{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Постановка задачи\n", "\n", "Крупная авиакомпания хочет повысить эффективность своей работы за счет сокращения времени задержки прибытия внутренних рейсов в будущем году.\n", "\n", "Сокращение времени задержки позволит снизить расходы компании и повысить ее привлекательность для клиентов.\n", "\n", "Для анализа доступны данные о полетах за текущий год.\n", "\n", "Задачи:\n", "- определение факторов, влияющих на задержку рейсов в текущем году;\n", "- прогнозировать задержку рейса для принятия соответствующих мер по сокращению задержки (при возможности).\n", "\n", "Эксперты сообщили, что на задержку рейсов влияют разные типы факторов:\n", "1. Некоторые факторы могут быть устранены или возможно снижение влияния данных факторов на задержку рейса (загруженность аэропорта, влияние типа аэропорта на задержу, проблемы с техобслуживанием или экипажем, чистка воздушного судна, загрузка багажа, заправка топливом).\n", "2. Некоторые факторы выходят из под контроля авиакомпании и не могут быть устранены (погода, проблемы с судном, проблемы обеспечения безопасности).\n", "3. Задержка судна в предыдущем рейсе не учитывается.\n", "4. Опозданием считается задержка более 15 минут." ] }, { "cell_type": "markdown", "metadata": { "id": "Y5dMmHXIRYEg" }, "source": [ "#### Загрузка и распаковка данных" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "from urllib.request import urlretrieve\n", "from zipfile import ZipFile\n", "\n", "ds_url = \"https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/raw/master/datasets/aa-domestic-delays-2018.csv.zip\"\n", "ds_zip_filename = \"data/aa-domestic-delays-2018.csv.zip\"\n", "urlretrieve(ds_url, ds_zip_filename)\n", "\n", "with ZipFile(ds_zip_filename, \"r\") as zObject:\n", " zObject.extractall(path=\"data\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Загрузка данных в Dataframe" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YvuKMosoRY7K", "outputId": "f9f05784-9c91-4869-a02e-e1ccc8115a8d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 899527 entries, 0 to 899526\n", "Data columns (total 23 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 FL_NUM 899527 non-null int64 \n", " 1 ORIGIN 899527 non-null object \n", " 2 DEST 899527 non-null object \n", " 3 PLANNED_DEP_DATETIME 899527 non-null object \n", " 4 CRS_DEP_TIME 899527 non-null int64 \n", " 5 DEP_TIME 899527 non-null float64\n", " 6 DEP_DELAY 899527 non-null float64\n", " 7 DEP_AFPH 899527 non-null float64\n", " 8 DEP_RFPH 899527 non-null float64\n", " 9 TAXI_OUT 899527 non-null float64\n", " 10 WHEELS_OFF 899527 non-null float64\n", " 11 CRS_ELAPSED_TIME 899527 non-null float64\n", " 12 PCT_ELAPSED_TIME 899527 non-null float64\n", " 13 DISTANCE 899527 non-null float64\n", " 14 CRS_ARR_TIME 899527 non-null int64 \n", " 15 ARR_AFPH 899527 non-null float64\n", " 16 ARR_RFPH 899527 non-null float64\n", " 17 ARR_DELAY 899527 non-null float64\n", " 18 CARRIER_DELAY 899527 non-null float64\n", " 19 WEATHER_DELAY 899527 non-null float64\n", " 20 NAS_DELAY 899527 non-null float64\n", " 21 SECURITY_DELAY 899527 non-null float64\n", " 22 LATE_AIRCRAFT_DELAY 899527 non-null float64\n", "dtypes: float64(17), int64(3), object(3)\n", "memory usage: 157.8+ MB\n" ] } ], "source": [ "import pandas as pd\n", "\n", "orig_df = pd.read_csv(\"data/aa-domestic-delays-2018.csv\")\n", "orig_df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Признаки\n", "\n", "Общие признаки:\n", "- FL_NUM – номер рейса.\n", "- ORIGIN – код аэропорта отправления.\n", "- DEST – код аэропорта назначения.\n", "\n", "Признаки вылета:\n", "- PLANNED_DEP_DATETIME – запланированные дата и время рейса.\n", "- CRS_DEP_TIME – запланированное время отправления.\n", "- DEP_TIME – фактическое время отправления.\n", "- DEP_AFPH – количество фактических вылетов в час. Данный признак показывает степень загруженности аэропорта отправления во время взлета.\n", "- DEP_RFPH – относительное количество вылетов в час. Данный признак показывает степень загруженности аэропорта отправления во время взлета.\n", "- TAXI_OUT – время, прошедшее между выездом самолета на взлетную полосу аэропорта отправления и отрывом колес самолета от земли.\n", "- WHEELS_OFF – момент времени, когда колеса самолета отрываются от земли.\n", "\n", "Полетные признаки:\n", "- CRS_ELAPSED_TIME – запланированное время, необходимое для полета.\n", "- PCT_ELAPSED_TIME – отношение фактического времени полета к запланированному времени полета для измерения относительной скорости самолета.\n", "- DISTANCE – расстояние между двумя аэропортами.\n", "\n", "Признаки прибытия:\n", "- CRS_ARR_TIME – запланированное время прибытия.\n", "- ARR_AFPH – число фактических прибытия в час. Этот признак показывает степень загруженности аэропорта назначения во время посадки самолета.\n", "- ARR_RFPH – относительное число прибытий в час. Этот признак показывает степень загруженности аэропорта назначения во время посадки самолета.\n", "- DEP_DELAY – суммарная задержка отправления в минутах.\n", "- ARR_DELAY – суммарная задержка прибытия в минутах, которая включает следующие параметры.\n", " - CARRIER_DELAY (целевая переменная) – задержка в минутах, вызванная обстоятельствами, находящимися в пределах контроля авиакомпании.\n", " - WEATHER_DELAY – задержка в минутах, вызванная метеорологическими условиями.\n", " - NAS_DELAY – задержка в минутах, предусмотренная национальной авиационной системой.\n", " - SECURITY_DELAY – задержка в минутах, вызванная процедурами обеспечения безопасности.\n", " - LATE_AIRCRAFT_DELAY – задержка в минутах, вызванная предыдущим рейсом того же самолета, который прибыл с опозданием." ] }, { "cell_type": "markdown", "metadata": { "id": "Hen4OWkxSEsb" }, "source": [ "#### Подготовка данных и конструирование признаков\n", "\n", "1. Преобразование даты (PLANNED_DEP_DATETIME) из строки в datetime.\n", "2. Получение месяца и дня недели вылета из даты для учета сезонности и особенностей дня недели (DEP_MONTH и DEP_DOW).\n", "3. Удаление столбца с датой (PLANNED_DEP_DATETIME).\n", "4. Определение признака хаба для аэропортов вылета и назначения (ORIGIN_HUB и DEST_HUB).\n", "5. Удаление лишних столбцов (FL_NUM, ORIGIN, DEST).\n", "6. Удаление столбца с общим временем задержки прибытия, так как данные значения будут иметь сильное влияние на результат (ARR_DELAY)." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "id": "qnyD6ZeLSGwL" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " CRS_DEP_TIME DEP_TIME DEP_DELAY DEP_AFPH DEP_RFPH TAXI_OUT \\\n", "0 1155 1149.0 -6.0 34.444444 0.956790 14.0 \n", "1 705 700.0 -5.0 17.454545 0.242424 16.0 \n", "2 1148 1145.0 -3.0 94.736842 0.947368 14.0 \n", "3 825 824.0 -1.0 33.559322 0.860495 16.0 \n", "4 1155 1147.0 -8.0 33.461538 0.929487 13.0 \n", "... ... ... ... ... ... ... \n", "899522 1534 1530.0 -4.0 35.357143 0.822259 20.0 \n", "899523 1751 1757.0 6.0 71.818182 1.040843 18.0 \n", "899524 2015 2010.0 -5.0 63.272727 1.193825 36.0 \n", "899525 1300 1323.0 23.0 70.843373 0.770037 11.0 \n", "899526 1435 1443.0 8.0 19.411765 0.924370 8.0 \n", "\n", " WHEELS_OFF CRS_ELAPSED_TIME PCT_ELAPSED_TIME DISTANCE ... \\\n", "0 1203.0 219.0 0.963470 1192.0 ... \n", "1 716.0 171.0 0.918129 1192.0 ... \n", "2 1159.0 212.0 0.971698 1558.0 ... \n", "3 840.0 271.0 0.918819 1558.0 ... \n", "4 1200.0 99.0 0.969697 331.0 ... \n", "... ... ... ... ... ... \n", "899522 1550.0 100.0 0.990000 331.0 ... \n", "899523 1815.0 181.0 0.972376 936.0 ... \n", "899524 2046.0 112.0 1.142857 511.0 ... \n", "899525 1334.0 50.0 0.820000 130.0 ... \n", "899526 1451.0 71.0 0.830986 130.0 ... \n", "\n", " ARR_RFPH CARRIER_DELAY WEATHER_DELAY NAS_DELAY SECURITY_DELAY \\\n", "0 0.854573 0.0 0.0 0.0 0.0 \n", "1 0.731707 0.0 0.0 0.0 0.0 \n", "2 1.092437 0.0 0.0 0.0 0.0 \n", "3 0.867379 0.0 0.0 0.0 0.0 \n", "4 1.006803 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... ... \n", "899522 0.837945 0.0 0.0 0.0 0.0 \n", "899523 0.697674 0.0 0.0 0.0 0.0 \n", "899524 0.482897 0.0 0.0 0.0 0.0 \n", "899525 0.888031 0.0 0.0 0.0 0.0 \n", "899526 1.011905 0.0 0.0 0.0 0.0 \n", "\n", " LATE_AIRCRAFT_DELAY DEP_MONTH DEP_DOW ORIGIN_HUB DEST_HUB \n", "0 0.0 1 0 1 1 \n", "1 0.0 1 0 1 1 \n", "2 0.0 1 0 0 1 \n", "3 0.0 1 0 1 0 \n", "4 0.0 1 0 1 1 \n", "... ... ... ... ... ... \n", "899522 0.0 12 0 1 1 \n", "899523 0.0 12 0 1 1 \n", "899524 0.0 12 0 1 0 \n", "899525 0.0 12 0 1 0 \n", "899526 0.0 12 0 0 1 \n", "\n", "[899527 rows x 22 columns]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = orig_df.copy()\n", "# Преобразование даты из строки в datetime\n", "df[\"PLANNED_DEP_DATETIME\"] = pd.to_datetime(df[\"PLANNED_DEP_DATETIME\"])\n", "# Получение месяца и дня недели вылета из даты для учета сезонности и особенностей дня недели\n", "df[\"DEP_MONTH\"] = df[\"PLANNED_DEP_DATETIME\"].dt.month\n", "df[\"DEP_DOW\"] = df[\"PLANNED_DEP_DATETIME\"].dt.dayofweek\n", "# Удаление столбца с датой\n", "df = df.drop([\"PLANNED_DEP_DATETIME\"], axis=1)\n", "# Список аэропортов-хабов\n", "hubs = [\"CLT\", \"ORD\", \"DFW\", \"LAX\", \"MIA\", \"JFK\", \"LGA\", \"PHL\", \"PHX\", \"DCA\"]\n", "# Определение признака хаба для аэропортов вылета и назначения\n", "is_origin_hub = df[\"ORIGIN\"].isin(hubs)\n", "is_dest_hub = df[\"DEST\"].isin(hubs)\n", "# Установка признака хаба для данных\n", "df[\"ORIGIN_HUB\"] = 0\n", "df.loc[is_origin_hub, \"ORIGIN_HUB\"] = 1\n", "df[\"DEST_HUB\"] = 0\n", "df.loc[is_dest_hub, \"DEST_HUB\"] = 1\n", "# Удаление лишних столбцов\n", "df = df.drop([\"FL_NUM\", \"ORIGIN\", \"DEST\"], axis=1)\n", "# Удаление столбца с общим временем задержки прибытия, так как данные значения будут иметь сильное влияние на результат\n", "df = df.drop([\"ARR_DELAY\"], axis=1)\n", "df" ] }, { "cell_type": "markdown", "metadata": { "id": "y6f3Z4UwUAUI" }, "source": [ "#### Формирование тестовой и обучающей выборок данных\n", "\n", "1. Задание фиксированного случайного состояния для воспроизводимости результатов (rand = 9).\n", "2. Выделение признака, который модель должна предсказать (CARRIER_DELAY).\n", "3. Формирование множества признаков, на основе которых модель будет обучаться (удаление столбца CARRIER_DELAY).\n", "4. Формирование выборок (85 % обучающая, 15 % тестовая).\n", "5. Создание классов для классификаторов в виде двоичных меток (опоздание свыше 15 минут – 1, иначе – 0)." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "id": "6LudeeYUUEPt" }, "outputs": [ { "data": { "text/html": [ "
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764597 rows × 21 columns

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" ], "text/plain": [ " CRS_DEP_TIME DEP_TIME DEP_DELAY DEP_AFPH DEP_RFPH TAXI_OUT \\\n", "31121 845 842.0 -3.0 16.842105 0.443213 21.0 \n", "633500 1315 1316.0 1.0 4.918033 0.983607 9.0 \n", "747737 1710 1704.0 -6.0 55.555556 1.028807 14.0 \n", "298943 1840 1920.0 40.0 28.200000 0.587500 18.0 \n", "843932 1830 1822.0 -8.0 28.846154 0.901442 13.0 \n", "... ... ... ... ... ... ... \n", "720822 1359 1410.0 11.0 53.239437 0.995130 26.0 \n", "459253 2209 2207.0 -2.0 80.689655 1.021388 16.0 \n", "711294 530 523.0 -7.0 12.452830 0.830189 17.0 \n", "872796 709 706.0 -3.0 120.000000 1.030043 12.0 \n", "516478 1925 2030.0 65.0 14.880000 1.352727 8.0 \n", "\n", " WHEELS_OFF CRS_ELAPSED_TIME PCT_ELAPSED_TIME DISTANCE ... \\\n", "31121 903.0 106.0 0.886792 331.0 ... \n", "633500 1325.0 121.0 1.107438 624.0 ... \n", "747737 1718.0 67.0 1.074627 304.0 ... \n", "298943 1938.0 161.0 0.888199 852.0 ... \n", "843932 1835.0 215.0 0.930233 1192.0 ... \n", "... ... ... ... ... ... \n", "720822 1436.0 147.0 1.006803 814.0 ... \n", "459253 2223.0 86.0 0.918605 413.0 ... \n", "711294 540.0 119.0 0.957983 666.0 ... \n", "872796 718.0 169.0 1.295858 1120.0 ... \n", "516478 2038.0 135.0 0.977778 761.0 ... \n", "\n", " ARR_AFPH ARR_RFPH WEATHER_DELAY NAS_DELAY SECURITY_DELAY \\\n", "31121 85.333333 1.145414 0.0 0.0 0.0 \n", "633500 111.891892 1.286114 0.0 0.0 0.0 \n", "747737 17.288136 0.751658 0.0 0.0 0.0 \n", "298943 38.780488 1.157627 22.0 0.0 0.0 \n", "843932 90.810811 1.121121 0.0 0.0 0.0 \n", "... ... ... ... ... ... \n", "720822 25.000000 1.136364 0.0 0.0 0.0 \n", "459253 2.352941 1.176471 0.0 0.0 0.0 \n", "711294 37.500000 0.892857 0.0 0.0 0.0 \n", "872796 12.336449 0.850790 0.0 47.0 0.0 \n", "516478 93.442623 1.112412 0.0 0.0 0.0 \n", "\n", " LATE_AIRCRAFT_DELAY DEP_MONTH DEP_DOW ORIGIN_HUB DEST_HUB \n", "31121 0.0 1 6 1 1 \n", "633500 0.0 9 4 0 1 \n", "747737 0.0 10 1 1 0 \n", "298943 0.0 5 5 0 1 \n", "843932 0.0 12 5 1 1 \n", "... ... ... ... ... ... \n", "720822 0.0 10 4 1 0 \n", "459253 0.0 7 6 1 0 \n", "711294 0.0 10 1 0 1 \n", "872796 0.0 12 3 1 0 \n", "516478 62.0 7 0 0 1 \n", "\n", "[764597 rows x 21 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "31121 0.0\n", "633500 0.0\n", "747737 0.0\n", "298943 0.0\n", "843932 0.0\n", " ... \n", "720822 0.0\n", "459253 0.0\n", "711294 0.0\n", "872796 0.0\n", "516478 0.0\n", "Name: CARRIER_DELAY, Length: 764597, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "31121 0\n", "633500 0\n", "747737 0\n", "298943 0\n", "843932 0\n", " ..