2932 lines
1.3 MiB
2932 lines
1.3 MiB
{
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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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"## Датасет №1: [Объекты вокруг Земли](https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects).\n",
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"\n",
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"### Описание датасета:\n",
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||
"Данный набор данных представляет собой коллекцию сведений о ближайших к Земле объектах (астероидах), сертифицированных NASA. Он содержит данные, которые могут помочь идентифицировать потенциально опасные астероиды, которые могут оказать влияние на Землю или на космические миссии. Набор данных включает в себя такие ключевые характеристики астероидов, как их размер, скорость, расстояние до Земли и информация о возможной опасности столкновения.\n",
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||
"\n",
|
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"---\n",
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"\n",
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"### Анализ сведений:\n",
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"**Проблемная область:**\n",
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||
"Основной проблемной областью является отслеживание и оценка рисков, связанных с приближением астероидов к Земле. С помощью данных о движении и характеристиках астероидов можно предсказать возможные столкновения и минимизировать угрозу для Земли, планируя превентивные действия.\n",
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"\n",
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"**Актуальность:**\n",
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"Набор данных высокоактуален для задач оценки рисков от космических объектов, мониторинга космического пространства и разработки превентивных мер по защите Земли. Также он важен для научных исследований в области астрономии и планетарной безопасности.\n",
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||
"\n",
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"**Объекты наблюдения:**\n",
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"Объектами наблюдения в данном наборе данных являются астероиды, классифицированные NASA как \"ближайшие к Земле объекты\" (Near-Earth Objects, NEO). Эти объекты могут проходить в непосредственной близости от Земли, что потенциально представляет опасность.\n",
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"\n",
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"**Атрибуты объектов:**\n",
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"- id: Уникальный идентификатор астероида.\n",
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"- name: Название, присвоенное астероиду NASA.\n",
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"- est_diameter_min: Минимальный оценочные диаметры астероида в километрах.\n",
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||
"- est_diameter_max: Максимальный оценочные диаметры астероида в километрах.\n",
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||
"- relative_velocity: Скорость астероида относительно Земли (в км/с).\n",
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"- miss_distance: Расстояние, на котором астероид пролетел мимо Земли, в километрах.\n",
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||
"- orbiting_body: Планета, вокруг которой вращается астероид.\n",
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"- sentry_object: Признак, указывающий на наличие астероида в системе автоматического мониторинга столкновений (система Sentry).\n",
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"- absolute_magnitude: Абсолютная величина, описывающая яркость объекта.\n",
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"- hazardous: Булев признак, указывающий, является ли астероид потенциально опасным.\n",
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"\n",
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"**Связь между объектами:**\n",
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"В данном наборе данных отсутствует явная связь между астероидами, однако на основе орбитальных параметров можно исследовать группы объектов, имеющие схожие орбиты или величины риска столкновения с Землей.\n",
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"\n",
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"---\n",
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"\n",
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"### Качество набора данных:\n",
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"**Информативность:**\n",
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"Датасет предоставляет важные сведения о ключевых характеристиках астероидов, такие как размер, скорость и расстояние от Земли, что позволяет проводить качественный анализ их потенциальной опасности.\n",
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"\n",
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"**Степень покрытия:**\n",
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"Набор данных включает данные о большом количестве астероидов (>90000 записей), что позволяет охватить значительную часть ближайших к Земле объектов. Однако не все астероиды могут быть обнаружены, так как данные зависят от возможности их наблюдения.\n",
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"\n",
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"**Соответствие реальным данным:**\n",
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"Данные в наборе предоставлены NASA, что указывает на высокую достоверность и актуальность информации. Тем не менее, параметры, такие как диаметр и расстояние, могут быть оценочными и подвергаться уточнению с новыми наблюдениями.\n",
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"\n",
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"**Согласованность меток:**\n",
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"Метрики в датасете четко обозначены, а булевы признаки, такие как \"hazardous\" (опасен или нет), соответствуют конкретным параметрам астероидов и легко интерпретируются.\n",
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"\n",
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"---\n",
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"\n",
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"### Бизес-цели:\n",
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"1. **Мониторинг космических угроз:**\n",
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"Создание системы, которая анализирует астероиды и предсказывает риски столкновения с Землей, помогая государственным агентствам и частным компаниям разрабатывать превентивные меры.\n",
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"2. **Поддержка космических миссий:**\n",
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"Предоставление точных данных для планирования и безопасного проведения космических миссий, минимизация рисков столкновения с космическими объектами.\n",
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"3. **Образовательные и научные исследования:**\n",
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"Использование данных для поддержки образовательных программ и научных исследований в области астрономии и космической безопасности.\n",
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"\n",
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"**Эффект для бизнеса:**\n",
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"Набор данных способствует развитию технологий космической безопасности, минимизирует финансовые риски от потенциальных катастроф и поддерживает стратегическое планирование космических миссий.\n",
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"\n",
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"---\n",
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"\n",
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"### Технические цели:\n",
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"1. **Моделирование риска столкновения:**\n",
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"Построение алгоритмов машинного обучения для прогнозирования вероятности столкновения астероидов с Землей.\n",
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"2. **Анализ и кластеризация астероидов:**\n",
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"Исследование взаимосвязей между астероидами, анализ орбитальных данных и выделение групп астероидов, имеющих схожие характеристики.\n",
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"3. **Оптимизация системы предупреждения угроз:**\n",
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"Создание системы раннего оповещения, которая будет автоматически анализировать данные и предупреждать о потенциальных угрозах в реальном времени.\n",
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"\n",
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"**Входные данные:**\n",
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"Диаметр, скорость, расстояние, орбитальные параметры астероидов.\n",
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"\n",
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"**Целевой признак:**\n",
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"Признак \"hazardous\" – бинарная метка, указывающая на потенциальную опасность астероида.\n",
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"\n",
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||
"---"
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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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||
"### Выгрузка данных из файла в DataFrame:"
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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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"source": [
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"from typing import Any\n",
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"from math import ceil\n",
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"\n",
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"import pandas as pd\n",
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"from pandas import DataFrame, Series\n",
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"from sklearn.model_selection import train_test_split\n",
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"from imblearn.over_sampling import ADASYN\n",
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"from imblearn.under_sampling import RandomUnderSampler\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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"df: DataFrame = pd.read_csv('..//static//csv//neo.csv')"
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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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"### Краткая информация о DataFrame:"
|
||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": 2,
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 90836 entries, 0 to 90835\n",
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"Data columns (total 10 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 id 90836 non-null int64 \n",
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" 1 name 90836 non-null object \n",
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" 2 est_diameter_min 90836 non-null float64\n",
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" 3 est_diameter_max 90836 non-null float64\n",
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" 4 relative_velocity 90836 non-null float64\n",
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" 5 miss_distance 90836 non-null float64\n",
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" 6 orbiting_body 90836 non-null object \n",
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" 7 sentry_object 90836 non-null bool \n",
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" 8 absolute_magnitude 90836 non-null float64\n",
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" 9 hazardous 90836 non-null bool \n",
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"dtypes: bool(2), float64(5), int64(1), object(2)\n",
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"memory usage: 5.7+ MB\n"
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||
]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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||
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>count</th>\n",
|
||
" <th>mean</th>\n",
|
||
" <th>std</th>\n",
|
||
" <th>min</th>\n",
|
||
" <th>25%</th>\n",
|
||
" <th>50%</th>\n",
|
||
" <th>75%</th>\n",
|
||
" <th>max</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
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" <tr>\n",
|
||
" <th>id</th>\n",
|
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" <td>90836.0</td>\n",
|
||
" <td>1.438288e+07</td>\n",
|
||
" <td>2.087202e+07</td>\n",
|
||
" <td>2.000433e+06</td>\n",
|
||
" <td>3.448110e+06</td>\n",
|
||
" <td>3.748362e+06</td>\n",
|
||
" <td>3.884023e+06</td>\n",
|
||
" <td>5.427591e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>est_diameter_min</th>\n",
|
||
" <td>90836.0</td>\n",
|
||
" <td>1.274321e-01</td>\n",
|
||
" <td>2.985112e-01</td>\n",
|
||
" <td>6.089126e-04</td>\n",
|
||
" <td>1.925551e-02</td>\n",
|
||
" <td>4.836765e-02</td>\n",
|
||
" <td>1.434019e-01</td>\n",
|
||
" <td>3.789265e+01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>est_diameter_max</th>\n",
|
||
" <td>90836.0</td>\n",
|
||
" <td>2.849469e-01</td>\n",
|
||
" <td>6.674914e-01</td>\n",
|
||
" <td>1.361570e-03</td>\n",
|
||
" <td>4.305662e-02</td>\n",
|
||
" <td>1.081534e-01</td>\n",
|
||
" <td>3.206564e-01</td>\n",
|
||
" <td>8.473054e+01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>relative_velocity</th>\n",
|
||
" <td>90836.0</td>\n",
|
||
" <td>4.806692e+04</td>\n",
|
||
" <td>2.529330e+04</td>\n",
|
||
" <td>2.033464e+02</td>\n",
|
||
" <td>2.861902e+04</td>\n",
|
||
" <td>4.419012e+04</td>\n",
|
||
" <td>6.292360e+04</td>\n",
|
||
" <td>2.369901e+05</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>miss_distance</th>\n",
|
||
" <td>90836.0</td>\n",
|
||
" <td>3.706655e+07</td>\n",
|
||
" <td>2.235204e+07</td>\n",
|
||
" <td>6.745533e+03</td>\n",
|
||
" <td>1.721082e+07</td>\n",
|
||
" <td>3.784658e+07</td>\n",
|
||
" <td>5.654900e+07</td>\n",
|
||
" <td>7.479865e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>absolute_magnitude</th>\n",
|
||
" <td>90836.0</td>\n",
|
||
" <td>2.352710e+01</td>\n",
|
||
" <td>2.894086e+00</td>\n",
|
||
" <td>9.230000e+00</td>\n",
|
||
" <td>2.134000e+01</td>\n",
|
||
" <td>2.370000e+01</td>\n",
|
||
" <td>2.570000e+01</td>\n",
|
||
" <td>3.320000e+01</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
|
||
" count mean std min \\\n",
|
||
"id 90836.0 1.438288e+07 2.087202e+07 2.000433e+06 \n",
|
||
"est_diameter_min 90836.0 1.274321e-01 2.985112e-01 6.089126e-04 \n",
|
||
"est_diameter_max 90836.0 2.849469e-01 6.674914e-01 1.361570e-03 \n",
|
||
"relative_velocity 90836.0 4.806692e+04 2.529330e+04 2.033464e+02 \n",
|
||
"miss_distance 90836.0 3.706655e+07 2.235204e+07 6.745533e+03 \n",
|
||
"absolute_magnitude 90836.0 2.352710e+01 2.894086e+00 9.230000e+00 \n",
|
||
"\n",
|
||
" 25% 50% 75% max \n",
|
||
"id 3.448110e+06 3.748362e+06 3.884023e+06 5.427591e+07 \n",
|
||
"est_diameter_min 1.925551e-02 4.836765e-02 1.434019e-01 3.789265e+01 \n",
|
||
"est_diameter_max 4.305662e-02 1.081534e-01 3.206564e-01 8.473054e+01 \n",
|
||
"relative_velocity 2.861902e+04 4.419012e+04 6.292360e+04 2.369901e+05 \n",
|
||
"miss_distance 1.721082e+07 3.784658e+07 5.654900e+07 7.479865e+07 \n",
|
||
"absolute_magnitude 2.134000e+01 2.370000e+01 2.570000e+01 3.320000e+01 "
|
||
]
|
||
},
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Краткая информация о DataFrame\n",
|
||
"df.info()\n",
|
||
"\n",
|
||
"# Статистическое описание числовых столбцов\n",
|
||
"df.describe().transpose()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Проблема пропущенных данных:\n",
|
||
"\n",
|
||
"**Проблема пропущенных данных** — это отсутствие значений в наборе данных, что может искажать результаты анализа и статистические выводы.\n",
|
||
"\n",
|
||
"Проверка на отсутствие значений, представленная ниже, показала, что DataFrame не имеет пустых значений признаков. Нет необходимости использовать методы заполнения пропущенных данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Присутствуют ли пустые значения признаков в колонке:\n",
|
||
"id False\n",
|
||
"name False\n",
|
||
"est_diameter_min False\n",
|
||
"est_diameter_max False\n",
|
||
"relative_velocity False\n",
|
||
"miss_distance False\n",
|
||
"orbiting_body False\n",
|
||
"sentry_object False\n",
|
||
"absolute_magnitude False\n",
|
||
"hazardous False\n",
|
||
"dtype: bool \n",
|
||
"\n",
|
||
"Количество пустых значений признаков в колонке:\n",
|
||
"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 \n",
|
||
"\n",
|
||
"Процент пустых значений признаков в колонке:\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Проверка пропущенных данных\n",
|
||
"def check_null_columns(dataframe: DataFrame) -> None:\n",
|
||
" # Присутствуют ли пустые значения признаков\n",
|
||
" print('Присутствуют ли пустые значения признаков в колонке:')\n",
|
||
" print(dataframe.isnull().any(), '\\n')\n",
|
||
"\n",
|
||
" # Количество пустых значений признаков\n",
|
||
" print('Количество пустых значений признаков в колонке:')\n",
|
||
" print(dataframe.isnull().sum(), '\\n')\n",
|
||
"\n",
|
||
" # Процент пустых значений признаков\n",
|
||
" print('Процент пустых значений признаков в колонке:')\n",
|
||
" for column in dataframe.columns:\n",
|
||
" null_rate: float = dataframe[column].isnull().sum() / len(dataframe) * 100\n",
|
||
" if null_rate > 0:\n",
|
||
" print(f\"{column} процент пустых значений: {null_rate:.2f}%\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
"\n",
|
||
"# Проверка пропущенных данных\n",
|
||
"check_null_columns(df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Проблема зашумленности данных:\n",
|
||
"\n",
|
||
"**Зашумленность** – это наличие случайных ошибок или вариаций в данных, которые могут затруднить выявление истинных закономерностей. Шум может возникать из-за ошибок измерений, неправильных записей или других факторов.\n",
|
||
"\n",
|
||
"**Выбросы** – это значения, которые значительно отличаются от остальных наблюдений в наборе данных. Выбросы могут указывать на ошибки в данных или на редкие, но важные события. Их наличие может повлиять на статистические методы анализа.\n",
|
||
"\n",
|
||
"Представленный ниже код помогает определить наличие выбросов в наборе данных и устранить их (при наличии), заменив значения ниже нижней границы (рассматриваемого минимума) на значения нижней границы, а значения выше верхней границы (рассматриваемого максимума) – на значения верхней границы."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка наличия выбросов в колонках:\n",
|
||
"Колонка est_diameter_min:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 8306\n",
|
||
"\tМинимальное значение: 0.0006089126\n",
|
||
"\tМаксимальное значение: 37.8926498379\n",
|
||
"\t1-й квартиль (Q1): 0.0192555078\n",
|
||
"\t3-й квартиль (Q3): 0.1434019235\n",
|
||
"\n",
|
||
"Колонка est_diameter_max:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 8306\n",
|
||
"\tМинимальное значение: 0.00136157\n",
|
||
"\tМаксимальное значение: 84.7305408852\n",
|
||
"\t1-й квартиль (Q1): 0.0430566244\n",
|
||
"\t3-й квартиль (Q3): 0.320656449\n",
|
||
"\n",
|
||
"Колонка relative_velocity:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 1574\n",
|
||
"\tМинимальное значение: 203.34643253\n",
|
||
"\tМаксимальное значение: 236990.1280878666\n",
|
||
"\t1-й квартиль (Q1): 28619.02064490995\n",
|
||
"\t3-й квартиль (Q3): 62923.60463276395\n",
|
||
"\n",
|
||
"Колонка miss_distance:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 6745.532515957\n",
|
||
"\tМаксимальное значение: 74798651.4521972\n",
|
||
"\t1-й квартиль (Q1): 17210820.23576468\n",
|
||
"\t3-й квартиль (Q3): 56548996.45139917\n",
|
||
"\n",
|
||
"Колонка absolute_magnitude:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 101\n",
|
||
"\tМинимальное значение: 9.23\n",
|
||
"\tМаксимальное значение: 33.2\n",
|
||
"\t1-й квартиль (Q1): 21.34\n",
|
||
"\t3-й квартиль (Q3): 25.7\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 5 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Проверка выбросов в DataFrame\n",
|
||
"def check_outliers(dataframe: DataFrame, columns: list[str]) -> None:\n",
