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{
"cells": [
{
"cell_type": "markdown",
"id": "c42ce76a-1533-4957-8eed-cfe46f9782d8",
"metadata": {},
"source": [
"Цель: \n",
"\n",
"Сравнить эффективность различных онлайн-алгоритмов машинного обучения при решении задачи распознавания рукописных букв из датасета EMNIST Letters . \n",
"Задача: \n",
"\n",
" Распознавание рукописных латинских букв (AZ).\n",
" Классификация изображений размером 28×28 пикселей.\n",
" Сравнение алгоритмов при разных пропорциях обучающей и тестовой выборок.\n",
" \n",
"\n",
"Используемые алгоритмы: \n",
"\n",
" SGDClassifier — стохастический градиентный спуск\n",
" Perceptron\n",
" PassiveAggressiveClassifier I/II\n",
" LogisticRegression с SAG-оптимизацией\n",
" \n",
"\n",
"Источник данных: \n",
"\n",
" Датасет: EMNIST Letters \n",
" Образцы: 145 600 изображений рукописных букв AZ\n",
" В работе используется ограниченная подвыборка (~1800 образцов) для сравнения с digits() из sklearn.\n",
" "
]
},
{
"cell_type": "markdown",
"id": "cdf3670c-57c6-4201-9f60-49f024da6ac5",
"metadata": {},
"source": [
"Препроцессинг данных"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6fb3efcb-1318-4960-92da-7cc65da23462",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Форма данных: (1800, 784) (1800,)\n"
]
}
],
"source": [
"import torch\n",
"from torchvision import datasets, transforms\n",
"import numpy as np\n",
"\n",
"# Преобразование изображений в одномерные массивы\n",
"transform = transforms.Compose([\n",
" transforms.ToTensor(),\n",
" lambda x: x.view(-1).numpy()\n",
"])\n",
"\n",
"# Загрузка EMNIST\n",
"train_dataset = datasets.EMNIST(\n",
" root='./data', split='letters', train=True, download=True, transform=transform\n",
")\n",
"test_dataset = datasets.EMNIST(\n",
" root='./data', split='letters', train=False, download=True, transform=transform\n",
")\n",
"\n",
"# Извлечение данных\n",
"X_train = [x for x, y in train_dataset]\n",
"y_train = [y - 1 for x, y in train_dataset] # Перенумерация меток: 1..26 → 0..25\n",
"\n",
"X_test = [x for x, y in test_dataset]\n",
"y_test = [y - 1 for x, y in test_dataset]\n",
"\n",
"# Объединение выборок\n",
"X = np.array(X_train + X_test)\n",
"y = np.array(y_train + y_test)\n",
"\n",
"# Ограничение до ~1800 образцов\n",
"SAMPLE_LIMIT = 1800\n",
"X = X[:SAMPLE_LIMIT]\n",
"y = y[:SAMPLE_LIMIT]\n",
"\n",
"print(\"Форма данных:\", X.shape, y.shape)"
]
},
{
"cell_type": "markdown",
"id": "7a413e1f-a89e-448c-aa15-07b6fcc6565b",
"metadata": {},
"source": [
"Обучение модели"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "496c57eb-04c6-45ac-a8ca-1ca84dcf6db4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучение: SGD\n",
"Обучение: ASGD\n",
"Обучение: Perceptron\n",
"Обучение: Passive-Aggressive I\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/max/lab4/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:738: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучение: Passive-Aggressive II\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/max/lab4/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:738: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\n",
"/home/max/lab4/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:738: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучение: SAG\n"
]
}
],
"source": [
"from sklearn.linear_model import (\n",
" LogisticRegression,\n",
" PassiveAggressiveClassifier,\n",
" Perceptron,\n",
" SGDClassifier,\n",
")\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Параметры эксперимента\n",
"heldout = [0.95, 0.90, 0.75, 0.50, 0.01] # Доли тестовой выборки\n",
"rounds = 10 # Число повторений для усреднения\n",
"rng = np.random.RandomState(42)\n",
"\n",
"# Модели\n",
"classifiers = [\n",
" (\"SGD\", SGDClassifier(max_iter=110)),\n",
" (\"ASGD\", SGDClassifier(max_iter=110, average=True)),\n",
" (\"Perceptron\", Perceptron(max_iter=110)),\n",
" (\"Passive-Aggressive I\", PassiveAggressiveClassifier(max_iter=110, loss=\"hinge\", C=1.0, tol=1e-4)),\n",
" (\"Passive-Aggressive II\", PassiveAggressiveClassifier(max_iter=110, loss=\"squared_hinge\", C=1.0, tol=1e-4)),\n",
" (\"SAG\", LogisticRegression(max_iter=110, solver=\"sag\", tol=1e-1, C=1.0e4 / X.shape[0])),\n",
"]\n",
"\n",
"xx = 1.0 - np.array(heldout) # Пропорция обучающих данных\n",
"\n",
"# Обучение и оценка моделей\n",
"results = {}\n",
"\n",
"for name, clf in classifiers:\n",
" print(f\"Обучение: {name}\")\n",
" yy = []\n",
" for test_size in heldout:\n",
" errors = []\n",
" for r in range(rounds):\n",
" X_train_part, X_test_part, y_train_part, y_test_part = train_test_split(\n",
" X, y, test_size=test_size, random_state=rng\n",
" )\n",
" clf.fit(X_train_part, y_train_part)\n",
" y_pred = clf.predict(X_test_part)\n",
" error_rate = 1 - np.mean(y_pred == y_test_part)\n",
" errors.append(error_rate)\n",
" yy.append(np.mean(errors))\n",
" results[name] = yy"
]
},
{
"cell_type": "markdown",
"id": "dcef6843-cd27-439a-8d58-db11164ff85c",
"metadata": {},
"source": [
"Визуализация"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c8ed30cb-505b-4a23-9778-4e095ddafc3c",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"\n",
"for name, values in results.items():\n",
" plt.plot(xx, values, label=name)\n",
"\n",
"plt.legend(loc=\"upper right\")\n",
"plt.xlabel(\"Пропорция обучающей выборки\")\n",
"plt.ylabel(\"Ошибка на тесте\")\n",
"plt.title(\"Сравнение онлайн-алгоритмов на уменьшенном EMNIST Letters\")\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ac0cf458-11f5-402c-955f-99b5124abd16",
"metadata": {},
"source": [
"Интерпретация результатов \n",
"\n",
"На графике видно, как ошибка классификации меняется в зависимости от доли обучающих данных , для каждого из используемых алгоритмов. \n",
"Что можно заметить: \n",
"\n",
" SGDClassifier может показывать хорошие результаты при больших наборах данных, но менее стабильный при малых.\n",
" ASGD (усреднённый SGD) часто более стабилен и быстрее сходится.\n",
" PassiveAggressive I/II хорошо подходят для потокового обучения и реагируют только на ошибки.\n",
" SAG (LogisticRegression с SAG) может быть наиболее точным при достаточном объёме данных.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "748cf616-eb27-4be3-adfb-90d1a785d2bd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "88a7ccea-cded-4a75-acb2-b7eade0f1d92",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "22c1549d-fff3-4355-81a4-03c84b4f505a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "97d9aefd-1ff6-41d2-836d-aa0c2b54e9ec",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}