\n", "720822 0\n", "459253 0\n", "711294 0\n", "872796 0\n", "516478 0\n", "Name: CARRIER_DELAY, Length: 764597, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 764597 entries, 31121 to 516478\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 CRS_DEP_TIME 764597 non-null int64 \n", " 1 DEP_TIME 764597 non-null float64\n", " 2 DEP_DELAY 764597 non-null float64\n", " 3 DEP_AFPH 764597 non-null float64\n", " 4 DEP_RFPH 764597 non-null float64\n", " 5 TAXI_OUT 764597 non-null float64\n", " 6 WHEELS_OFF 764597 non-null float64\n", " 7 CRS_ELAPSED_TIME 764597 non-null float64\n", " 8 PCT_ELAPSED_TIME 764597 non-null float64\n", " 9 DISTANCE 764597 non-null float64\n", " 10 CRS_ARR_TIME 764597 non-null int64 \n", " 11 ARR_AFPH 764597 non-null float64\n", " 12 ARR_RFPH 764597 non-null float64\n", " 13 WEATHER_DELAY 764597 non-null float64\n", " 14 NAS_DELAY 764597 non-null float64\n", " 15 SECURITY_DELAY 764597 non-null float64\n", " 16 LATE_AIRCRAFT_DELAY 764597 non-null float64\n", " 17 DEP_MONTH 764597 non-null int64 \n", " 18 DEP_DOW 764597 non-null int64 \n", " 19 ORIGIN_HUB 764597 non-null int64 \n", " 20 DEST_HUB 764597 non-null int64 \n", "dtypes: float64(15), int64(6)\n", "memory usage: 128.3 MB\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# Задание фиксированного случайного состояния для воспроизводимости результатов\n", "rand = 9\n", "# Выделение признака, который модель должна предсказать\n", "y = df[\"CARRIER_DELAY\"]\n", "# Формирование множества признаков, на основе которых модель будет обучаться (удаление столбца с y)\n", "X = df.drop([\"CARRIER_DELAY\"], axis=1).copy()\n", "X_train, X_test, y_train_reg, y_test_reg = train_test_split(\n", " X, y, test_size=0.15, random_state=rand\n", ")\n", "# Создание классов для классификаторов в виде двоичных меток (опоздание свыше 15 минут - 1, иначе - 0)\n", "y_train_class = y_train_reg.apply(lambda x: 1 if x > 15 else 0)\n", "y_test_class = y_test_reg.apply(lambda x: 1 if x > 15 else 0)\n", "\n", "display(X_train)\n", "display(y_train_reg)\n", "display(y_train_class)\n", "X_train.info()" ] }, { "cell_type": "markdown", "metadata": { "id": "aVjZol-yrYbH" }, "source": [ "#### Определение линейной корреляции признаков с целевым признаком с помощью корреляции Пирсона\n", "\n", "Существует сильная прямая корреляция между целевым признаком CARRIER_DELAY и признаком DEP_DELAY (70 %).\n", "\n", "Но это не значит, что в данных отсутствуют нелинейные зависимости, или что совокупность признаков не оказывает влияние на значение целевого признака." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_1e7XE_Orf0r", "outputId": "efbb620c-c92b-481e-d613-a34633a4ba86" }, "outputs": [ { "data": { "text/plain": [ "CARRIER_DELAY 1.000000\n", "DEP_DELAY 0.703935\n", "ARR_RFPH 0.101742\n", "LATE_AIRCRAFT_DELAY 0.083166\n", "DEP_RFPH 0.058659\n", "ARR_AFPH 0.035135\n", "DEP_TIME 0.030941\n", "NAS_DELAY 0.026792\n", "WHEELS_OFF 0.026787\n", "TAXI_OUT 0.024635\n", "PCT_ELAPSED_TIME 0.020980\n", "CRS_DEP_TIME 0.016032\n", "ORIGIN_HUB 0.015334\n", "DEST_HUB 0.013932\n", "DISTANCE 0.010680\n", "DEP_MONTH 0.009728\n", "CRS_ELAPSED_TIME 0.008801\n", "DEP_DOW 0.007043\n", "CRS_ARR_TIME 0.007029\n", "DEP_AFPH 0.006053\n", "WEATHER_DELAY 0.003002\n", "SECURITY_DELAY 0.000460\n", "Name: CARRIER_DELAY, dtype: float64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corr = df.corr()\n", "abs(corr[\"CARRIER_DELAY\"]).sort_values(ascending=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "xxPlvpOdrnY_" }, "source": [ "#### Использование регрессионных моделей для предсказания задержки рейса\n", "\n", "1. Линейная регрессия (linear).\n", "2. Полиномиальная регрессия (linear_poly).\n", "3. Полиномиальная регрессия без квадратичных членов (linear_interact).\n", "4. Гребневая регрессия (ridge).\n", "5. Дерево решений (decision_tree).\n", "6. k ближайших соседей (kNN).\n", "7. Случайный лес (random_forest).\n", "8. Многослойный персептрон (mlp)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2m81QO87rzal" }, "outputs": [], "source": [ "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n", "from sklearn import linear_model, tree, neighbors, ensemble, neural_network\n", "\n", "reg_models = {\n", " # Линейные модели\n", " \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n", " \"linear_poly\": {\n", " \"model\": make_pipeline(\n", " PolynomialFeatures(degree=2, interaction_only=False),\n", " linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n", " memory=None\n", " )\n", " },\n", " \"linear_interact\": {\n", " \"model\": make_pipeline(\n", " PolynomialFeatures(degree=2, interaction_only=True),\n", " linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n", " memory=None\n", " )\n", " },\n", " \"ridge\": {\"model\": linear_model.RidgeCV()},\n", " # Деревья\n", " \"decision_tree\": {\n", " \"model\": tree.DecisionTreeRegressor(max_depth=7, random_state=rand)\n", " },\n", " # Ближайшие соседи\n", " \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n", " # Ансамблевые методы\n", " \"random_forest\": {\n", " \"model\": ensemble.RandomForestRegressor(\n", " max_depth=7, random_state=rand, n_jobs=-1\n", " )\n", " },\n", " # Нейронные сети\n", " \"mlp\": {\n", " \"model\": neural_network.MLPRegressor(\n", " hidden_layer_sizes=(21,),\n", " max_iter=500,\n", " early_stopping=True,\n", " random_state=rand,\n", " )\n", " },\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "DFJudSP_tFec" }, "source": [ "#### Обучение и оценка регрессионных моделей\n", "\n", "Метрики: \n", "- RMSE – корень из средней квадратичной ошибки.\n", "- MAE – средняя абсолютная ошибка.\n", "- R-квадрат – коэффициент детерминации." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XZEDVAR0tIzN", "outputId": "4f1f55ff-85dd-49d9-ddaf-3758419bdd6a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: linear\n", "Model: linear_poly\n", "Model: linear_interact\n", "Model: ridge\n", "Model: decision_tree\n", "Model: knn\n", "Model: random_forest\n", "Model: mlp\n" ] } ], "source": [ "import math\n", "from sklearn import metrics\n", "\n", "for model_name in reg_models.keys():\n", " print(f'Model: {model_name}')\n", " fitted_model = reg_models[model_name][\"model\"].fit(\n", " X_train.values, y_train_reg.to_numpy().ravel()\n", " )\n", " y_train_pred = fitted_model.predict(X_train.values)\n", " y_test_pred = fitted_model.predict(X_test.values)\n", " reg_models[model_name][\"fitted\"] = fitted_model\n", " reg_models[model_name][\"preds\"] = y_test_pred\n", " reg_models[model_name][\"RMSE_train\"] = math.sqrt(\n", " metrics.mean_squared_error(y_train_reg, y_train_pred)\n", " )\n", " reg_models[model_name][\"RMSE_test\"] = math.sqrt(\n", " metrics.mean_squared_error(y_test_reg, y_test_pred)\n", " )\n", " reg_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train_reg, y_train_pred)\n", " reg_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(\n", " y_test_reg, y_test_pred\n", " )\n", " reg_models[model_name][\"R2_test\"] = metrics.r2_score(y_test_reg, y_test_pred)" ] }, { "cell_type": "markdown", "metadata": { "id": "6GD_HZhGHPXK" }, "source": [ "#### Вывод оценки в виде таблицы" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "id": "lvcbKDfmHQ6p" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 RMSE_trainRMSE_testMAE_trainMAE_testR2_test
mlp3.2435163.3085971.6101481.6286480.987025
random_forest5.1432676.0882491.3302111.3799380.956065
linear_poly6.2140106.3398434.3271154.3637710.952359
linear_interact6.4543146.5622844.5770354.6158060.948957
decision_tree6.5429247.4563351.5381631.5929850.934102
linear7.8196437.8828755.4861135.5181470.926347
ridge7.8320667.8981895.5024025.5366400.926060
knn7.3600989.2594221.8268882.2048400.898377
\n" ], "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg_metrics = pd.DataFrame.from_dict(reg_models, \"index\")[\n", " [\"RMSE_train\", \"RMSE_test\", \"MAE_train\", \"MAE_test\", \"R2_test\"]\n", "]\n", "reg_metrics.sort_values(by=\"RMSE_test\").style.background_gradient(\n", " cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE_train\", \"RMSE_test\"]\n", ").background_gradient(\n", " cmap=\"plasma\", low=0.3, high=1, subset=[\"MAE_train\", \"MAE_test\", \"R2_test\"]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Наилучший результат показали модели с типом черный ящик и аквариумные модели." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Определение сбалансированности выборки для классификации" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.061283264255549" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_train_class[y_train_class == 1].shape[0] / y_train_class.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Выборка данных имеет сильный дисбаланс в сторону отрицательного класса (y=0).\n", "\n", "Для решения данной проблемы используется гиперпараметр class_weight=\"balanced\" во многих моделях классификации." ] }, { "cell_type": "markdown", "metadata": { "id": "MZrTDbnjJMrB" }, "source": [ "#### Использование классификаторов для предсказания задержки рейса\n", "\n", "1. Логистическая регрессия (logistic).\n", "2. Гребневая классификация (ridge).\n", "3. Дерево решений (decision_tree).\n", "4. k ближайших соседей (knn).\n", "5. Наивный Байес (naive_bayes).\n", "6. Градиентно-бустированные деревья (gradient_boosting).\n", "7. Случайный лес (random_forest).\n", "8. Многослойный персептрон (mlp).\n", "\n", "Сбалансированность выборки: 0,061. Использование гиперпараметра class_weight=\"balanced\" позволяет адаптировать модели к данной особенности данных." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "c_1jMq5IJMSL" }, "outputs": [], "source": [ "from sklearn import naive_bayes\n", "\n", "class_models = {\n", " # Линейные модели\n", " \"logistic\": {\"model\": linear_model.LogisticRegression()},\n", " \"ridge\": {\n", " \"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")\n", " },\n", " # Дерево\n", " \"decision_tree\": {\n", " \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=rand)\n", " },\n", " # Ближайшие соседи\n", " \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n", " # Наивный Байес\n", " \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n", " # Ансамблевые методы\n", " \"gradient_boosting\": {\n", " \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n", " },\n", " \"random_forest\": {\n", " \"model\": ensemble.RandomForestClassifier(\n", " max_depth=11, class_weight=\"balanced\", random_state=rand\n", " )\n", " },\n", " # Нейронные сети\n", " \"mlp\": {\n", " \"model\": make_pipeline(\n", " StandardScaler(),\n", " neural_network.MLPClassifier(\n", " hidden_layer_sizes=(7,),\n", " max_iter=500,\n", " early_stopping=True,\n", " random_state=rand,\n", " ),\n", " memory=None\n", " )\n", " },\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "H8Z7-KugJ7xR" }, "source": [ "#### Обучение и оценка классификаторов\n", "\n", "Метрики:\n", "- Accuracy – верность/аккуратность.\n", "- Recall – полнота.\n", "- ROC AUC – площадь под ROC-кривой.\n", "- F1-мера.\n", "- MCC – коэффициент корреляции Мэтьюса." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "RyR1_5m4KBTs" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: logistic\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/user/Projects/python/ckexp/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model: ridge\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/user/Projects/python/ckexp/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model: decision_tree\n", "Model: knn\n", "Model: naive_bayes\n", "Model: gradient_boosting\n", "Model: random_forest\n", "Model: mlp\n" ] } ], "source": [ "import numpy as np\n", "\n", "for model_name in class_models.keys():\n", " print(f\"Model: {model_name}\")\n", " fitted_model = class_models[model_name][\"model\"].fit(\n", " X_train.values,\n", " y_train_class.to_numpy().ravel(),\n", " )\n", " y_train_pred = fitted_model.predict(X_train.values)\n", " y_test_prob = fitted_model.predict_proba(X_test.values)[:, 1]\n", " y_test_pred = fitted_model.predict(X_test.values)\n", "\n", " class_models[model_name][\"fitted\"] = fitted_model\n", " class_models[model_name][\"probs\"] = y_test_prob\n", " class_models[model_name][\"preds\"] = y_test_pred\n", "\n", " class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n", " y_train_class, y_train_pred\n", " )\n", " class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n", " y_test_class, y_test_pred\n", " )\n", " class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n", " y_train_class, y_train_pred\n", " )\n", " class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n", " y_test_class, y_test_pred\n", " )\n", " class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n", " y_test_class, y_test_prob\n", " )\n", " class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test_class, y_test_pred)\n", " class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n", " y_test_class, y_test_pred\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "NzCyXXXFKdx2" }, "source": [ "#### Вывод оценки в виде таблицы" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "VOhaFiEYKeN5" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 Accuracy_trainAccuracy_testRecall_trainRecall_testROC_AUC_testF1_testMCC_test