|
||
" for column in columns:\n",
|
||
" if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
|
||
" continue\n",
|
||
" \n",
|
||
" Q1: float = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
|
||
" Q3: float = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
|
||
" IQR: float = Q3 - Q1 # Вычисляем межквартильный размах\n",
|
||
"\n",
|
||
" # Определяем границы для выбросов\n",
|
||
" lower_bound: float = Q1 - 1.5 * IQR # Нижняя граница\n",
|
||
" upper_bound: float = Q3 + 1.5 * IQR # Верхняя граница\n",
|
||
"\n",
|
||
" # Подсчитываем количество выбросов\n",
|
||
" outliers: DataFrame = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)]\n",
|
||
" outlier_count: int = outliers.shape[0]\n",
|
||
"\n",
|
||
" print(f\"Колонка {column}:\")\n",
|
||
" print(f\"\\tЕсть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
|
||
" print(f\"\\tКоличество выбросов: {outlier_count}\")\n",
|
||
" print(f\"\\tМинимальное значение: {dataframe[column].min()}\")\n",
|
||
" print(f\"\\tМаксимальное значение: {dataframe[column].max()}\")\n",
|
||
" print(f\"\\t1-й квартиль (Q1): {Q1}\")\n",
|
||
" print(f\"\\t3-й квартиль (Q3): {Q3}\\n\")\n",
|
||
"\n",
|
||
"# Визуализация выбросов\n",
|
||
"def visualize_outliers(dataframe: DataFrame, columns: list[str]) -> None:\n",
|
||
" # Диаграммы размахов\n",
|
||
" plt.figure(figsize=(15, 10))\n",
|
||
" rows: int = ceil(len(columns) / 3)\n",
|
||
" for index, column in enumerate(columns, 1):\n",
|
||
" plt.subplot(rows, 3, index)\n",
|
||
" plt.boxplot(dataframe[column], vert=True, patch_artist=True)\n",
|
||
" plt.title(f\"Диаграмма размахов для \\\"{column}\\\"\")\n",
|
||
" plt.xlabel(column)\n",
|
||
" \n",
|
||
" # Отображение графиков\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"# Числовые столбцы DataFrame\n",
|
||
"numeric_columns: list[str] = [\n",
|
||
" 'est_diameter_min',\n",
|
||
" 'est_diameter_max', \n",
|
||
" 'relative_velocity', \n",
|
||
" 'miss_distance', \n",
|
||
" 'absolute_magnitude'\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Проверка наличия выбросов в колонках\n",
|
||
"print('Проверка наличия выбросов в колонках:')\n",
|
||
"check_outliers(df, numeric_columns)\n",
|
||
"visualize_outliers(df, numeric_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка наличия выбросов в колонках после их устранения:\n",
|
||
"Колонка est_diameter_min:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0.0006089126\n",
|
||
"\tМаксимальное значение: 0.32962154705\n",
|
||
"\t1-й квартиль (Q1): 0.0192555078\n",
|
||
"\t3-й квартиль (Q3): 0.1434019235\n",
|
||
"\n",
|
||
"Колонка est_diameter_max:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0.00136157\n",
|
||
"\tМаксимальное значение: 0.7370561859\n",
|
||
"\t1-й квартиль (Q1): 0.0430566244\n",
|
||
"\t3-й квартиль (Q3): 0.320656449\n",
|
||
"\n",
|
||
"Колонка relative_velocity:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 203.34643253\n",
|
||
"\tМаксимальное значение: 114380.48061454494\n",
|
||
"\t1-й квартиль (Q1): 28619.02064490995\n",
|
||
"\t3-й квартиль (Q3): 62923.60463276395\n",
|
||
"\n",
|
||
"Колонка miss_distance:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 6745.532515957\n",
|
||
"\tМаксимальное значение: 74798651.4521972\n",
|
||
"\t1-й квартиль (Q1): 17210820.23576468\n",
|
||
"\t3-й квартиль (Q3): 56548996.45139917\n",
|
||
"\n",
|
||
"Колонка absolute_magnitude:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 14.8\n",
|
||
"\tМаксимальное значение: 32.239999999999995\n",
|
||
"\t1-й квартиль (Q1): 21.34\n",
|
||
"\t3-й квартиль (Q3): 25.7\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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fk88ri3xet/J5ZT/zSsfu4uJiPProo8bRo0eN5s2bGyEhIcbp06erPG5c/sj95H5yf93P/aXv2YYNGxoHDx60WderVy+jY8eONu+pkpISo2vXrkabNm3MtvLOWXnvncTERMPBwcH49ddfzbaRI0faHOvZzv0eMWjQIMPb29s4c+aM2fb7778bjo6ONu+Jyo77fHbu3GlIMubMmWPT/sQTTxju7u7mMVb2+5Nh/PmZc/b7oPRzcvHixWZbUVGRERYWZri7u5vfRdauXWtIMp566qky4zz7e0jLli1tcviyZcvK/L8xDMMoLi42/vGPfxhRUVE27TNnzjQcHByM3bt3V3Bm6gamcylH6S1ZpVdrVEVycrIOHz5c7i8obm5u5p9PnTql3Nxc3XTTTZJUrV/ynJ2d9dhjj5mvXVxc9Nhjj+ngwYPKysqSJDk5OZlzSpWUlOjIkSM6c+aMQkJCyt3nDTfcoOuuu878Re7XX3/VF198UeGTcB955BGlpaXJarVKkhYtWqSwsDBdc801ZWLPPgd//PGHcnNz1a1bN504cUI7duyo1HGf++vzk08+KenPh0OUt5+8vDzl5uaqR48e2r17t/Ly8my2P3HihHJzc7V582bNmzdPPj4+Zcbu4+Ojfv36acGCBeY2H3zwgWJiYsqMz2KxmFcaFBcX6/Dhw3J3d9e1115b5pz37t1bjz32mCZOnKj77rtPrq6ueuONN2xiVq1apS5duuiWW24x29zd3TVixAjt3bvXvOXp3OPNzs7W1KlTVVJSottuu62cM1lW6dUGlZkHrDJXeRQUFKikpERNmjSp1P6rYtmyZfLw8NDtt9+u3NxccwkODpa7u7t5m2Pp1VQrVqwo97ahC7Vq1So1a9bM5raq+vXra8SIEZXa/uz36tGjR5WXl6du3bqV+/ezV69eNrePh4aGSpL69++vq666qkz77t27JUmbN2/Wrl279OCDD+rw4cPmuSooKFCvXr305ZdfnvcBaR999JFKSkr0wAMP2JxvX19ftWnTpsxtpRaLpdy/H5X16KOPmn92cnJSSEiIDMOwuQqjUaNGuvbaa83jPJ//+Z//sXndrVu3Sm+LyxP5nHxOPiefVxb5vG7l86p85nXo0EETJkzQW2+9pYiICOXm5mrRokU8GO5vitxP7if31/3cX6p///42f1ePHDmitWvX6oEHHjDfY7m5uTp8+LAiIiK0a9cu7d+/325/Z793CgoKlJubq65du8owDH3//ffVGmNUVJQOHjyojIwMs+3DDz9USUmJoqKiamTcZ7vmmmsUFBSk1NRUs624uFgffvih7rrrLvMYK/v9qTyrVq2Sr6+vBg0aZLbVq1dPTz31lI4fP25OX/Pvf/9bDg4O5X4mnnv3TWU4Ojpq8ODB+uSTT/THH3+Y7UuWLFHXrl0VGBhY5T4vNYro5fj111/l7Oxc5cSbl5enl19+WXFxceXe6njkyBGNGjVKPj4+cnNzU9OmTc03ybnJoDL8/PzUoEEDm7bShHH2vG6LFi1Sp06d5OrqqiZNmqhp06ZauXKl3X3GxMSYCWbhwoXq2rWr2rRpY3ccQUFB6tChg9555x0ZhmHeTlKebdu26d5775WHh4caNmyopk2b6qGHHpJU+XNw7lhatWolR0dHm2P+5ptvFB4ergYNGqhRo0Zq2rSpOYfjufuZOHGimjZtquuvv1579+5VRkaGzT9gSsXExGjp0qUqLCzUsmXL1Lhx43ITWklJiV599VW1adNGFotFXl5eatq0qbZs2VLuMU6fPl2enp7avHmzZs+eLW9vb5v1v/76a7lzQ5XeFnPubWiRkZFq2rSpWrZsqfHjx2vMmDHq379/me3Lk5ubq3r16lXqgVHHjh2TpArnQ2vSpInatGmjt956S5999pkOHjyo3NzccucXrapdu3YpLy9P3t7eatq0qc1y/PhxHTx4UJLUo0cP9e/fXxMmTJCXl5fuueceLViwoEbGIP15/lu3bl0miVR2Pq8VK1bopptukqurqzw9Pc3b0ct7r7Ro0cLmtYeHhyTJ39+/3PbSudB27dol6c/b8c49V2+99ZYKCwvP+/dv165dMgxDbdq0KdPH9u3bzfNdqnnz5hf0UJjyjtXV1dW8Jf3s9srM+VY6f9vZGjduXKltcfkin5PPyefk88oin9e9fF6Vz7xnn31WnTt31oYNGzRu3Di1b9++2mPG5Y3cT+4n99f93F/q3KLpzz//LMMwNHbs2DI5qrSQe26eOlt2draGDh0qT09P8zlYpdOwVOfvqSTzGSRnF7VTU1MVFBRk/p290HGfKyoqSt98841ZeM/IyNDBgwfNor1U+e9P5fn111/Vpk2bMtNMnfu+/OWXX+Tn5ydPT89Kj/18hgwZopMnT5pTB+3cuVNZWVl6+OGHa2wfFxM/z5dj586duvrqq6t89cKUKVPk6OioZ599tswDRqQ/5/9at26dnn32WQUFBcnd3V0lJSXq06fPea8aqa7Fixdr6NChioyM1LPPPitvb285OTkpMTFRv/zyS7nbPPTQQ3ruuef07bffatGiRRozZsx59/PII4/o9ddfV5cuXWS1WvXAAw+UeUDQsWPH1KNHDzVs2FATJ05Uq1at5Orqqk2bNun555+v9jk49x87v/zyi3r16qW2bdtq5syZ8vf3l4uLi1atWqVXX321zH4effRR9erVS7/99pteffVV9e/fX+vWrTP/4VKqX79+cnFx0fLly7VgwQJFR0eX+8Cal19+WWPHjtUjjzyiSZMmydPTU46Ojho9enS5x/j999+bH3Bbt261+TWwOqZPn67OnTvr9OnT+u677zR58mQ5OztXan6pvXv3qkWLFpX6VbH0aoWz5+kqT2pqqgYPHqyIiAib9nO/NFZVSUmJvL29tWTJknLXl35xdnBw0Icffqhvv/1Wn376qVavXq1HHnlEM2bM0LffflsjD0Srrq+++kp33323unfvrtdff13NmjVTvXr1tGDBAi1durRMvJOTU7n92Gs3/v9DaErfd9OmTSszd2Kp852HkpISOTg46P/+7//K3d+52599FUB1lLeP8x1nVfvDlY98Tj4nn5PPLwXyuX3VzedV/czbvXu3+SNDefNZ4++D3E/uJ/fX/dxf6twcU3qOn3nmmTL7LNW6dety24uLi3X77bfryJEjev7559W2bVs1aNBA+/fv19ChQ6v9HrVYLIqMjNTHH3+s119/XTk5Ofrmm2/08ssv18i4yxMVFaX4+HgtW7ZMo0eP1gcffCAPDw/16dPHZp+V+f5U17Rv317BwcFavHixhgwZosWLF8vFxUUPPPBAbQ+tUiiin6OwsFCbN2+2eRhHZRw4cECzZs1SYmKirrrqqjKJ9+jRo0pPT9eECROUkJBgtpd+2auOAwcOqKCgwOYD7KeffpIk8xbRDz/8UFdffbU++ugjmw/Tij6EmzRporvvvtu8naz0ds+KDB48WM8++6xGjRql+++/v9xffzMyMnT48GF99NFHNg8B3LNnT6WOt9SuXbtsfrH8+eefVVJSYh7zp59+qsLCQn3yySc2V7/Yu52ldevW5gdaeHi4WrRooaVLl5Z52KCzs7MefvhhvfTSS9q2bZvmz59fbn8ffvihbr31Vr399ts27ceOHStz1U1BQYFiYmLUvn17de3aVVOnTtW9996rG2+80Yxp2bKldu7cWWY/pbfMtWzZ0qY9ODjYfAJ33759tX//fk2ZMkVjx46t8Cn1Z86c0X//+1+bD+aK/Pjjj3JwcDjvFVrXX3+95s2bp27dumnixIm66aabNG3aNH3zzTeV2o89rVq10ueff66bb765Uv/Au+mmm3TTTTfppZde0tKlSzV48GC9//77evTRR6t1K1Kpli1b6ocffpBhGDb9lPf/7Fz//ve/5erqqtWrV9vcSld69UhNadWqlSSpYcOGCg8PrzDW3rlo1aqVDMNQYGBgubd3AnUN+Zx8Tj4nn1cF+bxuqcpnXklJiYYOHaqGDRtq9OjRevnll3X//febD5fF3we5n9xP7r88cr89V199taQ/pxY5X54719atW/XTTz9p0aJFGjJkiNm+Zs2aMrFV/b4QFRWlRYsWKT09Xdu3b5dhGDZXhV/IuMsTGBioLl26KDU1VbGxsfroo48UGRlp8x2jqt+fztayZUtt2bJFJSUlNu+rc9+XrVq10urVq3XkyJEqXY1+vvM7ZMgQxcXF6ffff9fSpUvVr18/NW7cuErHUFuYzuUcpbf49OrVq0rbTZgwQT4+PmXm3C1VesXFuVdMJiUlVWuc0p8flGfPuVVUVKQ33nhDTZs2VXBwsN39rl+/XpmZmRX2/cgjj2jLli0aMGBApa7s8fT01D333KMtW7aYTxM/V3ljKSoq0uuvv37e/s+WnJxs83rOnDmS/kwy9vaTl5dXqX/IlH7BsHeL0iOPPKKtW7eqe/fu5gfluZycnMr8f162bFm5c2A9//zzys7O1qJFizRz5kwFBAQoOjraZv933HGHNmzYYPP/rKCgQG+++aYCAgLOe7vqyZMndebMGZ05c6bCuM8++0x5eXm65557KoyT/nzv/fvf/1aXLl3O+/7Iz8/Xww8/rLvvvltjxoxReHi4mjVrdt59nM8DDzyg4uJiTZo0qdzxld6idvTo0TL/P0qv3io9z6W3vJVuUxV33HGHDhw4oA8//NBsO3HihN58883zbuvk5CQHBwcVFxebbXv37tXy5curPI6KBAcHq1WrVpo+fbqOHz9eZv2hQ4fMP5d+kT/3XNx3331ycnLShAkTypxPwzDKvWIHqE3k8z+Rz8nnFSGf/4V8XrfyeVU+82bOnKl169bpzTff1KRJk9S1a1c9/vjj5y0c4spD7v8TuZ/cX5G6kPvt8fb2Vs+ePfXGG2/o999/L7P+7Dx3rvLeO4ZhaNasWWVi7eVIe8LDw+Xp6anU1FSlpqaqS5cuNj8GXci47YmKitK3336r+fPnKzc316ZoL1X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",
|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 5 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Устранить выборсы в DataFrame\n",
|
||
"def remove_outliers(dataframe: DataFrame, columns: list[str]) -> DataFrame:\n",
|
||
" for column in columns:\n",
|
||
" if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
|
||
" continue\n",
|
||
" \n",
|
||
" Q1: float = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
|
||
" Q3: float = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
|
||
" IQR: float = Q3 - Q1 # Вычисляем межквартильный размах\n",
|
||
"\n",
|
||
" # Определяем границы для выбросов\n",
|
||
" lower_bound: float = Q1 - 1.5 * IQR # Нижняя граница\n",
|
||
" upper_bound: float = Q3 + 1.5 * IQR # Верхняя граница\n",
|
||
"\n",
|
||
" # Устраняем выбросы:\n",
|
||
" # Заменяем значения ниже нижней границы на нижнюю границу\n",
|
||
" # А значения выше верхней границы – на верхнюю\n",
|
||
" dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
|
||
" \n",
|
||
" return dataframe\n",
|
||
"\n",
|
||
"\n",
|
||
"# Устраняем выборсы\n",
|
||
"df: DataFrame = remove_outliers(df, numeric_columns)\n",
|
||
"\n",
|
||
"# Проверка наличия выбросов в колонках\n",
|
||
"print('Проверка наличия выбросов в колонках после их устранения:')\n",
|
||
"check_outliers(df, numeric_columns)\n",
|
||
"visualize_outliers(df, numeric_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Разбиение набора данных на выборки:\n",
|
||
"\n",
|
||
"**Групповое разбиение данных** – это метод разделения данных на несколько групп или подмножеств на основе определенного признака или характеристики. При этом наблюдения для одного объекта должны попасть только в одну выборку.\n",
|
||
"\n",
|
||
"**Основные виды выборки данных**:\n",
|
||
"1. Обучающая выборка (60-80%). Обучение модели (подбор коэффициентов некоторой математической функции для аппроксимации).\n",
|
||
"2. Контрольная выборка (10-20%). Выбор метода обучения, настройка гиперпараметров.\n",
|
||
"3. Тестовая выборка (10-20% или 20-30%). Оценка качества модели перед передачей заказчику.\n",
|
||
"\n",
|
||
"Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
|
||
"\n",
|
||
"Весь набор данных состоит из 90836 объектов, из которых 81996 (около 90.3%) неопасны (False), а 8840 (около 9.7%) опасны (True). Это говорит о том, что класс \"неопасные\" значительно преобладает.\n",
|
||
"\n",
|
||
"Все выборки показывают одинаковое распределение классов, что свидетельствует о том, что данные были отобраны случайным образом и не содержат явного смещения.\n",
|
||
"\n",
|
||
"Однако, несмотря на сбалансированность при разбиении данных, в целом данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать опасные объекты (True), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
|
||
"\n",
|
||
"Для получения более сбалансированных выборок данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Функция для создания выборок\n",
|
||
"def split_stratified_into_train_val_test(\n",
|
||
" df_input,\n",
|
||
" stratify_colname=\"y\",\n",
|
||
" frac_train=0.6,\n",
|
||
" frac_val=0.15,\n",
|
||
" frac_test=0.25,\n",
|
||
" random_state=None,\n",
|
||
") -> tuple[Any, Any, Any]:\n",
|
||
" \"\"\"\n",
|
||
" Splits a Pandas dataframe into three subsets (train, val, and test)\n",
|
||
" following fractional ratios provided by the user, where each subset is\n",
|
||
" stratified by the values in a specific column (that is, each subset has\n",
|
||
" the same relative frequency of the values in the column). It performs this\n",
|
||
" splitting by running train_test_split() twice.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" df_input : Pandas dataframe\n",
|
||
" Input dataframe to be split.\n",
|
||
" stratify_colname : str\n",
|
||
" The name of the column that will be used for stratification. Usually\n",
|
||
" this column would be for the label.\n",
|
||
" frac_train : float\n",
|
||
" frac_val : float\n",
|
||
" frac_test : float\n",
|
||
" The ratios with which the dataframe will be split into train, val, and\n",
|
||
" test data. The values should be expressed as float fractions and should\n",
|
||
" sum to 1.0.\n",
|
||
" random_state : int, None, or RandomStateInstance\n",
|
||
" Value to be passed to train_test_split().\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" df_train, df_val, df_test :\n",
|
||
" Dataframes containing the three splits.\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" if frac_train + frac_val + frac_test != 1.0:\n",
|
||
" raise ValueError(\n",
|
||
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
|
||
" % (frac_train, frac_val, frac_test)\n",
|
||
" )\n",
|
||
"\n",
|
||
" if stratify_colname not in df_input.columns:\n",
|
||
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
|
||
"\n",
|
||
" X: DataFrame = df_input # Contains all columns.\n",
|
||
" y: DataFrame = df_input[\n",
|
||
" [stratify_colname]\n",
|
||
" ] # Dataframe of just the column on which to stratify.\n",
|
||
"\n",
|
||
" # Split original dataframe into train and temp dataframes.\n",
|
||
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
|
||
" X, y, \n",
|
||
" stratify=y, \n",
|
||
" test_size=(1.0 - frac_train), \n",
|
||
" random_state=random_state\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Split the temp dataframe into val and test dataframes.\n",
|
||
" relative_frac_test: float = frac_test / (frac_val + frac_test)\n",
|
||
" df_val, df_test, y_val, y_test = train_test_split(\n",
|
||
" df_temp,\n",
|
||
" y_temp,\n",
|
||
" stratify=y_temp,\n",
|
||
" test_size=relative_frac_test,\n",
|
||
" random_state=random_state,\n",
|
||
" )\n",
|
||
"\n",
|
||
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
|
||
"\n",
|
||
" return df_train, df_val, df_test"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение количества наблюдений по меткам (классам):\n",
|
||
"hazardous\n",
|
||
"False 81996\n",
|
||
"True 8840\n",
|
||
"Name: count, dtype: int64 \n",
|
||
"\n",
|
||
"Проверка сбалансированности выборок:\n",
|
||
"Обучающая выборка: (54501, 10)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"False 49197\n",
|
||
"True 5304\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"False\": 90.27%\n",
|
||
"Процент объектов класса \"True\": 9.73%\n",
|
||
"\n",
|
||
"Контрольная выборка: (18167, 10)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"False 16399\n",
|
||
"True 1768\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"False\": 90.27%\n",
|
||
"Процент объектов класса \"True\": 9.73%\n",
|
||
"\n",
|
||
"Тестовая выборка: (18168, 10)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"False 16400\n",
|
||
"True 1768\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"False\": 90.27%\n",
|
||
"Процент объектов класса \"True\": 9.73%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок:\n",
|
||
"Для обучающей выборки аугментация данных требуется\n",
|
||
"Для контрольной выборки аугментация данных требуется\n",
|
||
"Для тестовой выборки аугментация данных требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Оценка сбалансированности\n",
|
||
"def check_balance(dataframe: DataFrame, dataframe_name: str, column: str) -> None:\n",
|
||
" counts: Series[int] = dataframe[column].value_counts()\n",
|
||
" print(dataframe_name + \": \", dataframe.shape)\n",
|
||
" print(f\"Распределение выборки данных по классам в колонке \\\"{column}\\\":\\n\", counts)\n",
|
||
" total_count: int = len(dataframe)\n",
|
||
" for value in counts.index:\n",
|
||
" percentage: float = counts[value] / total_count * 100\n",
|
||
" print(f\"Процент объектов класса \\\"{value}\\\": {percentage:.2f}%\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
"# Определение необходимости аугментации данных\n",
|
||
"def need_augmentation(dataframe: DataFrame,\n",
|
||
" column: str, \n",
|
||
" first_value: Any, second_value: Any) -> bool:\n",
|
||
" counts: Series[int] = dataframe[column].value_counts()\n",
|
||
" ratio: float = counts[first_value] / counts[second_value]\n",
|
||
" return ratio > 1.5 or ratio < 0.67\n",
|
||
" \n",
|
||
" # Визуализация сбалансированности классов\n",
|
||
"def visualize_balance(dataframe_train: DataFrame,\n",
|
||
" dataframe_val: DataFrame,\n",
|
||
" dataframe_test: DataFrame, \n",
|
||
" column: str) -> None:\n",
|
||
" fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
|
||
"\n",
|
||
" # Обучающая выборка\n",
|
||
" counts_train: Series[int] = dataframe_train[column].value_counts()\n",
|
||
" axes[0].pie(counts_train, labels=counts_train.index, autopct='%1.1f%%', startangle=90)\n",
|
||
" axes[0].set_title(f\"Распределение классов \\\"{column}\\\" в обучающей выборке\")\n",
|
||
"\n",
|
||
" # Контрольная выборка\n",
|
||
" counts_val: Series[int] = dataframe_val[column].value_counts()\n",
|
||
" axes[1].pie(counts_val, labels=counts_val.index, autopct='%1.1f%%', startangle=90)\n",
|
||
" axes[1].set_title(f\"Распределение классов \\\"{column}\\\" в контрольной выборке\")\n",
|
||
"\n",
|
||
" # Тестовая выборка\n",
|
||
" counts_test: Series[int] = dataframe_test[column].value_counts()\n",
|
||
" axes[2].pie(counts_test, labels=counts_test.index, autopct='%1.1f%%', startangle=90)\n",
|
||
" axes[2].set_title(f\"Распределение классов \\\"{column}\\\" в тренировочной выборке\")\n",
|
||
"\n",
|
||
" # Отображение графиков\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
"\n",
|
||
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
||
"print('Распределение количества наблюдений по меткам (классам):')\n",
|
||
"print(df.hazardous.value_counts(), '\\n')\n",
|
||
"\n",
|
||
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
||
" df, \n",
|
||
" stratify_colname=\"hazardous\", \n",
|
||
" frac_train=0.60, \n",
|
||
" frac_val=0.20, \n",
|
||
" frac_test=0.20\n",
|
||
")\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок:')\n",
|
||
"check_balance(df_train, 'Обучающая выборка', 'hazardous')\n",
|
||
"check_balance(df_val, 'Контрольная выборка', 'hazardous')\n",
|
||
"check_balance(df_test, 'Тестовая выборка', 'hazardous')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'hazardous', True, False) else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'hazardous', True, False) else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'hazardous', True, False) else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train, df_val, df_test, 'hazardous')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Приращение данных:\n",
|
||
"\n",
|
||
"**Аугментация данных** может быть полезна в том случае, когда имеется недостаточное количество данных и мы хотим сгенерировать новые данные на основе имеющихся, слегка модифицировав их.\n",
|
||
"\n",
|
||
"**Методы решения:**\n",
|
||
"1. **Выборка с избытком (oversampling).** Копирование наблюдений или генерация новых наблюдений на основе существующих с помощью алгоритмов SMOTE и ADASYN (нахождение k-ближайших соседей).\n",
|
||
"2. **Выборка с недостатком (undersampling).** Исключение некоторых наблюдений для меток с большим количеством наблюдений. Наблюдения можно исключать случайным образом или на основе определения связей Томека для наблюдений разных меток."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка сбалансированности выборок после применения метода oversampling:\n",
|
||
"Обучающая выборка: (99094, 21839)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"True 49897\n",
|
||
"False 49197\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"True\": 50.35%\n",
|
||
"Процент объектов класса \"False\": 49.65%\n",
|
||
"\n",
|
||
"Контрольная выборка: (33065, 11737)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"True 16666\n",
|
||
"False 16399\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"True\": 50.40%\n",
|
||
"Процент объектов класса \"False\": 49.60%\n",
|
||
"\n",
|
||
"Тестовая выборка: (33123, 11819)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"True 16723\n",
|
||
"False 16400\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"True\": 50.49%\n",
|
||
