diff --git a/README.md b/README.md index 0a33ff3..821cc68 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,4 @@ ФИО: Пучкина Анна\ Группа: ПИбд-32\ Ссылки на dataset: -1. https://www.kaggle.com/datasets/deepcontractor/car-price-prediction-challenge -2. https://www.kaggle.com/datasets/thebumpkin/14400-classic-rock-tracks-with-spotify-data -3. https://www.kaggle.com/datasets/shariful07/student-flexibility-in-online-learning \ No newline at end of file +https://www.kaggle.com/code/rustamovamalak/mobile-phone-specifications-first diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb deleted file mode 100644 index e68a077..0000000 --- a/lab_2/lab2.ipynb +++ /dev/null @@ -1,1303 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Лабораторная работа №2" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd \n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from sklearn.model_selection import train_test_split\n", - "from imblearn.over_sampling import RandomOverSampler\n", - "from imblearn.under_sampling import RandomUnderSampler\n", - "from sklearn.preprocessing import LabelEncoder\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Цены на автомобили**\n", - "https://www.kaggle.com/datasets/deepcontractor/car-price-prediction-challenge\n", - "\n", - "Этот набор данных предоставляет подробную информацию о продаже автомобилей, включая их уникальные идентификаторы, цены, сборы и налоги, а также характеристики производителя и модели. В данных представлены год производства, категория автомобиля, наличие кожаного салона, тип топлива, объем двигателя, пробег, количество цилиндров, тип коробки передач, привод, количество дверей, расположение руля, цвет и количество подушек безопасности. Эти данные могут быть использованы для анализа рынка автомобилей, прогнозирования цен на основе различных факторов, а также для изучения влияния технических и визуальных характеристик на стоимость автомобилей." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Выгрузка данных из csv файла \"Цены на автомобили\" в датафрейм" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['ID', 'Price', 'Levy', 'Manufacturer', 'Model', 'Prod. year',\n", - " 'Category', 'Leather interior', 'Fuel type', 'Engine volume', 'Mileage',\n", - " 'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel', 'Color',\n", - " 'Airbags'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "df1 = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", - "print(df1.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Преобразуем год производства в целочисленный тип\n", - "df1['Prod. year'] = df1['Prod. year'].astype(int)\n", - "\n", - "# Визуализация данных\n", - "plt.figure(figsize=(10, 6))\n", - "plt.scatter(df1['Prod. year'], df1['Price'])\n", - "plt.xlabel('Production Year')\n", - "plt.ylabel('Price')\n", - "plt.title('Scatter Plot of Price vs Production Year')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Выбросы:\n", - " ID Price Levy Manufacturer Model Prod. year \\\n", - "14 45732604 59464 891 HYUNDAI Santa FE 2016 \n", - "36 45369569 51746 1077 TOYOTA CHR Limited 2019 \n", - "47 45732544 55390 1017 HYUNDAI Santa FE 2017 \n", - "56 44316016 87112 - MERCEDES-BENZ GLA 250 2019 \n", - "73 45732043 53154 891 HYUNDAI Santa FE 2016 \n", - "... ... ... ... ... ... ... \n", - "19144 45733642 56814 1017 HYUNDAI Sonata 2017 \n", - "19161 45677230 64290 - LEXUS RX 450 F SPORT 2012 \n", - "19180 45803164 63886 1076 HYUNDAI Sonata 2020 \n", - "19188 45571892 61154 579 TOYOTA RAV 4 2017 \n", - "19211 45802856 50037 891 HYUNDAI Santa FE 2016 \n", - "\n", - " Category Leather interior Fuel type Engine volume Mileage Cylinders \\\n", - "14 Jeep Yes Diesel 2 76000 km 4.0 \n", - "36 Jeep No Petrol 2 10200 km 4.0 \n", - "47 Jeep Yes Diesel 2 100734 km 4.0 \n", - "56 Jeep Yes Petrol 2.0 Turbo 5323 km 4.0 \n", - "73 Jeep Yes Diesel 2 84506 km 4.0 \n", - "... ... ... ... ... ... ... \n", - "19144 Sedan Yes Petrol 2 67365 km 4.0 \n", - "19161 Jeep Yes Hybrid 3.5 97000 km 6.0 \n", - "19180 Sedan Yes LPG 2 5305 km 4.0 \n", - "19188 Jeep No Hybrid 2.5 71234 km 4.0 \n", - "19211 Jeep Yes Diesel 2 121902 km 4.0 \n", - "\n", - " Gear box type Drive wheels Doors Wheel Color Airbags \n", - "14 Automatic Front 04-May Left wheel White 4 \n", - "36 Tiptronic Front 04-May Left wheel Red 12 \n", - "47 Automatic Front 04-May Left wheel Black 4 \n", - "56 Tiptronic 4x4 04-May Left wheel Grey 0 \n", - "73 Automatic Front 04-May Left wheel Silver 4 \n", - "... ... ... ... ... ... ... \n", - "19144 Automatic Front 04-May Left wheel Black 4 \n", - "19161 Variator 4x4 04-May Left wheel Black 12 \n", - "19180 Automatic Front 04-May Left wheel Silver 4 \n", - "19188 Tiptronic 4x4 04-May Left wheel White 12 \n", - "19211 Automatic Front 04-May Left wheel Black 4 \n", - "\n", - "[1073 rows x 18 columns]\n" - ] - }, - { - "data": { - "image/png": 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mrgm6/fbbZV0Dcb3C+++/L5w7d044e/assGnTJiE2NlZQKBTC/v37BUGwvtanf//+QmRkpFBcXGx67ODBg4JSqRSmTZtmesyRNWQGg0GIjIwU4uPjhaqqKtPjX331lQBAWLRokekxcV2Y2EcpYtvt27cL586dE/744w/hk08+ESIiIoTAwEDh9OnTgiAIwvTp0wUAwuOPP26xj+nTp5u9bjt27DCtv2tMfJ3y8vIElUolPPfcc2bbDx06JPj5+Vk83tjtt98uREZGmq2byM/PF5RKpfDMM88IgiAIFy5cEAAIL730kt3r0Jh4vxw7dkw4d+6ckJubK7zzzjtCQECA0KFDB9PauhEjRggAhLffftvs+a+88ooAQFizZo3pMYPBICQkJAghISFCWVmZIAiC8OWXXwoAhBdffNHUrra2VkhMTLS4rxqvHRE15foLgu01ZOI9Id6XRUVFglqtFm644Qaz9/kbb7xheo807CMA4T//+Y/pscuXLwtRUVHCxIkTLY5lTV5enhAcHCyEh4cL/v7+wqFDhwRBEIRdu3YJAISPPvrIrH16errF43I/c2y9frZMmTJFiIiIsLrNYDAIERERwpNPPml67I477hD69etn1s7W2ixba8iWLVsmBAcHC7/++qvZ448//rigUqmEU6dOCYLw12dRWFiYUFRUJOt8YmJiBACS/xr209p1zcjIsHjNxXsoOTnZ7J57+OGHBZVKJVy8eFEQhL/urdTUVLN2TzzxhADA7hqyxve+eA0iIiKEkpIS0+Pr168XAAgbN240PdanTx+hc+fOQnl5uemx7777TgBg9b9DDYnv73Xr1tlsU1JSIgAQbr75ZtNjAITFixdbtG28Xs6RNWTW/ht0/fXXC3369DG7141Go3DNNdcIPXr0MD0mvk7Dhw+3WIOm1WqFOXPm2Dy/hq644gphzJgxstpS68Mpi0RNlJKSgoyMDIwfPx4HDx7Eiy++iFGjRqFTp05mUx6+/PJLGI1GLFq0CEql+VtOXP8BAIGBgab/X15ejvPnzyMxMRGVlZU4evRok/uZlZWF48eP44477kBxcTHOnz+P8+fPo6KiAtdffz127txpNqUHgM21H7bcfffdaN++PTp27IjU1FTTNC9xlKex/Px8ZGVl4c477zT7Zbpv375ISUnB5s2bHT9RAD/99BOKiopw//33m60XSE1NRc+ePZ3OPpecnIz27dujS5cumDx5MkJCQrBu3TqLX4tnz55td1//+9//oFAosHjxYott4n3xxRdfwGg0YtKkSabX7fz584iKikKPHj3w7bffSh7jtttuQ1FRkdk0ns8//xxGoxG33XYbgPr7Tq1W47vvvrOY0ibXlVdeifbt20On02HWrFno3r07Nm3aZDYSEBAQgLvuusvseZs3b0ZUVJTZ+jt/f388+OCDuHTpkmm65ebNm+Hn52d2XVUqFebOnduk/gLyrr8jtm/fDoPBgHnz5pm9z2fOnImwsDCLey8kJARTpkwx/a1WqzF48GDZ2UljYmKwePFilJSUYP78+abRzrVr10Kr1SIlJcXsnhk4cCBCQkLM7hlHPnOsvX62FBcXo23btla3bdmyBcXFxWav+e23346DBw/KnoJnzdq1a5GYmIi2bduanXdycjLq6uqwc+dOs/YTJ06UHGlvbMiQIdi2bZvFv3/+858WbRte15qaGhQXF6N79+5o06aNxVQ2oH49VcN7LjExEXV1dTh58iSAv+6tuXPnmrWbN2+e7P5bc9ttt5m9TuLMCvEePHv2LA4dOoRp06aZrXsaMWIE+vTpY3f/5eXlAIDQ0FCbbcRtLZ2VtaSkBDt27MCkSZNM9/758+dRXFyMUaNG4fjx42bT3IH693LjdaNt2rTBjz/+iLNnz9o9pnhvElnDKYtEThg0aBC++OILGAwGHDx4EOvWrcPLL7+MW265BVlZWdDr9Thx4gSUSiX0er3kvg4fPoynnnoKO3bssPiPk7VpMXIdP34cACSnd5WWlpr9h1mn0zl0jEWLFiExMREqlQrt2rVDr169bE5RAWD6onHllVdabOvVqxe2bt3q8KJpe/vt2bMndu/e7dD+GnvzzTdxxRVXwM/PDx06dMCVV15pEWT7+fmZ1hdIOXHiBDp27Cg5Ver48eMQBAE9evSwut3eYn1xzeCnn36K66+/HkD9dMX+/fvjiiuuAFD/RXvFihV45JFH0KFDBwwdOhQ33ngjpk2bhqioKLvnAdQHN2FhYfD390fnzp2tTuvr1KmTRdKEkydPokePHhbXUJx2JL6eJ0+eRHR0tMVieGuvs1xyrr8jbN17arUa3bp1M20Xde7c2SLwa9u2LX755RfZxxTXjTb84eP48eMoLS21uo4VME845MhnjrXXT4pgY6rzmjVroNPpEBAQgN9++w1A/TTQoKAgfPTRR3j++edlH6Oh48eP45dffrEZZDVOtOToZ1y7du2sromz9jlXVVWF5cuX44MPPsCZM2fMroW1z/KuXbua/S1+Fos/kIj3TuPPgfbt29sMfOWQe9zu3btbPLd79+5Wg8uGxGBLDMyskRO0NYfffvsNgiDg6aefxtNPP221TVFRkdmPbdbumRdffBHTp09Hly5dMHDgQIwdOxbTpk1Dt27dLNoKgtCkH3uodWBARuQCarUagwYNwqBBg3DFFVfgrrvuwtq1a63++m7NxYsXMWLECISFheGZZ55BXFwcNBoNMjMzsWDBAosRLEeIz33ppZfQv39/q20af9Ft+AuvHH369LG5gN+XDB482OaonyggIMAiwGgqo9EIhUKBLVu2WM3oZy9bV0BAAG666SasW7cOb731FgoLC7Fnzx6LL73z5s3DuHHj8OWXX2Lr1q14+umnsXz5cuzYscPmOqCGkpKSTOv7bHH0nmoqhUJhNRhonKjC3WxlaLQVyMhlNBoRGRmJjz76yOp2MWBx9DPHkdcvIiLC6mhrWVkZNm7ciOrqaqs/Mnz88cd47rnnmvSl1Wg0IiUlBY899pjV7eIPEKLmvB/nzp2LDz74APPmzUNCQgK0Wi0UCgUmT55s9bO8ue4Fe5r7uOIPK7/88gtuuukmq23EHyDs/WAJuPY9LL4Ojz76KEaNGmW1TeNA1No9M2nSJCQmJmLdunX4+uuv8dJLL2HFihX44osvLNaCXrhwweaPa0QMyIhcTPzCnp+fD6D+11+j0YicnBybAdF3332H4uJifPHFF0hKSjI9bi1Loa0vK7YeF0crwsLCPCZoiomJAQAcO3bMYtvRo0fRrl070+iYI1/OGu5XzPQnOnbsmGm7J4iLi8PWrVtRUlJic5QmLi4OgiBAp9NZfKGU67bbbsOHH36Ib775BkeOHIEgCKbpio2P9cgjj+CRRx7B8ePH0b9/f/zrX//CmjVrmnRcOWJiYvDLL7/AaDSaBbHidDnx9YqJicE333yDS5cumQWh1u6ftm3bWp3213iESs71B+Tffw3vvYa/jhsMBuTm5rbYey8uLg7bt2/HsGHDJIMORz5zHNWzZ0989NFHKC0thVarNT3+xRdfoLq6GitXrrQI4I8dO4annnoKe/bskczsKvU5d+nSJY/4jPv8888xffp0/Otf/zI9Vl1d3eTi9uK9dfz4cbN769y5c02eZuzIccWRzIasPdbY8OHD0aZNG3z88cd48sknrQaA//nPfwDArPB227ZtLa6VwWAw/TdV5Mxok3gd/f39nb5noqOjcf/99+P+++9HUVERBgwYgOeee84sIKutrcUff/yB8ePHO3Us8l1cQ0bURN9++63VXxLF9U/i1KWbbroJSqUSzzzzjMWvo+Lzxf9QNdyfwWDAW2+9ZbH/4OBgq9NexACm8X/IBg4ciLi4OPzzn//EpUuXLJ7XOL1yS4iOjkb//v3x4YcfmvU3OzsbX3/9NcaOHWt6zNZ5WXP11VcjMjISb7/9Ni5fvmx6fMuWLThy5AhSU1Nddg7OmjhxIgRBwNKlSy22iffBzTffDJVKhaVLl1rca4IgoLi42O5xkpOTER4ejk8//RSffvopBg8ebDb1prKyEtXV1WbPiYuLQ2hoqNk1bA5jx45FQUGBWTa52tpavP766wgJCTFlhxw7dixqa2uxcuVKU7u6ujq8/vrrFvuMi4vD0aNHze7rgwcPWpR4kHP9gfr7T869l5ycDLVajddee83s+f/+979RWlraYvfepEmTUFdXh2XLlllsq62tNZ2LI585jkpISIAgCDhw4IDZ42vWrEG3bt1w33334ZZbbjH79+ijjyIkJMTmyJ7I1usxadIkZGRkYOvWrRbbLl68iNraWqfOyREqlcri/fr66683eYQnOTkZ/v7+eP3118322zhzpKt17NgR8fHx+M9//mP2347vv/8ehw4dsvv8oKAgPProozh27JgpK2ZDmzZtwqpVqzBq1CizDItxcXEWa/7effddi+vnyH8bGouMjMS1116Ld955xyLQA+T9d7Gurs7iv8WRkZHo2LGjxWdnTk4OqqurTZmPiRrjCBlRE82dOxeVlZX429/+hp49e8JgMOCHH37Ap59+itjYWNMC+O7du+PJJ5/EsmXLkJiYiJtvvhkBAQHYv38/OnbsiOXLl+Oaa65B27ZtMX36dDz44INQKBRYvXq11YBv4MCB+PTTTzF//nwMGjQIISEhGDduHOLi4tCmTRu8/fbbCA0NRXBwMIYMGQKdTof33nsPY8aMQe/evXHXXXehU6dOOHPmDL799luEhYVh48aNLX358NJLL2HMmDFISEjAjBkzTGnvtVqtWQ2agQMHAqhP+T958mT4+/tj3LhxVteX+fv7Y8WKFbjrrrswYsQI3H777aa097GxsXj44Ydb6vTsuu666zB16lS89tprOH78OEaPHg2j0Yhdu3bhuuuuwwMPPIC4uDg8++yzWLhwIfLy8nDTTTchNDQUubm5WLduHe699148+uijksfx9/fHzTffjE8++QQVFRUWSQh+/fVXXH/99Zg0aRL0ej38/Pywbt06FBYWYvLkyc15CXDvvffinXfewZ133okDBw4gNjYWn3/+Ofbs2YNXXnnFtK5k3LhxGDZsGB5//HHk5eVBr9fjiy++sPrDxN13343/+7//w6hRozBjxgwUFRXh7bffRu/evc3WScm5/kD9/bd9+3b83//9Hzp27AidTochQ4ZYHLd9+/ZYuHAhli5ditGjR2P8+PE4duwY3nrrLQwaNMgsgUdzGjFiBGbNmoXly5cjKysLN9xwA/z9/XH8+HGsXbsWr776Km655RaHPnMcNXz4cERERGD79u2mkeqzZ8/i22+/xYMPPmj1OQEBARg1ahTWrl2L1157zea+Bw4ciJUrV+LZZ59F9+7dERkZiZEjR+If//gHNmzYgBtvvBF33nknBg4ciIqKChw6dAiff/458vLy7E6rdZUbb7wRq1evhlarhV6vR0ZGBrZv3y5ZBkRK+/bt8eijj2L58uW48cYbMXbsWPz888/YsmVLs5/T888/jwkTJmDYsGG46667cOHCBbzxxhuIj4+3+gNfY48//jh+/vlnrFixAhkZGZg4cSICAwOxe/durFmzBr169bKoO3jPPfeYCsynpKTg4MGD2Lp1q8W59u/fHyqVCitWrEBpaSkCAgIwcuRIm+snG3vzzTcxfPhw9OnTBzNnzkS3bt1QWFiIjIwMnD59GgcPHpR8fnl5OTp37oxbbrkF/fr1Q0hICLZv3479+/ebjY4CwLZt2xAUFISUlBRZfaNWqIWyORL5nC1btgh333230LNnTyEkJERQq9VC9+7dhblz5wqFhYUW7d9//33hqquuEgICAoS2bdsKI0aMELZt22bavmfPHmHo0KFCYGCg0LFjR1MafTRK63vp0iXhjjvuENq0aWORenj9+vWCXq8X/Pz8LFL8/vzzz8LNN98sRERECAEBAUJMTIwwadIk4ZtvvjG1EdOYnzt3TtY1sJWaujFrKYcFQRC2b98uDBs2TAgMDBTCwsKEcePGCTk5ORbPX7ZsmdCpUydBqVTKSoH/6aefmq51eHi48Pe//92Uml7UlLT39tpOnz5dCA4OtrmtcZro2tpa4aWXXhJ69uwpqNVqoX379sKYMWOEAwcOmLX73//+JwwfPlwIDg4WgoODhZ49ewpz5swRjh07ZrfvgiAI27ZtEwAICoVC+OOPP8y2nT9/XpgzZ47Qs2dPITg4WNBqtcKQIUOEzz77zO5+5d4vI0aMEHr37m11W2FhoXDXXXcJ7dq1E9RqtdCnTx+L+0QQBKG4uFiYOnWqEBYWJmi1WmHq1KnCzz//bPW+WrNmjdCtWzdBrVYL/fv3F7Zu3drk63/06FEhKSlJCAwMNEsx3jjtveiNN94QevbsKfj7+wsdOnQQZs+eLVy4cEHW9bDWRylS7793331XGDhwoBAYGCiEhoYKffr0ER577DHh7NmzpjZyP3OkXj9bHnzwQaF79+6mv//1r38JAMw+bxpbtWqVAEBYv369zXMrKCgQUlNThdDQUAGAWWrz8vJyYeHChUL37t0FtVottGvXTrjmmmuEf/7zn6ZyJOJnkSNlHmJiYoTU1FSr26z188KFC6Z7OiQkRBg1apRw9OhRi7Tttj5XrKWur6urE5YuXSpER0cLgYGBwrXXXitkZ2db7NORtPfWrgGspJz/5JNPhJ49ewoBAQFCfHy8sGHDBmHixIlCz549bV+0Burq6oQPPvhAGDZsmBAWFiZoNBqhd+/ewtKlS4VLly5Zbb9gwQKhXbt2QlBQkDBq1Cjht99+szhXQRCEtLQ0oVu3boJKpTI7bzlp7wVBEE6cOCFMmzZNiIqKEvz9/YVOnToJN954o/D555+b2th6nS5fviz84x//EPr16yeEhoYKwcHBQr9+/YS33nrL4pyGDBkiTJkyRdb1otZJIQjNvGqUiIiIWpXff/8dPXv2xJYtW0wZPsl39O/fH+3bt8e2bdvc3RWPl5WVhQEDBiAzM9PmOnIiriEjIiIil+rWrRtmzJiBF154wd1dISfU1NRYrL/77rvvcPDgQVx77bXu6ZSXeeGFF3DLLbcwGCNJHCEjIiIiIgt5eXlITk7GlClT0LFjRxw9ehRvv/02tFotsrOzm7wujojMMakHEREREVlo27YtBg4ciPfeew/nzp1DcHAwUlNT8cILLzAYI3IhjpARERERERG5CdeQERERERERuQkDMiIiIiIiIjfhGjIXMRqNOHv2LEJDQ6FQKNzdHSIiIiIichNBEFBeXo6OHTtCqZQeA2NA5iJnz55Fly5d3N0NIiIiIiLyEH/88Qc6d+4s2YYBmYuEhoYCqL/oYWFhbu4NERERERG5S1lZGbp06WKKEaQwIHMRcZpiWFgYAzIiIiIiIpK1lIlJPYiIiIiIiNyEARkREREREZGbMCAjIiIiIiJyEwZkREREREREbsKAjIiIiIiIyE0YkBEREREREbkJAzIiIiIiIiI3YUBGRERERETkJgzIiIiIiIiI3IQBGRERERERkZswICMiIiIiInITBmRERERERERuwoCMiIiIiIjITfzc3QEiIiIiX1ZnFLAvtwRF5dWIDNVgsC4cKqXC3d2iBgy1RqzOyMPJkkrEhAdhakIs1H7m4xZVhjo8vzkHecWViI0IwhNj9QhUq0zb5bzOpZU1uHvVPpwtrUZHrQbv3zkY2iB/h/oip6+uOM6xs+UY+/pO1AmASgFsnpuEKzuGmu2j4GI1bnx9J8qqaxGm8cNXc5MQ1Ubj8LW1d+18/T2kEARBcHcnfEFZWRm0Wi1KS0sRFhbm7u4QERGRB0jPzsfSjTnIL602PRat1WDxOD1Gx0e7sWckWr45B2m7cmFs8I1YqQBmJuqwcKweADDzP/uxLafI4rkp+kikTRsk63Ue8dIOnCyusthHTEQgvv/HSFl9kdNXucd5d2cuGgYBCgD3JtXvJ/bxTTavV94LqQCAXk9vQVWN0WJ7oL8SR5aNkXU+gP33iLe+hxyJDRiQuQgDMiIiImooPTsfs9dkovEXLfF3/ZVTBnj0F8rWYPnmHLyzM9fm9llJOvx+vsJqMCbq2zkMh06XSb7Oy7ccsRokiWIiAjG6d5RkX/p2DsMvp8sk+5p+uMDp48gR6K+0Gow13D4tIcbutb2qa1vJ98i9STqLwLHhdk9+DzEgcwMGZERERCSqMwoYvmKH2a/6DSkARGk12L1gpE9NvfImhlojej69xWz0pjEFYBEMOEIBoF2wH85V1DqxF/nHktNXZ8/JVRQAOoRpUFBm/T0C1I+m2Xp9PP095EhswKQeRERERC62L7fEZjAG1H8hzi+txr7ckpbrFJlZnZEnGYwBzgcuAtAiwZh4LFe2a24CIBmMAbaDMfH5vvIeYkBGRERE5GJF5dJfNB1tR653sqTS3V0gF/CF9xCzLBIRERG5WGSoxn4jB9p5Gl/IehcTHuTuLpALeOt7qCEGZEREREQuNlgXjmitBgWl1VaniInrXwbrwlu6a07z1qx3jU1NiMVzm4806xoyal7RXvoeaoxTFomIiIhcTKVUYPG4+rTejceNxL8Xj9N73aiSmDmy8fq4gtJqzF6TifTsfDf1zHFqPyVmJuok29ybpEOKPrKFekSOGt8v2uveQ9YwICMiIiJqBqPjo7FyygBEac2nVEVpNR6drtuWOqOApRtzrI4YiY8t3ZiDOnuZMjzIwrF6zErSofF3eqWiPi37wrF6pE0bZDMouzqmjcv6ovrzmNb60is61PqTWrkNB/O96n6zhWnvXYRp74mIiMgaX1hvBQAZJ4pxe9peu+3+O3MoEuIiWqBHrmOoNWJ1Rh5OllQiJjwIUxNiofYzH7eoMtTh+c05yCuuRGxEEJ4Yq4faT4nhK3ZITk0ND/JDcaX9TItbH0zClR1Drfalziig16J015ysj/HU+82R2IBryIiIiIiakUqp8MgvjI7y5cyRaj8lZiR2k2wTqFZh2U19LB5fPE6P2WsyLdabiSH3czf3xX1rMu324cqOoZJ9SdFHShaoHhjTBgdOXrR7HF/jjfdbY5yySERERER2+XrmyKaSMzU174VUyX3Y2w5Acupkij4Sn826BtFajcWaRZECQJC/7331Dw9Uu7sLTuMIGRERERHZ5cuZI501Oj4aKfooyampeS+k4tjZcox9fSfqhPo1Y5vnJplGxuRImzbI6tTJQLUKwF+jdbbcPUyHN7470fQT9UBHC8uReGV7d3fDKQzIiIiIiMguMXOk1PQ8RzJHttTaOjnHkbOG7FJ1LR7+9GeculCFrm0D8fJtVyFE49hX6Ss7huLEcunRMHt9USkViI0IhkKhQEx4kNm5jI6Pxr1JOqTtyjVL569UADMTdegR5XvJQf644P0FvpnUw0WY1IOIiIhaA1fUIWupWmZyjrN8c47NAGbh2PrSBePf2IVfTpdZ7L9v5zBseCDRZedjry/2totlCRp/uRdDtnnJV+Dl7b/K7o83eDq1l931f+7gSGzAgMxFGJARERFRa+HM6Ja9oMFVJQHkHOfnUxfwzs5cm/uYlaRDxu/FVoMxUUxEIE4VVzl9Pss350j2pW/nMMl+zEzU4atf8i1qxDUUGeKPoks1dvviLRQAjj07xmI00xMwIHMDBmRERERE0uqMAoav2GEzaBDXoe1eMNKp6YtyjtMhLABF5ZchVcZKqYDkdnsan4+tQNZQa8SVT2+BM9/KFQo49Xxv9asPBGRcQ0ZERERELWJfbonkCI4AIL+0GvtyS5wqFSDnOAVll+3ux9maww3Pp7TKYHNa4x8lVU4HU60xGAOA1Rl5Hjll0REMyIiIiIioRbRULTNPq021PacA7+/Js5jWWFBajdlrMtG3s9Yt/fIFJ0u8P6mH543vEREREZFPaqlaZp5WC21d1hmrpQLEx34tLG/J7viUmPAgd3fBaQzIiIiIiKhFiLXMpIoXR7uglpmc40SFBcDeMjW5y9ikjhMRrEZJhe1EGgKAqhqjvANJ9cH1FQM8nkIBTE2IdXc3nMaAjIiIiIhahFjLDLAMYppSy8yZ4ywZ3xszE3WS+5kxXAc/O30RN9s6zoT+He32t+F+bLF3Re4ZrkO0VnpkMNjft776B/mrmqV2XUvzrVeFiIiIiDza6PhorJwyAFGNgocorcZlKe/lHmfhWD1mJeksgiGloj7l/cieHVBrJ7OHUaiv72XrOCn6KFn9tZuUQwHMGB5rs69PpuqxeJxecrQu8Yr2svriLSoMddiXW+LubjiNST2IiIiIqEWNjo9Gij6qybXMXHmchWP1eOSGnlidkYeTJZWICQ/C1IRYqP2UWJ91RtZxYtsFYfeCkVaPU2cUEK3VoKC02uo6MgWAUI0fyqprJY8hCEBHbSCOLhtjta/i+a6cMsBmNsdTxZVIP1wo65y8haclcGkKBmRERERE1OJUSoVTqe1deRy1n9Jq6nRHkpDYOo44fXL2mkwoALOgTAwLr+raBt//et7ucU6WVNrsq0gqCH0p/ais8/EmnpbApSk4ZZGIiIiIyApXJSGxN30yqYe8qYRyMwrWGQXknC3FgZMXkHO2FHV/Trv0pcQfrkoA4wk4QkZEREREZIWc0S25SUikRq4MtUY8t/mIZCFqpcyMgss35yBtV67Zvp7bfAQzE3VI6hGJN749YXcfnsbZa+/pGJAREREROaHOKDT7WihyH3F0a8mGwygou2x6vENYAJaM721KQlJlqMPzm3OQV1yJ2IggPDFWj0C1ymxfhlojNh86a2rTv0sbBKpVUPspcX2vSGzLKbLZj+t7RULtp4Sh1mhzDdnyzTl4Z2euxXONAvDOzvogrU2QPy5W2k7D72mmJ3TF1zlFZmviov5cE+eqBDDuxoCMiIiIqInSs/NtJlDwlS+LJLKV2B6Y+Z/9ZsHUruPA6r2nkKKPRNq0QXbbvD3lamSfKZM8evaZMjy36TD+vTvP6ujXIzf0RNouy2CsoX/vzsVrt/XHA59kSZ+qB7lBH40nU3vbDEJ9gUIQ7CbZJBnKysqg1WpRWlqKsLAwd3eHiIiImll6dj5mr8m0yJwnfk13ZQp3ch97r3OfzmH45bTtYCpFHwkAkqNfA2Pa4MDJi071M6FbW2T8fsF+O104MrwoVfy867vj059Oe92PHo7EBgzIXIQBGRERUetRZxQwfMUOsy+JDSlQP61q94KRnL7oxey9zuQe3vCjhyOxge+M9RERERG1kH25JZJf0gUA+aXVPlG0tjWz9zqTe4ijSUs35pgySHozBmREREREDpJbjNYXita2Znz9PJcv/ejBgIyIiIjIQY4UDCbvxdfP8/lC0MyAjIiIiMhBrioYTJ7N3uvsSloNk583hS8EzQzIiIiIiBwkFgwGbCdD95Wita2ZnNdZjviO0kkdkntForS61rHOtXK+9KMHAzIiIiKiJhALBkdpzX+hj9JqPDr7GzlG6nW+ZUAnWfv421WdTOnvG0vRR+LW/vL201r5+o8eHBslIiIiaqLR8dFI0UdhX24JisqrERla/4u9L3xJpL/Yep03HDyLzzPP2H1+eEgA0qYNQpWhDs9vzkFecSViI4LwxFg9AtUqXPXM1hY4C++jAPD65P54bstRs2yXUV5Qh8wRDMiIiIiInKBSKpAQF+HublAzs/Y6R4XJW78ktgtUq7Dspj4W28s5XdEqAUBEaH09P1/+0YMBGRERERG1anVGoUlf+MWkH1K1yuSsc9L4KXHJYHS4361BUXm1z//owYCMiIiIiFqt9Ox8LN2YYxZURcucEicm/bhvTabNNnLWOU0a2AXvZ5x0rOOtRLuQAHd3odkxqQcRERER+awqQx2e/vIQpv77Rzz95SFUGepM29Kz8zF7TabFCFdBaTVmr8lEena+6bE6o4CME8VYn3UGGSeKUWcUXNaX63tHNfHsfJ+xzvHr7G04QkZEREREPmnmf/ZjW06R6e9dx4HVe08hRR+Jt6dcjaUbc2Dt676A+oQSSzfmIEUfhW05BVZH0Z5O7YVlm45I9kHcx31rfpLsS5sgf1ysrHHyjH3Pj3nFSLyyvbu70awUgiD4ftjZAsrKyqDValFaWoqwMOlaE0RERETUvBoHY40NjGmDAycv2t3Pw8lX4JXtv1oEbgrAajBnzdUxbfCTxLFS9JHo1i4Y7+zMtdlG7aeEobb1rTObc20c/jG6p7u74TBHYgOOkBERERGRT6ky1EkGYwBkBWMA8MGeXJujaHJJBWMAsC2nCB1CpddK1dRJB2NKBdCEWZQeL1itcncXmh3XkBERERGRT3l+c47L9nWxqmWmERaWX5bcbm9Omy8GYwCw58R5d3eh2TEgIyIiIiKfkldcKaudWqWArfyHCgBtAv1d1idqmtIq36/RxoCMiIiIiHxKbESQrHZDu9XXtmoclIl/3zUs1mV9oqaJ0mpckuHSk3ENGRERERH5lCfG6rF67ym77d6ZejW+/7XIIoNi1J91yFL0Ufhk/x8oKK12aM1YU7QJ9MPFVjAa5KhobQCGr9jRpDpx3oJZFl2EWRaJiIiIPIe9LIsp+kikTRsEoL7G2L7cEhSVVyMyVIPBunBTMWexVhlgnshDHEVL1kdKHuf6nu3xzdFzdvsrtx39de1XThngsUGZI7EBpywSERERkc9JmzYIKfpIq9saBmMAoFIqkBAXgQn9OyEhLsIUjAHA6PhorJwyAFFajdk+orQarJwyAGnTBmFWkg7KRvMelQpgVpIOdw/rJqu/macuyDwz9wr2gGV1YmC8dGOOT0xf5AiZi3CEjIiIiMjzVBnq8PzmHOQVVyI2IghPjNUjsAmp1KVG0QDAUGvE6ow8nCypREx4EKYmxELtp8Se387j7+/96MpTogb+O3MoEuIi3N0NC6xDRkREREQEIFCtwrKb+ji9H3EUzRa1nxIzEi1Hw85fkk5nT84pKq+238jDccoiEREREVEziQzV2G9ETeYL15cBGRERERFRMxmsC0e0ViNZ7yw8WN2SXfIJCtRnWxysC3d3V5zGKYtEREREbmZr/RE5z97aL0De9be3Fs3WcVRKBRaP05syNVpzU/+OeH9PnkvP25coYD3D5eJxeovX0hsxqYeLMKkHERERNcXyzTlI25WLhsnilApgZqIOC8fq3dcxH5CenW9RY6xxDSs5199WCn0xW6Oc40jt4+5h3XB72l6XnLOvuXtYLLZkF3hdHTJHYgMGZC7CgIyIiIgctXxzDt7ZmWtz+6wkBmVNJdYPa/xFt2ENq59PXbB7/X8/XyFZZ6xv5zAcOl3m1HFmJuqw9sBpXKyssdmmtfrvzKEYrAu3O8rpaRiQuQEDMiIiInKEodaInk9vgVQZJaUCOLpsDKcvOqjOKGD4ih1moyoNKQB0CAtAUfllyevvLLnHUQDQBvrjYhUDssYOLroB2iAPKH7mIBaGJiIiIvJwqzPy7AYDRqG+HTlmX26JzWAMqF+PVFDWvMGYI8cRAAZjNjy6NsvdXWh2DMiIiIiI3OBkSaVL29FffKE2FdU71QrufwZkRERERG4QEx7k0nb0F1+oTUX1ghtksvRVDMiIiIiI3GBqQizs5SVQKurbkWPk1P6KCguwe/2dJfc49e1s97c1G+QDdcbs8ZiA7IUXXoBCocC8efNMj1VXV2POnDmIiIhASEgIJk6ciMLCQrPnnTp1CqmpqQgKCkJkZCT+8Y9/oLa21qzNd999hwEDBiAgIADdu3fHqlWrLI7/5ptvIjY2FhqNBkOGDMG+ffua4zSJiIiIAABqPyVmJuok28xM1DGhRxOItb8AWAQ54t9Lxve2e/1nJenQt7N0QoaYiECb24Q/jyMnhd6S8dL9ba38VRwhaxH79+/HO++8g759+5o9/vDDD2Pjxo1Yu3Ytvv/+e5w9exY333yzaXtdXR1SU1NhMBjwww8/4MMPP8SqVauwaNEiU5vc3FykpqbiuuuuQ1ZWFubNm4d77rkHW7duNbX59NNPMX/+fCxevBiZmZno168fRo0ahaIi2ylOiYiIiJy1cKwes5J0FiMoSgVT3jtrdHw0Vk4ZgCit+fTFKK0GK6cMwOj4aLvX/7HRvXCu3CB5nLKqWsnt58qrLFLiNyYA0EdpcW+SDopGfVEogARdhJ09+K6EOHnnXmcUkHGiGOuzziDjRDHqmjtjiwu5Pe39pUuXMGDAALz11lt49tln0b9/f7zyyisoLS1F+/bt8fHHH+OWW24BABw9ehS9evVCRkYGhg4dii1btuDGG2/E2bNn0aFDBwDA22+/jQULFuDcuXNQq9VYsGABNm3ahOzsbNMxJ0+ejIsXLyI9PR0AMGTIEAwaNAhvvPEGAMBoNKJLly6YO3cuHn/8cVnnwbT3RERE1FSGWiNWZ+ThZEklYsKDMDUhliNjLlJnFOzWsLJ1/TNOFLdYwWa1SgFDne2v5QoFJEfalAo0e9ZIV1MAksFq2yB//PRUit2aY3IKc7c0r0p7P2fOHKSmpiI5Odns8QMHDqCmpsbs8Z49e6Jr167IyMgAAGRkZKBPnz6mYAwARo0ahbKyMhw+fNjUpvG+R40aZdqHwWDAgQMHzNoolUokJyeb2lhz+fJllJWVmf0jIiIiagq1nxIzErvhmQnxmJHYjcGYC6mUCiTERWBC/05IiIuw+uXe1vVvyWyNUsEYAKgaD501MrJnZLOviXM1tUq6w5Ou7iwrGJu9JtOizEFBaTVmr8lEena+0/1sbm59t3/yySfIzMzE8uXLLbYVFBRArVajTZs2Zo936NABBQUFpjYNgzFxu7hNqk1ZWRmqqqpw/vx51NXVWW0j7sOa5cuXQ6vVmv516dJF3kkTERERkVfwpGyNtXaGvw6dLvW6EbIaO0HohoP5klMP64wClm7MsTrKJj62dGOOx09fdFtA9scff+Chhx7CRx99BI3Gc252uRYuXIjS0lLTvz/++MPdXSIiIiIiF7KXrdGTFJZfdncXHGa0sz2/tBr7cktsbpdTANzePjyB2wKyAwcOoKioCAMGDICfnx/8/Pzw/fff47XXXoOfnx86dOgAg8GAixcvmj2vsLAQUVFRAICoqCiLrIvi3/bahIWFITAwEO3atYNKpbLaRtyHNQEBAQgLCzP7R0RERES+o2G2RnKPovJqmwk75E4p9fRC4W4LyK6//nocOnQIWVlZpn9XX301/v73v5v+v7+/P7755hvTc44dO4ZTp04hISEBAJCQkIBDhw6ZZUPctm0bwsLCoNfrTW0a7kNsI+5DrVZj4MCBZm2MRiO++eYbUxsiIiIiap1Gx0fjXhuZGFtz9sOWkne+AsNX7MDtaXvx0CdZuD1tL4av2IH07HzZU0o9aeqpNX7uOnBoaCji4+PNHgsODkZERITp8RkzZmD+/PkIDw9HWFgY5s6di4SEBAwdOhQAcMMNN0Cv12Pq1Kl48cUXUVBQgKeeegpz5sxBQEAAAOC+++7DG2+8gcceewx33303duzYgc8++wybNm0yHXf+/PmYPn06rr76agwePBivvPIKKioqcNddd7XQ1SAiIiIiT5SenY93d+ZarFMSBGBvbrFb+mSNN2ZZtEepAF7eftzicTFhx5t3DEC0VoOC0mqr68gUqC9zMNjDi0u7LSCT4+WXX4ZSqcTEiRNx+fJljBo1Cm+99ZZpu0qlwldffYXZs2cjISEBwcHBmD59Op555hlTG51Oh02bNuHhhx/Gq6++is6dO+O9997DqFGjTG1uu+02nDt3DosWLUJBQQH69++P9PR0i0QfRERERNR6yEka4Sl8LRgDbJ+TgPpga9mmHDyd2gtzPv7ZIoW+OKC5eJzebqZGd3N7HTJfwTpkRERERL6lJeuQUdP8d+ZQlFYZvLoOmUePkBERERERuYunJ4Og+tdoQv9OSNFH2S0A7qkYkBERERGRz6ozCk3+ou7pySDor9dILADujRiQEREREZFPSs/Od2oqm1iHzFbSCHIfb0nYIYfb0t4TERERETWX9Ox8zF6TaVE4WMzQl56db3cfDeuQNR5TU/z5r32IWnIfMRGBDvSarLF27QHvSNghBwMyIiIiIvIpcrIjLt2YYyowbKvwMFBfh2zllAGI0ppPX4zSavDmHQPgp5L+Om2oFSwCisaUCsugg+o9nHyF1Wu/csoAtyXscDVOWSQiIiIin7Ivt8RiZKwhAUB+aTX25ZbIytA3Oj7aatIIe8fBn8exxxdT1suhUgJ1Rtvbo7UaPDCyOx4Y2d1rE3bIwYCMiIiIiHyK3OyI23MK8P6ePIuRNHFaY8NRGGtJI5iF0TlXtA/GkcIKm9vjO4WZAi9vTdghB6csEhEREZFPkZsdcV3WGdnTGp05DlknFYwBwDdHimColRhC8xEMyIiIiIjIp4jZEW1NalMAiAhWo6SixuY+Gk5rdOY4UWHygrbIEH+uI2vEKACrM/Lc3Y1mx4CMiIiIiLySrWQc9rIjAsCE/h1lHUOcllhlqMPTXx7C1H//iKe/PIQqQ52s49w+uKus4/x9aKzk9iC1SnK7rwZzJ85fkky64gu4hoyIiIiIvI69GmNidsTGbaL+bKMNVOP9PXl2jxMZqsHM/+zHtpwi02O7jgOr955Cij4SadMGoWtEIE4WV5k9T0B9yvvYdkGyzie2XTDahahx7pLBYlv7EDWW3RSP+9Zk2ny+rl0Qfj9fKetY3uTwmVIMX7GjybXkvIFCEATfCjHdpKysDFqtFqWlpQgLC3N3d4iIiIh8llhjrPGXWHGUqGEyjjqjYDVDX51RQJ8lW1FpqLN5nGC1CglxEdh+pMhmmzCNH8qqa21uj2sfhBPn7AdKHcLUKCyzDMZEgf5KVNXYXk+ljwpBTsElu8fxBdZeZ0/jSGzAKYtEREREPsLXp3YBjtcYk9pPVY3tYAwAKg11ksEYAMlgDICsYAyAZDAGQDIYA9BqgjHAsdfZG3DKIhEREZEPsDeFz1e4qsbYmQtVsDdPzPu/6vuuhq+zt6fE5wgZERERkZcTp/A1DlTEelrp2flu6pnrya39tS2nQPKa7Dx+rjm6Ry3MF2rBMSAjIiIi8mKumsLnLeTW/voy66zkNfn51EVXdYncyBdqwTEgIyIiIvJijkzh8wVyan+FB/ujpML2miwB9Wu/FHZyxSt9NZe8j4jW1idp8XYMyIiIiIi8mNwpW74wtQuQV2Psb/07ydrXyCvbS26fmahDij5Ssk2YRjolQ9/OYVCrpCM7tUqB9iFqyTZ+dr61h2mk65T5ovH9oqHygaiZARkRERGRF5M7ZcsXpnaJxBpjHcICzB7vEBaAlVMGIFkfJWs/9yTGYVaSzmIkTKkAZiXpsHCsHmnTBtkMylL0kfhlySj07Ww9rXnfzmFYd/9wRIQEWN0uiggJsBtY2JtxWmmQzsLoizYczPeJqbjMskhERETkxcQpfAWl1VbXTClQXwzZF6Z2WbI+RubINUmIi8AjN/TE6ow8nCypREx4EKYmxELdYEhq4oDOOHjqIooaFG2ODFFj4oDOAIANDyTiUnUtHv70Z5y6UIWubQPx8m1XIUTjh4wTxZJTSgHY3Q7YD8hqfSAwcZSvZFlkYWgXYWFoIiIichcxyyJgnqrdGwroNoWcwtAAXHJNHClCbc36rDN46JMsu8ehpnl1cn9MkDlFtSWxMDQRERFRKyJO4YvSmk9LjNJqfC4Yk5tVMkUf5fQ1cUUGS1+aKuqJfOH6csoiERERkQ8YHR+NFH0U9uWWoKi8GpGh9VPyfCHpQUOOZJV09po4cixb0+bkTp+sMtTiYlWtrH75igCVApfrbAezKiVgNFov0O1LU3EZkBERERH5CJVS4fXraRqqMwoWwVRzZJW0dhyVUuHQsWytIROzQorTJ615OrUXnvgyW3Z/fYVUMAYAdcb6wEsB69NOF4/T+8QPDgzIiIiIiMjjpGfnY+nGHLMRqmitBpMHdZH1/MhQjc19LB6nN01ZlGojdzrcq9/8arZO7FhBOeKXbEXfzmHY8EAiRsdHo12IGucuWdZGaxeiRtvgAFysrJF1rNYm6Yp2+LXwktnrE9XoNfR2TOrhIkzqQUREROQaUok0BADBahUqDHU2n982yB/P3dQHcz6Wl/jDVps37xiAZZtyJKcbqpQKyQyHfTuHobSqBieLq2y2iQhRo9hKsEZAYo92WHXXYK+biutIbMARMiIiIiLyGHISaVRKBGMAYBQEPPOV9D6WbDgMQGGzjQLAsk05eDq1F+Z8/LPVaXMC7Keb/+V0meR2AAzGJHRsI13DzRcwICMiIiIij2EvkQZgPclDQ6VVtSi1kyCjoOyy3WPkl1ajbXB9senG0xqjtBpEhqpxUEbARU13+HQZhq/YITnt1NsxICMiIiIij+FIMo6WUFRejQn9O1nN1jj2tV3u7p7Py84vt3isoLQas9dk+kxJB9YhIyIiIiKP4Wl1paT607VtYAv2hERya8B5C46QEREREZHHsFe3CwCUCkAQbNenCgv0sztlEQC0gf4orbKd3TD6zzpXtjIxLki5AtuOFNk9DrmenBpw3oIjZERERETkMcS6XcBf2Q5FYk2qmYk6m9sB4O5h3WQdK7FHO8nt4/tFY1tOAWavybRY11ZQWo15n/9i0YfGFABCNSrJNv4enjHQk3naFNemYEBGRERERB5ldHw0Vk4ZgCit+XTBKK0GK6cMwMKxesntD4zsjmit9NTHqLAA/JR3QbLNhoP5WLLhsGS2RnsT5gQAl6qls0Lay9RItoUHqt3dBaexDpmLsA4ZERERkWvVGQXJ+lNS223VMgPqR63mJffAy9uPt8yJULN5cmwvzEySNyLakliHjIiIiIg8mr1gC6ifvii1Pkhq++j4aNybpEParlw0HIBSKuqnPMa2C3bJeXiKdsH+OF9hez2cr/rjQqW7u+A0BmRERERE1KJsJclwZW2p9Ox8vLsz12KETBCAd3fm4qHre7jkOJ7ATwm0C2mdAVmXtkHu7oLTuIaMiIiIiFqMOJXQWpKM2WsykZ6d7/Qx6owClm7MkVz79Z+MPKeP4ylqjcDRQu8bKXJFMpOeUaEu6Il7cYSMiIiIiFqEvUBJgfraUin6KKiUiiavIduXW2IR8DU+Vkll6xtN8jRKuylR7CupNLigJ+7FgIyIiIiIWoScQEmsLVVaZZCc1ig17fFyrbE5T4Nc5LILXiZPKyTeFAzIiIiIiKhFyK0ZtS2nAB/sybMYPxGnNd6bpLO6PkzcPi/5ClnHCQ/2x4WKGpuZGNsG+6PEyXVZwQEqGGqNqKmzPRrkr6ofDWT2e8f179LG3V1wGteQEREREVGLkDua8WXWWZvTGgUAabssgzFxOwB8sv+U3YLNSgXwzLh4ALYLTD87IR7RWo3NfclZAVVxuU4yGAOAmjoGY021Zm+eu7vgNAZkRERERNQiBuvC7QY44cH+KKmQXhckFbyI0x7txTdGAYgIDZAsMD22b0csHqe3uS/GUO63305xb2/AKYtERERE1CJUSgUWj9Nj9ppMKGAe0IhB2t/6d8K/9+S1SH+KyqsxoX8npOij7NZEI88UrFa5uwtO4wgZEREREbWY0fHRkqNSyfqoFuuLOIVSLDA9oX8nJMRFmIIxMSskea6bB3R2dxecxhEyIiIiImpRo+OjbY5K1RkFRGs1KJCYdqhU1Bd4tpWMI0qrgSAIKCy7LNlmsC5csp/2skLK0XgkkFwnOECFa7q3c3c3nMYRMiIiIiJqcbZGpcRpjYD1ZBsKADMTdTa3A8DicXosGd/bbht70xLlZoWUkqyPRIo+UrJNij4SMRGBTh+rtfnXrf18YmopAzIiIiIi8ij2pjUuHKuX3D46PtruPkbHR9vthytqXGWfKcPbU662GZSl6CORNm0Qvv/HSJtBmVbj/eukmmpEj3aICjN/HaK1Grwt8zX0BpyySEREREQeR2pao5ztcttIEbNCSk2ftEcsdD1xQGccOl2KgrLLpm1RYQGY2GAN1MIxvfDUF7/gfGWt6bF2QX5IujISX/x8tok98G5dI4Lw/l2DfTrpikIQBE5rdYGysjJotVqUlpYiLCzM3d0hIiIiIhdIz87H7DWZACyzQsr9En33sFirha7FkGLllAEAgNlrMq22ac1f1p/7W2/8fUisu7vhMEdiA05ZJCIiIiKyQWrq47zre8jah71C10s35mDJhsOSxa5bq//9dNrdXWh2nLJIRERERCTB1tTHvSeKZT3fXqFrZzM5+rJzly7bb+TlGJARERERkUPqjIJHrOkx1BqxOiMPJ0sqERMehKkJsVD7OT4B7FJ1LR7+9GeculCFrm0D8fJtVyFEY/9r8vkK3w8W3E0bqHZ3F5odAzIiIiIiki09Ox9LN+aYjepEazVYPE7folnvlm/OQdquXBgbzOl7bvMRzEzUYeFYvez9jH9jF345XWb6+1hBOeKXbEXfzmHY8EAiANvnPHlQV+dPhCTNT7nS3V1odkzq4SJM6kFERES+TkxwIZWcoiWCsuWbc/DOzlyb22clyQvKGgdjjfXtHIb7r+0uec7+fkoYao0296FWKWCo49ftpvr12TFNGvV0Nyb1ICIiIiKXqjMKWLoxRzLxxNKNOagzNm/wYag1Im2