mlp0.9984820.9985550.9871310.9888650.9998770.9882070.987437
gradient_boosting0.9917250.9916620.8929300.8938510.9988850.9292230.925619
random_forest0.9411660.9403250.9995520.9923750.9951450.6706750.685702
decision_tree0.9832970.9828950.8569690.8522150.9949320.8591820.850110
ridge0.9434530.9425260.9455790.9409340.9837770.6672100.673110
logistic0.9750540.9750310.6832920.6808280.9602870.7695460.763854
knn0.9728860.9651230.6806450.6077220.9483870.6809060.668176
naive_bayes0.9251190.9255390.2791260.2742680.8118690.3108580.274984
\n" ], "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n", " [\n", " \"Accuracy_train\",\n", " \"Accuracy_test\",\n", " \"Recall_train\",\n", " \"Recall_test\",\n", " \"ROC_AUC_test\",\n", " \"F1_test\",\n", " \"MCC_test\",\n", " ]\n", "]\n", "class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n", " cmap=\"plasma\", low=0.3, high=1, subset=[\"Accuracy_train\", \"Accuracy_test\"]\n", ").background_gradient(\n", " cmap=\"viridis\",\n", " low=1,\n", " high=0.3,\n", " subset=[\"Recall_train\", \"Recall_test\", \"ROC_AUC_test\", \"F1_test\", \"MCC_test\"],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Использование различных меток позволяют оценить качество моделей классификации с точки зрения различных свойств (точность и полнота), что особенно важно при несбалансированной выборке." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Интерпретация результатов для моделей типа белый ящик\n", "\n", "К моделям типа белого ящика в нашем случае относятся:\n", "- Линейные модели: разные виды регрессии.\n", "- Деревья решений.\n", "- Метод ближайших соседей.\n", "- Наивный Байес.\n", "\n", "К аквариумным моделям (серый ящик) относятся ансамблевые модели: случайный лес, градиентный бустинг.\n", "\n", "К моделям типа черного ящика относятся искусственные нейронные сети (MLP).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Линейные модели" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Линейная регрессия" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Коэффициенты и пересечение модели" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "coefficients:\t[ 4.54984539e-03 -5.25067742e-03 8.94125541e-01 -1.52961053e-02\n", " -4.69623002e-01 1.25277815e-01 -6.46744472e-04 -1.26240049e-02\n", " 4.50112895e+01 6.76385421e-04 -3.69920254e-04 5.47855860e-04\n", " 3.73866548e-01 -9.06364154e-01 -6.74052666e-01 -9.17411191e-01\n", " -9.29843952e-01 -3.96621856e-02 -1.79666480e-02 -1.02912927e+00\n", " -3.94934854e-01]\n", "intercept:\t-37.86177932752649\n", "y = -37.86 + 0.0045X1 + -0.0053X2 + 0.894X3 + ...\n" ] } ], "source": [ "coefs_lm = reg_models[\"linear\"][\"fitted\"].coef_\n", "intercept_lm = reg_models[\"linear\"][\"fitted\"].intercept_\n", "print(\"coefficients:\\t%s\" % coefs_lm)\n", "print(\"intercept:\\t%s\" % intercept_lm)\n", "print(\n", " \"y = %0.2f + %0.4fX1 + %0.4fX2 + %0.3fX3 + ...\"\n", " % (intercept_lm, coefs_lm[0], coefs_lm[1], coefs_lm[2])\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Вывод признаков и соответствующих им коэффициентам модели регрессии" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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featurecoef
0CRS_DEP_TIME0.004550
1DEP_TIME-0.005251
2DEP_DELAY0.894126
3DEP_AFPH-0.015296
4DEP_RFPH-0.469623
5TAXI_OUT0.125278
6WHEELS_OFF-0.000647
7CRS_ELAPSED_TIME-0.012624
8PCT_ELAPSED_TIME45.011289
9DISTANCE0.000676
10CRS_ARR_TIME-0.000370
11ARR_AFPH0.000548
12ARR_RFPH0.373867
13WEATHER_DELAY-0.906364
14NAS_DELAY-0.674053
15SECURITY_DELAY-0.917411
16LATE_AIRCRAFT_DELAY-0.929844
17DEP_MONTH-0.039662
18DEP_DOW-0.017967
19ORIGIN_HUB-1.029129
20DEST_HUB-0.394935
\n", "
" ], "text/plain": [ " feature coef\n", "0 CRS_DEP_TIME 0.004550\n", "1 DEP_TIME -0.005251\n", "2 DEP_DELAY 0.894126\n", "3 DEP_AFPH -0.015296\n", "4 DEP_RFPH -0.469623\n", "5 TAXI_OUT 0.125278\n", "6 WHEELS_OFF -0.000647\n", "7 CRS_ELAPSED_TIME -0.012624\n", "8 PCT_ELAPSED_TIME 45.011289\n", "9 DISTANCE 0.000676\n", "10 CRS_ARR_TIME -0.000370\n", "11 ARR_AFPH 0.000548\n", "12 ARR_RFPH 0.373867\n", "13 WEATHER_DELAY -0.906364\n", "14 NAS_DELAY -0.674053\n", "15 SECURITY_DELAY -0.917411\n", "16 LATE_AIRCRAFT_DELAY -0.929844\n", "17 DEP_MONTH -0.039662\n", "18 DEP_DOW -0.017967\n", "19 ORIGIN_HUB -1.029129\n", "20 DEST_HUB -0.394935" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coef_df = pd.DataFrame({\"feature\": X_train.columns.values.tolist(), \"coef\": coefs_lm})\n", "display(coef_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Интерпретация признаков:\n", "- Числовой признак (DEP_DELAY). Увеличение значения признака на 1 единицу увеличивает целевой признак на значение коэффициента в соответствующих единицах.\n", "- Бинарный признак (ORIGIN_HUB). Если значение отрицательное, то истинное значение признака уменьшает целевой признак на значение коэффициента.\n", "- Категориальные признаки (DEP_MONTH). При условии кодирования категориальные признаки позволяют отслеживать влияние отдельных признаков на целевой признак." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.061283264255549" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_train_class[y_train_class == 1].shape[0] / y_train_class.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Получение сведений о значимости признаков средствами библиотеки statsmodels" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 featureCoef.Std.Err.tP>|t|[0.0250.975]t_abs
2DEP_DELAY0.8941260.0003032951.0559780.0000000.8935320.8947192951.055978
16LATE_AIRCRAFT_DELAY-0.9298440.000509-1827.0180820.000000-0.930841-0.9288461827.018082
13WEATHER_DELAY-0.9063640.000911-995.3664230.000000-0.908149-0.904579995.366423
14NAS_DELAY-0.6740530.000813-829.1286570.000000-0.675646-0.672459829.128657
8PCT_ELAPSED_TIME45.0112890.117195384.0725660.00000044.78159245.240987384.072566
15SECURITY_DELAY-0.9174110.005465-167.8570850.000000-0.928123-0.906699167.857085
5TAXI_OUT0.1252780.001203104.1195790.0000000.1229200.127636104.119579
0CRS_DEP_TIME0.0045500.00007262.8716930.0000000.0044080.00469262.871693
1DEP_TIME-0.0052510.000092-57.1158950.000000-0.005431-0.00507057.115895
3DEP_AFPH-0.0152960.000321-47.7245060.000000-0.015924-0.01466847.724506
19ORIGIN_HUB-1.0291290.026669-38.5894110.000000-1.081399-0.97686038.589411
12ARR_RFPH0.3738670.01317128.3860310.0000000.3480520.39968128.386031
4DEP_RFPH-0.4696230.017169-27.3531790.000000-0.503273-0.43597327.353179
7CRS_ELAPSED_TIME-0.0126240.000660-19.1315160.000000-0.013917-0.01133119.131516
10CRS_ARR_TIME-0.0003700.000022-16.9386610.000000-0.000413-0.00032716.938661
20DEST_HUB-0.3949350.026256-15.0414590.000000-0.446397-0.34347315.041459
17DEP_MONTH-0.0396620.002641-15.0188080.000000-0.044838-0.03448615.018808
6WHEELS_OFF-0.0006470.000067-9.6461040.000000-0.000778-0.0005159.646104
9DISTANCE0.0006760.0000808.4288350.0000000.0005190.0008348.428835
18DEP_DOW-0.0179670.004487-4.0045610.000062-0.026760-0.0091734.004561
11ARR_AFPH0.0005480.0003321.6507880.098782-0.0001030.0011981.650788
\n" ], "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import statsmodels.api as sm\n", "\n", "linreg_mdl = sm.OLS(y_train_reg, sm.add_constant(X_train))\n", "linreg_mdl = linreg_mdl.fit()\n", "summary_df = linreg_mdl.summary2().tables[1]\n", "summary_df = (\n", " summary_df.drop([\"const\"]).reset_index().rename(columns={\"index\": \"feature\"})\n", ")\n", "summary_df[\"t_abs\"] = abs(summary_df[\"t\"])\n", "summary_df.sort_values(by=\"t_abs\", ascending=False).style.background_gradient(\n", " cmap=\"plasma_r\", low=0, high=0.1, subset=[\"P>|t|\"]\n", ").background_gradient(cmap=\"plasma_r\", low=0, high=0.1, subset=[\"t_abs\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Важными являются p-значение (P>|t|) и t-статистика (t_abs).\n", "\n", "Из результатов видно, что самыми значимыми признаками модели являются:\n", "- DEP_DELAY;\n", "- LATE_AIRCRAFT_DELAY;\n", "- WEATHER_DELAY;\n", "- NAS_DELAY." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Гребневая регрессия\n", "\n", "Интерпретация гребневой регрессии производится аналогично интерпретации линейной регрессии" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 featurecoef_linearcoef_ridge
0CRS_DEP_TIME0.0045500.004275
1DEP_TIME-0.005251-0.005485
2DEP_DELAY0.8941260.894229
3DEP_AFPH-0.015296-0.015304
4DEP_RFPH-0.469623-0.469623
5TAXI_OUT0.1252780.125284
6WHEELS_OFF-0.000647-0.000889
7CRS_ELAPSED_TIME-0.012624-0.012618
8PCT_ELAPSED_TIME45.01128945.010279
9DISTANCE0.0006760.000718
10CRS_ARR_TIME-0.000370-0.000546
11ARR_AFPH0.0005480.000550
12ARR_RFPH0.3738670.373865
13WEATHER_DELAY-0.906364-0.906358
14NAS_DELAY-0.674053-0.674045
15SECURITY_DELAY-0.917411-0.917411
16LATE_AIRCRAFT_DELAY-0.929844-0.929805
17DEP_MONTH-0.039662-0.039661
18DEP_DOW-0.017967-0.017967
19ORIGIN_HUB-1.029129-1.029140
20DEST_HUB-0.394935-0.394948
\n" ], "text/plain": [ "" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coefs_ridge = reg_models[\"ridge\"][\"fitted\"].coef_\n", "coef_ridge_df = pd.DataFrame(\n", " {\n", " \"feature\": X_train.columns.values.tolist(),\n", " \"coef_linear\": coefs_lm,\n", " \"coef_ridge\": coefs_ridge,\n", " }\n", ")\n", "coef_ridge_df.style.background_gradient(cmap=\"viridis_r\", low=0.3, high=0.2, axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Полиномиальная регрессия\n", "\n", "Для интерпретации и оценки важности признаков полиномиальной регрессии рекомендуется использовать возможности библиотеки statsmodels, так как модель содержит 253 линейных члена." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "253" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "232" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(\n", " reg_models[\"linear_poly\"][\"fitted\"].get_params()[\"linearregression\"].coef_.shape[0]\n", ")\n", "display(\n", " reg_models[\"linear_interact\"][\"fitted\"]\n", " .get_params()[\"linearregression\"]\n", " .coef_.shape[0]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Логистическая регрессия\n", "\n", "Интерпретация логистической регрессии выполняется аналогично интерпретации линейной регрессии\n", "\n", "Способ оценки важности признаков модели на основе логистической регрессии рассмотрен в предыдущей лекции" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "coefficients:\t[[-0.00132811 0.00034525 0.15746107 0.00349808 -0.00215053 -0.00445293\n", " 0.00029184 -0.05167613 -0.00175222 0.0055682 -0.00031922 -0.00757532\n", " -0.00273998 -0.15351444 -0.12133964 -0.00595224 -0.16451117 -0.01303235\n", " -0.0052911 0.00048854 -0.00206977]]\n", "intercept:\t[-0.00229272]\n" ] }, { "data": { "text/plain": [ "DEP_DELAY 6.969920\n", "CRS_ELAPSED_TIME 4.101834\n", "LATE_AIRCRAFT_DELAY 4.065346\n", "DISTANCE 3.616141\n", "NAS_DELAY 1.672065\n", "WEATHER_DELAY 1.604186\n", "CRS_DEP_TIME 0.665926\n", "ARR_AFPH 0.267888\n", "DEP_TIME 0.177772\n", "CRS_ARR_TIME 0.168589\n", "WHEELS_OFF 0.150765\n", "DEP_AFPH 0.124024\n", "DEP_MONTH 0.044475\n", "TAXI_OUT 0.043947\n", "DEP_DOW 0.010574\n", "SECURITY_DELAY 0.009756\n", "ARR_RFPH 0.001976\n", "DEP_RFPH 0.001215\n", "DEST_HUB 0.001007\n", "ORIGIN_HUB 0.000238\n", "PCT_ELAPSED_TIME 0.000185\n", "dtype: float64" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coefs_log = class_models[\"logistic\"][\"fitted\"].coef_\n", "intercept_log = class_models[\"logistic\"][\"fitted\"].intercept_\n", "print(\"coefficients:\\t%s\" % coefs_log)\n", "print(\"intercept:\\t%s\" % intercept_log)\n", "stdv = np.std(X_train, 0)\n", "abs(\n", " coefs_log.reshape(\n", " 21,\n", " )\n", " * stdv\n", ").sort_values(ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Дерево решений\n", "\n", "В библиотеке scikit-learn для построения дерева решений используется алгоритм CART https://ru.wikipedia.org/wiki/CART_(алгоритм)\n", "\n", "Оценочная функция, используемая алгоритмом CART, базируется на идее уменьшения неопределенности (неоднородности) в узле\n", "\n", "Степень неопределенности вычисляется на основе индекса Gini-Simpson (Gini impurity)\n", "\n", "Значение данного индекса позволяет рекурсивно расщеплять узел дерева в местах, где две ветви отличаются максимально" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Визуализация дерева решений\n", "\n", "Каждый узел содержит:\n", "1. Условие\n", "2. Значение индекса Джини-Симпсона\n", "3. Количество наблюдений, которые поступили на вход\n", "4. Количество наблюдения для левого и правого выходов\n", "\n", "Если в узле отсутствует условие, то такой узел является листом дерева и содержит конечное решение\n", "\n", "Путь от корня дерева до листа описывает некоторое правило дерева решений" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/yr/lls0svhd1fl_wljz9zn7kgzh0000gn/T/ipykernel_53152/2420969043.py:11: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n" ] }, { "data": { "image/png": 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4dwZiAQAAAAAaoCVLlsR2220X06dPL5pr27ZtVFRUxGqrrZasGwAApDZr1qzYaqutqp+TiznqqKPilltuSdYLAAAAAACo/3r27BmTJ0/OzD377LOx0047JekEAAA14YcffohOnTrFe++9VzS38cYbx+uvvx7NmzdP1g0AAAAAAKjfbrrppjj22GMzc6NGjYqzzz47SScAAKgphxxySNx9991FM02aNIkZM2bEZpttlqwXAABAXVFS0wUAAAAAAEhv4sSJmeOwBWPGjDEOCwBAvVe4WHJpXsa89dZb46GHHkrSCQAAAAAAqP+mTp0a5eXlmblTTjnFOCwAAPXeSiutFBMmTMjMvfXWW3HJJZck6QQAAAAAANR/X3/9dZx55pmZuc6dO0efPn2SdAIAgJo0bty46rM8xSxatCh69uwZ+Xw+WS8AAIC6Ipf32xIAAAAAQIPy0UcfRVlZWXz//fdFc3vssUc89thjkcvlknUDAICaMn/+/Oqh2HfffbdobqONNoqZM2dG8+bNk3UDAAAAAADqnyVLlsS2224br776atHc6quvHhUVFdGmTZtk3QAAoCYddthhcccddxTNNG7cOGbMmFF9Lh4AAAAAAGBZ9OjRI8rLy4tmCvfvPPvss7Hjjjsm6wUAADVpzJgxcfbZZ2fm/vznP8fvfve7JJ0AAADqipKaLgAAAAAAQFp9+vTJHIctXJgzefJk47AAADQYLVq0iIkTJ2bm3n777bjooouSdAIAAAAAAOqv8ePHZ47D/s/FOsZhAQBoSK644opo2bJl0czixYurL2vP5/PJegEAAAAAAPXPCy+8EFOmTMnMnXrqqcZhAQBocHdWbrnllpm5vn37xtdff52kEwAAQF1hIBYAAAAAoAG577774vbbb8/MnXPOOVFWVpakEwAA1Bb7779/HH744Zm5Sy+9NObMmZOkEwAAAAAAUP988MEHMWTIkMzcnnvuGb/73e+SdAIAgNpinXXWiQsvvDAz98QTT8Sf//znJJ0AAAAAAID6Z8mSJdGtW7fI5/NFc+3atYtLLrkkWS8AAKgNGjVqFOXl5ZHL5YrmPv/88+q7KwEAAPj/5PJZ3z4AAAAAAFAvzJ8/Pzp16hTvvvtu0dzGG28cr7/+ejRv3jxZNwAAqC0++uij6NChQ8ybN69obrfddqu+ZDLrEDsAAAAAAMC/OvTQQ+POO+8smmnSpEnMmDEjNttss2S9AACgNl3Ivu2228arr75aNNe2bduYO3dutGnTJlk3AAAAAACgfhgzZkycffbZmbkbbrghjj322CSdAACgtunRo0f1UGyWZ599NnbaaacknQAAAGq7kpouAAAAAABAGhdccEHmOGzBpEmTjMMCANBgrb322jF8+PDM3FNPPRXXXXddkk4AAAAAAED9cc8992SOwxacc845xmEBAGiwGjVqFFOmTIlcLlc098UXX8SAAQOS9QIAAAAAAOqH999/P84777zM3F577RXHHHNMkk4AAFAbXXLJJdGuXbvMXPfu3WPx4sVJOgEAANR2uXw+n6/pEgAAAAAArFgzZ86MrbfeOpYsWVI0d/TRR8fNN9+crBcAANRGlZWVsd1228Urr7xSNLfaaqvF3Llzq/8EAAAAAADI8sMPP0THjh2rL5gsZpNNNonXX389mjVrlqwbAADURr169YpJkyZl5p5++unYZZddknQCAAAAAADqvkMOOSTuvvvuopkmTZpUn+H5xS9+9u6x/gAA6tRJREFUkawXAADURjfddFMce+yxmblRo0bF2WefnaQTAABAbVZS0wUAAAAAAFixqqqqonv37pnjsK1bt47LLrssWS8AAKitSktLY8qUKZHL5Yrmvvzyy+jfv3+yXgAAAAAAQN02bNiwzHHYgsmTJxuHBQCAiLj44otjzTXXzMwVzssvXrw4SScAAAAAAKBuKwzDZo3DFgwaNMg4LAAARMRvf/vb2HvvvTNzQ4cOjffeey9JJwAAgNrMQCwAAAAAQD13zTXXxLPPPrvcLs8BAICGoGvXrtGrV6/M3J/+9Kd4+umnk3QCAAAAAADqrhkzZsTYsWMzc8ccc8xSXZ4DAAANQevWreOyyy7LzM2aNWupnrcBAAAAAICG7fvvv4/TTjstM7fpppvGgAEDknQCAIDaLpfLxaRJk6Jp06ZFc/Pnz69+3s7n88m6AQAA1Ea5vN+MAAAAAADqrc8//zzKysriq6++Kprbbrvt4rnnnovS0tJk3QAAoLb79ttvo0OHDvHPf/6zaK5jx44xffr0aNKkSbJuAAAAAABA3VFVVRW77LJLPP/885njVxUVFbHmmmsm6wYAALVd4VqU/fbbLx5++OGiuebNm8fs2bNjgw02SNYNAAAAAACoW84+++wYM2ZMZu6RRx6JvfbaK0knAACoKy644IIYOnRoZu7OO++MQw45JEknAACA2qikpgsAAAAAALDi9OvXL3MctqSkJMrLy43DAgDA/3EJ++WXX56ZK1wsuTQvgwIAAAAAAA3TVVddlTkOWzBixAjjsAAA8C9yuVxMmjQpmjZtWjS3YMGC6N27d/WgLAAAAAAAwL967bXXluq94WOPPdY4LAAA/B8GDBgQv/jFLzJzp512WsybNy9JJwAAgNool/dmAwAAAABAvfTEE0/EnnvumZk788wzY+zYsUk6AQBAXVM4VrH//vvHgw8+WDTXrFmzmDVrVmy00UbJugEAAAAAALXfZ599Fptttll88803RXPbb799PPfcc1FSUpKsGwAA1CXDhw+PIUOGZOZuv/32OPTQQ5N0AgAAAAAA6oaqqqrYeeed44UXXiiaW2WVVaKioiLWWGONZN0AAKAueeyxx2KvvfbKzPXt2zfGjBmTpBMAAEBtYyAWAAAAAKAeWrhwYWy55ZYxd+7corl11lknZs+eHSuvvHKybgAAUNe8/fbb0alTp/jxxx+L5n7961/HfffdF7lcLlk3AAAAAACgdjvuuOPihhtuKJopLS2Nl19+ufq8DwAA8J/PyG+11VbVl7IXs/baa8ecOXOckQcAAAAAAP5f5eXl0aNHj8zclClT4tRTT03SCQAA6vsZ+WnTplWf9wEAAGhoSmq6AAAAAAAAy9+oUaMyx2ELxo0b5+IbAADIsNFGG8WQIUMycw888EDcfvvtSToBAAAAAAC132OPPZZ58U1Bnz59jMMCAECGpk2bxuTJkzNzH330UZx33nlJOgEAAAAAALXfp59+Guecc05mbscdd4yTTz45SScAAKjLxowZE6uuumrRTGVlZXTr1q36TwAAgIYml8/n8zVdAgAAAACA5efNN9+MzTffPBYuXFg091//9V9x9913Ry6XS9YNAADqqkWLFsVWW20Vc+bMKZpr3759daZVq1bJugEAAAAAALVP4exO586d4x//+EfR3LrrrhuzZ8+Oli1bJusGAAB12QknnBDXXXdd0UxJSUm89NJL0aVLl2S9AAAAAACA2unYY4+Nm266qWimtLQ0XnnllerzPgAAQLY//vGPceqpp2bmJk2aFD169EjSCQAAoLYoqekCAAAAAAAsP/l8Pnr16pU5DtuiRYsYP368cVgAAFhKTZo0ifLy8szcxx9/HEOGDEnSCQAAAAAAqL1GjBiROQ5bUDjDYxwWAACW3qhRo6JNmzZFM1VVVdGtW7eorKxM1gsAAAAAAKh9Hnnkkcxx2IIzzzzTOCwAAPwEJ510Uuy0006ZuYEDB8Ynn3ySpBMAAEBtYSAWAAAAAKAeufXWW+Phhx/OzJ1//vmx/vrrJ+kEAAD1xW677RZ/+MMfMnMTJkyIl19+OUknAAAAAACg9nnjjTfi4osvzswddNBBcfDBByfpBAAA9cXqq68el156aWZu2rRpUV5enqQTAAAAAABQ+/z444/Rs2fPzNx6661XfRcPAACw9EpKSqrP5pSWlhbNffvtt9G3b99kvQAAAGqDXD6fz9d0CQAAAAAAlt0333wTHTp0iE8++aRobosttqgeq2rcuHGybgAAUF988cUXUVZWFl9++WXR3DbbbBNTp07NPMQOAAAAAADUL4VXNvfZZ5949NFHi+ZatGgRc+bMqb5gEgAA+Gmqqqpit912i2effbZorlWrVtXP3e3bt0/WDQAAAAAAqB0Ko6/Dhg3LzN19991x0EEHJekEAAD1Tf/+/WPUqFGZuYceeij23XffJJ0AAABqWklNFwAAAAAAYPk499xzM8dhC8rLy43DAgDAz9S2bdulOpT+8ssvx6RJk5J0AgAAAAAAao+bb745cxy2oHD5pHFYAAD4eUpKSmLKlCnRqFGjornvvvsu+vbtm6wXAAAAAABQO8ydOzcuueSSzNwhhxxiHBYAAJbB0KFDl+pcfM+ePWPBggVJOgEAANS0XD6fz9d0CQAAAAAAls2LL74YO+ywQ2T9k++pp55afREOAADw8xWeu/fYY4946qmniuZWXnnlmDNnTqy99trJugEAAAAAADXn66+/jrKysvjss8+K5jp37hzTpk2Lxo0bJ+sGAAD10cCBA2PEiBGZuQcffDB+9atfJekEAAAAAADU/HvAe++9dzz22GNFcyuttFL1e8Drrrtusm4AAFAf3XPPPXHQQQdl5oYMGRIXXHBBkk4AAAA1yUAsAAAAAEAdt2TJkth2223j1VdfLZpbffXVo6KiItq0aZOsGwAA1FezZ8+OrbbaKhYvXlw0d8QRR8Rtt92WrBcAAAAAAFBzevToEeXl5UUzuVwunn322dhxxx2T9QIAgPpq/vz50alTp3j33XeL5jbaaKOYOXNmNG/ePFk3AAAAAACgZtxwww1x3HHHZebGjBkTffv2TdIJAADqu0MPPTTuvPPOopnGjRvHjBkzoqysLFkvAACAmlBSIz8VAAAAAIDlZvz48ZnjsAVjx441DgsAAMtJx44do1+/fpm5v/zlL/HAAw8k6QQAAAAAANScF154IaZMmZKZO/XUU43DAgDActKiRYuYOHFiZu7tt9+Oiy66KEknAAAAAACg5nz11VdLNfq65ZZbxumnn56kEwAANARXXHFFtGzZsmhm8eLF0aNHj8jn88l6AQAA1IRc3m8+AAAAAAB11gcffBAdOnSIH374oWjul7/8ZTzyyCORy+WSdQMAgPpu/vz5sfnmm8c777xTNLfhhhvGzJkzqy+kBAAAAAAA6p/CRTVdu3aNGTNmFM21a9cuKioqYtVVV03WDQAAGoLDDz88br/99qKZxo0bx2uvvVZ9/h4AAAAAAKifunXrFldeeWXRTOH+neeffz623377ZL0AAKAhuOyyy6Jv376Zueuvvz6OO+64JJ0AAABqQkmN/FQAAAAAAJaLPn36ZI7DNmnSJCZNmmQcFgAAlrPC4GvhWTtLYUB2+PDhSToBAAAAAADpjRs3LnMctmDs2LHGYQEAYAW44ooromXLlkUzixcvju7du0c+n0/WCwAAAAAASOe5557LHIctKHxfYBwWAACWv9NOOy222mqrzNxZZ50VX331VZJOAAAANcFALAAAAABAHXXPPffEnXfemZk755xzYrPNNkvSCQAAGpr99tsvjjjiiMzcqFGjYtasWUk6AQAAAAAA6bz//vtx3nnnZeb22muvOOaYY5J0AgCAhmbttdeO4cOHZ+aeeuqpuO6665J0AgAAAAAA0lm8eHH18GuWNdZYIy6++OIknQAAoKFp1KhRTJkyJXK5XNHc559/HgMGDEjWCwAAILVcPp/PJ/+pAAAAAAAskx9++CE6duxYfcFkMZtsskm8/vrr0axZs2TdAACgofn444+jrKws5s2bVzS36667xhNPPBElJSXJugEAAAAAACvWIYccEnfffXfRTJMmTarP8PziF79I1gsAABqaysrK2G677eKVV14pmltttdVi7ty51X8CAAAAAAD1w6hRo6J///6ZuZtuuil++9vfJukEAAANVa9evWLSpEmZuWeeeSZ23nnnJJ0AAABScuMoAAAAAEAddMEFF2SOwxZMnjzZOCwAAKxg7du3j4suuigz9/TTT8e1116bpBMAAAAAALDiFYZhs8ZhCwYNGmQcFgAAVrDS0tKYMmVK5HK5orkvv/xyqS6IBwAAAAAA6ob33nsvzj///Mzc3nvvHUcffXSSTgAA0JAV7uFZc801M3Pdu3ePxYsXJ+kEAACQUi6fz+eT/kQAAAAAAJbJ66+/Hl26dIklS5YUzR1zzDFx4403JusFAAANWWVlZeywww4xbdq0ork2bdrE3Llzo23btsm6AQAAAAAAy9/3338fHTt2jA8++KBobtNNN40ZM2ZEs2bNknUDAICG7LTTTosJEyZk5p566qnYddddk3QCAAAAAABWjML16gcffHDcc889RXNNmzatvrOncJYHAABY8W655Zb47W9/m5kbOXJk9O/fP0knAACAVEqS/SQAAAAAAJZZVVVVdO/ePXMctnXr1jF27NhkvQAAoKErLS2N8vLyKCkpfhTjq6++in79+iXrBQAAAAAArBjnn39+5jhsweTJk43DAgBAQsOHD4+11lorM1c4l79o0aIknQAAAAAAgBXj7rvvzhyHLRg0aJBxWAAASOioo46KffbZZ6nO5b/77rtJOgEAAKRiIBYAAAAAoA65+uqr47nnnsvMjRgxItZYY40knQAAgP+2zTbbRO/evTNz1157bTz55JNJOgEAAAAAAMvfa6+9Fpdffnlm7thjj4299torSScAAOC/tW7deqme12fPnh1jx45N0gkAAAAAAFj+5s2bF6eddlpmbrPNNosBAwYk6QQAAPy3XC4XkyZNiqZNmxbNLViwoPq+nnw+n6wbAADAipbL+y0HAAAAAKBO+Oyzz6KsrCy+/vrrorkddtghnn322SgpKUnWDQAA+G/fffdddOjQIT7++OOiuULm1VdfjSZNmiTrBgAAAAAALLuqqqrYeeed44UXXiiaW2WVVaKioiLWWGONZN0AAID/VrhKZf/9948HH3ywaK558+Yxa9as2HDDDZN1AwAAAAAAlo++ffvGZZddlpl77LHHYs8990zSCQAA+N+GDx8eQ4YMyczdfvvtceihhybpBAAAsKJZBwAAAAAAqCPOPvvszHHY0tLSKC8vNw4LAAA1pFWrVnHFFVdk5ubMmROjR49O0gkAAAAAAFh+rrzyysxx2IIRI0YYhwUAgBqSy+Vi4sSJ0axZs6K5BQsWRK9evaoHZQEAAAAAgLpj+vTpS/U+73HHHWccFgAAalC/fv1is802y8ydfvrpMW/evCSdAAAAVjQLAQAAAAAAdcBjjz0Wf/7znzNzZ5xxRmy55ZZJOgEAAP+3ww47LPbff//M3IUXXhhvvfVWkk4AAAAAAMCy+/TTT+Occ87JzO24445xyimnJOkEAAD83zbaaKMYMmRIZu6BBx6I22+/PUknAAAAAABg2VVWVkb37t2jqqqqaG7VVVeN0aNHJ+sFAAD8u6ZNm0Z5eXlm7qOPPorzzjsvSScAAIAVLZfP5/Mr/KcAAAAAAPCzLVy4MDp37hz/+Mc/iubWXXfdmD17drRs2TJZNwAA4P/2zjvvRKdOnWLBggVFc/vtt1/cf//9kcvlknUDAAAAAAB+nmOPPTZuuummopnS0tJ45ZVXqs/7AAAANWvRokWx1VZbxZw5c4rm2rdvX51p1apVsm4AAAAAAMDPM3ny5OjZs2dm7sorr4xTTjklSScAAKC4448/Pq6//vqimZKSknjppZeiS5cuyXoBAACsCCUr5FMBAAAAAFhuRo4cmTkOWzB+/HjjsAAAUEtsuOGGcd5552XmHnzwwfjLX/6SpBMAAAAAAPDzPfLII5njsAVnnnmmcVgAAKglmjRpEuXl5Zm5jz/+OIYMGZKkEwAAAAAA8PN98sknMXDgwMzcTjvtFCeddFKSTgAAQLbRo0fHqquuWjRTVVUV3bt3j8rKymS9AAAAVoRcPp/Pr5BPBgAAAABgmb3xxhuxxRZbxMKFC4vmDjrooLj77ruT9QIAALItWrQott5665g9e3bR3JprrhkVFRXRunXrZN0AAAAAAICl9+OPP1aPvhbO8hSz3nrrVX8vsNJKKyXrBgAAZDvxxBPjmmuuKZopKSmJF198MbbZZptkvQAAAAAAgJ/mmGOOiZtvvrloplGjRvHKK69U39kDAADUHldddVWccsopmbkJEyZEr169knQCAABYEUpWyKcCAAAAALDM8vl89OzZM3MctnCh5Pjx45P1AgAAlk6TJk1iypQpmblPPvkkBg8enKQTAAAAAADw040YMSJzHLagcIbHOCwAANQ+l156aay22mpFM1VVVdGtW7eorKxM1gsAAAAAAFh6Dz/8cOY4bEHfvn2NwwIAQC104oknxs4775yZGzRoUPzzn/9M0gkAAGBFMBALAAAAAFBLFQ6kP/LII5m5YcOGxXrrrZekEwAA8NPssssucdJJJ2XmJk6cGC+99FKSTgAAAAAAwNKbO3duXHLJJZm5Qw45JA466KAknQAAgJ+mbdu2MWrUqMzcyy+/HJMmTUrSCQAAAAAAWHoLFiyInj17ZubWX3/9OO+885J0AgAAfpqSkpIoLy+PRo0aFc199913ceaZZybrBQAAsLzl8vl8frl/KgAAAAAAy+Trr7+OsrKy+Oyzz4rmOnfuHNOmTYvGjRsn6wYAAPw0X375ZfXz/RdffFE0t/XWW8eLL76YeYgdAAAAAABIo/D65d577x2PPfZY0dxKK60Uc+bMiXXXXTdZNwAA4Kc/3+++++7x9NNPF82tvPLKUVFREe3bt0/WDQAAAAAAKK4w+nrhhRdm5u6555448MADk3QCAAB+nnPOOSdGjhyZmXvwwQfjV7/6VZJOAAAAy1PJcv00AAAAAACWi0GDBmWOw+ZyuZgyZYpxWAAAqOVWW221GD16dGZu+vTpMXHixCSdAAAAAACAbDfeeGPmOGzBBRdcYBwWAABqucL5+/Ly8mjUqFHR3Lx58+KMM85I1gsAAAAAACiuoqIiRowYkZk79NBDjcMCAEAdcN5558UGG2yQmevZs2csWLAgSScAAIDlKZfP5/PL9RMBAAAAAFgmL7zwQuy0006R9c+33bp1q76gBgAAqP0Kz/d77rlnPPnkk0VzLVu2rH5Rde21107WDQAAAAAA+HdfffVVlJWVxeeff140t+WWW8a0adMyR6YAAIDaYdCgQXHJJZdk5u6///749a9/naQTAAAAAADwn9/P/eUvfxlPPPFE5vu5c+bMiXXWWSdZNwAA4Oe777774sADD8zMnXvuuTF8+PAknQAAAJYXA7EAAAAAALXIkiVLomvXrvHaa68VzbVr1656NGrVVVdN1g0AAFg2hRdLC5fEL168uGjusMMOi7/+9a/JegEAAAAAAP+uW7duceWVVxbN5HK5eP7552P77bdP1gsAAFg28+fPj8033zzeeeedorkNN9wwZs6cGS1atEjWDQAAAAAA+N+uv/76OP744zNzl112WZxxxhlJOgEAAMvH4YcfHrfffnvRTOPGjavv5uzQoUOyXgAAAMuqZJk/AQAAAACA5WbcuHGZ47AFY8eONQ4LAAB1TOGgef/+/TNzhYPr9913X5JOAAAAAADAv3vuuecyx2ELunfvbhwWAADqmMLg68SJEzNzhQHZ4cOHJ+kEAAAAAAD8u6+++irOOuuszNxWW20VvXv3TtIJAABYfi6//PJo2bJl0czixYujR48ekc/nk/UCAABYVrm832KA/4e9+w6zqjzbh33voTcFRMTepdqVWGMvsbdo7BoLTVFRFBFQEEGKIgjMoMYSe3ztGkuMvSAqInXsxorYUJDO7O/Y+/2SN/lF9wIG1rTzPI4czOx9rbWu5K+sNc967gAAAACgMvj000+jXbt28fPPPxfM7bvvvvH0009HJpNJrRsAALByzJ8/P7bccsv48MMPC+Y22mijmDZtWn5DSgAAAAAAID25DWS23377mDJlSsHcWmutFaWlpdG0adPUugEAACvPscceG/fdd1/BTO3atWPSpEnRvn371HoBAAAAAAD/66yzzoqbbrqpYCa3/8748eOjY8eOqfUCAABWnpEjR8b555+fmLv11lvj1FNPTaUTAABAeRWV+wwAAAAAAKwU5513XuJw2Hr16sXYsWMNhwUAgCqqQYMGMWbMmMTcJ598EgMGDEilEwAAAAAA8H+uu+66xOGwOSNGjDAcFgAAqvj/92/SpEnBzJIlS6JLly5RVlaWWi8AAAAAACDilVdeSRwOm5N7jm84LAAAVF3dunWLbbfdNjF30UUXxXfffZdKJwAAgPIyIBYAAAAAoBJ45JFH4qGHHkrMXXrppbH55pun0gkAAFg1DjjggDjuuOMSc9dcc01MnTo1lU4AAAAAAEDEP/7xj7jiiisSc/vuu2/84Q9/SKUTAACwaqyzzjpx1VVXJeZeeumluO2221LpBAAAAAAARCxevDg6d+6cmGvVqlUMGjQolU4AAMCqUbt27Rg3blxkMpmCuW+//TYuueSS1HoBAACURyabzWbLdQYAAAAAAMpl7ty50a5du/jss88K5rbYYouYPHly1KtXL7VuAADAqvHVV19FmzZt4qeffiqY23XXXePFF1+MoqKi1LoBAAAAAEBNlHvV8vDDD49HH320YC63dmfKlCmx+eabp9YNAABYNZYuXRq/+c1v4q233iqYW2ONNaK0tDRatGiRWjcAAAAAAKiphgwZEr169UrM3XPPPXHcccel0gkAAFi1zj333Bg9enRiLrcPz+67755KJwAAgBVl91AAAAAAgArWv3//xOGwOcXFxYbDAgBANbH22mvHoEGDEnOvvPJK3HLLLal0AgAAAACAmuzhhx9OHA6b07t3b8NhAQCgmqhVq1aMGzcuiooKb7/y3XffRc+ePVPrBQAAAAAANdUnn3yS34snyf777x/HHntsKp0AAIBVb+DAgfn9eJJ06dIlFi1alEonAACAFZXJZrPZFT4aAAAAAIByeeedd2L77bePpUuXFsyddNJJcfvtt6fWCwAAWPVy9wE777xzvPHGGwVzzZs3j9LS0lhzzTVT6wYAAAAAADXJnDlzol27dvH5558XzLVu3Tq/3qdevXqpdQMAAFa98847L0aNGpWYe/7552OPPfZIpRMAAAAAANQ0ua3SDz300Hj88ccL5nJrd6ZNmxabbrppat0AAIBV7y9/+Uscd9xxibnBgwdHr169UukEAACwIopW6CgAAAAAAMqtrKwsOnfunDgctmnTpjF8+PDUegEAAOmoVatWlJSURFFR4eUb33//ffTs2TO1XgAAAAAAUNNcfvnlicNhc4qLiw2HBQCAaujKK6+MddZZJzHXpUuXWLRoUSqdAAAAAACgpnnwwQcTh8Pm9OnTx3BYAACohn7/+9/HAQcckJgbMGBAfPzxx6l0AgAAWBEGxAIAAAAAVJAbb7wxxo8fn5gbMmRIrLXWWql0AgAA0rXddttF9+7dE3O33XZbPP/886l0AgAAAACAmuTtt9+OkSNHJuZOPvnk2GuvvVLpBAAApGu11VZbpvuCGTNmxPDhw1PpBAAAAAAANcmcOXOW6X3bNm3aRM+ePVPpBAAApCuTycSYMWOifv36BXPz58+Pbt26RTabTa0bAADA8shk3bEAAAAAAKTu66+/zi84nz17dsHczjvvHC+//HIUFRWl1g0AAEj/pdW2bdvGF198UTDXunXreOedd6JevXqpdQMAAAAAgOps6dKlscsuu8SECRMK5po1axalpaXRsmXL1LoBAADpym2/csghh8Rf//rXgrncBpRTp06NTTfdNLVuAAAAAABQ3V1wwQVx3XXXJeaee+652HPPPVPpBAAAVIyrrroq+vTpk5i777774phjjkmlEwAAwPIwUQAAAAAAoAJceOGFicNha9WqFSUlJYbDAgBANdekSZMYNWpUYu7dd9+NYcOGpdIJAAAAAABqghtuuCFxOGzOkCFDDIcFAIBqLpPJxOjRo6NBgwYFcwsWLIhzzjknP1AWAAAAAAAov4kTJy7Te7annnqq4bAAAFAD9OzZM9q2bZuYO++88+Knn35KpRMAAMDyMFUAAAAAACBlzzzzTNx5552JuR49esRWW22VSicAAKBiHXnkkXHIIYck5gYOHBgffPBBKp0AAAAAAKA6mzlzZlx66aWJuV122SXOOOOMVDoBAAAVa+ONN45+/fol5p588sm47777UukEAAAAAADV2dKlS6NTp05RVlZWMNe8efMYNmxYar0AAICKU7du3SguLk7Mffnll9G3b99UOgEAACyPTDabzS7XEQAAAAAArLAFCxbkh76+//77BXMbbLBBTJ8+PRo1apRaNwAAoGJ98skn0a5du5g/f37B3P7775/fZDKTyaTWDQAAAAAAqpsTTjgh7r777oKZ2rVrx8SJE2PLLbdMrRcAAFCxFi1aFNtuu21+PX8ha6+9dsyYMSNWX3311LoBAAAAAEB1M2bMmDjnnHMSczfddFOcccYZqXQCAAAqh9NPPz1uvfXWgpmioqKYMGFCbL/99qn1AgAASFKUmAAAAAAAYKW5+uqrE4fD5owePdpwWAAAqGE22mijuOKKKxJzTz/9dNx7772pdAIAAAAAgOoo96w9aThsTo8ePQyHBQCAGqZu3bpRUlKSmPvqq6+iT58+qXQCAAAAAIDq6Msvv4zevXsn5nbbbbf8YCgAAKBmGTZsWDRv3rxgpqysLDp37hxLly5NrRcAAECSTDabzSamAAAAAAAot/feey+/YeSiRYsK5o444oh48MEHU+sFAABUHosXL47tttsupk6dWjDXqlWrmDFjRjRt2jS1bgAAAAAAUB3Mnz8/v4bnww8/LJjbcMMNY9q0adGoUaPUugEAAJXHGWecETfffHPBTCaTiddffz123HHH1HoBAAAAAEB18Yc//CHuvffegpnatWvHpEmTon379qn1AgAAKo/c+p3cOp4k119/fZxzzjmpdAIAAEhSlJgAAAAAAKDcstlsdOnSJXE4bG5DyVGjRqXWCwAAqFzq1KkTJSUlibmZM2fGZZddlkonAAAAAACoTgYPHpw4HDZn9OjRhsMCAEANNnTo0FhjjTUS3xPo1KlTLFmyJLVeAAAAAABQHTz11FOJw2FzLrroIsNhAQCgBjvttNNi9913T8z17t07vvzyy1Q6AQAAJDEgFgAAAAAgBXfddVc8++yzibkBAwbE+uuvn0onAACgctp1113jzDPPTMwVFxfHhAkTUukEAAAAAADVQWlpaVx99dWJuaOOOioOOeSQVDoBAACVU2447PDhwxNzb7/9dowZMyaVTgAAAAAAUB3Mnz8/unbtmpjbaKONom/fvql0AgAAKqeioqL8Hju1a9cumJszZ06cf/75qfUCAAAoJJPNZrMFEwAAAAAAlMsPP/wQbdq0iVmzZhXMbbPNNvHGG28kLj4BAACqv++//z5at24d3377bcGc+wgAAAAAAFg2uVcp995773j++ecL5ho3bhwzZsyI9dZbL7VuAABA5b2P2GuvveKFF15IvI8oLS2NddddN7VuAAAAAABQVfXp0yeuuuqqxNzjjz8eBx10UCqdAACAyq13794xePDgxNxf//rX+N3vfpdKJwAAgF9T9KvfAAAAAACwUlx66aWJw2EzmUyUlJQY6gQAAOQ1b948rrnmmsTcpEmT4vrrr0+lEwAAAAAAVGW333574nDYnCuvvNJwWAAA4F/r/IuLi6NOnToFc3Pnzo3zzjsvtV4AAAAAAFBVzZgxI4YOHZqYO/roow2HBQAA/qVPnz6x8cYbJ+a6desW8+bNS6UTAADAr8lks9nsr34LAAAAAEC5vPbaa7HLLrsk5rp06RJjx45NpRMAAFA15JZ07LPPPvHcc88VzDVq1Cj/Quz666+fWjcAAAAAAKhKvv/++2jdunV8++23BXPbbLNNvPHGG1G7du3UugEAAFVjg8mrrroqMffYY4/FwQcfnEonAAAAAACoiu/N7rXXXvHCCy8UzDVp0iT/3uy6666bWjcAAKDye+KJJ+Kggw5KzPXu3XuZ1voAAACsKgbEAgAAAACsIosXL47tt98+pkyZUjC31lprRWlpaTRt2jS1bgAAQNXw7rvvxlZbbRWLFi0qmDvyyCPjgQceSK0XAAAAAABUJWeddVbcdNNNBTOZTCbGjx8fHTt2TK0XAABQNcyfPz86dOgQH330UcHcRhttFNOmTYuGDRum1g0AAAAAAKqKW2+9NU4//fTE3MiRI6N79+6pdAIAAKqWY489Nu67776CmTp16sSkSZOiXbt2qfUCAAD4d0X/8RsAAAAAACtNbrF50nDYnBEjRhgOCwAA/KLWrVvHJZdckph78MEH49FHH02lEwAAAAAAVCWvvPJK4nDYnC5duhgOCwAA/KIGDRrE2LFjE3OffPJJXHnllal0AgAAAACAquS7776Liy66KDG33XbbRbdu3VLpBAAAVD3XXXddNGnSpGBm8eLF0blz58hms6n1AgAA+HeZrDsSAAAAAICV7h//+Ee0a9cu5s2bVzC33377xVNPPRWZTCa1bgAAQNWyYMGC6NChQ3z44YcFcxtssEFMnz49GjVqlFo3AAAAAACozHIbu+Q2jZw6dWrBXKtWraK0tDRWX3311LoBAABVzx/+8Ie49957C2Zq164db7/9dn69DwAAAAAA8L/OOOOMuPnmmwtmcvvvTJgwIXbYYYfUegEAAFXP9ddfH927d0/M5e5BTj/99FQ6AQAA/Lui//gNAAAAAICVIrdgJGk4bL169WLs2LGGwwIAAAXVr18/iouLE3OffvppDBgwIJVOAAAAAABQFVx77bWJw2FzrrvuOsNhAQCARCNGjIjVVlutYGbJkiXRuXPnKCsrS60XAAAAAABUZi+99FLicNicbt26GQ4LAAAk6tq1a2y//faJuZ49e8a3336bSicAAIB/Z0AsAAAAAMBK9tBDD8UjjzySmLvssstis802S6UTAABQte23335x/PHHL9NG91OmTEmlEwAAAAAAVGaffPJJ9O/fPzG3//77x7HHHptKJwAAoGpbe+21Y9CgQYm5V155JW655ZZUOgEAAAAAQGW2aNGi6Ny58zI9gx84cGAqnQAAgKqtVq1aMW7cuCgqKjxy6bvvvouLL744tV4AAAD/lMlms9l//QYAAAAAQLnMnTs32rZtG59//nnBXOvWreOdd96JevXqpdYNAACo2mbOnBlt2rSJH3/8sWBu5513jpdffjlxETsAAAAAAFRXudcmDz300Hj88ccL5nJrd6ZNmxabbrppat0AAICqbenSpbHTTjvFm2++WTDXvHnzKC0tjTXXXDO1bgAAAAAAUNlcffXVcemllybm7r333jj22GNT6QQAAFQP3bt3j+uvvz4x98ILL8Rvf/vbVDoBAADk2AkUAAAAAGAluvzyyxOHw+YUFxcbDgsAACyXVq1axeDBgxNzr732WvzpT39KpRMAAAAAAFRGDz74YOJw2Jw+ffoYDgsAACyXWrVqxbhx46KoqPCWLd9//3307NkztV4AAAAAAFDZfPzxxzFgwIDE3IEHHhi///3vU+kEAABUHwMHDoy11147Mde5c+dYtGhRKp0AAAByMtlsNut/CgAAAACA8ps0aVLssMMOsXTp0oK5U045JW677bbUegEAANVHWVlZ7LLLLvH6668XzDVr1ixKS0ujZcuWqXUDAAAAAIDKYM6cOdG2bdv44osvCubatGmTX+9Tr1691LoBAADVxwUXXBDXXXddYu65556LPffcM5VOAAAAAABQWeS2PT/44IPjiSeeKJirX79+TJs2LTbZZJPUugEAANXHfffdF8cee2xi7qqrrorevXun0gkAAKCoogsAAAAAAFQHuaGwnTp1ShwOmxvSNGzYsNR6AQAA1UtRUVGUlJRErVq1CuZ++OGHuOiii1LrBQAAAAAAlUW/fv0Sh8PmFBcXGw4LAACssAEDBsS6666bmOvcuXMsXLgwlU4AAAAAAFBZ3H///YnDYXP69u1rOCwAALDCjjnmmPjd736XmLvyyivjo48+SqUTAACAAbEAAAAAACvBDTfcEBMmTEjMDR06NFq2bJlKJwAAoHraZptt4rzzzkvM3X777fHss8+m0gkAAAAAACqDiRMnxqhRoxJzp556auy5556pdAIAAKqnJk2aLNP9x7vvvhvDhg1LpRMAAAAAAFQGP/300zK9B9u2bdu46KKLUukEAABUT5lMJsaMGRP169cvmFuwYEF069Ytstlsat0AAICaK5N19wEAAAAAUC4zZ86MNm3axI8//lgwt+uuu8aLL74YRUVFqXUDAACqp7lz5+ZffP38888L5rbYYouYPHly1KtXL7VuAAAAAABQEZYuXRo777xzvPHGGwVzzZs3j9LS0lhzzTVT6wYAAFRPuS1bDj300Hj88ccL5nJrd6ZOnRqbbbZZat0AAAAAAKCi5IbDjho1KjH3wgsvxG9/+9tUOgEAANXb4MGDo3fv3om5v/zlL/H73/8+lU4AAEDNZQoBAAAAAEA59ejRI3E4bO3ataO4uNhwWAAAYKVo3LhxXH/99Ym59957L4YMGZJKJwAAAAAAqEglJSWJw2Fzhg4dajgsAACwUmQymRg9enQ0aNCgYG7hwoXRrVu3/EBZAAAAAACozt566638s/Mkp59+uuGwAADASnPhhRdGu3btEnPnnXde4t6hAAAA5WUSAQAAAABAOfztb3+Lu+++e5mGyG655ZapdAIAAGqGww8/PA499NDE3KBBg+L9999PpRMAAAAAAFSEL7/8Mnr37p2Y22233fKbSwIAAKwsG220UVx++eWJuaeffjruvffeVDoBAAAAAEBFWLp0aXTq1CnKysoK5tZYY40YOnRoar0AAIDqr27dulFSUpKY++qrr6JPnz6pdAIAAGquTDabzVZ0CQAAAACAqmjBggX5oa8ffPBBwdyGG24Y06ZNi0aNGqXWDQAAqBn+8Y9/RLt27WLevHkFc/vuu29+k8lMJpNaNwAAAAAASMsf/vCHxEFLtWvXjkmTJkX79u1T6wUAANQMixcvju222y6mTp1aMNeqVauYMWNGNG3aNLVuAAAAAACQluuvvz66d++emLv55pvj9NNPT6UTAABQs5xxxhn5e45CcvvvTJgwIXbYYYfUegEAADVLUUUXAAAAAACoqgYPHpw4HDZn9OjRhsMCAACrxIYbbhhXXHFFYu6ZZ56Ju+++O5VOAAAAAACQpqeeeipxOGzORRddZDgsAACwStSpUydKSkoSczNnzozLLrsslU4AAAAAAJCmL7/8cpmege++++5x2mmnpdIJAACoeYYMGRJrrLFGwUw2m41OnTrFkiVLUusFAADULJls7s4DAAAAAIDl8u6778ZWW20VixYtKpg76qij4v7770+tFwAAUPMsXrw4tt9++5gyZUrBXMuWLaO0tDSaNWuWWjcAAAAAAFiV5s+fHx06dIiPPvqoYG6jjTaKadOmRcOGDVPrBgAA1DxnnXVW3HTTTQUzmUwmxo8fHx07dkytFwAAAAAArGrHHnts3HfffQUzderUiUmTJkW7du1S6wUAANQ8t956a5x++umJuZEjR0b37t1T6QQAANQsRRVdAAAAAACgqslms9G5c+fE4bCNGzfOL/oAAABYlXIvxI4bNy4xN2vWrOjdu3cqnQAAAAAAIA1XXXVV4nDYnDFjxhgOCwAArHJDhgyJFi1aJL6P0KlTp1iyZElqvQAAAAAAYFV64oknEofD5vTs2dNwWAAAYJU79dRTY4899kjM9enTJ7744otUOgEAADWLAbEAAAAAAMvpjjvuiOeffz4xd+WVV8Z6662XSicAAKBm23nnnePss89OzOUGyY4fPz6VTgAAAAAAsCrNmDEjhg4dmpg7+uij46CDDkqlEwAAULM1b948rrnmmsTcpEmTYvTo0al0AgAAAACAVWnevHnRrVu3xNzGG28cl112WSqdAACAmi2TyURxcXHUqVOnYG7OnDlx/vnnp9YLAACoOTLZbDZb0SUAAAAAAKqK77//Ptq0aRPffPNNwdy2224bEyZMiNq1a6fWDQAAqNl++OGHaN26deL9ytZbbx1vvvmm+xUAAAAAAKqs3GuRe+21V7zwwgsFc40bN47S0tJYd911U+sGAADUbLn7lb333juef/75xPuVGTNmxHrrrZdaNwAAAAAAWNl69+4dgwcPTsw98cQTceCBB6bSCQAAIKdPnz5x1VVXJeYef/zxOOigg1LpBAAA1AxFFV0AAAAAAKAq6dWrV+KwpUwmEyUlJYYtAQAAqWrWrFlce+21ibl33nknRo0alUonAAAAAABYFW677bbE4bA5AwcONBwWAABIVe59guLi4qhTp07B3Ny5c6N79+6p9QIAAAAAgJVt2rRpMWzYsMTc73//e8NhAQCA1F122WWxySabJOa6desW8+bNS6UTAABQM2Sy2Wy2oksAAAAAAFQFr7zySuy2227LtMBj9OjRqXQCAAD4d7llIPvuu288++yzBXONGjWK6dOnxwYbbJBaNwAAAAAAWBm+++67aN26df7fQrbbbruYMGFC1KpVK7VuAAAA/9SvX7+48sorE3OPPPJIHHrooal0AgAAAACAlaWsrCz23HPPeOmllwrmmjRpEqWlpbHOOuuk1g0AAOCfnnzyyfjd736XmOvVq1cMHjw4lU4AAED1Z0AsAAAAAMAyWLx4cX7TyKlTpxbMtWrVKr8offXVV0+tGwAAwL979913Y6uttopFixYVzB1xxBHx4IMPptYLAAAAAABWhjPOOCNuvvnmgplMJpMfDrvDDjuk1gsAAODfzZ8/P7bccsv48MMPC+Y22GCDmD59ejRq1Ci1bgAAAAAAUF633HJL/PGPf0zMjRo1Ks4999xUOgEAAPyS4447Lv7yl78UzNSuXTvefvvt6NChQ2q9AACA6quoogsAAAAAAFQFI0aMSBwOm3PdddcZDgsAAFSo1q1bx6WXXpqYe+ihh+KRRx5JpRMAAAAAAKwML730UuJw2Jxu3boZDgsAAFSoBg0aRHFxcWLu008/jQEDBqTSCQAAAAAAVoZvv/02evbsmZjbfvvto2vXrql0AgAAKLSX6GqrrVYws2TJkujSpUuUlZWl1gsAAKi+MtlsNlvRJQAAAAAAKrNPPvkk2rVrF/Pnzy+YO+CAA+KJJ56ITCaTWjcAAIBfsmDBgthqq63i/fffL5hbf/31Y/r06dG4cePUugEAAAAAwIpYtGhRbLvttvnn2oWsvfbaMWPGjFh99dVT6wYAAPBrTjjhhLj77rsLZmrXrh0TJ06MLbfcMrVeAAAAAACwok4//fS49dZbC2aKiopiwoQJ+SGxAAAAFW306NFx7rnnJuZuuummOOOMM1LpBAAAVF9FFV0AAAAAAKAyy2azcc455yQOh61fv36MGTPGcFgAAKBSyN2jjB07NjH32WefRf/+/VPpBAAAAAAA5XHttdcmDofNue666wyHBQAAKtW9TNI9ypIlS6Jz585RVlaWWi8AAAAAAFgRL7zwQuJw2Jzcfj2GwwIAAJVFly5dYocddkjMXXzxxfHNN9+k0gkAAKi+DIgFAAAAACjgwQcfjMcffzwx16dPn9h0001T6QQAALAs9t133zjxxBMTcyNGjIh33nknlU4AAAAAALAiPv744xgwYEBi7sADD4zf//73qXQCAABYFq1atYrBgwcn5l599dX405/+lEonAAAAAABYEYsWLcoPVUqyzjrrxJVXXplKJwAAgGVRq1atGDduXBQVFR7T9P3330fPnj1T6wUAAFRPmWw2m63oEgAAAAAAldGcOXOibdu28cUXXxTMtWnTJiZNmhT16tVLrRsAAMCy+Prrr/P3LLNnzy6Y22mnneKVV15JXMQOAAAAAABpy70CefDBB8cTTzxRMFe/fv2YNm1abLLJJql1AwAAWBZLly6NXXbZJSZMmFAw16xZsygtLY2WLVum1g0AAAAAAJbVoEGD4rLLLkvM3XfffXHMMcek0gkAAGB5nH/++TFy5MjE3HPPPRd77rlnKp0AAIDqx66eAAAAAAC/ol+/fonDYXNKSkoMhwUAACqltdZaK66++urE3Pjx4+PGG29MpRMAAAAAACyP+++/P3E4bE7fvn0NhwUAACqlWrVqxbhx46KoqPA2Lz/88ENcdNFFqfUCAAAAAIBl9eGHH8aVV16ZmDvooIPi6KOPTqUTAADA8srd16y77rqJuS5dusTChQtT6QQAAFQ/mWw2m63oEgAAAAAAlc3bb78dO+ywQ5SVlRXMnXrqqXHrrbem1gsAAGB55e5rdt111/wQ2EKaNm0apaWl+aGyAAAAAABQGfz000/Rtm3b+PLLLwvmcplJkyZF3bp1U+sGAACwvHr06BEjRoxIzP3973+PvffeO5VOAAAAAACQJLeFeW7w65NPPlkw16BBg5g2bVpsvPHGqXUDAABYXvfff38cc8wxyzRMtk+fPql0AgAAqpeiii4AAAAAAFDZLF26NDp16pQ4HLZ58+YxbNiw1HoBAACsiKKioigpKYlatWoVzM2ePTsuvPDC1HoBAAAAAECSvn37Jg6Hzck9BzccFgAAqOz69+8f6623XmKuS5cusXDhwlQ6AQAAAABAkvvuuy9xOGxOv379DIcFAAAqvaOOOioOPvjgxNzAgQPjgw8+SKUTAABQvRgQCwAAAADw/xg3bly88cYbibmhQ4fGmmuumUonAACA8th6663j/PPPT8zdeeed8cwzz6TSCQAAAAAACnnrrbdi9OjRibnTTz89fvvb36bSCQAAoDyaNGkSo0aNSsy99957MWTIkFQ6AQAAAABAIT/++OMyvZ/arl276NGjRyqdAAAAyiOTyeTfVWjQoEHB3MKFC6Nbt26RzWZT6wYAAFQPmaw7CQAAAACAf/nqq6+iTZs28dNPPxXM7bbbbvHCCy9EUVFRat0AAADKY+7cufkXbD/77LOCuc033zwmT54c9evXT60bAAAAAAD8u6VLl8ZvfvOb/JDYQtZYY40oLS2NFi1apNYNAACgPHLbvBx++OHx6KOPFszVq1cvpkyZkl/LAwAAAAAAFeXcc8/ND05K8tJLL+X34wEAAKgqhgwZEr169UrM3XPPPXHcccel0gkAAKgeTC4AAAAAAPg3F1xwQeJw2Nq1a0dJSYnhsAAAQJXSuHHjZXoJ9/3334+rr746lU4AAAAAAPBLxo4dmzgcNmfYsGGGwwIAAFVKJpOJ66+/Pho2bFgwt3DhwujatWt+oCwAAAAAAFSEN954I8aMGZOYO+OMMwyHBQAAqpwePXpE+/btE3Pnn39+zJ49O5VOAABA9WB6AQAAAADA/++pp56Ke++9NzF30UUXLdNCDgAAgMrmsMMOi8MPPzwxN3jw4HjvvfdS6QQAAAAAAP/uyy+/jMsuuywxt/vuu8dpp52WSicAAICVacMNN4wrrrgiMffMM8/EPffck0onAAAAAAD4d0uWLIlOnTpFNpstmGvRokUMGTIktV4AAAArS506dWLcuHGJuZkzZ0afPn1S6QQAAFQPmWzSX1gAAAAAAGqA+fPnR4cOHeKjjz4qmNtoo41i2rRp0bBhw9S6AQAArEyffvpptGvXLn7++eeCub333ju/yWQmk0mtGwAAAAAAHHvssXHfffcVzNSuXTveeeed/PNuAACAqmjx4sWx/fbbx5QpUwrm1lprrSgtLY2mTZum1g0AAAAAAEaOHBnnn39+Yu7WW2+NU089NZVOAAAAq8JZZ50VN910U8FMbv+d8ePHR8eOHVPrBQAAVF1FFV0AAAAAAKAyGDRoUOJw2JwxY8YYDgsAAFRpG2ywQfTv3z8x9+yzz8Zdd92VSicAAAAAAMh54oknEofD5vTs2dNwWAAAoEqrU6dOlJSUJOa+/vrr6N27dyqdAAAAAAAg54svvog+ffok5vbYY4845ZRTUukEAACwqlx99dXRokWLgplsNhudO3eOJUuWpNYLAACoujLZ3F0EAAAAAEANNmPGjNh6661j8eLFBXPHHHPMMm1ACQAAUNnlFpvvsMMO8c477xTMtWzZMkpLS6NZs2apdQMAAAAAoGaaN29edOjQIT7++OOCuY033jimTp0aDRs2TK0bAADAqtKpU6e44YYbCmYymUy89tpr8Zvf/Ca1XgAAAAAA1Fy5PXbuv//+gpk6derk31Ft27Ztar0AAABWlT//+c9x6qmnJuZGjBgR559/fiqdAACAqquoogsAAAAAAFSkbDYbXbp0SRwO26RJk7juuutS6wUAALAq1a5dO0pKSvKbRxYya9asuPTSS1PrBQAAAABAzTVw4MDE4bA5Y8eONRwWAACoNgYPHhxrrrlm4nsPuUGyS5YsSa0XAAAAAAA10+OPP544HDbn4osvNhwWAACoNk4++eTYc889E3N9+/aNzz//PJVOAABA1WVALAAAAABQo/35z3+OF154YZk2oFx33XVT6QQAAJCGnXbaKb9xZJJx48bFa6+9lkonAAAAAABqpmnTpsWwYcMSc7///e/jwAMPTKUTAABAGpo3bx7XXnttYu6dd96JUaNGpdIJAAAAAICaad68eXHOOeck5jbZZJO47LLLUukEAACQhkwmE8XFxVGnTp2Cublz58Z5552XWi8AAKBqymSz2WxFlwAAAAAAqAjfffddtGnTJr799tuCue222y4mTJgQtWrVSq0bAABAGn744Yf8fdGsWbMK5rbccst46623EhexAwAAAADA8iorK4s999wzXnrppYK5Jk2aRGlpaayzzjqpdQMAAEhDbuuXfffdN5599tmCuUaNGsX06dNjgw02SK0bAAAAAAA1R69evWLIkCGJuSeffDIOOOCAVDoBAACkqW/fvjFw4MDE3KOPPhqHHHJIKp0AAICqp6iiCwAAAAAAVJRLLrkkcThsUVFRjBs3znBYAACgWmrWrFmMGDEiMTdlypQYOXJkKp0AAAAAAKhZbrvttsThsDlXXXWV4bAAAEC1lMlkYuzYsVG3bt2CuZ9//jnOO++81HoBAAAAAFBzTJ06Na655prE3HHHHWc4LAAAUG317t07Nt1008TcOeeck1/LAwAA8Esy2Ww2+4vfAAAAAABUY7lNJX/7298m5s4999wYNWpUKp0AAAAqQm7pyP777x/PPPNMwVzDhg1j+vTpseGGG6bWDQAAAACA6u3bb7+NNm3axHfffVcwt/3228frr78etWrVSq0bAABA2q644oro379/Yu7hhx+Oww47LJVOAAAAAABUf2VlZfl9eF555ZWCudVWWy1KS0tj7bXXTq0bAABA2p5++uk44IADEnMXX3xxDBkyJJVOAABA1WJALAAAAABQ4yxatCi22267mDZtWsFcbjH6jBkzYvXVV0+tGwAAQEV4//33Y8stt4yFCxcWzOU2lsxtMAkAAAAAACvDH//4x7jlllsKZoqKimLChAn5IbEAAADV2YIFC2KrrbbKr+UpZP3114/p06dH48aNU+sGAAAAAED19ac//SnOPPPMxNzo0aOjW7duqXQCAACoSMcff3zcc889BTO1a9eOiRMn5vfsAQAA+HdF//EbAAAAAEANcO211yYOh80ZOXKk4bAAAECNsPnmm0fv3r0Tc4888kg89NBDqXQCAAAAAKB6e/HFFxOHw+acc845hsMCAAA1Qv369WPs2LGJuc8++yz69++fSicAAAAAAKq3b775Ji6++OLE3I477hidO3dOpRMAAEBFGzFiROJepEuWLMnfJ5WVlaXWCwAAqBoy2Ww2W9ElAAAAAADS8vHHH0f79u1j/vz5BXMHHnhg/PWvf41MJpNaNwAAgIq0cOHC2GqrreK9994rmFtvvfVixowZ0bhx49S6AQAAAABQvSxatCi22Wab/PPmQtZZZ518ZrXVVkutGwAAQEU78cQT46677iqYqVWrVrz11lux9dZbp9YLAAAAAIDq57TTTovbbrutYKaoqCjeeOON2G677VLrBQAAUNHGjh0b3bp1S8zdeOONceaZZ6bSCQAAqBqKKroAAAAAAEBastlsnHPOOYnDYevXrx9jxowxHBYAAKhR6tWrF8XFxYm5zz//PC6//PJUOgEAAAAAUD0NHz48cThszsiRIw2HBQAAapxrr702Vl999YKZpUuXRufOnaOsrCy1XgAAAAAAVC/PP/984nDYnO7duxsOCwAA1DidOnWKjh07JuYuvvjimDVrViqdAACAqsGAWAAAAACgxnjggQfir3/9a2Kub9++sckmm6TSCQAAoDLZe++946STTlqmDfknTZqUSicAAAAAAKqXDz/8MK688srE3EEHHRRHH310Kp0AAAAqk7XWWiuuvvrqxNz48ePjxhtvTKUTAAAAAADVy8KFC6Nz586JuXXXXTcGDBiQSicAAIDKpFatWlFSUhJFRYVHO/3www9x0UUXpdYLAACo/DLZbDZb0SUAAAAAAFa1n376Kdq2bRtffvllwVy7du3i7bffjrp166bWDQAAoDKZNWtWtGnTJr/4vJCOHTvGq6++ml/MDgAAAAAAyyL3OmNu8OuTTz5ZMNegQYOYNm1abLzxxql1AwAAqEzKyspi1113zQ+BLaRp06ZRWlqaHyoLAAAAAADLauDAgdG3b9/E3P333x9HHXVUKp0AAAAqox49esSIESMSc88++2zstddeqXQCAAAqt6KKLgAAAAAAkIbcgvSk4bA5xcXFhsMCAAA1WsuWLWPIkCGJuQkTJsQNN9yQSicAAAAAAKqH++67L3E4bE6/fv0MhwUAAGq0oqKiKCkpiVq1ahXMzZ49Oy688MLUegEAAAAAUPV98MEH+QGxSQ4++OA48sgjU+kEAABQWfXv3z/WW2+9xFznzp1j4cKFqXQCAAAqt0w2m81WdAkAAAAAgFXprbfeio4dO0ZZWVnB3Omnnx4333xzar0AAAAqq9z90+677x6vvvpqwdzqq68epaWl0apVq9S6AQAAAABQNf3444/Rtm3b+Oqrrwrm2rVrF2+//XbUrVs3tW4AAACV1UUXXRTXXHNNYu6ZZ56JffbZJ5VOAAAAAABUXbntyA888MB4+umnC+YaNGgQ06dPj4022ii1bgAAAJXVgw8+GEcddVRibsCAAdG3b99UOgEAAJWXAbEAAAAAQLW2dOnS2GmnneLNN98smFtjjTXyQ41atGiRWjcAAIDKbMqUKbHtttvm76sKOf744+Ouu+5KrRcAAAAAAFXTueeeG6NHj07Mvfjii7H77run0gkAAKCymzt3brRr1y4+++yzgrnNN988Jk+eHPXr10+tGwAAAAAAVc8999yTfy80yZAhQ+Liiy9OpRMAAEBllxvtdPjhh8ejjz5aMFevXr38nj25tTwAAEDNVVTRBQAAAAAAVqXi4uLE4bA5w4YNMxwWAADg32y55ZbRo0ePxNzdd98df/vb31LpBAAAAABA1ZRbvzNmzJjE3B//+EfDYQEAAP5N48aN4/rrr0/Mvf/++/nN+gEAAAAA4NfMnj07LrjggsRchw4dlikHAABQU2QymfwanoYNGxbMLVy4MLp165YfKAsAANRcmay7AgAAAACgmvryyy+jTZs2MWfOnIK53/72t/H888/nF10AAADwf37++edo165dfPrppwVzm222WUyZMiXq16+fWjcAAAAAAKqGJUuWxG9+85uYOHFiwdwaa6wR7777bv5fAAAA/tMRRxwRDz/8cMFM3bp182t4tthii9R6AQAAAABQdeSGFI0dOzYx9/LLL8euu+6aSicAAICqZNiwYXHxxRcn5u666644/vjjU+kEAABUPkUVXQAAAAAAYFW54IILEofD1qlTJ4qLiw2HBQAA+AWNGjWK0aNHJ+Y++OCDGDx4cCqdAAAAAACoWsaMGZM4HDZn+PDhhsMCAAD8ilGjRuXX8hSyaNGi6NKlS2Sz2dR6AQAAAABQNUyYMCG/x06SM88803BYAACAX3H++efHlltuuUx7oc6ePTuVTgAAQOVjQCwAAAAAUC09+eST8Ze//CUx17Nnz2jXrl0qnQAAAKqiQw89NI488sjE3NVXXx3vvvtuKp0AAAAAAKgavvjii+jTp09ibo899ohTTz01lU4AAABV0QYbbBD9+/dPzD377LNx1113pdIJAAAAAICqYcmSJdGpU6fIZrMFcy1atIghQ4ak1gsAAKCqqVOnTpSUlCTmvv766+jdu3cqnQAAgMonk036qwwAAAAAQBUzf/78aN++fXz88ccFcxtvvHFMnTo1GjZsmFo3AACAquizzz6Ldu3axdy5cwvm9txzz/wmk5lMJrVuAAAAAABUXsccc0zcf//9iRukvPPOO9G2bdvUegEAAFRFixcvjh122CEmT55cMNeyZcsoLS2NZs2apdYNAAAAAIDK67rrrosLLrggMXfbbbfFKaeckkonAACAquzss8+OG2+8sWAmt//Oa6+9Fr/5zW9S6wUAAFQORRVdAAAAAABgZRs4cGDicNicsWPHGg4LAACwDNZff/0YMGBAYu7555+PO+64I5VOAAAAAABUbo8//njicNiciy++2HBYAACAZVCnTp0YN25cfvPIQmbNmhWXXnppar0AAAAAAKi8Pv/88+jbt29ibq+99oqTTz45lU4AAABV3dVXXx1rrrlmwUw2m41OnTrFkiVLUusFAABUDpls7o4AAAAAAKCamD59emyzzTaxePHigrljjz027r333tR6AQAAVHW5xeY77rhjTJo0qWAut3i9tLQ0mjdvnlo3AAAAAAAql3nz5kX79u3jk08+KZjbZJNNYurUqdGgQYPUugEAAFR1Xbp0iZKSksTcq6++GjvvvHMqnQAAAAAAqJyOOuqoePDBBwtm6tatG5MnT47WrVun1gsAAKCqu+OOO+Lkk09OzF1zzTXRo0ePVDoBAACVQ1FFFwAAAAAAWFmy2Wx07tw5cTjsaqutFiNGjEitFwAAQHVQu3btGDduXGQymYK5b775Jnr16pVaLwAAAAAAKp8rr7wycThsztixYw2HBQAAWE6DBg2Kli1bJuY6deqU+H4FAAAAAADV16OPPpo4HDbnkksuMRwWAABgOZ144omx9957J+b69esXn332WSqdAACAysGAWAAAAACg2rj11lvjpZdeSsxdddVVsc4666TSCQAAoDrp2LFjdOnSJTF34403xiuvvJJKJwAAAAAAKpepU6fG8OHDE3PHHXdcHHDAAal0AgAAqE6aNWsWI0aMSMxNmTIlRo4cmUonAAAAAAAql59//jnOOeecxNymm24avXv3TqUTAABAdZLJZGLs2LFRt27dxPuz7t27p9YLAACoeJlsNput6BIAAAAAAOX17bffRps2beK7774rmNthhx1i/PjxUatWrdS6AQAAVCezZ8+Otm3bxsyZMwvmOnToEBMnTow6deqk1g0AAAAAgIpVVlYWe+yxR7z88ssFc6uttlqUlpbG2muvnVo3AACA6iS3Xcz+++8fzzzzTMFcw4YNY8aMGbHBBhuk1g0AAAAAgIp3ySWXxNChQxNzTz/9dOy3336pdAIAAKiOLr/88hgwYEBi7uGHH47DDjsslU4AAEDFKqrg6wMAAAAArBQXX3xx4nDYoqKiKCkpMRwWAACgHJo2bRojRoxIzE2dOnWZcgAAAAAAVB+33HJL4nDYnEGDBhkOCwAAUA6ZTCbGjh0b9erVK5ibN29enHvuuan1AgAAAACg4k2ZMiWuvfbaxNzxxx9vOCwAAEA5XXrppbHZZpsl5nJreH7++edUOgEAABUrk81msxXcAQAAAACgXF588cXYY489EnPdu3ePkSNHptIJAACgOsstNznggAPib3/7W8FcgwYNYvr06bHRRhul1g0AAAAAgIrxzTffRJs2beL7778vmNthhx1i/PjxUatWrdS6AQAAVFf9+/ePK664IjH34IMPxhFHHJFKJwAAAAAAKk5ZWVnsvvvu8eqrrxbMrb766lFaWhqtWrVKrRsAAEB19cwzz8R+++2XmLvoooti2LBhqXQCAAAqjgGxAAAAAECVtmjRothmm21ixowZBXPrrLNOPrPaaqul1g0AAKA6++CDD6JDhw6xcOHCgrlDDjkkHnnkkchkMql1AwAAAAAgfaeddlrcdtttBTNFRUXxxhtvxHbbbZdaLwAAgOost3Znq622ivfee69gbr311su/U9G4cePUugEAAAAAkL4bb7wxzj777MTc2LFjo0uXLql0AgAAqAlOPPHEuOuuuwpmatWqFRMnTsyv9wEAAKqvooouAAAAAABQHsOHD08cDpszatQow2EBAABWos022yz69OmTmHvsscfiwQcfTKUTAAAAAAAV4/nnn08cDpvTvXt3w2EBAABWonr16kVxcXFi7vPPP4/LL788lU4AAAAAAFSMWbNmxSWXXJKY69ix4zINkQUAAGDZXXPNNbH66qsXzCxdujQ6deoUZWVlqfUCAADSl8lms9kKuC4AAAAAQLl99NFH0b59+1iwYEHB3EEHHZQfSJTJZFLrBgAAUBMsXLgwtt5663j33XcL5tZdd92YMWNGNGnSJLVuAAAAAACkw7NiAACAinfyySfHHXfcUTBTq1atePPNN2ObbbZJrRcAAAAAAOk55ZRT4vbbby+Y8awYAABg1SkpKYkuXbok5saNGxdnn312Kp0AAID0FVXANQEAAAAAyi2bzUa3bt0Sh8M2aNAgRo8ebTgsAADAKlCvXr38wvQkX3zxRfTr1y+VTgAAAAAApGvYsGGJw2FzRo0aZTgsAADAKnLNNddE06ZNC2aWLl0anTt3zv8LAAAAAED18uyzzyYOh80577zzDIcFAABYRXJDX3faaafE3CWXXBJff/11Kp0AAID0GRALAAAAAFRJ//M//xNPPvlkYi43gGjjjTdOpRMAAEBNtOeee8Ypp5yyTBv/v/3226l0AgAAAAAgHR988EEMHDgwMXfwwQfHkUcemUonAACAmqhly5YxZMiQxNzrr78eN954YyqdAAAAAABIx8KFC6NLly6JufXWWy/69++fSicAAICaqKioKEpKSqJWrVoFc7Nnz46LLrootV4AAEC6MtlsNpvyNQEAAAAAyuXHH3+Mtm3bxldffVUw1759+/zwoTp16qTWDQAAoCb65ptvonXr1vHDDz8UzO24447x2muvJS5iBwAAAACg8su9mnjggQfG008/XTDXoEGDmD59emy00UapdQMAAKiJysrKYrfddsuvzylk9dVXj9LS0mjVqlVq3QAAAAAAWHUGDBgQl19+eWLuwQcfjCOOOCKVTgAAADVZbvjrNddck5h75plnYp999kmlEwAAkJ6iFK8FAAAAALBS9O3bN3E4bE5JSYnhsAAAAClYc801Y+jQoYm5N954I8aNG5dKJwAAAAAAVq177703cThsTm7zScNhAQAAVr2ioqL8exS1atUqmPvxxx+jR48eqfUCAAAAAGDVef/992PQoEGJuUMPPTQOP/zwVDoBAADUdFdccUWsv/76ibkuXbrEggULUukEAACkJ5PNZrMpXg8AAAAAoFzefPPN6NixYyQ92jzjjDPipptuSq0XAABATVdWVha//e1v45VXXimYW2211aK0tDTWXnvt1LoBAAAAALByzZ49O9q2bRszZ84smOvQoUNMnDgx6tSpk1o3AACAmu7iiy+OYcOGJeaefvrp2G+//VLpBAAAAADAypfbf2f//fePZ555pmCuYcOGMX369Nhwww1T6wYAAFDTPfzww3HEEUcs0zDZyy+/PJVOAABAOgyIBQAAAACqjKVLl+aHw+Y2jSykRYsW+WFDa6yxRmrdAAAAiJg6dWpsu+22sWTJkoK54447Lu65557UegEAAAAAsHJ169Ytxo4dm5h7+eWXY9ddd02lEwAAAP/r559/jnbt2sWnn35aMLfZZpvFlClTon79+ql1AwAAAABg5bn77rvjhBNOSMwNHTo0evbsmUonAAAA/s/hhx8ejzzySMFM3bp182t4tthii9R6AQAAq1bRKj4/AAAAAMBKM2bMmMThsDnDhw83HBYAAKACdOjQIS688MLE3L333htPPfVUKp0AAAAAAFi5JkyYEMXFxYm5M88803BYAACACtCoUaMYPXp0Yu6DDz6IwYMHp9IJAAAAAICVa/bs2XHBBRck5rbccss4//zzU+kEAADAf7r++uvza3kKWbRoUXTt2jWy2WxqvQAAgFUrk/X/8AEAAACAKuCLL76Itm3bxpw5cwrm9thjj3juuecik8mk1g0AAID/M2/evGjfvn188sknBXObbLJJTJ06NRo0aJBaNwAAAAAAymfJkiWx4447xqRJkwrmWrRoEe+++240b948tW4AAAD8p6OOOioefPDBgpm6devG5MmTo3Xr1qn1AgAAAACg/Lp06RIlJSWJuVdffTV23nnnVDoBAADw36655pq46KKLEnN33HFHnHjiial0AgAAVq2iVXx+AAAAAICV4vzzz08cDlunTp38wnXDYQEAACpOw4YNY/To0Ym5jz76KAYNGpRKJwAAAAAAVo7c89+k4bD/3MDEcFgAAICKNXLkyGjcuHHBzKJFi/JDBLLZbGq9AAAAAAAon/Hjx8e4ceMSc2effbbhsAAAABWse/fusdVWWyXmevToET/88EMqnQAAgFXLgFgAAAAAoNL761//Gv/zP/+TmLvkkkuiTZs2qXQCAADg1x188MFx9NFHJ+aGDBkSM2bMSKUTAAAAAADl8/nnn0ffvn0Tc3vuuWecfPLJqXQCAADg162//voxYMCAxNxzzz0Xd9xxRyqdAAAAAAAonyVLlkTnzp0jm80WzK255ppx9dVXp9YLAACAX1anTp0YN25cZDKZgrlZs2bFpZdemlovAABg1clkk/6SAwAAAABQgebNmxft27ePTz75pGBu0003jSlTpkSDBg1S6wYAAEDhQQFt27aNuXPnFsztscce+U0mkxaxAwAAAABQsY4++uh44IEHEjcumTx5crRp0ya1XgAAABQeFLDjjjvGpEmTEgcFlJaWRvPmzVPrBgAAAADA8rv22mvjwgsvTMzdfvvtcdJJJ6XSCQAAgGSdO3fOD4pN8uqrr8bOO++cSicAAGDVKFpF5wUAAAAAWCmuvPLKxOGwOWPGjDEcFgAAoBJZb731YuDAgYm5F154If785z+n0gkAAAAAgBXz2GOPJQ6HzenVq5fhsAAAAJVI7dq1o6SkJDKZTMHcN998k7+nAwAAAACg8vr000+jX79+ibm99947TjzxxFQ6AQAAsGwGDx4cLVu2XKZBsosXL06lEwAAsGpkstlsdhWdGwAAAACgXKZNmxbbbLNNLFmypGDuuOOOi3vuuSe1XgAAACyb3P1cx44d4+233y6Ya9GiRZSWlsYaa6yRWjcAAAAAAJbNzz//HO3atctvMFnIpptuGlOmTIkGDRqk1g0AAIBl07Vr1yguLk7Mvfzyy7Hrrrum0gkAAAAAgOVz5JFHxkMPPVQwU7du3Zg8eXK0bt06tV4AAAAsmzvvvDNOOumkxNywYcPioosuSqUTAACw8hWtgnMCAAAAAJRbWVlZdO7cOXE47GqrrRYjRoxIrRcAAADLrnbt2jFu3LjIZDIFc99++21ccsklqfUCAAAAAGDZDRgwIHE4bE5u0JDhsAAAAJXToEGDolWrVom53HscixcvTqUTAAAAAADL7pFHHkkcDptz6aWXGg4LAABQSZ1wwgmxzz77JOYuv/zyZXqPAwAAqJwMiAUAAAAAKqVbbrklXn755WXapGTttddOpRMAAADLb8cdd4yuXbsm5v70pz/FSy+9lEonAAAAAACWzZQpU+Laa69NzB1//PGx3377pdIJAACA5de0adMYMWJEYm7q1KnLlAMAAAAAID1z586Nc845JzG3+eabR69evVLpBAAAwPLLZDIxduzYqFu3bsHcvHnz4txzz02tFwAAsHJlstlsdiWfEwAAAACgXL755pto06ZNfP/994lDhl577bWoVatWat0AAABYfj/++GO0bds2vvrqq4K59u3bx8SJExMXsQMAAAAAsOqVlZXF7rvvHq+++mrB3Oqrrx6lpaXRqlWr1LoBAACw/HJbzBxwwAHxt7/9rWCuQYMGMX369Nhoo41S6wYAAAAAwK/r2bNnDB8+PDGXe/677777ptIJAACAFde/f/+44oorEnMPPfRQHH744al0AgAAVp6ilXguAAAAAICVtig9aThsUVFRjBs3znBYAACAKiA3HOC6665LzE2bNi2uvfbaVDoBAAAAAFDYn/70p8ThsDmDBw82HBYAAKAKyGQyMXbs2KhXr17B3Pz58+Pcc8/ND5QFAAAAAKBivfPOOzFixIjE3Iknnmg4LAAAQBXRq1ev2GKLLRJzuTU8c+fOTaUTAACw8mSyVuMDAAAAAJXI888/H3vttVdi7vzzz1+mxesAAABUDrklKr/73e/iqaeeKphr0KBBflDsxhtvnFo3AAAAAAD+06xZs6JNmzbxww8/FMx17NgxP0S2Vq1aqXUDAACgfK688sro169fYu6BBx6II488MpVOAAAAAAD8t7Kysth1111j/PjxBXNNmzaN0tLSWGuttVLrBgAAQPk8++yzsc8++yTmLrzwwhg+fHgqnQAAgJXDgFgAAAAAoNJYuHBhbLPNNvkF54Wsu+66MWPGjGjSpElq3QAAACi/Dz/8MDp06BALFiwomDvooIPisccei0wmk1o3AAAAAAD+zymnnBK33357wUxRUVG89dZb+fU+AAAAVK13N7beeut49913C+a8uwEAAAAAULHGjRsXnTt3TsyVlJREp06dUukEAADAynPyySfHHXfcUTBTq1at/LsbufU+AABA1VBU0QUAAAAAAP5p+PDhicNhc0aNGmWDEQAAgCpo0003jT59+iTm/vrXv8YDDzyQSicAAAAAAP7Tc889lzgcNue8884zHBYAAKAKqlevXhQXFyfmvvjii7j88stT6QQAAAAAwH/6+uuvo1evXom5nXbaKc4666xUOgEAALDy92Bt2rRpwczSpUujU6dOUVZWllovAACgfDLZbDZbznMAAAAAAJTbhx9+GB06dIgFCxYUzB1yyCHxyCOPRCaTSa0bAAAAK8+iRYvyAwNmzJhRMLfOOuvkM6uttlpq3QAAAAAAarqFCxfGVlttFe+9917B3HrrrRfTp0+PJk2apNYNAACAlevUU0+NP//5zwUzRUVF8eabb8a2226bWi8AAAAAACJOOumkuPPOOwtmatWqFW+99VZsvfXWqfUCAABg5brhhhvyA2CTFBcXR+fOnVPpBAAAlE9ROY8HAAAAACi3bDYbXbt2TRwO26BBg7j++usNhwUAAKjC6tatm19wnuTLL7+Mvn37ptIJAAAAAID/NWTIkMThsDmjRo0yHBYAAKCKGzZsWDRr1qxgpqysLL8B5dKlS1PrBQAAAABQ0/39739PHA6bc/755xsOCwAAUMWdeeaZsfPOOyfmevXqFTNnzkylEwAAUD4GxAIAAAAAFe4vf/lLPP3004m5K664IjbaaKNUOgEAALDq7LHHHnHaaacl5kaPHh1vvfVWKp0AAAAAAGq6999/PwYNGpSYO/TQQ+OII45IpRMAAACrTsuWLWPo0KGJuTfeeCPGjRuXSicAAAAAgJpuwYIF0aVLl8Tc+uuvn9+LBwAAgKqtqKgoSkpKolatWgVzP/74Y1x44YWp9QIAAFZcJpvNZstxPAAAAABAucyePTvatm0bM2fOLJjr0KFDTJw4MerUqZNaNwAAAFadb7/9Nlq3bh3ff/99wdwOO+wQ48ePT1zEDgAAAADAisu9Zrj//vvHM888UzDXsGHDmD59emy44YapdQMAAGDVKSsri9/+9rfxyiuvFMytttpqUVpaGmuvvXZq3QAAAAAAaqLc0Nf+/fsn5h5++OE47LDDUukEAADAqtezZ88YPnx4Yu7pp5+O/fbbL5VOAADAiilaweMAAAAAAFaKPn36JA6HzRk3bpzhsAAAANVIixYtYtiwYYm5N998M4qLi1PpBAAAAABQU91zzz2Jw2H/uQGl4bAAAADVR1FRUZSUlETt2rUL5n766afo0aNHar0AAAAAAGqi9957LwYPHpyYO/zwww2HBQAAqGZy72tssMEGibmuXbvGggULUukEAACsmEw2m82u4LEAAAAAAOUyYcKE2GmnnSLpMeVZZ50VN9xwQ2q9AAAASEdZWVnsueee8dJLLxXMNWnSJEpLS2OdddZJrRsAAAAAQE0xe/bsaNOmTXz99dcFc1tuuWW89dZbUadOndS6AQAAkI5evXrFkCFDEnNPPfVU7L///ql0AgAAAACoSXL77+y7777x7LPPFsw1atQopk+fvkxDgwAAAKhaHn300TjssMMSc/369Yv+/fun0gkAAFh+BsQCAAAAABViyZIl0bFjx3j77bcL5tZcc838EKDmzZun1g0AAID0TJs2LbbZZpv8fWIhxx57bNx7772p9QIAAAAAqCm6du0axcXFiblXXnkldtlll1Q6AQAAkK6ff/452rdvH//4xz8K5jbddNOYMmVKNGjQILVuAAAAAAA1wZ133hknnXRSYm748OFx4YUXptIJAACA9B155JHx0EMPFczUrVs3Jk+eHK1bt06tFwAAsOyKliMLAAAAALDSjB49OnE4bM4111xjOCwAAEA1lttYsmfPnom5v/zlL/Hkk0+m0gkAAAAAoKZ4/fXXo6SkJDF39tlnGw4LAABQjTVq1CjGjBmTmPvwww9j0KBBqXQCAAAAAKgpfvjhh+jRo0dibuutt47zzjsvlU4AAABUjFGjRuXX8hSyaNGi6Nq1a2Sz2dR6AQAAyy6T9f/WAQAAAICUff7559G2bduYO3duwdxee+0Vf//73yOTyaTWDQAAgPTNmzcvOnToEB9//HHB3MYbbxzTpk2LBg0apNYNAAAAAKC6WrJkSeywww7xzjvvFMytueaaUVpaGs2bN0+tGwAAABXj6KOPjgceeKBgpk6dOjF58uRo06ZNar0AAAAAAKqzzp07x7hx4wpmcvvvvPrqq7HTTjul1gsAAICKce2118aFF16YmLv99tvjpJNOSqUTAACw7IqWIwsAAAAAsFKcd955icNh69atG8XFxYbDAgAA1AANGzaM0