"Процент объектов класса \"False\": 49.51%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок после применения метода oversampling:\n",
|
||
"Для обучающей выборки аугментация данных не требуется\n",
|
||
"Для контрольной выборки аугментация данных не требуется\n",
|
||
"Для тестовой выборки аугментация данных не требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Метод приращения с избытком (oversampling)\n",
|
||
"def oversample(df: DataFrame, column: str) -> DataFrame:\n",
|
||
" X: DataFrame = pd.get_dummies(df.drop(column, axis=1))\n",
|
||
" y: DataFrame = df[column] # type: ignore\n",
|
||
" \n",
|
||
" adasyn = ADASYN()\n",
|
||
" X_resampled, y_resampled = adasyn.fit_resample(X, y) # type: ignore\n",
|
||
" \n",
|
||
" df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return df_resampled\n",
|
||
"\n",
|
||
"\n",
|
||
"# Приращение данных (oversampling)\n",
|
||
"df_train_oversampled: DataFrame = oversample(df_train, 'hazardous')\n",
|
||
"df_val_oversampled: DataFrame = oversample(df_val, 'hazardous')\n",
|
||
"df_test_oversampled: DataFrame = oversample(df_test, 'hazardous')\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
|
||
"check_balance(df_train_oversampled, 'Обучающая выборка', 'hazardous')\n",
|
||
"check_balance(df_val_oversampled, 'Контрольная выборка', 'hazardous')\n",
|
||
"check_balance(df_test_oversampled, 'Тестовая выборка', 'hazardous')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'hazardous')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка сбалансированности выборок после применения метода undersampling:\n",
|
||
"Обучающая выборка: (10608, 21839)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"False 5304\n",
|
||
"True 5304\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"False\": 50.00%\n",
|
||
"Процент объектов класса \"True\": 50.00%\n",
|
||
"\n",
|
||
"Контрольная выборка: (3536, 11737)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"False 1768\n",
|
||
"True 1768\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"False\": 50.00%\n",
|
||
"Процент объектов класса \"True\": 50.00%\n",
|
||
"\n",
|
||
"Тестовая выборка: (3536, 11819)\n",
|
||
"Распределение выборки данных по классам в колонке \"hazardous\":\n",
|
||
" hazardous\n",
|
||
"False 1768\n",
|
||
"True 1768\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"False\": 50.00%\n",
|
||
"Процент объектов класса \"True\": 50.00%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок после применения метода undersampling:\n",
|
||
"Для обучающей выборки аугментация данных не требуется\n",
|
||
"Для контрольной выборки аугментация данных не требуется\n",
|
||
"Для тестовой выборки аугментация данных не требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Метод приращения с недостатком (undersampling)\n",
|
||
"def undersample(df: DataFrame, column: str) -> DataFrame:\n",
|
||
" X: DataFrame = pd.get_dummies(df.drop(column, axis=1))\n",
|
||
" y: DataFrame = df[column] # type: ignore\n",
|
||
" \n",
|
||
" undersampler = RandomUnderSampler()\n",
|
||
" X_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n",
|
||
" \n",
|
||
" df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return df_resampled\n",
|
||
"\n",
|
||
"\n",
|
||
"# Приращение данных (undersampling)\n",
|
||
"df_train_undersampled: DataFrame = undersample(df_train, 'hazardous')\n",
|
||
"df_val_undersampled: DataFrame = undersample(df_val, 'hazardous')\n",
|
||
"df_test_undersampled: DataFrame = undersample(df_test, 'hazardous')\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок после применения метода undersampling:')\n",
|
||
"check_balance(df_train_undersampled, 'Обучающая выборка', 'hazardous')\n",
|
||
"check_balance(df_val_undersampled, 'Контрольная выборка', 'hazardous')\n",
|
||
"check_balance(df_test_undersampled, 'Тестовая выборка', 'hazardous')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок после применения метода undersampling:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'hazardous')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Датасет №2: [Зарплаты в области Data Science](https://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary).\n",
|
||
"\n",
|
||
"### Описание датасета:\n",
|
||
"Данный набор данных предназначен для исследования факторов, влияющих на заработную плату специалистов по данным (Data Scientists) в 2023 году. Набор данных содержит информацию о различных характеристиках работников, таких как уровень опыта, тип занятости, местоположение сотрудника и компании, удалённость работы и размер компании. Этот анализ помогает понять, какие факторы наиболее значимо влияют на уровень зарплат в области Data Science, и как изменяются заработные платы в зависимости от этих факторов.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Анализ сведений:\n",
|
||
"**Проблемная область:**\n",
|
||
"Основная задача – изучить, как различные факторы, такие как опыт, тип занятости, местоположение и удалённость работы, влияют на уровень зарплаты специалистов по данным. Это важно для понимания рыночных тенденций и формирования конкурентоспособной системы оплаты труда.\n",
|
||
"\n",
|
||
"**Актуальность:**\n",
|
||
"Данный набор данных актуален для компаний, стремящихся выстроить конкурентоспособные стратегии оплаты труда, а также для специалистов по данным, желающих оценить свои зарплатные ожидания в зависимости от их опыта, географии и типа занятости.\n",
|
||
"\n",
|
||
"**Объекты наблюдения:**\n",
|
||
"Объектами наблюдения являются специалисты по данным, работающие в различных компаниях и странах, с разным уровнем опыта и типом занятости.\n",
|
||
"\n",
|
||
"**Атрибуты объектов:**\n",
|
||
"- work_year: Год, в который была выплачена зарплата.\n",
|
||
"- experience_level: Уровень опыта сотрудника.\n",
|
||
" - EN: Начальный.\n",
|
||
" - MI: Средний.\n",
|
||
" - SE: Старший.\n",
|
||
" - EX: Исполнительный.\n",
|
||
"- employment_type: Тип занятости.\n",
|
||
" - PT: Полная.\n",
|
||
" - FT: Частичная.\n",
|
||
" - CT: Контрактная.\n",
|
||
" - FL: Фриланс.\n",
|
||
"- job_title: Должность, которую занимал сотрудник.\n",
|
||
"- salary: Общая сумма выплаченной заработной платы.\n",
|
||
"- salary_currency: Валюта, в которой выплачена зарплата.\n",
|
||
"- salary_in_usd: Заработная плата, конвертированная в доллары США (USD).\n",
|
||
"- employee_residence: Страна проживания сотрудника в год выплаты зарплаты.\n",
|
||
"- remote_ratio: Доля удалённой работы (например, 100% удалённо или частично удалённо).\n",
|
||
"- company_location: Страна, в которой расположена основная офисная компания работодателя.\n",
|
||
"- company_size: Среднее количество сотрудников, работающих в компании.\n",
|
||
"\n",
|
||
"**Связь между объектами:**\n",
|
||
"Набор данных позволяет исследовать взаимосвязи между факторами, такими как уровень опыта, тип занятости и местоположение сотрудника, с уровнем его заработной платы. Взаимосвязи между этими факторами могут дать полезную информацию о влиянии определённых условий работы на доход.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Качество набора данных:\n",
|
||
"**Информативность:**\n",
|
||
"Датасет предоставляет важную информацию для анализа различных факторов, влияющих на зарплату специалистов по данным. Он включает множество атрибутов, которые можно использовать для построения моделей и анализа.\n",
|
||
"\n",
|
||
"**Степень покрытия:**\n",
|
||
"Набор данных охватывает специалистов по данным с разным опытом, работающих в различных странах, что позволяет провести сравнительный анализ и выявить региональные и глобальные тренды.\n",
|
||
"\n",
|
||
"**Соответствие реальным данным:**\n",
|
||
"Заработные платы специалистов по данным, приведенные в датасете, отражают реальную ситуацию на рынке труда в 2023 году, предоставляя точные данные для анализа текущих рыночных условий.\n",
|
||
"\n",
|
||
"**Согласованность меток:**\n",
|
||
"Все категории, такие как уровни опыта или типы занятости, имеют четко определённые метки, что упрощает анализ и моделирование.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Бизес-цели:\n",
|
||
"1. **Оптимизация структуры оплаты труда:**\n",
|
||
"Компании могут использовать данный анализ для создания конкурентных предложений по оплате труда, основываясь на опыте, географии и других значимых факторах.\n",
|
||
"2. **Планирование найма и удержание специалистов:**\n",
|
||
"Помогает работодателям понять, какие факторы могут привлечь или удержать специалистов по данным, и оптимизировать HR-процессы для сокращения текучести кадров.\n",
|
||
"3. **Анализ глобальных и региональных зарплатных трендов:**\n",
|
||
"Позволяет компаниям проводить сравнительный анализ зарплат по регионам, уровням опыта и типам занятости, помогая в принятии решений о расширении бизнеса в разные страны.\n",
|
||
"\n",
|
||
"**Эффект для бизнеса:**\n",
|
||
"Компании, использующие данную информацию, могут предлагать конкурентоспособные зарплаты, улучшить процессы найма и удержания специалистов, а также сократить издержки, связанные с высокими зарплатными ожиданиями. Это также помогает улучшить планирование бюджета на персонал.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Технические цели:\n",
|
||
"1. **Построение модели прогнозирования зарплат:**\n",
|
||
"Создание модели, которая будет предсказывать уровень зарплаты специалиста по данным на основе таких факторов, как опыт, регион и удалённость работы.\n",
|
||
"2. **Анализ влияния опыта и удалённости на зарплату:**\n",
|
||
"Исследование того, как уровень опыта и удалённость работы влияют на заработную плату, что может помочь компаниям лучше планировать условия найма.\n",
|
||
"3. **Оптимизация найма специалистов:**\n",
|
||
"С помощью анализа компания может определить наиболее значимые факторы для назначения зарплат, чтобы предлагать более конкурентные условия и привлекать лучших специалистов.\n",
|
||
"\n",
|
||
"**Входные данные:**\n",
|
||
"Год, уровень опыта, тип занятости, должность, зарплата, страна проживания, удалённость работы.\n",
|
||
"\n",
|
||
"**Целевой признак:**\n",
|
||
"Признак \"salary_in_usd\" – заработная плата в долларах США.\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Выгрузка данных из файла в DataFrame:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df: DataFrame = pd.read_csv('..//static//csv//ds_salaries.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Краткая информация о DataFrame:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 3755 entries, 0 to 3754\n",
|
||
"Data columns (total 11 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 work_year 3755 non-null int64 \n",
|
||
" 1 experience_level 3755 non-null object\n",
|
||
" 2 employment_type 3755 non-null object\n",
|
||
" 3 job_title 3755 non-null object\n",
|
||
" 4 salary 3755 non-null int64 \n",
|
||
" 5 salary_currency 3755 non-null object\n",
|
||
" 6 salary_in_usd 3755 non-null int64 \n",
|
||
" 7 employee_residence 3755 non-null object\n",
|
||
" 8 remote_ratio 3755 non-null int64 \n",
|
||
" 9 company_location 3755 non-null object\n",
|
||
" 10 company_size 3755 non-null object\n",
|
||
"dtypes: int64(4), object(7)\n",
|
||
"memory usage: 322.8+ KB\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>count</th>\n",
|
||
" <th>mean</th>\n",
|
||
" <th>std</th>\n",
|
||
" <th>min</th>\n",
|
||
" <th>25%</th>\n",
|
||
" <th>50%</th>\n",
|
||
" <th>75%</th>\n",
|
||
" <th>max</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>work_year</th>\n",
|
||
" <td>3755.0</td>\n",
|
||
" <td>2022.373635</td>\n",
|
||
" <td>0.691448</td>\n",
|
||
" <td>2020.0</td>\n",
|
||
" <td>2022.0</td>\n",
|
||
" <td>2022.0</td>\n",
|
||
" <td>2023.0</td>\n",
|
||
" <td>2023.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>salary</th>\n",
|
||
" <td>3755.0</td>\n",
|
||
" <td>190695.571771</td>\n",
|
||
" <td>671676.500508</td>\n",
|
||
" <td>6000.0</td>\n",
|
||
" <td>100000.0</td>\n",
|
||
" <td>138000.0</td>\n",
|
||
" <td>180000.0</td>\n",
|
||
" <td>30400000.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>salary_in_usd</th>\n",
|
||
" <td>3755.0</td>\n",
|
||
" <td>137570.389880</td>\n",
|
||
" <td>63055.625278</td>\n",
|
||
" <td>5132.0</td>\n",
|
||
" <td>95000.0</td>\n",
|
||
" <td>135000.0</td>\n",
|
||
" <td>175000.0</td>\n",
|
||
" <td>450000.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>remote_ratio</th>\n",
|
||
" <td>3755.0</td>\n",
|
||
" <td>46.271638</td>\n",
|
||
" <td>48.589050</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>100.0</td>\n",
|
||
" <td>100.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" count mean std min 25% \\\n",
|
||
"work_year 3755.0 2022.373635 0.691448 2020.0 2022.0 \n",
|
||
"salary 3755.0 190695.571771 671676.500508 6000.0 100000.0 \n",
|
||
"salary_in_usd 3755.0 137570.389880 63055.625278 5132.0 95000.0 \n",
|
||
"remote_ratio 3755.0 46.271638 48.589050 0.0 0.0 \n",
|
||
"\n",
|
||
" 50% 75% max \n",
|
||
"work_year 2022.0 2023.0 2023.0 \n",
|
||
"salary 138000.0 180000.0 30400000.0 \n",
|
||
"salary_in_usd 135000.0 175000.0 450000.0 \n",
|
||
"remote_ratio 0.0 100.0 100.0 "
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Краткая информация о DataFrame\n",
|
||
"df.info()\n",
|
||
"\n",
|
||
"# Статистическое описание числовых столбцов\n",
|
||
"df.describe().transpose()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Проблема пропущенных данных:\n",
|
||
"\n",
|
||
"Проверка на отсутствие значений, представленная ниже, показала, что DataFrame не имеет пустых значений признаков. Нет необходимости использовать методы заполнения пропущенных данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Присутствуют ли пустые значения признаков в колонке:\n",
|
||
"work_year False\n",
|
||
"experience_level False\n",
|
||
"employment_type False\n",
|
||
"job_title False\n",
|
||
"salary False\n",
|
||
"salary_currency False\n",
|
||
"salary_in_usd False\n",
|
||
"employee_residence False\n",
|
||
"remote_ratio False\n",
|
||
"company_location False\n",
|
||
"company_size False\n",
|
||
"dtype: bool \n",
|
||
"\n",
|
||
"Количество пустых значений признаков в колонке:\n",
|
||
"work_year 0\n",
|
||
"experience_level 0\n",
|
||
"employment_type 0\n",
|
||
"job_title 0\n",
|
||
"salary 0\n",
|
||
"salary_currency 0\n",
|
||
"salary_in_usd 0\n",
|
||
"employee_residence 0\n",
|
||
"remote_ratio 0\n",
|
||
"company_location 0\n",
|
||
"company_size 0\n",
|
||
"dtype: int64 \n",
|
||
"\n",
|
||
"Процент пустых значений признаков в колонке:\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Проверка пропущенных данных\n",
|
||
"check_null_columns(df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Проблема зашумленности данных:\n",
|
||
"\n",
|
||
"Представленный ниже код помогает определить наличие выбросов в наборе данных и устранить их (при наличии), заменив значения ниже нижней границы (рассматриваемого минимума) на значения нижней границы, а значения выше верхней границы (рассматриваемого максимума) – на значения верхней границы."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка наличия выбросов в колонках:\n",
|
||
"Колонка work_year:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 76\n",
|
||
"\tМинимальное значение: 2020\n",
|
||
"\tМаксимальное значение: 2023\n",
|
||
"\t1-й квартиль (Q1): 2022.0\n",
|
||
"\t3-й квартиль (Q3): 2023.0\n",
|
||
"\n",
|
||
"Колонка salary:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 113\n",
|
||
"\tМинимальное значение: 6000\n",
|
||
"\tМаксимальное значение: 30400000\n",
|
||
"\t1-й квартиль (Q1): 100000.0\n",
|
||
"\t3-й квартиль (Q3): 180000.0\n",
|
||
"\n",
|
||
"Колонка salary_in_usd:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 63\n",
|
||
"\tМинимальное значение: 5132\n",
|
||
"\tМаксимальное значение: 450000\n",
|
||
"\t1-й квартиль (Q1): 95000.0\n",
|
||
"\t3-й квартиль (Q3): 175000.0\n",
|
||
"\n",
|
||
"Колонка remote_ratio:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0\n",
|
||
"\tМаксимальное значение: 100\n",
|
||
"\t1-й квартиль (Q1): 0.0\n",
|
||
"\t3-й квартиль (Q3): 100.0\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Числовые столбцы DataFrame\n",
|
||
"numeric_columns: list[str] = [\n",
|
||
" 'work_year',\n",
|
||
" 'salary',\n",
|
||
" 'salary_in_usd',\n",
|
||
" 'remote_ratio'\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Проверка наличия выбросов в колонках\n",
|
||
"print('Проверка наличия выбросов в колонках:')\n",
|
||
"check_outliers(df, numeric_columns)\n",
|
||
"visualize_outliers(df, numeric_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка наличия выбросов в колонках после их устранения:\n",
|
||
"Колонка work_year:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 2020.5\n",
|
||
"\tМаксимальное значение: 2023.0\n",
|
||
"\t1-й квартиль (Q1): 2022.0\n",
|
||
"\t3-й квартиль (Q3): 2023.0\n",
|
||
"\n",
|
||
"Колонка salary:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 6000.0\n",
|
||
"\tМаксимальное значение: 300000.0\n",
|
||
"\t1-й квартиль (Q1): 100000.0\n",
|
||
"\t3-й квартиль (Q3): 180000.0\n",
|
||
"\n",
|
||
"Колонка salary_in_usd:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 5132.0\n",
|
||
"\tМаксимальное значение: 295000.0\n",
|
||
"\t1-й квартиль (Q1): 95000.0\n",
|
||
"\t3-й квартиль (Q3): 175000.0\n",
|
||
"\n",
|
||
"Колонка remote_ratio:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0\n",
|
||
"\tМаксимальное значение: 100\n",
|
||
"\t1-й квартиль (Q1): 0.0\n",
|
||
"\t3-й квартиль (Q3): 100.0\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Устраняем выборсы\n",
|
||
"df: DataFrame = remove_outliers(df, numeric_columns)\n",
|
||
"\n",
|
||
"# Проверка наличия выбросов в колонках\n",
|
||
"print('Проверка наличия выбросов в колонках после их устранения:')\n",
|
||
"check_outliers(df, numeric_columns)\n",
|
||
"visualize_outliers(df, numeric_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Разбиение набора данных на выборки:\n",
|
||
"\n",
|
||
"Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
|
||
"\n",
|
||
"Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
|
||
"\n",
|
||
"Чтобы решить эту проблему введём категории для значения зарплаты. Вместо того, чтобы использовать точные значения зарплаты для стратификации, мы создадим категории зарплат, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении зарплаты. Это позволит создать более крупные классы, что устранит проблему с редкими значениями\n",
|
||
"\n",
|
||
"Категории для разбиения зарплат:\n",
|
||
"- Низкая зарплата: зарплаты ниже первого квартиля (25%) — это значения меньше 95000.\n",
|
||
"- Средняя зарплата: зарплаты между первым квартилем (25%) и третьим квартилем (75%) — это зарплаты от 95000 до 175000.\n",
|
||
"- Высокая зарплата: зарплаты выше третьего квартиля (75%) и до максимального значения — это зарплаты выше 175000.\n",
|
||
"\n",
|
||
"Весь набор данных состоит из 3755 объектов, из которых 1867 (около 49.7%) имеют средний уровень зарплаты (medium), 956 (около 25.4%) – низкий уровень зарплаты (low), и 932 (около 24.8%) – высокий уровень зарплаты (high).\n",
|
||
"\n",
|
||
"Все выборки показывают одинаковое распределение классов, что свидетельствует о том, что данные были отобраны случайным образом и не содержат явного смещения.\n",
|
||
"\n",
|
||
"Однако, несмотря на сбалансированность при разбиении данных, в целом данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать низкие или высокие зарплаты (low или high), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
|
||
"\n",
|
||
"Для получения более сбалансированных выборок данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение количества наблюдений по меткам (классам):\n",
|
||
"salary_in_usd\n",
|
||
"100000.0 99\n",
|
||
"150000.0 98\n",
|
||
"120000.0 91\n",
|
||
"160000.0 84\n",
|
||
"130000.0 82\n",
|
||
" ..\n",
|
||
"39916.0 1\n",
|
||
"26005.0 1\n",
|
||
"22611.0 1\n",
|
||
"5679.0 1\n",
|
||
"40038.0 1\n",
|
||
"Name: count, Length: 1002, dtype: int64 \n",
|
||
"\n",
|
||
"Статистическое описание целевого признака:\n",
|
||
"count 3755.000000\n",
|
||
"mean 136959.779760\n",
|
||
"std 61098.121137\n",
|
||
"min 5132.000000\n",
|
||
"25% 95000.000000\n",
|
||
"50% 135000.000000\n",
|
||
"75% 175000.000000\n",
|
||
"max 295000.000000\n",
|
||
"Name: salary_in_usd, dtype: float64 \n",
|
||
"\n",
|
||
"Распределение количества наблюдений по меткам (классам):\n",
|
||
"salary_category\n",
|
||
"medium 1867\n",
|
||
"low 956\n",
|
||
"high 932\n",
|
||
"Name: count, dtype: int64 \n",
|
||
"\n",
|
||
"Проверка сбалансированности выборок:\n",
|
||
"Обучающая выборка: (2253, 12)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"medium 1120\n",
|
||
"low 574\n",
|
||
"high 559\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"medium\": 49.71%\n",
|
||
"Процент объектов класса \"low\": 25.48%\n",
|
||
"Процент объектов класса \"high\": 24.81%\n",
|
||
"\n",
|
||
"Контрольная выборка: (751, 12)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"medium 373\n",
