1174 lines
421 KiB
Plaintext
1174 lines
421 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Лабораторная работа №2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Ход выполнения работы. \n",
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"1. Выбрать три набора данных, которые не соответствуют Вашему варианту задания.\n",
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"2. Провести анализ сведений о каждом наборе данных со страницы загрузки в Kaggle.\n",
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"Какова проблемная область?\n",
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"3. Провести анализ содержимого каждого набора данных. Что является\n",
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"объектом/объектами наблюдения? Каковы атрибуты объектов? Есть ли связи между\n",
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"объектами?\n",
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"4. Привести примеры бизнес-целей, для достижения которых могут подойти\n",
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"выбранные наборы данных. Каков эффект для бизнеса?\n",
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"5. Привести примеры целей технического проекта для каждой выделенной ранее\n",
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"бизнес-цели. Что поступает на вход, что является целевым признаком?\n",
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"6. Определить проблемы выбранных наборов данных: зашумленность, смещение,\n",
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"актуальность, выбросы, просачивание данных.\n",
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"7. Привести примеры решения обнаруженных проблем для каждого набора данных.\n",
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"8. Оценить качество каждого набора данных: информативность, степень покрытия,\n",
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"соответствие реальным данным, согласованность меток.\n",
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"9. Устранить проблему пропущенных данных. Для каждого набора данных\n",
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"использовать разные методы: удаление, подстановка константного значения (0 или\n",
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"подобное), подстановка среднего значения.\n",
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"10. Выполнить разбиение каждого набора данных на обучающую, контрольную и\n",
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"тестовую выборки.\n",
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"11. Оценить сбалансированность выборок для каждого набора данных. Оценить\n",
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"необходимость использования методов приращения (аугментации) данных.\n",
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"12. Выполнить приращение данных методами выборки с избытком (oversampling) и\n",
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"выборки с недостатком (undersampling). Должны быть представлены примеры\n",
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"реализации обоих методов для выборок каждого набора данных.\n",
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"13. Все выводы и программный код должны быть оформлены в виде ноутбука. Для\n",
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"выполнения данной лабораторной работы следует создать новый файл-ноутбук."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Пункты 1-5."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Были выбраны датасеты:\n",
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"1. Объекты вокруг Земли (https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects)\n",
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"7. Экономика стран (https://www.kaggle.com/datasets/pratik453609/economic-data-9-countries-19802020)\n",
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"18. Цены на мобильное устройство (https://www.kaggle.com/datasets/dewangmoghe/mobile-phone-price-prediction)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Объекты вокруг Земли**\n",
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"\n",
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"Проблемная область: космические объекты и их угроза для Земли\n",
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"\n",
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"Объект наблюдения: астероиды и другие малые тела Солнечной системы\n",
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"\n",
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"Атрибуты: имя объекта, минимальный и максимальный оценочные диаметры, относительная скорость, расстояние промаха, орбитальное тело, объекты программы \"Сентри\", абсолютная звездная величина, опасность\n",
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"\n",
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"Пример бизнес-цели:\n",
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"\n",
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"1. Разработка и продажа страховых продуктов для космических рисков. Цель технического проекта: разработка системы оценки рисков и ценообразования для страховых продуктов, защищающих от космических угроз.\n",
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"\n",
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"2. Разработка и продажа технологий для мониторинга и предотвращения космических угроз. Цель технического проекта: создание системы мониторинга и прогнозирования траекторий небесных тел для предотвращения космических угроз.\n",
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"\n",
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"3. Образовательные программы и сервисы. Цель технического проекта: разработка интерактивных образовательных материалов и сервисов, основанных на данных о небесных телах.\n",
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"Актуальность: Исследования астероидов и разработка технологий для их отклонения не только помогают защитить Землю от потенциальных угроз, но и стимулируют научные открытия в различных областях, включая астрономию, физику, инженерию и образование. Эта тема имеет важное значение для будущего нашей планеты и человечества в целом."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Экономика стран**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Проблемная область: экономические данные по странам\n",
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"\n",
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"Объект наблюдения: каждая страна (например, США, Франция) выступает отдельным объектом наблюдения.\n",
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"\n",
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"Атрибуты: фондовый индекс: среднегодовая цена индекса, инфляция (inflationrate): годовой уровень инфляции, цена на нефть (oil prices): среднегодовая стоимость нефти, обменный курс (exchange_rate): курс национальной валюты к доллару США, ВВП в процентах (gdppercent): прирост ВВП, доход на душу населения (percapitaincome): средний доход на человека в стране.\n",
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"\n",
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"Пример бизнес-цели:\n",
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"1) Определение факторов, влияющих на рост фондового рынка в различных странах (эффект для бизнеса: позволяет финансовым компаниям и инвесторам создавать более эффективные стратегии вложений)\n",
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"2) Прогнозирование инфляции и курсов валют (эффект для бизнеса: помогает компаниям адаптироваться к колебаниям на валютных рынках и снижать риски при операциях с иностранной валютой)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Цены на мобильные устройства**"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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"Проблемная область: цены, характеристики мобильных устройств разных компаний.\n",
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"\n",
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"Объект наблюдения: каждый мобильный телефон представляет собой отдельный объект.\n",
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"\n",
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"Атрибуты: фондовый индекс: рейтинг, спецификации, количество SIM-карт, поддержка сетей (3G, 4G, 5G), оперативная память (RAM), батарея: емкость батареи, экран (размер и разрешение), камера: характеристика камеры (основной и фронтальной), встроенная и внешняя память, версия Android, процессор, поддержка быстрой зарядки.\n",
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"\n",
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"Пример бизнес-цели:\n",
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"1) Анализ характеристик, влияющих на рейтинг и популярность модели.\n",
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"Эффект для бизнеса: помогает производителям оптимизировать параметры устройства для увеличения спроса.\n",
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"2) Оптимизация ценовой стратегии в зависимости от характеристик.\n",
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"Эффект для бизнеса: позволяет разработать ценовые сегменты, соответствующие характеристикам, и повысить конкурентоспособность."
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||
]
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},
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||
{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"**№6.** Сперва напишем функции для определения проблем выбранных наборов данных: зашумленности, смещения, актуальности, выбросов, просачивания данных.\n"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"#Проверка на зашумленность\n",
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"def check_noise(dataframe):\n",
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" total_values = dataframe.size\n",
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" missing_values = dataframe.isnull().sum().sum()\n",
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" noise_percentage = (missing_values / total_values) * 100\n",
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" return f\"Зашумленность: {noise_percentage:.2f}%\"\n",
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"\n",
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"\n",