X7WAMANJ25UoGSUD9NEWpYAwAfjldhsUSyTaEP/sjpYbBmFP2/HoOdUYBGSeKsT7rDDJOFDf7PdbSOGWRiIiIiOzal1simXxCwF81txLiIpqtH6sz8mDv+7hRqG83I7GbzTYPf/qzrOMVljm3Tsy3QoeWt2jjYdSuz3b7FNnmxBEyIiIiIrKrqFxeJkC57ZrqZEmlS9qdulDliu5QM/vjQpXFDwEFpdWYvSYT6dn5buqVazEgIyIiIiK7IkM19hs50K6pYsKDXNKua9tAV3SH3KAlp8i2BAZkRERERGTXYF04orUa2Epur0D9VLLBuvBm7cfUhFjYy7CvVNS3k/LybVfJOl6HsACb5yyHG6oBtAoNp8h6OwZkRERERGSXSqnA4nH1mQsbxxji34vH6Zu9HpnaT4mZiTrJNjMTdXYz84Vo/NC3s3T2u76dw7B0fG8A1s9ZASBFH2m3LzER0qNxMRGBUKt8K3KzF2SEybj+cjT3FNmWwICMiIiIiGQZHR+NlVMGIEprPi0xSqtxOOW9M5nzFo7VY1aSzmL0SamQn/IeADY8kGgzKBDrkNk757RpgyT78tjoXjDUSp9btaEOtT4w9a4he3F5cIAf1t0/3Ob179ZO3tTU5p4i2xJYh8xFWIeMiIiIWos6o4B9uSUoKq9GZGj9NEVHRsZcVVzaUGvE6ow8nCypREx4EKYmxDapZtWl6lo8/OnPOHWhCl3bBuLl265CiMY8Gbm9Y9nannGiGLen7XW4T63Bf2cORWmVAYs3HDbLZtkhLACLb+yNZZtyUFBabTVTpQL1QfHuBSObfVS2KRyJDRiQuQgDMiIiIiL7PKW4tCOcCSDXZ53BQ59kNXMPvdOMYbF4f0+ezXvh3iQd3v2zALhgZXvDe6W0sgZ3r9qHs6XV6KjV4P07B0Mb5N+c3ZfEwtBERERE5HE8pbi0I8QAsqmp18OD1M3ZPa/2xc9nJO+FDQfz8eYdV9mdIjvipR3o98zXOHDqIvJLq3Hg1EX0e+ZrjHhpR/OegIuwMDQRERERtQhPKS4tl70AUoH6ADJFH2Vz2tzRgrLm7KJXu1BZY3ObeC+0DQ7A7gUjbU6RHfHSDpwstl5T7mRxFUa8tAPf/2Nkc3TfZRiQEREReTBn1+oQNYem3peOFpdu6rotR/tqaz+OBJD9u7TB85tzkFdcidiIIDwxVo9AtQp/sAC1U4rKq6FSKqwG6KWVNTaDMdHJ4iqUVta4dfqiPQzIiIiIPJSrEh8QuZIz96UjxaWXb85B2q5cNJy9+NzmI5iZWJ9F0d52uX2V2o++o1ZWf5/bnIPsM3+NhO06Dqzeewop+kgM1bl/pM+bSU35vHvVPln7uHvVPvzv/mGu6pLLMSAjIiLyQLYSH4jrVjwx8QH5Pmfvy8G6cLQJ8sdFialqbYL8seNoIdJ25VpsMwrAOztzkfF7MX45bTkVUNwOAFd1bWu3rz+fumBqb20/N/aV9x5rGIw1tC2nCLV1nrMezhsdLShH4hXtrW47KzF62ZR27sKAjIiIyMO4Yt0Kkau12H0pCPj3bssgqSFrwVhDabty0T7EdsIIsa8Fdr6ob/olH0oF4EyOkW+PnWv6kwl/XKhEwcVq3Pj6TpRV1yJM44ev5iYhqo0GHbUaySmloo5az65VxiyLREREHsaRdStELcUV9+W+3BLJ0TEAuFhV61QABNQHUIXlBpvbxb7aO4wA54Ixct7HP57E0Be+wfmKGhjqBJyvqMHQF75Br6e34P07B8vah9x27sKAjIiIyMM4mviAqCW44r7kPUuOqjVaf7yqxoihy7cjJiJQ8vkxEYEendADYEBGRETkcRxJfEDUUlxxX/KeJVeqqjHi05nX2AzKYiICPT7lPcA1ZERERB5nsC4c0VoNCmxMqVKgvjDqYF14S3eNWjFX3JfiPqSmPkaFBaCo/LJTUwWVCqB9iBpF5QbJvto6l4btOoRpUFDGkT1PdePrO/HT0zegtLIGd6/ah7Ol1eio1eD9Owd7/MiYiCNkREREHkalVGDxuPq03Y1TI4h/Lx6nZ0IPalGuuC9VSgXG95POXDihf0fMTNRJtunbOUxy+8xEHZZOiLcZbAl/9jVZHym5n2R9JPrYORa5V1l1LQBAG+SP/90/DBkLr8f/7h/mNcEYwICMiIjII42Oj8bKKQMQ1Sg7WJRWw5T35DbO3pd1RgEbDuZLttlwMB+Pje6FWUk6NI7tlApgVpIOGx5IlNwu1iGTYjQK2J93QbLN/rwL2J5TZHdf5D5hGu+f8Of9Z0BEROSjRsdHI0UfhX25JSgqr0ZkaP10MI6MkTs5c1/ay9QI/JWpceFYPR65oSdWZ+ThZEklYsKDMDUhFmq/+vGEq7q2RfuQM2bZFNuHqHFV17amFP22KAA8+eUhXKysleyLvYyQ5H5fzU1ydxecxoCMiIjIg6mUCiTERbi7G0RmmnpfOpqpUe2nxIzEbhbbbRWoLio3YPaaTMxL7mE3Rf8FO8EYeb5AfyWi2nh/ohhOWSQiIiKiFuGKTI32ClQDwAd78hzuG7W8mLbSKetnJekQ6G89XAn0V+LIsjHN0a0W59aAbOXKlejbty/CwsIQFhaGhIQEbNmyxbS9uroac+bMQUREBEJCQjBx4kQUFhaa7ePUqVNITU1FUFAQIiMj8Y9//AO1tea/eHz33XcYMGAAAgIC0L17d6xatcqiL2+++SZiY2Oh0WgwZMgQ7Nu3r1nOmYiIiKi1ErMs2prcqAAQbSdTo5wC1RerPGuqoUZpmQhFpACgaqWzkFPtJHi5qmtbHFk2Bnsfvx7tgv2hVinQLtgfex+/3meCMcDNAVnnzp3xwgsv4MCBA/jpp58wcuRITJgwAYcPHwYAPPzww9i4cSPWrl2L77//HmfPnsXNN99sen5dXR1SU1NhMBjwww8/4MMPP8SqVauwaNEiU5vc3FykpqbiuuuuQ1ZWFubNm4d77rkHW7duNbX59NNPMX/+fCxevBiZmZno168fRo0ahaIiLuIkIiIiaqzOKCDjRDHWZ51Bxoli1MnMUd8wU6MtDTM1llbWYOJbe5Cw/BtMfGsPSitrZE97bBPoLxkERYUFoI2dTHzBapWsY9lznT5Kcrs3ZQR0pX/vypXc/sjag6gzCghUqxATEYyIkADERAQj0EWvi6dQCILgRJUH1wsPD8dLL72EW265Be3bt8fHH3+MW265BQBw9OhR9OrVCxkZGRg6dCi2bNmCG2+8EWfPnkWHDh0AAG+//TYWLFiAc+fOQa1WY8GCBdi0aROys7NNx5g8eTIuXryI9PR0AMCQIUMwaNAgvPHGGwAAo9GILl26YO7cuXj88cdl9busrAxarRalpaUIC2N6VCIiIvJN6dn5WLoxx2yUKlqrweJxetnZP5dvzkHarlyzWmNKRX26ejFD4oiXduBkcZXFczuEqVFYZrB4vLGHk6/Ay9t/tbn97SkDAAD3rcls8j7keuC6OGz85azV84mJCIQCCuQVVzp9HF8UGaJG0SXL19vTiz47Eht4zBqyuro6fPLJJ6ioqEBCQgIOHDiAmpoaJCcnm9r07NkTXbt2RUZGBgAgIyMDffr0MQVjADBq1CiUlZWZRtkyMjLM9iG2EfdhMBhw4MABszZKpRLJycmmNtZcvnwZZWVlZv+IiIiIfJmYTKPxlMGC0mrMXpOJ9GzplPbiPt7dmWtR+FkQgHd35iI9O99mMAYAhWUGmyNforZB/ugRGWK3L//LPC25/dCZixap9Zti0yHrwRgAnCyuQlnlZecP4mHsXTa519VaMAbUX7cRL+1wrFMeyu0B2aFDhxASEoKAgADcd999WLduHfR6PQoKCqBWq9GmTRuz9h06dEBBQQEAoKCgwCwYE7eL26TalJWVoaqqCufPn0ddXZ3VNuI+rFm+fDm0Wq3pX5cuXZp0/kRERETeQE4yjaUbcySnL8rZx+INh20GL43b2mIUBDzzlXTa+yUbcrDNTo2x7UeKLALHpsg9L30+JVV1zh/Ew9h/jZw/xsniKpT6QGkCtwdkV155JbKysvDjjz9i9uzZmD59OnJybL+BPMXChQtRWlpq+vfHH3+4u0tEREREzUZOMg2xhpgz+ygsc360qLSqFgVl0seR2k7e4+5V3p+Iz+11yNRqNbp37w4AGDhwIPbv349XX30Vt912GwwGAy5evGg2SlZYWIioqPqFkVFRURbZEMUsjA3bNM7MWFhYiLCwMAQGBkKlUkGlUlltI+7DmoCAAAQEBDTtpImIiIi8jKM1xJzZB5FcZ+0UGvcGbh8ha8xoNOLy5csYOHAg/P398c0335i2HTt2DKdOnUJCQgIAICEhAYcOHTLLhrht2zaEhYVBr9eb2jTch9hG3IdarcbAgQPN2hiNRnzzzTemNkREREStnStqiMndB5FcHbXef0+5dYRs4cKFGDNmDLp27Yry8nJ8/PHH+O6777B161ZotVrMmDED8+fPR3h4OMLCwjB37lwkJCRg6NChAIAbbrgBer0eU6dOxYsvvoiCggI89dRTmDNnjmn06r777sMbb7yBxx57DHfffTd27NiBzz77DJs2bTL1Y/78+Zg+fTquvvpqDB48GK+88goqKipw1113ueW6EBEREXkasYZYQWm11fVBCgBRdmqIydlHZFiAU9MWFQDaBPnhQmWt3bYtQQH766mo6d6/c7C7u+A0t46QFRUVYdq0abjyyitx/fXXY//+/di6dStSUlIAAC+//DJuvPFGTJw4EUlJSYiKisIXX3xher5KpcJXX30FlUqFhIQETJkyBdOmTcMzzzxjaqPT6bBp0yZs27YN/fr1w7/+9S+89957GDVqlKnNbbfdhn/+859YtGgR+vfvj6ysLKSnp1sk+iAiIiJqrRrWEGucIE/8u2ENsabuY+n43oiJCJTsS/sQNRQS+7j5qs6Szxf16SSdjjxFH4n2IWrJNn52vk336Rxmdx9hGt+qqyWXrl2Q5PZAf+mLGxMR6BM13DyuDpm3Yh0yIiIiag1cUYdMzj5spb4X609J7SM0wB9///ePdvvx0YwhWJWRazXbYoo+Em9PuRrDV+yQTERijwKAQiGdVVBpZ7uvaqPxw8Vq2yOZ0VoN/FXAqRLL6+9LdcgYkLkIAzIiIiJqLeqMAvbllqCovBqRofXTFKVGxpq6j9LKGty9ah/Ollajo1aD9+8cbDYiYmsfe347j7+/JyMgu2cIhnVvhypDHZ7fnIO84krERgThibF6BKpVyDhRjNvT9jp0XuRa/505FProMMn7wBM5Ehu4PcsiEREREXkXlVKBhLiIZt+HNsgf/7t/mMP7OH9J3ho0sV2gWoVlN/Wx2M6skO5XVF6NwbpwPDqqpynwDtH4VgjjW2dDRERERK1euxB5pYnstQsPkl77Rc0v91yFxbRRR6fIejqPS3tPREREROQUuQty7LQ7WlDudFfIOa98c9xiDV9BaTVmr8lEena+m3rlWhwhIyIiImol5KzbcsX6MDkMtUaszsjDyZJKxIQHYWpCLNT2UhbK3Mf5CplTFv9sZ+uc/7hQ6fB5WaNWKWCoY9oGVxFQnyxl6cYcpOijmuX+bEkMyIiIiIhaATmZDV2RQVGO5ZtzkLYr1yyz4HObj2Bmog4Lx+qd3se1V8orXRQZqpE855hw6bTscjEYcz0BQH5pNfbllji9ntHdOGWRiIiIyMelZ+dj9ppMyalfctq4wvLNOXhnZ65FmnejALyzMxfLN+c4vY8dRwsRrdVY1CkTKVAfdF2ouCx5zh1CNfJPjNzCFxKvMCAjIiIi8mF1RgFLN+ZYXS4lPrZ0Yw6WbDhst02dk8WyDLVGpO3KlWyTtisXhlqjU/v49+5cPDmmJwDbxaOfTtVj2aYjkuf87OYjksch95ObwMWTMSAjIiIi8mH7ckskCxuLU78Kymyvu2o4PcwZqzPy7BZANgr17ZzdR2H5ZaycMgBRWvNRriitBiunDEDbYLXd61JQ5v2jLz7PB2aDcg0ZERERkQ9z5ZQuZ/d1skRekgypdo7sY0ZiN6Too6wm7FifdUbWfsizyU3g4skYkBERERH5sEgXroOSuy9bWQvlJsmQaufoPmwVj3bldSH38YXXkQEZERERkQ8brAtHtFaDgtJqq7O7FKifxicIAgrLLku2GawLt3s8qayFUxNi8dzmI5JTDpUKYGpCrM3trtgH8Nd1kZq2GKrxQ3l1reR+yD0cuSc9HdeQEREREfkwlVKBxePqU8nbSnCxeJweS8b3ttvGXr0ne5kadxwtxMxEneQ+ZibqJOuRqf2UTu8DqL8u4/tJp/Lv0tb5tPc92gc7vY/Wzpl70hswICMiIiLycaPjoyUTXIyOj5bVRorcbI6Pje6FWUk6NP4erVQAs5LM65DVGQVknCjG+qwzyDhRbMryuHCs3ul91BkFbDgoncr/VPElye1y5BW7prh0a5UYF4EOYU27J70FpywSERERtQKj46NtJrhwpI0tcrM57sstwcKxejxyQ0+szsjDyZJKxIQHYWpCrNmolr0i1c7uQxsonWURAC4ZbKffl6vGyVIBrV3fTlocP2ceGAuCb11TheBrZ+QmZWVl0Gq1KC0tRVhYmLu7Q0RERNSi1medwUOfZNlt9+rk/pjQv5PNxB/AX1MfG39JFcNCOaMj9vZx97BY/HtPnt3+kmdSQN594C6OxAYcISMiIiIip8nNdhcZqpEcuUrRR0lOfVSgfupjij7K5sidvemTCgBf/HxaVn/JMwmwfx94C64hIyIiIiKniVkLbX01VqA+6LpQYZBM/PHGjt9kT320Rc70yQuVzZ89UQFYrHMj13FFsXJPwICMiIiIiExsJcGw16ZhNkdbnk7thWWbpBN/fPBDrqx+FpVX2+yrK4thq1XSEVWAnWyOIX6MyJrT7+fK3N0Fp3HKIhEREREBsJ9IQ06bZH0ktuUUWew7WR+JtsEBdkeuLlbWyOpr3vlKDF+xw2o/XFkseHy/Tvg80/b0xlcn98db3/2GX05bBgZ9Oofh93MVAOpc1h8yt/K73/H3odJlEDwdk3q4CJN6EBERkTeTk0gDgGQbW8GY6Pqe7fHN0XN2+xKkVqHSYDuICf5zu61+vHnHACzblCNZDDss0A+lVfanLUYE+6O4wnaQGOivRFWN7WyMASoFLtfx63ZzaRPoj6zFN7i7GxYciQ04ZZGIiIiolZNbQ2zJhsM22wiAZDAGADuO2Q/GAEBhZ5ZfZY1lMCb2AwCWbcrB06m96vfVeN9//u+0ITGy+iIVjAGQDMYAMBhrZu1D1O7ugtMYkBERERG1cnJriBWUXXbqOIIAhGr8JBN/RASrUXFZeoqf1Pwusa9tgwMkC12XVBkc6jt5pnH9PDPtvSO4hoyIiIiolXNlEgx7BnRtg52/nocCMBvlEoO0Cf074n0X1AcrKq/GhP6dbBa6/ujHU04fg9zvvJ0RTG/AETIiIiKiVs6VSTDsSerRXnLk6vqeHVxyHPGcVEoFEuIiMKF/JyTERZhqVsVGBLnkOOReMeHe/zpyhIyIiIiolRNriEklwYjSaiAIAgrLLlttI4dSAUxNiIXaT2lz5GrPb+edOJO/+jpYFy7Z7omxeqzey1EybybeT96OI2RERERErVzDGmK2kmAsHqfHkvG9bbZRAEjRR0oeZ2aiDuo/63bZGrk6f0n+OjWpvqrsVGQOVKvQt7N09ru+ncMQpuH4RXPxt1PjLThAJbm94f3kzbz/DIiIiIgIgLyizraMjo+WnEo4Oj7abpu0aYMwK0mHxrGQUgHMStJh4VjpwtGA/OmTDydfIdlXe+qMAs6VSyf2KCq7jEuX7afGp6YZ2aOdKZhvSHzsX7f2c/p+8gasQ+YirENGRERE7iSnqLMcdUbB6lRCR9oYao1YnZGHkyWViAkPMk1TlHv84St22J0+uXvBSABocl8zThTj9rS9sq8LuZ5aCYzoab12XYo+EmnTBgFw7n5yF0diAwZkLsKAjIiIiNxFTlFnR4IydxPPB7CeiVHu+UgFqZdrjXjokyzXdZpczptHwVgYmoiIiKiVkFvU2ZHpi+4mZ/qkPWJQ17i+WkFpNWavyUTuuQqX9plcL21XLgy10oW3fQFXKRIRERF5MblFnfflliAhLqLlOuak0fHRNjMx2mMvSFUA+M/ek67uMrmYUQBWZ+RhRmI3d3elWTEgIyIiIvJicos6F5VXy1of5gruPo6cILWkQjqhB3mGkyWV7u5Cs2NARkREROTF5GYlzDtfgeErdjid9MMeVyUXsbcfe+vDyDf4QuFne5jUw0WY1IOIiIjcQU5WwjZB/rhQWWN1G+C6pB+uSi5ibz/3Junw7s5cm9vnJffAy9uPO9R3alkKwG6BcYUCOLZsjMdnVLSGST2IiIiIWgk5RZ1tffF1ZdIPVyUXkbOftF2WwVjD7f/ddwptgvwljxNip+gwNS8B9WnvpahVymaZ6uppGJAREREReTmprITzkq/ARSujY6KGST+c4UhyEWf3IxXTCQAKyi6jtk468KuuqZPcTs3PYGdm6eVaI/aeKG6ZzriRU2vIDAYDcnNzERcXBz8/LkcjIiIichdbWQm/+uWsrOfLTQ7i7PPttXO2H6JLl2slt3OZmXfI+P08hvVo5+5uNKsmjZBVVlZixowZCAoKQu/evXHq1CkAwNy5c/HCCy+4tINEREREJI9KqUBCXAQm9O+EhLgIqJQK2Uk/5LZz9vn22rULDnCqH+RbBNRPY804UYz1WWeQcaLYq2rqydGkgGzhwoU4ePAgvvvuO2g0f72pkpOT8emnn7qsc0RERETknMG6cERrNRbry0QK1GcnHKwL94zj+P6SIXJAQWk1hq/YgdvT9uKhT7Jwe9peDF+xA+nZ+e7umss0KSD78ssv8cYbb2D48OFQKP561/Tu3RsnTpxwWeeIiIiIyDlykn4sHqc3JU9o6miEo8ex5fyly7KO5wqB/tJfhVXMtuB2/8s8Y7GmsKC0GrPXZPpMUNakhV/nzp1DZGSkxeMVFRVmARoRERERuZ+Y9KNx3a6oRvXBnK0hJvc4UpydOumIqhrphWR1XGfmkQTUB/lLN+YgRR/l9ZkYmxSQXX311di0aRPmzp0LAKYg7L333kNCQoLrekdERERELmEr6Yf4ZdZW7S9xNEJuDTF7xxHVGQWrbcSpj7bqqhEB5lk7E+Ii3N0dpzQpIHv++ecxZswY5OTkoLa2Fq+++ipycnLwww8/4Pvvv3d1H4mIiIioGdmr/eXoaISYXMQWeyNxi8fpMXtNpkXxYDnFhKl1cVVWTndq0szY4cOHIysrC7W1tejTpw++/vprREZGIiMjAwMHDnR1H4mIiIjISenZ+TaTI7iqhpjcfsxekym5LkiqrtotAzo53QfyHS05xbW5NLl4WFxcHNLS0lzZFyIiIiJqBvamI941LFbWfpwdjXBkJM7W1McNWWfweeYZu8cK8leiUmKNmFqlgMFO8WjyfH06ad3dBac1aYRs8+bN2Lp1q8XjW7duxZYtW5zuFBEREVFrUnLJgBv+7zv0X/o1bvi/71ByyeCyfdsLggBgfZa84tHOjkY4OhJXZxSQc7YUB05eQM7ZUtQZBURpA2UdS6m0l0GRKRR9wQtbjri7C05r0gjZ448/brUAtCAIePzxxzFmzBinO0ZERETUGgx6dhvONQjALlbVYMCz29A+RI39T6U4vX85QVBxhQHhwf64UFFjNXBToH66oLO1yuSOsBWVV2P55hyk7cpFw6z7z20+ghnDdYjWaiTPKTzYHyUVNZLHqKqpk9UX8mwHT190dxec1qSfBo4fPw69Xm/xeM+ePfHbb7853SkiIiKi1qBxMNbQuUsGDHp2m9PHkBsE/a1//dosZ2qI2SN3hG1bTiHe2WkejAGAUQDSduWifaha8vlXdWnTxB5a8vKM6j4vTOPv7i44rUkBmVarxe+//27x+G+//Ybg4GCnO0VERETk60ouGWwGY6JzlwxOT1+UGwQl66NsJtKQm/LeHjGlva0YRwEgKiwAmw9JF/z95XSZ5PbMkxea1kErZNbFJje55xqdu7vgtCZNWZwwYQLmzZuHdevWIS4uDkB9MPbII49g/PjxLu0gERERkS+a/O4Pstt9Pf/aJh/HXl2vhtMRVUqFrBpiTaVSKiRT2gPA8O7t8XnmaaeOc6Gq1qnnk/dQB6jc3QWnNWmE7MUXX0RwcDB69uwJnU4HnU6HXr16ISIiAv/85z9d3UciIiIin1NULm/kS247W8QgCJA3HVGsITahfyckxEW4LBgTSaW0XzllAIJ84As2tZzzly67uwtOa9IImVarxQ8//IBt27bh4MGDCAwMRN++fZGUlOTq/hERERH5pMhQNS5WSSeeENs5SwyCGhdjjmpQjFlUZxSabYSsYX9sjcSduVDl0mORb2sXHODuLjhNIQgCZ8a6QFlZGbRaLUpLSxEWFubu7hAREZGHK7lkwAAZSTsyn0pBeIjzQRlgP9hKz863CNqirQRtzclQa0TPp7c4tXarQ6ga5y4ZJPfReMokeaeP7hmCYd3bubsbFhyJDWSPkL322mu49957odFo8Nprr0m2ffDBB+XuloiIiKhVCg9Ro32IWjKxR/sQtcuCMeCv6YjW2Cse3TCxR3OOoqn9lJiZqMM7O3NttomJCMTJYtsjaX27tEFhWbVk8o/wIDWKK11X740cFxLgh0uXnVvv5wtTFmWPkOl0Ovz000+IiIiATmc7m4lCobCagdHXcYSMiIiImsJW6ntX1SGTo84oYPiKHTZre4mJP3YvGIltOQUtMopmrQ6ZUgHcNUyHD/ZYpsRvTKmQzpDIETLf8N+ZQ23+yOBOjsQGnLLoIgzIiIiIqKlKLhkw+d0fUFRuQGSoGp/ce41LR8bsyThRjNvT9tpt93DyFXhl+68WgYw4Nuaq9PgiQ60RqzPycLKkEjHhQZiaEIvVGXlYtumIy45hj8ZPiepaY4sdj+QLDlDhl8WjXL7G0RWaZcqiqKamBj179sRXX32FXr16NbmTRERERFQvPETtVGp7Z8ktHv3Bnlyro0oC6oOypRtzkKKPcmmafH1HLdqFBiAyVAOVUoGTJZUu2bdcDMY8V+XlOtQZBY8MyBzhcEDm7++P6mp5b1oiIiIi8nxyi0dLZYUUAOSXVmNfbolLppDZSjAyzAOnp5F7CABWZ+RhRmI3d3fFKU2qQzZnzhysWLECtbUsukdERETk7cTi0bbGGRQA2gT6y9qX3NE2KWKCkcZr2gpKq/G/zDM2+0mtT0uPmDaHJtUh279/P7755ht8/fXX6NOnD4KDg822f/HFFy7pHBERERG5jq3siGLx6NlrMi2SXYjBz13DYvHy9uN2jyF3tE2qj0s35khOjQwKUKHicp3NfYzrG4WNvxQ41Q/yDjHhQe7ugtOaFJC1adMGEydOdHVfiIiIiKiZ2KsxZq94dIo+Cp/s/wMFpdVWgyUxE+NgXbhT/dyXW2Iz2yNQH5RVXK5Dt/ZB+P2c5ehI385heGXyAPx00nbWSADoEBqAwnL7KdOZjdGz3TEkxt1dcJpDAZnRaMRLL72EX3/9FQaDASNHjsSSJUsQGBjYXP0jIiIiIifJrTE2Oj4aKfoomzXG7I2iLR6ndzrBgtwpj9aCMQD45XQZXkw/gto66WQcRkGwG2wpAIRq/FBWzWU6nirrj4semfbeEQ6tIXvuuefwxBNPICQkBJ06dcJrr72GOXPmNFffiIiIiMhJ9qYAAvXZEev+LNolFo+e0L8TEuIizAIscRQtSms+LTFKq3FZyvtg/yZN4DKTtitXsuA2AJy7ZLA78iUADMY8nCvWLLqbQ3f8f/7zH7z11luYNWsWAGD79u1ITU3Fe++9B6WySflBiIiIiKgZyZkC6Eh2RHujaHLZWs/21nf216nZY69oNPkOZ9csegKHArJTp05h7Nixpr+Tk5OhUChw9uxZdO7c2eWdIyIiIiLnyB1BcGSkQRxFayqp9Wz5ZfbXdRG5as2iJ3AoIKutrYVGYx6F+vv7o6bGdk0KIiIiInIfuSMILTXSYG89W2yE92fNI9eSs2bR1oirN3AoIBMEAXfeeScCAgJMj1VXV+O+++4zS33PtPdEREREnkGsMdbc2RHlkJPSvrDU+bpSSgWnLfqK3tGhOJxfbvaYACBFH2las2gvg6inc2jh1/Tp0xEZGQmtVmv6N2XKFHTs2NHsMSIiIiLyDGKNMQAWBZVdmR1RDjnr2SpdkENjZqIOYRrpcYcgNfMfeIPGwZhoW04Rlm/OkSwiPntNJtKz81uim05RCILA3w9coKysDFqtFqWlpQgLC3N3d4iIiIjMeMIowvqsM3jokyyX7CtBF44f80rMRsKUivpg7JEbeqLn01s4SubjFAA6hGlQUGY9yBdHf3cvGNni0xcdiQ2czytKRERERB7PVdkRneHKdWrv3zUYKqUCqzPycLKkEjHhQZiaEAu1nxL/3vU7g7FWQABsBmPidkcyiLoLAzIiIiKiVsLZ7IjOkrOeLTxYjeIK6RpiwF8FgWckdrPYdrLE+XVo5Ds8vVYZJ88SERERUYuQs55tQv+OsvYl9SW7S1tmaqS/eHqtMgZkRERERNRiRsdHY+WUAYjSmn9JjtJqsHLKAKToo2TtR+pLds+oUKf6SN5DqbAM7kUK1K+T9PRaZZyySERERC7jzbWArPG282mp/jp7nNHx0RhxRSSe35yDvOJKxEYE4YmxegSqVagzCojWaiSzMYpfsk+dr8ToV79HVY0Rgf5KpD80Al3bBaGk0v6UR/INndpocPpCtaxaZZ6KARkRERG5hCdk8XMlbzufluqvK46zfHMO0nblmhJv7DoOfPTjKcxM1GHhWD3G94vGOztzbT5/fL9oXPnUZtQa/3qsssaIpH9+Cz8lsHrG0CadG3mfZ8bF47LRaHFPRnnwe7Uxpr13Eaa9JyKi1kysBdT4S4X4u/TKKQO84ouRyNvOp6X664rjLN+cIxlszUzU4d+7c53KkqhSAoIgXRy68YgKeR8/pQLHnh0DlVLhcaPZjsQGXENGRERETqkzCli6Mcfql1vxsaUbc1DnJXnIve18Wqq/jhzHUGvEv3f9jkXrs/HvXb/D8OdQlqHWiLRdtoMxAGYjZ03vq3Qw1rDP5L3euOMqU9AlZhCd0L8TEuIiPH6aYkOcskhERERO2ZdbIrnex1tqAYm87Xxaqr9yjzPvk0xsOlRgFhA9t/kIZibqEBmqYX0wMvFT1gfPtkogRGk1uLFvFNJ25Vlsn5Wk86hRame4dYRs+fLlGDRoEEJDQxEZGYmbbroJx44dM2tTXV2NOXPmICIiAiEhIZg4cSIKCwvN2pw6dQqpqakICgpCZGQk/vGPf6C2ttaszXfffYcBAwYgICAA3bt3x6pVqyz68+abbyI2NhYajQZDhgzBvn37XH7OREREvkZujR9PrwUk8rbzaan+yn3+xl8KLIIuowC8szMXX/x82qk+kG8J0fgDsF0CYXy/aLxnJRhTAHh3Zy7Ss/Obs3stxq0B2ffff485c+Zg79692LZtG2pqanDDDTegoqLC1Obhhx/Gxo0bsXbtWnz//fc4e/Ysbr75ZtP2uro6pKamwmAw4IcffsCHH36IVatWYdGiRaY2ubm5SE1NxXXXXYesrCzMmzcP99xzD7Zu3Wpq8+mnn2L+/PlYvHgxMjMz0a9fP4waNQpFRUUtczGIiIi8lNwaP66sBVRnFJBxohjrs84g40SxS6cPuuN8nNFS/XXF+R4+W+70Psh36CICbZZAePOOAdhwMN9rpg47w6OSepw7dw6RkZH4/vvvkZSUhNLSUrRv3x4ff/wxbrnlFgDA0aNH0atXL2RkZGDo0KHYsmULbrzxRpw9exYdOnQAALz99ttYsGABzp07B7VajQULFmDTpk3Izs42HWvy5Mm4ePEi0tPTAQBDhgzBoEGD8MYbbwAAjEYjunTpgrlz5+Lxxx+36Ovly5dx+fJl099lZWXo0qULk3oQEVGrU2cUMHzFDhSUVktOPdq9YKRL1nU0dzbBlj4fZ4n9tZcm3tn+2rsucjGZBonmXdcd80ZdaTUhx77cEtyettfuPv47c6hHTB1uzGuTepSWlgIAwsPri7cdOHAANTU1SE5ONrXp2bMnunbtioyMDABARkYG+vTpYwrGAGDUqFEoKyvD4cOHTW0a7kNsI+7DYDDgwIEDZm2USiWSk5NNbRpbvnw5tFqt6V+XLl2cPX0iIiKvpFIqsHicHoDtqUeuqgUkZvlrHHwUlFZj9ppMl0xhasnzcQWVUoHx/aQD0fH9ok2Z6Jo6sijnusgR4Cf99TPQ3/mvpwoAYRrpVAkaFxyHnLPlcIHNbd42ddgZHpPUw2g0Yt68eRg2bBji4+MBAAUFBVCr1WjTpo1Z2w4dOqCgoMDUpmEwJm4Xt0m1KSsrQ1VVFS5cuIC6ujqrbY4ePWq1vwsXLsT8+fNNf4sjZERERK3R6PhorJwyoFlrAdnL8qdA/RSmFH2U08FSS5yPq9QZBWw4KB2IbjiYj36d22LZJudGFkfHR+PeJB3SduWi4RwrhQK4MjIERwsv2d1HdcPiYVZU1Uhvl0OhAMqqayXbVLvgOOQcg9Foc8T75gGdZO2jTYB/c3WvxXhMQDZnzhxkZ2dj9+7d7u6KLAEBAQgICHB3N4iIiDzG6PhopOijmq0WUEtnP2zu83EVe9cFqL8u93+cafG4OLIot05ZenY+3t2ZaxEUGwXICsZaig8sK2oVOoUFWK1rV1BajTe/PSFrH9uOFmJEr0jXd64FeURA9sADD+Crr77Czp070blzZ9PjUVFRMBgMuHjxotkoWWFhIaKiokxtGmdDFLMwNmzTODNjYWEhwsLCEBgYCJVKBZVKZbWNuA8iIiKyT6wF1BzcMYWpOc/HVZw5X0dGFqVGKF2N68xah+yzZZJJO+Q4WVLpqu64jVsnzwqCgAceeADr1q3Djh07oNPpzLYPHDgQ/v7++Oabb0yPHTt2DKdOnUJCQgIAICEhAYcOHTLLhrht2zaEhYVBr9eb2jTch9hG3IdarcbAgQPN2hiNRnzzzTemNkRERORe3pb9sKU4e74NRxalyBmJc5W2Qd4/DY3su1hd5/Q+YiOCXNAT93LrCNmcOXPw8ccfY/369QgNDTWt+dJqtQgMDIRWq8WMGTMwf/58hIeHIywsDHPnzkVCQgKGDh0KALjhhhug1+sxdepUvPjiiygoKMBTTz2FOXPmmKYU3nfffXjjjTfw2GOP4e6778aOHTvw2WefYdOmTaa+zJ8/H9OnT8fVV1+NwYMH45VXXkFFRQXuuuuulr8wREREZGGwLhzRWo3d7IeDdeEt3TW3GqwLR5sgf1ysrHFqP/ZG2loyeUK7YH+UOHk+1Do8MVbv7i44za0B2cqVKwEA1157rdnjH3zwAe68804AwMsvvwylUomJEyfi8uXLGDVqFN566y1TW5VKha+++gqzZ89GQkICgoODMX36dDzzzDOmNjqdDps2bcLDDz+MV199FZ07d8Z7772HUaNGmdrcdtttOHfuHBYtWoSCggL0798f6enpFok+iIiIyD3ELH+z12RaTGnzxOyHrmYtNbgrz7VdiPTaeFeMPHbSBuBM6WW77U7LaEOk9lNCbSdrpzfwqDpk3syRWgNERETUdMs35yBtV65Z4galApiZqMNCH/i13Bqp2mvaQLWsek32fDRjCIb1aGdzu9z6bO1D1PjlTJnF9r6dw/BIypWY/sF+p/tKJGIdMiIiIqIWJGb5a5xFzygA7+7MdUkdMk9jr/bathzbtZwccb5CelRKTh2y8f2icchKMKYAcOh0GXb+es75jrYwHx1w9Rm+UIeMARkRERF5BTlZ/pZuzHGo2LGns1d7DQC+zDrrkmPJmZIo1meL0pq3jdJq8OYdV2HDwXzJvn7x82nnO9rCfOh28knhgWoAcKroubt5RNp7IiIiIntaug6ZJ5BzziUVBqeO4WgyFFv12eT09UKldLFmIkcdLSxHRU2tzSm9nlTE3RYGZEREROQV3FGHzN1cfS6uSoZirT6bL1138h67fzuHnb+et1pc2pGi5+7EKYtERETkFVpjHTJ7mQ8d8XByD6tTDV31hdWXrjt5j8xTFyWnyXrDNGaOkBEREZFXaJV1yGR+j2z7Zx0yqevywMgeeGBkj2ZLnS/n9ekQFoCCMqa0J9dQKIDyatvTYL1lGjNHyIiIiMgryMny52t1yOxlPhT97apOAOxfF3Gq4YT+nZAQF9Hka2UtgYKc12fJ+N5I0UdK7rtPJ5YPInlGXtleVjtPn07LgIyIiIi8hlSWP29YK+IoudMAU/RRLXZd0rPzMXzFDtyethcPfZKF29P2YviKHUjPzpf1+qRNG4S+na0HXX07h+EJH60lR5YGx7aR3G4veO/bWfr5Ik+fTsvC0C7CwtBEREQtp84oNNvUO08itxjz7gUjoVIqmv26iDXRGvdFPIIYdEn1w9Y+xP28eccAPPr5QVQa6lzWb/JNapUC4cEBKCyT9/5oSSwMTURERD7NVVPvPJ2j0zSb87rIqYkmJlCoMwrIOVuKAycvIOdsqSmpgpxacss25TAYI1kMdQJuHShvuq4nY1IPIiIiIg8mTgNsXGcpqoXrLMmtAzfvk0xsOlRgVlD5uc1HMDNRh2uv7CBrH0Ry/fzHRY94fziDARkRERGRh7NVjLklf/mXmxhh4y8FFo8ZBeCdnbn4reiSq7tFrVxpVY1HvD+cwYCMiIiIWqWWWocm5ziu6ktznpMrEiPsOHrOBT0h+ou+YygA68XKvQUDMiIiImp10rPzLaY4RTfDFCc5x2nJNs4Q64w5M6VQABCq8ZOsHWVvO/kOlQKoczK9oC4ixDWdcSMm9SAiIqJWRczy1ziwKCitxuw1mUjPzm+x47RkG2eplAqM7+d8YKfV+Etu1/jx62lroQAwK0mHxoO4SgWgjw6VtY+zPrDmkCNkRERE1GrYyxSoQH2mwBR9lFNT/eQcZ8mGwwAULdLGVee04aDzgd2Zi1WS289dMjh9DPIOAX4KLByrx7zkK/H85hzkFVciNiIIT4zV4+MfTyJn0xG7+4gJD2qBnjYvBmRERETUasjNFLgvt8Sp9ShyjlNQdllyH65s0xLnJBcL4JLIX6WymGq76ziw/UgRnhzTE0oFzLJ1NqZUAFMTYlums82IY8JERETUasjNFCi3XXM9vzn44jmRd6uqqbM51XbuJ1mI7yRdUPn6XpFQ+8AUV+8/AyIiIiKZ5GYKdDajoCsyErpau5AAp57viedE3u1ynWBzqq0AIPtMmeTzs8+UmYqOezMGZERERNRqiJkCba2kUqA+M+FgXXizHycqLABRYc63CQ+STpJh4uT3VvGciFqKvVhLnIrr7RiQERERUauhUiqweJweACyCHPHvxeP0TtfuangcW5aM740l46X7IqfNTVd1ktWn8xXSa83sUSkVdqeQ2cMvnuRq4lTaOqOAjBPFWJ91Bhknir1q5IzvCyIiImpVRsdHY+WUAYhqNNoTpdVg5ZQBLqtDNjo+GvfaSOl9b5IOo+OjZfVF3I+i0X4Uf+4nRR8lqz/OTjk01BrxzZEip/ZhdOrZRJYiQzVIz87H8BU7cHvaXjz0SRZuT9uL4St2uKyERXNjlkUiIiJqdUbHRyNFH4V9uSUoKq9GZGj9NEVnR8YaSs/Ox7s7cy1mCgoC8O7OXFzVta0p4JLqi639GP/cT7/ObRGt1aCgtNrqrEQF6gM8Z6dhrs7IszuFjKiliPf1hQoD5nycaXHvizX4XPkjS3NhQEZEREStkkqpcCoNvBRH653Z6ovUfkTLNuXg6dRemPPxzzbbuGIa5smSSqeeT+RqT6f2wrJNzV9XsLlxyiIRERGRizlS78wV+2kbHGB3eqSzfKEAL3mfDqFqm/d12+AAl7zP3I0BGREREZGLuaremdz9bM8pwLs7cy2mFIrTI12xlmZqQqzFF2Oi5lZYbrB5X2/LKZC1D0+voccpi0ReqM4oNOu6B6Lm4En3rSf1pTXyputvqDVidUYeTpZUIiY8CFMTYmUVonW03pmtayJ3P+uyzrhs2patvqj9lJiZqMM7O3Nl9YmouYj39fqss7Lae3oNPQZkRF4mPTsfSzfmmA3RR2s1WDxO7/GLVqn18qT71pP60hp50/VfvjkHabvMR52e23wEMxN1WDhWOqW9WLNLTqINqWuSoo9CtFYjOS0rIliN4gqDze0Np23ZWzNn7/VZOFaPLzLP4Nwly+OFqpUoNzCPIrUMAUBxhQHhwf64UFHTrAltmhunLBJ5kfTsfMxek2nxH2Yxk5C3pHel1sWT7ltP6ktr5E3Xf/nmHLxjZQqgUQDe2ZmL5ZtzJJ8vt97ZtpwCyWuyLacA4/tJB6r9u2jtnQ4A+9O25Lw+49/YZTUYA8BgjNzib/3r6/A1Z13B5saAjMhL2MvYBdRPSfGmQojk+zzpvvWkvrRG3nT9DbVGpO2SnpaXtisXhlrpAMRejbEUfZSsa2JvWlbmqQuS20XtggNsbpPz+jy97hf8crpM1rGIWsq1V0a2SF3B5sQpi0RewpGMXc2VxpnIUZ5033pSX1ojb7r+cuptGYX6djMSu0m2k6oxlnGiWNY1sedCZa3dNgAshxAakPP6nKuQeRyiFnS0oAwzk+IwsmeHJq339AQMyIi8hKsydhG1JE+6bz2pL62RN11/ufW25LazVWOspc+1qPyyxDb3X3eipjhVUml17eN7u3M9cm2qNd4RNhKRwxm7iDyBJ923ntSX1sibrr/celvO1uUKD1I79XxHlVyyHZB5wnUnaoqTxZVeszbVFgZkRF5CzNhla8aJAvWZsDw9kxC1Lp5033pSX1ojb7r+cuptKRX17ZxxtKBcVruQAKXkddNq5E14Cg+2HQCKr4+U9sGcWEWeJ+uPC16xNlUKAzIiLyE3Y5enZxKi1sWT7ltP6ktr5E3XX6y3JWVmos7p9Sl/XJA35fHqmPog1dZ1u3u4dF9FUdpA1BkFZJwoxvqsM8g4UWz6oqpSKuxmc7x5YBfERATKOpYtgf6e9dXTXyV9v7n/biR7yqrrbG5ruDbVk3nWu4KIJNnL2OUN86Sp9fGk+9aT+tIaedP1XzhWj1lJOouRMqUCmJVkvw6ZHF3aygtuhnVvJ3nd7k2Kk7Wfc6VVGL5iB25P24uHPsnC7Wl7MXzFDqRn56POKGDDQempXRsO5uNyjXOp7aucfL6r1dRJj5x49rgKKWRGzJ6+RlIhCALvNRcoKyuDVqtFaWkpwsLC3N0d8nF1RsFqxi4iT+ZJ960n9aU18qbrb6g1Nlvmtl2/nsPU9/fZbbf67sFIvKK9zev2712/Y9mmI03qg3jV5yX3wMvbjzdpH0Se7r8zh7Z49lZHYgNOBibyQrYydhF5Mk+6bz2pL62RN11/tZ/Sbmr7piqptF5g2VY7W9ctr1je1EdrBNQHZf/e9XuT90HkTlqNH8qqa62OZipQP5LsCWtTpXDKIhEREZEbuC7zpHOTnQQAZZdtr8Mh8mQp+g4APH9tqhQGZERERERu4KrMk/07t3F114i8xrAe7b1mbaotnLJIRERE5AZi5snZazKhgPk4l7Vf922tIevY1rl6aETeLDI0AMO6t0OKPspr1qY2xoCMiIiIyE1Gx0cjWR+JbTlFZo8LAFL0kaZf99Oz87F0Y45Z8dtorQaLx+mRoo9CtFZjURjXUQoFIJXqrXHQSOQR/rwpvWltamOcskhERETkJss351gEY6JtOUVYvjkH6dn5mL0m0yLgKiitxuw1mdiWU2C3hpgcfnZGE7xksIFamaJLl93dBadxhIyIiIjIDQy1RqTtypVsk7YrF+1DzlodmRIzJC7ZcBiuKGFsryaXnc1EblHCgIyIiIiImmJ1Rh6MdoIcowAUltv+wikAKCjz/i+kRE0VHqx2dxecximLRERERG5wsqTp9cOIqF6UNtDdXXAaAzIiIiIiN+jC7IhETlEqgIExbd3dDacxICMiIiJyg55RobLaBamlv661CfRDVJh0PTOphBwKAJEh3j/ti1ofowAcOHnB3d1wGgMyajZ1RgEZJ4qxPusMMk4Uo87eRHmSjdfW8xlqjfj3rt+xaH02/r3rdxhqje7uEhG5ia3P7JJKg6zn2/v4MNQJWHSjHoBlag/x75mJOpvPFwA8c1M8Av2lvxaqVUyzSJ6nqLw+++j2zHzEPr7J9G97Zr6beyYfk3pQs5Cql+INFdM9Ga+t51u+OQdpu3LNFus/t/kIZibqsHCs3n0dI6IWJ/WZHRmqkbUPez/oVBrqoA30x71JOqTtyjWrJaZQ1AdjV3VtC8B2RsfaWiMu2zmOvSyMRI5SKZzP3hkZqkHs45ssHr/ns0zgMyDvhVTnDtACFIIgVQKQ5CorK4NWq0VpaSnCwsLc3R23EuulNL6xxN/VVk4ZwMChiXhtPd/yzTl4Z6ftLz2zkhiUEbUW9j6z37zjKizbdAQFpdVW09orAAQFqFBxuc7uscbGd8CW7EKbhZvVfkrJwM5fpWDART7LHUGZI7EBpyySS9UZBSzdmGOzXgoALN2Ywyl2TcBr6/nk1hTi9EUi3yfnM3vZpiN4OlV6qmFSj/ayjvf98fM2gzHA/igbgzHyZZ4+fZEBGbnUvtwSs2kZjQkA8kursS+3pOU65SN4bT2f3JpCqzPyWqQ/ROQ+cj+z2warsXLKAERpzacvRmk1WDllAKYMjZF1PDmjaESt1T2fZbq7C5K4hoxcSlxY6ap29BdeW88nt6YQaw8R+T5HPrMn9O+EFH0U9uWWoKi8GpGhGgzWhUOlVMBQa4RCAUgtMFEAkqNjROTZGJCRS8ldoCy3Hf2F19bzxYTLqykktx0ReS9HP7NVSgUS4iIsth84eUEyGANaNhhj8EfkepyySC41WBeOaK10LZRobf0vf+QYXlvPNzUhVrLWD1BfC2hqQmyL9IeI3Ef8zJYi5zNb7khbm0B/2X1zBoMx8kbvTRrg7i5IYkBGLqVSKrB4nPQC5cXj9FDZ+9ZKFnhtPZ/aTylZ6weoTz+t9uNHL5GvUykVaB8qXWy5faja7me23JG24T3aSW63W2PMj//tIN+VPMCzM1DzWwG53Oj4aMkFykzL3nS8tp5v4Vg9ZiXpLEbKlAqmvCdqTaoMdfjldJlkm19Ol6HKIJ2MQ87siKiwAPyUd0G6PzXSWRYNtRz7Iu+0+u7BkttZh6wVYR0yS3VGweoCZXIer63nM9QasTojDydLKhETHoSpCbEcGSNqRZ7+8hBW7z1lt93UoV2x7KY+km3EemaA+ZRB8VN/XnIPvLz9eBN7SuTdHriuOx4ddSW+/uks7v38Z9Pj795yFW64uqPb+uVIbMCkHtRsbC1QJufx2no+tZ8SMxK7ubsbROQmecXysqnKaSfOjli6MccslX6UVoPF4/S4zNqG1KoJSM/Ox9JtR8weXbztCIwahVfMHmJARkRERORisRFB2CVj0Co2oj7rqr1R9dHx0TZT42ecKG6u0yDyeAoFMHtNpkXCmYLSasxek+kVSzo4ZdFFOGWRiIiIRFWGOvRalG633ZFnRuOV7ceQtivXrLC8UlGfBEjOutM6o4CBz27DxcoaZ7pM5JXaBvrhQlWt1W0K1I8k714wssWXdnDKIhEREVELsbauN1CtQt/OYZKJPfp2DsMr24/hnZ25FtuMAkyPi0GZ1CiaveQgRL7KVjAG1K+5zC+txr7cEo9e6sGAjIiIiKiJ0rPzLdZ2RWs1eDpVj3PlBsnnFpZWI22XZTDWUNquXDxyQ0/86+ujFqNoz20+gpmJOgzv3p7ryIgkyK3n5y4MyIiIiIiaQMx+aG3tyv0fZ9p9fqGdgA2oHymb9v6P2Pt7idVt7+zMxY4j5+R22SoFAKUSqGNMRy1MoQBaYvGU3Hp+7sIczEREREQOqjMKWLoxxyIYA2D1MWdYC8YaOn7ukux9NV5FI/6tVvErIbU8Z4OxNkH+iAoLkKzTF62tn0bsyfjuIyIiInLQvtwSs2mK3qBdsJ9FsCgASNZH2vxCS+TJXri5D5aM7w3A9o8Ni8fpPb5WKwMyIiIiIge5Yk2KVuMHe98TXfk18nyF9eQH23KKoFIw6TZ5poeu746osACzx6LCAvD2n+nsxTp9UVrzaYlRWo1XpLwHuIaMiIioVbCWCdDRX41dsQ9vY+ucXbEmRalUYMbwWKTtyrPZZoguHHtzpacsukK5gQEZeaZ+ndtiznU9mlynzxswICMiIvJxtjIBLh6nl/3rsSv24W2kzjlFH4VorQYFpdVNXjN2obIGI3tGQalQ2KxDNjyuPfbm7nPuRIi82Ir0I3jyy1qz9+F7u3MtPntUSoVHp7aXwsLQLsLC0ERE5IlsZQIUfzeWM6XHFfvwNnLOGQBmr6nPpig0aiP3y9Wrk/tjQv9ONmuMrc86g4c+yWr6iRD5IG/47HEkNuAaMiIiIh8lJxPg0o05qDPaDh9csQ9vI/ecU/RRNteuPHhdd1nHCg9SAwDUfkrMSOyGZybEY0ZiN9N0rBA/VVNPg8hnWfvsqTMKyDhRjPVZZ5BxotirPpM4ZZGIiMhH2csEKADIL63GvtwSm1N9XLEPb+PIOdtau/L+7t9lHetoQRkSr2hvc/ubO39ztPtErULD92FplcGrp1QzICMiIvJRcjMBSrVzxT48la2EHY6es7W1K39cqJK1D7GdrSmL+aWXHTgjotZnW04BPtiTZ7VA++w1mR49rVHEgIyIiMhHyc0EKNXOFfvwRFIJO1xxzjHhQbL2ERMehOWbcyySejy3+QhmJurQUavxunpnRC3py6yzNqcXK/DX9GJPzrjINWREREQ+arAuHNFajc1aVgrUByGDdeHNug9PIybsaBzoiL+oX6gwOH3OUxNiobBXY0wBnC2txjs7zYMxADAKwDs7c9GrY6j9EyJqxUoqDDa3NZzW6MkYkBEREfkolVKBxeP0ACwLDIt/Lx6nl/zl2NF9ePrCejkJO5ZtysHTqb0AOHfdAv2lE3JoVAp8sCdXss1He/+Q3G7aF+c8Ednk6VOqGZARERH5sNHx0TYzAcpdWyF3H+nZ+Ri+YgduT9uLhz7Jwu1pezF8xQ6kZ+e77oScJDdhR9vgAKeu277cElQa6iTbVNUKFiNj1vojx9WxEfCz8a3OcydqEbUMT59S7daAbOfOnRg3bhw6duwIhUKBL7/80my7IAhYtGgRoqOjERgYiOTkZBw/ftysTUlJCf7+978jLCwMbdq0wYwZM3Dp0iWzNr/88gsSExOh0WjQpUsXvPjiixZ9Wbt2LXr27AmNRoM+ffpg8+bNLj9fIiIidxgdH43dC0bivzOH4tXJ/fHfmUOxe8FIhxa629uHvWmAnhKUOZKww5nr1tK/yB88fRG1RuvbPGuMksi11CqF10+pdmtAVlFRgX79+uHNN9+0uv3FF1/Ea6+9hrfffhs//vgjgoODMWrUKFRX//Uh9/e//x2HDx/Gtm3b8NVXX2Hnzp249957TdvLyspwww03ICYmBgcOHMBLL72EJUuW4N133zW1+eGHH3D77bdjxowZ+Pnnn3HTTTfhpptuQnZ2dvOdPBERUQsSMwFO6N8JCXERTVrgbmsf3lSrzNGEHU29bi39i3x5tfRoHJGvGqKrz3Da1OnFnkAhCIL7Px0BKBQKrFu3DjfddBOA+tGxjh074pFHHsGjjz4KACgtLUWHDh2watUqTJ48GUeOHIFer8f+/ftx9dVXAwDS09MxduxYnD59Gh07dsTKlSvx5JNPoqCgAGp1ffHFxx9/HF9++SWOHj0KALjttttQUVGBr776ytSfoUOHon///nj77bdl9d+RatxERES+JONEMW5P22u33X9nDnV7rbI6o4DhK3agoLTaagCpQP20xN0LRjr1JU48jtT0yA6hapy7ZJCctqhQAJ7xTY3ItZQK2J2yK8f9I+LQt4vW4+qQORIbeOwastzcXBQUFCA5Odn0mFarxZAhQ5CRkQEAyMjIQJs2bUzBGAAkJydDqVTixx9/NLVJSkoyBWMAMGrUKBw7dgwXLlwwtWl4HLGNeBxrLl++jLKyMrN/RERErZE31SpzRaITuccZ30/6i+BNV3XC9b0iJdv07sgfeck3uWrAXKl0zbRsd/LYnDwFBQUAgA4dOpg93qFDB9O2goICREaaf5D5+fkhPDzcrI1Op7PYh7itbdu2KCgokDyONcuXL8fSpUubcGZERES+xVNrldkq/CwmKVm8/jAKy/8qvNwhLABLxvc2+xJnq2BzQ5eqa/Hwpz/j1IUqdG0biJdvuwqBahU2HJReN7fhYD7sTVTKv+j+IJbIkyV0awfAeoF2b+GxAZmnW7hwIebPn2/6u6ysDF26dHFjj4iIiNxDrFVmbxpgSy6slyr8PDo+Gj+fuoBzly6bPaeo/DJ+PnXBFJBJFWxeOLZ+lG38G7vwy+m/ZskcKyhH/JKtiGsfZLegs5yCz8UVBoQH+6OkosZmmzYaFS5yDRm1UgNi2rq7C07z2IAsKioKAFBYWIjo6L9+qSosLET//v1NbYqKisyeV1tbi5KSEtPzo6KiUFhYaNZG/NteG3G7NQEBAQgICGjCmREREfkWcRrg7DWZUMA8q587FtaLGR8bB4dixsdkfSS25RRZPE8sxixq+P+ttcn4vdgsGGvoxLnKpp9AIyEBfpIBWdeIYFw8w6UT1Dp9/ONJzEjs5u5uOMVj15DpdDpERUXhm2++MT1WVlaGH3/8EQkJCQCAhIQEXLx4EQcOHDC12bFjB4xGI4YMGWJqs3PnTtTU/PVBtm3bNlx55ZVo27atqU3D44htxOMQERGRNFfUO3MFexkfBcBqMNbQuztzkbZLumBz2q5cm8GYq50qqZLc/guDMWrF8opd9+OHu7h1hOzSpUv47bffTH/n5uYiKysL4eHh6Nq1K+bNm4dnn30WPXr0gE6nw9NPP42OHTuaMjH26tULo0ePxsyZM/H222+jpqYGDzzwACZPnoyOHTsCAO644w4sXboUM2bMwIIFC5CdnY1XX30VL7/8sum4Dz30EEaMGIF//etfSE1NxSeffIKffvrJLDU+ERERSRsdH40UfZTVdVstxV7hZzkE2M9s6AEZ/IkIgC9U2nNrQPbTTz/huuuuM/0trsmaPn06Vq1ahcceewwVFRW49957cfHiRQwfPhzp6enQaP769e2jjz7CAw88gOuvvx5KpRITJ07Ea6+9Ztqu1Wrx9ddfY86cORg4cCDatWuHRYsWmdUqu+aaa/Dxxx/jqaeewhNPPIEePXrgyy+/RHx8fAtcBSIiIt/h7oX1npDJkYhaTv/ObdzdBad5TB0yb8c6ZERERO4ntyYaETUvjZ8S1bVG29v9laiusb1dLk+ob2iNT9QhIyIiInKUmPHR2UmS9mZZtuAsTCKvJBWMAUB1jRHhwWrJNgo777O2Qf4tmr21