|
||
"low 191\n",
|
||
"high 187\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"medium\": 49.67%\n",
|
||
"Процент объектов класса \"low\": 25.43%\n",
|
||
"Процент объектов класса \"high\": 24.90%\n",
|
||
"\n",
|
||
"Тестовая выборка: (751, 12)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"medium 374\n",
|
||
"low 191\n",
|
||
"high 186\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"medium\": 49.80%\n",
|
||
"Процент объектов класса \"low\": 25.43%\n",
|
||
"Процент объектов класса \"high\": 24.77%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок:\n",
|
||
"Для обучающей выборки аугментация данных требуется\n",
|
||
"Для контрольной выборки аугментация данных требуется\n",
|
||
"Для тестовой выборки аугментация данных требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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UKQCPcnRubi7i4+MxefLkIj/zWgVztPZxHh4eqFGjBjp16oRDhw5hyJAhAB7l6AcPHmDGjBmws7ODEAIeHh4ICQmBn58f5s+fDyAvT2pPXJ2dnfHgwQN4enpi8ODBWLlyJcLCwvDVV1+hf//+6NixI9zc3NCkSRMkJiaiZ8+eGDFiBFavXo0tW7bonbC6u7sjPj4e7dq1Q8uWLbFlyxbExMTo7Wfm6JIxR5cdczRztNQ5urDiPsdl+awY4/PPP8ebb74JAIiNjcX777+v9/1Q2NGjR7FlyxZMnjwZtra2WLZsGfr27YuzZ8/qig7Xr19H9+7d4eLigo8//hjW1tb4+eef4efnh6NHj6JLly4G29Xut4JxVCYhBAYOHIjDhw9j3LhxaN++Pfz9/fHRRx8hPDy8yA5LrcKdYqV9vgFg8ODBGDRoEObMmaNb1qlTJ7z00kvYv3+/bsCCVlFt5gkTJkCtVut+T05OBgBs3rwZrq6uePnllxEYGIhr166hQ4cOiI6O1k1XOWrUKKxduxaurq4YPHgwYmJicPz4cfTv3x/Hjx9H165dddtNSEhATEwMnJycMHjwYJw5cwaBgYFF7ov169dj1apVGDx4MKKiopCRkQGFQoF+/frh0qVLcHFx0eW3V199FatXr0br1q1x8+ZNtG/fHj4+PszHzMd6mI+Zj6XMx5WRE6OiovDEE0/oOpS9vLywZ88ejBs3DsnJyZgyZUqFXqfWunXrcO3atSL/plar0bdvXzzxxBOYO3cu9u7di+nTpyM3N1d3f4ay5sXBgwfjpZdeQm5uLk6dOoUVK1YgIyMD69atAwBkZGTAz88PQUFBeOedd9CwYUNs3boVo0ePRmJiYqXe7DoqKgpdu3ZFeno6Jk+eDA8PD6xZswYDBw7EH3/8gcGDBxf72H///Re7d+8u0/N99tlnaNmyJTIyMnSD82vWrIlx48aV+zXExcXh/PnzsLKywqRJk9C4cWNs374d48ePR1xcHD799FMAZX/fjDlmC7ty5QoGDRqE/v3746efftItN+mxLcpg9erVAoA4cOCAiImJEffv3xebN28WHh4ewt7eXjx48EAIIURmZqZQq9V6jw0JCRG2trZi1qxZumWrVq0SAMT8+fMNnkuj0egeB0DMmzfPYJ3WrVuLHj166H4/fPiwACDq1q0rkpOTdct///13AUAsWrRIt+2mTZuKPn366J5HCCHS09NFw4YNxXPPPWfwXF27dhVt2rTR/R4TEyMAiOnTp+uWhYaGCpVKJb799lu9x167dk1YWVkZLL9z544AINasWaNbNn36dFHwbTl27JgAIDZs2KD32L179xos9/HxEQMGDDCIfdKkSaLwW1049o8//ljUrFlTdOzYUW+frlu3TiiVSnHs2DG9x//vf/8TAMSJEycMnq+gHj166Lb3zz//CCsrKzF16tQi1zVmfwiR9z4VlJ2dLdq0aSN69uypty2lUikGDx5scCxq3/PExETh7OwsunTpIjIyMopcJzs7W9SsWVO0adNGb52///5bABBfffWVbtmoUaOEj4+P3nZWrFghAIizZ88W+ZqN8ddffwkA4ty5cyWuZ8x+ESLvOBk1apTud2M/r9rPV6NGjQye6+effxYAxM2bN/We39PTU++5SnPo0CEBQEyePNngb4U/q4X16dNHNGrUSG9Z4e8Ira+//lo4OjqKwMBAveWffvqpUKlU4t69e0IIIbZt2yYAiIULF+rWUavVomfPngKAWL16tW55+/btRc2aNUVcXJxu2ZUrV4RSqRQjR47ULdMe08OGDTOIa9iwYaJOnTp678fFixcNnqs8Cn4WtT777DMBQERHRxf7OGM/A9rvahsbG739GhMTIzw8PETHjh11y4w9Xp555hnx9NNP68WjfZ6C+6Oo42HTpk0CgPj33391y7Q5LCQkRAghRMeOHYWrq6uIjIzUrRMYGCisra3Fyy+/rFumfc9iYmJ0y86dO1fk+1L4mEtJSRFubm7irbfe0lsvMjJSuLq66i0v/B0SEBAglEql6Nevn17cxenWrZtBjn7rrbcEAGFra6vL0Q0aNBD9+/fXe2zBz7z2s/7xxx8LAGLixIkCgPD09BSxsbFCiLzPY2BgoLCystLL0YMGDRI2NjYiODhYty8ePnwonJ2dRbt27XQ5etmyZQKA6Nixo9i4caMuR8+dO1cAEHXq1NHlaO13Vnp6uvD09BQAxJIlS/Ti79Kli1Aqlbr9qc3RU6dO1e1n5mhDzNHM0czR8s7RhR9b3Oe4rJ8VR0dHg+fZunWrACAOHz5s8LeicnNBAAQAcf78ed2ysLAwYWdnJwYPHqxbVjCHaGlzSOHzASHy8t4zzzxTYhw9evQQrVu3LjKuwjFOmjTJYPmAAQP0vju2b98uAIhvvvlGb70hQ4YIhUIhgoKCdOcbAMRHH32kazPb29sLAKJt27aiR48epX6+tbmgYJu5a9euolWrVrr1tZ/Ln376qdg2s5ubm7C1tdX93qdPHwFAODg46LWZhwwZIgDocmlUVJQAIDw8PERubq5uvfnz5wsAevu1R48eAoDw9fXVtZmzsrJEmzZtBADxxRdfCCEefVcplUoxc+ZM4efnJ2rWrCmCgoL08nHB/FYw/2jz29ixY5mPmY/1MB/nYT5ebbB+WVQkH2uZKieOGzdOeHt769pfWq+99ppwdXXVvb/aY27r1q0Gz+Xo6Kh3bBVuD2dmZooGDRro2psFYx41apQAIN59913dMo1GIwYMGCBsbGx0bWNj8mLB117wO1UIYZDXFi5cKACI9evX65ZlZ2eLJ598Ujg5Oeny1po1awQAcffuXb3tFX4Py7J/pkyZIgDofZenpKSIhg0bCl9fX90xp91mwXOiLl266PZj4ddYWFGPz8zMFEqlUkycOFG3rKh+iMIKf1f5+PgIAOK3337TLcvNzRXPPvussLW11R1PZX3fjDlmC+ai0NBQ4e3tLZ566imD3GHssW2Mck0/1atXL3h5eaF+/fp47bXX4OTkhL/++ktXEbW1tdXNF6hWqxEXFwcnJyc0b94cFy9e1G1n27Zt8PT0xLvvvmvwHMWNrDXGyJEj4ezsrPt9yJAh8Pb21lXNLl++jDt37mD48OGIi4tDbGwsYmNjkZaWhmeffRb//vuvweV0mZmZepftFuXPP/+ERqPB0KFDdduMjY1F7dq10bRpUxw+fFhv/ezsbAB5+6s4W7duhaurK5577jm9bXbs2BFOTk4G28zJydFbLzY2ttRRvuHh4ViyZAm+/PJLg8vNt27dipYtW6JFixZ629ROOVb4+Ytz9uxZDB06FC+//DLmzZtX5DrG7A8AuqttgLwRQUlJSejevbvesbV9+3ZoNBp89dVXBnNXao+t/fv3IyUlBZ9++qnBe6td5/z584iOjsbEiRP11hkwYABatGhhcBmaRqPR7aPLly9j7dq18Pb2RsuWLUt8TSXRjlb5+++/kZOTU+x6xuyXohj7edUaNWqU3nMBwNChQ2FnZ4cNGzbolvn7+yM2NhZvvPFGqa9Ra9u2bVAoFJg+fbrB3wp+JxR8/qSkJMTGxqJHjx64e/cukpKSSn2erVu3onv37qhRo4becd2rVy+o1WrdZXR79+6FtbU13nrrLd1jlUqlbmSLVkREBC5fvozRo0fD3d1dt7xdu3Z47rnniqzYF778Dsj77nr48KHe52rDhg2wt7fHyy+/XOrrKo32+yEmJganTp3CX3/9hXbt2sHT07PYx5T1M/Diiy+iadOmut+1N527cOECoqKiABh/vNSsWRMPHjwo9XUVPB4yMzMRGxuLJ554AgCKPIYTEhIQGBiICxcu4PXXX9eNRgKApk2bYuDAgdi7d6/eqMby2r9/PxITEzFs2DC9Y02lUqFLly4lfodOmzYNjz32mG66CmMVzNErV66ESqXC5s2bdTlaoVAgNzcXsbGxiI6ORlRUVJGf+X///Reenp546aWXAABjxozRXe2hUORN7dSrVy8Aed99arUa+/btw6BBg9CoUSPddry9vTF8+HDdCKCRI0fq3rPx48fjtdde0+Xo//73v1CpVHj48KEuR6vVamRmZuKPP/5AXFwcVCoVJk6cqPeaY2JioNFodPtZe+WQUqnU7Wfm6OIxRzNHG4M52vxydEElfY7L+lkBYPBdlZKSUqHX9+STT6Jjx4663xs0aIAXX3wR/v7+UKvVpeaQ48eP664w0MrOzi71OwnI++xoX4f2u6wo2nOIgv8Kf653794NlUqFyZMn6y2fOnUqhBDYs2eP3vJ58+bp2syZmZmYOHGi7jg05vOdnp6OTZs2wd3dHXXr1sWVK1fw7LPP6rav/eylpaUByBvhaqzXX39dr82snZpCO7XUr7/+CiBvCpKEhATdPnn11VdhbW2Nmzdv6rWZrays4ObmpjvGbGxsdCO1IyIi9J5bCIHTp0/j1KlT2LhxI1xdXfXyccH8pj1/zMrKMpj2Wov52BDzcdGYj5mPS1PRfGyM0nKiEALbtm3DCy+8ACGE3v7v06cPkpKSDI67lJQUg++10vz000+Ii4sr8ljSKjg1o3ZkfXZ2Ng4cOACg7HkxPT0dsbGxiIyMxLZt2wzy2u7du1G7dm29e09ZW1tj8uTJSE1NxdGjRwHk9RUAMKq/ADBu/+zevRudO3fGU089pVvm5OSE8ePHIzQ0VG9Wg4L+/PNPnDt3Dt9//71RsWhpP5f37t3D3LlzodFodHmjoPj4eF2ftTFq1aqFESNG6H5XqVSYMmUKsrKyyv2+lXbMFhQXF4c+ffrA2dkZO3fu1MsL5Tm2S1Ku6ad++uknNGvWDFZWVqhVqxaaN2+ulwS18/stW7YMISEhei9Q2yEC5E1b1bx5c1hZmWQWLJ2CHWpA3gevSZMmurnWtHOFFnU5nFZSUhJq1Kih+z02NtZgu4XduXMHQohi1yt8yat2+o2i5q0tuM2kpCTdB7aw6Ohovd/37dsHLy+vEuMsbPr06ahTpw4mTJhgMM/cnTt3cPPmzWK3Wfj5ixIeHo4BAwYgLS0NcXFxxRasjNkfQN6JyjfffIPLly/rzVFacLvBwcFQKpVo1apVsdvRTptW0jy7YWFhAIDmzZsb/K1FixY4fvy43rL79+/r7Stvb29s27at1NdUkh49euDll1/GzJkzsWDBAvj5+WHQoEEYPny43smsMfulKMZ+XrUaNmxosMzNzQ0vvPACNm7ciK+//hpA3olF3bp1i/xSLk5wcDDq1Kmjd5JTlBMnTmD69Ok4deoU0tPT9f6WlJRU6k0P79y5g6tXr5Z6XIeFhcHb21t3Cb5WkyZN9H4v6Thp2bIl/P39DW5sVtR+fO655+Dt7Y0NGzbg2WefhUajwaZNm/Diiy/qNTrL6+TJk3qvuWnTpti+fXuJx4ixnwHtNlq0aGGwnraBEhoailq1ahl9vHTt2hVbtmzBwoUL8dprr8HKyspgXmQgL8nPnDkTmzdvNvhOKuqE/bHHHtP9XNx7tm3bNsTGxuoVPMpDm2+K+xy4uLgUufz48ePYtWsXDh48WOapRwry9PTE9u3b0a1bN90yIUSxuaLgZ/7hw4d6Obqo91b7WUhLS0NMTAzS09OL3acif27OgjmyadOmejnayclJN51ZwRy9ZcsWbNmyBUBeQ7Jwjo6PjwdguJ+1HQIuLi7M0cVgjmaONhZztPnlaK3SPsdl/aykpaWV+buqNEV99zZr1gzp6emIiYkBgBJziEajwf3799G6dWvd8sTERPj4+JT63Ldu3dK9Hu29I6ZPn24wrc+vv/6q68gvqOBzhIWFoU6dOgbvufZcJywsTC/GESNGYOzYsbppkBYuXIjnnnsOgHGf74Id26+++ir69u2LOXPmGMT48ccfA8ibtnLu3Ll44YUXsGDBghLPYwpPt+zm5gYbGxtdTtUORvjxxx/x448/FrmNgvm4Tp06iI+P13uvtdNJF54vvWCHiXaAhJa1tTVycnIM8tv48eN1Pxfu2GE+NsR8zHzMfFw+FcnHxiotJyqVSiQmJmLFihVYsWJFkdso/L1S+P4xpUlKSsJ3332HDz74oNhcoVQq9QYaaOMEoOtfNSYvFjRv3jy93FY4r4WFhaFp06YGhdbC2+vQoQPs7Owwc+ZMLF++XJeLcnJyipxu0Jj9ExYWVuRUlwWfu/B3oVqtxmeffYbXX38d7dq1K/U5Cho0aJDuZ6VSiS+++KLIwlzBz0zNmjXx1ltvYebMmVCpVAbrKhQKNGvWrNj9V973rbRjtnbt2rrlzz//PG7fvo2aNWsa3J8jJiamzMd2ScpVTejcubNunr6ifPfdd/jyyy8xduxYfP3113B3d4dSqcSUKVMMroCQgjaGefPmoX379kWuUzChZmdnIyIiQncCWtJ2FQoF9uzZU+TBVThJR0ZGAoDem1/UNmvWrKlXzS+ocILp0qULvvnmG71lS5cuxY4dO4p8/M2bN/Hbb79h/fr1RX7wNRoN2rZtq5sLvbD69esXG7tWUFAQHnvsMSxYsAAjRozAmjVriiwoGbM/jh07hoEDB+Lpp5/GsmXL4O3tDWtra6xevdrgRmVSqFWrFtavXw8gL0msWrUKffv2xfHjx9G2bdtybVN7U6PTp09j165d8Pf3x9ixY/Hjjz/i9OnTcHJyqtB+KevntfCIE62RI0di69atOHnyJNq2bYudO3di4sSJBl+mFRUcHIxnn30WLVq0wPz581G/fn3Y2Nhg9+7dWLBggVHfMRqNBs8995yuAViYqe6lUJKi9qNKpcLw4cOxcuVKLFu2DCdOnMDDhw/LNHKnJO3atdM1SmNiYrB48WL4+fnh4sWLJX7ujFHccVEcY46X8ePHw9/fH++//36Jc2QPHToUJ0+exEcffYT27dvDyckJGo0Gffv2LfJ4WL9+PdLT0/Uax5VF+/zr1q0rch8XV9T/5JNP0KdPH/Ts2dPg5nqlWbp0KZo3b677DtLOe639DtIWevr27YvHHnsM2dnZ+P3333H//n2DBg9Q9vfWVLQ5+vXXX0d0dDQ6deqEDh06YMWKFbhw4YKuEyQ7O1s3gli7n5OSkjBkyBCMGDECI0eOhJWVFTZv3swcXQTmaOZoU2KOLr+K5GhjP8fGsrOzw65du/SWHTt2TDd/trmIjIxEnz59Sl3P19dXdxVCXFwcFi9ejBEjRqBRo0a6KzuBvKtNC98s/IsvvtB9/5VHo0aNUKtWLWzfvt3gu9yYz/eIESNw5MgRuLi44J133sHXX3+N559/HgcOHNDrYJs8eTIWL16McePGwdfXF7NmzUJiYmKRI5+NuboFgK4j4tNPP9UbRQvkjZ5WqVR6uVMIgcjIyFLbzFpTp07Frl27kJGRgf/973+wsbEB8OjGrdr8pj32tAMrgbwrDM6ePavbFvOxPuZj5mPm4/KrzDazsbTvzxtvvFFsPi/cgf7VV18Z3MPjhRdeKPY55syZA6VSiY8++kh3lXtV0LbPNBoN7t69W2xeK02tWrWwZMkSTJo0yeBY7NGjh8H6Zd0/xvr1118RGhoKf3//Mj/2hx9+wH/+8x/k5OTg3Llz+Oabb2BlZWVw5cy2bdvg4uKC9PR0/PXXX/j222919x8rTKq2e0G3bt3Cnj17MHToUEydOhWrV6/W/a08x3ZJTHuJRL4//vgDzzzzjMFIl8TERL1Ltho3bowzZ84UW0krL+3IWC0hBIKCgnQ7RjtixMXFxWBkSFGuXLmCnJycEgs52u0KIdCwYUOjvuBv3LgBhUJRZKW64DYPHDiAbt26GXVwenp6Grymkm5MNm3aNLRv3x6vvvpqsc+vvRysvJVp7bQitWrVwo4dOzB16lT079/f4OTSmP2xbds22NnZwd/fX++EvOCHRBu3RqPBjRs3ii1caY+DgIAAg1EEWtqRWbdv3zYYPXH79m2D0WF2dnZ6+3/gwIFwd3fH0qVL8fPPPxf7uozxxBNP4IknnsC3336LjRs34vXXX8fmzZvx5ptvGr1fimLs57U0ffv2hZeXFzZs2IAuXbogPT1d75I3YzRu3Bj+/v6Ij48vduTJrl27kJWVhZ07d6JBgwa65UVd1l3cMdu4cWOkpqaW+vn38fHB4cOHkZ6erjfyJCgoyGA9IO+YKOzWrVvw9PTUG3FSkpEjR+LHH3/Erl27sGfPHnh5eRnVYDdGjRo19F6zn58f6tSpg9WrV2PatGlFPsbYz4CnpyecnJyK3QcA9G78ZszxYmdnh3/++QeBgYG4f/8+hBCIiorSO2FNSEjAwYMHMXPmTHz11Ve65YXzQEHdunWDo6Mjxo8fX2y8jo6OJrnEWPs9U7NmTaPyDZD3nX3q1KkyXXZZUJcuXXT5qqjvoPT0dHh4eOhdUjp48GB069ZN78qWOnXq4Pbt26hXrx6Aoo9v7eg9R0dHeHl5wcHBodh9qlAoIITAnTt3dDnyzp07uhvBtWvXDqmpqboRiNocbW9vj7Zt2+Lw4cOwt7fHlStXMHHiRFy9ehV2dna4cuWKbrScdj9rLydu1KiRbr+fO3eOOboIzNHM0cZijja/HK1V2ue4rJ8VlUplsP8Lj7Qvq6LycmBgIBwcHHRxlpRDlEqlXsfwgwcPkJKSYtR0NY6Ojnqvp3v37qhbty727dunV9SoV6+eweteuHChXlHDx8cHBw4cQEpKit7oRu25TlFXjhT3XW7M57tRo0aIj4/HmTNnMG7cOLi6umL48OE4ffo0nnzySd1jtN/NLVq0wIcffoh79+5hzZo1yM3NNYjH29sbQN5UR9ob2wJ5ncvZ2dm6EbvaK07S0tL09kt2drZuCpqCbfiHDx9CrVbrtZm15wlF3fx5/PjxGDFiBB5//HGcOnVKN3Id0M9vp0+fhkKhwMiRI3VFlMIj/5mP9TEfMx8zH5dfRfKxsYzJic7OzlCr1Ua3Idu2bWuwblEDuYC87+tFixZh9uzZcHZ2LraooS08FGw7BQYGAnjUti9rXizYPgNgkNd8fHxw9epVaDQavWJfUdt788038dJLLyEgIEA3Nd/UqVOLfC3G7B8fH58S+zMKv5b09HTMnDkTEydONOrK0cI6duwIPz8/AEC/fv0QHh6OOXPm4Msvv9R77U8//bTue2bgwIE4ceIE9u7dW2RRo2HDhrh48WKx+6+875sxx6zWzp070b17d8yePRvvvPMO3njjDd3gCC8vrzIf2yUxbTk4n0qlMrjEZOvWrQgPD9db9vLLLyM2NhZLly412Ebhx5fF2rVr9eZ9/eOPPxAREYF+/foByDtwGjdujB9++AGpqakGj9deAl0wdpVKheeff77E533ppZegUqkwc+ZMg/iFEHpfFLm5udi2bRs6d+5c4mWWQ4cOhVqt1jvJK7iNijQwTp06hR07duD7778vNpENHToU4eHhutFNBWVkZBg1p1uzZs10J8dLliyBRqPBe++9p7eOsftDpVJBoVDoXe4ZGhpqcBI6aNAgKJVKzJo1y2AUgva96d27N5ydnTF79myDOVS16zz++OOoWbMm/ve//+ldnrpnzx7cvHkTAwYMKPG1Z2dnIzc3V++xZZWQkGBwPGlPOrXbNXa/FMXYz2tprKysMGzYMPz+++/47bff0LZt2zJffvfyyy9DCIGZM2ca/E0bozbxFIw5KSmpyJNRR0fHIj8jQ4cOxalTp4qspicmJuoagH369EFOTo7e8a/RaPDTTz/pPcbb2xvt27fHmjVr9J4vICAA+/btQ//+/Ut41fratWuHdu3a4ZdffsG2bdt00y5VBu28yyUdn8Z+BpRKJfr27YsdO3bozXMcHx+PNWvW4PHHH9e7rLUsx0uzZs3w7LPPolevXnrTKAFFHw9AXidESby8vPDYY49h48aNet/5wcHB2LlzJ/r161fsSWBZ9OnTBy4uLvjuu++KnN+3cL7RXr46fPjwYhuXZVHUd5C2uFDQwYMHDR779NNPIzY2Flu2bMHjjz+ONWvW6Kb/EkIgODhYNyenUqmESqVC7969sWPHDt1lrQAQFRWFjRs36kberV27VnfsrVixAps3b9bl6OXLl0OtVqN27dp6Ofqxxx6Do6MjlEol5s6di9DQUN2IYW2OdnJyKnE/M0cXjTmaOdpYzNHml6O1SvscV/SzYgqFi/X379/Hjh070Lt3b6hUqlJzyFNPPaU3ZePmzZsBFD+9Y0m03zvlyfP9+/eHWq02aL8uWLAACoVC197UKum7vDxt5sLHRXFtZm1nRlH5Qzu1xsaNG/XazNorWPv27QsAuvnxf/31V731fv31V919CApSq9VQKpW6NnN2djbWrFkD4FEhRUupVGLmzJlo164dPvzwQ8yZMwcBAQG6fKzNb//73/8M8k9GRobBvVGYj/UxH+dhPmY+NoWy5GNjGZMTX375ZWzbtg0BAQEGjy/chiyrmTNnolatWkXes6SwgvlOCIGlS5fC2tpa10ld1rxYWOH9279/f0RGRuqmHgbyvvuWLFkCJycng6sw3N3d8fTTT6NXr17o1auX3hTFZdW/f3+cPXsWp06d0i1LS0vDihUr4OvrazBV36JFi5CWlobPP/+83M9ZUEZGBnJzc4sckKAlhIAQothzmKL2n3b6PFtbW10hoTznMyUdswVpzw8mTpyIrl27YsKECbr32dTHdqV86p9//nnMmjULY8aMQdeuXXHt2jVs2LDBYC62kSNHYu3atfjggw9w9uxZdO/eHWlpaThw4AAmTpyIF198sVzP7+7ujqeeegpjxoxBVFQUFi5ciCZNmuhOzJRKJX755Rf069cPrVu3xpgxY1C3bl2Eh4fj8OHDcHFxwa5du5CWloaffvoJixcvRrNmzXDkyBHdc2g7Wq5evYpTp07hySefROPGjfHNN99g2rRpCA0NxaBBg+Ds7IyQkBD89ddfGD9+PD788EMcOHAAX375Ja5evWpwaXdhPXr0wIQJEzB79mxcvnwZvXv3hrW1Ne7cuYOtW7di0aJFGDJkSLn20759+/Dcc8+VWB0bMWIEfv/9d7z99ts4fPgwunXrBrVajVu3buH333+Hv79/qVewFFS7dm3MmzcPb775Jt544w3079+/TPtjwIABmD9/Pvr27Yvhw4cjOjoaP/30E5o0aYKrV6/q1mvSpAk+//xzfP311+jevTteeukl2Nra4ty5c6hTpw5mz54NFxcXLFiwAG+++SY6deqE4cOHo0aNGrhy5QrS09OxZs0aWFtbY86cORgzZgx69OiBYcOGISoqCosWLYKvr6/BlDhpaWl6l9KuW7cOmZmZGDx4sNH7qLA1a9Zg2bJlGDx4MBo3boyUlBSsXLkSLi4uusRv7H4pirGfV2OMHDkSixcvxuHDh4uc77c0zzzzDEaMGIHFixfjzp07uumDjh07hmeeeQbvvPMOevfuDRsbG7zwwguYMGECUlNTsXLlStSsWdPgJoQdO3bE8uXL8c0336BJkyaoWbMmevbsiY8++gg7d+7E888/j9GjR6Njx45IS0vDtWvX8McffyA0NBSenp4YNGgQOnfujKlTpyIoKAgtWrTAzp07dXMNF2zYzJs3D/369cOTTz6JcePGISMjA0uWLIGrqytmzJhR5v344YcfAoDJLqMF8joGtMdnbGwsfv75Z1hZWZVYsC3LZ2DWrFnYu3cvnnrqKUycOBG2trZYuXIlkpKSipyLuaLHC5A3mv/pp5/G3LlzkZOToxt5WfgGkkWZO3cu+vbtiyeeeAITJkxAbm4uli5dCjs7O3z77bcG6x86dEjXoaIdqXDt2jXdDTWBvNygVCpx9OhR9OjRAy4uLli+fDlGjBiBxx57DK+99hq8vLxw7949/PPPP+jWrZveicSDBw90l4aX1+7du3Hr1q1iv4Ps7e0RHx+P3r17o2XLlrh+/ToOHz4MpVKpdxLYu3dvnD17Fh988AGeeeYZxMTEoHnz5qhRowYef/xxHDp0CLa2tnpFhG+++Qb79+/HU089hZycHGRkZKBr167IysrChAkTMGnSJLi7u2P27NkAgHv37uGNN96Al5cXbt68iZUrV+Kpp57CrFmz0L9/f7Ru3RpJSUkIDAzEjBkzdDn6k08+wezZs5GRkYHly5ejWbNm8PPzw//+9z80atRId9+UzZs3Y+PGjejduzeWLl3KHF0K5uiyY45mjjaV8uToohT1OS7rZ6UytGnTBn369MHkyZNha2uLZcuWAYBep1zBHDJx4kRYWVnh559/RlZWFubOnQsgbz9Nnz4dv/zyC1577bUi7/dUWGpqqi5Xx8fHY/HixbC2ti5XMeeFF17AM888g88//xyhoaH4z3/+g3379mHHjh2YMmUKGjdujGPHjunWL+m73JjP99WrV9GoUSO0bNkSH3zwAaytreHm5objx49j9uzZuo4D7c1Lb9y4gZkzZ2Lt2rV48cUXi+z00M5Rn5aWhvr166NXr166ufMdHBywYMECAHlTe4waNQpr1qxBzZo18cwzzyAzMxNHjhyBs7Mz9u3bh/feew9paWm6+38pFAqMHTsW3t7eOH36tG5E7/Xr1/U6icaNG4eVK1ciNDQUAwYMgJubG3r37q27IesHH3yAZcuWYeLEiVAoFJgwYQIWLVqky2+F5xxnPtbHfMx8zHxcfqbKxyUxJid+//33OHz4MLp06YK33noLrVq1Qnx8PC5evIgDBw7o9m957Nu3Dxs2bNBN+1ccOzs77N27F6NGjUKXLl2wZ88e/PPPP/jss890o/ONyYsFXb16FevXr9cNklu8eDHq1aun+44cP348fv75Z4wePRoXLlyAr68v/vjjD5w4cQILFy40yX1TivPpp59i06ZN6NevHyZPngx3d3esWbMGISEh2LZtm8E0cfv27cO3335b5H11jLF//348ePBAN/3Uhg0bMHDgQIP3RdsPoZ1+KigoCFOmTClym+PGjcPy5csxevRonD9/Hg0bNsT27dtx8OBBfP/997pYy/q+GXPMFqZQKPDLL7+gffv2mD59uu5czqTHtiiD1atXCwDi3LlzJa6XmZkppk6dKry9vYW9vb3o1q2bOHXqlOjRo4fo0aOH3rrp6eni888/Fw0bNhTW1taidu3aYsiQISI4OFgIIURISIgAIObNm2fwPK1bt9bb3uHDhwUAsWnTJjFt2jRRs2ZNYW9vLwYMGCDCwsIMHn/p0iXx0ksvCQ8PD2Frayt8fHzE0KFDxcGDB/Weu7R/o0aN0tvutm3bxFNPPSUcHR2Fo6OjaNGihZg0aZK4ffu2EEKId999Vzz99NNi7969BjFNnz5dFPW2rFixQnTs2FHY29sLZ2dn0bZtW/Hxxx+Lhw8f6tbx8fERAwYMMHjspEmTDLYJQCgUCnHhwgW95UW9R9nZ2WLOnDmidevWwtbWVtSoUUN07NhRzJw5UyQlJRk8X2nbE0KInj17igYNGoiUlJQy749ff/1VNG3aVNja2ooWLVqI1atXF7vfVq1aJTp06KCLu0ePHmL//v166+zcuVN07dpV2NvbCxcXF9G5c2exadMmvXW2bNmi2467u7t4/fXXxYMHD/TWGTVqlN5x4eTkJB577DGxbt26EvdRaS5evCiGDRsmGjRoIGxtbUXNmjXF888/L86fP1+u/eLj46N3zBr7edV+vrZu3VpivK1btxZKpdJg/xgrNzdXzJs3T7Ro0ULY2NgILy8v0a9fP71jdefOnaJdu3bCzs5O+Pr6ijlz5ohVq1YJACIkJES3XmRkpBgwYIBwdnYWAPReT0pKipg2bZpo0qSJsLGxEZ6enqJr167ihx9+ENnZ2br1YmJixPDhw4Wzs7NwdXUVo0ePFidOnBAAxObNm/ViP3DggOjWrZvuWHrhhRfEjRs39NbRvicxMTHF7oOIiAihUqlEs2bNyrUPi9KjRw+949PNzU1069ZN7N6926jHG/MZECLveO3Tp49wdHQUDg4Ows/PTxw7dqzY7Zb1eNF+L69evVq37MGDB2Lw4MHCzc1NuLq6ildeeUU8fPhQABDTp0/XrafNYQWPkQMHDoiuXbsKOzs74ezsLPr37y+uXr2q95za96ws/3x8fPS2cfjwYdGnTx/h6uoq7OzsROPGjcXo0aP1Psfa75D33ntP77FFxV2Ubt26GfUd1KBBA731rKysROfOnUWHDh1Ejx49dJ/1w4cP6+VoKysrYW1tLZRKpXBychIDBgwQe/fuNcjR2mNAqVQKpVIpnnnmGXHy5Em9HD1gwAABQNja2ooGDRoIV1dX4eTkJF5//XURFxcnhHiUo7XbKZijMzMzRaNGjYx6Lxo3bqy3n5mjS96eEMzRZcEczRxtChXJ0cZ8jrWM/aw4OjoabG/r1q263FBYUbm5IABi0qRJYv369brPQYcOHYrcljaHODk5CQcHB10O0Tpx4oRo0qSJmDFjhsjKyio1juL27Z49e4qMsbABAwYY5PSUlBTx/vvvizp16ghra2vRtGlTMW/ePKHRaIQQj/J2ad/lpX2+C+cz7blVwTbzhg0b9NbR5svJkyeLhIQEIYQQbm5uwtbWVvf82u+LV155Rbi6uuribNSokS4XFvTpp58KJycn3XM4OzuLwYMHl6vNXPAco2A+trOzEwBE9+7ddTFMmjRJNGzYUPj4+Bjkt08//ZT5uADmY+Zj5mPTqGibWQjT5sSoqCgxadIkUb9+fd13/7PPPitWrFihW6ekY87R0VHvONbmp/bt2+tyVnExa88HgoODRe/evYWDg4OoVauWmD59ulCr1XrPU1peLPjaC+bH2rVri5deekncvHnT4HWPGTNGeHp6ChsbG9G2bdti92dhZflMFt4/QggRHBwshgwZItzc3ISdnZ3o3Lmz+Pvvv/XW0W7T29tbpKWlGbzGgn0QRdE+vmB7vHDuFsKwH8Le3l60atVKLFiwQLdO4e8qIYSIjo4WY8eO1e2/Nm3aiJUrVxrEUZb3zZhjtrgcM3PmTGFlZSUuXryoW2bMsW2MMhU1zJ2xCcRY2g92SR1J06dPNziAiKqz9u3bi549e0odRqX666+/BABx/PjxStl+TEyMsLKyErNmzaqU7ZsTSzxeDh8+bNABQvo52thBEiVhjiYqO0v8zi2MOdr8FFcwIGlUVpu5adOmxa7DfEykj/m44uSaj+WSE4sb5EDVjzkfs5VyTw0iqp7Onz+Py5cvY+TIkVKHYjLauf+01Go1lixZAhcXF90UN6b222+/Qa1Wl/mmcXJjiccLEZG5ssTvXOZoIiKSG+Zj02A+JqLKuZOOhXBycsLrr79e4k242rVrhzp16lRhVETmJyAgABcuXMCPP/4Ib29vvPrqq3p/V6vVpd7wx8nJqcTPmlTeffddZGRk4Mknn0RWVhb+/PNPnDx5Et999x3s7e1N+lyHDh3CjRs38O2332LQoEHw9fU16fbNRWnHi9y5u7sb3MCMTI85msg4zNGmUV1yNFFZOTk5oWbNmgZzjRfEfEzEfGwqzMdEpMWiRgk8PT11NwgqzksvvVRF0RCZrz/++AOzZs1C8+bNsWnTJtjZ2en9/f79+7qbEhZn+vTpZb45WFXo2bMnfvzxR/z999/IzMxEkyZNsGTJErzzzjsmf65Zs2bh5MmT6NatG5YsWWLy7ZuL0o4XuWvXrh3WrFkjdRgWjzmayDjM0aZRXXI0UVl5enqiZcuWiI2NLXYd5mMi5mNTYT4mIi2FEEJIHQQRWbbMzEwcP368xHUaNWqERo0aVVFEREREBDBHExERmQPmYyKismFRg4iIiIiIiIiIiIiIZIE3CiciIiIiIiIiIiIiIllgUYOIiIiIiIiIiIiIiGSBRQ0iIiIiIiIiIiIiIpIFFjWIiIiIiIiIiIiIiEgWWNQgIiIiIiIiIiIiIiJZYFGDiIiIiIiIiIiIiIhkgUUNIiIiIiIiIiIiIiKSBRY1iIiIiIiIiIiIiIhIFljUICIiIiIiIiIiIiIiWWBRg4iIiIiIiIiIiIiIZIFFDSIiIiIiIiIiIiIikgUWNYiIiIiIiIiIiIiISBZY1CAiIiIiIiIiIiIiIllgUYOIiIiIiIiIiIiIiGSBRQ0iIiIiIiIiIiIiIpIFFjWIiIiIiIiIiIiIiEgWWNQgIiIiIiIiIiIiIiJZYFGDiIiIiIiIiIiIiIhkgUUNIiIiIiIiIiIiIiKSBRY1iIiIiIiIiIiIiIhIFljUICIiIiIiIiIiIiIiWWBRg4iIiIiIiIiIiIiIZIFFDSIiIiIiIiIiIiIikgUWNYiIiIiIiIiIiIiISBZY1CAiIiIiIiIiIiIiIllgUYOIiIiIiIiIiIiIiGSBRQ0iIiIiIiIiIiIiIpIFFjWIiIiIiIiIiIiIiEgWWNQgIiIiIiIiIiIiIiJZYFGDiIiIiIiIiIiIiIhkgUUNIiIiIiIiIiIiIiKSBRY1iIiIiIiIiIiIiIhIFljUICIiIiIiIiIiIiIiWWBRg4iIiIiIiIiIiIiIZIFFDSIiIiIiIiIiIiIikgUWNYiIiIiIiIiIiIiISBZY1CAiIiIiIiIiIiIiIllgUYOIiIiIiIiIiIiIiGSBRQ0iIiIiIiIiIiIiIpIFFjWIiIiIiIiIiIiIiEgWWNQgIiIiIiIiIiIiIiJZYFGDiIiIiIiIiIiIiIhkgUUNIiIiIiIiIiIiIiKSBRY1iIiIiIiIiIiIiIhIFljUICIiIiIiIiIiIiIiWWBRg4iIiIiIiIiIiIiIZMFK6gCIqPrQaATSsnORnq1GVo4GaiGgEQJCCCitUgFlBhQKBZQKJZQKJexUdnCwdoCDlQMUCoXU4RMREVU7xuZulUIFpUIJW5UtHK0dYW9lz9xNREQkAba7iag6YFGDiMolK1eN6OQsRKdkISYlE1HJWYhOyUR0chbi0rKRmpmLtOxcpGXlIjVLjfTsXGTkqCFE0dvr/sQJXE7aVeTfFFDA3soeDtYOcLR2hIOVAxysHeBs7QwPew942nvCy94LXg5euv897D1grbSuxD1AREQkLyXl7tjULKRm5SItS52fv9VIy8pFZm7FcrejtaOuo6S03O1p7wkrJZsnREREWtm5GkQlZ5bY7k7NykV6tpHt7i4ncDm5fO1ubc4umMPZ7iYiqbDVQETFik/LRkhsKu7GpCEk9tG/yORMJKbnVFkcAgLpuelIz01HbEasUY9RQIEadjVQ27E2fJx94OPqAx8XH/i6+MLHxQfONs6VHDUREVHVKyp3h8alIyIpQ7LcjQzjHlNS7vZ18YWTjVPlBk1ERCSBxPRsBOfn7bsxqWaRu9nuJiJzx6IGESE5MwcB4UkICE/CrcgU3I1JQ2hcWpWeQJmagEB8ZjziM+NxI+6Gwd/d7dzzOklcfdGsRjO09miN5u7NYW9lL0G0REREZZOSmYOA8GQEhCfhZmRytcrdDV0bommNpszdREQkKwVz963IFNyNzStgVIfc7evqi+Y1mqOVRyvmbiIyCRY1iKqZpIwcXA9PwrX8fwHhSQiLTy/28lRLpT3xuhh9UbdMpVChoWtDtPJohZbuLdHKoxVauLeAg7WDhJESEVF1x9ydp7Tcrf3XvEZz5m4iIpJUwYGD1/ILGaFxaczdeJS7tW1utruJqDwUQlS3r1Si6iUqOROnguNw+m4czobEI8RMT6RKmpdbSkqFEk3cmuDxWo/j8dqP4/Faj6OGXQ2pwyIiIgumzd1nQuJw5i5zd1kpFUo0dmuMx2s9jk61OzF3ExFRpZNNu7uEe2pIqWC7u1PtTuhYqyNzNxGViEUNIgsTmZSJ03fjdP9C49KlDsko5toxUpgCCjR2a4xOtTuxo4SIiEyCubtyFc7dnWp1gpudm9RhERGRjBUsYsgqd5tpUaMwtruJqDQsahDJXI5ag7Mh8dh/IwpHA2MQEpsmdUjlIpeOkcIUUKBJjSZ4uu7T8Kvvh3Ze7aBUKKUOi4iIzJg2dx+4GYUjt5m7q5oCCjSt0RTd63Zn7iYiIqMUbHf/GxiDu3LN3TIpahSmzd096vVAj/o90NazLXM3UTXHogaRDCWmZ+PI7Rjsv5l3QpWSmSt1SBUm146Rwtzt3HWdJF3rdOW8oEREBIC525xpc/cz9Z/Bk3WeZO4mIiIAQFJ6Dg7fjsaBm3kDCC0id8u0qFGYu507nq73NPzq+TF3E1VTLGoQycSDhHTsuRaJ/TejcCEsAWqNZX10LaVjpCAbpQ06eXfCM/WeQS+fXvCw95A6JCIiqkLM3fJjo7RBZ+/O8Kvnh2d9noWnvafUIRERURW6H5+OvQGROJCfu3MtLXdbSFGjIBulDTrV7gS/+n54zuc5truJqgkWNYjMWEJaNv65FoHtl8Jx4V6CWd5ozFQssWOkICuFFbrU6YIBDQfg2QbPciQJEZGFSkzPxt9XI7DjcjjOhzF3y5lKocIT3k9gQCPmbiIiS6bN3dWi3W2BRY2C2O4mqj6spA6AiPRl5qix/0YUdlwOx9HAGOSoLfiMqhrJFbk4EX4CJ8JPwN7KHn71/fB8o+fRtU5XWCn5VUxEJGfM3ZZJLdQ48fAETjxk7iYisjSZOWocuBmF7ZeYuy2JQbu7nh8GNBqArnW7wlppLXV4RGRCvFKDyEycvhuH38/fx77rUUjNkv9cnWVl6aM9i1PDtgZ6+/bGy01fRkuPllKHQ0REZcDcXT1zt7udO3r79MbgpoPRyqOV1OEQEVEZnL4bh63nH8D/emT1zN0WfqVGcdxs3dDHtw/b3UQWhEUNIgmlZOZg24UH2HDmHu5Ep0odjqSqa8dIQe0822Fo86Ho27AvbFW2UodDRERFYO5+hLkbaOvZFkObD0W/hv2Yu4mIzFRyZg7+ZO4GUH2LGgWx3U1kGVjUIJJAQHgSNpwJw47LD5GerZY6HLPAjpFHXG1d8WLjFzG0+VD4uPhIHQ4REYG5uyjM3Y9oc/erzV9FA5cGUodDREQArj9MwvrTzN0FsajxCNvdRPLGogZRFcnKVePvKxFYfyYMl+4lSh2O2WHHiCEFFOji3QWvNX8NzzR4BkqFUuqQiIiqFebukjF3G1JAgSe8n8CrLV6FXz0/qJQqqUMiIqpWsnLV+OdqBNadZu4uCosahtjuJpIn3uGOqJKlZuVi/ekwrDoeguiULKnDIRkREDgdcRqnI06jgXMDjG4zGi82fhE2KhupQyMismjM3VReAgKnIk7hVMQp1Heuj9GtR2NQk0HM3URElSwlMwfrT9/DqhMhiGHupjIo2O72cfHBqNaj2O4mkgFeqUFUSWJTs7DqeAjWnw5Dcmb1uwFZWXG0p3G87L3wRqs38GrzV+Fo7Sh1OEREFoW5u2yYu43jae+JN1rm5W4nGyepwyEisija3L3udBhSmLtLxSs1jKNtdw9tNpS5m8hMsahBZGL349Px87/B2Hr+AbJyNVKHIxvsGCkbZxtnvNr8VbzR8g142HtIHQ4Rkawxd5cPc3fZOFs749UWzN1ERKZwPz4dK/69i60X7iMzh7nbWCxqlI2ztTOGNh+KN1q9AU97T6nDIaICWNQgMpG7MalYfPAOdl2NgFrDj1VZsWOkfGxVthjcZDDGtxsPLwcvqcMhIpIV5u6KYe4uHzuVHQY1GYQ3276JWo61pA6HiEhWgmNSsfRQEHZdeYhc5u4yY1GjfGxVthjUZBAmtJvAdjeRmWBRg6iCIpMysfBAIP648IAnVRXAjpGKsbeyx7AWwzC2zVi42rpKHQ4RkVlj7jYN5u6KsVPZYViLYRjXdhxzNxFRKbS5e+uFBxyIUAEsalQM291E5oNFDaJySkzPxk+Hg7D2VBinqjABdoyYhrONM8a2GYvXW74Oeyt7qcMhIjIrienZWHYkGGtOhjJ3mwBzt2k42zhjTOsxeKPVG8zdRESFMHebFosapqHN3a+3fB0O1g5Sh0NULbGoQVRG6dm5+PVYCFYcu8sbkZkQO0ZMy9PeE+PbjceQZkNgrbSWOhwiIkkxd1cO5m7T8rT3xIR