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"#Проверка на смещение \n",
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"def check_bias(dataframe, target_column):\n",
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" if target_column in dataframe.columns:\n",
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" unique_values = dataframe[target_column].nunique()\n",
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" total_values = len(dataframe)\n",
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" bias_percentage = (unique_values / total_values) * 100\n",
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" return (\n",
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" f\"Смещение по {target_column}: {bias_percentage:.2f}% уникальных значений\"\n",
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" )\n",
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" return \"Целевой признак не найден.\"\n",
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"\n",
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"\n",
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"#Проверка на дубликаты\n",
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"def check_duplicates(dataframe):\n",
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" duplicate_percentage = dataframe.duplicated().mean() * 100\n",
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" return f\"Количество дубликатов: {duplicate_percentage:.2f}%\"\n",
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"\n",
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"\n",
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"#Проверка на выбросы\n",
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"def check_outliers(dataframe, column):\n",
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" if column in dataframe.columns:\n",
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" Q1 = dataframe[column].quantile(0.25)\n",
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" Q3 = dataframe[column].quantile(0.75)\n",
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" IQR = Q3 - Q1\n",
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" lower_bound = Q1 - 1.5 * IQR\n",
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" upper_bound = Q3 + 1.5 * IQR\n",
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" outlier_count = dataframe[\n",
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" (dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)\n",
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" ].shape[0]\n",
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" total_count = dataframe.shape[0]\n",
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" outlier_percentage = (outlier_count / total_count) * 100\n",
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" return f\"Выбросы по {column}: {outlier_percentage:.2f}%\"\n",
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" return f\"Признак {column} не найден.\"\n",
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"\n",
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"\n",
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"#Проверка на просачивание данных\n",
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"def check_data_leakage(dataframe, target_column):\n",
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" if target_column in dataframe.columns:\n",
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" correlation_matrix = dataframe.select_dtypes(include=[np.number]).corr()\n",
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" leakage_info = correlation_matrix[target_column].abs().nlargest(10)\n",
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" leakage_report = \", \".join(\n",
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" [\n",
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" f\"{feature}: {value:.2f}\"\n",
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" for feature, value in leakage_info.items()\n",
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" if feature != target_column\n",
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" ]\n",
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" )\n",
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" return f\"Признаки просачивания данных: {leakage_report}\"\n",
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" return \"Целевой признак не найден.\""
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]
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||
},
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||
{
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||
"cell_type": "markdown",
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"metadata": {},
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"source": [
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||
"Датасет 1. Объекты вокруг земли"
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]
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||
},
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||
{
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||
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['id', 'name', 'est_diameter_min', 'est_diameter_max',\n",
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" 'relative_velocity', 'miss_distance', 'orbiting_body', 'sentry_object',\n",
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" 'absolute_magnitude', 'hazardous'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"# Вывод всех столбцов\n",
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"\n",
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"df_neo = pd.read_csv(\"neo.csv\")\n",
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"print(df_neo.columns)"
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]
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},
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{
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||
"cell_type": "code",
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"execution_count": 19,
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||
"metadata": {},
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||
"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Зашумленность: 0.00%\n",
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"Смещение по miss_distance: 99.67% уникальных значений\n",
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"Количество дубликатов: 0.00%\n",
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"Выбросы по relative_velocity: 1.73%\n",
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"Признаки просачивания данных: est_diameter_min: 0.56, est_diameter_max: 0.56, relative_velocity: 0.35, id: 0.28, miss_distance: 0.26\n"
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]
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}
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||
],
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"source": [
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||
"noise_columns = check_noise(df_neo)\n",
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||
"bias_info = check_bias(df_neo, \"miss_distance\")\n",
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"duplicate_count = check_duplicates(df_neo)\n",
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"outliers_data = check_outliers(df_neo, \"relative_velocity\")\n",
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"leakage_info = check_data_leakage(df_neo, \"absolute_magnitude\")\n",
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"\n",
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"print(noise_columns)\n",
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"print(bias_info)\n",
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"print(duplicate_count)\n",
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"print(outliers_data)\n",
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"print(leakage_info)"
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||
]
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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 7. Экономика стран"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n",
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||
" 'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n",
|
||
" 'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n",
|
||
" 'tradebalance', 'USTreasury'],\n",
|
||
" dtype='object')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df_ed = pd.read_csv(\"economic_data.csv\")\n",
|
||
"print(df_ed.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Зашумленность: 4.51%\n",
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||
"Смещение по index price: 85.64% уникальных значений\n",
|
||
"Количество дубликатов: 0.00%\n",
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||
"Выбросы по unemploymentrate: 4.88%\n",
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||
"Признаки просачивания данных: percapitaincome: 0.51, USTreasury: 0.49, year: 0.47, log_indexprice: 0.34, gdppercent: 0.26, oil prices: 0.22, unemploymentrate: 0.18, manufacturingoutput: 0.11, index price: 0.08\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"noise_columns = check_noise(df_ed)\n",
|
||
"bias_info = check_bias(df_ed, \"index price\")\n",
|
||
"duplicate_count = check_duplicates(df_ed)\n",
|
||
"outliers_data = check_outliers(df_ed, \"unemploymentrate\")\n",
|
||
"leakage_info = check_data_leakage(df_ed, \"inflationrate\")\n",
|
||
"\n",
|
||
"print(noise_columns)\n",
|
||
"print(bias_info)\n",
|
||
"print(duplicate_count)\n",
|
||
"print(outliers_data)\n",
|
||
"print(leakage_info)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 18. Цена на мобильные устройства"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Index(['Unnamed: 0', 'Name', 'Rating', 'Spec_score', 'No_of_sim', 'Ram',\n",
|
||
" 'Battery', 'Display', 'Camera', 'External_Memory', 'Android_version',\n",
|
||
" 'Price', 'company', 'Inbuilt_memory', 'fast_charging',\n",
|
||
" 'Screen_resolution', 'Processor', 'Processor_name'],\n",
|
||
" dtype='object')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df_mp = pd.read_csv(\"mobile_phone_price_prediction.csv\")\n",
|
||
"print(df_mp.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Зашумленность: 2.36%\n",
|
||
"Смещение по Name: 97.37% уникальных значений\n",
|
||
"Количество дубликатов: 0.00%\n",
|
||
"Выбросы по Spec_score: 1.24%\n",