uTAgIyIiohZVZxSQcaIY67POIONEMepcuCBLTuFnOcbE2860DAAzhuvg54KozF8lvV3jx8iPfNf4ftFQwPp7VQEg0M4bxFem+TEgIyIiohaTnp2P4St24Pa0vXjokyzcnrYXw1fsQHq2dBFlR0hlfBwb30HWPnTtgpGij7S6LUUfiZE9O6DWBYFkjZ3yYdW1vvKVk8hSl7ZBNt+r85KvQKVB+g1ysbIG+3JLmrOLLcJj65ARERGRb7FXH8yV6fFtZXx8eduvAArtPv/EuQpst5Eef3tOEbq2DXJJP4las/CQAIyOj8bInh2wOiMPJ0sqERMehKkJsdgi80caMZFPnVFwa4ZXZzAgIyIiomZnrz6YAsDSjTlI0Ue57EuUtYyPQ3TheONb+8/de6JYcjrUuqwzznWOiBAZGoD07Hws2ZCDgrK/MqSm7crF7YO7ytyHBunZ+Vi6Mces5EW0VoPF4/QtVgPRGZyySERERM3OXn0wAUB+aXWzTz9S2ssS8KcLVTU2twkASipqEB6sdjp5CFFrti+3BPetyTQLxgCgoKwaL2//FW2C/CWfH63V4ELFZcxek2nx+SKOvLtyOnRzYUBGREREzU5ufbDmriN2vuKyy/Z1U/+OAJxLHkLUmr2784Tk9io7a8hu7BuNZZuO2Bx5B+pH3l2ZOKg5MCAjInJQc2aII/JVkaEa+40caNfc/ZDj+p4dsHLKAHQIs0xI8HByD5cdh8hXVdmpQ3bZTur8LzLPeMTIu7O4hoyIyAHePk+dyF3E+mAFpdVWf81WoD6Qae6aQmI/pL7EtQ3yx4VK21MWTUxDYeZnJAgCekSG2D2O1PUgIvuKKwyy2jX3yLuzOEJGRCSTmCHOm+epE7mLnPpgi8fpmz0rmkqpQPtQ6WK0IZr/b+/Ow6Oq7j6Af2eSTJJJmGxkA0ISZJEIsokYkCCILFIFpa8W2UTLotJWRbRpsVD7KlTfCvbRCvIqVKgbVZQC4hsJmxhZgxASIISENYsQspCQdc77R5iRyczce5O5syXfz/PwtJl7cu65J3fG+d1zzu8oe16dnlOMp9YfRlGF5TTI4opaPPNRJqpqGyR/v6HRiI7B0m0J9ONXNSJHOXvk3VF8lxORQ9rL9D25DHGAd8xTJ3Inqf3B1Ex5L+V6XSOOXqiQLHO+9LqiujYeuWj3M0EAqKiRDsh+ulaHn65JP+GXm9JFRPL6x4W6uwmSOGWRiFqtPU3fa0mGuOZptonoZ/b2B3PVfkGvbc1WVE6v85HclDYiSKd4uhQROYchwFf2wQcAfLTvLJ4c3s0FLWodjpARUau0t+l7npIhjoiUszWCX3ClWtHvhsmk2+4XF6JGE4naLTUewcSFBSoqd7ZU2fveXThCRkQt5o4NXt3NUzLEEXk7V42s2ztPj6hgRb9fKjP69eP5cofaR9TeqTHB/9xVZYFWfLhehbM5D0fIiKjFPGWDV1cyZWazF15q0PRlz9kZ4oi8matG1qXOszv3sqI65NZuXamqQ7C/T6vbSESOq6xphNxe71oNMD05wSXtaS0GZETUYu1x+p6nZIgj8nT2Ev24KjGOkhF8f1/prz99OxsUnUswhw+R243o0VHy+OzhidDJvOfdjVMWiajF2uv0PVOGuObToGLaaCITopaSmo4YEqhzSWIcJSP4tQ1GDIoPxaGzZVbH70uKwhPDumHK6h9kz1UlkfSDiFwjLlyP+5KikJZdYnXsvqQopN6f5IZWtQwDMiJqMU/Z4NUd3J0hjshTmaYJNv9MME1HfGJYgqJ6HB1ZV/r7M5ITsP7JGLy2NRsFV6qREKHHH+5PQqDOB41GIfsZZwjwRbmC7G5E5FwFV6rxnZ2pyN9ml2BbVqHHPzD17PE7IvJI7X36no9Wg+RbIjCxf2ck3xLRZq+TSCkl0xE3HrmoqC5HR9ZbMoIfqPPBXyb1xbonh+Avk/oiUNe0JkzJZ9y9vaMcaicRqeNgQalkghBv2COUARkRtYonbPBKRJ5ByTTB0qp6hAfpnJ4YR60EPHKfcUbu10zkMB8VnmdKJeDxliRjnLJIRK3G6XtEBCifJjipfyes2VsADSxTXqs5sm4a3Xpq/WGHzyP1GffF4QsOtZOIgEYXDVx5epIxjpARkUM4fY+IlE4TvC8pBu9OG4hog+Mj6/ayOQLKR/Cl6pAzOKH1iUeIyLU8PckYR8iIiIjIIS1J9JOWXYTmW8KKFuaPV7K5tNwIvpI6pMrMHJqA177OcSj1faCfVna/MyKSZnoM7M1JxjSipZ+CZFNFRQVCQkJQXl4Og0HZ/iVERERthSnLImB7muC70wYCgM1MjDeXkRsls5fNUY06TPUobevnhy/YTLV9c1mpL1lyx4lIno8GMA1u2/vscce69pbEBpyySERERA6TmyZ4X1KMwxtDq7G5tFQdpnqWbDqOJZvsn0fcOM+xC+V2z3Nzm1p7nIjk+ftqvT7JGKcsEhERkSqkpglm5F1xeGNoJdkcHa0DAIoqaiWP48Z5iMj95qQken2SMQZkREREpBpTop/mlGY5kyqnRh1F5dcV1UFE3uHpkT0B2P/s8QacskhERERO15INm51ZR2lVnaI6iMjzzU1JhM7X+8MZ778CIiIi8nhqbNh8Z2I4QvV+kucJ0/uZ67CV1j482L+VV2At0I9fo4icbdbQBJuvz01JROr9Sa5tjJNwyiIRUQs1GoXXzlMncpeWbNjsyHvMVK+9lPWP3tFFjcsBAPhotQCYtt5TxRr8USixHtBPA9Qzs4rH25hpexP2bceL2kxAxrT3KmHae6L2QcneRURkn9x7SOp4SKAOU1b/IHuO50b3wIpvc22mrFf6pSdM74ur1Q12j4cH+aG0ql5hbeQOHYP8MCA+zObWBPclRSE9pwSN/Bbs1eIjArFr4Sh3N8OmlsQGDMhUwoCMqO1TY/8jIrI/yiz3HntiWALe31sgW39ooB/KrjsWLD05LAEf7C2wu1fZY0O64F/7bD+5J88QEuCDippGu39DfgFuG3780xiEyExldgfuQ0ZEpDI19j8iz2FrbRG5jikb2sT+nZF8S4R5mqLce+yLzIuK6nc0GAOA0UkxeHfaQMQ229so9sbeRicKrzl8DnKuqjrbwRi5htwk4wCVknE8sXa/KvW4E9eQEREpoMb+R+QZOO3UMyl5j12tds0UQVNyER+txu7eRsvTcl3SFmq9BonlfQzUnE+uj2uk/kAtcKkN7AnIETIiIgXU2P+I3M80Ja75F/+i8ho8tf4wtmUVuqll5EnvnQf7xZqTiNgazQOAruGB7mwiEd3QKUTZdhiejAEZEZECaux/RO7V3qedqjFNs67BiPf3nMGfvsrC+3vOoE6lJ9yAuu+dIJ1WcrqUXMLGTT8WyvbP8kcHtLxhRKS61TMGu7sJDuOURSIiBUx7KBWV19hdIB4js4cSuVd7nnaqxjTNpVuzsXpPPm6OU17dmoPZw9XZC0jJeyzI3wfXahtl6xqbFIMvjlyye1wuFlVyHwQH+CI+IhBnr1yXbQ8ROU9OYQWG9ejo7mY4hCNkREQKmPZQAqwXKjffQ4k8U3uddqrGNM2lW7Oxane+VSBjFMCq3flYujXb4XYqeY/dER+mqK7S6jqH2yN3HzQaBX6qdPw85PkC/fi57skyzlx2dxMcxoCMiEihcX1i8e60gYhpNl895kbWNSaE8GztcdppS6Zp2pvSWNdgxOo9+ZLnWb0nX5Xpi3LvseE9IhXVc+jcVYfb0jHIX/L497mXUV0nP1pH3u86d4/2cN4fMHPKIhFRC4zrE2s36xp5tvY47VTpNM2303Px0b6zKL5pxCe6gw5/ntgHF69el53iZxTAuowCPDm8m8NtHtcnFqNujca6jAKcLa1GfLge05MToPPVoq7BiFe35ki2R6sBKmtUCJRk3tKfZ3IPMiJP0BammDMgIyJqIVPWNfIupilxT60/bLUpbFuddqp0+uXyb61TuBdX1mHe+sO4p6eytRlnS6tb1DZ7bK13+9/v8s3r3WYPT8Sq3fZH7O7pFYn0Ez853I7L12olj1fVNjh8DiJyTJC/D+7q5v3/PeaURSIiajfa27RTNaZf7s27oqhcfLgegLJsjvbKKFnvlnp/EuamJFplStRqgLkpiXjybsdH6QCgY7D0lMWoDtLHicj5/HzaRijDETIiImpX2tO0U7lpmkrUN8r/plYDTE9OUJTN0V6Zlyf0xl+25Nhd76ZB03q3+5JikHp/EhaMudXmtMa9ueos8DfKzNMMCuBXKGq/tADU2/Si9cqq69tEZlx+mhARUbvTXqadyk3TVBqkdY8Mwumfquwenz08EeknivHU+sNWdZpGt96dNhAAbJYpLK/B0x9lSrah+bYEOl+tzTVrl6ukpxoqtS+/FMN72k8iUiyxNo8c05J7k9zDz0eDWomHNV0NvjhX4ZppvUUV3v9ebBvjfERERGST1DTNfl1CFNXRLTJYcprgi+N6K8rmuGTTcYe/aMuti5PLjqicTEtF2xtR9RQMxjxfvcwI8gUXBWMAUCqz3tMbcISMiIiojbM3TfOD7/Lx44Vy2d8fnBCO2Snd8OzoXnhtazYKrlQjIUKPP9yfhECdDzLyrijK5qgG2XVxKsVJyd2kk5lo+Eib2jHZzKuuaQYAIDxI58KzOQcDMiIionbA1jTNmUMT8NrXORASX640mqZy27IKsWRTtnl60J5cIC27BEseTEKtCnuQKaHVAINkNoeWy46oRJDOB3fd6KtGo7C53rBjB+//EtjW3dUtHD+cKbV/PDEUP+SXua5B5BThgd7/XmRARkRE1E7pfLWYI5NGfs6N9WHz1h+2OlZUUYN56w/judE9ndlMM6MADp29Krn+T43MkvU3ZX20l6TkcIHjm0+Tfb1jOyCnsNKhOh4ZFId+XULw3u58q/WTc1IScXf3SPyQv9+hc5D7pZ0oxojeUXYfnngDBmREXsibP3SIyLOk3p8EAFi9J99iGpJW05Ss48VxvTHov9Mk61j7fT5iDP4orqi1u+l2SKAfyq7XO9xeuTVkdyaGI0jng6q61m8OXddgxIpvT+Ht9NN2k5TEhjge+JF9vx6WiFMllTbvyzsTpUe+TK5W10lm5PzqyEUnXgG5SsGVKkUZXj0ZAzIiL+PtHzpE5Hkk08ifvoyyaulA6mp1PZ69twfe2p5rd9PtWcMSbG5A3VKmpB3X6xptrmdrNApUOxCMmazaaR2MAT+n4L/SBhIJeLJOYXpMviPO5rrFrUcvKQrIQgP9ADRN103qFIKOHfwR1SHA/ABTjdFUcr+LpVWyGV49/fsRAzIiL2LaNNWbP3SIyDPZSyOfoXBj6AajwLvTBlo9MIq58cDovqQYfHLgvOPJPTTA7A8PIC27xPzSnlxg3Q/ncF9SFO5KjFAlS1+dxLI4AUim/CZpoQE+KKuxHzTHhjTN/Ghat3gcRRVNwW/TusVi3N1dOuGKSdn1esmHmCN6Rjl2IeQR8kttf6Y037/Qk2cSMSAj8hKNRiGZVtpbPnSIyNsoDTyE7Kbbix9IsrkWrSWWfp2DrIsVNo+lZZfgxCXbx5yhQ4AvKmtcl967rRiUEI7tJ36ye/zBfrFIyy6ys26xFv8+rGyq4YWy6/hvG5uNmx5iTh7YWVE93BfNezXfv9BTMWkrkZfYn1+qKK30/nz5aRxE5DkajQIZeVfw1ZGLyMi7gka5fNIuJpf+vaXlHGUvGDM5X+a6TWKvMRhrlZ0n7QdjALD2+7N46fOjDp/ny8yLknvjbc0qUlSPny+/Lns7ubWn7sYRMiIvofTDxNM/dIjaE7kEPN6wJvSuWyIQqveTXEcWqvfDXbdESF7PfUkx+PN/siXPpdXY399IA8DfT4uaelfucCTNs0Jn7yE327O2wajKVgpXJe5ZAShea1jnom0dyHk8fb0gAzIiL6H0w8TTP3SI2gu5YMtb1oT6aDVY9nBfyamGyx7ui7TsIsnreXZ0T9n1Y6ZgzF5ikKgO/jhXer2ll0DkVnpfoJqDqU4TduOBkb0MrzE31iR6Mo7BEnmJOxPDERsSAHurwzT4eSE0kbeqazDi/T1n8KevsvD+njNe+2TaFGw1D0BMwcnWo5ck14QCTWtCPWX64rg+sVg5bSBiDP4Wr8cY/LFy2kDz6JfU9azZa3+vs5uN7BUJTbMPOo2mad+ouLDAljeeyAV0Prb/6xwZrMOMYdbJckg9kwY0rQVs/hcw/bz4gSSPX1uvEUJ4xqe9l6uoqEBISAjKy8thMBjc3Rxqo0xf8gDbT4895Yk6UWss3Zptdy8s015Z3qDRKHD3X9PtjgZpAIQF+aG0Sn5Pro9n3+VRC9HtTcHMyLuCKat/cOq5NQAe7BeDr35Utu6HSG5vvJiQAFTXNaD8uv3hK38fjaKMmpHBfvjpmvV7+vYuBgTrfPD9GW4k7iwvT+iNzmGBHjf9uyWxAacsEnmRcX1iJdNKMxhrf9rKJuFLt2Zj1W7rERSjgPl1bwnKlCTgURKMAZ63JtRHq7EZILqqnd/mSCeDMHF0Y2jyfvclRWHywC52p9oKAC9PSMIfvjwmWY+Pj0Z+0RtgMxgDgKMXKjgdzclCA/1kM7x6OgZkRF7G2z90SD3ekBBCiboGI1bvkZ7OtnpPPhaMuRU6L8h2pmZw4i1rQl3RTgEoDrJ6RQfj8Ply5zaIPNrkgV1ky+SWXJPd9LxaakM6hbxz4rX3KLve9De098DIGzAgI/JC3vyhQ+rwloQQSqzLKLCbWc/EKJrK2dq42NMoDU7Cg3S4WlXnsoXoaoym2qvDtMa1qLzG7vWEBPqZvzg5W/6VKpechzyTaV9OqVU5GgBrvle2rpE8W3iwv3whD8eAjIjIy7S1TcLPllarWs7dlAQnMSEBeHlCEp756LDdjIJqLkRXYzRVro7FDyThqfX2r+fxoQlYsT3X4WsJ9vfFtVr7a36C/X1xlSnt2jXTvpxyZeRGx8g7xBi8YyaBFM+f+0FERBba2ibh8eF6Vcu5m49Wg8UPNK13k8r6df/tTWtCY0Isv0zEhASoOsIpl/FxW1ahKnWY1rjau57BKoz2hen98Prk2yXLPHKH/FQ1ss1P5luhv6/nP+BpqdBAP8nsxeTZ9DqfNpFdmiNkRERepq1tEj49OQGvbs2RnLao1TSV8xZKE/CotSbU3lRCudFUQH40tSUjslLX89WRiy26JlsEgLF9YrBy2kAs2XQcRRW15mMxBn8sefA2hATq8MHeAofPFRceiPMSe551i9TjzE/eMWqrlL+fL+olRh99tVrUwjXJUu6ID8XBs2VOP8+sYQlY8W2u3ZHdYYkh2JvP9Yie6np9IxqNwitmg0hhQEZE5GXa2ibhOl8tZg9PtJll0WT28ESXJ/RwdM2V0mDL0TWhUlMJQwJ1slO3TKOp9trQkhHZ5Fsi7F6PGvdjWXU99ueXYlyfWIy6NRrrMgpwtrQa8eF6TE9OgM5Xi0ajkJwyqtTrk/th6dfZOHqhwurY7V0M+Pe8Ybj15a9l1z96i3AFWzG4KnOlVgN8NDsZv1y513b/dzbgp2t1stOChRCyae/nj+qBXjEd7D486eDvh73v71Pr0khlwovWF0thQEZE5GWUrlHypmkcppT2nrIPmVoZLJ2dgEcuucvjQxMU1VNUYT/gUmtE9s7EcITq/Rxet1NSWYNtWYVYsinbot2r9+RjyYPy69kE5NPih+n9cGdiODbNH45rNQ147tNMnLt6HV3DArH80QEIDvBFRt6VNhOMAcCAuFBsP6FsWwFnMwrg3Z15OGYjGAOAYxcrMCclEe/tzpdcgwlAcl2jaZ2m1MMTT9mcnezzlvXFUhiQERF5GdMaJSVfNLxJ6v1JWDDmVpujHq5kL8gp9LAMlkqmEn5++IKiukqv1dodEfS0EdmCy9VY/u0pq9eLKmowb/1hrLzx97E3ZdS895REQHZznwYH+GL1zME2z9eWZJ4vc8l5FG7rhTV78yVHODf9WIh3HhuIv2yRnhasdO9Oew9Pypn4w+N5y/piKQzIiIi8UFvdJFznq3Xr1BOpIAdo+qLekgyWzty4W8lUwooaZdkGL1ytxt1/Tbc5InhfUowqI7L780sdGh3TAIg2+MumKk/94pjkejYl7TBNjUy+JcLu3/ByZa1kHd6mtKoeGk3TFDB7mj8Aao0APy2qFOztJbVFgmmabFiQDrsWjpR8iOPoOs1HV+1VVI7c57Eh8e5ugsMYkBEReSluEq4+uSAHkF9zZeLsjbvVTNqy5vuzVq/dvKedGiOyLWmvvfP8anAcVmw/Lfm7V6vr8cOZKxjWvaPNUY+WTMGU+htera5TVI83kQrGAMeDMQCKgjGl0rKL8PxnRyz+Pv/7Xb6qD6UKJBK7kGc4cr5M8uGJN2BARkTkxbhJuLqKypV9+ZIr54qNu5VvQC2drEGrgc21UDdnUPzupVEOj8gqbe9zo3vgkwPnbZ4n66LtNUXNZeQ1BWSOtKPgcjVWfHvK7t9w3G1Riuoh57GVTbP51OKm9Ya2M3IquW81clEqud2lq9XYllXn1AdgzsaAjIiI6IbSKmWjHlLlXLVx96D4MLvBlIlWA7zywG34zSdH7I5uSP3+zRkUHR2RVZqMZv6oHpg/qofN82RdVJp+3P5FKWlHtMEfH+8/J/k33HP6ssK2OE5uqqAaUwkjgnS4ovD+d4UOAb64VtNg97qk7n3T1GKjUeDpjzKtjhdV1FqsNwTsTy8O8tei7rp6o3qkvo2ZF7A3r9SpD8CcjRtDExER3RAe7O9wOVdt3H3o7FXZLH9GAUR0aNqYOcZgOTIUGxKAJ4clKDqXaZqfaUR2Yv/O5hT3SindMNtHq7F7nuRutke9mpMqp6QdU+7sKpm0QwC4Vuu6L+lywZYA8Ivbpb9wxkcESh4f0DUUsSEBkpskx4YEQOfj2BSwkEBlYwGTB3Y2n7d5OwDpBwlA03vsxc+PSpb5/RfH0GgU2JZViLv/mo4pq3/A7z45gimrf8Ddf03HtqxC+Pv5KWovuU/GGetgDLDca9HTs2UyICMiIrqhedDSmnKu2rhbaZa/n8tZfiERQqBDgLIvxx0VBqpyTMloopv1X0xIgKKn2IMTw+0GDCaaG+Xk2jEnJRGaZpVpNMCclEQkdAySOYvnCQ+SDhzOyayFSj9Rgj+O7w3AfhD0x/G9Ue/gF9vhPSIVlRt7W9O9EtXB8t6LNvhj1lBlSRyu1Urvm1ZWXY+/b8/FU+sPWz1EMY2uiEbPGTUk26Sydqr1AMzZGJARERHdYJrOJiVWJqOgq9LEl15TluVvb+5PeGr9YYs1NABQXFErmyDDTPWHy9bBoRKHzl5VNFp06OxVyTLbsgrx3u58q1EWowDe252PgstVitoTHqRTVM4VNhyU3uJArouNAiiurGkaTQ2xHTAXV9bI1iPnYEGp7IMP03ss89xV/NTsPi+prMWJwkrHGnGT/91zRnJ0pbjKs0dWSBk1kyA5AwMyIiJqUxqNAhl5V/DVkYvIyLvSoqkqpulsGtgeJdBAPqOgKaiTm/rl6MbdSoOBtOxiyS+cSlyuUifFuynZia3g8Kn1h7Etq1Dy91s+KmhNbmsDAPh4/znEGOT/hv89sY/de0UpuX32/BVOEbxe7/gUyrOl1RjXJxa7Fo7EyxN6Y0ZyPF6e0Bu7Fo7EuD6xqmzAW1RRizsSwiTLPNgvFq9vy8EqO0FzhoqjHVIbhDMUaztctU9iazGpB5EX8ubUrkTOpEaqeUf3eHPVxt0xIdJrgkzKFe5FJkWNLzNqJDtROiooVU7JGr+iilo8N7onVnx7SvJvOK5PLN7V2r5Xht0SgX8fvijbVrnbQKNVuJOyCuLD9TeyEmZbBLWr9+RjyYNJqm3Am36iRPL4x/vPoVJmuiGRiVxyozC9n8MPwJyNAVkbc72uEa9tzUbBlWokROjxh/uTEKjzcXez3K4tBTDO3tuIyFupmWre0YyCrti42zQSJxVchOr9HN6MWcmmz0q0JNmJva0clI4KSpVTOnUpoaNe0d/Q3r3SaBT4IvOi5BdFDYAamZGtmnqjKlkU5Wg1QHSHAMxbf9jqWFFFDeatP4y3fzVAlbZUS4xKAUBFDYMxUk5uEkRdg+dnyWRA1obM/vAA0rJ/fuq0JxdY98M53JcUhdUzBruxZe7VlgIYV+xtROSNnJFq3tE93py9cffNI3H20rfPGpqI5d+eUlSfM0fzAHWSnSgdFZQq15I1fsm3RGDUrdFYl1GAs6XViA/XY3pygtU0Q1v3io9Wg9nDE7Fqd77dc4y6NRLbT/wk2xY/X63kl0p/Xy1qHfzSOerWSCzalCVZZtGmLIeCMQ0Avc5Hcpogkdqq6hrNm8V7Kq4hayOaB2M3S8suwewPD7i4RZ7BFMDYy54kt17Bk8h94QS8I7UrkTO4KtV8SzmSJl4J00hc80QksTeSMMwf1V3RerZ/PGY/kYNaD3nUSHaiRtKVlqzx25ZViBFv7MBftuTgw4yz+MuWHIx4Y4fi/3ak3p+EuSmJNteYzU1JxK+H36KoHrkn/I4GYwBw6GyZ7GiqI6OtQNP7cHgPz/1STG3XXhfuG9gaDMiaeeedd5CQkICAgAAMGTIE+/fvd3eTZF2va7QbjJmkZZfgejt7ItXWAhhP/cJJ5AlclWreE43rE4vvXhqFj2ffhbd+1R8fz74L3700CuP6xCre++v+2+3XoRY1kp3cnHTFXh1yI3pK+yQtu0iVB3oDuoZZpfmPNgRgQNcw8+beUpqn5neWqw4GW0ptO17skvMQ3exSmfS2D+7GgOwmn376KZ5//nksXrwYhw8fRr9+/TB27FiUlEgHO+722tZsVcu1FW0tgGnPXziJ5Lgq1bynkhqJM42iyY2AOXs0ryUbQ0uRGxVUEkTK9cl9STGqPND7Oauk5edyccWNaeY782TXvyhNM++dq6KJXKNzqLLpzu7CNWQ3efPNNzF79mzMmjULALBy5Ups2bIFH3zwAX7/+9+7uXX2FVxRloZWabm2oq0FMO39CyeRFNPoS1F5jd31VGolp/BGzl7P1pJ2qJHsRI3rkaojI++KwwlIlKxrXPO9/fVlN5NLpKHRKA/ciNqju7q1fj2wKzAgu6Gurg6HDh1Camqq+TWtVovRo0cjIyPDqnxtbS1qa39Oq1tRUeGSdtqSEKHHnlxl5dqTthbA8AsnkX2uSjXvzRxNUqIWtYJDNa7HXh1qPNBTMktD6Zos2c2wGYwRSdK6au5vK3HK4g2XL19GY2MjoqOjLV6Pjo5GUVGRVfmlS5ciJCTE/C8uLs5VTbXyh/uTVC3XVrhqc1ZXUWu6D1FbpXRqHrmfs6dHOkqNB3pKg7rQQD/J/06FBvopqoeI7FNrc3tnYUDWSqmpqSgvLzf/O3/+vNvaEqjzwX1JUZJl7kuKanf7kbXFAIZfOImkSSW4IFJKjQd6SoO6WcMSzHU2P8fNx4mo9Tx9NhQDshs6duwIHx8fFBdbZv8pLi5GTEyMVXl/f38YDAaLf+60esZgu0FZe96HrC0GMPzCSSTN00dfyPOp8UBPaVA3f1QPyf9OzR/VQ1E9IYHSq1Ck3gWmOmYPT5Ss49cMDluFn0D2yW1joZcZTJg9PLFNzIZiQHaDTqfDoEGDsH37dvNrRqMR27dvR3JyshtbptzqGYOR88o4TL+rK4b36Ijpd3VFzivj2m0wZtIWAxh+4SQici5HH+i1JKhTY+uCv06+XbI9c27shyZVxx8nNO2bZsvclEQseuA2u8dNbu8i/YB6bkqiolk9cueR+8+ezkej6Dwrpw2ULLNy2kDZttjaa85EAyB/2QRF54mPkM4EGOgn/bVd6fXIlYkM1kkej48IVNQnBcsmSJYpWDZBdhuLNx/ph7kpiVZ/b62m6Rx/nJDUJmZDaYTgUlCTTz/9FDNnzsSqVatw5513YsWKFfjss89w4sQJq7VlzVVUVCAkJATl5eVuHy0jIiIidTQahUMJSLZlFVpllYxtYVZJpfVsyyrEkk3HUVTx83qZGIM/ljx4G8b1iVXclroGI9ZlFOBsaTXiw/WYnpwAne/PwcDSrdl4b3e+VfKcOSmJSL0/CUu3ZmPVbusMknNvHAeA2R8esLmH6s2zeuTq6fnHrahrtP4aq/PR4NSr9ys+z7asQiz87Agq637eYNug0+L1R/qb+0XJNZ0uuobxf9+FeiPgpwW+/u0IdI8JNpfdllWI5/91GNU3NTlIA/xt6s8B/og30nH2ivWeWfERgdi1cJTi6/nN+sO4OWWMDsDfb3qQsC2rEPPWH7aqZ+WNMnLtUNonALDzaDEe/+ig+ee1j92Be27/+Xu1kvtS7p5U632mppbEBgzImnn77bfxxhtvoKioCP3798ff//53DBkyRPb3GJARERGRLY4GdS2pR66MWm2R+4IsdxwArtc14rWt2Si4Uo2ECD3+cH+S1Xp3uXoull7H+L/vQlVtI4L8ffD1b0egc3hgi8+jpF+UXJMcJecpr67HE2v341J5DTqFBOCDx+9EiP7n5C5qXY9cGbl2qNUnStvrijrUxIDMDRiQERERERER0LLYgGvIiIiIiIiI3IQBGRERERERkZswICMiIiIiInITBmRERERERERuwoCMiIiIiIjITRiQERERERERuQkDMiIiIiIiIjdhQEZEREREROQmDMiIiIiIiIjchAEZERERERGRmzAgIyIiIiIichMGZERERERERG7CgIyIiIiIiMhNfN3dgLZCCAEAqKiocHNLiIiIiIjInUwxgSlGkMKATCWVlZUAgLi4ODe3hIiIiIiIPEFlZSVCQkIky2iEkrCNZBmNRly6dAkdOnSARqNxd3NkVVRUIC4uDufPn4fBYHB3c9oU9q3zsG+dh33rPOxb52HfOg/71nnYt87jSX0rhEBlZSU6deoErVZ6lRhHyFSi1WrRpUsXdzejxQwGg9tv2LaKfes87FvnYd86D/vWedi3zsO+dR72rfN4St/KjYyZMKkHERERERGRmzAgIyIiIiIichMGZO2Uv78/Fi9eDH9/f3c3pc1h3zoP+9Z52LfOw751Hvat87BvnYd96zze2rdM6kFEREREROQmHCEjIiIiIiJyEwZkREREREREbsKAjIiIiIiIyE0YkBEREREREbkJAzIvtnv3bjzwwAPo1KkTNBoNvvzyS4vjxcXFePzxx9GpUyfo9XqMGzcOubm5NusSQmD8+PE26zl37hwmTJgAvV6PqKgoLFy4EA0NDU66Ks+gRt/ec8890Gg0Fv/mzZtnUYZ92/r7NiMjA6NGjUJQUBAMBgNSUlJw/fp18/HS0lJMnToVBoMBoaGhePLJJ3Ht2jVnX55bOdq3BQUFVves6d+GDRvM5Xjftu6+LSoqwvTp0xETE4OgoCAMHDgQn3/+uUUZ3ret69u8vDw89NBDiIyMhMFgwCOPPILi4mKLMu2tb5cuXYrBgwejQ4cOiIqKwqRJk3Dy5EmLMjU1NXjmmWcQERGB4OBgTJ482arflLzfd+7ciYEDB8Lf3x/du3fH2rVrnX15bqVW3/72t7/FoEGD4O/vj/79+9s819GjRzF8+HAEBAQgLi4Or7/+urMuyyOo0bc//vgjpkyZgri4OAQGBqJ379546623rM7lSfctAzIvVlVVhX79+uGdd96xOiaEwKRJk3DmzBl89dVXyMzMRHx8PEaPHo2qqiqr8itWrIBGo7F6vbGxERMmTEBdXR2+//57/POf/8TatWvxpz/9ySnX5CnU6tvZs2ejsLDQ/O/mD1L2bev7NiMjA+PGjcOYMWOwf/9+HDhwAPPnz4dW+/NH2tSpU3H8+HGkpaVh8+bN2L17N+bMmeOSa3QXR/s2Li7O4n4tLCzEn//8ZwQHB2P8+PEAeN86ct/OmDEDJ0+exKZNm3Ds2DE8/PDDeOSRR5CZmWkuw/vWkpK+raqqwpgxY6DRaJCeno69e/eirq4ODzzwAIxGo7mu9ta3u3btwjPPPIMffvgBaWlpqK+vx5gxYyzuyeeeew7/+c9/sGHDBuzatQuXLl3Cww8/bD6u5P2en5+PCRMmYOTIkThy5AieffZZ/PrXv8Y333zj0ut1JTX61uSJJ57Ao48+avM8FRUVGDNmDOLj43Ho0CG88cYbWLJkCd577z2nXZu7qdG3hw4dQlRUFNavX4/jx4/jj3/8I1JTU/H222+by3jcfSuoTQAgNm7caP755MmTAoDIysoyv9bY2CgiIyPF6tWrLX43MzNTdO7cWRQWFlrVs3XrVqHVakVRUZH5tXfffVcYDAZRW1vrtOvxJK3t2xEjRojf/e53dutl37a+b4cMGSIWLVpkt97s7GwBQBw4cMD82tdffy00Go24ePGiuhfhoRz5TLhZ//79xRNPPGH+mfdt6/s2KChIfPjhhxZ1hYeHm8vwvm1d337zzTdCq9WK8vJyc5mysjKh0WhEWlqaEIJ9K4QQJSUlAoDYtWuXEKKpj/z8/MSGDRvMZXJycgQAkZGRIYRQ9n5/8cUXxW233WZxrkcffVSMHTvW2ZfkMVrTtzdbvHix6Nevn9Xr//jHP0RYWJjFZ+tLL70kevXqpf5FeChH+9bk6aefFiNHjjT/7Gn3LUfI2qja2loAQEBAgPk1rVYLf39/fPfdd+bXqqur8dhjj+Gdd95BTEyMVT0ZGRno27cvoqOjza+NHTsWFRUVOH78uBOvwHMp7VsA+Ne//oWOHTuiT58+SE1NRXV1tfkY+9aakr4tKSnBvn37EBUVhaFDhyI6OhojRoyw6PuMjAyEhobijjvuML82evRoaLVa7Nu3z0VX41lact+aHDp0CEeOHMGTTz5pfo33rTWlfTt06FB8+umnKC0thdFoxCeffIKamhrcc889AHjf2qKkb2tra6HRaCw2gg0ICIBWqzWXYd8C5eXlAIDw8HAATe/v+vp6jB492lzm1ltvRdeuXZGRkQFA2fs9IyPDog5TGVMd7UFr+laJjIwMpKSkQKfTmV8bO3YsTp48iatXr6rUes+mVt+Wl5eb6wA8775lQNZGmW7O1NRUXL16FXV1dfjrX/+KCxcuoLCw0Fzuueeew9ChQzFx4kSb9RQVFVl8EAMw/1xUVOS8C/BgSvv2sccew/r167Fjxw6kpqZi3bp1mDZtmvk4+9aakr49c+YMAGDJkiWYPXs2tm3bhoEDB+Lee+81ryspKipCVFSURd2+vr4IDw9n38rctzd7//330bt3bwwdOtT8Gu9ba0r79rPPPkN9fT0iIiLg7++PuXPnYuPGjejevTsA3re2KOnbu+66C0FBQXjppZdQXV2NqqoqvPDCC2hsbDSXae99azQa8eyzz2LYsGHo06cPgKY+0el0CA0NtSgbHR1t7hMl73d7ZSoqKizW9bZVre1bJdr7561affv999/j008/tZii7Gn3LQOyNsrPzw9ffPEFTp06hfDwcOj1euzYsQPjx483r7PZtGkT0tPTsWLFCvc21sso6VsAmDNnDsaOHYu+ffti6tSp+PDDD7Fx40bk5eW5sfWeTUnfmtaEzJ07F7NmzcKAAQOwfPly9OrVCx988IE7m+/RlN63JtevX8dHH31kMTpGtint25dffhllZWX49ttvcfDgQTz//PN45JFHcOzYMTe23rMp6dvIyEhs2LAB//nPfxAcHIyQkBCUlZVh4MCBNu/t9uiZZ55BVlYWPvnkE3c3pc1h3zqPGn2blZWFiRMnYvHixRgzZoyKrVOXr7sbQM4zaNAgHDlyBOXl5airq0NkZCSGDBlinrKRnp6OvLw8q6cMkydPxvDhw7Fz507ExMRg//79FsdNmWxsTXFsL+T61pYhQ4YAAE6fPo1bbrmFfWuHXN/GxsYCAJKSkix+r3fv3jh37hyApv4rKSmxON7Q0IDS0lL2rcL79t///jeqq6sxY8YMi9d539om17d5eXl4++23kZWVhdtuuw0A0K9fP+zZswfvvPMOVq5cyfvWDiX37ZgxY5CXl4fLly/D19cXoaGhiImJQbdu3QC078+E+fPnm5OYdOnSxfx6TEwM6urqUFZWZvE9oLi42NwnSt7vMTExVtkDi4uLYTAYEBgY6IxL8hiO9K0S9vrWdKwtU6Nvs7Ozce+992LOnDlYtGiRxTFPu2/56KgdCAkJQWRkJHJzc3Hw4EHz9MTf//73OHr0KI4cOWL+BwDLly/HmjVrAADJyck4duyYxX/I0tLSYDAYrL4Qt0f2+tYWU/+aAgr2rTR7fZuQkIBOnTpZpcE9deoU4uPjATT1bVlZGQ4dOmQ+np6eDqPRaA6M2zMl9+3777+PBx98EJGRkRav876VZq9vTetHm4/Y+Pj4mEd9ed9KU3LfduzYEaGhoUhPT0dJSQkefPBBAO2zb4UQmD9/PjZu3Ij09HQkJiZaHB80aBD8/Pywfft282snT57EuXPnkJycDEDZ+z05OdmiDlMZUx1tkRp9q0RycjJ2796N+vp682tpaWno1asXwsLCHL8QD6RW3x4/fhwjR47EzJkz8eqrr1qdx+PuW7ekEiFVVFZWiszMTJGZmSkAiDfffFNkZmaKs2fPCiGE+Oyzz8SOHTtEXl6e+PLLL0V8fLx4+OGHJetEswxXDQ0Nok+fPmLMmDHiyJEjYtu2bSIyMlKkpqY689LcztG+PX36tHjllVfEwYMHRX5+vvjqq69Et27dREpKirkM+7b19+3y5cuFwWAQGzZsELm5uWLRokUiICBAnD592lxm3LhxYsCAAWLfvn3iu+++Ez169BBTpkxx6bW6mlqfCbm5uUKj0Yivv/7a6hjv29b1bV1dnejevbsYPny42Ldvnzh9+rT4n//5H6HRaMSWLVvM5Xjftu6+/eCDD0RGRoY4ffq0WLdunQgPDxfPP/+8RZn21rdPPfWUCAkJETt37hSFhYXmf9XV1eYy8+bNE127dhXp6eni4MGDIjk5WSQnJ5uPK3m/nzlzRuj1erFw4UKRk5Mj3nnnHeHj4yO2bdvm0ut1JTX6Voimz9rMzEwxd+5c0bNnT/P7wJRVsaysTERHR4vp06eLrKws8cknnwi9Xi9WrVrl0ut1JTX69tixYyIyMlJMmzbNoo6SkhJzGU+7bxmQebEdO3YIAFb/Zs6cKYQQ4q233hJdunQRfn5+omvXrmLRokWyaambB2RCCFFQUCDGjx8vAgMDRceOHcWCBQtEfX29k67KMzjat+fOnRMpKSkiPDxc+Pv7i+7du4uFCxdapGUWgn3ryH27dOlS0aVLF6HX60VycrLYs2ePxfErV66IKVOmiODgYGEwGMSsWbNEZWWlKy7RbdTq29TUVBEXFycaGxttnof3bev69tSpU+Lhhx8WUVFRQq/Xi9tvv90qDT7v29b17UsvvSSio6OFn5+f6NGjh/jb3/4mjEajRZn21re2+hSAWLNmjbnM9evXxdNPPy3CwsKEXq8XDz30kCgsLLSoR8n7fceOHaJ///5Cp9OJbt26WZyjLVKrb0eMGGGznvz8fHOZH3/8Udx9993C399fdO7cWSxbtsxFV+keavTt4sWLbdYRHx9vcS5Pum81Qgjh6CgbERERERERtRzXkBEREREREbkJAzIiIiIiIiI3YUBGRERERETkJgzIiIiIiIiI3IQBGRERERERkZswICMiIiIiInITBmRERERERERuwoCMiIiIiIjITRiQERFRu/H4449j0qRJTj9PQkICVqxY4fTzEBGR92NARkREbvX4449Do9FAo9FAp9Ohe/fueOWVV9DQ0ODupslau3YtQkNDrV4/cOAA5syZ47Tzrlu3DkFBQTh9+rTF65cuXUJYWBjefvttp52biIjUxYCMiIjcbty4cSgsLERubi4WLFiAJUuW4I033rBZtq6uzsWta7nIyEjo9Xqn1T99+nSMHTsWjz/+OIxGo/n12bNnY9CgQXjmmWdUP6c39DsRkTdiQEZERG7n7++PmJgYxMfH46mnnsLo0aOxadMmAD9PM3z11VfRqVMn9OrVCwBw7NgxjBo1CoGBgYiIiMCcOXNw7do1c52NjY14/vnnERoaioiICLz44osQQlic19bUwv79+2PJkiXmn8vKyjB37lxER0cjICAAffr0webNm7Fz507MmjUL5eXl5hE+0+81r/fcuXOYOHEigoODYTAY8Mgjj6C4uNh8fMmSJejfvz/WrVuHhIQEhISE4Fe/+hUqKyvt9tmqVatw6tQpvPnmmwCaRuv27t2LNWvWoK6uDi+88AI6d+6MoKAgDBkyBDt37jT/7pUrVzBlyhR07twZer0effv2xccff2xR/z333IP58+fj2WefRceOHTF27Fi7bSEiotZjQEZERB4nMDDQYkRm+/btOHnyJNLS0rB582ZUVVVh7NixCAsLw4EDB7BhwwZ8++23mD9/vvl3/va3v2Ht2rX44IMP8N1336G0tBQbN25sUTuMRiPGjx+PvXv3Yv369cjOzsayZcvg4+ODoUOHYsWKFTAYDCgsLERhYSFeeOEFm3VMnDgRpaWl2LVrF9LS0nDmzBk8+uijFuXy8vLw5ZdfYvPmzdi8eTN27dqFZcuW2W1bZGQk3nvvPbz88stIS0vDc889h7feegtxcXGYP38+MjIy8Mknn+Do0aP4r//6L4wbNw65ubkAgJqaGgwaNAhbtmxBVlYW5syZg+nTp2P//v0W5/jnP/8JnU6HvXv3YuXKlS3qOyIiUkgQERG50cyZM8XEiROFEEIYjUaRlpYm/P39xQsvvGA+Hh0dLWpra82/895774mwsDBx7do182tbtmwRWq1WFBUVCSGEiI2NFa+//rr5eH19vejSpYv5XEIIER8fL5YvX27Rnn79+onFixcLIYT45ptvhFarFSdPnrTZ9jVr1oiQkBCr12+u9//+7/+Ej4+POHfunPn48ePHBQCxf/9+IYQQixcvFnq9XlRUVJjLLFy4UAwZMsTmeW82Y8YModVqzdd19uxZ4ePjIy5evGhR7t577xWpqal265kwYYJYsGCB+ecRI0aIAQMGyJ6fiIgc4+vugJCIiGjz5s0IDg5GfX09jEYjHnvsMYtpg3379oVOpzP/nJOTg379+iEoKMj82rBhw2A0GnHy5EkEBASgsLAQQ4YMMR/39fXFHXfcYTVtUcqRI0fQpUsX9OzZs9XXlpOTg7i4OMTFxZlfS0pKQmhoKHJycjB48GAATdMcO3ToYC4TGxuLkpIS2fpffvllfPjhh1i0aBGApqmcjY2NVm2ura1FREQEgKbpnK+99ho+++wzXLx4EXV1daitrbVa9zZo0KDWXTQRESnGgIyIiNxu5MiRePfdd6HT6dCpUyf4+lr+5+nmwEtNWq3WKkCrr683///AwECnnNcWPz8/i581Go1Fwg57TH1l+t9r167Bx8cHhw4dgo+Pj0XZ4OBgAMAbb7yBt956CytWrEDfvn0RFBSEZ5991ipxh7P6nYiIfsY1ZERE5HZBQUHo3r07unbtahWM2dK7d2/8+OOPqKqqMr+2d+9eaLVa9OrVCyEhIYiNjcW+ffvMxxsaGnDo0CGLeiIjI1FYWGj+uaKiAvn5+eafb7/9dly4cAGnTp2y2Q6dTofGxkbZtp4/fx7nz583v5adnY2ysjIkJSXJXmtLDRgwAI2NjSgpKUH37t0t/sXExABo6quJEydi2rRp6NevH7p162b3GomIyLkYkBERkdeZOnUqAgICMHPmTGRlZWHHjh34zW9+g+nTpyM6OhoA8Lvf/Q7Lli3Dl19+iRMnTuDpp59GWVmZRT2jRo3CunXrsGfPHhw7dgwzZ860GFUaMWIEUlJSMHnyZKSlpSE/Px9ff/01tm3bBqBpmuG1a9ewfft2XL58GdXV1VZtHT16NPr27YupU6fi8OHD2L9/P2bMmIERI0bgjjvuUL1vevbsialTp2LGjBn44osvkJ+fj/3792Pp0qXYsmULAKBHjx5IS0vD999/j5ycHMydO9ci6yMREbkOAzIiIvI6er0e33zzDUpLSzF48GD88pe/xL333muxIfKCBQswffp0zJw5E8nJyejQoQMeeughi3pSU1MxYsQI/OIXv8CECRMwadIk3HLLLRZlPv/8cwwePBhTpkxBUlISXnzxRfOo2NChQzFv3jw8+uijiIyMxOuvv27VVo1Gg6+++gphYWFISUnB6NGj0a1bN3z66adO6Jkma9aswYwZM7BgwQL06tULkyZNwoEDB9C1a1cAwKJFizBw4ECMHTsW99xzD2JiYjBp0iSntYeIiOzTiJasbiYiIiIiIiLVcISMiIiIiIjITRiQERERERERuQkDMiIiIiIiIjdhQEZEREREROQmDMiIiIiIiIjchAEZERERERGRmzAgIyIiIiIichMGZERERERERG7CgIyIiIiIiMhNGJARERERERG5CQMyIiIiIiIiN/l/lHXYWyotWAEAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Преобразуем год производства в целочисленный тип\n", - "df1['Prod. year'] = df1['Prod. year'].astype(int)\n", - "\n", - "# Статистический анализ для определения выбросов\n", - "Q1 = df1['Price'].quantile(0.25)\n", - "Q3 = df1['Price'].quantile(0.75)\n", - "IQR = Q3 - Q1\n", - "\n", - "# Определение порога для выбросов\n", - "threshold = 1.5 * IQR\n", - "outliers = (df1['Price'] < (Q1 - threshold)) | (df1['Price'] > (Q3 + threshold))\n", - "\n", - "# Вывод выбросов\n", - "print(\"Выбросы:\")\n", - "print(df1[outliers])\n", - "\n", - "# Обработка выбросов\n", - "# В данном случае мы заменим выбросы на медианное значение\n", - "median_price = df1['Price'].median()\n", - "df1.loc[outliers, 'Price'] = median_price\n", - "\n", - "# Визуализация данных после обработки\n", - "plt.figure(figsize=(10, 6))\n", - "plt.scatter(df1['Prod. year'], df1['Price'])\n", - "plt.xlabel('Production Year')\n", - "plt.ylabel('Price')\n", - "plt.title('Scatter Plot of Price vs Production Year (After Handling Outliers)')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Очистим от строк с пустыми значениями наш датасет" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Количество удаленных строк: 0\n", - "\n", - "DataFrame после удаления строк с пропущенными значениями:\n", - " ID Price Levy Manufacturer Model Prod. year Category \\\n", - "0 45654403 13328 1399 LEXUS RX 450 2010 Jeep \n", - "1 44731507 16621 1018 CHEVROLET Equinox 2011 Jeep \n", - "2 45774419 8467 - HONDA FIT 2006 Hatchback \n", - "3 45769185 3607 862 FORD Escape 2011 Jeep \n", - "4 45809263 11726 446 HONDA FIT 2014 Hatchback \n", - "... ... ... ... ... ... ... ... \n", - "19232 45798355 8467 - MERCEDES-BENZ CLK 200 1999 Coupe \n", - "19233 45778856 15681 831 HYUNDAI Sonata 2011 Sedan \n", - "19234 45804997 26108 836 HYUNDAI Tucson 2010 Jeep \n", - "19235 45793526 5331 1288 CHEVROLET Captiva 2007 Jeep \n", - "19236 45813273 470 753 HYUNDAI Sonata 2012 Sedan \n", - "\n", - " Leather interior Fuel type Engine volume Mileage Cylinders \\\n", - "0 Yes Hybrid 3.5 186005 km 6.0 \n", - "1 No Petrol 3 192000 km 6.0 \n", - "2 No Petrol 1.3 200000 km 4.0 \n", - "3 Yes Hybrid 2.5 168966 km 4.0 \n", - "4 Yes Petrol 1.3 91901 km 4.0 \n", - "... ... ... ... ... ... \n", - "19232 Yes CNG 2.0 Turbo 300000 km 4.0 \n", - "19233 Yes Petrol 2.4 161600 km 4.0 \n", - "19234 Yes Diesel 2 116365 km 4.0 \n", - "19235 Yes Diesel 2 51258 km 4.0 \n", - "19236 Yes Hybrid 2.4 186923 km 4.0 \n", - "\n", - " Gear box type Drive wheels Doors Wheel Color Airbags \n", - "0 Automatic 4x4 04-May Left wheel Silver 12 \n", - "1 Tiptronic 4x4 04-May Left wheel Black 8 \n", - "2 Variator Front 04-May Right-hand drive Black 2 \n", - "3 Automatic 4x4 04-May Left wheel White 0 \n", - "4 Automatic Front 04-May Left wheel Silver 4 \n", - "... ... ... ... ... ... ... \n", - "19232 Manual Rear 02-Mar Left wheel Silver 5 \n", - "19233 Tiptronic Front 04-May Left wheel Red 8 \n", - "19234 Automatic Front 04-May Left wheel Grey 4 \n", - "19235 Automatic Front 04-May Left wheel Black 4 \n", - "19236 Automatic Front 04-May Left wheel White 12 \n", - "\n", - "[19237 rows x 18 columns]\n" - ] - } - ], - "source": [ - "# Удаление строк с пропущенными значениями\n", - "df_dropna = df1.dropna()\n", - "\n", - "# Вывод количества удаленных строк\n", - "num_deleted_rows = len(df1) - len(df_dropna)\n", - "print(f\"\\nКоличество удаленных строк: {num_deleted_rows}\")\n", - "\n", - "print(\"\\nDataFrame после удаления строк с пропущенными значениями:\")\n", - "print(df_dropna)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Теперь создадим выборки" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки: 11542\n", - "Размер контрольной выборки: 3847\n", - "Размер тестовой выборки: 3848\n" - ] - } - ], - "source": [ - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", - "\n", - "# Разделение данных на обучающую и временную выборки\n", - "train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n", - "\n", - "# Разделение остатка на контрольную и тестовую выборки\n", - "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n", - "\n", - "# Проверка размеров выборок\n", - "print(\"Размер обучающей выборки:\", len(train_df))\n", - "print(\"Размер контрольной выборки:\", len(val_df))\n", - "print(\"Размер тестовой выборки:\", len(test_df))\n", - "\n", - "# Сохранение выборок в файлы\n", - "train_df.to_csv(\"..//static//csv//train_data.csv\", index=False)\n", - "val_df.to_csv(\"..//static//csv//val_data.csv\", index=False)\n", - "test_df.to_csv(\"..