2E/Bys5eZu4mo2kvPzsWq4yH4+V/mblNiUcO0POw8ML7deLzS7BVYq5i7iaoSixpERlJrBDaeCcOig0GITc2SOhyLw46RylHPqR7e6/ge+vr2lToUIqIqp9YIbDx7D4sO3GHurgTM3ZWjvnN9TO4wGX0bMncTUfWjzd2LD95BTApzt6mxqFE56jrVxZSOU9juJqpCLGoQGeFsSDy+2hGAW5EpUodisdgxUrk61+6MaZ2noUmNJlKHQkRUJc6FxuOrHddxMyJZ6lAsFnN35Xq81uP4rMtnaFqjqdShEBFVCebuyseiRuXqXLszPu38KXM3URVgUYOoBNHJmfhu901sv/xQ6lAsHjtGKp+VwgqvtXgNE9tPhLONs9ThEBFVCubuqsPcXfmYu4moOohOycTs3bfw16VwqUOxeCxqVD7mbqKqwaIGURFy1BqsPhGCxQeDkJrF+TurAjtGqo6HnQfee+w9DGoyCAqFQupwiIhMgrm76jF3Vx0POw9M6TgFLzZ+kbmbiCxGrlqD1SdCsejgHebuKsKiRtVxt3PHlMemsN1NVElY1CAq5ERQLKbvvI6g6FSpQ6lW2DFS9dp5tcNnXT5Da4/WUodCRFQhzN3SYO6ueu292uOzLp+hpUdLqUMhIqqQk/m5+w5zd5ViUaPqtfNsh8+eYLubyNSUUgdAZC6SMnIw9fcreP2XM+wUoWrhasxVvP7P65h/fj6y1LwJHxHJD3M3VTeXYy7jtX9ew7xz85CZmyl1OEREZZaUnoMPfr+M4b+cYUGDqoWrsfnt7gtsdxOZEosaRAAO3IjCc/OPYtvFB1KHQlSl1EKN1ddXY8jOIbgcfVnqcIiIjMbcTdWVRmiw9sZavLLrFeZuIpKVAzei8NyCo/jzIu+dQdWLWqixOoDtbiJTYlGDqrXE9GxM2XwJb649j+gUVsyp+gpNDsWovaMw5+wcZORmSB0OEVGxmLuJ8mhz99xzc3nVBhGZNeZuojxsdxOZDosaVG3tDYhEr/n/Yvvlh1KHQmQWNEKD9TfXY8jOITgfeV7qcIiIDDB3E+nTCA3W3ViHIbuG4GLURanDISIysO96JJ5bwNxNpKVtd7+882WcizwndThEssWiBlU7CWnZeGfjRby9/gJiUzlKhKiweyn3MNZ/LL478x1HfhKRWUhMZ+4mKklYchjG+I/B92e/Z+4mIrOQmJ6NyZsuYfy6C4jh1RlEBu6n3Mc4/3H49vS3ZnmvDT8/P0yZMqXYvysUCmzfvt3o7R05cgQKhQKJiYkVjo0IYFGDqpkzd+PQb9Ex/H01QupQiMyagMCmW5sw7J9hCEoIkjocPTy5IqpezobEM3cTGUEjNNhwcwNe+/s13Em4I3U4epi7iaqXM3fj0HfhMey8wqsziEoiILD59ma89vdrCE4MljqcMomIiEC/fv2kDoOqMRY1qFrQaAQWHgjE8F/OIDKZo9eIjBWUGIRh/wzD1sCtUodiNJ5cEVkGbe4etvI0IpKYu4mMFZwUjOH/DMfvt3+XOhSjMXcTWQa2u4nKJygxCK/9/Zqs2t21a9eGra2t1GFQNcaiBlm8yKRMDP/lNBYeuAO1RkgdDpHsZKozMevULEw9MhUp2SlSh1MqnlwRyR9zN1HFZKoz8fXpr/Hh0Q+Zu4moSkQlM3cTVYQ5trs1Gg0+/vhjuLu7o3bt2pgxY4bub4Wvsjx58iTat28POzs7PP7449i+fTsUCgUuX76st80LFy7g8ccfh4ODA7p27Yrbt29XzYshi8OiBlm0Q7ei0H/xMZy+Gy91KESyty9sH17Z9QquxFyROhSeXBFZsIM3mbuJTMU/1B+v7HoF12KuSR0KczeRBTt8Kxr9FzF3E5mCObW716xZA0dHR5w5cwZz587FrFmzsH//foP1kpOT8cILL6Bt27a4ePEivv76a3zyySdFbvPzzz/Hjz/+iPPnz8PKygpjx46t7JdBFopFDbJIOWoNvv77BsatOY/4tGypwyGyGOGp4Ri9ZzR+vfYrhJBuBBZProgsT45ag1m7mLuJTC08NRwj947EqoBVzN1EZFI5ag2+/ecGxq45hzjmbiKTKdjullK7du0wffp0NG3aFCNHjsTjjz+OgwcPGqy3ceNGKBQKrFy5Eq1atUK/fv3w0UcfFbnNb7/9Fj169ECrVq3w6aef4uTJk8jM5HR1VHZWUgdAZGqxqVn47/oLOBeaIHUoRBYpV+Ri4cWFuB53Hd90+wYO1g5VHoP25AoAmjZtiqVLl+LgwYN47rnn9NYreHJlZ2eHVq1aITw8HG+99ZbBNrUnVwDw6aefYsCAAcjMzISdnV3lvyCiao65m6hy5WpyseDCAlyJvoLZ3WczdxNRhcWk5OXu82HM3USVwVza3QV5e3sjOjraYL3bt2+jXbt2evm3c+fOpW7T29sbABAdHY0GDRqYImSqRnilBlmUaw+SMHDJcXaKEFWB/WH78caeN/Ag5UGVP3dVnlwRUeVi7iaqOofuH8Lru1/H/ZT7Vf7czN1EluPagyS8uPQ4CxpEVWB/2H6M2DNCkna3tbW13u8KhQIajcZk21QoFABQ4W1S9cSiBlmMHZfD8crPJ/EwiZetEVWVOwl3MOyfYTgdcbpKn5cnV0SWgbmbqOoFJQYxdxNRuTF3E1W9wIRASXK3sZo3b45r164hKytLt+zcuXMSRkTVAYsaJHsajcDs3Tfx3ubLyMxhI4aoqiVmJeLt/W9j3Y11UodigCdXROaJuZtIWklZSXh7/9tYf2O91KEYYO4mMk8ajcDsPczdRFIx53b38OHDodFoMH78eNy8eRP+/v744YcfADwadEBkaixqkKwlZeRg7Jpz+Pnfu1KHQlStqYUac8/NxRfHv0C22nxuEsiTKyLzw9xNZB7UQo055+bgyxNfIkedI3U4OszdROYnOTMH49acw89HmbuJpKRtd39+/HNkqbNKf0AVcXFxwa5du3D58mW0b98en3/+Ob766isA4H2uqNLwRuEkW/fi0jF69VncjU2TOhQiyrcjeAdCk0OxtOdSuNm5SR2O7uTqv//9L9q3b4+2bdviq6++wvDhw3lyRSSB+/HpGLX6LO7GMHcTmYvtQdsRkhSCxT0Xw93OXepwmLuJzMy9uHSM/o25m8ic7AzeiXvJ97D02aVwtXWtlOc4cuSIwbLt27frfhZC6P2ta9euuHLliu73DRs2wNraWncDcD8/P4PHtG/f3mAZkbEUgkcPyVBAeBJGrz6H2FTzqUxTxXR/4gQuJ+2SOgwyEV8XX/z83M+o41RH6lAMbNiwAWPGjEFSUhLs7e2lDoeo2ggIT8KY384hJoW521Iwd1sWHxcf/K/X/1DPuZ7UoRhg7iaSBtvdlqd7lxO4nMzcbSkaujbE/3r9zyza3WvXrkWjRo1Qt25dXLlyBe+88w78/Pywfr35TXVJloHTT5HsHL8Ti9dWnOaJFZEZC00OxRu738Dt+NtSh4K1a9fi+PHjCAkJwfbt2/HJJ59g6NCh7BQhqkLa3M2CBpH5CksOw4g9I3Ar/pbUoTB3E5kBtruJzF9IUojZtLsjIyPxxhtvoGXLlnj//ffxyiuvYMWKFVKHRRaMRQ2SlR2XwzHmt7NIzcqVOhQiKkVMRgxG7x2NsxFnJY2DJ1dE0mLuJpKP2IxYjNk7BmcizkgaB3M3kbSYu4nkQ9vuljp3f/zxxwgNDUVmZiZCQkKwYMECODg4SBoTWTZOP0WysfLfu/huz03wiLVMnMLCclkrrfHdU9+hb8O+UodCRFWMuduyMXdbLuZuourrl2N38e1u5m5LxemnLJe10hpfd/saAxoNkDoUoirBKzXI7Akh8M3fN3hiRSRTOZocfPzvx1h7fa3UoRBRFWHuJpI3be7ecHOD1KEQURURQuC73TfxzT/M3URylKPJwbRj07Dm+hqpQyGqEixqkFnTaAQ+3HoVvxwPkToUIqoAAYF55+dh6aWlUodCRJWMuZvIMggIfH/2eyy5tETqUIiokqnzc/eKf+9KHQoRVYCAwA/nf2C7m6oFFjXIbOWqNZiy5TK2XXwgdShEZCI/X/0Z8y/MlzoMIqokzN1ElmfF1RWYf565m8hS5ao1eJ+5m8iisN1N1QGLGmSWctQavLvpEnZeeSh1KERkYqsDVmPO2TlSh0FEJsbcTWS5Vl9n7iayRDlqDSZvZu4mskSrA1bj+7PfSx0GUaVhUYPMTo5ag4kbLmJPQKTUoRBRJVl/cz2+Pf2t1GEQkYkwdxNZvvU31+Ob099IHQYRmUiOWoNJGy5i9zXmbiJLteHmBra7yWKxqEFmRXtitf9GlNShEFEl23x7M747853UYRBRBTF3E1UfW25vYe4msgC5ag3e2XgR+5i7iSze5tubWdggi8SiBpmNXLUG7268xBMrompk061NmH1mttRhEFE5sVOEqPrZdGsTp6IikrHc/Oki/a8zdxNVFxxQSJaIRQ0yCxqNwHtbLmPvdV76SlTdbLy1kTcxI5IhjUZgypbL7BQhqobW31yPH879IHUYRFRG2nY3p4skqn423dqEBRcWSB0GkcmwqEFm4csdAfjnaoTUYRCRRFYHrMZvAb9JHQYRlcFXOwPwN3M3UbW15sYa/HLtF6nDIKIy+ILtbqJqbVXAKra7yWKwqEGSm78/EBvO3JM6DCKS2I8XfsT2oO1Sh0FERpi/PxDrTzN3E1V3iy4uwl93/pI6DCIywvz9gdjIdjdRtcd2N1kKFjVIUmtPhWLxwTtSh0FEZmLGyRk4cv+I1GEQUQmYu4mooJmnZuLwvcNSh0FEJWDuJqKCZpycgUP3DkkdBlGFsKhBktl15SFm7LwudRhEZEbUQo0Pj36IC1EXpA6FiIqwk7mbiApRCzU+/vdj5m4iM/X3VeZuItKnzd3nIs9JHQpRubGoQZI4dicGU3+/Ao2QOhIiMjdZ6iy8e/Bd3I6/LXUoRFTAv4ExmPr7ZeZuIjKQqc7Eu4feRWBCoNShEFEBx+/E4oMtbHcTkaEsdRYmH5qMW/G3pA6FqFxY1KAqd+V+It5edwHZao3UoRCRmUrJScHbB95GeGq41KEQEYCrDxLx3/UXkKNmrwgRFS0lOwVv72fuJjIXVx8kYsK682x3E1GxUnNSMWH/BOZukiUWNahKRSRl4M2155GWrZY6FCIyc7EZsXjn4DtIy0mTOhSiai0iKQNvrmHuJqLSxWTEYOKBiUjNTpU6FKJq7WFiBsb+xtxNRKWLz4zHu4feZbubZIdFDaoyGdlqvLX2PGJSsqQOhYhkIigxCJ/++yk0giPMiKSgzd3RzN1EZKS7SXfx8b8fM3cTSUSbu2NTmbuJyDh3Eu7gk38/Ye4mWWFRg6qEEAJTt15GQHiy1KEQkcwceXAECy8ulDoMompHCIEPt15h7iaiMjsWfgwLLyyUOgyiakebu68/ZO4morI5+uAoczfJCosaVCUWHbyD3dcipQ6DiGRqdcBq7AzeKXUYRNXKooN38M+1CKnDICKZWn2duZuoqi0+GMTcTUTltvr6auwI2iF1GERGYVGDKt3uaxFYdPCO1GEQkczNPDkTl6MvSx0GUbXwz1XmbiKquJknZ+JKzBWpwyCqFvZci8DCg4FSh0FEMjfz1Excir4kdRhEpWJRgypVQHgSpv5+BUJIHQkRyV22JhtTDk9BRCpHnxFVpoDwJHy4lbmbiCpOm7sj03jFNlFluv4wCR+w3U1EJpCjycGUw1PwMPWh1KEQlYhFDao0calZGL/2PDJy1FKHQkQWIi4zDu8dfg/Z6mypQyGySPFp2XiLuZuITCg2IxaTD01GZm6m1KEQWaS8dvcF5m4iMpn4zHhMOTyF7W4yayxqUKUQQuD936/gYRIbL0RkWjfjb2LO2TlSh0FkcYQQeH/LZUQwdxORid2Mv4nvz34vdRhEFkcIgSlbLiM8MUPqUIjIwtyMv4m55+ZKHQZRsVjUoEqx7Egw/g2MkToMIrJQvwf+jr0he6UOg8iiLDsSjKPM3URUSbbd2Ybdd3dLHQaRRVl2JBjH7sRKHQYRWagtt7dgT8geqcMgKhKLGmRyZ0PiMX8/b1BGRJVrxqkZCEsOkzoMIotwLpS5m4gq36zTs5i7iUyE7W4iqgozT81EaFKo1GEQGWBRg0wqPi0bkzddglrDO5QRUeVKy0nDh0c/RJY6S+pQiGQtPi0b725k7iaiyqfN3Zyjm6hi2O4moqrCdjeZKxY1yGS0c3FHJnMubiKqGrfib/H+GkQVIITAB78zdxNR1bkVf4tzdBNVgBACU5m7iagK3U64jdlnZksdBpEeFjXIZJYf5VzcRFT1tgZu5RzdROW0/Ggwjtxm7iaiqrXl9hb4h/pLHQaRLK349y4OM3cTURXbdmcbdgXvkjoMIh0WNcgkLt5LwPx9nM+TiKQx6/QshKeGSx0GkaxcYu4mIgnNODkD91PuSx0GkaxcvJeAef63pQ6DiKqpb898i4epD6UOgwgAixpkApk5anz4+xXkcj5PIpJIWk4avjrxFYTg9xCRMTJz1Ji6lbmbiKSTmpOKL45/wdxNZKTMHDWmst1NRBJKy0nDlye+ZO4ms8CiBlXY3L23cTc2TeowiKiaOxt5FhtvbZQ6DCJZmOd/G3djmLuJSFoXoy9i/c31UodBJAtz9t5CCNvdRCQxtrvJXLCoQRVy5m4cVp8MkToMIiIAwKKLixCWHCZ1GERm7WxIPFafYO4mIvOw+OJi5m6iUpy5G4ffToZKHQYREQBg4YWFCE0KlToMquZY1KByS8/OxUd/XAWvOiMic5GRm4Evjn8BjdBIHQqRWUrPzsWHW6+AM1cQkbnIVGcydxOVID07Fx9vY7ubiMxHpjoTX5z4AmqNWupQqBpjUYPK7bvdN3EvPl3qMIiI9FyOuYzfrv8mdRhEZom5m4jM0eWYy1h7fa3UYRCZpe/33EJYHHM3EZmXKzFXsPr6aqnDoGqMRQ0qlxNBsdhw5p7UYRARFemnSz8hKCFI6jCIzMrxO8zdRGS+ll5eirtJd6UOg8isnAyOxbrTnJ6NiMzTssvLcCfhjtRhUDXFogaVWVpWLj7mtFNEZMayNdn48sSXnMqCKF9aVi4+4dQVRGTGstRZ+OI4p7Ig0mK7m4jMXY4mB1+d+IrtbpIEixpUZgsPBCI8MUPqMIiIShQQF4Ctt7dKHQaRWVh08A5zNxGZvWux17D59mapwyAyCwv2B+JBAnM3EZm3gLgA/BH4h9RhUDXEogaVye3IFKw+ESp1GERERll0aRHiMuKkDoNIUoFRKVh1PETqMIiIjPLTpZ8QmxErdRhEkrodmYLfToZKHQYRkVEWXlzIdjdVORY1qEy+3BGAXA2vfyUieUjJTsH8C/OlDoNIUl9sZ+4mIvlIyUnB/PPM3VS9sd1NRHLCdjdJgUUNMtqfFx/gbEi81GEQEZXJzuCdOB95XuowiCSx7QJzNxHJz667u3Ah6oLUYRBJgu1uIpKjncE7cS7ynNRhUDXCogYZJTkzB9/tviV1GERE5fLtmW+Rq8mVOgyiKpWUkYPZe25KHQYRUbl8c/ob5m6qdtjuJiI5+/b0t8jR5EgdBlUTLGqQUX7wv43Y1CypwyAiKpegxCCsu7FO6jCIqlRe7s6WOgwionIJSgzChpsbpA6DqErN3xfIdjcRyVZwUjDWXl8rdRhUTbCoQaUKCE/C+tNhUodBRFQhy68sR2RapNRhEFWJgPAkbDjD3E1E8rb8ynJEp0dLHQZRlbj+MAnr2O4mIpn7+erPiEqLkjoMqgZY1KBSzfr7BniPMiKSu4zcDCy9tFTqMIiqxNfM3URkAdJy0rDwwkKpwyCqEjN33oCayZuIZC4jNwNLL7PdTZWPRQ0q0cGbUbxJGRFZjF13dyEwIVDqMIgq1aFbUTjD3E1EFuKfkH9wO/621GEQVaoDN6JwNpS5m4gsw87gnbiTcEfqMMjCsahBxdJoBObs5U3KiMhyaISGIz7Jomk0AnP2sPOPiCyHRmiw4OICqcMgqjQajcBcf7a7ichyaIQG8y/MlzoMsnAsalCx/rj4AIFRqVKHQURkUsfCj+Fc5DmpwyCqFH9cfIDbUSlSh0FEZFInwk/gbMRZqcMgqhRsdxORJToefpy5myoVixpUpMwcNRbs5xQtRGSZ5p+fDyE4ZzFZFuZuIrJkCy7wag2yPJk5aixk7iYiCzX/AtvdVHlY1KAirT4RioikTKnDICKqFAFxAfAP9Zc6DCKTYu4mIkvG3E2WaM3JUDxk7iYiC3U97jr2hOyROgyyUCxqkIHE9GwsPxIkdRhERJVq8aXFyNHkSB0GkUkwdxNRdbDk0hLkanKlDoPIJJLSc7DsSLDUYRARVarFlxYjR812N5meldQBkPlZfiQYyZlsLBCZQszfMUi+kIysiCworBVwaOKA2kNrw9bb1mBdIQTC5och9VoqGrzbAC4dXYrdrjpTjaitUUi+mAx1qho2Xjbw6OUB957uunUiNkUg8XgiFLYK1B5SG25d3XR/SzqbhMQTifB538ekr1dO7qfcx5+Bf+LVFq9KHQpRhS0/ytxNZEqVlb9zk3IR+XskUq+nQp2uhmMzR3i/4Q3b2o+2y/xdvLDkMPx5508MbT5U6lCIKmzZ0SAkZbCjj8gU2O42X+Gp4fgr6C/mbjI5XqlBehLSsrHudJjUYRBZjLRbaXDv6Y5GXzaC70e+EGqB0B9CocnSGKwbty8OUBi33chNkUi9lop64+uh6XdN4dHbAw/XP0TypWQAQPKlZCSdSoLvh76oPbQ2wleHIzclr8NTna5G1LYoeI/0NtnrlKtVAas44pNkLzE9G+tPMXcTmVJl5G8hBMIWhyE7JhsNJjdAk5lNYO1pjdB5j7bL/F26lddW8kpLkr34tGysY+4mMhm2u80b291UGVjUID2rToQgPVstdRhEFsP3Q1/U6F4DdnXtYN/AHvXerIecuBxkhGborZcRloHYvbGoO7auUdtND0qHWzc3OLV0go2XDdz93GFX3w4Zd/O2mxWRBccWjrBvaA+3J9ygtFciOyYbABD5eyTce7rDxsPGtC9Whh6mPcTfd/+WOgyiCll1IhRpzN1EJlUZ+Ts7KhsZwRmoM6oOHBo5wNbbFnVG1oEmW4PE04kAmL+NEZkWiV3Bu6QOg6hCVrPdTWRSbHebt/DUcLa7yeRY1CCdlMwcrDkZKnUYRBZNnZHXeFE5qnTLNFkaPPj5AeqMqANrN2ujtuPQxAEpl1OQk5ADIQRSb6YiOyobTm2cACDvRCs0A+o0NTJCMyCyBWxr2SItMA2ZYZnweM7D9C9Opn699is0wnAED5EcMHcTVQ1T5G+RIwAACutHw0MVSgUU1gqkB6YDYP421qqAVVBr2CFM8sTcTVT52O42P79c+4XtbjIp3lODdNadDuN83ESVSGgEIjdGwqGpA+zq2emWR2yKgEMTB7g8VvxcnoV5v+GNh789xO33bwMqQKFQoM6YOnBs7ggAcG7rjPQn0xE8MxgKGwXqvVUPClsFHq59iHpv1kP8oXjEHYiDlZMV6oypA7u6dqU8o+UKTQ6Ff6g/+jXsJ3UoRGW27nQY5+MmqmSmyt+23raw9rBG1NYo1B1dFwpbBeL845Abn4vcpLxzcOZv44Qlh2Ff2D7mbpIltruJKhfb3eYpLDkMe0P2on+j/lKHQhaCRQ0CAGTmqLHqeIjUYRBZtIh1Ech8kIlGnzfSLUu+lIy0m2loPLNxmbYVfyAe6cHpaPBeA9h42iDtdhoi1kXA2s0aTq3zRo3UGlwLtQbX0j0mens0nFo5QaFSIGZnDJp80wQpV1LwYMUDNJnZxDQvUqZWXluJvr59oVAYObkqkRnIyFbj12PM3USVzVT5W2GlQIN3GyD813DcnHQTUAJOrZzg1M4JEI/WY/42zi/XfmFRg2SH7W6iysd2t/laeW0l+jXsx3Y3mQSLGgQA2HT2HmJTs6UOg8hiPVz3EMlXktFoWiNYuz+61DXtRhqyo7Nxc+JNvfXvLb0Hh2YOaDStUeFNQZOtQdQfUWjwbgM4t3cGkHfZa+a9TMTuidWdXBWU9TALiacS0XhmYyQeS4RDcwdYuVjBtbMrwn8NhzpDDZW9yuBx1cWdhDs4fP8wejboKXUoREbbdPYe4tKYu4kqkynzNwDY+9qjyddNoE5XQ+QKWLlYIXhWMOx97Ytcn/m7eIEJgTh6/yh61O8hdShERtvMdjdRpWK727wFJQbh4L2D6OXTS+pQyAKwqEHIztVgxb93pQ6DyCIJIRCxPgLJF5LR8NOGsPHSv0mY5wBP1OhRQ29Z0BdB8B7urTtxMtimWkCoheFdkZR5z1dUDOFrwlH7tdpQ2akgNPmPByBy89fn1JZYeXUlixokG8zdRJWrMvJ3QSqHvA6NrMgsZIRkoOZLNYuMgfm7ZCuurWBRg2QjR83cTVRZ2O6Wj5XXVrKoQSbBG4UTdlwOR0RSptRhEFmkiHURSDyZiPpv14fSTomcxBzkJOZAk513NmPtZg27enZ6/wDA2t1a70Qs8NNAJF9IBgCo7FVwaO6AyC2ReTcqi8lGwrEEJJ5IhEtHw/lBE44mwMrZCi4d8v7m0NQBaTfTkB6Ujth9sbCtY6t3A7XqKiAuAOcjz0sdBpFRdlwOR2QyczdRZamM/A0ASWeT8nJ3dDaSLyYjdF4oXB5zgXMbww4V5u/SXY25inOR56QOg8go2y+F4yHb3USVgu1u+bgRdwMXoi5IHQZZAF6pQVhzKlTqEIgsVvyheABAyPf6c+fWHVcXNbrXKOohRcqOzIY6Xa37vf5/6yPqjyg8+PkB1GlqWHtYo9bLteD+jLve43KTchGzKwaNvnh0Oa1DIwd49vVE2IIwWLlYoe5bdcvz0izSxlsb8Xjtx6UOg6hUzN1Elauy8nduUi4iNkdAnaSGlZsV3Lq6wetFL4PHMX8bb/2N9ehUu5PUYRCV6reToVKHQGSx2O6Wl403N6JjrY5Sh0EypxBFXTNF1caFsAS8vPyk1GEQofsTJ3A5aZfUYVA1Z6Wwwp6X96C2Y22pQyEqFnM3mQvmbjIHKoUKe17aA28nb6lDISrWhbB4vLz8lNRhEKF7lxO4nMzcTdJiu5tMgdNPVXNrOdKTiEgnV+Ti99u/Sx0GUYmYu4mIHlELNX4PZO4m87bmZJjUIRARmQ22u8kUWNSoxqJTMrH7WoTUYRARmZVtd7YhW50tdRhERWLuJiIy9OedP5m7yWzFpGRhb0Ck1GEQEZkVtrupoljUqMY2nbmPHDVnHyMiKig+Mx57Q/dKHQZRkZi7iYgMxWfGwz/UX+owiIq0+ew9ZKs1UodBRGRW4jPjsSdkj9RhkIyxqFFN5ao12HiWl8ASERVl482NUodAZCBHrcGGM8zdRERF2Xxrs9QhEBnIa3ffkzoMIiKztOHmBqlDIBljUaOa2ns9ElHJWVKHQURklq7HXceVmCtSh0GkZ29AJKJTmLuJiIpyNfYqrsddlzoMIj37b0QhIilT6jCIiMzSzfibbHdTubGoUU1tPMPRIkREJfnrzl9Sh0CkZ/M55m4iopJsubVF6hCI9Kw7zSssiYhKsj1ou9QhkEyxqFENhSdm4NTdOKnDICIya/6h/shSc1Q8mYeHiRk4FczcTURUEv9Qf2TkZkgdBhEA4EFCOtvdRESl8A9hu5vKh0WNamj7pXAI3mOUiKhEqTmpOHTvkNRhEAEA/roUDg1zNxFRidJz05m7yWzsuPyQ7W4iolKk5KTg8L3DUodBMsSiRjX016VwqUMgIpKFncE7pQ6BCABzNxGRsXbd3SV1CEQAgD8vPpA6BCIiWdgRvEPqEEiGWNSoZq7cT0RQdKrUYRARycKph6cQmxErdRhUzTF3ExEZ7/TD08zdJLmrDxIRHJMmdRhERLJw6uEpxKTHSB0GyQyLGtUMR4sQERlPLdT45+4/UodB1RxzNxGR8dRCjd13d0sdBlVzf17kFZZERMZiu5vKg0WNaiRHrcGuqxFSh0FEJCucgoqklKPWYOeVh1KHQUQkK3/f/VvqEKgay1VrsIu5m4ioTDgFFZUVixrVyJHbMYhPy5Y6DCIiWQlMCMSt+FtSh0HV1KFb0UhIz5E6DCIiWbkZfxN3Eu5IHQZVU0cDYxDHdjcRUZkEJQbhZtxNqcMgGWFRoxrZcZmXwBIRlYd/qL/UIVA1xZGeRETlw6s1SCp/XWK7m4ioPPaH7Zc6BJIRFjWqiexcDY7c5k13iIjK4+C9g1KHQNUQczcRUfkxd5MUsnLVOHwrWuowiIhk6dC9Q1KHQDLCokY1cSI4FqlZuVKHQUQkSyFJIQhJCpE6DKpmmLuJiMovLDkMwYnBUodB1czJoDikZaulDoOISJaCk4IRmhQqdRgkEyxqVBP7rkdJHQIRkaxxxCdVNeZuIqKKOXz/sNQhUDWz7wZzNxFRRRy4d0DqEEgmWNSoBoQQOHCTJ1dERBVx+B47RqjqMHcTEVUcczdVJSEEDjJ3ExFVCKegImOxqFENXLyXiJiULKnDICKStWux1xCdzjmSqWowdxMRVdy12GuISee9iahqXL6fiGjmbiKiCgmIDUBUGgvEVDoWNaqBfTcipQ6BiEj2BARHfFKVYe4mIqo4AcEpqKjKcOopIqKKExCc+pmMwqJGNbCfc3ITEZnEofu8FJaqBnM3EZFpsKhBVWU/ixpERCbBKajIGCxqWLi7Mam4G5smdRhERBbhbORZpOekSx0GWbiQ2DTmbiIiEzkbcRZpOfxOpcoVEpuGoOhUqcMgIrIIF6MvIiM3Q+owyMyxqGHhjgfFSh0CEZHFyNXk4mL0RanDIAvH3E1EZDrZmmycizwndRhk4Y7e5n3XiIhMJUeTgwtRF6QOg8wcixoW7gQ7RoiITOpMxBmpQyALd5K5m4jIpJi7qbKdDI6TOgQiIovC3E2lYVHDgmk0AqfvxksdBhGRReHJFVUmjUbg1F12jBARmRKv1KDKpNEInAlhu5uIyJROR5yWOgQycyxqWLDrD5ORlJEjdRhERBblVvwtJGYmSh0GWagbEclITGfuJiIypcCEQOZuqjQ3ItjuJiIytdvxt5GQmSB1GGTGWNSwYCeDOX0FEZGpCQicjTwrdRhkoThtJBGR6TF3U2Viu5uIyPQEBGdJoBKxqGHBOK8nEVHl4MkVVRbmbiKiysGiBlUW5m4iosrBKaioJCxqWKgctQbnQjmvJxFRZeDJFVUG5m4iosrDAQlUGXLVGpzj/TSIiCoF291UEhY1LNTl+4lIz1ZLHQYRkUW6l3IPkWmRUodBFoa5m4io8oQmhyImPUbqMMjCXHmQhDTmbiKiShGeGs52NxWLRQ0LdSGMN9MhIqpMV2OuSh0CWZiLzN1ERJXqfNR5qUMgC3OWV2kQEVWqgNgAqUMgM8WihoW6cj9R6hCIiCwaT67I1K48SJQ6BCIii8bcTabGdjcRUeW6GsvBhFQ0FjUs1NUHSVKHQERk0a7FXpM6BLIwV+4zdxMRVSYWNcjUrnJAAhFRpboWw3Y3FY1FDQsUm5qF8MQMqcMgIrJoN+JuQCM0UodBFoK5m4io8t2Kv8XcTSYTm5qFh0mZUodBRGTR2O6m4rCoYYF4CSwRUeVLz01HcGKw1GGQhWDuJiKqfOm56bibeFfqMMhC8CoNIqLKl56bjqDEIKnDIDPEooYFusKpp4iIqgSnsSBTYVGDiKhqBMQxd5NpcNpIIqKqwXY3FYVFDQvEESNERFWD99UgU+GABCKiqsGOETIVtruJiKrG1RjeLJwMsahhgXiTcCKiqsGOETIVdowQEVWNG3E3pA6BLMS1cLa7iYiqwvW461KHQGaIRQ0L8zAxA/Fp2VKHQURULQQnBkOtUUsdBsncw8QMJKTnSB0GEVG1cDv+NnI0/M6linmYmIHYVLa7iYiqQkhSCG8WTgZY1LAwd6JTpQ6BiKjayNZkIzw1XOowSOaCmLuJiKpMtiYb95PvSx0GydztqBSpQyAiqjay1Fl4kPJA6jDIzLCoYWHYMUJEVLXuJt2VOgSSOeZuIqKqFZIUInUIJHPBzN1ERFWK7W4qjEUNCxMcw5MrIqKqxJMrqqgg5m4ioioVksyiBlVMcEya1CEQEVUrwYnBUodAZoZFDQvDESNERFXrbiKLGlQxzN1ERFWLV2pQRXEwIRFR1eJgQiqMRQ0Lw5MrIqKqxY4RqijmbiKiqhWaFCp1CCRzd3mlBhFRleJgQiqMRQ0LkpSeg9jUbKnDICKqVjhihCoiMT2buZuIqIpx+imqiKSMHMSmZkkdBhFRtcJ2NxXGooYFCYpJkToEIqJqJzUnFdHp0VKHQTLFqzSIiKpeSnYKYjNipQ6DZIq5m4io6qXnpiMiNULqMMiMsKhhQXizMiIiaXAaCyqv4GjmbiIiKXD6SCov3guLiEga91PuSx0CmREWNSzI/fh0qUMgIqqWItI4YoTK50ECczcRkRTuJd+TOgSSqbA45m4iIilEpkdKHQKZERY1LEhEUqbUIRARVUuRaTy5ovKJTGbuJiKSAqeOpPJiu5uISBpsd1NBLGpYkCh2jBARSSIqPUrqEEim2DFCRCQN5m4qL7a7iYikwaIGFcSihgVhxwgRkTTYMULlFcncTUQkCV6pQeXFqyyJiKTBogYVxKKGBWHHCBGRNHhyReXF3E1EJA0WNai8opi7iYgkwXtqUEEsaliI1KxcpGblSh0GEVG1xCs1qDxSs3KRwtxNRCQJFjWoPNKYu4mIJMPBhFQQixoWIjIpQ+oQiIiqraSsJGTk8nuYyoa5m4hIOglZCchWZ0sdBskM76dBRCSdlOwUpOekSx0GmQkWNSxEZFKW1CEQEVVrUWm8WoPKhvfCIiKSFq/WoLLi/TSIiKTFKahIi0UNC8ERI0RE0orLjJM6BJKZmBQOSCAikhKLGlRWbHcTEUkrKStJ6hDITLCoYSGSMnKkDoGIqFpLyU6ROgSSGeZuIiJpJWQmSB0CyUxcKqcsIyKSEtvdpMWihoVIyeTNyoiIpMSTKyor5m4iImml5DB3U9kwdxMRSSs5O1nqEMhMsKhhIVIyOdqTiEhKLGpQWTF3ExFJKy0nTeoQSGZSs1jUICKSUnIWixqUh0UNC8ERI0RE0mJRg8oqOYO5m4hISqnZqVKHQDKTynY3EZGk2O4mLRY1LERKFkd7EhFJKTWHHSNUNszdRETS4pUaVFbM3URE0mJRg7RY1LAQvFKDiEhaPLmShp+fH6ZMmaL73dfXFwsXLpQsnrLglRpERNLigAQqK7a7iYikxfthkRaLGgXIumOEJ1dERJLiDcvMw7lz5zB+/HipwzAK76lBRCQtTj8lDTm3u3lPDSIiafGeGqRlJXUA5uzcuXNwdHSUOgyjWGrHSNLprUg8ugbOHQfCvVdeJ1VOQgQSDv+KrAc3INQ5sG/YEe7PTYDKsUax23mwfCzUydEGy506DIBH7/8CAOIPrkRawEEorO3g1mMUnFo/o1sv7dZxpAUcRM0h0038ConIUrBjxDx4eXlJHYLRLHW0J3M3EckFr9QwD/Jqd1tm7iYikgtOHUlavFKjBF5eXnBwcJA6DKOkZ6mlDsHksiICkXJ5L6y9fHXLNNmZiP79S0ChQK1h36H2G/MgNLmI3jYLQmiK3Zb3qAWoN2md7l/NV78BADi26AYASA86g7SbR1Fz6Neo4TcG8XuXQJ2elPecWWlI/Hct3PM7UIiIipKlzpI6BLPi5+eHd999F1OmTEGNGjVQq1YtrFy5EmlpaRgzZgycnZ3RpEkT7NmzR/eYgIAA9OvXD05OTqhVqxZGjBiB2NhY3d/T0tIwcuRIODk5wdvbGz/++KPB8xYc7RkaGgqFQoHLly/r/p6YmAiFQoEjR44AAI4cOQKFQgF/f3906NAB9vb26NmzJ6Kjo7Fnzx60bNkSLi4uGD58ONLT0026jzJymLuZu4lISuwYMQ9yanenWeiVGkmntyJszvOIP7BCtywnIQLRf36D+4uH496CVxCz/Xuo0xJK3I7QqJH47zo8+N843PvxJYT//CYST2yCEOLRc535E/eXvI77S15H8tk/9R6f9fA2In57D0JjeedIRGQa2ZpsqUMgMyGLogY7RkqXqym+U0CONNkZiN31Azz6vgulnZNueVb4DeQmRcOz//uw8fKFjZcvPAe8j+yIIGSGXS12eyoHV6icauj+ZQSdhZWbN2zrtwUA5MTdh139trD1bgrHVj2gsHFAblIUACDh8Go4d+gPK5ealfuiiUjWcoVlNnIrYs2aNfD09MTZs2fx7rvv4r///S9eeeUVdO3aFRcvXkTv3r0xYsQIpKenIzExET179kSHDh1w/vx57N27F1FRURg6dKhuex999BGOHj2KHTt2YN++fThy5AguXrxoklhnzJiBpUuX4uTJk7h//z6GDh2KhQsXYuPGjfjnn3+wb98+LFmyxCTPpZWjFqWvJCPM3UQkNzkay7zavbzY7i5ddq5ltbsB0w5ISD6zDSmX98D9ubdR583lcOsxGsln/0TKhV0AgOzoECQd3wDPgR/D84WPkHhsPbJjQgHkFUTi/H+Ce59JUChVlfmSiUjGmLtJSxZFDYAdI6XJ1VhWx0j8/uWwb9wJ9r7t9ZYLdd6Xl0JlrVumUNkACgWyHlw3attCnYO0G0fg1O45KBQKAICNV0NkRwZBnZmKrMggiNwsWNWog8wH15EdFQznji+Y5oURkcXK1bCoUdh//vMffPHFF2jatCmmTZsGOzs7eHp64q233kLTpk3x1VdfIS4uDlevXsXSpUvRoUMHfPfdd2jRogU6dOiAVatW4fDhwwgMDERqaip+/fVX/PDDD3j22WfRtm1brFmzBrm5ptnv33zzDbp164YOHTpg3LhxOHr0KJYvX44OHTqge/fuGDJkCA4fPmyS59KytAEJzN1EJDfM3YbY7i6ZpbW7TT0gISv8JuybdIFD406wcq0FxxZPwd63A7IjAgEAOXEPYO3lC3uf/8Detz2svXyRE/cAQF5BxK5+a9h6N6vcF01EspajZlGD8sjmnhrajhEAmDZtGr7//ntdxwgAfPXVV1i+fDmuXr2KAwcO6DpGtFatWoX69esjMDAQderUwa+//or169fj2WefBZB38lavXj2TxKrtGAGAcePGYdq0aQgODkajRo0AQNcx8sknn5jk+QBAbUGjPdNuHEV2ZDC8Ry0w+JttnRZQWNsh4chquPUYCQgg8ehvgNBAnVrypbBa6YGnoclMhWObZ3XL7Bt1hGNrP0SueR8KKxt4DngfSmtbxPsvg8eA95FyaTdSLv4Nlb0L3Pu8AxsvH1O9XCKyEOwYMdSuXTvdzyqVCh4eHmjbtq1uWa1atQAA0dHRuHLlCg4fPgwnJyeD7QQHByMjIwPZ2dno0qWLbrm7uzuaN29u8lhr1aoFBwcHXd7WLjt79qxJnksrl7mbuZuIJKUWnOKmMLa7S6axsKJGwQEJSSc365aXNiCh8AAGLdu6LZFyeS9y4sNh7V4X2dF3kfngBmr0HAcAsPHyRW5COHKTowEB5MaHw8bTBzkJEUi9dgDeoxZW2msloqqjUqjy/ilVUCmU+b8rYaVQQalQ5v+ct1wJRf7PSqighArI/zlvuRLI/1kBFRSoa8crsSmPbIoa7BgpmaWMGMlNjkH8wZWo9erXUFjZGPxd5eAKr0GfIn7fsrxLWBUKOLbqAZtajYH8kZulSb26D/aNOsLK2UNvudtTr8Ptqdd1vyce3wg73/ZQKFVIOrUFdcb+hIygs4j7Zz68Ry+q2AslkiEFFHknHfknHMr8f3nLFHonKEooHq2j/RkKqBQKKKHM/z/vpESZf3KS93c8Wg7tzwX/z0tcSpG/rgBUEHnrCujWUQlRxP8i738hCizXFPhdA5Umfz2NJv9vmvzHaKDSaPLXyV+uEVAKtW49OyVHjBRmbW2t97tCodBbph1xr9FokJqaihdeeAFz5swx2I63tzeCgoLK/PxKZd4FqQXncc7JKfp9KhxXUbFrTHxlhaVcqcHcTURypea8/QbY7i6ZpbS7gcoZkODyxBBostLxcOXbgFIJaDRwe3oEnFo/AwCw9qwPt6dHImrLlwAAtx6jYO1ZH1GbP0cNvzHICLmIpBMbAaUV3HuNh139NpXy2olMSdtOzuvA13biKx917Cvy/m5VoK2s14mf3x7O+z+vMz+vEx+6drIVkL8cUOW3e1XIbxvnL7OCgEoASiF0P6vy28Cq/Hatla5tK2AlNHl/12igym/XqjQCKk3ez1b5/ys1algJdd5yjTpvfU0uVOpcWAk1lBo1VJpcWKlz8x9bybnVqwXwXOU+BcmDbIoa7BgpmUZYxslVdmQQNOmJiPjtvUcLhQZZ968j5eLfaPDhX7Bv+BjqTvgF6vQkKJQqKO2ccH/pG3Bwq13q9nOTopEZdgVegz8rcb2cuPtIu3EY3qMXI/XqftjVawOVgyscWnRH3J5F0GSlQ2krj5vZVWf6ne6POuJVUOr/btDxrnrU6a7XGf+oE/5Rx3vBjnj9DnldBzuK7oTXnowohSjh/wKd8Hod8xr9DnpRTCe80MAq/0REmX8yoxTq/L+pH3XMa7QnI2r9v6vVur8rYBnfM5XHuvRVqFiPPfYYtm3bBl9fX1hZGZ6eNG7cGNbW1jhz5gwaNGgAAEhISEBgYCB69OhR5Da9vLwAABEREejQoQMA6M3RLTULSd3M3WS28grrSkABKKGEIj9/KxRKKPL/rlAooIB2ef7P+f8X+TcooFAgf7287eT9BbrzA+i2kXdeUPDvup/z/1co9NcxfAx0z6EE9J8vf11A6Lb3aJ1H8wwrhXabosD2835WCMPHKITQ25YSAPIHA+QtE1CI/L8Jkbc/8v/+6LEi/2dF/t+0z5f3s1Lkb1ORt54yf3sKoSmwnbzzHO3PivzHKfKXPXoOTf5yTX6seQMW8p5Dk/98eevpHis0UEDAGhyQUBjb3SWzlHZ3ZQ1ISL95DGk3jsDzhQ9h7eWD7Ki7SDi4EionDzi1zbtax7lDfzh36K97TOq1g1DY2MO2bguEr3wb3iPnQ50Sh9idc1F3wq9QWPEc29xo29lWBUfjI78TX5k/Gj+/LW0wGj9/+aNO/LwR+Np2tJXeYLb8Tnwo8jrmkd+RLx4NZDPsvC/8TwMr5H3vW2m0nfuaRx35+Z372javlXZwmyY3r1Nft15eJ75KkwsrTf7f839Xsp1ctSzke5gqTjZFjbKojh0jlsLO5z/wHrtUb1nc7kWw9qgHly4v690wTOXgCgDICLsCTVoSHJp0QWlSr+2HysEV9o07FbuOEAJx/j+hRs83obSxB4QGQjutjPb/Em6ORuXzaroGU3M9HnXOG3TC5/8T4lHne/4oAGX+iUbh5URViidXFTJp0iSsXLkSw4YNw8cffwx3d3cEBQVh8+bN+OWXX+Dk5IRx48bho48+goeHB2rWrInPP/9c1/lRFHt7ezzxxBP4/vvv0bBhQ0RHR+um1CDTYe6uvl5N1+D9XI/8DmttZ3F+R3PBzmldB7WmwHp5Hc8osJ5SaB49Vvu3/J8VhR5bcD2F0Oj9zM4FMprCTuoIZI3tbvmqrAEJCUdWw/WJIXBslff+2nj5Ijc5Gkmnt+qKGgWp05OQdGIjag2fg6yHgbB2rwNr97qwdq8Loc5FTkI4bArcwJwqbmi6wNRcd71R99pOfStNbv4o/IKd+Or80fj5I/M52I2kxnN6ymeRRQ12jMiX0tbB4KRFYW0LpZ2zbnnq1f2w9qgPpYMrsh7eQsKBFXDu9CKsPR7NzRq1+TPYN30SLgVuEiqEBqnXDsCxzbN6HSyFpV7xh8reRdfRYlu3JRKPb0RW+C1k3L0Aa48GejdRI9NomhGH5vcvSR0GUQXw5L4i6tSpgxMnTuCTTz5B7969kZWVBR8fH/Tt21eXn+fNm6cbFers7IypU6ciKSmpxO2uWrUK48aNQ8eOHdG8eXPMnTsXvXv3roqXVCpLOWKYu6uvZhmxaMbcTVRtVcd2t5GzJpq9yhqQIHKyAIX++6tQKIvthEw49AucOw2ClYsnsiMDIdQFBqZp1ICFTNVpTppkxqPl/ctSh0FUAZbSiqKKssiiRnXsGKlOcuLDkfDvGmgyUmHlWhOuTw6Fc6dB+uskRMI2I1lvWWboZaiTY+DUrvjJ99RpCUg69TtqvzFPt8y2TnO4dB6M6D9mQungCs8B75v09VAeFXhlBZElOXLkiMGy0NBQg2UFp5do2rQp/vzzz2K36eTkhHXr1mHdunW6ZR999FGJz9GyZUucPHmy2Of08/PT+x0ARo8ejdGjR+stmzFjBmbMmFFsbOVhIf0iRmHutkxWzN0kd5bSQy0Rtrvlq7IGJNg36Yykk1ugcvGCjWcDZEcFI/nc9iLzeEbIJeTEh8MjP0fb1G6G3PgHyAg+j9yUWECpgpV73crZAdWYEiwUkcwpih/oRNWLQhRuyZMstZnuj9SsXKnDICq3w01+R8MH26UOg6j8arcF3j4udRQkI22n+yOFuZtk7GiTzfB5sFPqMIjKr2YrYOIpqaMgGWn11V6kZ1tmQTdy46ewqdkI7r3GAwASjvyG1IADugEJzu37wbnTIN19VQDgwfKxcGr7LNyeeh0AoMlKR+Kx9Ui/cwqa9CSonNzh0LIH3Lq9BoXq0b0xNDlZiPhtMrwGfgKbWo9u7p5yxR+Jx9ZBobKGe++JcChh6kkqnwNNt6HJ/W1Sh0FUft7/ASb8K3UUZAYs8kqN6sjOWsWiBsmaFXj8ksxZ2UsdAcmMnY2KRQ2SNRVzN8mdFe+pQWVjb62y2KJG7eHf6/1ew280aviNLvEx9f67Su93pa0D3HuN1xVGiqO0tkXdt342WO78nz5w/k8f4wKmclHyfgQkd2x3U77iJ7MkWbG34VtJ8qYU7BghmbPmyRWVjb01L50mebNi7ia5s3GUOgKSGXsb5m6SN04/RbJnzQEJlIc94RbCwZoX3ZC88Z4aJHvWDlJHQDLDogbJnYpFDZI7FjWojBxY1CCZY1GDZI9XalA+FjUshB1PrkjmVIJFDZI5XqlBZcTcTXLHqyxJ9jgggcrI3oaDCUneWNQg2eOVGpSPRQ0LYW/Nt5LkTcmiBskdO0aojJi7Se54pQbJng1zN5WNA6+yJJlju5tkj1dqUD62pi2EA0eMkMxxtCfJHkeMUBlx+imSO6UmR+oQiCrGmtNPUdlw+imSOyWnfSa5Y7ub8rGoYSHYMUJyx6IGyR6nn6Iy4s1GSe6Yu0n2eKUGlZGDLQcTkrwpBaefIpnjlRqUj0UNC8GOEZI7XgZLsmfjJHUEJDO8ypLkjkUNkj3eKJzKiNNPkdwpeE8Nkjs7V6kjIDPBooaFcHe0kToEogphxwjJnoOH1BGQzHgwd5PMcfopkj0bZ6kjIJlxsuOABJI3XqlBsufIdjflYVHDQrBjhOROqWFRg2TOqabUEZDMeDgxd5O8MXeT7Dl6Sh0ByYynk63UIRBVCAcTkuw5ekkdAZkJFjUsBE+uSO4UPLkiuePJFZURczfJnYJXapDcOdWSOgKSGS9n5m6SN04/RbLHdjflY1HDQnjy5IpkjqM9SfYceaUGlQ2LGiR3HJBAsseiBpURixokd5x+imSPRQ3Kx6KGhfDkFBYkc+wYIdlz4skVlQ2nnyK545UaJHucOpLKiO1ukjuFUEsdAlHFcOpIyseihoXw4mhPkjkFr9QgOVPZAnauUkdBMsPcTXKnULOoQTJmZQfYu0kdBckMr9QguWNRg2RNaQ3Y15A6CjITLGpYCHdHGygUUkdBVH4sapCs8RJYKgfmbpI7XqlBssZpI6kcPBxtoWTuJhnj9FMka7xKgwpgUcNCWKmUqOHAS2FJvljUIFnj1FNUDlYqJdzsraUOg6j8mLtJzpx5Pw0qO5VSAXdHtrtJvhTglRokYyxqUAEsaliQmrwUluSM99QgOXOqLXUEJFO1XOykDoGo/NTZUkdAVH68STiVkyenjyQZU/BKDZIz1wZSR0BmhEUNC1Lf3UHqEIjKjVNYkKy5N5Q6ApIpHw/mbpInlULDjhGSN2dvqSMgmapXw17qEIjKjffUIFlju5sKYFHDgviyY4TkTMOTK5Ix90ZSR0Ay5evhKHUIROVipxJSh0BUMR6NpY6AZMqHuZtkTMF2N8lZDV+pIyAzwqKGBeHJFcmamldqkIxxxAiVUwMOSCCZclCyU4Rkzp1FDSofX0+2u0m+eJUlyRrb3VQAixoWhFNYkFwpFIKXwZK81eDJFZWPjzs7Rkie7FXsFCGZ45UaVE4NOZiQ5IztbpIztrupABY1LAinsCC5slOyY4RkTGkFuPlIHQXJFAckkFzZKTn9FMkYczdVAHM3yRkHE5JsKVSAG28UTo+wqGFB6rjZw1qlkDoMojJjxwjJmmt9QGUldRQkU8zdJFf2KnaKkIy5NWDupnKr62YPGyt2pZA8cfopki3XeoDKWuooyIwwE1sQlVKBejU4aoTkh1dqkKxxXk+qAOZukivmbpI13k+DKkCpVKB+DXupwyAqH5ErdQRE5cN2NxXCooaF4aWwJEe27BghOXNvJHUEJHPM3SRHdrxROMkZ76dBFdSQNwsnmVJo2PYmmWK7mwphUcPCNK3pJHUIRGVmx5uNkpx5Npc6ApK55rWcpQ6BqMx4o3CSNV6pQRXEogbJFu+pQXJVq7XUEZCZYVHDwrSu4yp1CERlxis1SNa820kdAclcqzouUodAVGa2vFKD5KxmS6kjIJlr6c3cTfLEG4WTbNVmu5v0sahhYdgxQnLEogbJlkIJ1GojdRQkc63YMUIyZKtg7ia5UnBAAlVYm7ocTEgypWFRg2RIoeSVGmSARQ0L09jLCXbWfFtJXljUINlybwTYcto/qphGzN0kQ7xROMlWDV/Ajh3SVDFsd5Ns8UoNkqMaDQEbTvtH+piFLYxKqeDc3CQ7NhztSXLFS2DJBJi7SY5sOP0UyZX3f6SOgCyASqlA89q80pJkiFdqkBzVbit1BGSGWNSwQK14Xw2SGV6pQbLFkysyEU4fSXJjq2CnCMkUixpkIq2Zu0mOeKUGyRHb3VQEFjUsEDtGSG5Y1CDZ4pzcZCK8rwbJjR2v1CC5qtNe6gjIQrThYEKSIYVg25tkiDMkUBFY1LBAHDFCcmPDogbJVW2O9iTT4IAEkhtrsKhBMuXdXuoIyEKw3U1yw3Y3yRav1KAisKhhgVrWdoGVUiF1GERGs+U9NUiOnL0BJy+poyAL0crbFdYq5m6SD04/RbLkWh9wcJc6CrIQzWs7s91NsmKtFFKHQFR2TrUBF2+poyAzxKKGBbK3UXHUCMmKDTtGSI7qd5Y6ArIgebmb01iQfPBG4SRLvJ8GmZCdtQotOX0kyYiNgkUNkiGfJ6WOgMwUixoWqnNDjkAi+bBmxwjJkU83qSMgC8PcTXJizQEJJEc+XaWOgCwMczfJCa/UIFliu5uKwaKGherky5Mrkg9OP0WyxI4RMjHmbpITG95Tg+SIHSNkYixqkJxYs91NctSAV2pQ0VjUsFCdG7pDwek9SSY42pNkx84NqNla6ijIwnTyrcHcTbLBqSNJduxcgdrtpI6CLExnX7a7ST5sWNQgubFzBWq2kjoKMlMsalgoNwcbNK3p9P/27jvOyvLO+/j3Pn16rzAFptLBYRg6A9JEERULoogKgj2WJJs1+2SfJLubxH2SmGgSNa4tGjWaWGJPohAxatREsxoLdhABQarMwJTz/HEQGQWmcM657vJ5v168SFhgvtmXzve+zu++rst0DKBHeLiC45SPlXxUKOKL7oaTBNVuOgLQO+Xj6G7EXU5aSLWFGaZjAD0S5FsgnKaMdTcOjn8yXIytsHAKtsHCcTi+AgnCEVRwCnZZwnHobiRI00C6G84Q9LHuhsNw5DMOgaGGi/HBCJwixNuecBo+GEGC8EICnCLInRpwmsqJphPApehuOEXQ4qJwOAxDDRwCQw0XaxqQZzoC0CMB3hiBk4TSpZIRplPApfhgBE7B8VNwlHAm3Y2EobvhFJyQAEcJpEilo0yngI0x1HCx4qwIZ3PDEXjbE45SPlbyB0yngEuVZKWoroizuWF/AbobTlI+VvL5TaeASxVmRFTNuhsOEPKxUwMOMmCS5A+aTgEbY6jhctPqC01HALrFGyNwlJqZphPA5ZrrC0xHALoVtNipAQcZMMV0Arjc1Dq6G/bHCQlwFNbd6AZDDZebylADDsBODThK7SzTCeByU+vobthfIMpQAw5Sd5TpBHA51t1wgoDYqQEHYd2NbjDUcLnRFTnKjHBMCuwtYDHUgEMU1Es5laZTwOXobjgBx0/BMfJqpLwq0yngcmMqc5VBd8Pmghw/BacoGCRll5tOAZtjqOFyAb9Pk2rZCgt744MROAZviyAJAn6fJtXQ3bA3Pzs14BTs0kASBPw+TWbdDZsLiOOn4BC1HD2F7jHU8ACOsYDdcfwUHKN2tukE8AiOsYDdBcRQAw7BUANJciTdDZvjonA4BvdpoAcYanhAc12BLMt0CuDg2KkBR4hkS2VNplPAI+hu2B07NeAIKbl0N5Kmua5QProbNua32KkBB4hkSWVjTaeAAzDU8ID89LCG9882HQM4KO7UgCNUT5d8ftMp4BH56WEN75dlOgZwUH52asAJambS3Uia3LSQRpZlm44BHFTQYqcGHKBqmuTnjiJ0j6GGR8waUmQ6AnBQgShDDTgAR08hyWYPLTEdATgofydDDThAHd2N5DpyEOtu2FeAoQacoG6O6QRwCIYaHjF3eKnpCMBB8bYnbC8Q4ZJwJN3cEQw1YF90N2zPH4rtsgSS6JjhdDfsixMSYHuBFIYa6DGGGh5RlpvKVljYVkCc7Qmbq5kpRTJNp4DH9M9J1ajybNMxgAPyd7aZjgAcWs1MKZxhOgU8piIvTSNYd8OmOH4Ktlc3Wwqnm04Bh2Co4SFzR7BbA/bE256wvWEnmU4Aj2KnJezKx0XhsDu6G4Ycy7obNhXgonDY3dATTSeAgzDU8JC5w0vks0ynAL7ML7bBwsYiWRw9BWOOobthUww1YGvhLKnuKNMp4FGsu2FXDDVga+EsqWaG6RRwEIYaHlKYGdGYAbmmYwBf4ueicNjZoLlSIGw6BTyK7oZd+bgoHHY2mO6GOYWZEY0dmGc6BvAlHPsMW2PdjV5iqOExx47oZzoC8CV+3vaEnXF8BQzj+EjYkS/KnRqwsWEnm04Aj+MIKthRgDs1YGfD5ptOAIdhqOExRw0tVtDPXljYC8dPwbbSi6XKyaZTwOPmDC1RgHMsYDMWOzVgVxmlUuUk0yngcUcNK1HIz8ctsBeOn4JtpRVKA6aYTgGHoWU9JictpCm1haZjAF34OH4KdjX0BMlHVcKsnLSQptbT3bAXXyc7NWBTw+bT3TAuKyWoKXUFpmMAXfgZasCuhhwn+fymU8BheNrzoIVNZaYjAF1w/BRsa/gpphMAkqSFY8pNRwC6YKcGbIvuhk2cPJp1N+wlII6fgk2NWmQ6ARyIoYYHNdcWql92iukYwD4+hhqwo5KRUulI0ykASdKU2gK6G7ZisVMDdlQ8LPYDsIFp9YUqyYqYjgHsE+DYZ9hR6SipZLjpFHAghhoe5PNZvDUCW+H4KdhSw5mmEwD7+HyWTmmku2EjDDVgR6PPNp0A2MdPd8Nm/FwUDjs6YrHpBHAohhoedUpjmfxcOgqbYKcGbCeULg07yXQKoAu6G3bCTg3YDt0NGzp1TLkCdDdsgovCYTvBNGnYiaZTwKEYanhUcVZE07h0FDbBUAO2M+xEKZxuOgXQRVFmRFPr6G7YRAfdDZsZdpIUzjCdAuiiKJN1N+yDi8JhO8PpbvQdQw0P49JR2AVDDdhO4zmmEwAHtLCJYyxgE+zUgN00LjGdADig08ZWmI4ASJL8XBQOu2lcajoBHIyhhodx6SjswupkqAEbKR8vFQ81nQI4oObaQrobxgV9UVlR3vaEjZSP54Jw2NbkmnyV5dLdMI+LwmErZWPpbhyWgOkAMMfns3Ta2HJd+egbpqPA49x+Ufj3ntqt373eptc3dSolYGl8mV8/mB5WXb5/3+9pvvlTrXy/6/8fljcEde0xB18AnXlfi255ueubsrOq/Hr09DRJ0u72qJb+vlX3v96m4nSffn50RNMHfv5t/7+f3q0PtnXq6jkssroYw9sisC+fz9KicRX6/iOvm44CD4v46O2+9Pb+zn2wRde92KYfzwrrkrFhSfT2YWlaZjoBcFCWZWnhmAr94FG6G2b55e4XEuhvh2GXBg4TQw2PO62pQj974i19usfdi1PYm9t3aqx8v10XNIbUWOpXe6d0xRO7NfO2Xfrn+elKC31+ceA5RwT1nanhff89Ndj9pYKzq/26ad7nD0dh/+d/5voX2/Tiug49syRNj7zVroW/bdGGr6bLsiy9u6VTv/xbm15Ylhan/5UukVEiDTrWdArgkBY2leuaJ97Szt3u/t4J+0r1u/tDkUT2tiTd+1qbnl3bodKMrr+f3u6jjFKpfq7pFMAhnTqmTFc/sVq7WHfDIL/l7uOn6G8HyewvDTnOdAo4HMdPeVxWSlALuFsDhlkuv1Pj0dPTdObIkIYU+jWi2K+b50X0wbaoXvyo66ImNWipON2370dmuPuHq7C/65/JSfn8z7y2qUPH1gU0pNCvCxpD+nhXVJt2xR5kz3uoRT+YHu7R1/CUsedL/qDpFMAhZUaCOnUMd2vAnBSfu4caieztD7d36qJHWnX7CSkKfmElRm/30Zilkp939WBv2akhndJId8Msn8t3atDfDjKOdTcOH0MN6OyJAxTw8Q0W5rh9p8YXbdsd+zk3peu/d7f/b5vyr9yhoT/fqX/9Y6t2tXX/Js2K99pV+N87VHfNTp33YIs27/r8QXVEkV+rPuhQS1tUj73drpJ0S/mplm7/R5siAUvHD+IhootItjT6LNMpgB45e+IABf10N8yIuHynxhfFq7c7o1EturdFXxsf+8Dli+jtPghncXwFHGPppIGsu2GU1+7UoL9tKpItHbHYdAq4AK+0QP2yU3TM8BLd99I601HgUVZnW/e/ySU6o1Fd8mirJpT5NXS/B6KFw4KqyPKpNMPSPzZ06l/+2Ko3Nnfqd6ekHvTvml0d0AmDAhqQ7dPbWzp1xZ9266jbd+mZJWny+yydPSqof2zo0OCf71R+qqXfnJSiLa3St1a0asXiNP3bE62685U2VeX6dOOxKeqX6fE5d+NSKZxhOgXQIyVZKTpmeKnu/fuHpqPAg1L9nZJH3keIZ2//YNUeBXzSxU2hA/7f6e0+aFwiRbJMpwB6pF92iuaOoLthjtvv1Ngf/W1jjUulcLrpFHABhhqQJC2fUsVQA+Z0eueNkQseatUrGzu06uyuZ2oua/j8AWlYkV8lGZaOvHWX3v6kU1W5B37oWTA02OXPDC/yq+qnO7XivQ4dOTCgoN/Sz47uehnZWfe36OIxIf19fYfue71dL5+briuf3q2LH23Vb08++IOc6wVSpLHnmU4B9MqyyQP5YARGhF1+Ufj+4tXbL67r0E+e26O/LU+TZR34TW16u5cCKbFjIwEHWT6F7oY5frn7To390d82FYhITeeaTgGX8Ph4EJ8ZVJKpSTX5pmPAo7yyU+PCh1v04Op2Pbk4Tf27eTujqV/sbZK3Pun52zQDc3zKT7UO+meefLddr27s0IVjQlrxXofm1ASUFrJ08pCgVrznnQ+oDmjU6VIa3wPhLHQ3TIlY3uiMePb2Ux+0a+OnUZX/eKcC39muwHe26/1tUV3++G5VXrXjgH+G3u7GEYuk9ALTKYBeqS/OVHMd/9zCDJ9Hjp+iv21s5Gl0N+KGnRrYZ/nkKj21epPpGPAil9+pEY1GddEjrbr39XatWJyqATndz5NfWh972CnJ6Pm5u2u3d2rzrugB/0xre1QXPBy72Mzvs9TRKUX3vqjT1il1dHrnrZ0v8QWk8ReZTgH0Cd0NE9x+p0YienvR8KCmD+y69Jp12y4tGh7UWSO/fNY2vd0NX1Aaf7HpFECfLJ9cpRVvfGw6BjzIb9HfX0R/J5HlZ92NuGKnBvaZWJOvUeXZpmPAi1w+1Ljg4Vbd9o82/fqEFGWELa3f2an1OzvVsvdCsrc/6dR3V+7Wi+s69N7WTj3wRpvOuK9FkytiR0p9pv6anbr3tdiulp17ovra4616dm273tvaqT+90655d+5Sda5Ps6q+PK/+7srdmlMT0KiS2N83odyv373epn9s6NA1f92jCeUennEPOUHKqTCdAuiTiTX5Gl2RYzoGPCbsc/eHIono7bxUn4YW+rv8CPqk4nRLdflfvnSU3u7GsJOk7DLTKYA+GVeVp5Fl2aZjwIP8UXfvEqC/bW7wPCl3gOkUcBEP/9uEA/nqzDqddsNzpmPAYyyXDzV+8ULsgaj5ll1dfv2meRGdOTKkkF/647vtuuq5Pfp0T1RlWT7NHxTUv00Od/n9b2zu1LbdsQcyvyX9Y2OHbnm5TVtboyrNsDSzKqDvTg0rHOj6lskrGzv0m3+266Xln58neuLggFa8F9Ckmz5VXZ5Pv57v1XM9LWnipaZDAIfl8pl1OvWXz5qOAQ+JuPxOjUT0dm/Q292wfHQ3HO+yGbU648a/mo4Bj/G5/KJw+tvGLJ805eumU8BlrGg06uG9TziQU69/Vs+8s9l0DHhExNeh10OLTMeAVw07SZp/g+kUwGFb+Mtn9Ze36W4kx+LSD/XtT75mOga8avBx0sm3mE4BHLYF1z+jZ9/5xHQMeMjDNb/X4DV3mI4BLxq+QDrhOtMp4DIcP4Uv+eqsOtMR4CFhP3NVGOILSlO/aToFEBeXz6S7kTxhl+/UgI35AtKR3zKdAoiLr7HuRpK5facGbMoXlKb+q+kUcCGGGviShoocTasvNB0DHhFx+bncsLGGxZzpCddoqMjR1LoC0zHgEWGLoQYMOeIMKa/KdAogLhoqcll3I6kYasCIhjOlnErTKeBCDDVwQJfPrJVldf/7gMPl9stGYVPBNGkyZ3rCXS6fWUd3IylCFt0NA4Jp0pRvmE4BxNVX6W4kEUMNJF0wVZrMkaVIDIYaOKAhpVmaM7TEdAx4QMTH8VMwYOy5UkaR6RRAXA3tl6XZQ4pNx4AHcPwUjBh7Ht0N1xlcmqmjh7HuRnL4RX8jyZpYdyNxGGrgoC6dUSu/j9dGkFh8MIKkS8mRJnzFdAogIS6fWasA3Y0EC3H8FJItNY/uhmtdPrOO7kZS+KLs1EASRbLobiQUQw0cVHVhuk5rKjcdAy4XZqcGkm3ipbEHLMCFqgsz6G4kXMhqNx0BXjPpcimSaToFkBAD8tN0cmOZ6RjwAI6fQlJN+IqUkm06BVyMoQYO6bIZtcpODZqOAReL+HmwQhJllUtjlptOASTUZTPqlEN3I4FCHF+BZMoqlxqXmk4BJNRXZ9YpK4XuRmJZDDWQLNkV0tgLTKeAyzHUwCFlp4Z02Yxa0zHgYhxhgaSa9Z9SMGI6BZBQWalBuhsJFeLoSCTTjP8rBcKmUwAJlZsW0qXTa0zHgMv5o/Q3kmT291h3I+EYaqBbpzVVqL44w3QMuFTYx9siSJKqadLgY02nAJJiId2NBAqyUwPJMmCKNHS+6RRAUiwaV0l3I6Es7tRAMlRPl+qPNp0CHsBQA93y+yx9a+5g0zHgUgw1kBT+kHTUf5tOASQN3Y1ECoo7NZAE/pB09A9NpwCSxu+z9O9zh5iOARfjTg0knD8kzf6B6RTwCIYa6JHxVfk6amix6RhwoZDFgxWSYNwFUn616RRAUo2vytfsIXQ34i/I0ZFIhnEXSPkcxwNvGVeVp6OHlZiOAZfysdMSiTb2PNbdSBqGGuixK+YMUjjAPzKIrzDnciPRMvtLk79mOgVgxDePprsRfxw/hYTL7C9N/rrpFIARVxw9SClBv+kYcCFfJ/2NBMoopbuRVKxy0WNluam6cCoTV8QXOzWQcLP+QwqlmU4BGFGWm6qLj+RNZ8QXx08h4WZ/Twqlmk4BGNEvO0XnN1eZjgEXsjh+Cok087tSON10CngIQw30yrnNVVxehrgKsVMDiTRgijTkeNMpAKOWTx6oQSWZpmPARQIcP4VEqp4uDT7WdArAqOVTqlRbxIeDiC9flP5GglROkoadaDoFPIahBnol6PfpyhOHy++zTEeBS7BTAwkTTJWO+bHpFIBxAb9PV86nuxE/gSg7NZAggYh01JWmUwDGhQI+XXniCLobccVODSREME069mrTKeBBDDXQa8P7Z2vJxAGmY8AlgjxYIVGO/JaUx9Z9QJKG9c/SUrobccLxU0iYqd+ku4G9Rpax7kZ8WVHW3kiAI78l5fK9CsnHUAN9ctmMWlXmcc4tDl/Ix4MVEqB8vNR0rukUgK1cSncjTvxcFI5EKBsrjbvQdArAVi6bUasB+dwNh/jwsdMS8VY+XmpabjoFPIqhBvokEvTreycMl8VuWBymIOdyI96CqdK8a8Q3KKAruhvxEmCnBuItmCod93PJx/IU2F8k6NcP5tPdiA92aiCuAimsu2EUT43os3FVeVrQWG46BhwuxFAD8caxU8BBxbq7zHQMOJyfNz0Rb3Q3cFBjBuTq9KYK0zHgAgw1EFdH/h+6G0Yx1MBhuWJOvfplp5iOAQcLcKcG4oljp4BuXTFnkPrn0N3oOy4KR1xVTKS7gW584yjW3Th8VpQXChEnZU1S03mmU8DjGGrgsGREgrpqwUj5fWw3Q99w/BTiJpgqHfcztr8C3ciIBPUTuhuHgZ0aiJtgGt0N9EBaOKAfnjxCVDcOBzs1EBeBiDTvZxwZCeP4JxCHrbEyVxdOrTYdAw4V5LJRxMuM70i5A02nAByhoSJXF02ju9E3fu7UQLzM/I6UU2k6BeAIYwfm6QLW3TgMFi8lIB5m/ZeUX2M6BcBQA/Fx8ZE1Gl2RYzoGHIidGoiL+mOkMeeYTgE4ykXTatRYSXej93ydfCiCOKg9Shq9xHQKwFEumV6rBtbd6CN2auCwDZ4nNdLdsAeGGogLv8/SVQtGKjMSMB0FDhO0eLDCYcouj21/BdArfp+lH58yUhl0N3rJH20zHQFOl1UmHfdzjp0Cesnvs/QT1t3oI+7UwGHJLpeOvdp0CmAfhhqIm/45qfrP44eZjgGHCXCEBQ6HLyideJOUkm06CeBI/XNS9V90N3rJx4ciOByfdXdqrukkgCP1z0nV9+cPNx0DTkR/o698gVh3R7JMJwH2YaiBuJo7olQnNfQ3HQMOEhA7NXAYjvw/Uv/RplMAjjZ3RKnmH0F3o+d8nMmNw3Hkt6SyRtMpAEebM6xECxrLTMeAw1idDDXQR9NYd8N+GGog7r49b4iqCtJMx4BDBLgoHH1VM1Maf7HpFIArfGfeENUWpZuOAYfwdXL8FPqodrY0/iLTKQBX+Pe5Q1RdSHej5zh+Cn1SdaQ04SumUwBfwlADcZcaCuj6M0YrI8w5n+geQw30SUapdNy1nMUNxElaOKDrFo3mfg30CBeFo08y+0vH/YLuBuIkJeTXtacfoXTW3egpLgpHb6UXScdfR3fDlhhqICGqCtL141NG8n0P3QpYfDCCXvIFpRP/R0rLM50EcJUB+Wn68cl0N7pncVE4essXkE7iHg0g3qoLM/TDk0fQ3egZdmqgN/wh6eRbpfQC00mAA2KogYSZPrhIF0+rMR0DNufnwQq9NedKqWK86RSAK9Hd6AmOn0KvzfovqWyM6RSAK80aUqyL6G70AMdPoVfm/D+pfKzpFMBBMdRAQl0yvUbTBxWajgEb4/gp9ErjUmn02aZTAK5Gd6M7FsdPoTcazpKalptOAbjapdNrNH1QkekYsDsuCkdPNZ4jNSw2nQI4JIYaSCjLsvTjU0ZqIBeH4yD8DDXQU5WTpNk/MJ0CcD3LsvSjU0ZqYD7djQOz2KmBnqqcJM35b9MpANezLEtXLRjJxeE4NIYa6InKSdLs75tOAXSLoQYSLiMS1PWLGrjADAcUiPK2J3ogpzJ2nqef7yNAMmRGgrpuUYPSQn7TUWBDVgdDDfRAzoC93R00nQTwhPRwQNcvalBGhOdlHATHT6E72RWsu+EYDDWQFNWFGfrJgpHy+7jBDF351Gk6AuwulCGdeheXiwJJVlOUoasXjqK78WUcP4XuhDOlU++ku4EkG1iQrp8uGCWqGwfETg0cSihdOvUOuhuOwVADSXPkoCJ9Z94Q0zFgM37xwQgOwfJJ82+QCutNJwE8aVp9kb47b6jpGLCbjj2mE8DOLL904o10N2DI1PpC/ftc1t3oyrKishQ1HQO2ZUnHXysV8b0DzsFQA0l1WlOFzm+uMh0DNuLn+CkcyvRvS3WzTacAPG1hUzndjS64UwOHNOM7Us0M0ykAT1s8vlLLpww0HQM2ErY4IQGHMPt70qC5plMAvcJQA0n3tVl1On5UP9MxYBN+zvXEwYy7UJpwsekUABTr7uNGlpqOARsI+/hQBIfQdJ40/kLTKQBI+sbsetbd2CfoY5cGDmLCJdLY80ynAHqNoQaSzrIsXXnicI2vyjMdBTbATg0c0PAF0sz/MJ0CwF6x7h6hcQPpbq9L8fMyAg5i+CmxNz0B2MJn6+6J1fmmo8AGQgw1cCAjFkozvm06BdAnDDVgRNDv07WLGlRfnGE6CgzzMdTAF9XMlOb9TLK44RCwk1DAp+vOaFBtUbrpKDAoxc+HIjiAmpnSvJ/T3YDNBP0+/eL0IzSoJNN0FBgWtOhvfEHNTOnYq02nAPqMoQaMyYwEddNZjSrJipiOAoN8HD+F/fUfI510i+QPmE4C4AAyI0HdfNYYutvDUjh+Cl9U1kR3AzaWEQnqlrMa1S87xXQUGBTkTg3sr99ouhuOx1ADRpVkpei2pU3KTw+ZjgJD2KmBfQrqpYV3SaFU00kAHEJpdopuX9qk/PSw6SgwIOLjZQTsp3Aw3Q04QGFmRLecPUZ5aay7vYrjp7BPXo102t10NxyPoQaMqypI16+WNCk7NWg6Cgyw2KkBScrsL53+Oyk113QSAD0wsCBdty0dQ3d7UMTPm57YK7s81t0pOaaTAOiB6sLYujsrhe72Ii4KhyQpq0xaxLob7sBQA7YwqCRTt5w1Rhlhtr55DTs1oIwSafEDUlY/00kA9EJ9Md3tRRw/BUlSepG06D4ps8R0EgC9MLg0U79aQnd7EXdqQBmlsXV3drnpJEBcMNSAbYwoy9bNZzcqLeQ3HQVJ5OtkqOFp6cXS4gelvCrTSQD0Ad3tPWGOn0J6kXTmQ3Q34FDD+9PdXsRODY9LL5bOfFDKHWg6CRA3DDVgKw0Vubr57DE8YHkIx095WHpR7MEqv9p0EgCH4bPuTqW7PSHCTg1v+2ygkV9jOgmAw9BQkatbWHd7SsBi3e1Z6UXS4t/zMgJch6EGbKexMlc3ntnIhyMewU4Nj8ooie3Q4EMRwBXobu9gqOFh6cUMNAAXGV3JYMNLQnz6500ZJbHuLqg1nQSIO76twZaaBubp1rPHKCPCWZ9uZ0XbTEdAsmX258EKcKGxA/P0qyVjlEl3u1rYx8sInpTZTzrrYQYagMuMrozttuSODfcLiJcSPCejlJcR4GoMNWBboytzdeeyscpPD5mOggSyOtkG6ylZ5dJZnMMNuFVDRa7uXDZO+elh01GQIGGLD0U8J7s8NtCguwFXaqzM1R3LxiovjXW3m3GnhsdklbHuhusx1ICtDSnN0t3njle/7BTTUZAgVic7NTwjvy72oUhOpekkABJocGmm7j53HN3tUmGOn/KW3CrprEfobsDlhvbLortdLsBLCd5ROFha8jiXgsP1GGrA9gbkp+me88apqiDNdBQkAndqeENZk3T2o1J2mekkAJKA7nYvjp/ykH4NsQ9FsvqbTgIgCQYWpOue88apujDddBQkQJChhjeUj4u9jJBZajoJkHAMNeAIJVkpuvvc8RrWL8t0FMSZFeXDEderPUo6434pNdd0EgBJRHe7U4gPRbyhZpa0+EEpLd90EgBJVJKVoruXj9OIsmzTURBnQYvjp1yvbo606F4pJTtuf2Vzc7MuueSSuP19QDwx1IBj5KaFdMeysWoawAejrtLBUMPVjjhDWnC7FIzfVnYerADnyE0L6dfnNNHdLhK26G3XO+IM6dQ7pFBq3P5Kuhtwjpy0kH69tEkTqxlqukmAOzXcbdQi6ZTb4rruBuyOoQYcJT0c0K1LxmjeSLbSuQbHT7nX5K9Lx14t+fymkwAwKCMS1K+WNOmEUf1MR0EccFG4yzX/K90NQGnhgG48s1HHDC8xHQVxEhD97VqTLpfmXUN3w3MYasBxwgG/frJglC6dXivLMp0Gh4uLwl3I8klH/0ia9k3TSQDYRCjg049OGanLZ9DdThe0OkxHQCJYfmnuT6Xmb5hOAsAmQgGfrj51lC4+ssZ0FMQBF4W7kOWXjrpSOvJbSflyW7Zs0RlnnKGcnBylpqbqqKOO0urVqyVJ0WhUBQUFuueee/b9/pEjR6qk5PPB6KpVqxQOh7Vr166k5IX7MdSAY31leo1+umCUwgH+MXaqkI8HK9cJZ0oL7pAalyTly/FgBTjLRUfW6JpTj1AkSHc7VYjjp9wnlB47bqphcVK+HN0NOIdlWbpsRq1+eirrbqcLcvyUu0SypNN+IzUtT9qXPPPMM/XCCy/ogQce0DPPPKNoNKo5c+aora1NlmVp8uTJWrFihaRY17/22mtqaWnR66+/LklauXKlGhsblZoav+Mt4W20Ehxt7ohS3blsrAoywqajoA/CDDXcJa9aWvonqW520r4kD1aA8xw9vER3Lhun/HS624m4KNxlcqti3V07K2lfku4GnOfYEaW6a/k4FbLudqwg/e0eeTXS0iek6ulJ+5KrV6/WAw88oBtuuEGTJk3SiBEjdPvtt+vDDz/UfffdJyl2f9Zn/f3nP/9Zo0aN6vJrK1as0JQpU5KWGe7HUAOON6o8R/ddMEH1xRmmo6CXUvy8LeIa1dNjH4oU1CbtS/JgBTjXyLJs3X8h3e1EQbFTwzWqZ0jnPCEV1iftS9LdgHN91t1DSjNNR0Ef+MXa2xWqZ0jn/EnKr07ql33ttdcUCATU1NS079fy8vJUV1en1157TZI0ZcoU/fOf/9THH3+slStXqrm5eV9/t7W16S9/+Yuam5uTmhvuxlADrtAvO0W/PW+8ZgwuMh0FvRDmXG53mPAVaeHdUkp2Ur8sD1aAs/XLTtE9543XrCF0t5Nwp4ZLTLhEWvgbuhtAr5Rkpeiec8dr9pBi01HQS9yp4QLjL4p1dyTLdJIDGjZsmHJzc7Vy5cou/b1y5Uo9//zzamtr0/jx403HhIsw1IBrpIUDun5Rg/5ldr38Pm4hdYIIOzWcLZAizf8facZ3JJ8964QHK8De0sMBXbdotK6YU68A3e0IQTHUcLRgqnTijdKMb9PdAPokJeTXL04/Ql+dWcu620ECYqjhWIGIdPx10sz/MNbdgwYNUnt7u5577rl9v7Z582a98cYbGjx4sKTYHTyTJk3S/fffr1dffVUTJ07U8OHDtXv3bl133XUaPXq00tLSjOSHO9nzSRboI8uydF5zlX69tInzPh0gwp0azpVVJp39qDTsRGMReLAC3GPZ5CrdsWysijLpbrtjqOFg2eXSkselofONRaC7AXewLEsXTqvRbUtYdzsFOzUcKrtCOuthacQCozFqamo0b948nXPOOVq1apVefvllnX766erXr5/mzZu37/c1Nzfrjjvu0MiRI5Weni6fz6fJkyfr9ttv5+hIxB1DDbhS08A8PXTxJI2vyjMdBYfAZaMONWiudO5TUulIozF4sALcpbEyVw9eRHfbXVBtpiOgL+rmSMtWSsXDjMaguwF3GVfFutspAhanJDjO4ONi6+5+DaaTSJJuuukmNTQ06JhjjtG4ceMUjUb18MMPKxgM7vs9U6ZMUUdHR5djIpubm7/0a0A8MNSAaxVkhPWrJU26cGq1LHbF2lLEz1DDUQIR6egfSqfcJqXkmE4jiQcrwG0+6+4LplbR3TYVYKeGswQi0pz/J516h5SaazqNJLobcJuCjLBuW9Kki4+sEadR2ZefFwqdIxCRjv6RdPItxu/PWLFiha666ipJUk5Ojm699VZt3bpVu3bt0qOPPqqampouv3/kyJGKRqP6/ve/v+/XLrnkEkWjUc2aNSuZ0eEBVjQaZVwL13vyjY267K6XtGUXbxfaybS8Lbrx0wtMx0BPFNTHzuAuGmI6CQCPeOL1Dfrq3f/QJ5/uMR0F+3mw5iENXXO76RjoCbobQJI9tfpjXXrXS9q0k+62m+8OeFWLPvpP0zHQnfxa6cSbpOKhppMAtsdODXjC1LpCPXbJZDXXFZiOgv2Efbzt6QhHnCGd8yQfigBIqmn1RXr0kkmaVl9oOgr2E1C76QjoiYYzpWUr6G4ASTWppkAPXTxJk2ryTUfBF7BTwwFGLIx1NwMNoEcYasAzCjMjuvmsMfrP44cqNeQ3HQeSQlwUbm/hzNgbnsdeLYVSTacB4EGFGRHdeGajvnfCMKXR3bbAUMPmItnSybdKc38iBVNMpwHgQUWZEf1qSZO+exzrbjvhonAbi2RJx18nHf8LKZRmOg3gGAw14DmnNVXoka9M0ugKe9wJ4GVcFG5j1dOl85+Rhs43nQQAdOqYcj1Md9tCIMpQw7YGNkvnrpIGz+v2twJAoi0aG1t3N1bS3XYQEGtvW6qZKZ3/rDRigekkgOMw1IAnVeSl6TfLx+lfZtcr5OdfA1MYathQJEua93Pp9N9KWf1NpwGAfT7r7q/PrqO7DfKzU8N+wlmxXZVn3C9ll5lOAwD7VOSl6a5l43TFnHqFAnS3SX5xna6tRLKk434hnXa3lFlqOg3gSLQKPMvns3Rec5Xuv3CCBpVkmo7jSdypYTN1c6QL/iqNOs10EgA4IJ/P0vnN1brvggkaTHcb4Y/S3bZSN0e64LnY/VcAYEM+n6Vlk6v00EUTNaxfluk4nsWdGjZSO1s6/zlp5ELTSQBHY6gBzxtUkqnfXzhBV8yp58zPJGOnhk2k5Erz/0c69Q4po9h0GgDo1uDSTP3+oon6t6MH0d1J5o+2mY4ASUrN+7y7M0tMpwGAbtUUZeje88fr67PrlBKku5PNL15KMC6SLR13rbTwLrobiAOGGoCkgN+nZZOr9IfLpmj6oCLTcTwjyEXh5g0+LrY7Y9iJppMAQK/4fZaWThpIdycZd2rYwND5dDcARwr4fTq/uVqPXzpZ0wcVmo7jKQGOnzJr8HGxnZUjTzWdBHANhhrAfvplp+iGxaN13aIGlWZFTMdxvRCXlZmTXyud/jvp5Fuk9ALTaQCgz/bv7hK6O+F8DDXMya2SFt4tnXijlJZvOg0A9FlZbqpuWNyo6xc1qF92iuk4nsDxU4bk18XuvDr5Fk5FAOIsYDoAYEezhhRrUk2+fvyHN3XT0++pvZO3GhIhZLEFNulCGVLzv0hN50r+oOk0ABA3s4YUa2J1vn74+Ju65Zn31EF3JwRDDQNCGdLkr0pjz5cCIdNpACBuZg4p1qSaAv3kT6v1P6veUVsH3Z0oPl4oTC7W3UDCsVMDOIjUUEDfPHqwfn/RRI2vyjMdx5WCXBSeRJY04lTpohel8RfxYAXAldLCAX1r7mA9eNFETazmTfZE8DPUSKL9unviJQw0ALhSSsivbxxVr4cunqSmAbmm47hWgDs1kmf4KdJFL7DuBhKMoQbQjUElmfr1OWN105mNqi1KNx3HVbgoPElKRkhLHpeOv1bK4Nx5AO43qCRTty1t0k1n0d3x5utkqJEUpUdIS/9IdwPwjNqiDN21fJx+ecZoVRfS3fHm506NxCsaJp31qHTC9Rw1BSQBx08BPTS1vlCTawt01/Nr9OM/vqmPd+w2HcnxgrwtkliZ/WNbXkeeLvmYYQPwnql1hZpcE+vuH/3hTW3aSXcfLivaZjqCu2WUStO+KY08TbIs02kAIOlmDC7StPpC1t1xxvFTCZRdLjVfEduhwbobSBqGGkAv+H2WFjaVa97IUl3353f0yz+/o5Y2PpjvqyB3aiRGar406XKpcYkUCJtOAwBG7d/d1658Wzc89S7dfRjYqZEgqfnSxEulxqVSkAvvAXjbZ9193KhSXb933f3pHrr7cHBReAKkFUiTvyY1nMURkYABVjQaZQ8a0EcbtrfqJ39arXteWKs9HTwk9NZPq1/QsWt/ZDqGe4SzYud2jj1PCrNlGwAOZP22Vl3z5Gr95nm6uy9WF/2bgtveMR3DPSJZ0ji6GwAO5eMdu3XVH9/UXc+vUXsnH2H1xe9qHtcRa242HcMdwpnS+IvpbsAwhhpAHKzb2qJfrHhbd72wRnva+YCkp35R/VcdtfYq0zGcL5gqjVkWu0Q0Jcd0GgBwhI+2xbr7zufp7t54q+DrCuxYazqG8wXTpLHnxl5GoLsBoEfWfLJLv1j5Ni8V9sH9NY9oxJpfmY7hbIFIbEflpMulVC61B0xjqAHE0YbtrXs/IPlArW08ZHXnhppnNH3N1aZjOFcwTWpYLE24hEtEAaCPPuvuO/76gXYz3OjW23mXyf/petMxnCuYKjWcKU28TEovMJ0GABxp/bZWXbuSdXdvPFjzkIauud10DGcKZUijz5TGXiBllphOA2AvhhpAAmzc0arrV76j25/7gHO7D+HmmlVqXvNz0zGcJ61AGrM8dmcGb4gAQFxs3N6qa1e+o1//9X0+IDmEd3Iukq9ls+kYzpOaH9tVOeYcuhsA4uTjHbt1w1Pv6LZn3+fOjW48XPN7DV5zh+kYzpJWIDWdG9udkZJtOg2AL2CoASTQ5p27dcsz7+vXz72vTTv3mI5jO7fVrNTENdeZjuEcuQOlcRdKI0/jElEASJBNO3frtmff123PfqBNO3ebjmM772adK2v3dtMxnGNfdy+Ugimm0wCAK23dtUc3rnpXv3r2fW3Z1WY6ji09VnO/6tbcZTqGM+RUxo6HHHk6627AxhhqAEmwu71Dv3/5I938l3f1yod8EPCZu2qeVNOaX5qOYX+lo2JHTA06VvL5TKcBAE/Y096p37+8TjfR3V28m7FUVtsu0zHsr39j7BLR+mPobgBIkta2Dj3w0jrd/Jf39M+P6O79/aHmXtWsudt0DHsrHi5N+Io05HjJ5zedBkA3GGoASfb8e5/opqff1WOvblBHp7f/9fttzR/UsOYm0zHsyR+SBs2VRi+RKieYTgMAnvbXdz/Rjave1R9eo7vfTT1DVme76Rj25AtKg46JHRFZMc50GgDwtOff+0Q3P/2eHnt1vdo93t2S9Kfqe1S19nemY9iPPyQNnic1niOVN5lOA6AXAqYDAF7TWJmrxspcfbi1Rbc+857ueWGtNn/qzaOp/OLc0y/JKo9d/n3EYi4QBQCbGDMgV2MG5Grtll361TPv67d/+9CzR1Mx0DiA7PJYbx9xhpReaDoNAECfr7s/2tai2559X3f+dY1n192S5BP3hXWRXR7r7SMW092AQ7FTAzCsraNTT7y+Ufe8uFZPvr7RU2+RPFjzkIauud10DPN8Qal+TuyhauA0jqkAAJtr7+jUk298rLtfWKMn39iotg5vdHeKv0OvBReZjmEP/pBUd5Q06gypiu4GALvb3d6hP722Ub99ca1Wvvmxp9bdkrSy+k5VrH3AdAyzfIFYdzecybobcAGGGoCNbNq5W/f9/UPd8+Javb5+h+k4Cfdozf2q9/JlZSUjpGEnSSNOldLyTacBAPTB5p27da9Hujs32Ka/+RebjmFW0dBYb49YQHcDgENt2rlb97+0Tr99ca1n7t54qvp2la19yHQMM/o1xNbdQ+ezKwNwEYYagE298uE23fPiWj34j3XatNOd22Q9eVlZfl3sYWrofCm/2nQaAEAc/e/abbrnxTV65JX12rjDfcdT9Yvs1tM6y3SM5Muvk4aeIA05QSqoNZ0GABBHr320Xb99ca3ue2mdq4+WfLrqVvX78FHTMZInvy42yBg2X8odaDoNgARgqAHYXGdnVM+/94keeWW9Hn91vdZtazUdKW6eqL5bA9feazpG4uVUxj4IGTpfKh5qOg0AIME6O6N68YMteuR/1+uxV9frw60tpiPFRVVqi/7UucR0jOTIGfD5IIPuBgDX6+iM6rl3N+vxVzfosVfX6yMXrbsl6dmqm1X84eOmYyRWZv9Ydw87SSoZbjoNgARjqAE4zMtrtuqRV2Ifkry76VPTcQ6Lq8/1LBoq1cyQ6o+R+o82nQYAYNDLa7bq4Vc+0qOvrNf7m3eZjtNnQzN26sG2ZaZjJE7BIKl2pjTkeKl0lOk0AACDXl6zVY+9Glt3v/2xs9fdkvTcwBtVtO6PpmPEX/EwqXZ27Ee/BsmyTCcCkCQMNQAHe2P9Dv3xtQ16avXH+tv7W7Wno9N0pF5ZVX27+rvlXM9QujRgSmyQUTNTyupnOhEAwIb+uW67Vry5UatWb9IL72/RnnbndPforB26Z/dy0zHiZ193T5eqZ0jZZaYTAQBs6K2NO/XYq+v15Osb9dKarY68ZPz5gTeoYN0TpmMcvmCqNLA5tuaunSVllppOBMAQhhqAS+za067n3v1Eq1Zv0qrVm/TGBvtfVvpM1S0q+fAx0zH6Lr9Wqp4ee6CqmCAFQqYTAQAcpGVPh559d7NWrd6kp1Z/rDc37DQd6ZAm5m7TbbvOMx3j8BTU7+3uGVL5eLobANArn+5u13PvbtbTb23W02/F1t1O+FTtxQHXKe+jlaZj9IEV6+4Bk2Lr7spJUjBiOhQAGwiYDgAgPlJDAU2tK9TUukJJ0sbtrVr11iY9/dZm/e2DLbY8qsoX7TAdoed8Aal4uFQ+TqoYF/s5Ld90KgCAg6WE/F26e8P2Vj21epOefWez/v7BFr2z6VNbfVASsRzU29Le7h4W6+yyptjPGUWmUwEAHCwtHNC0+iJNq4/1yaadu/X0W5v0l7c265l3NuuDT+x5zKTllLW35Y/dh1ExQaoYH+vu1FzTqQDYEDs1AI/Y8ukevbR2q/7+wVb9/YMtennNVm1vbTea6YWB1yt/3QqjGQ4qlCH1GxV7i7NinNS/UQqlmU4FAPCQbbva9Pc1W/S3vd390pqt2mGwu48p2KRrdlxs7Ot3K5QhlTVKZWOl8rGxO63obgBAEm3euVsvr92ql9Zs00trtuofa7dq664207H0UuU1yl7/F9MxviySHXsBoWxMbIhR1iSFM0ynAuAA7NQAPCInLdTlbdBoNKq3P/5Uf/9gi15dt11vbdypNzfs0MYdu5OWyddpdqiyT3a5VDRMKh4au+C7eJiUU8klYwAAo7JSg2quK1Tzft391sad+vuarXpj/Q69uSH2Y8P25HR3xGeT3pa1t7uHSkVDPu/vnAGSz2c6HADAw/LSw112ckjSe5s+1UtrtuqlNVv1+vrY2nvTzj1JzWVFbXCHV1ZZbK1dPDz2c8nwWJ8DQB8w1AA8yrIsVRemq7owXSft9+vbdrVp9cYdenNDbMjx1sademvjTm3Y0Rr3IzB8SuIW2EAk9sCUUxn7kVez90OQIVIkK3k5AADoI8uyVFOUoZqirm8wbmtp2zfgeHN9rMPf3fSpNu5oVTzvMg37kvyBiD+8t7srYt1dOCg2vCgcLEUyk5sFAIA+qsxPU2V+mo4b1W/fr23dtUdvbdyp1XvX26s37tTbG3dq3baWhBw9mbTjpwKRWGfnDoy9bJA7IHYXZfEwjpECEFcMNQB0kZUa1OjKXI2u7PrAsae9U+u3terDrS1a99mPbS36cGur1m1t0Ybtrdq5u71XD2Bx26nhD8fut0jNi/2cVvj5ByCf/cgoYecFAMCVslKCaqzMVeMXurutI9bda7e07OvvD/f7z5s/3aMdrW09HnyEfXH8QMQfjvV2ap6Ulidl9pOyKz7v7+wKKaOY7gYAuFJ2auiA6+6WPR36aFuLPtrWGvuxtUUfbd/7895f29bS++OsfIrDiwn+sJReuHfNXRBbd2cUfT68yB3IuhtA0nCnBoC46eyMantrm7buatO2ltiPrXt/3t7Spl172tXWEdWe9k7t6ejU11IeVE7L+1K0U+rsiP3sD8be7ghEpEA49nNwv/8eTJVScrsOMThzEwCAPunsjGpHa7u2tuzR1l2x3t66a8++7t7d3qk97Z3a3d6poeH1OrHld1K0Y29v7x1yBCKSP7S3q0OxDz0C4c97PJT++fDis0EG3Q0AQJ/sae/U9tZYT29vbdf2ljbtaG3f79fa1NrWqfaOTrV1RtXe0akr0h5Qdssaxd5C3PsxoD8cW2sHU2Lr7EAk9nPws59T9xtgFLBLEoCtMNQAAAAAAAAAAACOwE16AAAAAAAAAADAERhqAAAAAAAAAAAAR2CoAQAAAAAAAAAAHIGhBgAAAAAAAAAAcASGGgAAAAAAAAAAwBEYagAAAAAAAAAAAEdgqAEAAAAAAAAAAByBoQYAAAAAAAAAAHAEhhoAAAAAAAAAAMARGGoAAAAAAAAAAABHYKgBAAAAAAAAAAAcgaEGAAAAAAAAAABwBIYaAAAAAAAAAADAERhqAAAAAAAAAAAAR2CoAQAAAAAAAAAAHIGhBgAAAAAAAAAAcASGGgAAAAAAAAAAwBEYagAAAAAAAAAAAEdgqAEAAAAAAAAAAByBoQYAAAAAAAAAAHAEhhoAAAAAAAAAAMARGGoAAAAAAAAAAABHYKgBAAAAAAAAAAAcgaEGAAAAAAAAAABwBIYaAAAAAAAAAADAERhqAAAAAAAAAAAAR2CoAQAAAAAAAAAAHIGhBgAAAAAAAAAAcASGGgAAAAAAAAAAwBEYagAAAAAAAAAAAEdgqAEAAAAAAAAAAByBoQYAAAAAAAAAAHAEhhoAAAAAAAAAAMARGGoAAAAAAAAAAABHYKgBAAAAAAAAAAAcgaEGAAAAAAAAAABwBIYaAAAAAAAAAADAERhqAAAAAAAAAAAAR2CoAQAAAAAAAAAAHIGhBgAAAAAAAAAAcASGGgAAAAAAAAAAwBEYagAAAAAAAAAAAEdgqAEAAAAAAAAAAByBoQYAAAAAAAAAAHAEhhoAAAAAAAAAAMARGGoAAAAAAAAAAABHYKgBAAAAAAAAAAAcgaEGAAAAAAAAAABwBIYaAAAAAAAAAADAERhqAAAAAAAAAAAAR2CoAQAAAAAAAAAAHIGhBgAAAAAAAAAAcASGGgAAAAAAAAAAwBH+Pzug7E020dVmAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
