", + "# Разделение данных на обучающую и тестовую выборки\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, \n", + " test_size=0.2, \n", + " random_state=42\n", + ")\n", + "\n", + "# Масштабирование данных (нормализация пикселей)\n", + "X_train = X_train / 255.0\n", + "X_test = X_test / 255.0\n", + "\n", + "# Создание и обучение модели SVM\n", + "model = SVC(kernel='rbf', C=10) # Используем радиальное ядро\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Предсказание и оценка точности\n", + "y_pred = model.predict(X_test)\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f'Точность модели: {accuracy:.3f}')\n", + "\n", + "# Визуализация ошибок\n", + "errors = np.where(y_pred != y_test)[0]\n", + "\n", + "print(f'\\nПримеры ошибок (всего {len(errors)}):')\n", + "for i in errors[:10]: # Показываем первые 10 ошибок\n", + " plt.figure(figsize=(4, 4))\n", + " plt.imshow(X_test[i].reshape(28, 28), cmap='gray')\n", + " plt.title(f'True: {y_test[i]}, Pred: {y_pred[i]}')\n", + " plt.axis('off')\n", + " plt.show()\n", + "\n", + "# Построение матрицы ошибок\n", + "print('\\nМатрица ошибок:')\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n", + "disp.plot(cmap='Blues')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3c0545b3-e4f1-4a16-84b8-137e93c43228", + "metadata": {}, + "source": [ + "## 4. Результаты\n", + "Метрики:\n", + "\n", + "Датасет\t Размер изображения\t Точность\n", + "load_digits\t 8x8 пикселей\t 0.9889\n", + "MNIST\t 28x28 пикселей\t 0.9671\n", + "\n", + "## 5. Интерпретация результатов\n", + "Ключевые наблюдения:\n", + "\n", + "Модель показывает высокую точность на обоих датасетах (>96%)\n", + "\n", + "Наибольшие трудности возникают с цифрами:\n", + "\n", + "4 ↔ 9\n", + "\n", + "5 ↔ 6\n", + "\n", + "3 ↔ 8\n", + "\n", + "Основные причины ошибок:\n", + "\n", + "Схожесть начертания цифр\n", + "\n", + "Разный стиль написания (у некоторых людей цифры выглядят нетипично)\n", + "\n", + "Шум в данных (нечеткие или поврежденные изображения)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}