|
||
"Признаки просачивания данных: Spec_score: 0.06, Unnamed: 0: 0.03\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"noise_columns = check_noise(df_mp)\n",
|
||
"bias_info = check_bias(df_mp, \"Name\")\n",
|
||
"duplicate_count = check_duplicates(df_mp)\n",
|
||
"outliers_data = check_outliers(df_mp, \"Spec_score\")\n",
|
||
"leakage_info = check_data_leakage(df_mp, \"Rating\")\n",
|
||
"\n",
|
||
"print(noise_columns)\n",
|
||
"print(bias_info)\n",
|
||
"print(duplicate_count)\n",
|
||
"print(outliers_data)\n",
|
||
"print(leakage_info)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 1. Объекты вокруг Земли"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"id 0\n",
|
||
"name 0\n",
|
||
"est_diameter_min 0\n",
|
||
"est_diameter_max 0\n",
|
||
"relative_velocity 0\n",
|
||
"miss_distance 0\n",
|
||
"orbiting_body 0\n",
|
||
"sentry_object 0\n",
|
||
"absolute_magnitude 0\n",
|
||
"hazardous 0\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_neo.isnull().sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Пропущенных значений нет, поэтому пропускаем."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 7. Экономика стран"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"stock index 0\n",
|
||
"country 0\n",
|
||
"year 0\n",
|
||
"index price 52\n",
|
||
"log_indexprice 0\n",
|
||
"inflationrate 43\n",
|
||
"oil prices 0\n",
|
||
"exchange_rate 2\n",
|
||
"gdppercent 19\n",
|
||
"percapitaincome 1\n",
|
||
"unemploymentrate 21\n",
|
||
"manufacturingoutput 91\n",
|
||
"tradebalance 4\n",
|
||
"USTreasury 0\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_ed.isnull().sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Имеются пустые значения. На их место поставим \"No value\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_ed[\"index price\"] = df_ed[\"index price\"].fillna(\"No value\")\n",
|
||
"df_ed[\"inflationrate\"] = df_ed[\"inflationrate\"].fillna(\"No value\")\n",
|
||
"df_ed[\"exchange_rate\"] = df_ed[\"exchange_rate\"].fillna(\"No value\")\n",
|
||
"df_ed[\"gdppercent\"] = df_ed[\"gdppercent\"].fillna(\"No value\")\n",
|
||
"df_ed[\"percapitaincome\"] = df_ed[\"percapitaincome\"].fillna(\"No value\")\n",
|
||
"df_ed[\"unemploymentrate\"] = df_ed[\"unemploymentrate\"].fillna(\"No value\")\n",
|
||
"df_ed[\"manufacturingoutput\"] = df_ed[\"manufacturingoutput\"].fillna(\"No value\")\n",
|
||
"df_ed[\"tradebalance\"] = df_ed[\"tradebalance\"].fillna(\"No value\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_ed[\"index price\"] = df_ed[\"index price\"].replace(\"No value\", 0)\n",
|
||
"df_ed[\"inflationrate\"] = df_ed[\"inflationrate\"].replace(\"No value\", 0)\n",
|
||
"df_ed[\"exchange_rate\"] = df_ed[\"exchange_rate\"].replace(\"No value\", 0)\n",
|
||
"df_ed[\"gdppercent\"] = df_ed[\"gdppercent\"].replace(\"No value\", 0)\n",
|
||
"df_ed[\"percapitaincome\"] = df_ed[\"percapitaincome\"].replace(\"No value\", 0)\n",
|
||
"df_ed[\"unemploymentrate\"] = df_ed[\"unemploymentrate\"].replace(\"No value\", 0)\n",
|
||
"df_ed[\"manufacturingoutput\"] = df_ed[\"manufacturingoutput\"].replace(\"No value\", 0)\n",
|
||
"df_ed[\"tradebalance\"] = df_ed[\"tradebalance\"].replace(\"No value\", 0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Снова проверим датафрейм на наличие пустых значений:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"stock index 0\n",
|
||
"country 0\n",
|
||
"year 0\n",
|
||
"index price 0\n",
|
||
"log_indexprice 0\n",
|
||
"inflationrate 0\n",
|
||
"oil prices 0\n",
|
||
"exchange_rate 0\n",
|
||
"gdppercent 0\n",
|
||
"percapitaincome 0\n",
|
||
"unemploymentrate 0\n",
|
||
"manufacturingoutput 0\n",
|
||
"tradebalance 0\n",
|
||
"USTreasury 0\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_ed.isnull().sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 18. Цены на мобильные устройства"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Unnamed: 0 0\n",
|
||
"Name 0\n",
|
||
"Rating 0\n",
|
||
"Spec_score 0\n",
|
||
"No_of_sim 0\n",
|
||
"Ram 0\n",
|
||
"Battery 0\n",
|
||
"Display 0\n",
|
||
"Camera 0\n",
|
||
"External_Memory 0\n",
|
||
"Android_version 443\n",
|
||
"Price 0\n",
|
||
"company 0\n",
|
||
"Inbuilt_memory 19\n",
|
||
"fast_charging 89\n",
|
||
"Screen_resolution 2\n",
|
||
"Processor 28\n",
|
||
"Processor_name 0\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_mp.isnull().sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_mp[\"Android_version\"] = df_mp[\"Android_version\"].fillna(\"No value\")\n",
|
||
"df_mp[\"Inbuilt_memory\"] = df_mp[\"Inbuilt_memory\"].fillna(\"No value\")\n",
|
||
"df_mp[\"fast_charging\"] = df_mp[\"fast_charging\"].fillna(\"No value\")\n",
|
||
"df_mp[\"Screen_resolution\"] = df_mp[\"Screen_resolution\"].fillna(\"No value\")\n",
|
||
"df_mp[\"Processor\"] = df_mp[\"Processor\"].fillna(\"No value\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_mp[\"Android_version\"] = df_mp[\"Android_version\"].replace(\"No value\", 0)\n",
|
||
"df_mp[\"Inbuilt_memory\"] = df_mp[\"Inbuilt_memory\"].replace(\"No value\", 0)\n",
|
||
"df_mp[\"fast_charging\"] = df_mp[\"fast_charging\"].replace(\"No value\", 0)\n",
|
||
"df_mp[\"Screen_resolution\"] = df_mp[\"Screen_resolution\"].replace(\"No value\", 0)\n",
|
||
"df_mp[\"Processor\"] = df_mp[\"Processor\"].replace(\"No value\", 0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 67,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Unnamed: 0 0\n",
|
||
"Name 0\n",
|
||
"Rating 0\n",
|
||
"Spec_score 0\n",
|
||
"No_of_sim 0\n",
|
||
"Ram 0\n",
|
||
"Battery 0\n",
|
||
"Display 0\n",
|
||
"Camera 0\n",
|
||
"External_Memory 0\n",
|
||
"Android_version 0\n",
|
||
"Price 0\n",
|
||
"company 0\n",
|
||
"Inbuilt_memory 0\n",
|
||
"fast_charging 0\n",
|
||
"Screen_resolution 0\n",
|
||
"Processor 0\n",
|
||
"Processor_name 0\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 67,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_mp.isnull().sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"10. Выполнить разбиение каждого набора данных на обучающую, контрольную и тестовую выборки."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"df_neo Dataset:\n",
|
||
"Train: 80.00%\n",
|
||
"Validation: 10.00%\n",
|
||
"Test: 10.00%\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Разбиение df_neo\n",
|
||
"\n",
|
||
"original_df_neo_size = len(df_neo)\n",
|
||
"train_df_neo, temp_df_neo = train_test_split(df_neo, test_size=0.2, random_state=42)\n",
|
||
"val_df_neo, test_df_neo = train_test_split(temp_df_neo, test_size=0.5, random_state=42)\n",
|
||
"\n",
|
||
"print(\"df_neo Dataset:\")\n",
|
||
"print(f\"Train: {len(train_df_neo)/original_df_neo_size*100:.2f}%\")\n",
|
||
"print(f\"Validation: {len(val_df_neo)/original_df_neo_size*100:.2f}%\")\n",
|
||
"print(f\"Test: {len(test_df_neo)/original_df_neo_size*100:.2f}%\\n\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"df_ed Dataset:\n",
|
||
"Train: 79.95%\n",
|
||
"Validation: 10.03%\n",
|
||
"Test: 10.03%\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Разбиение df_ed\n",
|
||
"\n",
|
||
"original_df_ed_size = len(df_ed)\n",
|
||
"train_df_ed, temp_df_ed = train_test_split(df_ed, test_size=0.2, random_state=42)\n",
|
||
"val_df_ed, test_df_ed = train_test_split(temp_df_ed, test_size=0.5, random_state=42)\n",
|
||
"\n",
|
||
"print(\"df_ed Dataset:\")\n",
|
||
"print(f\"Train: {len(train_df_ed)/original_df_ed_size*100:.2f}%\")\n",
|
||
"print(f\"Validation: {len(val_df_ed)/original_df_ed_size*100:.2f}%\")\n",
|
||
"print(f\"Test: {len(test_df_ed)/original_df_ed_size*100:.2f}%\\n\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 88,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"df_mp Dataset:\n",
|
||
"Train: 80.00%\n",
|
||
"Validation: 10.00%\n",
|
||
"Test: 10.00%\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Разбиение df_mp\n",
|
||
"\n",
|
||
"original_df_mp_size = len(df_mp)\n",
|
||
"train_df_mp, temp_df_mp = train_test_split(df_mp, test_size=0.2, random_state=42)\n",
|
||
"val_df_mp, test_df_mp = train_test_split(temp_df_mp, test_size=0.5, random_state=42)\n",
|
||
"\n",
|
||
"print(\"df_mp Dataset:\")\n",
|
||
"print(f\"Train: {len(train_df_mp)/original_df_mp_size*100:.2f}%\")\n",
|
||
"print(f\"Validation: {len(val_df_mp)/original_df_mp_size*100:.2f}%\")\n",
|
||
"print(f\"Test: {len(test_df_mp)/original_df_mp_size*100:.2f}%\\n\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"11. Оценить сбалансированность выборок для каждого набора данных. Оценить необходимость использования методов приращения (аугментации) данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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pKQDKR7+IjIxE69atLcpt2bIFWVlZGDt2rEWsSqUSERERVV5PN17Pubm5VZ4Lo9FYqeyNPwtv3boVJSUlmD59uvk9CgAmT54MnU5X7RBnQgjMnj0bo0ePrrGW/0YVr72bvXdUuNk1DZTXtisUinrXzI0fPx6XL1+2eD2tXr0aarUaDz74IIDydrwVbZ1NJhMyMjJQVlaGHj16VNl8oiYV5/zf//63xfv6jb9MALX/7KiLX375BUqlEk8//bTF8ueeew5CCPz6668Wy6Oioix+fezcuTN0Oh0uXLhw0/34+/vjH//4h3mZVqvFlClT6hV3Tf71r39Z3O/Xrx+uXr1qfv+p7WfRoUOHkJaWhqeeesriGh0+fDjatm3LIf+uYRMIMlu+fDlat24NBwcH6PV6tGnTxuLDpKJ36wcffID4+HgYjUbzuut/QoqLi0ObNm3g4NCwl1erVq0s7kuShJYtW5rbpJ07dw4A8Oijj1a7jezsbHh4eJjvGwyGStu90blz5yCEqLbcjT+tVrQ5rKqd4/XbzM7Ohq+vb5XrKzpTVPj999/r3NFq7ty5CAgIwBNPPIHvv/++0v5jYmKq3eaN+78V586dw4kTJ2q9r6ysrBrPXXp6OrKysvDxxx9XO8zQjdscOXKk+X+FQoGXX34Zo0ePNm+voKAAbdq0qbSddu3awWQyITExER06dLBYl52djTfeeAPPPvss9Hp9tfHWR32OsTFeH127dkXHjh3x5ZdfYubMmVi5ciVefPHFSu0NK7Zd8aXiRjqdzuJ+fn5+ra/ns2fP3rTspUuXAKDSc6hWq9GiRQvz+hutWrUKp0+fxpo1a7B69epaxQPA3PbZzc2tVuVvdk0D5SNTvP/++3jmmWfwwgsvwM3NrVJ/gJqMGTMGzz77LFavXo2BAweiqKgI69evx9ChQy2e0y+++AL//e9/cfbsWYv23dc3yamNinN643Xn4+NjsT+g9p8ddd1/QECAxZc+oPw1e318FYKDgyttw8PD46bn+NKlS2jZsmWlypuq3i9u1Y0xVpzHzMxM6HS6Wn8WVfd6AIC2bdvizz//bMiwbRYTYDLr1auXeRSIqrzxxht45ZVX8Pjjj+PVV1+Fp6cnFAoFpk+fbhXD01TEsHjx4mrbsl3/IVRSUoLk5GTcfffdN92uJEn49ddfq+wFfeMHW0VtmZ+fX43b9PX1xapVq6pcf+MHfkREBF577TWLZe+//z42btxY5eNjYmKwcuVKfP3111W2fTSZTOjUqRPefvvtKh8fFBRUbex1ZTKZcPfdd+OFF16ocv2NtYkpKSk3PXdAeY1XdcncjW3vlixZgi5duqC0tBQHDx7Ea6+9BgcHh1uquV20aBEUCgVmzpyJq1ev1ns7VanPMdZ2m7V9fVR4/PHH8cEHH6BXr15ISUnBQw89hP/+979Vbvurr76q8rm78cuwo6MjfvrpJ4tlf/zxBxYsWFDpsc2bN8cnn3xisWzt2rW3PMZqSUkJXnnlFUyaNKnSNXgzFV8qatuuOSUlBSEhITWWGTNmDI4cOYJly5bV69h8fX1x991344cffsDy5cvx008/ITc3F+PGjTOX+frrr/HYY49h5MiRmDlzJnx9faFUKrFw4UJzn4/GYA2fHdWNYCFu6DAnp5vFWNfPIqoZE2Cqte+//x533nknPv30U4vlWVlZ8Pb2Nt8PCwvD/v37UVpa2qBD11TUMlUQQuD8+fPmRKDi5y2dToeoqKibbu/48eMoLS2tMemv2K4QAqGhobX6oDxz5gwkSaqxhiAsLAxbt25Fnz59zB2UauLt7V3pmGrqqDZ79mx07doVDz/8cLX7P378OAYNGnRLzVJqIywsDHl5ebV6ToDy83d9R54