||
"print('Распределение количества наблюдений по меткам (классам):')\n",
|
||
"print(df.salary_in_usd.value_counts(), '\\n')\n",
|
||
"\n",
|
||
"# Статистическое описание целевого признака\n",
|
||
"print('Статистическое описание целевого признака:')\n",
|
||
"print(df['salary_in_usd'].describe().transpose(), '\\n')\n",
|
||
"\n",
|
||
"# Определим границы для каждой категории зарплаты\n",
|
||
"bins: list[float] = [df['salary_in_usd'].min() - 1, \n",
|
||
" df['salary_in_usd'].quantile(0.25), \n",
|
||
" df['salary_in_usd'].quantile(0.75), \n",
|
||
" df['salary_in_usd'].max() + 1]\n",
|
||
"labels: list[str] = ['low', 'medium', 'high']\n",
|
||
"\n",
|
||
"# Создаем новую колонку с категориями зарплат#\n",
|
||
"df['salary_category'] = pd.cut(df['salary_in_usd'], bins=bins, labels=labels)\n",
|
||
"\n",
|
||
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
||
"print('Распределение количества наблюдений по меткам (классам):')\n",
|
||
"print(df['salary_category'].value_counts(), '\\n')\n",
|
||
"\n",
|
||
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
||
" df,\n",
|
||
" stratify_colname=\"salary_category\", \n",
|
||
" frac_train=0.60, \n",
|
||
" frac_val=0.20, \n",
|
||
" frac_test=0.20\n",
|
||
")\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок:')\n",
|
||
"check_balance(df_train, 'Обучающая выборка', 'salary_category')\n",
|
||
"check_balance(df_val, 'Контрольная выборка', 'salary_category')\n",
|
||
"check_balance(df_test, 'Тестовая выборка', 'salary_category')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train, df_val, df_test, 'salary_category')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Приращение данных:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка сбалансированности выборок после применения метода oversampling:\n",
|
||
"Обучающая выборка: (3360, 240)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"low 1121\n",
|
||
"medium 1120\n",
|
||
"high 1119\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.36%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.30%\n",
|
||
"\n",
|
||
"Контрольная выборка: (1119, 154)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"low 373\n",
|
||
"medium 373\n",
|
||
"high 373\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Тестовая выборка: (1122, 159)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"low 374\n",
|
||
"medium 374\n",
|
||
"high 374\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок после применения метода oversampling:\n",
|
||
"Для обучающей выборки аугментация данных не требуется\n",
|
||
"Для контрольной выборки аугментация данных не требуется\n",
|
||
"Для тестовой выборки аугментация данных не требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Приращение данных (oversampling)\n",
|
||
"df_train_oversampled: DataFrame = oversample(df_train, 'salary_category')\n",
|
||
"df_val_oversampled: DataFrame = oversample(df_val, 'salary_category')\n",
|
||
"df_test_oversampled: DataFrame = oversample(df_test, 'salary_category')\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
|
||
"check_balance(df_train_oversampled, 'Обучающая выборка', 'salary_category')\n",
|
||
"check_balance(df_val_oversampled, 'Контрольная выборка', 'salary_category')\n",
|
||
"check_balance(df_test_oversampled, 'Тестовая выборка', 'salary_category')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'salary_category')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка сбалансированности выборок после применения метода undersampling:\n",
|
||
"Обучающая выборка: (1677, 240)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"low 559\n",
|
||
"medium 559\n",
|
||
"high 559\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Контрольная выборка: (561, 154)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"low 187\n",
|
||
"medium 187\n",
|
||
"high 187\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Тестовая выборка: (558, 159)\n",
|
||
"Распределение выборки данных по классам в колонке \"salary_category\":\n",
|
||
" salary_category\n",
|
||
"low 186\n",
|
||
"medium 186\n",
|
||
"high 186\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок после применения метода undersampling:\n",
|
||
"Для обучающей выборки аугментация данных не требуется\n",
|
||
"Для контрольной выборки аугментация данных не требуется\n",
|
||
"Для тестовой выборки аугментация данных не требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Приращение данных (undersampling)\n",
|
||
"df_train_undersampled: DataFrame = undersample(df_train, 'salary_category')\n",
|
||
"df_val_undersampled: DataFrame = undersample(df_val, 'salary_category')\n",
|
||
"df_test_undersampled: DataFrame = undersample(df_test, 'salary_category')\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок после применения метода undersampling:')\n",
|
||
"check_balance(df_train_undersampled, 'Обучающая выборка', 'salary_category')\n",
|
||
"check_balance(df_val_undersampled, 'Контрольная выборка', 'salary_category')\n",
|
||
"check_balance(df_test_undersampled, 'Тестовая выборка', 'salary_category')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок после применения метода undersampling:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'salary_category')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Датасет №3: [Экономика стран](https://www.kaggle.com/datasets/pratik453609/economic-data-9-countries-19802020).\n",
|
||
"\n",
|
||
"### Описание датасета:\n",
|
||
"Данный набор данных содержит информацию о ключевых макроэкономических показателях для восьми стран (Китай, Франция, Германия, Индия, Япония, Испания, Великобритания, США) и одного специального административного района (Гонконг) за период с 1980 по 2020 год. В наборе представлены данные о таких макроэкономических переменных, как инфляция, уровень безработицы, ВВП, обменные курсы (по отношению к доллару США), доход на душу населения и цены на основные фондовые индексы каждой страны. Этот датасет полезен для анализа взаимосвязей между экономическими показателями и динамикой фондовых индексов стран, что может быть использовано для экономического моделирования и прогноза.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Анализ сведений:\n",
|
||
"**Проблемная область:**\n",
|
||
"Основная задача – исследование взаимосвязей между макроэкономическими переменными и ценами на фондовые индексы, а также анализ влияния таких факторов, как инфляция, ВВП и обменные курсы на фондовые рынки и экономическую стабильность стран.\n",
|
||
"\n",
|
||
"**Актуальность:**\n",
|
||
"Этот датасет актуален для исследователей, аналитиков и экономистов, занимающихся изучением макроэкономических трендов, финансовых рынков и их взаимосвязей. Он предоставляет данные, необходимые для анализа экономических кризисов, изменений на фондовых рынках и долгосрочных макроэкономических прогнозов.\n",
|
||
"\n",
|
||
"**Объекты наблюдения:**\n",
|
||
"Страны (или регионы), данные по которым включены в датасет: Китай, Франция, Германия, Гонконг, Индия, Япония, Испания, Великобритания и США. Для каждого из них собирались данные за период с 1980 по 2020 год.\n",
|
||
"\n",
|
||
"**Атрибуты объектов:**\n",
|
||
"- stock index: Название основного фондового индекса страны.\n",
|
||
"- country: Название страны.\n",
|
||
"- year: Год, к которому относятся данные.\n",
|
||
"- index price: Средняя цена фондового индекса за год.\n",
|
||
"- log_indexprice: Логарифмическое значение цены индекса для учета валютных различий.\n",
|
||
"- inflationrate: Уровень инфляции в стране.\n",
|
||
"- oil prices: Цены на нефть в долларах США.\n",
|
||
"- exchange_rate: Обменный курс валюты страны по отношению к доллару США.\n",
|
||
"- gdppercent: Рост ВВП (в процентах).\n",
|
||
"- percapitaincome: Доход на душу населения.\n",
|
||
"- unemploymentrate: Уровень безработицы (в процентах).\n",
|
||
"- manufacturingoutput: Объем производства в промышленном секторе страны.\n",
|
||
"- tradebalance: Торговый баланс.\n",
|
||
"- USTreasury: Облигации.\n",
|
||
"\n",
|
||
"**Связь между объектами:**\n",
|
||
"Данные позволяют исследовать взаимосвязи между макроэкономическими факторами и ценами на фондовые индексы, а также между другими экономическими показателями. Например, можно анализировать, как инфляция и обменный курс влияют на фондовый рынок каждой страны или как колебания цен на нефть отражаются на экономике разных стран.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Качество набора данных:\n",
|
||
"**Информативность:**\n",
|
||
"Датасет включает широкий спектр макроэкономических показателей и цены фондовых индексов за 40-летний период, что делает его очень полезным для анализа долгосрочных экономических трендов и финансовых рынков.\n",
|
||
"\n",
|
||
"**Степень покрытия:**\n",
|
||
"Набор данных охватывает данные по ведущим экономикам мира, представляя достаточно полную картину макроэкономической динамики в разных странах и регионах за большой временной период (1980–2020 гг.).\n",
|
||
"\n",
|
||
"**Соответствие реальным данным:**\n",
|
||
"Все представленные макроэкономические показатели и цены фондовых индексов являются официальными экономическими данными, которые используются для анализа и прогнозирования в реальных условиях.\n",
|
||
"\n",
|
||
"**Согласованность меток:**\n",
|
||
"Названия признаков определены недостаточно чётко. Обычно названия признаков записываются в стиле \"snake_case\" – слова пишутся строчными буквами и разделяются знаком нижнего подчеркивания. В данном же случае некоторые названия переменных записаны в стиле \"snake_case\", у некоторых слова разделяются пробелом, у некоторых вовсе не разделяются и пишутся слитно. Также в описании датасета отсутствовала расшифровка нескольких столбцов датасета – их предназначение пришлось домысливать самому, основываясь лишь на собственной логике. Сами данные представляют собой легко интерпретируемые экономические показатели, что упрощает их анализ и использование в эконометрических моделях.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Бизес-цели:\n",
|
||
"1. **Оценка влияния макроэкономических факторов на фондовые рынки:**\n",
|
||
"Анализ взаимосвязей между инфляцией, обменными курсами, ВВП и динамикой фондовых индексов для прогнозирования изменений на фондовых рынках.\n",
|
||
"2. **Прогнозирование экономических кризисов:**\n",
|
||
"Использование данных для создания моделей, позволяющих прогнозировать экономические кризисы или спады на основе динамики ключевых макроэкономических переменных.\n",
|
||
"3. **Оптимизация инвестиционных решений:**\n",
|
||
"Помощь инвесторам и финансовым аналитикам в понимании влияния экономических факторов на фондовые рынки для принятия более обоснованных инвестиционных решений.\n",
|
||
"\n",
|
||
"**Эффект для бизнеса:**\n",
|
||
"Компании, использующие данные для анализа и прогнозирования, могут лучше управлять рисками, связанными с изменениями на фондовых рынках и макроэкономическими условиями. Это может привести к более точным инвестиционным стратегиям и повышению эффективности управления активами.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Технические цели:\n",
|
||
"1. **Построение модели прогнозирования цен на фондовые индексы:**\n",
|
||
"Создание модели машинного обучения для прогнозирования цен на фондовые индексы на основе макроэкономических показателей, таких как инфляция, ВВП и обменные курсы.\n",
|
||
"2. **Анализ корреляций между макроэкономическими переменными:**\n",
|
||
"Проведение анализа корреляций между такими показателями, как инфляция, доход на душу населения, цены на нефть и курс валют для выявления ключевых факторов, влияющих на фондовые рынки.\n",
|
||
"3. **Прогнозирование долгосрочных экономических трендов:**\n",
|
||
"Использование данных для построения прогнозов долгосрочных макроэкономических трендов и их влияния на экономику и финансовые рынки.\n",
|
||
"\n",
|
||
"**Входные данные:**\n",
|
||
"Инфляция, ВВП, обменные курсы, цены на нефть, цены фондовых индексов, доход на душу населения, уровень безработицы, объем производства в промышленном секторе страны, торговый баланс, облигации.\n",
|
||
"\n",
|
||
"**Целевой признак:**\n",
|
||
"Признак \"index_price\" – средняя цена фондового индекса страны.\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Выгрузка данных из файла в DataFrame:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df: DataFrame = pd.read_csv('..//static//csv//economic_data.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Краткая информация о DataFrame:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 369 entries, 0 to 368\n",
|
||
"Data columns (total 14 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 stock index 369 non-null object \n",
|
||
" 1 country 369 non-null object \n",
|
||
" 2 year 369 non-null float64\n",
|
||
" 3 index price 317 non-null float64\n",
|
||
" 4 log_indexprice 369 non-null float64\n",
|
||
" 5 inflationrate 326 non-null float64\n",
|
||
" 6 oil prices 369 non-null float64\n",
|
||
" 7 exchange_rate 367 non-null float64\n",
|
||
" 8 gdppercent 350 non-null float64\n",
|
||
" 9 percapitaincome 368 non-null float64\n",
|
||
" 10 unemploymentrate 348 non-null float64\n",
|
||
" 11 manufacturingoutput 278 non-null float64\n",
|
||
" 12 tradebalance 365 non-null float64\n",
|
||
" 13 USTreasury 369 non-null float64\n",
|
||
"dtypes: float64(12), object(2)\n",
|
||
"memory usage: 40.5+ KB\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>count</th>\n",
|
||
" <th>mean</th>\n",
|
||
" <th>std</th>\n",
|
||
" <th>min</th>\n",
|
||
" <th>25%</th>\n",
|
||
" <th>50%</th>\n",
|
||
" <th>75%</th>\n",
|
||
" <th>max</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>year</th>\n",
|
||
" <td>369.0</td>\n",
|
||
" <td>2000.000000</td>\n",
|
||
" <td>11.848225</td>\n",
|
||
" <td>1980.00</td>\n",
|
||
" <td>1990.00</td>\n",
|
||
" <td>2000.00</td>\n",
|
||
" <td>2010.0000</td>\n",
|
||
" <td>2020.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>index price</th>\n",
|
||
" <td>317.0</td>\n",
|
||
" <td>7898.648297</td>\n",
|
||
" <td>7811.336862</td>\n",
|
||
" <td>168.61</td>\n",
|
||
" <td>2407.10</td>\n",
|
||
" <td>5160.10</td>\n",
|
||
" <td>10279.5000</td>\n",
|
||
" <td>47751.33</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>log_indexprice</th>\n",
|
||
" <td>369.0</td>\n",
|
||
" <td>3.610542</td>\n",
|
||
" <td>0.482481</td>\n",
|
||
" <td>2.23</td>\n",
|
||
" <td>3.32</td>\n",
|
||
" <td>3.60</td>\n",
|
||
" <td>3.9800</td>\n",
|
||
" <td>4.68</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>inflationrate</th>\n",
|
||
" <td>326.0</td>\n",
|
||
" <td>0.041748</td>\n",
|
||
" <td>0.039579</td>\n",
|
||
" <td>-0.04</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" <td>0.03</td>\n",
|
||
" <td>0.0575</td>\n",
|
||
" <td>0.24</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>oil prices</th>\n",
|
||
" <td>369.0</td>\n",
|
||
" <td>39.743171</td>\n",
|
||
" <td>25.452654</td>\n",
|
||
" <td>11.35</td>\n",
|
||
" <td>19.41</td>\n",
|
||
" <td>28.52</td>\n",
|
||
" <td>57.8800</td>\n",
|
||
" <td>98.56</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>exchange_rate</th>\n",
|
||
" <td>367.0</td>\n",
|
||
" <td>27.897548</td>\n",
|
||
" <td>49.620521</td>\n",
|
||
" <td>0.90</td>\n",
|
||
" <td>1.33</td>\n",
|
||
" <td>5.44</td>\n",
|
||
" <td>15.0550</td>\n",
|
||
" <td>249.05</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>gdppercent</th>\n",
|
||
" <td>350.0</td>\n",
|
||
" <td>0.037114</td>\n",
|
||
" <td>0.037850</td>\n",
|
||
" <td>-0.11</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" <td>0.03</td>\n",
|
||
" <td>0.0600</td>\n",
|
||
" <td>0.15</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>percapitaincome</th>\n",
|
||
" <td>368.0</td>\n",
|
||
" <td>20719.964674</td>\n",
|
||
" <td>17435.037783</td>\n",
|
||
" <td>27.00</td>\n",
|
||
" <td>2090.25</td>\n",
|
||
" <td>19969.50</td>\n",
|
||
" <td>36384.0000</td>\n",
|
||
" <td>65280.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>unemploymentrate</th>\n",
|
||
" <td>348.0</td>\n",
|
||
" <td>0.068908</td>\n",
|
||
" <td>0.043207</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" <td>0.04</td>\n",
|
||
" <td>0.06</td>\n",
|
||
" <td>0.0900</td>\n",
|
||
" <td>0.26</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>manufacturingoutput</th>\n",
|
||
" <td>278.0</td>\n",
|
||
" <td>328.084820</td>\n",
|
||
" <td>622.395923</td>\n",
|
||
" <td>0.59</td>\n",
|
||
" <td>80.38</td>\n",
|
||
" <td>188.16</td>\n",
|
||
" <td>271.9775</td>\n",
|
||
" <td>3868.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>tradebalance</th>\n",
|
||
" <td>365.0</td>\n",
|
||
" <td>-15.996384</td>\n",
|
||
" <td>154.557170</td>\n",
|
||
" <td>-770.93</td>\n",
|
||
" <td>-25.37</td>\n",
|
||
" <td>-0.14</td>\n",
|
||
" <td>19.0800</td>\n",
|
||
" <td>366.14</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>USTreasury</th>\n",
|
||
" <td>369.0</td>\n",
|
||
" <td>0.059024</td>\n",
|
||
" <td>0.033086</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>0.03</td>\n",
|
||
" <td>0.05</td>\n",
|
||
" <td>0.0800</td>\n",
|
||
" <td>0.14</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" count mean std min 25% \\\n",
|
||
"year 369.0 2000.000000 11.848225 1980.00 1990.00 \n",
|
||
"index price 317.0 7898.648297 7811.336862 168.61 2407.10 \n",
|
||
"log_indexprice 369.0 3.610542 0.482481 2.23 3.32 \n",
|
||
"inflationrate 326.0 0.041748 0.039579 -0.04 0.02 \n",
|
||
"oil prices 369.0 39.743171 25.452654 11.35 19.41 \n",
|
||
"exchange_rate 367.0 27.897548 49.620521 0.90 1.33 \n",
|
||
"gdppercent 350.0 0.037114 0.037850 -0.11 0.02 \n",
|
||
"percapitaincome 368.0 20719.964674 17435.037783 27.00 2090.25 \n",
|
||
"unemploymentrate 348.0 0.068908 0.043207 0.02 0.04 \n",
|
||
"manufacturingoutput 278.0 328.084820 622.395923 0.59 80.38 \n",
|
||
"tradebalance 365.0 -15.996384 154.557170 -770.93 -25.37 \n",
|
||
"USTreasury 369.0 0.059024 0.033086 0.01 0.03 \n",
|
||
"\n",
|
||
" 50% 75% max \n",
|
||
"year 2000.00 2010.0000 2020.00 \n",
|
||
"index price 5160.10 10279.5000 47751.33 \n",
|
||
"log_indexprice 3.60 3.9800 4.68 \n",
|
||
"inflationrate 0.03 0.0575 0.24 \n",
|
||
"oil prices 28.52 57.8800 98.56 \n",
|
||
"exchange_rate 5.44 15.0550 249.05 \n",
|
||
"gdppercent 0.03 0.0600 0.15 \n",
|
||
"percapitaincome 19969.50 36384.0000 65280.00 \n",
|
||
"unemploymentrate 0.06 0.0900 0.26 \n",
|
||
"manufacturingoutput 188.16 271.9775 3868.46 \n",
|
||
"tradebalance -0.14 19.0800 366.14 \n",
|
||
"USTreasury 0.05 0.0800 0.14 "
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Краткая информация о DataFrame\n",
|
||
"df.info()\n",
|
||
"\n",
|
||
"# Статистическое описание числовых столбцов\n",
|
||
"df.describe().transpose()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Проблема пропущенных данных:\n",
|
||
"\n",
|
||
"Проверка на отсутствие значений, представленная ниже, показала, что некоторые колонки DataFrame содержат пустые значения признаков.\n",
|
||
"\n",
|
||
"Решения проблемы отсутствия значений:\n",
|
||
"1. Удаление примеров с пустыми значениями (допустимо для набора данных с большим количеством наблюдений).\n",
|
||
"2. Использовать метод машинного обучения, который способен обработать пустые значения (например, деревья решений).\n",