aNHJ+ZyA2QHDhyYSicAAAAAgOpu1KhRicNh/7mxiOGwAAAANcPIkSOjcePGBTOLFy/ODyvIZrOp9QIAAAAAqK5ee+21xOGwOZ06dTIcFgAAoIbo3r17bL311om5Hj16xPfff59KJwAAYNkZEAsAAAAApOqxxx6LBx54IDF3ySWXROvWrVPpBAAAQMU76KCD4phjjknMDRs2LKZPn55KJwAAAACA6urTTz+Nfv36Jeb23nvvOPHEE1PpBAAAQMVbb7314sorr0zMvfDCC/HnP/85lU4AAAAAANXV4sWL84Nfk7Rs2TIGDRqUSicAAAAqXu3atWPcuHGRyWQK5r755pvo1atXar0AAIBlk8lms9llzAIAAAAAlMvPP/8c7du3j3/84x8Fc5tttllMmTIl6tevn1o3AAAAKt6XX34Zbdq0iTlz5hTM7b777vlNJpMWsQMAAAAA8MuOPPLIeOihhwpm6tatG5MnT47WrVun1gsAAICKt2TJkujYsWO8/fbbBXMtWrSI0tLSWGONNVLrBgAAAABQnQwfPjx69uyZmLvzzjvjhBNOSKUTAAAAlUfXrl2juLg4MffKK6/ELrvskkonAAAgmQGxAAAAAMAy+/HHH+O+++7LD29t1apVHH/88bHRRhst8/GXXHJJDB06NDH39NNPx3777VfOtgAAAFRF119/fXTv3j0xd/PNN8fpp5++zOfNbVj52GOPxbfffhu77bZbHHrooVG/fv1ytgUAAAAASN/ixYvjiSeeiL///e/RvHnz2H///WPnnXde5uMfeeSROPzwwxNzl19+eVxxxRXlbAsAAEBV9MYbb8RvfvObSNqW5owzzoibbrppmc/71Vdfxe23357/d7PNNos//OEPBswCAAAAAFVS7vnp66+/Hvfff3/Uq1cvttlmmzj66KMjk8ks0/H/+Mc/ol27djFv3ryCuX333Te/F8+ynhcAAIDqY/bs2dGmTZv4+uuvC+Y6dOgQEydOjDp16izTeefPnx+33nprfPzxx9G4ceM48cQTY9NNN11JrQEAAANiAQAAAIBlMnny5Nhrr73i+++//9dnuUE6I0eOjLPPPjvx+NxQ2e222y6WLFlSMJcbOnvXXXetlM4AAABUPUuXLs1vLvnWW28VzOU2hiwtLY0WLVoknrNXr14xdOjQ/9iwcvPNN48XXngh1l577ZXSGwAAAAAgDT/88EPss88+8fbbb//H5506dYri4uLEjSDnzp2b31jys88+K5jLPUPNrRfKrQ8CAACgZjrnnHNizJgxibmXXnopdtttt8TcPffck3//ZM6cOf/6rFGjRvnBBrvssku5+wIAAAAApKWsrCxOO+20uP322//j89yz0kceeSSaNWuWeI7DDz88ny0kN3g2t2dPbi0PAAAANdPdd98dJ5xwQmIut7dOz549E3OTJk2KI488Mj755JP/+Hzs2LHRpUuXcnUFAAD+lwGxAAAAAECi+fPnx8Ybbxxff/31f31Xu3bt/NCerbbaquCi9t133z1effXVgtdZffXV88N9WrVqtVJ6AwAAUDXl7jM7duyYv58s5PTTT4+bb765YObBBx+Mo4466he/y13j9ddfL1dXAAAAAIA0HXTQQfHEE0/84nc33XRTnHHGGQWPz232MXz48MTr/O1vf4t99913hXsCAABQ9f3444/Rpk2bmDlzZsFc+/btY+LEiVG3bt1fzXz88cfRoUOHmDdv3n99t9pqq8WHH34YLVq0WCm9AQAAAABWtauuuir69Omzwu89PvTQQ/lhPEn69+8f/fr1W+GeAAAAVH25sVIHHHBA/j2PQho2bBjTp0+PDTfc8FczCxcujO233z6mTZv2i98/++yzsddee5W7MwAA1HRFFV0AAAAAAKj8nnzyyV8cDpuzZMmSKCkpKXh8btF60nDYnMGDBxsOCwAAQH4hebdu3RJzt9xyS7z44osFM9dff/2vfjdhwoSYMWPGCnUEAAAAAEhbbiBPbh3Prxk5cmTB4ydPnhwjRoxIvM4JJ5xgOCwAAACx+uqrx3XXXZeYy20YmXS/mXuv5JeGw+b89NNP8fDDD69wTwAAAACAtAfz3HrrrQXfe5wzZ86vfj937tw499xzE6+zxRZbxCWXXLLCPQEAAKgeMplMjB07NurVq1cwl1ubc8455+TvW3/Nc88996vDYXNuv/32cnUFAAD+lwGxAAAAAECil156qeD3H3744a9+N2vWrLj44osTr/Gb3/wmOnXqtEL9AAAAqH4GDhwYa6+9dmKuc+fOsWjRol/9/qOPPip4fNKAWQAAAACAyuKVV14puFFH7nnor31fVlaWX5uzdOnSxOE/1157bbm7AgAAUD0ce+yxccABByTm+vfvHx9//PEKr+F54YUXVqgfAAAAAEDavv766/jggw8KZgo9E7388svj888/T7xOcXFx4vAfAAAAaobNNtssLrvsssTcY489Fg899NCvfm8NDwAApMOAWAAAAAAg0cyZM1f42J49e8YPP/xQMFOrVq0oKSmJoiKPLAEAAPhfq622WowcOTIxN2PGjLjmmmt+9ftCwxL++TI2AAAAAEB1WcOTyWR+8fMbb7wxxo8fn3j81VdfHWuttdYK9QMAAKD6yd1njhkzJurXr18wN3/+/DjnnHN+da2ONTwAAAAAQHVRnn14Jk2atEzvTZ500kmx9957r/B1AAAAqH4uvvjiaN26dWLu3HPPjTlz5vzid9bwAABAOkxbAAAAAADKvTD91zaFfO655+LPf/5z4vnPO++82GabbVa4HwAAANXTMcccE7/73e8ScwMGDIiPPvroF79LGmRQnpexAQAAAACqwhqe3AYdvXr1Sjz/TjvtFGefffYK9wMAAKB62nTTTaNPnz6Jub/+9a/xwAMP/OJ31vAAAAAAANXFsjzP/KVnokuXLo3OnTvn/y2kadOmcc0115SrIwAAANVPvXr1ori4ODH3xRdfxOWXX75Ca3h+/vnnmDt37gp3BAAA/pcBsQAAAABAotwmkYW0atXqvz5buHBhflF6kvXWWy/69+9frn4AAABUT5lMJkaPHh3169cvmFuwYEF069YtstnsMt2zLs89LwAAAABAVV7Dk3PhhRfG7NmzCx5bq1atKCkpiaIirxwCAADw33r27Blt27ZNzHXv3j1++umn//rcGh4AAAAAoLpIep6ZW3+z5ppr/tfnN9xwQ7z++uuJ5x8yZEi0bNmyXB0BAAConvbaa684+eSTE3MjR46Mt99+e7nX8ORYxwMAAOXnbW0AAAAAoNx/oF9rrbX+67OhQ4fGe++9l3ju66+/Pho3blyufgAAAFRfm2yySfTr1y8x9+STT8b//M//LNM967+zKB0AAAAAqM5reP7+97/HnXfemXju888/P7beeuty9QMAAKD6qlu3bhQXFyfmvvzyy19c65O0huebb76JpUuXlqsjAAAAAEBlWMPTokWLqFWr1n98NnPmzLj00ksTz73LLrvEmWeeWe6OAAAAVF/Dhw+PZs2aFcyUlZVFp06d/ms9TtIanhx78QAAQPkZEAsAAAAAFLR48eL47rvvCmb+3z/yv//++3HVVVclnvuwww6LI444otwdAQAAqN4uvPDCaNeuXWLuvPPOix9//HG5FqbnXqwGAAAAAKgKkp5n/r/PQxcsWBBdunRJPO/6668fV1xxRbn7AQAAUL3tsccecdpppyXmrr/++pg4ceJyreHJbUr57bfflrsjAAAAAEBlW8OT06NHj/969/H/lRsqW1JSEkVFtgwHAADg17Vs2TKGDh2amHvjjTdi3Lhxyz0g1l48AABQfv7aAwAAAAAUNGvWrMRMq1at/vVzNpuNbt26xcKFCwse07Bhwxg1atRK6QgAAED1Vrdu3fyLzUm++uqr6Nu376/es/6Sr7/+utz9AAAAAADSkPQ88/99HjpkyJB4//33l2lwT+PGjcvdDwAAgOpv2LBh0bx588Rhr506dYqlS5cu8xqeHOt4AAAAAIDquIbnb3/7W9x9992J580Nkd1yyy3L3Q8AAIDq749//GPsuuuuiblLL730Pwa+NmnSJBo0aFDwGGt4AACg/AyIBQAAAADK/cf5tdZa618/33PPPfmF6Un69+8fG264Ybn7AQAAUDPsvvvu+cXpSUaPHh1vvvnmL96z/pK5c+fGzz//vFI6AgAAAACsKtlsNnEdz78/D33vvfdi0KBBiec9/PDD8/8BAACAZdGiRYv8kNgkufU7xcXFy7yGJ8fmkgAAAABAVbA8a3gWLFgQXbt2TTznBhtsEJdffvlK6QcAAED1V1RUlF+bU7t27YK5n376KS644IJ//Z7JZBLX8VjDAwAA5WdALAAAAABQ0MyZMxMz//wD/+zZs//jj/+/Zsstt4zzzjtvpfQDAACg5hgyZEisscYaiUMSOnXqFEuXLs3/bnNJAAAAAKA6yG3KkdswspB/Pg/NPSfNbSy5aNGigvlGjRrFqFGjVmpPAAAAqr/TTjstdtttt8Rc796948svv8z/vOaaa66U91cAAAAAACpa0rPMf3+ncfDgwfHBBx8knnP06NH5tTwAAACwrHL7uvbo0SMxd88998TTTz/9r9+T9uKxhgcAAMrPgFgAAAAAoFxDcmrVqvWv4Ty5zTuWZajOuHHjok6dOiutIwAAADVDixYtYvjw4Ym5iRMnxpgxY/I/t2rVKjFvQCwAAAAAUNkty3PMfz4Pveuuu+Lvf/97Yr5///6xwQYbrJR+AAAA1BxFRUVRUlIStWvXLpibM2dOXHDBBfmfc++Q5Nb+FGINDwAAAABQFSQ9y/znGp533303rr766sT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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "\n", "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(16, 8), dpi=600)\n", "tree.plot_tree(\n", " class_models[\"decision_tree\"][\"fitted\"],\n", " feature_names=X_train.columns.values.tolist(),\n", " filled=True,\n", " max_depth=2,\n", ")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В левом узле 629167 наблюдений отнесены к классу 0 (без опозданий)\n", "\n", "Для данного листа можно сформулировать следующее правило: Если DEP_DELAY <= 15.5 Тогда 0\n", "\n", "Аналогичное правило можно наблюдать в множестве правил, который можно извлечь из дерева решений:\\\n", "IF DEP_DELAY <= 20.5 AND DEP_DELAY <= 15.5 THEN class: 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Вывод правил из модели дерева решений" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "|--- DEP_DELAY <= 20.50\n", "| |--- DEP_DELAY <= 15.50\n", "| | |--- class: 0\n", "| |--- DEP_DELAY > 15.50\n", "| | |--- PCT_ELAPSED_TIME <= 0.99\n", "| | | |--- PCT_ELAPSED_TIME <= 0.98\n", "| | | | |--- PCT_ELAPSED_TIME <= 0.96\n", "| | | | | |--- CRS_ELAPSED_TIME <= 65.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 0.94\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- PCT_ELAPSED_TIME > 0.94\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- CRS_ELAPSED_TIME > 65.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 0.95\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- PCT_ELAPSED_TIME > 0.95\n", "| | | | | | | |--- class: 0\n", "| | | | |--- PCT_ELAPSED_TIME > 0.96\n", "| | | | | |--- CRS_ELAPSED_TIME <= 140.50\n", "| | | | | | |--- DEP_DELAY <= 18.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 18.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- CRS_ELAPSED_TIME > 140.50\n", "| | | | | | |--- DEP_DELAY <= 19.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 19.50\n", "| | | | | | | |--- class: 0\n", "| | | |--- PCT_ELAPSED_TIME > 0.98\n", "| | | | |--- DEP_DELAY <= 18.50\n", "| | | | | |--- DISTANCE <= 326.50\n", "| | | | | | |--- LATE_AIRCRAFT_DELAY <= 0.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- LATE_AIRCRAFT_DELAY > 0.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- DISTANCE > 326.50\n", "| | | | | | |--- DEP_DELAY <= 17.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 17.50\n", "| | | | | | | |--- class: 0\n", "| | | | |--- DEP_DELAY > 18.50\n", "| | | | | |--- LATE_AIRCRAFT_DELAY <= 1.50\n", "| | | | | | |--- DISTANCE <= 1358.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- DISTANCE > 1358.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- LATE_AIRCRAFT_DELAY > 1.50\n", "| | | | | | |--- class: 0\n", "| | |--- PCT_ELAPSED_TIME > 0.99\n", "| | | |--- LATE_AIRCRAFT_DELAY <= 1.50\n", "| | | | |--- WEATHER_DELAY <= 2.00\n", "| | | | | |--- NAS_DELAY <= 17.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 1.00\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- PCT_ELAPSED_TIME > 1.00\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- NAS_DELAY > 17.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 1.09\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- PCT_ELAPSED_TIME > 1.09\n", "| | | | | | | |--- class: 1\n", "| | | | |--- WEATHER_DELAY > 2.00\n", "| | | | | |--- class: 0\n", "| | | |--- LATE_AIRCRAFT_DELAY > 1.50\n", "| | | | |--- LATE_AIRCRAFT_DELAY <= 3.50\n", "| | | | | |--- DEP_DELAY <= 18.50\n", "| | | | | | |--- DISTANCE <= 153.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- DISTANCE > 153.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- DEP_DELAY > 18.50\n", "| | | | | | |--- WEATHER_DELAY <= 2.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- WEATHER_DELAY > 2.50\n", "| | | | | | | |--- class: 0\n", "| | | | |--- LATE_AIRCRAFT_DELAY > 3.50\n", "| | | | | |--- LATE_AIRCRAFT_DELAY <= 4.50\n", "| | | | | | |--- DEP_DELAY <= 19.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 19.50\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- LATE_AIRCRAFT_DELAY > 4.50\n", "| | | | | | |--- class: 0\n", "|--- DEP_DELAY > 20.50\n", "| |--- LATE_AIRCRAFT_DELAY <= 11.50\n", "| | |--- NAS_DELAY <= 27.50\n", "| | | |--- DEP_DELAY <= 35.50\n", "| | | | |--- PCT_ELAPSED_TIME <= 0.96\n", "| | | | | |--- DEP_DELAY <= 28.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 0.93\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- PCT_ELAPSED_TIME > 0.93\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- DEP_DELAY > 28.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 0.92\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- PCT_ELAPSED_TIME > 0.92\n", "| | | | | | | |--- class: 1\n", "| | | | |--- PCT_ELAPSED_TIME > 0.96\n", "| | | | | |--- WEATHER_DELAY <= 4.50\n", "| | | | | | |--- LATE_AIRCRAFT_DELAY <= 6.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- LATE_AIRCRAFT_DELAY > 6.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- WEATHER_DELAY > 4.50\n", "| | | | | | |--- WEATHER_DELAY <= 10.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- WEATHER_DELAY > 10.50\n", "| | | | | | | |--- class: 0\n", "| | | |--- DEP_DELAY > 35.50\n", "| | | | |--- WEATHER_DELAY <= 16.50\n", "| | | | | |--- DEP_DELAY <= 44.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 0.93\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- PCT_ELAPSED_TIME > 0.93\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- DEP_DELAY > 44.50\n", "| | | | | | |--- SECURITY_DELAY <= 20.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- SECURITY_DELAY > 20.50\n", "| | | | | | | |--- class: 0\n", "| | | | |--- WEATHER_DELAY > 16.50\n", "| | | | | |--- WEATHER_DELAY <= 23.50\n", "| | | | | | |--- DEP_DELAY <= 57.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 57.50\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- WEATHER_DELAY > 23.50\n", "| | | | | | |--- DEP_DELAY <= 88.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 88.50\n", "| | | | | | | |--- class: 0\n", "| | |--- NAS_DELAY > 27.50\n", "| | | |--- PCT_ELAPSED_TIME <= 1.11\n", "| | | | |--- NAS_DELAY <= 31.50\n", "| | | | | |--- PCT_ELAPSED_TIME <= 1.07\n", "| | | | | | |--- DEP_DELAY <= 69.00\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 69.00\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- PCT_ELAPSED_TIME > 1.07\n", "| | | | | | |--- WEATHER_DELAY <= 10.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- WEATHER_DELAY > 10.50\n", "| | | | | | | |--- class: 0\n", "| | | | |--- NAS_DELAY > 31.50\n", "| | | | | |--- DEP_DELAY <= 471.50\n", "| | | | | | |--- CRS_ELAPSED_TIME <= 420.00\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- CRS_ELAPSED_TIME > 420.00\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- DEP_DELAY > 471.50\n", "| | | | | | |--- NAS_DELAY <= 388.00\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- NAS_DELAY > 388.00\n", "| | | | | | | |--- class: 0\n", "| | | |--- PCT_ELAPSED_TIME > 1.11\n", "| | | | |--- NAS_DELAY <= 64.50\n", "| | | | | |--- WEATHER_DELAY <= 20.50\n", "| | | | | | |--- DEP_DELAY <= 43.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- DEP_DELAY > 43.50\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- WEATHER_DELAY > 20.50\n", "| | | | | | |--- WHEELS_OFF <= 36.00\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- WHEELS_OFF > 36.00\n", "| | | | | | | |--- class: 0\n", "| | | | |--- NAS_DELAY > 64.50\n", "| | | | | |--- PCT_ELAPSED_TIME <= 1.44\n", "| | | | | | |--- NAS_DELAY <= 78.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- NAS_DELAY > 78.