b+fj4wNXVFUajsdbbDA8PN/f8Hzp0KK5cuYJFixbhlVdegY+PD7RaLWJjYys97uzZs1AoFJW+ECQlJeHdd9/FwoUL4erq2uAJcH2OsaFfHxXGjRuHmTNn4plnnsE//vGPSjVu12/b19e3VttWKpWVyt04WkMFZ2fnSmVvnCihIrmMjY21aMZSUlKC+Pj4KmP64IMPkJaWVuue+tc7dOgQANz0vQMob750/vx5DBkypMZyCoUCS5YswcmTJxEfH48PPvgAqampGD9+fK3jGjduHDZv3oxff/0Vq1evhk6nw7333mte//3336NFixZYt26dxeu+Pl8EK875uXPnLM55enp6pVrV2n521OW9KCQkBFu3bkVubq7FNXn27FmL+G5VSEgITp06BSGERXxVvV80ttp+Fl3/erjxV5nY2NgGOze2jm2AqdaUSmWlb8tr166tNKzK6NGjYTAY8P7771faxq182/7yyy8t2gl+//33SE5OxtChQwGUJzlhYWFYsmQJ8vLyKj0+PT29UuxKpbLKIcauN2rUKCiVSsyfP79S/EIIi+SnrKwMP/zwA3r16lXjt/GHHnoIRqMRr776aqV1ZWVl1SYDtREdHY2NGzfizTffrPYD5aGHHsKVK1cq1awB5b2e8/Pz673/qvYVHR2N3377rdK6rKwslJWVme8fOnQIcXFx1f6UDpRfh6NHj8YPP/yAU6dOVVp/4/NclcLCQpSVlaGsrAxKpRL33HMPNm7caDHEU2pqKlavXo2+fftW+gl//vz50Ov1ldrsNZT6HGNDvz4qeHp64v7778eJEyfw+OOPV1lm8ODB0Ol0eOONNyoNm1bTthtKVFQU1Go13nvvPYvX6Keffors7GwMHz7conxubi5ef/11zJgxo8ZfG6rz/fffo02bNmjbtu1Ny27cuBGFhYU1XtMVli1bhu3bt2PVqlWIiopCnz596hTXyJEjodVq8cEHH+DXX3/FqFGjLNqAVtQaXn+O9u/fj+jo6DrtByg/5yqVCsuWLbPYXlWjutT2s8PZ2RlA9V+Grjds2DAYjcZKnzPvvPMOJEkyX/e3atiwYUhKSrJoRlZQUCDLLG+1/Szq0aMHfH198eGHH1oM1/brr78iJiam0uvBXrEGmGptxIgRWLBgASZOnIjevXvj5MmTWLVqlcW3f6C8M86XX36JZ599FgcOHEC/fv2Qn5+PrVu34qmnnsL9999fr/17enqib9++mDhxIlJTU7F06VK0bNnSPIOTQqHA//73PwwdOhQdOnTAxIkT0axZM1y5cgU7duyATqfDTz/9hPz8fCxfvhzvvfceWrdubTFuZ0VicOLECURHRyMyMhJhYWF47bXXMHv2bFy8eBEjR46Eq6sr4uPjsX79ekyZMgXPP/88tm7dildeeQUnTpyo9PPujQYMGIAnnngCCxcuxLFjx3DPPfdApVLh3LlzWLt2Ld59912LThd18fvvv+Puu++usSbukUcewZo1a/Cvf/0LO3bsQJ8+fWA0GnH27FmsWbMGv/32201rt/Ly8iqNo1lRK7Jr1y6oVCo0a9YMM2fOxI8//ogRI0bgscceQ3h4OPLz83Hy5El8//33uHjxIry9vbFgwQK8++67aNGihXloqeq8+eab2LFjByIiIjB58mS0b98eGRkZOHLkCLZu3YqMjAyL8lu2bMHly5fNTSBWrVqF++67z9wh6LXXXsOWLVvQt29fPPXUU3BwcMBHH32E4uJivPXWW1We41WrVjXq5Al1PcaGen1UZeXKlVi+fLlFbd31dDodVqxYgUceeQTdu3fHmDFj4OPjg4SEBPz888/o06dPlV+IG4qPjw9mz56N+fPnY8iQIbjvvvsQGxuLDz74AD179qxUi3rkyBF4e3tX2yynOhcuXMBbb72FAwcOYNSoURYzAB48eBBA+bUWHBwMPz8/zJ07Fx988AF69+6Ne+65p8Ztnz59Gi+88ALmzZtnHg+3rlxcXDBy5Ehze+brmz8A5e/h69atwwMPPIDhw4cjPj4eH374Idq3b1/ll6Ka+Pj44Pnnn8fChQsxYsQIDBs2DEePHsWvv/5a6Tqp7WdHWFgY3N3d8eGHH8LV1RXOzs6IiIiosn3yvffeizvvvBMvvfQSLl68iC5duuD333/Hxo0bMX36dIsOb7di8uTJeP/99zFhwgQcPnwY/v7++Oqrr2SZHbW2n0UqlQqLFi3CxIkTMWDAAIwdO9Y8DFrz5s1rNbSeXbht402Q1apuaKYbFRUVieeee074+/sLJycn0adPHxEdHV1pWCEhyodWeumll0RoaKhQqVTCz89P/OMf/zAPM1WfYdC++eYbMXv2bOHr6yucnJzE8OHDxaVLlyo9/ujRo2LUqFHCy8tLaDQaERISIh566CGxbds2i33f7Hb9MD5CCPHDDz+Ivn37CmdnZ+Hs7Czatm0rpk6dKmJjY4UQQvz73/8W/fv3F5s3b64U043DoFX4+OOPRXh4uHBychKurq6iU6dO4oUXXhBJSUnmMnUdBk2SJHH48GGL5VU9RyUlJWLRokWiQ4cOQqPRCA8PDxEeHi7mz58vsrOzK+3vxu3d7PxdP5RRbm6umD17tmjZsqVQq9XC29tb9O7dWyxZskSUlJQIIYQIDAwUjz/+uMWxX38Obnw+UlNTxdSpU0VQUJD5Ghs0aJD4+OOPzWUqrp2Km4ODgwgJCRFPP/20yMzMtNjekSNHxODBg4WLi4vQarXizjvvFHv37rUoU/Fa6dq1q8XQTxXXVEMOg1bXY2yo14cQNx/Oqbr1O3bsEIMHDxZubm7C0dFRhIWFiccee0wcOnTIXKYxhkGr8P7774u2bdsKlUol9Hq9ePLJJys9zxXX7jvvvFPlMdWk4vmrzbV/+fJlERQUJKZPn17l6+n657uoqEh07txZ9O3bV5SVlZnL1GUYtAo///yzACD8/f0rDT1mMpnEG2+8IUJCQoRGoxHdunUTmzZtqjTE2I3xXX/s159zo9Eo5s+fb/5MGDhwoDh16lSl12tdPjs2btwo2rdvLxwcHCxeU1XFmJubK2bMmCECAgKESqUSrVq1EosXL7Z4bVYcy9SpUyudq6reV6py6dIlcd999wmtViu8vb3FM888Yx5iriGHQbvxcVWdcyFu/llU4bvvvhPdunUTGo1GeHp6inHjxpmHNSUhJCGsqAU4URV27tyJO++8E2vXrq13rej1Ll68iNDQUMTHx1fbiWXevHm4ePHiTWf2oqo1b94c8+bNqzR7GzW8hn59UPVWrlxpfm+ozsCBA/HYY4/x2ieycmwDTERERER2hW2Aye64uLhg3LhxNXZS69y5s3lqZ6q7AQMGmCdQIWoqwsLCLKaFrsrdd9/dYO1Pybbl5eXdtG21j49PtcOfUeNiAkx2x9vb26LzSlVGjRp1m6Jpmr744gu5QyBqcP369UO/fv1qLPPSSy/dpmjI2i1ZsgTz58+vsUxNTfGocbENMBEREVEDu3Dhwk2nWu7bt2+tp9SmhsUEmIiIiIjsCjvBEREREZFdYRvgWjCZTEhKSoKrq2ujTxtLRERERHUnhEBubi4CAgKgUNRcx8sEuBaSkpIQFBQkdxhEREREdBOJiYkIDAyssQwT4FpwdXUFUH5CdTqdzNEQERER0Y1ycnIQFBRkzttqwgS4FiqaPeh0OibARERERFasNs1VZe0Et3v3btx7770ICAiAJEnYsGGDxXohBObMmQN/f384OTkhKioK586dsyiTkZGBcePGQafTwd3dHZMmTao08PSJEyfQr18/ODo6IigoCG+99VZjHxoRERERWSlZE+D8/Hx06dIFy5cvr3L9W2+9hffeew8ffvgh9u/fD2dnZwwePBhFRUXmMuPGjcPp06exZcsWbNq0Cbt378aUKVPM63NycnDPPfcgJCQEhw8fxuLFizFv3jx8/PHHjX58RERERGR9rGYcYEmSsH79eowcORJAee1vQEAAnnvuOTz//PMAgOzsbOj1eqxcuRJjxoxBTEwM2rdvj4MHD6JHjx4AgM2bN2PYsGG4fPkyAgICsGLFCrz00ktISUmBWq0GAPznP//Bhg0bcPbs2VrFlpOTAzc3N2RnZ7MJBBEREZEVqku+ZrXjAMfHxyMlJQVRUVHmZW5uboiIiEB0dDQAIDo6Gu7u7ubkFwCioqKgUCiwf/9+c5n+/fubk18AGDx4MGJjY5GZmVnlvouLi5GTk2NxIyIiIqKmwWoT4JSUFACAXq+3WK7X683rUlJS4Ovra7HewcEBnp6eFmWq2sb1+7jRwoUL4ebmZr5xCDQiIiKipsNqE2A5zZ49G9nZ2eZbYmKi3CERERERUQOx2gTYz88PAJCammqxPDU11bzOz88PaWlpFuvLysqQkZFhUaaqbVy/jxtpNBrzkGcc+oyIiIioabHaBDg0NBR+fn7Ytm2beVlOTg7279+PyMhIAEBkZCSysrJw+PBhc5nt27fDZDIhIiLCXGb37t0oLS01l9myZQvatGkDDw+P23Q0RERERGQtZE2A8/LycOzYMRw7dgxAece3Y8eOISEhAZIkYfr06Xjttdfw448/4uTJk5gwYQICAgLMI0W0a9cOQ4YMweTJk3HgwAHs2bMH06ZNw5gxYxAQEAAA+Oc//wm1Wo1Jkybh9OnT+O677/Duu+/i2WeflemoiYiIiEhOsg6DtnPnTtx5552Vlj/66KNYuXIlhBCYO3cuPv74Y2RlZaFv37744IMP0Lp1a3PZjIwMTJs2DT/99BMUCgVGjx6N9957Dy4uLuYyJ06cwNSpU3Hw4EF4e3vj3//+N2bNmlXrODkMGhEREZF1q0u+ZjXjAFszJsBERERE1q1JjANMRERERNQYmAATERERkV1hAkxEREREdsVB7gCIGkJCQgIMBkODbMvb2xvBwcENsi0iIiKyPkyAyeYlJCSgbbt2KCwoaJDtOWm1OBsTwySYiIioiWICTDbPYDCgsKAA42Ythj447Ja2lZoQh1WLZsJgMDABJiIiaqKYAFOToQ8OQ2CrDnKHQURERFaOneCIiIiIyK4wASYiIiIiu8IEmIiIiIjsChNgIiIiIrIrTICJiIiIyK4wASYiIiIiu8IEmIiIiIjsChNgIiIiIrIrTICJiIiIyK4wASYiIiIiu8IEmIiIiIjsChNgIiIiIrIrTICJiIiIyK4wASYiIiIiu8IEmIiIiIjsChNgIiIiIrIrTICJiIiIyK4wASYiIiIiu8IEmIiIiIjsioPcARDJKbeoFBn5JdCqHeCsUUIIuSMiIiKixsYEmOxOmcmEv1LyEJOSg8uZhRbrnJQq6HqNRn6JSaboiIiIqLGxCQTZlaJSI344fAVbYlLNya+7VgUnlRIAUGiU4HHnREzZlIb//h6L4jKjnOESERFRI2ANMNmNvOIybDh6BVfzS6BxUKB7iAfa6l2hc1IBAMqMJuw/EYu9sZcBn+ZYtv08tsakYenDXdHGz1Xm6ImIiKihsAaY7EJOUSnWHkrE1fwSOGuUeDA8EL2ae5qTXwBwUCoQ4mJC8mfT8HykOzy0KsQk5+De9//EV/suyRg9ERERNSQmwNTkCSGw5UwqcorK4OakwoPhQfBy0dT4mN5BTvhtRn/c2cYHJWUmvLLhFF7ZcAplRrYNJiIisnVMgKnJO51U3tnNQSHhgW7N4HZdrW9NfF0d8dljPTFrSFtIEvDVvkuYuPIgsgtLGzliIiIiakxMgKlJyysuwx/nDQCAyDCvWie/FSRJwpMDw/Dh+HBo1Ur8cc6Ahz+KRlpOUWOES0RERLcBE2Bq0nbGpqGkzAS9ToOuQe713s7gDn5Y+69I+LpqcDYlF6NW7EW8Ib/hAiUiIqLbhgkwNVmXruYjLj0fCgkY1FYPhSTd0vY6BLjhhyd7o7mXFpczC/Hgh3tx6kp2A0VLREREtwsTYGqyDl3MBAB0DnSHj2vNnd5qK8hTi7X/6o0OAToY8kow5uN9iI672iDbJiIiotuDCTA1Sak5RbicVQiFBHQPdm/Qbfu4avDtlDtwRwtP5BWX4dHPDmDzqZQG3QcRERE1HibA1CQduVRe+9tK7wpXx7p1fKsNV0cVVk7shcEd9CgxmvDUqsP49kBCg++HiIiIGh5ngqMmJ7uwFOfS8gAA4cEejbYfR5USH4wLx0vrT+Lbg4n4z7qTuJpfgqcGhkGSJCQkJMBgMDTIvry9vREcHNwg2yIiIrJ3TICpyTmakAkBINhTW++2vzExMbUu+2BzgdI8Z/wQk4/Fv8Ui5sJlDNbn46GHHkRRYWG99n8jJ60WZ2NimAQTERE1ACbA1KQUlxpxOikHQP3a/uZkpAMAxo8fX+fHuva4D56DpmDTuXx8u347iopL8MDTCxDapmOdt3W91IQ4rFo0EwaDgQkwERFRA2ACTE3KubQ8lJkEPJ3VCPbU1vnxhXnlyfPwJ15Cm87hdX58Qn4ZDl1VwqXjXVA4uULr6Y/AVh3qvB0iIiJqPEyAqUk5m5ILAGjr5wrpFsb99QoIqVfiGgjA35CPTccvQxvWEyeKS9Ch1AhHlbLesRAREVHD4igQ1GQUlAFXssrb3LbRu8oWR6i3MzoiAcbCXOSY1Fh7+DJyi0pli4eIiIgsMQGmJiOxoPxyDnB3hM6p4Yc+qwsdCpG6ehbUkhEZ+SVYc+gyMvJLZI2JiIiIyjEBpiYjMb/8cm6r18kcSblSQwK6aQzw0KqQV1yGtYcSkZJdJHdYREREdo8JMDUJKu9gZJcqoJCAVnoXucMxc1QY8WB4EPQ6DYrKTPjhyGVcvJovd1hERER2jQkwNQnO7QcCAJp7OVtdhzMntRKjuwcixFOLMpPAT8eTcDYlR+6wiIiI7BYTYLJ5Qgg4tx8AAGjjJ1/nt5qolArc2yUAbfSuMAngt9OpOJKQKXdYREREdokJMNm8i9llcHDTQykJtPB2ljucaikVEgZ30KNrkDsA4I9zBvx53gAhhLyBERER2RkmwGTzDieVdyzzdRRwUFr3JS1JEvq38kafMC8AwOFLmdgSkwqTiUkwERHR7WLd2QJRLRxKLgYA+DuZZI6kdiRJQo/mnohq5wsJQExyLjadTEap0TbiJyIisnVMgMmmGfKKce5q+SQTfo62lUB2CHDDiM7+UCokxBvysf7oFRSVGuUOi4iIqMljAkw2bWdsOgSA4pTzcLLBib1b+LjggW7NoHFQIDm7CN8fvoy8ojK5wyIiImrSmACTTdt+NhUAUBh3UOZI6q+ZuxP+ER4IZ40SV/NLsOZwImeNIyIiakRMgMlmlZSZsPsvAwDbToABwNtFg4fCg+CuVSG3qHzWuMSMArnDIiIiapKYAJPNOngxA3nFZXDTKFCSfE7ucG6ZzkmFB8MDzbPGrT92BccvZ8kdFhERUZPDBJhs1raYNABAuL8GQNMYRkyrdsA/ugeijZ8rhChv43z4qhKSg1ru0IiIiJoMJsBks3b+dS0BDtDIHEnDclAqMLi9Hn1alo8VfDFfCb8JbyMxu1TmyIiIiJoGJsBkk1Kyi3AhPR8KCejs27QSYODaWMEhnuUjRCgE1D7NMXOrAV/vu8RJM4iIiG4RE2CySXvOl3d+69TMDc7qpnsZB3tqEeVfisKLR1FiBF7ecApjPt6HuPQ8uUMjIiKyWU03c6AmbW/cVQBA75beMkfS+ByVQNp3czCxqw5OKiUOXMzA0Hf/wOLfziK3iM0iiIiI6ooJMNkcIQT2xpXXAPcO85I5mttF4N7Wzvh9Rn/0b+2DkjITlu+Iw4DFO7FyTzxnkCMiIqoDq06AjUYjXnnlFYSGhsLJyQlhYWF49dVXIcTfbSCFEJgzZw78/f3h5OSEqKgonDtnOSRWRkYGxo0bB51OB3d3d0yaNAl5efwJ2VbFG/KRnF0EtVKBHiGecodzWwV5avHFxJ746JFwtPBxRkZ+Ceb9dAb93tqBD3aeR3Yha4SJiIhuxqoT4EWLFmHFihV4//33ERMTg0WLFuGtt97CsmXLzGXeeustvPfee/jwww+xf/9+ODs7Y/DgwSgqKjKXGTduHE6fPo0tW7Zg06ZN2L17N6ZMmSLHIVEDqGj+0D3EHU5qpczR3H6SJGFwBz/8Pr0/XhvZEQFujkjPLcZbm2PRe+E2vLbpDJKyCuUOk4iIyGo5yB1ATfbu3Yv7778fw4cPBwA0b94c33zzDQ4cOACgvPZ36dKlePnll3H//fcDAL788kvo9Xps2LABY8aMQUxMDDZv3oyDBw+iR48eAIBly5Zh2LBhWLJkCQICAuQ5OKq3v5s/NP32vzVxUCow/o4QPNwzCD8dT8JHuy4gNjUX//szHiv3XsR9XQPwRP8wtPFztXhcQkICDAZDg8Tg7e2N4ODgBtkWERHR7WLVCXDv3r3x8ccf46+//kLr1q1x/Phx/Pnnn3j77bcBAPHx8UhJSUFUVJT5MW5uboiIiEB0dDTGjBmD6OhouLu7m5NfAIiKioJCocD+/fvxwAMPVNpvcXExiouLzfdzcnIa8SipLkwmgehrNcAV4+TaO5VSgVHdA/FAt2bY+Vc6PtoVh30XMrDuyBWsO3IFd7X1xdQ7wxAe4omEhAS0bdcOhQUNM82yk1aLszExTIKJiMimWHUC/J///Ac5OTlo27YtlEoljEYjXn/9dYwbNw4AkJKSAgDQ6/UWj9Pr9eZ1KSkp8PX1tVjv4OAAT09Pc5kbLVy4EPPnz2/ow6EGEJOSg8yCUjirlegc6C53OFZFkiTc2cYXd7bxxbHELHy8Ow6/nkrB9rNp2H42DVHtfDE82ITCggKMm7UY+uCwW9pfakIcVi2aCYPBwASYiIhsilUnwGvWrMGqVauwevVqdOjQAceOHcP06dMREBCARx99tNH2O3v2bDz77LPm+zk5OQgKCmq0/VHt7T1fXvsb0cILKqVVN2GXVdcgd3wwLhzxhnx8tCsOaw9fxtaYNGyLATzv/he8AsMQ2KqD3GESERHJwqoziJkzZ+I///kPxowZg06dOuGRRx7BjBkzsHDhQgCAn58fACA1NdXicampqeZ1fn5+SEtLs1hfVlaGjIwMc5kbaTQa6HQ6ixtZh30XyhPgyBZs/lAbod7OeHN0Z/w+oz+GdvSDAODafQS2JKuQmNEwzSCIiIhsjVUnwAUFBVAoLENUKpUwmUwAgNDQUPj5+WHbtm3m9Tk5Odi/fz8iIyMBAJGRkcjKysLhw4fNZbZv3w6TyYSIiIjbcBTUUEwmgUOXMgEAES3sa/izWxXm44IV48Mxb4AnyrJTUWCUsO7oFeyNM1gMK0hERGQPrDoBvvfee/H666/j559/xsWLF7F+/Xq8/fbb5o5rkiRh+vTpeO211/Djjz/i5MmTmDBhAgICAjBy5EgAQLt27TBkyBBMnjwZBw4cwJ49ezBt2jSMGTOGI0DYmL/ScpFdWAqtWon2/qyVr4/Oeg2SPpuGUJfyiTMOXszE5lMpKDOaZI6MiIjo9rHqNsDLli3DK6+8gqeeegppaWkICAjAE088gTlz5pjLvPDCC8jPz8eUKVOQlZWFvn37YvPmzXB0dDSXWbVqFaZNm4ZBgwZBoVBg9OjReO+99+Q4JLoFBy+W1/52D/aAA9v/1psoKUR3TyNaBgdgW0wq/krLQ05RGe7rGgAnlf2Nq0xERPbHqhNgV1dXLF26FEuXLq22jCRJWLBgARYsWFBtGU9PT6xevboRIqTb6WB8BgCgZ3M2f2gI7f110Dk6YNOJZKTkFGHjsSsY1S0Qagd+uSAioqaNn3RkE4QQOHjxWgIc6iFzNE1HoIcWD4YHwlGlQGpOMTadSEKZic0hiIioabPqGmCiCpczC5GcXQQHhYRuQfaZAMfExDTKNrxcNLi/azOsO3IZiZmF2HwqBcM6+UMhSbe8PyIiImvEBJhswqFL5bW/HZu5wUltX+1UczLSAQDjx49vsG3m5eVZ3PfTOWJE5wD8eCwJcen5OBifgQgONUdERE0UE2CyCQfiyzvA9Qq1v/a/hXnlU3EPf+IltOkcfkvbijmwC79+8S6KiooqrQv21GJQO1/8fiYV++IzEODuhCBP7S3tj4iIyBoxASabYG7/a8cd4LwCQm559rbUhLga17fz1+FyZiHOJOdg8+kU/LNXMJw1fJsgIqKmhZ3gyOpl5JfgfFr5T/Y9Quyz/e/tNLCND7yc1SgoMeK3MymcKIOIiJocJsBk9Q5dq/1t5esCD2e1zNE0fSqlAkM7+sFBISExoxCnk3PkDomIiKhBMQEmq3f42vTHPey4+cPt5uWiQeS1TnB7zhlQUFImc0REREQNhwkwWb0jCeUJcDibP9xWXYPc4e2iRlGZCX+eM8gdDhERUYNhAkxWraTMhBOXswEA3YPd5Q3GzigUEga11QMAYlJykZhRIHNEREREDYMJMFm1mOQcFJeZ4K5VIdTbWe5w7I6fmyM6NXMDAOyITYPJxA5xRERk+5gAk1WraP7QLcgdEmcmk0WfMC84qhTILCjFGXaIIyKiJoAJMFm1IwlZAIDuwWz/KxeNSmkef3l/fAbKjCaZIyIiIro1TIDJqh25NgJEd3aAk1XnZm5wdXRAXnEZjl3OkjscIiKiW8IEmKxWWk4RrmQVQiEBXYLc5Q7HrjkoFbjj2rBohy5moqjUKHNERERE9ccEmKxWRfvf1npXuHA6Xtm19XOFl7MaxWUm89jMREREtogJMFmtoxXtf9n8wSooJAm9w8prgY9fzkIJmwITEZGNYgJMVquiBpgd4KxHqLczvF3UKDUKxOXy7YOIiGwTP8HIKnECDOskSRJ6hJSPCHE+VwlJpZE5IiIiorpjAkxWiRNgWK9Wvi5wc1KhxCTBpfNgucMhIiKqM/YsItkkJCTAYDBUue6Xc/kAgBY6BY4ePVrjdmJiYho8NqqeQiEhPMQD28+mQdfrAZQaOTscERHZFibAJIuEhAS0bdcOhQUFVa73GjYDLp0GYfv3n2H9jG9qtc28vLyGDJFq0M7fFXvPpaJI54PdlwoR0VPuiIiIiGqPCTDJwmAwoLCgAONmLYY+OKzS+t+TVMgtA4Y98BD8//lgjduKObALv37xLoqKihorXLqBg0KBVq5GnMxywE/n8vG8EJyqmoiIbAYTYJKVPjgMga06WCwrLjMiN+ECAKBD29bQqmu+TFMT4hotPqpecxcTjqcVIiHbCdFxV9G7pbfcIREREdUKO8GR1UnLKQYA6Bwdbpr8knzUCiD/1DYAwGd7LsobDBERUR0wASark5JT3pRBr3OUORK6mZzDPwEAtp1NxaWr+TJHQ0REVDtMgMnqpF5LgP2YAFu9sowr6OangRDAF3svyR0OERFRrTABJquTeq0JBGuAbcPwVloAwJpDicgtKpU5GiIioptjAkxWJa+4DHnFZZAA+LhyljFb0NVPgxY+zsgrLsO6I1fkDoeIiOimmACTValo/uDpoobagZenLVBIEibcEQIAWL0/AUJwYgwiIrJuzDDIqlQkwHpXNn+wJQ90D4SjSoHY1FwcvpQpdzhEREQ1YgJMVqWi/S87wNkWNycV7u0cAKC8FpiIiMiaMQEmqyGE+LsGWMf2v7Zm3LVmEJtOJiMzv0TmaIiIiKrHBJisRlZhKYrLTFAqJHi5MAG2NV0C3dDeX4eSMhN+OHJZ7nCIiIiqxQSYrEZF7a+vqwZKhSRzNFRXkiRh3B3BAIDVB9gZjoiIrBcTYLIaqdnXxv9lBzibdX/XZnBWK3EhPR/7LmTIHQ4REVGVmACT1UjNvdb+143NH2yVi8YB93drBqC8FpiIiMgaMQEmq2A0CaTlcga4puCfvcqbQWw+lQxDXrHM0RAREVXGBJiswtW8YhhNAhoHBdydVHKHQ7egYzM3dAlyR6lR4PvD7AxHRETWhwkwWYWK8X/1OkdIEjvA2bpx12qBV+9PgMnEznBERGRdmACTVUjh+L9Nyogu/nB1dEBCRgH2xBnkDoeIiMgCE2CyCn9PgMH2v02BVu2AUdc6w63ax85wRERkXZgAk+xKykzIuDZzGKdAbjr+GVE+M9yWmFTzFxwiIiJrwASYZJeeWwyB8iG0nDUOcodDDaSNnyt6NveA0SSw5mCi3OEQERGZMQEm2bH9b9M17lot8DcHEmBkZzgiIrISTIBJdmz/23QN6egHD60KSdlF2BmbJnc4REREAJgAkxVgAtx0OaqU+Ed4IABg1X52hiMiIuvABJhkVWwEcorKAAB6VzaBaIrGXhsTeEdsGi5nFsgcDRERERNgkllmSfmkF+5OKmhUSpmjocbQwscFfVp6QQjg2wPsDEdERPJjAkyyyrqWAPuyA1yTVtEZ7tuDiSg1mmSOhoiI7B3HnCJZZZaUfwfzdWX7X1sVExNz0zLeJgF3RwUMecX4aFM0egc5Wa739kZwcHBjhUhERGSBCTDJqqIGmEOg2Z6cjHQAwPjx42tV3r3feLj1HoNXv9mBtO9esVjnpNXibEwMk2AiIrotmACTbBROOhQYyxNgH3aAszmFeTkAgOFPvIQ2ncNvWj6/DNicJODUvBsmL10HV1X58tSEOKxaNBMGg4EJMBER3RZMgEk2ar+WAAB3rQoaB3aAs1VeASEIbNWhVmXPFl/BxasFMKh80a6VTyNHRkREVDV2giPZVCTAvqz9tRudAt0AAGeSc1DGznBERCQTJsAkG42+PAHWswOc3Wju5QwXjQOKSk04n54ndzhERGSnmACTbNT+12qA2QHObigkCR2b6QAAJy5nyxwNERHZKybAJIvsIiMcdL4A2AHO3nQMcINCApKzi5BybRpsIiKi24kJMMkiLrMUAODiINgBzs44axzQWu8KADiWmCVvMEREZJeYAJMsKhJgDzU7QtmjrkHuAIBzqbkoLJM3FiIisj9MgEkWfyfAQuZISA56nSMC3BxhEsCFPP4CQEREtxcTYJJFXEZ5AuzOBNhuVdQCX8hTAEqVvMEQEZFdYQJMt50hrxhXC00QwsQE2I6F+bjA1dEBJSYJLh0Gyh0OERHZESbAdNudvFI+/FVZxhWoeAXaLYVCQpdAdwCArtcomAS/DBER0e3B9INuu5PXxn8tTjkvcyQkt47NdFBJAiqvIBxMKpY7HCIishNWnwBfuXIF48ePh5eXF5ycnNCpUyccOnTIvF4IgTlz5sDf3x9OTk6IiorCuXPnLLaRkZGBcePGQafTwd3dHZMmTUJeHmehkktFDXBJyrmblKSmTuOgRAvX8pFA1sXkQbAWmIiIbgOrToAzMzPRp08fqFQq/Prrrzhz5gz++9//wsPDw1zmrbfewnvvvYcPP/wQ+/fvh7OzMwYPHoyior8H2B83bhxOnz6NLVu2YNOmTdi9ezemTJkixyERgFPmBDhO5kjIGrR0NcJUWoxzGaXYdyFD7nCIiMgOOMgdQE0WLVqEoKAgfP755+ZloaGh5v+FEFi6dClefvll3H///QCAL7/8Enq9Hhs2bMCYMWMQExODzZs34+DBg+jRowcAYNmyZRg2bBiWLFmCgICA23tQdi49txjJ2UWQAJSkMgEmwFEJ5J3YAl34CKzYFYfIMC+5QyIioibOqmuAf/zxR/To0QMPPvggfH190a1bN3zyySfm9fHx8UhJSUFUVJR5mZubGyIiIhAdHQ0AiI6Ohru7uzn5BYCoqCgoFArs37+/yv0WFxcjJyfH4kYNo6L2t5nOAaKU0+BSudyD66GQgN1/pZuvESIiosZi1QnwhQsXsGLFCrRq1Qq//fYbnnzySTz99NP44osvAAApKSkAAL1eb/E4vV5vXpeSkgJfX1+L9Q4ODvD09DSXudHChQvh5uZmvgUFBTX0odmtE9c6wIV5cNxX+ltZdir6BjkCAJZuZdtwIiJqXFadAJtMJnTv3h1vvPEGunXrhilTpmDy5Mn48MMPG3W/s2fPRnZ2tvmWmJjYqPuzJxUd4JgA040ebO8KhQRsjUnFscQsucMhIqImzKoTYH9/f7Rv395iWbt27ZCQkAAA8PPzAwCkpqZalElNTTWv8/PzQ1pamsX6srIyZGRkmMvcSKPRQKfTWdyoYZxiAkzVaKZzwAPdAgEAb2/5S+ZoiIioKbPqBLhPnz6IjY21WPbXX38hJCQEQHmHOD8/P2zbts28PicnB/v370dkZCQAIDIyEllZWTh8+LC5zPbt22EymRAREXEbjoIqpOUWISWnCJIENHe36v6XJJNnBrWCg0LC7r/ScfAiR4QgIqLGYdUJ8IwZM7Bv3z688cYbOH/+PFavXo2PP/4YU6dOBQBIkoTp06fjtddew48//oiTJ09iwoQJCAgIwMiRIwGU1xgPGTIEkydPxoEDB7Bnzx5MmzYNY8aM4QgQt1lF7W9LHxc4cQo4qkKwlxYP9ihvc//f32NvUpqIiKh+rDoL6dmzJ9avX49vvvkGHTt2xKuvvoqlS5di3Lhx5jIvvPAC/v3vf2PKlCno2bMn8vLysHnzZjg6OprLrFq1Cm3btsWgQYMwbNgw9O3bFx9//LEch2TXTl4uH02jUzM3mSMha/bvu1pCrVRg34UM7PorXe5wiIioCbL636FHjBiBESNGVLtekiQsWLAACxYsqLaMp6cnVq9e3RjhUR1UdIDr2MwNQKa8wZDVCnB3wiORIfj0z3i8/vMZ9AnrBwelVX9XJyIiG8NPFbptTiddnwATVe/pu1rBXavCX6l5WHPostzhEBFRE8MEmG6LjPwSJGeXT3zRzt9V5mjI2rlpVXhmUCsAwNtbYpFbVCpzRERE1JTUuwlEfn4+du3ahYSEBJSUlFise/rpp285MGpaKmp/Q72d4erIIdDo5sbfEYKvoi/hgiEfH+yMw6whbeUOiYiImoh6JcBHjx7FsGHDUFBQgPz8fHh6esJgMECr1cLX15cJMFVy6kp5B7j2ARxTmWpHpVRg9rB2mPzlIXz6Zzwe6hGEUG9nucMiIqImoF5NIGbMmIF7770XmZmZcHJywr59+3Dp0iWEh4djyZIlDR0jNQEVNcAdmABTHUS180W/Vt4oKTNhzsZTEELIHRIRETUB9UqAjx07hueeew4KhQJKpRLFxcUICgrCW2+9hRdffLGhY6Qm4ExSeQ1wxwB2gKPakyQJC+7vCLWDAn+cM2DTiWS5QyIioiagXk0gVCoVFIry3NnX1xcJCQlo164d3NzckJiY2KABku3LKy7DBUM+ANYAU/ViYmKqXfdAGy2+O52HOeuPw6MoCdoaJlLx9vZGcHBwY4RIRERNRL0S4G7duuHgwYNo1aoVBgwYgDlz5sBgMOCrr75Cx44dGzpGsnExyeW1v/5ujvBy0cgcDVmbnIzyyS7Gjx9ffSGlCgGPv49Mz2a476VPkLmt+olsnLRanI2JYRJMRETVqlcC/MYbbyA3NxcA8Prrr2PChAl48skn0apVK3z22WcNGiDZvtNX2P6XqleYV/4FafgTL6FN5/Bqy6UWSfgzDdD1uBf3DhsKb8fK7YFTE+KwatFMGAwGJsBERFSteiXAPXr0MP/v6+uLzZs3N1hA1PScSqoYAYLtf6l6XgEhCGzVodr1gQAyzqTiTHIOjuVqMa59MFScIY6IiOqhXp8ed911F7Kysho4FGqqTps7wLEGmG5N/9becNE4ILuwFHvOG+QOh4iIbFS9EuCdO3dWmvyCqCrFZUacSy1vLtOBUyDTLdI4KBHVzhcAcPxyNhIzCmSOiIiIbFG9fz+UJKkh46Am6q+UPJSZBNy1KgS4OcodDjUBIV7O6HTty9SWmFSUlJlkjoiIiGxNvadCfuCBB6BWq6tct3379noHRE1LxQQYHQPc+KWJGkzflt64dDUfOUVl+ONcOga108sdEhER2ZB6J8CRkZFwcXFpyFioCTrFGeCoEagdFLi7vR4/HLmCU0k5aOnrghAvTpNMRES1U68EWJIkzJw5E76+vg0dDzUxFR3g2P6XGlqghxZdAt1w/HI2tsakYXwEhz0jIqLaqVcbYCEqj79JdCOjSZgnwWANMDWGPi294eakQl5xGXadS5c7HCIishH1SoDnzp3L5g90UxfS81BUaoJWrUQof56mRqBSKnBPez0kADHJubhSwHbmRER0c/VqAjF37lwAQHp6OmJjYwEAbdq0gY+PT8NFRjavovlDe38dFAomJtQ4AtydEB7igUOXMnEkwwEKrbvcIRERkZWrVw1wQUEBHn/8cQQEBKB///7o378/AgICMGnSJBQUcFxOKneaHeDoNolo4QlvFzVKTBK8hvybzbSIiKhG9UqAZ8yYgV27duHHH39EVlYWsrKysHHjRuzatQvPPfdcQ8dINurUlYr2v+wAR43LQaHA4A5+UEBA2yoC2+ML5Q6JiIisWL0S4B9++AGffvophg4dCp1OB51Oh2HDhuGTTz7B999/39Axkg0SQvxdA9yMNcDU+LxdNGjvbgQAfHosh7PEERFRterdBEKvrzzwvK+vL5tAEADgcmYhcorKoFJKaOXrKnc4ZCdau5pQlHgKRWUCz605DqOJTSGIiKiyeiXAkZGRmDt3LoqKiszLCgsLMX/+fERGRjZYcGS7Kmp/W+tdoXao94zbRHUiScDVn9+Bo4OEAxcz8OmfF+QOiYiIrFC9RoFYunQphgwZgsDAQHTp0gUAcPz4cTg6OuK3335r0ADJNlWMANGR7X/pNivLTsXErjqsOJSNJb/9hTvb+KKVnr9CEBHR3+pVNdepUyecO3cOCxcuRNeuXdG1a1e8+eabOHfuHDp06NDQMZINOnWF7X9JPlGhTrirrS9KjCa88MMJNoUgIiIL9aoB3r17N3r37o3Jkyc3dDzURJinQGYNMMlAkiS8/kBH3PP2bhxNyMIXey/i8b6hcodFRERWol41wHfeeScyMjIaOhZqItJyi5CWWwxJAtr586dnkoe/mxNmD2sHAFj8WyxHhSAiIrN6JcAcZJ5qUlH728LbGVp1vX5kIGoQY3oG4Y4WnigsNWL2upN87yIiIgD1bAIBANHR0fDw8KhyXf/+/esdENm+MxUd4Jqx+QPJS6GQ8Oaozhi8dDf+PG/A2sOX8VCPILnDIiIimdU7AX7ggQeqXC5JEoxGY70DIuuWkJAAg8FQY5k/T2cCANxMuThy5EiVZWJiYho8NqKqNPd2xnP3tMYbv5zFa5vOYGBrH/jqHOUOi4iIZFTvBDglJQW+vr4NGQtZuYSEBLRt1w6FN5nsJGDKJ1B5+GPxi0/j1UvHayybl5fXkCESVenxPqHYdCIZJy5nY87G0/jwkXC5QyIiIhnVKwGWJKmh4yAbYDAYUFhQgHGzFkMfHFZlmRIT8NNlNQDg/56bC7Wy6m3FHNiFX79412IyFaKGUtUvDI+1V2HmFWDz6RQs/3EPIgOdbrodb29vBAcHN0aIREQko3olwOxIYt/0wWEIbFX1eM+XMwuAy1fg6uiAFm1bVbuN1IS4xgqP7FhORjoAYPz48VWud+s7Du59xmLh7xeQ9Mm/IEpq/jXDSavF2ZgYJsFERE1MvRJgk8nU0HFQE5GWWwwA8HXVyBwJ2aPCvPIOmMOfeAltOldu5mAUwNZkgTwXT9w15xt08ai+v0JqQhxWLZoJg8HABJiIqImpVwK8cOFC6PV6PP744xbLP/vsM6Snp2PWrFkNEhzZnvRrCbAPE2CSkVdASLW/UkR55WPDsSTE5SnRq10or1UiIjtUr3GAP/roI7Rt27bS8g4dOuDDDz+85aDIdjEBJmsX4uWMlr4uEALYEZvGJl1ERHaoXglwSkoK/P39Ky338fFBcnLyLQdFtqnMaEJGQQkAwNeVw0yR9erfyhsqpYTk7CLEpOTKHQ4REd1m9UqAg4KCsGfPnkrL9+zZg4CAgFsOimyTIa8EQgBOKiWcqxv+gcgKuDqq0CvUEwDw5zkDiko5djkRkT2pVxvgyZMnY/r06SgtLcVdd90FANi2bRteeOEFPPfccw0aINmOtNzyIc18XTUcKo+sXrcgD8Qk5SKjoATRF67izjYc15yIyF7UKwGeOXMmrl69iqeeegolJeU/eTs6OmLWrFmYPXt2gwZItoPtf8mWKBUSBrbxwbqjV3DycjY6+Os4QxwRkZ2oVxMISZKwaNEipKenY9++fTh+/DgyMjIwZ86cho6PbEh6HodAI9sS5KlFa70LBIAdsensEEdEZCfqPRUyALi4uKBnz54NFQvZMKNJwJBX/msAa4DJlvRr5YOLhgKk5BThdFIOOjZzkzskIiJqZPVOgA8dOoQ1a9YgISHB3Ayiwrp16245MLItmQUlMJoE1EoF3JxUcodDVGsuGgdEtPDEH+cM2Bt3Fa30LtA4sBMnEVFTVq8mEN9++y169+6NmJgYrF+/HqWlpTh9+jS2b98ONzfWntijtOva/7IDHNmaLoHu8NCqUFhqxP74DLnDISKiRlavBPiNN97AO++8g59++glqtRrvvvsuzp49i4ceeohThtopdoAjW6ZUSOjf2gcAcDwxCxn5JTd5BBER2bJ6JcBxcXEYPnw4AECtViM/Px+SJGHGjBn4+OOPGzRAsg0VCTA7wJGtau7ljFBvZ5gEsPtcutzhEBFRI6pXAuzh4YHc3PLZk5o1a4ZTp04BALKyslBQUNBw0ZFNEEKwBpiahH6tvKGQgEtXC5BcyKY8RERNVb0S4P79+2PLli0AgAcffBDPPPMMJk+ejLFjx2LQoEENGiBZv+zCUpQYTVAqJHhq1XKHQ1RvHlo1ugV5AABOZDoAilsaKIeIiKxUvd7d33//fRQVlc/69dJLL0GlUmHv3r0YPXo0Xn755QYNkKxfRe2vt4saCgVrzci29Qz1QExKDvJKjND1uFfucIiIqBHUKQHOyckpf5CDA1xcXMz3n3rqKTz11FMNHx3ZBPMIEC5s/kC2T+OgRO8wL2yNSYNb77HIKjLKHRIRETWwOiXA7u7utRriymjkB4Y9+bsDHKeRpaahvb8Oh+NSkAktVp3MxV295Y6IiIgaUp0S4B07dljcF0Jg2LBh+N///odmzZo1aGBkG4QQFmMAEzUFkiShi4cRO1MV2B5fiBOXs9A50F3usIiIqIHUKQEeMGBApWVKpRJ33HEHWrRo0WBBke3ILzaisNQISSpvA0zUVHhpBPJObYdLx7sw/6cz+P5fkZzkhYioiajXKBBEFdLyyjtDemrVcFDycqKmJWvXF3B0kHD4UiY2HkuSOxwiImogt5SxJCYmoqCgAF5eXg0VD9mY9Bw2f6Cmy5h3FaPaugAAFv4ag/ziMpkjIiKihlCnJhDvvfee+X+DwYBvvvkGd911F9zc3Bo8MLIN6XlMgKlpu6+NM/5IKkNiRiFW7IzD84PbyB0SERHdojolwO+88w6A8g4i3t7euPfeeznur51L4xTI1MSplRJeGtYe//r6MD7+4wIe7hmEIE+t3GEREdEtqFMCHB8f31hxkA0qLDUit6j8J2HWAFNTNriDHr3DvLA37ipe/zkGHz4SLndIRER0C9hrieqtYvxfNycVNA5KmaMhajySJGHuvR2gVEjYfDoFe88b5A6JiIhuARNgqrd0jv9LdqSNnyvGRwQDAOb/dAZlRpPMERERUX0xAaZ6S8stHwKNCTDZixl3t4a7VoXY1Fx8cyBB7nCIiKiemABTvaWzAxzZGXetGs/d3RoA8N8tfyGroETmiIiIqD6YAFO9lBpNyCwoBQD4uDABJvsxtlcw2vq5IqugFG9v+UvucIiIqB6YAFO9VNT+OquVcNbUaTARIpvmoFRgzoj2AICv913C2ZQcmSMiIqK6sqkE+M0334QkSZg+fbp5WVFREaZOnQovLy+4uLhg9OjRSE1NtXhcQkIChg8fDq1WC19fX8ycORNlZZzR6VawAxzZs94tvTGkgx9MAljw0xkIIeQOiYiI6sBmEuCDBw/io48+QufOnS2Wz5gxAz/99BPWrl2LXbt2ISkpCaNGjTKvNxqNGD58OEpKSrB371588cUXWLlyJebMmXO7D6FJ+XsCDEeZIyGSx0vD20HtoMDeuKv47XSK3OEQEVEd2MRv13l5eRg3bhw++eQTvPbaa+bl2dnZ+PTTT7F69WrcddddAIDPP/8c7dq1w759+3DHHXfg999/x5kzZ7B161bo9Xp07doVr776KmbNmoV58+ZBrVZX2l9xcTGKi4vN93Ny+BPnjTgFMtmLmJiYatfd10qL72PyMHf9cXgUJkGllKot6+3tjeDg4MYIkYiI6sgmEuCpU6di+PDhiIqKskiADx8+jNLSUkRFRZmXtW3bFsHBwYiOjsYdd9yB6OhodOrUCXq93lxm8ODBePLJJ3H69Gl069at0v4WLlyI+fPnN+5B2TCTAK7mcQQIatpyMtIBAOPHj6+2jKRyRMDkj5AKL9wz7XXkHFhfbVknrRZnY2KYBBMRWQGrT4C//fZbHDlyBAcPHqy0LiUlBWq1Gu7u7hbL9Xo9UlJSzGWuT34r1lesq8rs2bPx7LPPmu/n5OQgKCjoVg6jSckplWASgMZBAVdHq7+EiOqlMK/8l5/hT7yENp2rn/r4Yp4ChzMAn7sexyPjH4GmikkRUxPisGrRTBgMBibARERWwKqzl8TERDzzzDPYsmULHB1vX1tTjUYDjYY1m9XJKin/mdfHVQNJqv4nX6KmwCsgBIGtOlS7vpkQSDiYiPTcYiTAB3e28r2N0RERUX1YdSe4w4cPIy0tDd27d4eDgwMcHBywa9cuvPfee3BwcIBer0dJSQmysrIsHpeamgo/Pz8AgJ+fX6VRISruV5ShuqlIgNn8gQiQJAn9W3kDAE4mZZubBxERkfWy6gR40KBBOHnyJI4dO2a+9ejRA+PGjTP/r1KpsG3bNvNjYmNjkZCQgMjISABAZGQkTp48ibS0NHOZLVu2QKfToX379rf9mJqC62uAiQgI9NAizMcZQgB/nDfIHQ4REd2EVTeBcHV1RceOHS2WOTs7w8vLy7x80qRJePbZZ+Hp6QmdTod///vfiIyMxB133AEAuOeee9C+fXs88sgjeOutt5CSkoKXX34ZU6dOZTOH+pAUyCqtqAHmEGhEFfq29Ea8IR+Xrhbg0tV8hHg5yx0SERFVw6prgGvjnXfewYgRIzB69Gj0798ffn5+WLdunXm9UqnEpk2boFQqERkZifHjx2PChAlYsGCBjFHbLgcPfxiFBAeFBHetSu5wiKyGu1aNLkHuAIDd5wwwmTg5BhGRtbLqGuCq7Ny50+K+o6Mjli9fjuXLl1f7mJCQEPzyyy+NHJl9UOvDAADeLhoo2AGOyEJEc0/EJOcgI78Ep5Ky0TnQXe6QiIioCjZfA0y3V0UCzA5wRJVpVErcEeoFANh3IQPFZUaZIyIioqowAaY6qUiA2QGOqGodm7nBQ6tCYakRhy9lyh0OERFVgQkw1ZoQAmp9CwBMgImqo1RI6NuyfFi0owlZyC8ukzkiIiK6ERNgqjVDgQlKJx0kCHi5qOUOh8hqhXo7w9/NEWUmgf3xGXKHQ0REN2ACTLV2IasUAKBTCTgoeOkQVUeSJPQJK68FPp2UjbxSmQMiIiILzGKo1uIzyz/F3dUc3onoZpp5OCHESwuTAM5kK+UOh4iIrsMEmGqtogaYCTBR7VTUAicWKKHyDZU5GiIiqsAEmGrtQkUNsIoJMFFt+Lhq0FrvAgDwGPCozNEQEVEFJsBUK4a8YmQUmiCECW6sASaqtcgWXpAg4NSiB06lFcsdDhERgQkw1dLppBwAQFlGElS8aohqzV2rRqiLCQDw9clcCMEvkEREcmMqQ7VyOikbAFCSdkHmSIhsT1s3I0wlRfjrail+P5MqdzhERHaPCTDVyukr5TXAJalxMkdCZHuclEDuoY0AgMW/xcJoYi0wEZGcmABTrZyqqAFOOS9zJES2KfvAOrioJZxPy8OmE0lyh0NEZNeYANNNZReW4tLVAgCsASaqL1GcjxGtnAEAy7afZy0wEZGMmADTTZ2+Ul77q3dWwlSUJ3M0RLZreCtn6BwdcD4tD7+cTJY7HCIiu8UEmG7q5LUEuIWHSuZIiGybs1qBx/uWT4ixbPs5mFgLTEQkCybAdFNMgIkazsQ+oXB1dMBfqXn49VSK3OEQEdklJsB0U6euJcBhTICJbpmbkwoT+5TXAr+77S/WAhMRyYAJMNUop6gUF691gGMCTNQwJvUJhaumvBZ482nWAhMR3W5MgKlGFeP/NnN3gquGlwtRQ3DTqjCxT3MAwHvb2BaYiOh2Y0ZDNapo/tCxmU7mSIialsf7hsJF44CzKbn4/QxrgYmIbicmwFSjig5wnZq5yRwJUdPirlXjsd7NAQDvbjvPWmAiotuICTDV6O8aYCbARA1tUt9QOKuViEnOwZaYVLnDISKyG0yAqVq5RaW4YMgHwBpgosbg4azGo9dqgd/bdg5CsBaYiOh2cJA7ALJeZ5LKO8AFuDnCy0WDSzLHQ2TrYmJiKi3r5WbCZw4STifl4JOfo9EjwPGm2/H29kZwcHBjhEhEZBeYAFO1Ktr/dmDtL9EtyclIBwCMHz++yvXuAyfCLWI05q6JRspXz990e05aLc7GxDAJJiKqJybAVK1T7ABH1CAK88p/TRn+xEto0zm80voiI/BrkoAmoC3GLVkPvVP1TSFSE+KwatFMGAwGJsBERPXEBJiqxREgiBqWV0AIAlt1qHJdZykdxy5nIb5Uh/DOgbc5MiIi+8JOcFSl/OIycwc4jgBB1Pi6h7hDKUm4klWIK5mFcodDRNSkMQGmKp1JzoEQgJ/OET6uGrnDIWryXB1VaBfgCgDYf/GqzNEQETVtTICpSicvcwY4otutZ4gnFBKQmFGI5GzWAhMRNRYmwFQlToBBdPvpnFRo61f+pfNAfIbM0RARNV1MgKlK7ABHJI8ezT0gAbh4tQBpOUVyh0NE1CQxAaZKCkrKEJeeB4AJMNHt5qFVo7W+vC3wgYusBSYiagxMgKmSmOQcmATg46qBr+7ms1IRUcPq2dwDABCXng9DXrHM0RARNT1MgKmSig5wrP0lkoeXiwYtfV0AAAdZC0xE1OCYAFMlJ6+Uz1rFDnBE8unV3BMA8FdqHjLzS2SOhoioaWECTJVwCmQi+fm4ahDq7QwAOHiJtcBERA2JCTBZKCwx4lxaLgAmwERy6xVaXgt8NiUX2YWlMkdDRNR0MAEmCzEp5R3gvF3U0Os4AxyRnPx0jgjx1EII4BDbAhMRNRgmwGThRGIWgPLaX0mS5A2GiMy1wGeSc5BTxFpgIqKGwASYLBy/NgJElyB3eQMhIgBAgLsTAj2cYBKcHY6IqKEwASYLx6/VADMBJrIevcO8AJTXAueyEpiI6JYxASaz7IJSXDDkAwC6BLrLGwwRmfm7OSHU2xlCAGeylXKHQ0Rk85gAk9mJK1kAgBAvLTyd1fIGQ0QWIluU1wJfLlBC5RMqczRERLaNCTCZmZs/sPaXyOr4uGrQ+trscO79H5E5GiIi28YEmMyOJbIDHJE1uyPMCxIEtC174XR6sdzhEBHZLCbABAAQQuDYtRrgrkGcAIPIGnlo1WjuYgIArDyWC5NJyBwREZFtYgJMAIDk7CIY8oqhVEjoEMAEmMhatXczwlRcgLjMUmw4dkXucIiIbBITYALwd/vftn6ucFSxlzmRtXJUAtnRawAAi3+LRWGJUeaIiIhsDxNgAgAcu5wFgO1/iWxBzqGN8NEqkZxdhE/+uCB3OERENocJMAH4uwa4K0eAILJ+xlKM7+wKAPhg53kkZhTIHBARkW1hAkwwmgROcgpkIpvSN8gREaGeKCo1Yd6PpyEEO8QREdUWE2DC+bQ85JcYoVUr0fLaOKNEZN0kScLrD3SESilh29k0/HY6Ve6QiIhsBhNgwpGETADlE2AoFZLM0RBRbbX0dcWU/i0AAPN/Oo284jKZIyIisg1MgAmHL5UnwOEhHjJHQkR19e+7WiHYU4vk7CIs+S1W7nCIiGwCE2Ay1wB3D3GXNxAiqjNHlRKvjuwIAFi59yL2nDfIHBERkfVjAmznMvNLcCE9HwDQLYg1wES2aEBrH/wzIhgA8Pza48guLJU5IiIi68YE2M4dTSyv/W3h4wwPZ7XM0RBRfb00rB2ae5U3hZi78ZTc4RARWTUmwHbuyKUsAED3YNb+EtkyZ40D3n64KxQSsOFYEjZymmQiomoxAbZz7ABH1HR0D/bAtLtaAQD+88NJnE3JkTkiIiLrxATYjpUZTTh+bQpk1gATNQ3PDGqFvi29UVhqxBNfHWZ7YCKiKjjIHQA1voSEBBgMlXuGX8gsRUGJEVqVhNwrf+FIUs1jAMfExDRWiERURzW9Hid3UCI2SYlLVwvw+Me7MLuvBxRS5de3t7c3goODGzNMIiKrxAS4iUtISEDbdu1QWFBQaZ1Lt2HwuucpXP3rMHr2mFPrbebl5TVkiERUBzkZ6QCA8ePH11hOrQ+DftxbOJwM3PPCh8jc/r9KZZy0WpyNiWESTER2x6oT4IULF2LdunU4e/YsnJyc0Lt3byxatAht2rQxlykqKsJzzz2Hb7/9FsXFxRg8eDA++OAD6PV6c5mEhAQ8+eST2LFjB1xcXPDoo49i4cKFcHCw6sNvEAaDAYUFBRg3azH0wWEW6w4alEgoALp37Yz2A9bddFsxB3bh1y/eRVFRUWOFS0Q3UZhX3q53+BMvoU3n8BrLJuQrcPAqoOs5Er3vHoE2OpN5XWpCHFYtmgmDwcAEmIjsjlVngLt27cLUqVPRs2dPlJWV4cUXX8Q999yDM2fOwNnZGQAwY8YM/Pzzz1i7di3c3Nwwbdo0jBo1Cnv27AEAGI1GDB8+HH5+fti7dy+Sk5MxYcIEqFQqvPHGG3Ie3m2lDw5DYKsOFsu2pl8EUIo2ocEI9HK+6TZSE+IaJzgiqjOvgJBKr+kbBQJwvJSJP84bcCrLAQEBerTz192eAImIrJhVJ8CbN2+2uL9y5Ur4+vri8OHD6N+/P7Kzs/Hpp59i9erVuOuuuwAAn3/+Odq1a4d9+/bhjjvuwO+//44zZ85g69at0Ov16Nq1K1599VXMmjUL8+bNg1ptn2Pf5hWXIbuwFBIAPzdHucMhokbSPcQD+SVlOJKQhS0xqVAqJLTWu8odFhGRrGxqFIjs7GwAgKenJwDg8OHDKC0tRVRUlLlM27ZtERwcjOjoaABAdHQ0OnXqZNEkYvDgwcjJycHp06er3E9xcTFycnIsbk1NUlYhAMDHVQONg1LmaIioMfVt6Y12/q4QAth8KgVnk5veexoRUV3YTAJsMpkwffp09OnTBx07ls97n5KSArVaDXd3d4uyer0eKSkp5jLXJ78V6yvWVWXhwoVwc3Mz34KCghr4aOR3JbM8AQ5wd5I5EiJqbJIkIaqdHu39dRAAfjuTivg8m3n7JyJqcDbzDjh16lScOnUK3377baPva/bs2cjOzjbfEhMTG32ft9uVazXAzZgAE9kFhSQhqp0vOjVzAwAcyXCAW+8xEELIHBkR0e1nEwnwtGnTsGnTJuzYsQOBgYHm5X5+figpKUFWVpZF+dTUVPj5+ZnLpKamVlpfsa4qGo0GOp3O4taUFJYacTW/BAAQ4M72v0T2QpIk3NnGxzzzo3u/8fjgUDZKjaabPJKIqGmx6gRYCIFp06Zh/fr12L59O0JDQy3Wh4eHQ6VSYdu2beZlsbGxSEhIQGRkJAAgMjISJ0+eRFpamrnMli1boNPp0L59+9tzIFamov2vp7MaWrVV94MkogYmSRL6tvRGV48yCJMR2+ILMeHTAzDkFcsdGhHRbWPVCfDUqVPx9ddfY/Xq1XB1dUVKSgpSUlJQWFiewLm5uWHSpEl49tlnsWPHDhw+fBgTJ05EZGQk7rjjDgDAPffcg/bt2+ORRx7B8ePH8dtvv+Hll1/G1KlTodFo5Dw82bD5AxGFuZqQ/sOrcHSQEH3hKka89yeOJGTKHRYR0W1h1QnwihUrkJ2djYEDB8Lf3998++6778xl3nnnHYwYMQKjR49G//794efnh3Xr/p7UQalUYtOmTVAqlYiMjMT48eMxYcIELFiwQI5DsgoVHeCYABPZt8ILh7BokBda+DgjJacID38UjS+jL7JdMBE1eVb9+3dt3oQdHR2xfPlyLF++vNoyISEh+OWXXxoyNJtVUmZCem75T51s/0tEQW4qbJzaHbN+OIFfTqZgzsbTOHIpE2+M6sQmUkTUZFl1DTA1vOTsQggAbk4quDqq5A6HiKyAq6MKy//ZHS8PbwelQsKGY0l4YPleXEjPkzs0IqJGwQTYzlw2j//L2l8i+pskSfi/fi2w+v8i4O2iQWxqLu5d9ic2Hrsid2hERA2OCbCdYQc4IqpJRAsv/PJ0X0SEeiK/xIhnvj2G2etOoqjUKHdoREQNhgmwHSkuMyIlpwgAEOihlTkaIrJWvjpHrPq/CDx9V0tIEvDNgQSMXL4HcWwSQURNBHs42JErWYUQorz9r5sT2/8SERATE1PtuoHegHs/Tyzdn4WzKbkY/u5uPNnDDf2CK/+C5O3tjeDg4MYMlYiowTABtiOJGeXNH4I82fyByN7lZKQDAMaPH3/TskoXT3jfOxMI7oR39mVhwYffIHPbJxBlJeYyTlotzsbEMAkmIpvABNiOJGYUAACC2fyByO4V5uUAAIY/8RLadA6/aXmTAGKyjTibo4Br16EI7DkYEd5lcFUBqQlxWLVoJgwGAxNgIrIJTIDtRKERuJpfXlsT6MkEmIjKeQWEILBVh1qVDQbQ9mo+fjudiuxSYGeaIwZ30EPfuCESETU4doKzE2lF5U+1r6sGTiqlzNEQka0K8XLGuIhgNHN3QonRhJ9OJCMmWwFAkjs0IqJaYwJsJ9KKyj+cglj7S0S3yFnjgAe6NUPnQDcAwJlsB3iP/A8KS00yR0ZEVDtMgO1ERQ1wMBNgImoASoWEO9v4YlBbX0gQcG7TBy9uv4qEqwVyh0ZEdFNMgO2Ag1cgiowSlAoJAW6cAY6IGk7HZm4YoC+DMS8Tl7LLcN/yP7H3vEHusIiIasQE2A44hXQFUD79sYOSTzkRNSwvjUDyl9PR0lOFrIJSTPjsAL49kCB3WERE1WI2ZAccQ7sDAII4/BkRNRJj7lW8dqcX7usSgDKTwH/WncQbv8TAaBJyh0ZEVAkT4CauuEzAMaQLACDU21nmaIioKVMrJbw7pitmRLUGAHy8+wKe+Oow8ovLZI6MiMgSE+Am7mRaMRQqDbRKAS9ntdzhEFETJ0kSnolqhffGdoPaQYGtMal48MNoJGcXyh0aEZEZE+Am7lBSMQDAz8kESeI4nUR0e9zXJQDfTL4DXs5qnEnOwcjle3DycrbcYRERAeBMcE2aEAKHk4sAAP5OHJ+TiBpXTEyMxX0JwOsD3fD6H5lIzCnG6BV/YnqEO+4IdKpxO97e3pxSmYgaFRPgJiwmORdXC00wlRTBx5GV/UTUOHIy0gEA48ePr3K9pNbC5/4XgBY98NbeLGTufAc5+3+odntOWi3OxsQwCSaiRsMEuAnbfjYVAFB06RiULbvLHA0RNVWFeTkAgOFPvIQ2ncOrLGMSwIlMI+LylPAYOBFdh09Ad08jFDe0zEpNiMOqRTNhMBiYABNRo2EC3IRtO5sGACiMOwgMYgJMRI3LKyAEga06VLs+GMCxxCzs/isdl/KVKFO5YHhnfziplLcvSCIisBNck3U1rxjHErMAAIVxh+QNhojomq5B7rivSwDUSgWuZBVizcFEZBaUyB0WEdkZJsBN1I7YdAgBtPBwgDHvqtzhEBGZNfd2xoM9AuHq6ICswlJ8dzARiRkFcodFRHaECXAT9evJZABAzwBHmSMhIqrM20WDh3sEwU/niOIyEzYcu4KTV7IhOHEcEd0GTICboOzCUuw+V94ru3cgE2Aisk7OGgeM7t4MrfUuMAlg+9k0HLyqhKSueZg0IqJbxQS4CdpyJhWlRoE2elcEuankDoeIqFoOSgWGdPBD7zAvSBKQWKCE/4R3cCGzVO7QiKgJYwLcBP18IgkAMLyzv8yREBHdnCRJ6NncE6O7B8JJKaDyCsSsrQYs23YOZUZO4kNEDY8JcBOTXVCKP84ZAADDOjEBJiLb0czdCYP8SpEfuwdGAfx3y18YvWIvYlNy5Q6NiJoYJsBNzG9nUlBmEmjr54qWvi5yh0NEVCcaJWDYsBDPRLhD5+iA45ezMfy9P7Bo81kUlhjlDo+ImghOhNHE/HyifPSH4az9JSIb5ltwEf+Nao1PjmbjwJVirNgZhx8OXsQjnV3RO9ARkiTdfCMAvL29OaMcEVXCBLgJycwvwZ7z15o/sP0vEdmgnIzyEWzGjx9vXubUshc87/4X0uCL/0Zn4Y2kWGTu+AzFl0/fdHtOWi3OxsQwCSYiC0yAm5BNJ5NRZhJo569DmA+bPxCR7SnMywEADH/iJbTpHG5eXmYCzuWWITZHCU1AG/iNWwR/JxM6updBV81gN6kJcVi1aCYMBgMTYCKywAS4CVlzMBEAMLp7M5kjISK6NV4BIQhs1cFiWXMAvYvLsD8+A6eSspFcqEBKoRrtA3To1dwTOicO+0hEtcMEuIk4k5SDk1eyoVJKGNU9UO5wiIgahbPGAXe19UXXIHfsOW/ABUM+TiflICY5B+38dejZ3BNuTISJ6CaYADcRaw6V1/7e3V4PT2e1zNEQETUuT2c17u0SgCtZhdgffxWJGYU4nZSDM8k5aOvnip7NPeUOkYisGBPgJqCo1Ij1R68AAB7qESRzNEREt08zdyeM6haIpKxCHIjPwKWMAsQk5+Jsci4CtUqofFvIHSIRWSGOA9wE/H4mFdmFpQhwc0S/Vj5yh0NEdNsFuDthZLdmeLhHEJp7aSFQPq1ywMT3MH/XVfx5zgAhhNxhEpGVYALcBFR0fvtHeCCUitqNjUlE1BT5uTni/q7NMLZXEIK0RgiTEcdTSzD+0/0YsexP/Hg8idMrExETYFt36Wo+9sSVj/37IJs/EBEBAHxdHdHL24ikj6dgWEstHFUKnE7KwdPfHMXAJTvxxd6LnFmOyI4xAbZxn/4ZDyGAgW18EOSplTscIiKrUpadiv/r7oa9/xmEGVGt4emsxuXMQsz98TQi39yGN36JQcLVArnDJKLbjAmwDcvMLzGP/jClHzt6EBFVx9NZjWeiWmHPrLvw6v0dEOypRVZBKT7efQEDluzAxM8PYMfZNBhNbCdMZA84CoQNW7X/EopKTegQoENkmJfc4RARWT0ntRKPRDbHPyNCsONsGr7cdwm7/0rHjtjyW5CnE8b0DMYD3ZohwN1J7nCJqJEwAbZRRaVGrNx7CQAwpX8LSBI7vxER1ZZSISGqvR5R7fWIN+Tj632XsPZQIhIzCrH4t1gs+T0WkS28MKp7IIZ29IOzhh+XRE0JX9E2auOxKzDkFcPfzRHDOvnLHQ4Rkc0K9XbGKyPa4/l72uCnE0n44fBl7I/PwN64q9gbdxWvbDiFIR39MLyTP/q28oajSil3yER0i5gA2yCjSeDj3RcAAI/3CYVKyabcRETViYmJqXXZlgpgVk8N0tr7YNelQuy8WIjkvPLJhtYfvQIXjQPubOuLoR39MLCND7RqfowS2SK+cm3QuiOXEZeeDzcnFR7uxaHPiIiqkpORDgAYP378LW1HHdAGzu0GwLltH+TBCz8dT8JPx5PgqFKgfysf3NXWF3e29YVe59gQYRPRbcAE2MYUlRrxzpa/AABT7wyDzlElc0RERNapMC8HADD8iZfQpnP4LW0rNSEOqxY9hjc+Xo0UBz/su1yE1Hwjfj+Tit/PpAIAQt0dEO7viO7+GrTyVFU7MZG3tzeCg4NvKR4iujVMgG3MV9GXkJRdhAA3R0yIbC53OEREVs8rIASBrTrc0jbKa5MFXpwy1rxM5dsC2pa94BTWA2r/1ojPKkN8Vh6+j8mDqSgPRQknUZRwAkWXjqPUkGB+nJNWi7MxMUyCiWTEBNiGZBeW4v0d5wEAM+5uzY4YRES3yc1qk4uMZUgtUiClUEJqoQKlji7Qto6EtnUkAECjEPB1NEFVkI49KxciJS2dCTCRjJgA25AVO+OQXViK1noXjOoeKHc4RER2p6ba5JbX/pqEQFpuMS5nFCAxsxBJWYUoNgGJBUoAfvB/7F2MX5+Croej0S3YHd2C3NHOX4cgDy0U1TSbIKKGxQTYRsQk5+B/f5SP/DBrSNtq25YREZG8FJIEP50j/HSO6NEcKDOZkJpdjMuZBYhPNiApqwAlji44EJ+BA/EZ5sc5q5Vo4+eKtv46tPPXoY3eFS19XeDprJbvYIiaKCbAViwhIQEGgwFGk8B/tl1FmUkgopkG7gWXceTIlVptoy7D/xARUcNzUCjQzMOp/GZMwdvzx+Lt/61CmVsQYq+W4EJWKRKzy5BfYsSRhCwcSciyeLxOo0CgzgFBOgcEXrsF6Rzg4aiAj48Pm1IQ1QMTYCuVkJCAtu3aobCgALpeo+Fx50QYi/Lww+xHsCY/s87by8vLa4QoiYioLio60z37f/+0XCEpoPJsBpVvKNQ+oVD7hkLlHQQHNz1yik04k16CM+klFg8xFefDmLEb998Zga4t9Gjp64JWvq5o5u7EphREN8EE2EoZDAYUFhRg1Kx3cVRqAZMAegU4ovlbn9ZpOzEHduHXL95FUVFRI0VKRES1Vdeh2cpMJcgtk5BbKiGn9O+/+WWAQuMMhX9r/HI2E7+c/btixMlBQpiHCmGeKoR5qNDSUwW9sxKSVHNSzOHZyJ4wAbZikoMG51WhMJVKCPbUok/XgJu+gd0oNSGukaIjIqL6utWh2cpMJhzZtxc/fvMZVN7BUHkFld88A1EIFU6ll+DUdTXGxsJclCT/heKkWBQnx6IkKRamIstfBjk8G9kTJsBWSggBr6FPI7tUASeVElHtfOuc/BIRUdPkoFBAWZiBgtg9GD5woLk22SQEcktLkVkimW/ZJRLg5AqnFuFwavF3rbOLg4Cn2gRPjQAyE7Fx8XQYDAYmwGQXmABbqY2x+XBuPwASBIZ38ocrZ3wjIqIq3Kw22WgSMOQVIyWnCCnZ5beswlLklUnIK1MioQAAQhE0fQ1e2m5A3+Qz6BbsgW7B7vB3c7ptx0F0OzEBtkK7/krH1ydzAQBdPIxo5sE3ICIiqh+lQoJe5wi9zhFdrg0hX1hqRGp2EZJzipCaXYSkzHyUqjSIMZQi5o94APEAAL1Og44Bbmild0UbPxe01rsizMeFEzGRzWMCbIUOxF+FSQC5x39DixF3yh0OERE1MU4qJZp7O6O5tzMAIPGv01g2599485NvkSG54VhiFs6m5CI1pxipOWnYdjbN/FiFBIR4OSPES4tgz+tuXlo0c3fiL5ZkE5gAW6GZg9vCpSQDTy1ZAeleJsBERNS4JAkoy7gCv8JLuLNdO4wOcUZRmRMuZJbhUnYpErLLkJhThoTsUuSVCMQb8hFvyK9yW44OEnxcVGjm6WKuefZ11VT630nNWmSSDxNgKxXRzBEwlskdBhER2YHy8YmB8ePH37Ss0tkDKq8gOLj7wcHdHw7uevP/SidXFJUJJGaVIDEro8btaFUSvLVKeDspy/9qFfDSlv/vo1XCy0kJfz0n+qDGwQSYiIjIztV1fOLqlJlK8NfZU9jx4xooXTyhdPGCg4vntf89zcsUakcUlAokZJchIbv6yh5j3hl0bRmIVv4eCLquqUWwpxY+LhpO+EH1xgSYiIiIANz6+MQAcPVyHIoTT1WbTAsBlIkSFBqBwjIJBUbp2l+goExCobH8f5OQoHTxxMmUApxMKai0HY2D4u+k+Ia2yEEeWjaxoBoxASYiIqIGdyvJtBACF2LP4JM3XsCMl1+Hg7sfUvPLkJpnRGq+EekFRhSXmXA+LQ/n0/Kq3Ia7owJ6ZyX8XBygd1YizM8d7ZoHwNNZDQ9nNTy1aibJdowJMBEREVkVSZJQnJ2OkpTzWDTt4coFFEo4uHpfa3t83c3NDw4e/lA6uiCryISsIhNir5aWP+ZMHoDLFptxVCngqb2WEDur4a5Vw83JAe5Oarg5qeDmpILOSQV3rcp8381JBa365lNLk3WzqwR4+fLlWLx4MVJSUtClSxcsW7YMvXr1kjssIiIiusGttEsuMZUgv0xCfhmQXybBkJWL+IsXEdq2M0okFXKKTSgzAUWlJiRlFyEpu6hO21dKgItaARe1BGe1Ai5qBVzVCug0f//VaSS4ahTQqRVwvbZcWUWbZW9vb3b0k4HdJMDfffcdnn32WXz44YeIiIjA0qVLMXjwYMTGxsLX11fu8IiIiKgKDdEu+cz+ndj/3StIu26ZpHaC0kkHhZMOCq3u7/8dXSxuSkdXKBydzfclpQpGAWQXm5BdDADGWsdhLMqDqSAHpsIcGAvL/ypKCzH9qf9DiwBfc020m5MKjg5KOKoVcFQp4eighEopsda5AdlNAvz2229j8uTJmDhxIgDgww8/xM8//4zPPvsM//nPf2SOjoiIiBpLQ41yIYTAmUPbsfWHLzFw/AwEtGyPEhNQYpLK/xrL/xabJJQYr/29th4AlI4uUDq6AAiw2O7H+1IBpNa4b6VCgqPDtYRYpYSDUoJSIUEplf9VSBKMZaUwGcugkAClJEEh4e+b4tr9ig1KFn8gQTLfESYjFApl+d1K5a6VuXaruFP5voAAoFFr8OJ9XdDOX1erc3y72EUCXFJSgsOHD2P27NnmZQqFAlFRUYiOjq5Uvri4GMXFxeb72dnZAICcnJzGD/aavLzyRv2Xz51GcWHl3q+1lZoQBwBIufgX4py1txQTt2XbMVnrtqwxJnvYljXGZA/bssaYrHVbjRFTaUnxLX2mAoCxuADGXAOURZnQifzyjFB57VbNJHgmAZSZgGLxd5JcYpKQkZmJ04ej0feuIVA6uSC3xITcYhPySwVKjECpUZiTShOAUgC5txS9PAaHatDMuW2j76ciTxNC3KQkIInalLJxSUlJaNasGfbu3YvIyEjz8hdeeAG7du3C/v37LcrPmzcP8+fPv91hEhEREdEtSkxMRGBgYI1l7KIGuK5mz56NZ5991nzfZDIhIyMDXl5ejd7+JicnB0FBQUhMTIROZ10/F9gKnsNbw/N363gObx3P4a3jObx1PIe37naeQyEEcnNzERAQcNOydpEAe3t7Q6lUIjXVsn1Namoq/Pz8KpXXaDTQaDQWy9zd3RszxEp0Oh1fbLeI5/DW8PzdOp7DW8dzeOt4Dm8dz+Gtu13n0M3NrVblFDcvYvvUajXCw8Oxbds28zKTyYRt27ZZNIkgIiIioqbPLmqAAeDZZ5/Fo48+ih49eqBXr15YunQp8vPzzaNCEBEREZF9sJsE+OGHH0Z6ejrmzJmDlJQUdO3aFZs3b4Zer5c7NAsajQZz586t1ASDao/n8Nbw/N06nsNbx3N463gObx3P4a2z1nNoF6NAEBERERFVsIs2wEREREREFZgAExEREZFdYQJMRERERHaFCTARERER2RUmwFZk+fLlaN68ORwdHREREYEDBw7IHdJtsXDhQvTs2ROurq7w9fXFyJEjERsba1Fm4MCBkCTJ4vavf/3LokxCQgKGDx8OrVYLX19fzJw5E2VlZRZldu7cie7du0Oj0aBly5ZYuXJlpXhs8XmYN29epfPTtu3f864XFRVh6tSp8PLygouLC0aPHl1pYhh7Pn8A0Lx580rnUJIkTJ06FQCvwRvt3r0b9957LwICAiBJEjZs2GCxXgiBOXPmwN/fH05OToiKisK5c+csymRkZGDcuHHQ6XRwd3fHpEmTkJeXZ1HmxIkT6NevHxwdHREUFIS33nqrUixr165F27Zt4ejoiE6dOuGXX36pcyxyqOkclpaWYtasWejUqROcnZ0REBCACRMmICkpyWIbVV23b775pkUZez2HAPDYY49VOj9DhgyxKMPrsOZzWNX7oiRJWLx4sbmMTV6HgqzCt99+K9Rqtfjss8/E6dOnxeTJk4W7u7tITU2VO7RGN3jwYPH555+LU6dOiWPHjolhw4aJ4OBgkZeXZy4zYMAAMXnyZJGcnGy+ZWdnm9eXlZWJjh07iqioKHH06FHxyy+/CG9vbzF79mxzmQsXLgitViueffZZcebMGbFs2TKhVCrF5s2bzWVs9XmYO3eu6NChg8X5SU9PN6//17/+JYKCgsS2bdvEoUOHxB133CF69+5tXm/v508IIdLS0izO35YtWwQAsWPHDiEEr8Eb/fLLL+Kll14S69atEwDE+vXrLda/+eabws3NTWzYsEEcP35c3HfffSI0NFQUFhaaywwZMkR06dJF7Nu3T/zxxx+iZcuWYuzYseb12dnZQq/Xi3HjxolTp06Jb775Rjg5OYmPPvrIXGbPnj1CqVSKt956S5w5c0a8/PLLQqVSiZMnT9YpFjnUdA6zsrJEVFSU+O6778TZs2dFdHS06NWrlwgPD7fYRkhIiFiwYIHFdXn9e6c9n0MhhHj00UfFkCFDLM5PRkaGRRlehzWfw+vPXXJysvjss8+EJEkiLi7OXMYWr0MmwFaiV69eYurUqeb7RqNRBAQEiIULF8oYlTzS0tIEALFr1y7zsgEDBohnnnmm2sf88ssvQqFQiJSUFPOyFStWCJ1OJ4qLi4UQQrzwwguiQ4cOFo97+OGHxeDBg833bfV5mDt3rujSpUuV67KysoRKpRJr1641L4uJiREARHR0tBCC568qzzzzjAgLCxMmk0kIwWuwJjd+aJpMJuHn5ycWL15sXpaVlSU0Go345ptvhBBCnDlzRgAQBw8eNJf59ddfhSRJ4sqVK0IIIT744APh4eFhPn9CCDFr1izRpk0b8/2HHnpIDB8+3CKeiIgI8cQTT9Q6FmtQVeJxowMHDggA4tKlS+ZlISEh4p133qn2MfZ+Dh999FFx//33V/sYXoeWanMd3n///eKuu+6yWGaL1yGbQFiBkpISHD58GFFRUeZlCoUCUVFRiI6OljEyeWRnZwMAPD09LZavWrUK3t7e6NixI2bPno2CggLzuujoaHTq1MliYpPBgwcjJycHp0+fNpe5/hxXlKk4x7b+PJw7dw4BAQFo0aIFxo0bh4SEBADA4cOHUVpaanFcbdu2RXBwsPm4eP4slZSU4Ouvv8bjjz8OSZLMy3kN1k58fDxSUlIsjsPNzQ0REREW15y7uzt69OhhLhMVFQWFQoH9+/eby/Tv3x9qtdpcZvDgwYiNjUVmZqa5TE3ntDax2Irs7GxIkgR3d3eL5W+++Sa8vLzQrVs3LF682KLZDc9hebMjX19ftGnTBk8++SSuXr1qXsfrsG5SU1Px888/Y9KkSZXW2dp1aDczwVkzg8EAo9FYaVY6vV6Ps2fPyhSVPEwmE6ZPn44+ffqgY8eO5uX//Oc/ERISgoCAAJw4cQKzZs1CbGws1q1bBwBISUmp8vxVrKupTE5ODgoLC5GZmWmzz0NERARWrlyJNm3aIDk5GfPnz0e/fv1w6tQppKSkQK1WV/rQ1Ov1Nz03FetqKtMUzt+NNmzYgKysLDz22GPmZbwGa6/ieKs6juvPha+vr8V6BwcHeHp6WpQJDQ2ttI2KdR4eHtWe0+u3cbNYbEFRURFmzZqFsWPHQqfTmZc//fTT6N69Ozw9PbF3717Mnj0bycnJePvttwHwHA4ZMgSjRo1CaGgo4uLi8OKLL2Lo0KGIjo6GUqnkdVhHX3zxBVxdXTFq1CiL5bZ4HTIBJqsydepUnDp1Cn/++afF8ilTppj/79SpE/z9/TFo0CDExcUhLCzsdodpdYYOHWr+v3PnzoiIiEBISAjWrFkDJycnGSOzTZ9++imGDh2KgIAA8zJegySX0tJSPPTQQxBCYMWKFRbrnn32WfP/nTt3hlqtxhNPPIGFCxda3dSzchgzZoz5/06dOqFz584ICwvDzp07MWjQIBkjs02fffYZxo0bB0dHR4vltngdsgmEFfD29oZSqazUKz81NRV+fn4yRXX7TZs2DZs2bcKOHTsQGBhYY9mIiAgAwPnz5wEAfn5+VZ6/inU1ldHpdHBycmpSz4O7uztat26N8+fPw8/PDyUlJcjKyrIoc/1x8fz97dKlS9i6dSv+7//+r8ZyvAarVxFrTcfh5+eHtLQ0i/VlZWXIyMhokOvy+vU3i8WaVSS/ly5dwpYtWyxqf6sSERGBsrIyXLx4EQDP4Y1atGgBb29vi9ctr8Pa+eOPPxAbG3vT90bANq5DJsBWQK1WIzw8HNu2bTMvM5lM2LZtGyIjI2WM7PYQQmDatGlYv349tm/fXulnkqocO3YMAODv7w8AiIyMxMmTJy3eyCo+LNq3b28uc/05rihTcY6b0vOQl5eHuLg4+Pv7Izw8HCqVyuK4YmNjkZCQYD4unr+/ff755/D19cXw4cNrLMdrsHqhoaHw8/OzOI6cnBzs37/f4prLysrC4cOHzWW2b98Ok8lk/nIRGRmJ3bt3o7S01Fxmy5YtaNOmDTw8PMxlajqntYnFWlUkv+fOncPWrVvh5eV108ccO3YMCoXC/LO+vZ/DG12+fBlXr161eN3yOqydTz/9FOHh4ejSpctNy9rEdVjnbnPUKL799luh0WjEypUrxZkzZ8SUKVOEu7u7RY/ypurJJ58Ubm5uYufOnRZDqBQUFAghhDh//rxYsGCBOHTokIiPjxcbN24ULVq0EP379zdvo2IIqnvuuUccO3ZMbN68Wfj4+FQ5BNXMmTNFTEyMWL58eZVDUNni8/Dcc8+JnTt3ivj4eLFnzx4RFRUlvL29RVpamhCifBi04OBgsX37dnHo0CERGRkpIiMjzY+39/NXwWg0iuDgYDFr1iyL5bwGK8vNzRVHjx4VR48eFQDE22+/LY4ePWoeoeDNN98U7u7uYuPGjeLEiRPi/vvvr3IYtG7duon9+/eLP//8U7Rq1cpi+KmsrCyh1+vFI488Ik6dOiW+/fZbodVqKw2d5ODgIJYsWSJiYmLE3Llzqxw66WaxyKGmc1hSUiLuu+8+ERgYKI4dO2bx3ljRk37v3r3inXfeEceOHRNxcXHi66+/Fj4+PmLChAnmfdjzOczNzRXPP/+8iI6OFvHx8WLr1q2ie/fuolWrVqKoqMi8DV6HNb+WhSgfxkyr1YoVK1ZUerytXodMgK3IsmXLRHBwsFCr1aJXr15i3759cod0WwCo8vb5558LIYRISEgQ/fv3F56enkKj0YiWLVuKmTNnWozBKoQQFy9eFEOHDhVOTk7C29tbPPfcc6K0tNSizI4dO0TXrl2FWq0WLVq0MO/jerb4PDz88MPC399fqNVq0axZM/Hwww+L8+fPm9cXFhaKp556Snh4eAitViseeOABkZycbLENez5/FX777TcBQMTGxlos5zVY2Y4dO6p83T766KNCiPIhi1555RWh1+uFRqMRgwYNqnRer169KsaOHStcXFyETqcTEydOFLm5uRZljh8/Lvr27Ss0Go1o1qyZePPNNyvFsmbNGtG6dWuhVqtFhw4dxM8//2yxvjaxyKGmcxgfH1/te2PF2NSHDx8WERERws3NTTg6Oop27dqJN954wyK5E8J+z2FBQYG45557hI+Pj1CpVCIkJERMnjy50pdJXoc1v5aFEOKjjz4STk5OIisrq9LjbfU6lIQQou71xkREREREtoltgImIiIjIrjABJiIiIiK7wgSYiIiIiOwKE2AiIiIisitMgImIiIjIrjABJiIiIiK7wgSYiIiIiOwKE2AiIiIisitMgImIGtjAgQMxffp08/3mzZtj6dKlNT5GkiRs2LDhlvfdUNupq3nz5qFr166Vlun1etliIiKqjoPcARARWYt7770XpaWl2Lx5c6V1f/zxB/r374/jx4+jc+fOddruwYMH4ezs3FBhAihPLjds2IBjx45ZLE9OToaHh0eD7qs+YmJiMH/+fKxfvx533HGHVcRERFSBCTAR0TWTJk3C6NGjcfnyZQQGBlqs+/zzz9GjR486J78A4OPj01Ah3pSfn99t21dN4uLiAAD3338/JEmSORoiIktsAkFEdM2IESPg4+ODlStXWizPy8vD2rVrMWnSJFy9ehVjx45Fs2bNoNVq0alTJ3zzzTc1bvfGJhDnzp1D//794ejoiPbt22PLli2VHjNr1iy0bt0aWq0WLVq0wCuvvILS0lIAwMqVKzF//nwcP34ckiRBkiRzzDc2Nzh58iTuuusuODk5wcvLC1OmTEFeXp55/WOPPYaRI0diyZIl8Pf3h5eXF6ZOnWreV3XefPNN6PV6uLq6YtKkSSgqKjKvmzdvHu69914AgEKhqFUCXJs4iouL8fzzz6NZs2ZwdnZGREQEdu7cabGdH374AR06dIBGo0Hz5s3x3//+96b7JiL7wwSYiOgaBwcHTJgwAStXroQQwrx87dq1MBqNGDt2LIqKihAeHo6ff/4Zp06dwpQpU/DII4/gwIEDtdqHyWTCqFGjoFarsX//fnz44YeYNWtWpXKurq5YuXIlzpw5g3fffReffPIJ3nnnHQDAww8/jOeeew4dOnRAcnIykpOT8fDDD1faRn5+PgYPHgwPDw8cPHgQa9euxdatWzFt2jSLcjt27EBcXBx27NiBL774AitXrqz0JeB6a9aswbx58/DGG2/g0KFD8Pf3xwcffGBe//zzz+Pzzz8HAHN8tXGzOKZNm4bo6Gh8++23OHHiBB588EEMGTIE586dAwAcPnwYDz30EMaMGYOTJ09i3rx5eOWVV2o8FiKyU4KIiMxiYmIEALFjxw7zsn79+onx48dX+5jhw4eL5557znx/wIAB4plnnjHfDwkJEe+8844QQojffvtNODg4iCtXrpjX//rrrwKA+P927i+kqTeMA/jXjaL0gCSEpqSibrpS5p9KdBcWKvNKBctBSxdNiEJJMQXxHyrihTcZdtOFQy9kdtGuvBARkbGVSpBYDJchs4tSQm8USZzv7+o3PP6pWdmPX+f7gXNx3vPseZ+dmz17ec9xOBxHztHb2yuysrIC5+3t7UKv1x+I25vn+fPn4ty5c2JjYyNwfXR0VKhUKvHlyxchhBAWi0XExcWJnZ2dQMytW7eEyWQ6spacnBzx8OFD2Vh2drasHofDIY7zE/OjOnw+n1Cr1bL7JoQQ+fn5oqmpSQghxO3bt0VhYaHsekNDg7h06VLQdRCRMnAFmIhoj5SUFOTm5mJgYAAAsLi4CKfTCavVCgDw+/3o6upCWloaIiIiIEkSxsbGsLy8HFR+j8eDixcvIjo6OjCWk5NzIG5kZAQGgwFRUVGQJAktLS1Bz7F3Lr1eL3sAz2AwYHd3FwsLC4Gxy5cvQ61WB84vXLiA1dXV7+bNzs6WjR32HY7re3XMz8/D7/dDq9VCkqTAMTU1Fdhv7PF4YDAYZDkNBgM+fPgAv9//y/UR0d+DD8EREe1jtVpRU1ODZ8+ewWazITExEXl5eQCA3t5e9PX14cmTJ0hLS0NYWBhqa2uxvb392+Z/9eoVzGYzOjo6YDQaER4eDrvdfmL7WU+dOiU7DwkJwe7u7onM9bN1bGxsQK1W482bN7ImGQAkSfpjNRLR34ErwERE+5SXl0OlUmF4eBhDQ0O4d+9e4EEul8uFkpIS3LlzB3q9HgkJCfB6vUHn1ul0+PTpk2xf7OvXr2UxbrcbcXFxaG5uxpUrV6DRaODz+WQxp0+f/uGqpk6nw9zcHDY3NwNjLpcLKpUKycnJQdd8WN7p6WnZ2P7v8LtlZGTA7/djdXUVSUlJsuPfN1/odDq4XC7Z51wuF7Ra7YGmmYiUjQ0wEdE+kiTBZDKhqakJnz9/xt27dwPXNBoNxsfH4Xa74fF4cP/+faysrASdu6CgAFqtFhaLBXNzc3A6nWhubpbFaDQaLC8vw2634+PHj3j69CkcDocsJj4+HktLS3j79i2+fv2Kb9++HZjLbDbjzJkzsFgsePfuHSYnJ1FTU4OKigpERkYe76bs8ejRIwwMDMBms8Hr9aK9vR3v37//6XzB0Gq1MJvNqKysxMuXL7G0tISZmRn09PRgdHQUAFBfX4+JiQl0dXXB6/VicHAQ/f39ePz48YnWRkT/P2yAiYgOYbVasb6+DqPRKNuv29LSgszMTBiNRly/fh1RUVEoLS0NOq9KpYLD4cDW1hauXbuGqqoqdHd3y2KKi4tRV1eH6upqpKenw+12o7W1VRZTVlaGoqIi3LhxA+fPnz/0VWyhoaEYGxvD2toarl69ips3byI/Px/9/f3Huxn7mEwmtLa2orGxEVlZWfD5fHjw4MEv5QyGzWZDZWUl6uvrkZycjNLSUszOziI2NhYAkJmZiRcvXsButyM1NRVtbW3o7OyU/YEhIgKAECH2vOuHiIiIiOgvxxVgIiIiIlIUNsBERHTi9r66bP/hdDr/6/KISGG4BYKIiE7c4uLikddiYmJw9uzZP1gNESkdG2AiIiIiUhRugSAiIiIiRWEDTERERESKwgaYiIiIiBSFDTARERERKQobYCIiIiJSFDbARERERKQobICJiIiISFH+AWzqCbvWBBubAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_sample_balance(y, sample_name):\n",
|
||
" plt.figure(figsize=(8, 5))\n",
|
||
" sns.histplot(y, bins=30, kde=True)\n",
|
||
" plt.title(f\"Распределение целевой переменной для {sample_name}\")\n",
|
||
" plt.xlabel(sample_name)\n",
|
||
" plt.ylabel(\"Частота\")\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"# Оценка сбалансированности выборок\n",
|
||
"plot_sample_balance(train_df_neo[\"relative_velocity\"], \"Train df_neo\")\n",
|
||
"plot_sample_balance(val_df_neo[\"relative_velocity\"], \"Validation df_neo\")\n",
|
||
"plot_sample_balance(test_df_neo[\"relative_velocity\"], \"Test df_neo\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Кажется, выборки сбалансированы."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Оценка сбалансированности выборок\n",
|
||
"plot_sample_balance(train_df_ed[\"inflationrate\"], \"Train df_ed\")\n",
|
||
"plot_sample_balance(val_df_ed[\"inflationrate\"], \"Validation df_ed\")\n",
|
||
"plot_sample_balance(test_df_ed[\"inflationrate\"], \"Test df_ed\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Выборки выглядят схоже, но у всех трех разное количество значений. В дальнейшем мы не сможем обучить какую-либо модель. \n",
|
||
"Если модель обучается на несбалансированных данных, она будет предсказывать какой-то гораздо чаще, даже если в тестовой выборке классы распределены более равномерно."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 90,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Оценка сбалансированности выборок\n",
|
||
"plot_sample_balance(train_df_mp[\"Ram\"], \"Train df_mp\")\n",
|
||
"plot_sample_balance(val_df_mp[\"Ram\"], \"Validation df_mp\")\n",
|