|
||
"3. Использовать методы подстановки данных:\n",
|
||
" - Заполнить средним значением признака (среднее по колонке).\n",
|
||
" - Подставить магическое число (число за диапазоном доступных значений).\n",
|
||
" - Обучить модель для предсказания пропущенного значения на основе других значений наблюдения.\n",
|
||
"\n",
|
||
"Воспользуемся методом подстановки среднего значения признака."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"До заполнения пустых значений:\n",
|
||
"Присутствуют ли пустые значения признаков в колонке:\n",
|
||
"stock index False\n",
|
||
"country False\n",
|
||
"year False\n",
|
||
"index price True\n",
|
||
"log_indexprice False\n",
|
||
"inflationrate True\n",
|
||
"oil prices False\n",
|
||
"exchange_rate True\n",
|
||
"gdppercent True\n",
|
||
"percapitaincome True\n",
|
||
"unemploymentrate True\n",
|
||
"manufacturingoutput True\n",
|
||
"tradebalance True\n",
|
||
"USTreasury False\n",
|
||
"dtype: bool \n",
|
||
"\n",
|
||
"Количество пустых значений признаков в колонке:\n",
|
||
"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 \n",
|
||
"\n",
|
||
"Процент пустых значений признаков в колонке:\n",
|
||
"index price процент пустых значений: 14.09%\n",
|
||
"inflationrate процент пустых значений: 11.65%\n",
|
||
"exchange_rate процент пустых значений: 0.54%\n",
|
||
"gdppercent процент пустых значений: 5.15%\n",
|
||
"percapitaincome процент пустых значений: 0.27%\n",
|
||
"unemploymentrate процент пустых значений: 5.69%\n",
|
||
"manufacturingoutput процент пустых значений: 24.66%\n",
|
||
"tradebalance процент пустых значений: 1.08%\n",
|
||
"\n",
|
||
"После заполнения пустых значений:\n",
|
||
"Присутствуют ли пустые значения признаков в колонке:\n",
|
||
"stock index False\n",
|
||
"country False\n",
|
||
"year False\n",
|
||
"index price False\n",
|
||
"log_indexprice False\n",
|
||
"inflationrate False\n",
|
||
"oil prices False\n",
|
||
"exchange_rate False\n",
|
||
"gdppercent False\n",
|
||
"percapitaincome False\n",
|
||
"unemploymentrate False\n",
|
||
"manufacturingoutput False\n",
|
||
"tradebalance False\n",
|
||
"USTreasury False\n",
|
||
"dtype: bool \n",
|
||
"\n",
|
||
"Количество пустых значений признаков в колонке:\n",
|
||
"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 \n",
|
||
"\n",
|
||
"Процент пустых значений признаков в колонке:\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Заполнить пропущенные данные средним значением\n",
|
||
"def fill_null_columns(dataframe: DataFrame) -> DataFrame:\n",
|
||
" for column in dataframe.columns:\n",
|
||
" null_rate: float = dataframe[column].isnull().sum() / len(dataframe) * 100\n",
|
||
" if null_rate > 0:\n",
|
||
" # Замена пустых данных на медиану\n",
|
||
" df[column] = df[column].fillna(df[column].median())\n",
|
||
" \n",
|
||
" return dataframe\n",
|
||
"\n",
|
||
"\n",
|
||
"# Проверка пропущенных данных\n",
|
||
"print('До заполнения пустых значений:')\n",
|
||
"check_null_columns(df)\n",
|
||
"\n",
|
||
"# Заполнение пропущенных значений\n",
|
||
"df: DataFrame = fill_null_columns(df)\n",
|
||
"\n",
|
||
"# Проверка пропущенных данных\n",
|
||
"print('После заполнения пустых значений:')\n",
|
||
"check_null_columns(df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Проблема зашумленности данных:\n",
|
||
"\n",
|
||
"Представленный ниже код помогает определить наличие выбросов в наборе данных и устранить их (при наличии), заменив значения ниже нижней границы (рассматриваемого минимума) на значения нижней границы, а значения выше верхней границы (рассматриваемого максимума) – на значения верхней границы."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка наличия выбросов в колонках:\n",
|
||
"Колонка year:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 1980.0\n",
|
||
"\tМаксимальное значение: 2020.0\n",
|
||
"\t1-й квартиль (Q1): 1990.0\n",
|
||
"\t3-й квартиль (Q3): 2010.0\n",
|
||
"\n",
|
||
"Колонка index price:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 33\n",
|
||
"\tМинимальное значение: 168.61\n",
|
||
"\tМаксимальное значение: 47751.33\n",
|
||
"\t1-й квартиль (Q1): 2846.5\n",
|
||
"\t3-й квартиль (Q3): 9484.47\n",
|
||
"\n",
|
||
"Колонка log_indexprice:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 3\n",
|
||
"\tМинимальное значение: 2.23\n",
|
||
"\tМаксимальное значение: 4.68\n",
|
||
"\t1-й квартиль (Q1): 3.32\n",
|
||
"\t3-й квартиль (Q3): 3.98\n",
|
||
"\n",
|
||
"Колонка inflationrate:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 38\n",
|
||
"\tМинимальное значение: -0.04\n",
|
||
"\tМаксимальное значение: 0.24\n",
|
||
"\t1-й квартиль (Q1): 0.02\n",
|
||
"\t3-й квартиль (Q3): 0.05\n",
|
||
"\n",
|
||
"Колонка oil prices:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 11.35\n",
|
||
"\tМаксимальное значение: 98.56\n",
|
||
"\t1-й квартиль (Q1): 19.41\n",
|
||
"\t3-й квартиль (Q3): 57.88\n",
|
||
"\n",
|
||
"Колонка exchange_rate:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 85\n",
|
||
"\tМинимальное значение: 0.9\n",
|
||
"\tМаксимальное значение: 249.05\n",
|
||
"\t1-й квартиль (Q1): 1.33\n",
|
||
"\t3-й квартиль (Q3): 13.9\n",
|
||
"\n",
|
||
"Колонка gdppercent:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 41\n",
|
||
"\tМинимальное значение: -0.11\n",
|
||
"\tМаксимальное значение: 0.15\n",
|
||
"\t1-й квартиль (Q1): 0.02\n",
|
||
"\t3-й квартиль (Q3): 0.05\n",
|
||
"\n",
|
||
"Колонка percapitaincome:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 27.0\n",
|
||
"\tМаксимальное значение: 65280.0\n",
|
||
"\t1-й квартиль (Q1): 2099.0\n",
|
||
"\t3-й квартиль (Q3): 36354.0\n",
|
||
"\n",
|
||
"Колонка unemploymentrate:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 23\n",
|
||
"\tМинимальное значение: 0.02\n",
|
||
"\tМаксимальное значение: 0.26\n",
|
||
"\t1-й квартиль (Q1): 0.04\n",
|
||
"\t3-й квартиль (Q3): 0.08\n",
|
||
"\n",
|
||
"Колонка manufacturingoutput:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 35\n",
|
||
"\tМинимальное значение: 0.59\n",
|
||
"\tМаксимальное значение: 3868.46\n",
|
||
"\t1-й квартиль (Q1): 101.07\n",
|
||
"\t3-й квартиль (Q3): 245.75\n",
|
||
"\n",
|
||
"Колонка tradebalance:\n",
|
||
"\tЕсть выбросы: Да\n",
|
||
"\tКоличество выбросов: 77\n",
|
||
"\tМинимальное значение: -770.93\n",
|
||
"\tМаксимальное значение: 366.14\n",
|
||
"\t1-й квартиль (Q1): -24.12\n",
|
||
"\t3-й квартиль (Q3): 18.15\n",
|
||
"\n",
|
||
"Колонка USTreasury:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0.01\n",
|
||
"\tМаксимальное значение: 0.14\n",
|
||
"\t1-й квартиль (Q1): 0.03\n",
|
||
"\t3-й квартиль (Q3): 0.08\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 12 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Числовые столбцы DataFrame\n",
|
||
"numeric_columns: list[str] = [\n",
|
||
" 'year',\n",
|
||
" 'index price',\n",
|
||
" 'log_indexprice',\n",
|
||
" 'inflationrate',\n",
|
||
" 'oil prices',\n",
|
||
" 'exchange_rate',\n",
|
||
" 'gdppercent',\n",
|
||
" 'percapitaincome',\n",
|
||
" 'unemploymentrate',\n",
|
||
" 'manufacturingoutput',\n",
|
||
" 'tradebalance',\n",
|
||
" 'USTreasury'\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Проверка наличия выбросов в колонках\n",
|
||
"print('Проверка наличия выбросов в колонках:')\n",
|
||
"check_outliers(df, numeric_columns)\n",
|
||
"visualize_outliers(df, numeric_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка наличия выбросов в колонках после их устранения:\n",
|
||
"Колонка year:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 1980.0\n",
|
||
"\tМаксимальное значение: 2020.0\n",
|
||
"\t1-й квартиль (Q1): 1990.0\n",
|
||
"\t3-й квартиль (Q3): 2010.0\n",
|
||
"\n",
|
||
"Колонка index price:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 168.61\n",
|
||
"\tМаксимальное значение: 19441.424999999996\n",
|
||
"\t1-й квартиль (Q1): 2846.5\n",
|
||
"\t3-й квартиль (Q3): 9484.47\n",
|
||
"\n",
|
||
"Колонка log_indexprice:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 2.3299999999999996\n",
|
||
"\tМаксимальное значение: 4.68\n",
|
||
"\t1-й квартиль (Q1): 3.32\n",
|
||
"\t3-й квартиль (Q3): 3.98\n",
|
||
"\n",
|
||
"Колонка inflationrate:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: -0.025000000000000005\n",
|
||
"\tМаксимальное значение: 0.095\n",
|
||
"\t1-й квартиль (Q1): 0.02\n",
|
||
"\t3-й квартиль (Q3): 0.05\n",
|
||
"\n",
|
||
"Колонка oil prices:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 11.35\n",
|
||
"\tМаксимальное значение: 98.56\n",
|
||
"\t1-й квартиль (Q1): 19.41\n",
|
||
"\t3-й квартиль (Q3): 57.88\n",
|
||
"\n",
|
||
"Колонка exchange_rate:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0.9\n",
|
||
"\tМаксимальное значение: 32.755\n",
|
||
"\t1-й квартиль (Q1): 1.33\n",
|
||
"\t3-й квартиль (Q3): 13.9\n",
|
||
"\n",
|
||
"Колонка gdppercent:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: -0.025000000000000005\n",
|
||
"\tМаксимальное значение: 0.095\n",
|
||
"\t1-й квартиль (Q1): 0.02\n",
|
||
"\t3-й квартиль (Q3): 0.05\n",
|
||
"\n",
|
||
"Колонка percapitaincome:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 27.0\n",
|
||
"\tМаксимальное значение: 65280.0\n",
|
||
"\t1-й квартиль (Q1): 2099.0\n",
|
||
"\t3-й квартиль (Q3): 36354.0\n",
|
||
"\n",
|
||
"Колонка unemploymentrate:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0.02\n",
|
||
"\tМаксимальное значение: 0.14\n",
|
||
"\t1-й квартиль (Q1): 0.04\n",
|
||
"\t3-й квартиль (Q3): 0.08\n",
|
||
"\n",
|
||
"Колонка manufacturingoutput:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0.59\n",
|
||
"\tМаксимальное значение: 462.77\n",
|
||
"\t1-й квартиль (Q1): 101.07\n",
|
||
"\t3-й квартиль (Q3): 245.75\n",
|
||
"\n",
|
||
"Колонка tradebalance:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: -87.52499999999999\n",
|
||
"\tМаксимальное значение: 81.55499999999999\n",
|
||
"\t1-й квартиль (Q1): -24.12\n",
|
||
"\t3-й квартиль (Q3): 18.15\n",
|
||
"\n",
|
||
"Колонка USTreasury:\n",
|
||
"\tЕсть выбросы: Нет\n",
|
||
"\tКоличество выбросов: 0\n",
|
||
"\tМинимальное значение: 0.01\n",
|
||
"\tМаксимальное значение: 0.14\n",
|
||
"\t1-й квартиль (Q1): 0.03\n",
|
||
"\t3-й квартиль (Q3): 0.08\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 12 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Устраняем выборсы\n",
|
||
"df: DataFrame = remove_outliers(df, numeric_columns)\n",
|
||
"\n",
|
||
"# Проверка наличия выбросов в колонках\n",
|
||
"print('Проверка наличия выбросов в колонках после их устранения:')\n",
|
||
"check_outliers(df, numeric_columns)\n",
|
||
"visualize_outliers(df, numeric_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Разбиение набора данных на выборки:\n",
|
||
"\n",
|
||
"Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
|
||
"\n",
|
||
"Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
|
||
"\n",
|
||
"Чтобы решить эту проблему введём категории для значения цены фондового рынка. Вместо того, чтобы использовать точные значения цен для стратификации, мы создадим категории, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении. Это позволит создать более крупные классы, что устранит проблему с редкими значениями.\n",
|
||
"\n",
|
||
"Категории для разбиения зарплат:\n",
|
||
"- Низкая цена индекса: значения ниже первого квартиля (25%) — это цены фондовых индексов ниже 2846.50.\n",
|
||
"- Средняя цена индекса: значения между первым квартилем (25%) и третьим квартилем (75%) — это цены от 2846.50 до 9484.47.\n",
|
||
"- Высокая цена индекса: значения выше третьего квартиля (75%) и до максимального значения — это цены выше 9484.47.\n",
|
||
"\n",
|
||
"Весь набор данных состоит из 369 объектов, из которых 184 (около 49.9%) имеют средний уровень цены фондового рынка (medium), 93 (около 25.2%) – низкий уровень цены (low), и 92 (около 24.9%) – высокий уровень цены (high).\n",
|
||
"\n",
|
||
"Все выборки показывают одинаковое распределение классов, что свидетельствует о том, что данные были отобраны случайным образом и не содержат явного смещения.\n",
|
||
"\n",
|
||
"Однако, несмотря на сбалансированность при разбиении данных, в целом данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать низкие или высокие цены фондовых рынков (low или high), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
|
||
"\n",
|
||
"Для получения более сбалансированных выборок данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение количества наблюдений по меткам (классам):\n",
|
||
"index price\n",
|
||
"5160.100 53\n",
|
||
"19441.425 33\n",
|
||
"1000.000 2\n",
|
||
"285.430 1\n",
|
||
"248.890 1\n",
|
||
" ..\n",
|
||
"1647.170 1\n",
|
||
"1986.530 1\n",
|
||
"2099.320 1\n",
|
||
"2263.410 1\n",
|
||
"203.150 1\n",
|
||
"Name: count, Length: 284, dtype: int64 \n",
|
||
"\n",
|
||
"Статистическое описание целевого признака:\n",
|
||
"count 369.000000\n",
|
||
"mean 6948.930095\n",
|
||
"std 5682.147273\n",
|
||
"min 168.610000\n",
|
||
"25% 2846.500000\n",
|
||
"50% 5160.100000\n",
|
||
"75% 9484.470000\n",
|
||
"max 19441.425000\n",
|
||
"Name: index price, dtype: float64 \n",
|
||
"\n",
|
||
"Распределение количества наблюдений по меткам (классам):\n",
|
||
"index_price_category\n",
|
||
"medium 184\n",
|
||
"low 93\n",
|
||
"high 92\n",
|
||
"Name: count, dtype: int64 \n",
|
||
"\n",
|
||
"Проверка сбалансированности выборок:\n",
|
||
"Обучающая выборка: (221, 15)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"medium 110\n",
|
||
"low 56\n",
|
||
"high 55\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"medium\": 49.77%\n",
|
||
"Процент объектов класса \"low\": 25.34%\n",
|
||
"Процент объектов класса \"high\": 24.89%\n",
|
||
"\n",
|
||
"Контрольная выборка: (74, 15)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"medium 37\n",
|
||
"high 19\n",
|
||
"low 18\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"medium\": 50.00%\n",
|
||
"Процент объектов класса \"high\": 25.68%\n",
|
||
"Процент объектов класса \"low\": 24.32%\n",
|
||
"\n",
|
||
"Тестовая выборка: (74, 15)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"medium 37\n",
|
||
"low 19\n",
|
||
"high 18\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"medium\": 50.00%\n",
|
||
"Процент объектов класса \"low\": 25.68%\n",
|
||
"Процент объектов класса \"high\": 24.32%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок:\n",
|
||
"Для обучающей выборки аугментация данных требуется\n",
|
||
"Для контрольной выборки аугментация данных требуется\n",
|
||
"Для тестовой выборки аугментация данных требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
||
"print('Распределение количества наблюдений по меткам (классам):')\n",
|
||
"print(df['index price'].value_counts(), '\\n')\n",
|
||
"\n",
|
||
"# Статистическое описание целевого признака\n",
|
||
"print('Статистическое описание целевого признака:')\n",
|
||
"print(df['index price'].describe().transpose(), '\\n')\n",
|
||
"\n",
|
||
"# Определим границы для каждой категории цен фондового рынка\n",
|
||
"bins: list[float] = [df['index price'].min() - 1, \n",
|
||
" df['index price'].quantile(0.25), \n",
|
||
" df['index price'].quantile(0.75), \n",
|
||
" df['index price'].max() + 1]\n",
|
||
"labels: list[str] = ['low', 'medium', 'high']\n",
|
||
"\n",
|
||
"# Создаем новую колонку с категориями зарплат\n",
|
||
"df['index_price_category'] = pd.cut(df['index price'], bins=bins, labels=labels)\n",
|
||
"\n",
|
||
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
||
"print('Распределение количества наблюдений по меткам (классам):')\n",
|
||
"print(df['index_price_category'].value_counts(), '\\n')\n",
|
||
"\n",
|
||
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
||
" df,\n",
|
||
" stratify_colname=\"index_price_category\", \n",
|
||
" frac_train=0.60, \n",
|
||
" frac_val=0.20, \n",
|
||
" frac_test=0.20\n",
|
||
")\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок:')\n",
|
||
"check_balance(df_train, 'Обучающая выборка', 'index_price_category')\n",
|
||
"check_balance(df_val, 'Контрольная выборка', 'index_price_category')\n",
|
||
"check_balance(df_test, 'Тестовая выборка', 'index_price_category')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train, df_val, df_test, 'index_price_category')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Приращение данных:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка сбалансированности выборок после применения метода oversampling:\n",
|
||
"Обучающая выборка: (335, 31)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"low 115\n",
|
||
"medium 110\n",
|
||
"high 110\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 34.33%\n",
|
||
"Процент объектов класса \"medium\": 32.84%\n",
|
||
"Процент объектов класса \"high\": 32.84%\n",
|
||
"\n",
|
||
"Контрольная выборка: (110, 31)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"high 40\n",
|
||
"medium 37\n",
|
||
"low 33\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"high\": 36.36%\n",
|
||
"Процент объектов класса \"medium\": 33.64%\n",
|
||
"Процент объектов класса \"low\": 30.00%\n",
|
||
"\n",
|
||
"Тестовая выборка: (115, 31)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"low 42\n",
|
||
"medium 37\n",
|
||
"high 36\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 36.52%\n",
|
||
"Процент объектов класса \"medium\": 32.17%\n",
|
||
"Процент объектов класса \"high\": 31.30%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок после применения метода oversampling:\n",
|
||
"Для обучающей выборки аугментация данных не требуется\n",
|
||
"Для контрольной выборки аугментация данных не требуется\n",
|
||
"Для тестовой выборки аугментация данных не требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Приращение данных (oversampling)\n",
|
||
"df_train_oversampled: DataFrame = oversample(df_train, 'index_price_category')\n",
|
||
"df_val_oversampled: DataFrame = oversample(df_val, 'index_price_category')\n",
|
||
"df_test_oversampled: DataFrame = oversample(df_test, 'index_price_category')\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
|
||
"check_balance(df_train_oversampled, 'Обучающая выборка', 'index_price_category')\n",
|
||
"check_balance(df_val_oversampled, 'Контрольная выборка', 'index_price_category')\n",
|
||
"check_balance(df_test_oversampled, 'Тестовая выборка', 'index_price_category')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'index_price_category')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Проверка сбалансированности выборок после применения метода undersampling:\n",
|
||
"Обучающая выборка: (165, 31)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"low 55\n",
|
||
"medium 55\n",
|
||
"high 55\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Контрольная выборка: (54, 31)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"low 18\n",
|
||
"medium 18\n",
|
||
"high 18\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Тестовая выборка: (54, 31)\n",
|
||
"Распределение выборки данных по классам в колонке \"index_price_category\":\n",
|
||
" index_price_category\n",
|
||
"low 18\n",
|
||
"medium 18\n",
|
||
"high 18\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент объектов класса \"low\": 33.33%\n",
|
||
"Процент объектов класса \"medium\": 33.33%\n",
|
||
"Процент объектов класса \"high\": 33.33%\n",
|
||
"\n",
|
||
"Проверка необходимости аугментации выборок после применения метода undersampling:\n",
|
||
"Для обучающей выборки аугментация данных не требуется\n",
|
||
"Для контрольной выборки аугментация данных не требуется\n",
|
||
"Для тестовой выборки аугментация данных не требуется\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Приращение данных (undersampling)\n",
|
||
"df_train_undersampled: DataFrame = undersample(df_train, 'index_price_category')\n",
|
||
"df_val_undersampled: DataFrame = undersample(df_val, 'index_price_category')\n",
|
||
"df_test_undersampled: DataFrame = undersample(df_test, 'index_price_category')\n",
|
||
"\n",
|
||
"# Проверка сбалансированности выборок\n",
|
||
"print('Проверка сбалансированности выборок после применения метода undersampling:')\n",
|
||
"check_balance(df_train_undersampled, 'Обучающая выборка', 'index_price_category')\n",
|
||
"check_balance(df_val_undersampled, 'Контрольная выборка', 'index_price_category')\n",
|
||
"check_balance(df_test_undersampled, 'Тестовая выборка', 'index_price_category')\n",
|
||
"\n",
|
||
"# Проверка необходимости аугментации выборок\n",
|
||
"print('Проверка необходимости аугментации выборок после применения метода undersampling:')\n",
|
||
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
|
||
" \n",
|
||
"# Визуализация сбалансированности классов\n",
|
||
"visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'index_price_category')"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "aimenv",
|
||
"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
|
||
}
|