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- PCT_ELAPSED_TIME > 1.44\n", "| | | | | | |--- NAS_DELAY <= 119.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- NAS_DELAY > 119.50\n", "| | | | | | | |--- class: 0\n", "| |--- LATE_AIRCRAFT_DELAY > 11.50\n", "| | |--- DEP_DELAY <= 75.50\n", "| | | |--- DEP_DELAY <= 41.50\n", "| | | | |--- LATE_AIRCRAFT_DELAY <= 14.50\n", "| | | | | |--- DEP_DELAY <= 29.50\n", "| | | | | | |--- DEP_DELAY <= 27.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 27.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- DEP_DELAY > 29.50\n", "| | | | | | |--- PCT_ELAPSED_TIME <= 0.97\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- PCT_ELAPSED_TIME > 0.97\n", "| | | | | | | |--- class: 1\n", "| | | | |--- LATE_AIRCRAFT_DELAY > 14.50\n", "| | | | | |--- LATE_AIRCRAFT_DELAY <= 20.50\n", "| | | | | | |--- DEP_DELAY <= 32.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 32.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- LATE_AIRCRAFT_DELAY > 20.50\n", "| | | | | | |--- DEP_DELAY <= 38.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 38.50\n", "| | | | | | | |--- class: 0\n", "| | | |--- DEP_DELAY > 41.50\n", "| | | | |--- LATE_AIRCRAFT_DELAY <= 29.50\n", "| | | | | |--- PCT_ELAPSED_TIME <= 0.94\n", "| | | | | | |--- DEP_DELAY <= 55.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 55.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- PCT_ELAPSED_TIME > 0.94\n", "| | | | | | |--- WEATHER_DELAY <= 0.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- WEATHER_DELAY > 0.50\n", "| | | | | | | |--- class: 0\n", "| | | | |--- LATE_AIRCRAFT_DELAY > 29.50\n", "| | | | | |--- LATE_AIRCRAFT_DELAY <= 38.50\n", "| | | | | | |--- DEP_DELAY <= 59.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 59.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- LATE_AIRCRAFT_DELAY > 38.50\n", "| | | | | | |--- DEP_DELAY <= 60.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 60.50\n", "| | | | | | | |--- class: 0\n", "| | |--- DEP_DELAY > 75.50\n", "| | | |--- LATE_AIRCRAFT_DELAY <= 60.50\n", "| | | | |--- WEATHER_DELAY <= 0.50\n", "| | | | | |--- NAS_DELAY <= 38.50\n", "| | | | | | |--- DEP_DELAY <= 88.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- DEP_DELAY > 88.50\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- NAS_DELAY > 38.50\n", "| | | | | | |--- TAXI_OUT <= 63.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- TAXI_OUT > 63.50\n", "| | | | | | | |--- class: 0\n", "| | | | |--- WEATHER_DELAY > 0.50\n", "| | | | | |--- WEATHER_DELAY <= 18.50\n", "| | | | | | |--- LATE_AIRCRAFT_DELAY <= 31.50\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- LATE_AIRCRAFT_DELAY > 31.50\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- WEATHER_DELAY > 18.50\n", "| | | | | | |--- DEP_AFPH <= 99.64\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_AFPH > 99.64\n", "| | | | | | | |--- class: 0\n", "| | | |--- LATE_AIRCRAFT_DELAY > 60.50\n", "| | | | |--- DEP_DELAY <= 114.50\n", "| | | | | |--- LATE_AIRCRAFT_DELAY <= 71.50\n", "| | | | | | |--- DEP_DELAY <= 95.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 95.50\n", "| | | | | | | |--- class: 1\n", "| | | | | |--- LATE_AIRCRAFT_DELAY > 71.50\n", "| | | | | | |--- DEP_DELAY <= 96.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 96.50\n", "| | | | | | | |--- class: 0\n", "| | | | |--- DEP_DELAY > 114.50\n", "| | | | | |--- LATE_AIRCRAFT_DELAY <= 98.50\n", "| | | | | | |--- WEATHER_DELAY <= 1.00\n", "| | | | | | | |--- class: 1\n", "| | | | | | |--- WEATHER_DELAY > 1.00\n", "| | | | | | | |--- class: 0\n", "| | | | | |--- LATE_AIRCRAFT_DELAY > 98.50\n", "| | | | | | |--- DEP_DELAY <= 171.50\n", "| | | | | | | |--- class: 0\n", "| | | | | | |--- DEP_DELAY > 171.50\n", "| | | | | | | |--- class: 0\n", "\n" ] } ], "source": [ "text_tree = tree.export_text(\n", " class_models[\"decision_tree\"][\"fitted\"],\n", " feature_names=X_train.columns.values.tolist(),\n", ")\n", "print(text_tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Библиотека позволяет получить важность признаков модели дерева решений" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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featureimportance
2DEP_DELAY0.527482
16LATE_AIRCRAFT_DELAY0.199153
8PCT_ELAPSED_TIME0.105381
13WEATHER_DELAY0.101649
14NAS_DELAY0.062732
15SECURITY_DELAY0.001998
9DISTANCE0.001019
7CRS_ELAPSED_TIME0.000281
5TAXI_OUT0.000239
6WHEELS_OFF0.000035
3DEP_AFPH0.000031
0CRS_DEP_TIME0.000000
19ORIGIN_HUB0.000000
18DEP_DOW0.000000
17DEP_MONTH0.000000
10CRS_ARR_TIME0.000000
12ARR_RFPH0.000000
11ARR_AFPH0.000000
1DEP_TIME0.000000
4DEP_RFPH0.000000
20DEST_HUB0.000000
\n", "
" ], "text/plain": [ " feature importance\n", "2 DEP_DELAY 0.527482\n", "16 LATE_AIRCRAFT_DELAY 0.199153\n", "8 PCT_ELAPSED_TIME 0.105381\n", "13 WEATHER_DELAY 0.101649\n", "14 NAS_DELAY 0.062732\n", "15 SECURITY_DELAY 0.001998\n", "9 DISTANCE 0.001019\n", "7 CRS_ELAPSED_TIME 0.000281\n", "5 TAXI_OUT 0.000239\n", "6 WHEELS_OFF 0.000035\n", "3 DEP_AFPH 0.000031\n", "0 CRS_DEP_TIME 0.000000\n", "19 ORIGIN_HUB 0.000000\n", "18 DEP_DOW 0.000000\n", "17 DEP_MONTH 0.000000\n", "10 CRS_ARR_TIME 0.000000\n", "12 ARR_RFPH 0.000000\n", "11 ARR_AFPH 0.000000\n", "1 DEP_TIME 0.000000\n", "4 DEP_RFPH 0.000000\n", "20 DEST_HUB 0.000000" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_imp_df = pd.DataFrame(\n", " {\n", " \"feature\": X_train.columns.values.tolist(),\n", " \"importance\": class_models[\"decision_tree\"][\"fitted\"].feature_importances_,\n", " }\n", ").sort_values(by=\"importance\", ascending=False)\n", "dt_imp_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "k ближайших соседей\n", "\n", "Интерпретировать модель на основе алгоритма k ближайших соседей можно только локально: на уровне отдельных наблюдений\n", "\n", "Например, используем наблюдение с id = 721043 из тестовой выборки\n", "\n", "Значения целевого признака для случая 721043:\n", "- Фактическое ($y$): 1\n", "- Предсказанное ($\\hat{y}$): 0" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CRS_DEP_TIME 655.000000\n", "DEP_TIME 1055.000000\n", "DEP_DELAY 240.000000\n", "DEP_AFPH 90.800000\n", "DEP_RFPH 0.890196\n", "TAXI_OUT 35.000000\n", "WHEELS_OFF 1130.000000\n", "CRS_ELAPSED_TIME 259.000000\n", "PCT_ELAPSED_TIME 1.084942\n", "DISTANCE 1660.000000\n", "CRS_ARR_TIME 914.000000\n", "ARR_AFPH 40.434783\n", "ARR_RFPH 1.064073\n", "WEATHER_DELAY 0.000000\n", "NAS_DELAY 22.000000\n", "SECURITY_DELAY 0.000000\n", "LATE_AIRCRAFT_DELAY 221.000000\n", "DEP_MONTH 10.000000\n", "DEP_DOW 4.000000\n", "ORIGIN_HUB 1.000000\n", "DEST_HUB 0.000000\n", "Name: 721043, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "1" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(X_test.loc[721043, :])\n", "display(y_test_class[721043])\n", "display(class_models[\"knn\"][\"preds\"][X_test.index.get_loc(721043)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Библиотека scikit-learn позволяет получить информацию об указанном количестве соседей (7) для некоторого элемента из тестовой выборки (id = 721043)\n", "\n", "Первый элемент кортежа – расстояние от соседей в обучающей выборке до текущей точки в тестовой выборке\n", "\n", "Второй элемент – идентификаторы соседей в тестовой выборке" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[143.3160128 , 173.90740076, 192.66705727, 211.57109221,\n", " 243.57211853, 259.61593993, 259.77507391]]),\n", " array([[105172, 571912, 73409, 89450, 77474, 705972, 706911]]))" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_models[\"knn\"][\"fitted\"].kneighbors(\n", " X_test.loc[721043, :].values.reshape(1, 21), 7\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Данные о целевом признаке CARRIER_DELAY соседей для случая 721043 из обучающей выборки" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3813 0\n", "229062 1\n", "283316 0\n", "385831 0\n", "581905 1\n", "726784 1\n", "179364 0\n", "Name: CARRIER_DELAY, dtype: int64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train_class.iloc[[105172, 571912, 73409, 89450, 77474, 705972, 706911]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Можно сделать вывод, что значение $\\hat{y}$ для случая 721043 было определено как мода от значений целевой переменной соседей.\n", "\n", "Мода – одно или несколько значений во множестве наблюдений, которое встречается наиболее часто." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Наивный байесовский классификатор\n", "\n", "Алгоритм обучения модели на основе наивного байесовского классификатора вычисляет априорные вероятности принадлежности наблюдения некоторому классу\n", "\n", "$\\hat{y} = P(y=1|X) = P(y=1) \\prod^{n}_{i=1} P(x_{i} | y=1)$\n", "\n", "$P(x_{i} | y=1) = \\frac{ 1 }{ \\sqrt{2 \\pi \\sigma_{i}^{2}}} e^{\\frac{ (x_{i} \\theta_{i})^2 }{ 2 \\sigma^{2}_{i} }}$\n", "\n", "Библиотека scikit-learn позволяет получить вероятности $P(y=0|X)$ и $P(y=1|X)$" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.93871674, 0.06128326])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_models[\"naive_bayes\"][\"fitted\"].class_prior_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Вероятность принадлежности классу 1 соответствует показателю сбалансированности выборки " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для получения значения дисперсии $\\sigma_{i}$ и среднего значения $\\theta_{i}$ признаков в одном классе по сравнению с другим используются атрибуты var_ и theta_ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Дисперсия" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2.50123026e+05, 2.61324730e+05, 9.21572605e+02, 1.26123968e+03,\n", " 2.08339528e-01, 9.58074414e+01, 2.62606651e+05, 6.30102550e+03,\n", " 1.13475535e-02, 4.22470414e+05, 2.75433641e+05, 1.25314386e+03,\n", " 3.48655340e-01, 1.11234714e+02, 1.91877186e+02, 2.80302201e+00,\n", " 5.06561612e+02, 1.17346654e+01, 3.99122491e+00, 2.39015406e-01,\n", " 2.34996222e-01],\n", " [2.60629652e+05, 2.96009867e+05, 1.19307931e+04, 1.14839167e+03,\n", " 1.99929921e+00, 1.20404927e+02, 3.08568277e+05, 6.29066219e+03,\n", " 1.38936741e-02, 4.10198938e+05, 3.28574000e+05, 1.09023147e+03,\n", " 3.08997044e+00, 7.79140423e+01, 1.56184090e+02, 9.12112286e-01,\n", " 2.11279954e+03, 1.02712368e+01, 4.02943162e+00, 1.77750796e-01,\n", " 2.50208354e-01]])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_models[\"naive_bayes\"][\"fitted\"].var_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Средние значения" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.30740577e+03, 1.31006271e+03, 5.14196506e+00, 5.45864877e+01,\n", " 1.09377996e+00, 1.87120810e+01, 1.33552258e+03, 1.70734929e+02,\n", " 9.71131781e-01, 1.01824369e+03, 1.48438931e+03, 5.39873058e+01,\n", " 1.09644787e+00, 7.39971299e-01, 2.85434558e+00, 2.41814585e-02,\n", " 4.14674395e+00, 6.55045281e+00, 2.95035528e+00, 6.06800513e-01,\n", " 6.24199571e-01],\n", " [1.41305545e+03, 1.48087887e+03, 8.45867640e+01, 6.14731036e+01,\n", " 1.25429654e+00, 1.99378321e+01, 1.49409412e+03, 1.72229998e+02,\n", " 9.83974416e-01, 1.04363666e+03, 1.54821862e+03, 4.26486417e+01,\n", " 1.36373798e+00, 4.50733082e-01, 4.71991378e+00, 2.11281132e-02,\n", " 1.40744819e+01, 6.73367907e+00, 3.04251232e+00, 7.69575517e-01,\n", " 4.85391724e-01]])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_models[\"naive_bayes\"][\"fitted\"].theta_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Эти значения можно использовать для глобальной и локальной интерпретации модели." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Результат\n", "\n", "Все алгоритмы обучения показали высокие значения метрик качества, но выявили некорректные закономерности.\n", "\n", "Как видно из результатов интерпретации моделей, значение целевого признака CARRIER_DELAY сильно зависит от признака DEP_DELAY.\n", "\n", "Зная суммарную задержку отправления в минутах (DEP_DELAY) можно с высокой степенью качества спрогнозировать задержку в минутах, вызванную обстоятельствами, находящимися в пределах контроля авиакомпании (CARRIER_DELAY).\n", "\n", "Если в производственной среде значение признака DEP_DELAY недоступно или станет доступным после применения модели, то модель будет иметь низкое качество." ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": ".venv (3.11.12)", "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": "3.11.12" } }, "nbformat": 4, "nbformat_minor": 0 }