||
"plot_sample_balance(test_df_mp[\"Ram\"], \"Test df_mp\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Выборки явно не сбалансированы, пока что не можем обучить модель."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"12. Выполнить приращение данных методами выборки с избытком (oversampling) и выборки с недостатком (undersampling). Должны быть представлены примеры реализации обоих методов для выборок каждого набора данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 93,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from imblearn.over_sampling import SMOTE"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 1."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 97,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"После oversampling (df_neo): hazardous\n",
|
||
"False 81996\n",
|
||
"True 81996\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X_df_neo = df_neo.drop(\"hazardous\", axis=1)\n",
|
||
"y_df_neo = df_neo[\"hazardous\"]\n",
|
||
"\n",
|
||
"# Кодирование категориальных признаков\n",
|
||
"for column in X_df_neo.select_dtypes(include=[\"object\"]).columns:\n",
|
||
" X_df_neo[column] = X_df_neo[column].astype(\"category\").cat.codes\n",
|
||
"\n",
|
||
"# Теперь применяем SMOTE\n",
|
||
"smote = SMOTE(random_state=42)\n",
|
||
"X_resampled_df_neo, y_resampled_df_neo = smote.fit_resample(X_df_neo, y_df_neo)\n",
|
||
"\n",
|
||
"# Получаем результаты\n",
|
||
"print(f\"После oversampling (df_neo): {pd.Series(y_resampled_df_neo).value_counts()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 98,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"После undersampling (df_neo): hazardous\n",
|
||
"False 8840\n",
|
||
"True 8840\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from imblearn.under_sampling import RandomUnderSampler\n",
|
||
"\n",
|
||
"# Undersampling df_neo\n",
|
||
"undersample = RandomUnderSampler(random_state=42)\n",
|
||
"X_under_df_neo, y_under_df_neo = undersample.fit_resample(X_df_neo, y_df_neo)\n",
|
||
"\n",
|
||
"print(f\"После undersampling (df_neo): {pd.Series(y_under_df_neo).value_counts()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 7."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"После oversampling (df_ed): country\n",
|
||
"United States of America 41\n",
|
||
"United Kingdom 41\n",
|
||
"India 41\n",
|
||
"Japan 41\n",
|
||
"Hong Kong 41\n",
|
||
"China 41\n",
|
||
"Germany 41\n",
|
||
"France 41\n",
|
||
"Spain 41\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X_df_ed = df_ed.drop(\"country\", axis=1)\n",
|
||
"y_df_ed = df_ed[\"country\"]\n",
|
||
"\n",
|
||
"# Кодирование категориальных признаков\n",
|
||
"for column in X_df_ed.select_dtypes(include=[\"object\"]).columns:\n",
|
||
" X_df_ed[column] = X_df_ed[column].astype(\"category\").cat.codes\n",
|
||
"\n",
|
||
"# Теперь применяем SMOTE\n",
|
||
"smote = SMOTE(random_state=42)\n",
|
||
"X_resampled_df_ed, y_resampled_df_ed = smote.fit_resample(X_df_ed, y_df_ed)\n",
|
||
"\n",
|
||
"# Получаем результаты\n",
|
||
"print(f\"После oversampling (df_ed): {pd.Series(y_resampled_df_ed).value_counts()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 102,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"После undersampling (df_ed): country\n",
|
||
"China 41\n",
|
||
"France 41\n",
|
||
"Germany 41\n",
|
||
"Hong Kong 41\n",
|
||
"India 41\n",
|
||
"Japan 41\n",
|
||
"Spain 41\n",
|
||
"United Kingdom 41\n",
|
||
"United States of America 41\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from imblearn.under_sampling import RandomUnderSampler\n",
|
||
"\n",
|
||
"# Undersampling df_ed\n",
|
||
"undersample = RandomUnderSampler(random_state=42)\n",
|
||
"X_under_df_ed, y_under_df_ed = undersample.fit_resample(X_df_ed, y_df_ed)\n",
|
||
"\n",
|
||
"print(f\"После undersampling (df_ed): {pd.Series(y_under_df_ed).value_counts()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Датасет 18."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 107,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"ename": "ValueError",
|
||
"evalue": "Expected n_neighbors <= n_samples_fit, but n_neighbors = 6, n_samples_fit = 1, n_samples = 1",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[1;32mIn[107], line 10\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# Теперь применяем SMOTE\u001b[39;00m\n\u001b[0;32m 9\u001b[0m smote \u001b[38;5;241m=\u001b[39m SMOTE(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m---> 10\u001b[0m X_resampled_df_mp, y_resampled_df_mp \u001b[38;5;241m=\u001b[39m \u001b[43msmote\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_df_mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_df_mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Получаем результаты\u001b[39;00m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mПосле oversampling (df_mp): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpd\u001b[38;5;241m.\u001b[39mSeries(y_resampled_df_mp)\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
|
||
"File \u001b[1;32mc:\\Users\\nemar\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\imblearn\\base.py:208\u001b[0m, in \u001b[0;36mBaseSampler.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m 188\u001b[0m \n\u001b[0;32m 189\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[38;5;124;03m The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m 206\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m--> 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"File \u001b[1;32mc:\\Users\\nemar\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\imblearn\\base.py:112\u001b[0m, in \u001b[0;36mSamplerMixin.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 106\u001b[0m X, y, binarize_y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_X_y(X, y)\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampling_strategy_ \u001b[38;5;241m=\u001b[39m check_sampling_strategy(\n\u001b[0;32m 109\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampling_strategy, y, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampling_type\n\u001b[0;32m 110\u001b[0m )\n\u001b[1;32m--> 112\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 114\u001b[0m y_ \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 115\u001b[0m label_binarize(output[\u001b[38;5;241m1\u001b[39m], classes\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39munique(y)) \u001b[38;5;28;01mif\u001b[39;00m binarize_y \u001b[38;5;28;01melse\u001b[39;00m output[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 116\u001b[0m )\n\u001b[0;32m 118\u001b[0m X_, y_ \u001b[38;5;241m=\u001b[39m arrays_transformer\u001b[38;5;241m.\u001b[39mtransform(output[\u001b[38;5;241m0\u001b[39m], y_)\n",
|
||
"File \u001b[1;32mc:\\Users\\nemar\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:389\u001b[0m, in \u001b[0;36mSMOTE._fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 386\u001b[0m X_class \u001b[38;5;241m=\u001b[39m _safe_indexing(X, target_class_indices)\n\u001b[0;32m 388\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnn_k_\u001b[38;5;241m.\u001b[39mfit(X_class)\n\u001b[1;32m--> 389\u001b[0m nns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnn_k_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkneighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_class\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_distance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m[:, \u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m 390\u001b[0m X_new, y_new \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_samples(\n\u001b[0;32m 391\u001b[0m X_class, y\u001b[38;5;241m.\u001b[39mdtype, class_sample, X_class, nns, n_samples, \u001b[38;5;241m1.0\u001b[39m\n\u001b[0;32m 392\u001b[0m )\n\u001b[0;32m 393\u001b[0m X_resampled\u001b[38;5;241m.\u001b[39mappend(X_new)\n",
|
||
"File \u001b[1;32mc:\\Users\\nemar\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neighbors\\_base.py:834\u001b[0m, in \u001b[0;36mKNeighborsMixin.kneighbors\u001b[1;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[0;32m 832\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 833\u001b[0m inequality_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_neighbors <= n_samples_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 834\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 835\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minequality_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 836\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_neighbors = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_neighbors\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, n_samples_fit = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_samples_fit\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 837\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mX\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# include n_samples for common tests\u001b[39;00m\n\u001b[0;32m 838\u001b[0m )\n\u001b[0;32m 840\u001b[0m n_jobs \u001b[38;5;241m=\u001b[39m effective_n_jobs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n\u001b[0;32m 841\u001b[0m chunked_results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||
"\u001b[1;31mValueError\u001b[0m: Expected n_neighbors <= n_samples_fit, but n_neighbors = 6, n_samples_fit = 1, n_samples = 1"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X_df_mp = df_mp.drop(\"Battery\", axis=1)\n",
|
||
"y_df_mp = df_mp[\"Battery\"]\n",
|
||
"\n",
|
||
"# Кодирование категориальных признаков\n",
|
||
"for column in X_df_mp.select_dtypes(include=[\"object\"]).columns:\n",
|
||
" X_df_mp[column] = X_df_mp[column].astype(\"category\").cat.codes\n",
|
||
"\n",
|
||
"# Теперь применяем SMOTE\n",
|
||
"smote = SMOTE(random_state=42)\n",
|
||
"X_resampled_df_mp, y_resampled_df_mp = smote.fit_resample(X_df_mp, y_df_mp)\n",
|
||
"\n",
|
||
"# Получаем результаты\n",
|
||
"print(f\"После oversampling (df_mp): {pd.Series(y_resampled_df_mp).value_counts()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"В данном случае у нас есть два датасета, предназначенных для решения задачи классификации (df_neo, df_ed). Проблему дисбаланса в них мы решили применив undersampling и oversampling.\n",
|
||
"\n",
|
||
"Последний датасет не подходит для обучения, т.к предназначен для решения задачи регрессии (цены мобильного устройства), поэтому выполнять приращение данных не требуется."
|
||
]
|
||
}
|
||
],
|
||
"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": "3.12.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|