//static//csv//test_data.csv\", index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Проанализируем сбалансированность выборок" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение Category в обучающей выборке:\n", - "Category\n", - "Sedan 5289\n", - "Jeep 3246\n", - "Hatchback 1684\n", - "Minivan 396\n", - "Coupe 318\n", - "Universal 216\n", - "Microbus 184\n", - "Goods wagon 151\n", - "Pickup 31\n", - "Cabriolet 20\n", - "Limousine 7\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 45.82%\n", - "Процент автомобилей категории 'Джип': 28.12%\n", - "\n", - "Распределение Category в контрольной выборке:\n", - "Category\n", - "Sedan 1697\n", - "Jeep 1109\n", - "Hatchback 608\n", - "Minivan 129\n", - "Coupe 105\n", - "Universal 73\n", - "Microbus 57\n", - "Goods wagon 42\n", - "Pickup 17\n", - "Cabriolet 9\n", - "Limousine 1\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 44.11%\n", - "Процент автомобилей категории 'Джип': 28.83%\n", - "\n", - "Распределение Category в тестовой выборке:\n", - "Category\n", - "Sedan 1750\n", - "Jeep 1118\n", - "Hatchback 555\n", - "Minivan 122\n", - "Coupe 109\n", - "Universal 75\n", - "Microbus 65\n", - "Goods wagon 40\n", - "Cabriolet 7\n", - "Pickup 4\n", - "Limousine 3\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 45.48%\n", - "Процент автомобилей категории 'Джип': 29.05%\n", - "\n", - "Необходима аугментация данных для балансировки классов.\n", - "Необходима аугментация данных для балансировки классов.\n", - "Необходима аугментация данных для балансировки классов.\n" - ] - } - ], - "source": [ - "train_df = pd.read_csv(\"..//static//csv//train_data.csv\")\n", - "val_df = pd.read_csv(\"..//static//csv//val_data.csv\")\n", - "test_df = pd.read_csv(\"..//static//csv//test_data.csv\")\n", - "\n", - "# Оценка сбалансированности\n", - "def check_balance(df, name):\n", - " counts = df['Category'].value_counts()\n", - " print(f\"Распределение Category в {name}:\")\n", - " print(counts)\n", - " print(f\"Процент автомобилей категории 'Седан': {counts['Sedan'] / len(df) * 100:.2f}%\")\n", - " print(f\"Процент автомобилей категории 'Джип': {counts['Jeep'] / len(df) * 100:.2f}%\")\n", - " print()\n", - "\n", - "# Определение необходимости аугментации данных\n", - "def need_augmentation(df):\n", - " counts = df['Category'].value_counts()\n", - " ratio = counts['Sedan'] / counts['Jeep']\n", - " if ratio > 1.5 or ratio < 0.67:\n", - " print(\"Необходима аугментация данных для балансировки классов.\")\n", - " else:\n", - " print(\"Аугментация данных не требуется.\")\n", - " \n", - "check_balance(train_df, \"обучающей выборке\")\n", - "check_balance(val_df, \"контрольной выборке\")\n", - "check_balance(test_df, \"тестовой выборке\")\n", - "\n", - "need_augmentation(train_df)\n", - "need_augmentation(val_df)\n", - "need_augmentation(test_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "По результатам анализа требуется приращение, соотношения отзывов вне допустимого диапазона" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Оверсэмплинг:\n", - "Распределение Category в обучающей выборке:\n", - "Category\n", - "Jeep 5289\n", - "Hatchback 5289\n", - "Sedan 5289\n", - "Goods wagon 5289\n", - "Cabriolet 5289\n", - "Universal 5289\n", - "Minivan 5289\n", - "Microbus 5289\n", - "Coupe 5289\n", - "Pickup 5289\n", - "Limousine 5289\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 9.09%\n", - "Процент автомобилей категории 'Джип': 9.09%\n", - "\n", - "Распределение Category в контрольной выборке:\n", - "Category\n", - "Jeep 1697\n", - "Sedan 1697\n", - "Minivan 1697\n", - "Coupe 1697\n", - "Hatchback 1697\n", - "Goods wagon 1697\n", - "Universal 1697\n", - "Microbus 1697\n", - "Pickup 1697\n", - "Cabriolet 1697\n", - "Limousine 1697\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 9.09%\n", - "Процент автомобилей категории 'Джип': 9.09%\n", - "\n", - "Распределение Category в тестовой выборке:\n", - "Category\n", - "Jeep 1750\n", - "Hatchback 1750\n", - "Sedan 1750\n", - "Coupe 1750\n", - "Minivan 1750\n", - "Goods wagon 1750\n", - "Microbus 1750\n", - "Universal 1750\n", - "Cabriolet 1750\n", - "Pickup 1750\n", - "Limousine 1750\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 9.09%\n", - "Процент автомобилей категории 'Джип': 9.09%\n", - "\n", - "Андерсэмплинг:\n", - "Распределение Category в обучающей выборке:\n", - "Category\n", - "Cabriolet 7\n", - "Coupe 7\n", - "Goods wagon 7\n", - "Hatchback 7\n", - "Jeep 7\n", - "Limousine 7\n", - "Microbus 7\n", - "Minivan 7\n", - "Pickup 7\n", - "Sedan 7\n", - "Universal 7\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 9.09%\n", - "Процент автомобилей категории 'Джип': 9.09%\n", - "\n", - "Распределение Category в контрольной выборке:\n", - "Category\n", - "Cabriolet 1\n", - "Coupe 1\n", - "Goods wagon 1\n", - "Hatchback 1\n", - "Jeep 1\n", - "Limousine 1\n", - "Microbus 1\n", - "Minivan 1\n", - "Pickup 1\n", - "Sedan 1\n", - "Universal 1\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 9.09%\n", - "Процент автомобилей категории 'Джип': 9.09%\n", - "\n", - "Распределение Category в тестовой выборке:\n", - "Category\n", - "Cabriolet 3\n", - "Coupe 3\n", - "Goods wagon 3\n", - "Hatchback 3\n", - "Jeep 3\n", - "Limousine 3\n", - "Microbus 3\n", - "Minivan 3\n", - "Pickup 3\n", - "Sedan 3\n", - "Universal 3\n", - "Name: count, dtype: int64\n", - "Процент автомобилей категории 'Седан': 9.09%\n", - "Процент автомобилей категории 'Джип': 9.09%\n", - "\n" - ] - } - ], - "source": [ - "# Загрузка данных\n", - "train_df = pd.read_csv(\"..//static//csv//train_data.csv\")\n", - "val_df = pd.read_csv(\"..//static//csv//val_data.csv\")\n", - "test_df = pd.read_csv(\"..//static//csv//test_data.csv\")\n", - "\n", - "# Преобразование категориальных признаков в числовые\n", - "def encode(df):\n", - " label_encoders = {}\n", - " for column in df.select_dtypes(include=['object']).columns:\n", - " if column != 'Category': # Пропускаем целевую переменную\n", - " le = LabelEncoder()\n", - " df[column] = le.fit_transform(df[column])\n", - " label_encoders[column] = le\n", - " return label_encoders\n", - "\n", - "# Преобразование целевой переменной в числовые значения\n", - "def encode_target(df):\n", - " le = LabelEncoder()\n", - " df['Category'] = le.fit_transform(df['Category'])\n", - " return le\n", - "\n", - "# Применение кодирования\n", - "label_encoders = encode(train_df)\n", - "encode(val_df)\n", - "encode(test_df)\n", - "\n", - "# Кодирование целевой переменной\n", - "le_target = encode_target(train_df)\n", - "encode_target(val_df)\n", - "encode_target(test_df)\n", - "\n", - "# Проверка типов данных\n", - "def check_data_types(df):\n", - " for column in df.columns:\n", - " if df[column].dtype == 'object':\n", - " print(f\"Столбец '{column}' содержит строковые данные.\")\n", - "\n", - "check_data_types(train_df)\n", - "check_data_types(val_df)\n", - "check_data_types(test_df)\n", - "\n", - "# Функция для выполнения oversampling\n", - "def oversample(df):\n", - " if 'Category' not in df.columns:\n", - " print(\"Столбец 'Category' отсутствует.\")\n", - " return df\n", - " \n", - " X = df.drop('Category', axis=1)\n", - " y = df['Category']\n", - " \n", - " oversampler = RandomOverSampler(random_state=42)\n", - " X_resampled, y_resampled = oversampler.fit_resample(X, y)\n", - " \n", - " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", - " return resampled_df\n", - "\n", - "# Функция для выполнения undersampling\n", - "def undersample(df):\n", - " if 'Category' not in df.columns:\n", - " print(\"Столбец 'Category' отсутствует.\")\n", - " return df\n", - " \n", - " X = df.drop('Category', axis=1)\n", - " y = df['Category']\n", - " \n", - " undersampler = RandomUnderSampler(random_state=42)\n", - " X_resampled, y_resampled = undersampler.fit_resample(X, y)\n", - " \n", - " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", - " return resampled_df\n", - "\n", - "# Применение oversampling и undersampling к каждой выборке\n", - "train_df_oversampled = oversample(train_df)\n", - "val_df_oversampled = oversample(val_df)\n", - "test_df_oversampled = oversample(test_df)\n", - "\n", - "train_df_undersampled = undersample(train_df)\n", - "val_df_undersampled = undersample(val_df)\n", - "test_df_undersampled = undersample(test_df)\n", - "\n", - "# Обратное преобразование целевой переменной в строковые метки\n", - "def decode_target(df, le_target):\n", - " df['Category'] = le_target.inverse_transform(df['Category'])\n", - "\n", - "decode_target(train_df_oversampled, le_target)\n", - "decode_target(val_df_oversampled, le_target)\n", - "decode_target(test_df_oversampled, le_target)\n", - "\n", - "decode_target(train_df_undersampled, le_target)\n", - "decode_target(val_df_undersampled, le_target)\n", - "decode_target(test_df_undersampled, le_target)\n", - "\n", - "# Проверка результатов\n", - "def check_balance(df, name):\n", - " if 'Category' not in df.columns:\n", - " print(f\"Столбец 'Category' отсутствует в {name}.\")\n", - " return\n", - " \n", - " counts = df['Category'].value_counts()\n", - " print(f\"Распределение Category в {name}:\")\n", - " print(counts)\n", - " \n", - " if 'Sedan' in counts and 'Jeep' in counts:\n", - " print(f\"Процент автомобилей категории 'Седан': {counts['Sedan'] / len(df) * 100:.2f}%\")\n", - " print(f\"Процент автомобилей категории 'Джип': {counts['Jeep'] / len(df) * 100:.2f}%\")\n", - " else:\n", - " print(\"Отсутствуют одна или обе категории (Седан/Внедорожник).\")\n", - " print()\n", - "\n", - "# Проверка сбалансированности после oversampling\n", - "print(\"Оверсэмплинг:\")\n", - "check_balance(train_df_oversampled, \"обучающей выборке\")\n", - "check_balance(val_df_oversampled, \"контрольной выборке\")\n", - "check_balance(test_df_oversampled, \"тестовой выборке\")\n", - "\n", - "# Проверка сбалансированности после undersampling\n", - "print(\"Андерсэмплинг:\")\n", - "check_balance(train_df_undersampled, \"обучающей выборке\")\n", - "check_balance(val_df_undersampled, \"контрольной выборке\")\n", - "check_balance(test_df_undersampled, \"тестовой выборке\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Классические рок-треки (по данным Spotify)**\n", - "https://www.kaggle.com/datasets/thebumpkin/14400-classic-rock-tracks-with-spotify-data\n", - "\n", - " Этот набор данных, содержащий 1200 уникальных альбомов и 14 400 треков, представляет собой не просто коллекцию — это хроника эволюции классического рока. Каждый трек тщательно каталогизирован с 18 столбцами данных, включая ключевые метаданные, такие как название трека, исполнитель, альбом и год выпуска, наряду с функциями Spotify audio, которые позволяют получить представление о звуковом ландшафте этих неподвластных времени мелодий. Бизнес-цель может заключаться в улучшении стратегии маркетинга и продвижения музыкальных треков. Предположим как этот набор может быть полезен для бизнеса: Персонализированные рекомендации: Создание алгоритмов, которые будут рекомендовать пользователям музыку на основе их предпочтений. Цель технического проекта: Разработать и внедрить систему рекомендаций, которая будет предсказывать и рекомендовать пользователям музыкальные треки на основе их предпочтений и поведения. Входные данные: Данные о пользователях: Идентификатор пользователя, история прослушиваний, оценки треков, время прослушивания, частота прослушивания. Данные о треках: Атрибуты треков (название, исполнитель, альбом, год, длительность, танцевальность, энергичность, акустичность и т.д.). Данные о взаимодействии: Время и частота взаимодействия пользователя с определенными треками. Целевой признак: Рекомендации: Булева переменная, указывающая, должен ли конкретный трек быть рекомендован пользователю (1 - рекомендуется, 0 - не рекомендуется)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Выгрузка данных из csv файла \"Данные о клиентах\" в датафрейм" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['Track', 'Artist', 'Album', 'Year', 'Duration', 'Time_Signature',\n", - " 'Danceability', 'Energy', 'Key', 'Loudness', 'Mode', 'Speechiness',\n", - " 'Acousticness', 'Instrumentalness', 'Liveness', 'Valence', 'Tempo',\n", - " 'Popularity'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "df = pd.read_csv(\"..//static//csv//UltimateClassicRock.csv\")\n", - "print(df.columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Анализируем датафрейм при помощи \"ящика с усами\". Есть смещение в сторону меньших значений, это можно исправить при помощи oversampling и undersampling." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Box plot для столбца 'Popularity'\n", - "plt.figure(figsize=(10, 6))\n", - "sns.boxplot(x=df['Popularity'])\n", - "plt.title('Box Plot для Popularity')\n", - "plt.xlabel('Popularity')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Решим проблему пустых значений при помощи удаления таких строк." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "df_cleaned = df.dropna()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Разбиение набора данных на обучающую, контрольную и тестовую выборки" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки: 8650\n", - "Размер контрольной выборки: 2884\n", - "Размер тестовой выборки: 2884\n" - ] - } - ], - "source": [ - "# Разделение на обучающую и тестовую выборки\n", - "train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную\n", - "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", - "\n", - "print(\"Размер обучающей выборки:\", len(train_df))\n", - "print(\"Размер контрольной выборки:\", len(val_df))\n", - "print(\"Размер тестовой выборки:\", len(test_df))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Оценка сбалансированности выборок, по результатам видно что баланса тут мало" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение Popularity в обучающей выборке:\n", - "Popularity\n", - "23 258\n", - "15 250\n", - "26 246\n", - "21 245\n", - "14 245\n", - " ... \n", - "84 1\n", - "87 1\n", - "91 1\n", - "79 1\n", - "86 1\n", - "Name: count, Length: 88, dtype: int64\n", - "\n", - "Распределение Popularity в контрольной выборке:\n", - "Popularity\n", - "17 90\n", - "26 86\n", - "21 83\n", - "24 83\n", - "28 80\n", - " ..\n", - "85 1\n", - "83 1\n", - "84 1\n", - "80 1\n", - "77 1\n", - "Name: count, Length: 85, dtype: int64\n", - "\n", - "Распределение Popularity в тестовой выборке:\n", - "Popularity\n", - "22 86\n", - "21 85\n", - "12 84\n", - "20 82\n", - "26 81\n", - " ..\n", - "76 2\n", - "71 2\n", - "79 1\n", - "82 1\n", - "80 1\n", - "Name: count, Length: 80, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "def check_balance(df, name):\n", - " counts = df['Popularity'].value_counts()\n", - " print(f\"Распределение Popularity в {name}:\")\n", - " print(counts)\n", - " print()\n", - "\n", - "check_balance(train_df, \"обучающей выборке\")\n", - "check_balance(val_df, \"контрольной выборке\")\n", - "check_balance(test_df, \"тестовой выборке\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Выполним овер- и андер- слемпинг." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение Popularity в обучающей выборке после oversampling:\n", - "Popularity\n", - "44 258\n", - "20 258\n", - "30 258\n", - "27 258\n", - "8 258\n", - " ... \n", - "78 258\n", - "79 258\n", - "74 258\n", - "81 258\n", - "86 258\n", - "Name: count, Length: 88, dtype: int64\n", - "\n", - "Распределение Popularity в контрольной выборке после oversampling:\n", - "Popularity\n", - "21 90\n", - "11 90\n", - "28 90\n", - "23 90\n", - "37 90\n", - " ..\n", - "61 90\n", - "84 90\n", - "80 90\n", - "77 90\n", - "0 90\n", - "Name: count, Length: 85, dtype: int64\n", - "\n", - "Распределение Popularity в тестовой выборке после oversampling:\n", - "Popularity\n", - "14 86\n", - "47 86\n", - "27 86\n", - "13 86\n", - "66 86\n", - " ..\n", - "63 86\n", - "79 86\n", - "71 86\n", - "82 86\n", - "80 86\n", - "Name: count, Length: 80, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "def oversample(df):\n", - " X = df.drop('Popularity', axis=1)\n", - " y = df['Popularity']\n", - " \n", - " oversampler = RandomOverSampler(random_state=42)\n", - " X_resampled, y_resampled = oversampler.fit_resample(X, y)\n", - " \n", - " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", - " return resampled_df\n", - "\n", - "train_df_oversampled = oversample(train_df)\n", - "val_df_oversampled = oversample(val_df)\n", - "test_df_oversampled = oversample(test_df)\n", - "\n", - "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", - "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", - "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение Popularity в обучающей выборке после undersampling:\n", - "Popularity\n", - "0 1\n", - "1 1\n", - "2 1\n", - "3 1\n", - "4 1\n", - " ..\n", - "84 1\n", - "85 1\n", - "86 1\n", - "87 1\n", - "91 1\n", - "Name: count, Length: 88, dtype: int64\n", - "\n", - "Распределение Popularity в контрольной выборке после undersampling:\n", - "Popularity\n", - "0 1\n", - "1 1\n", - "2 1\n", - "3 1\n", - "4 1\n", - " ..\n", - "82 1\n", - "83 1\n", - "84 1\n", - "85 1\n", - "87 1\n", - "Name: count, Length: 85, dtype: int64\n", - "\n", - "Распределение Popularity в тестовой выборке после undersampling:\n", - "Popularity\n", - "0 1\n", - "1 1\n", - "2 1\n", - "3 1\n", - "4 1\n", - " ..\n", - "76 1\n", - "77 1\n", - "79 1\n", - "80 1\n", - "82 1\n", - "Name: count, Length: 80, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "def undersample(df):\n", - " X = df.drop('Popularity', axis=1)\n", - " y = df['Popularity']\n", - " \n", - " undersampler = RandomUnderSampler(random_state=42)\n", - " X_resampled, y_resampled = undersampler.fit_resample(X, y)\n", - " \n", - " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", - " return resampled_df\n", - "\n", - "train_df_undersampled = undersample(train_df)\n", - "val_df_undersampled = undersample(val_df)\n", - "test_df_undersampled = undersample(test_df)\n", - "\n", - "check_balance(train_df_undersampled, \"обучающей выборке после undersampling\")\n", - "check_balance(val_df_undersampled, \"контрольной выборке после undersampling\")\n", - "check_balance(test_df_undersampled, \"тестовой выборке после undersampling\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Онлайн обучение**\n", - "\n", - "https://www.kaggle.com/datasets/shariful07/student-flexibility-in-online-learning\n", - "\n", - "\n", - "Этот набор данных предоставляет информацию о студентах и их характеристиках, связанных с обучением и использованием технологий. В данных представлены следующие атрибуты: уровень образования студента (например, бакалавриат, магистратура), тип учебного заведения (государственное или частное), пол, возраст, тип используемого устройства, является ли студент IT-специалистом, местоположение, финансовое состояние, тип интернета, тип сети и уровень гибкости в обучении. Эти данные могут быть использованы для анализа влияния различных факторов на успеваемость студентов, оптимизации образовательных программ и разработки стратегий поддержки студентов в условиях цифровизации образования." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Выгрузка данных из csv файла \"Онлайн обучение\" в датафрейм" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['Education Level', 'Institution Type', 'Gender', 'Age', 'Device',\n", - " 'IT Student', 'Location', 'Financial Condition', 'Internet Type',\n", - " 'Network Type', 'Flexibility Level'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "df = pd.read_csv(\"..//static//csv//students_adaptability_level_online_education.csv\")\n", - "print(df.columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "При помощи ящика с усами и колонки возраста проверим набор на баланс." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Box plot для столбца 'Age'\n", - "plt.figure(figsize=(10, 6))\n", - "sns.boxplot(x=df['Age'])\n", - "plt.title('Box Plot для Age')\n", - "plt.xlabel('Age')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Теперь проверим на шум" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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RIr3++uv64YcflJmZaV/u6jiRLt9D5WxYKGicxMbGauPGjQ7LAgICcl0mW65cOYf39ejRo6pSpUqu/T2v57Aiv9r9/PxUo0YN+/oc1apVyxVwy5Urpz179hRpXQCcR7AC4BZ+fn5q1qyZmjVrpltvvVUDBw7UkiVLNH78eKcen5SUpHbt2ik0NFSTJk1SzZo1FRAQoJ07d2r06NGWpqzOeez06dPzvR/k2j+yrj5b4IwGDRqUqtntcm6gd/bDaU+ePKno6OgC2zz44IPauXOnZs6cmW+Qyc/BgwftkzPUrl071/rFixfbg5Wzcrbro48+mm9obtiwoVN97d+/XwsXLtR7773ncN9Lcclv9jhz1f18ffv21ebNm/X888+rcePGCg4OVnZ2tjp37pzneHCmz+vx3nvvKS4uTj169NDzzz+vihUrytvbW1OnTrXfS+iK2NhY7dq1S7/88osiIyMt1XYtT56Vr7i2H4DrR7AC4HY5l7+dOHFC0uWbu7Ozs7Vv3758g82GDRuUmJio+Ph4tW3b1r48r1n18rtsKb/lOTeXh4aGlprwkxNiDhw4kGvdDz/8oPLly9vPVhV0mVZ+vvnmG0nK91LEq2VmZurQoUPq3Llzge28vLz02muv6bvvvlNCQoJmz56tU6dO5TvV+NUWL14sX19f/etf/8r1B+TGjRv11ltv6eeff1ZUVJSio6OVnZ2thIQEhxB26NAhh8dVqFBBISEhunTpkuXtOmbMGDVu3Fj9+vWz1E9ROXfunNauXauJEydq3Lhx9uU5Z1+vhzPjMC9Lly5VjRo1FB8f77AvOvtPk2t169ZNH3zwgd57771CP4rg6nGSM9NmjgMHDhT6z4D8+ly7dq1+//13h3+o5DUW8+LseLy69ho1atiXZ2RkKCEhodQciwDkj3usAJSY9evX5/nf1Jz7g3IugenRo4e8vLw0adKkXP9pz3l8zh/bV/eXkZGh2bNn5+o/KCgoz0uhcoLItVMZN23aVDVr1tRrr72m33//Pdfjfvvtt3xfY3GpUqWKGjdurEWLFjnUu3fvXn355Zf6wx/+YF+W3+sqyNKlS1WnTh3FxsYW2nbFihVKS0vL9YdrXmbOnKl169bZP3+qdevWTtWzePFitWnTRv369dMDDzzg8JUznfwHH3wgSerUqZMk5dr2M2fOdPjZ29tbvXv31r///W/t3bs313M6u123bNmiFStW6JVXXrmuEFsc8hoPkjRjxozr7tOZcehsLdu2bdOWLVuuq44HHnhADRo00JQpU/LsIzU1VS+99JKky/8YqFixoubMmaOLFy/a26xatUr79+/Pc6bIwvzhD39QVlaW3n77bfuyS5cu5dq/8hMUFOTUWOzYsaP8/Pz01ltvObx38+bNU3Jy8nXVDqBkccYKQIl55plndOHCBfXs2VOxsbHKyMjQ5s2b9dFHHykmJkYDBw6UdPnemJdeekmTJ09WmzZt1KtXL/n7+2v79u2KiIjQ1KlT1apVK5UrV04DBgzQiBEjZLPZ9K9//SvPP/iaNm2qjz76SKNGjVKzZs0UHBysbt26qWbNmipbtqzmzJmjkJAQBQUFqUWLFqpevbreffdddenSRfXq1dPAgQNVtWpV/frrr1q/fr1CQ0P16aeflvTbp+nTp6tLly5q2bKlHn/8cft062FhYQ6frdS0aVNJl6eaf/DBB+Xr66tu3brlef/VkSNHNG3aNH399dfq1auX3nvvPfu6nEvxVq9eraioKFWuXFnjx4/X7Nmz1apVq3yn287x/fff64UXXtCECRPsnxnmjG3btunQoUN6+umn81xftWpV3X777Vq8eLFGjx6tpk2bqnfv3poxY4YSExPt063/+OOPkhzPGLzyyitav369WrRooSFDhqhu3bo6e/asdu7cqTVr1ujs2bOF1vfll1/q3nvvLVVnEEJDQ9W2bVtNmzZNmZmZqlq1qr788stCPxetIM6Mw7z88Y9/VHx8vHr27KmuXbsqISFBc+bMUd26dfP8R0VhfH19FR8fr44dO6pt27bq27evWrduLV9fX33//fd6//33Va5cOU2ZMkW+vr569dVXNXDgQLVr104PPfSQfbr1mJgYl6b6z9GtWze1bt1aL774on766SfVrVtX8fHxTt/H2bRpU61Zs0ZvvPGGIiIiVL16dbVo0SJXuwoVKmjMmDGaOHGiOnfurO7du+vAgQOaPXu2mjVr5tSZXgBu5pa5CAHclFatWmUGDRpkYmNjTXBwsPHz8zO1atUyzzzzjDl16lSu9vPnzzdNmjQx/v7+ply5cqZdu3Zm9erV9vWbNm0yd955pwkMDDQRERH26dt1zRThv//+u3n44YdN2bJljSSH6axXrFhh6tata3x8fHJNnbxr1y7Tq1cvEx4ebvz9/U10dLTp27evWbt2rb1NzvTKv/32m1PvQc5060uWLCmwXV7TrRtjzJo1a0zr1q1NYGCgCQ0NNd26dTP79u3L9fjJkyebqlWrGi8vrwKnXs+Z8rmwrwULFphjx46ZyMhIM3LkSJOcnJyrL101dXZ6erpp2LChueuuu0xWVlau11XQdOvPPPOMkWQOHz6cb5sJEyYYSebbb781xhhz/vx5M3z4cHPLLbeY4OBg06NHD3PgwAEjybzyyisOjz116pQZPny4iYyMNL6+vqZy5crmnnvuMXPnzs33+a5+jTabzezYscNh+bVTjucnOjradO3aNd/1+U23ntf7dfX7bYwxx44dMz179jRly5Y1YWFhpk+fPub48eO52uW3z147/XeOwsbhta89Ozvb/O1vfzPR0dHG39/fNGnSxHz22We5Xlter6Eg586dM+PGjTMNGjQwZcqUMQEBAaZ+/fpmzJgxDlP6G2PMRx99ZK/5lltuMY888og5duyYQ5sBAwaYoKCgXM+T15TpiYmJ5rHHHjOhoaEmLCzMPPbYY2bXrl1OTbf+ww8/mLZt25rAwEAjyT71en7v96xZs0xsbKzx9fU1lSpVMsOGDTPnzp1zaNOuXTtTr169XLXn9R4DKDk2Y7jLEQBuVgsXLtSECRPsk1fkpX379oqLi1NcXFyJ1VUUdu/erSZNmui9997TI4884u5yAAA3OO6xAgB4vLS0tFzLZsyYIS8vL4fJTQAAKC7cYwUAN7GaNWuqZ8+eBba599577TMlllbTpk3Tjh071KFDB/n4+GjVqlVatWqVhg4dWuRTdAMAkBcuBQQAeLzVq1dr4sSJ2rdvn37//XdFRUXpscce00svvVTgBy8DAFBUCFYAAAAAYBH3WAEAAACARQQrAAAAALCIC8/zkJ2drePHjyskJMThgyUBAAAA3FyMMUpNTVVERIS8vPI/L0WwysPx48eZRQoAAACA3S+//KJq1arlu55glYeQkBBJl9+80NBQN1cDAAAAwF1SUlIUGRlpzwj5IVjlIefyv9DQUIIVAAAAgEJvEWLyCgAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIs8Pli1b99eI0eOLLBNTEyMZsyYUSL1AAAAALj5+Li7gLzExcVp0aJFeuKJJzRnzhyHdcOHD9fs2bM1YMAALVy4UPHx8fL19XVTpcXr2LkLSk3PUkpapsICfRUc4KNq5cq4uyxJ1AbgxsZxBLiC8YCS5qn7XKkMVpIUGRmpDz/8UG+++aYCAwMlSenp6Xr//fcVFRVlb3fLLbe4q8RidTTxvP6y7DttOpRoX3ZXrXBN6dlA0eFBbqyM2gDc2DiOAFcwHlDSPHmfK7WXAt5+++2KjIxUfHy8fVl8fLyioqLUpEkT+7JrLwU8ffq0unXrpsDAQFWvXl2LFy8uybKLxLFzF3LtUJK08VCiXlr2nY6du+CmyqgNwI2N4whwBeMBJc3T97lSG6wkadCgQVqwYIH95/nz52vgwIEFPiYuLk6//PKL1q9fr6VLl2r27Nk6ffp0gY+5ePGiUlJSHL7cKTU9K9cOlWPjoUSlpmeVcEVXUBuAGxnHEeAKxgNKmqfvc6U6WD366KPauHGjjh49qqNHj2rTpk169NFH823/448/atWqVXrnnXd05513qmnTppo3b57S0tIKfJ6pU6cqLCzM/hUZGVnUL8UlKWmZBa5PTS94fXGiNgA3Mo4jwBWMB5Q0T9/nSu09VpJUoUIFde3aVQsXLpQxRl27dlX58uXzbb9//375+PioadOm9mWxsbEqW7Zsgc8zZswYjRo1yv5zSkqKW8NVaGDBk3GEBLhvsg5qA3Aj4zgCXMF4QEnz9H2uVJ+xki5fDrhw4UItWrRIgwYNKpbn8Pf3V2hoqMOXO4UE+OiuWuF5rrurVrhCAtyXh6kNwI2M4whwBeMBJc3T97lSH6w6d+6sjIwMZWZmqlOnTgW2jY2NVVZWlnbs2GFfduDAASUlJRVzlUWrWrkymtKzQa4dK2dGFHdON0ltAG5kHEeAKxgPKGmevs+V7tgnydvbW/v377d/X5A6deqoc+fOeuKJJ/T222/Lx8dHI0eOtE/X7kmiw4P0Su+GSk3PUmp6pkICfBVSSubwpzYANzKOI8AVjAeUNE/e50p9sJLk0qV5CxYs0ODBg9WuXTtVqlRJL7/8ssaOHVuM1RWf0rwDURuAGxnHEeAKxgNKmqfuczZjjHF3EaVNSkqKwsLClJyc7Pb7rQAAAAC4j7PZoNTfYwUAAAAApR3BCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACART7X86C1a9dq7dq1On36tLKzsx3WzZ8/v0gKAwAAAABP4XKwmjhxoiZNmqQ77rhDVapUkc1mK466AAAAAMBjuBys5syZo4ULF+qxxx4rjnoAAAAAwOO4fI9VRkaGWrVqVRy1AAAAAIBHcjlYDR48WO+//35x1AIAAAAAHsnlSwHT09M1d+5crVmzRg0bNpSvr6/D+jfeeKPIigMAAAAAT+BysNqzZ48aN24sSdq7d6/DOiayAAAAAHAzcjlYrV+/vjjqAAAAAACPZekDgo8dO6Zjx44VVS0AAAAA4JFcDlbZ2dmaNGmSwsLCFB0drejoaJUtW1aTJ0/O9WHBAAAAAHAzcPlSwJdeeknz5s3TK6+8otatW0uSNm7cqAkTJig9PV1Tpkwp8iIBAAAAoDSzGWOMKw+IiIjQnDlz1L17d4flK1as0FNPPaVff/21SAt0h5SUFIWFhSk5OVmhoaHuLgcAAACAmzibDVy+FPDs2bOKjY3NtTw2NlZnz551tTsAAAAA8HguB6tGjRpp1qxZuZbPmjVLjRo1KpKiAAAAAMCTuHyP1bRp09S1a1etWbNGLVu2lCRt2bJFv/zyiz7//PMiLxAAAAAASjuXz1i1a9dOP/74o3r27KmkpCQlJSWpV69eOnDggNq0aVMcNQIAAABAqeby5BU3AyavAAAAACA5nw2cuhRwz549ql+/vry8vLRnz54C2zZs2NC1SgEAAADAwzkVrBo3bqyTJ0+qYsWKaty4sWw2m/I60WWz2XTp0qUiLxIAAAAASjOnglVCQoIqVKhg/x4AAAAAcIVTwSo6Otr+/dGjR9WqVSv5+Dg+NCsrS5s3b3ZoCwAAAAA3A5dnBezQoUOeHwScnJysDh06FElRAAAAAOBJXA5WxhjZbLZcyxMTExUUFFQkRQEAAACAJ3H6A4J79eol6fIEFXFxcfL397evu3Tpkvbs2aNWrVoVfYUAAAAAUMo5HazCwsIkXT5jFRISosDAQPs6Pz8/3XnnnRoyZEjRVwgAAAAApZzTwWrBggWSpJiYGP35z3/msj8AAAAA+P9sJq8PpLrJOfvpygAAAABubM5mA6fOWN1+++1au3atypUrpyZNmuQ5eUWOnTt3ul4tAAAAAHgwp4LV/fffb5+sokePHsVZDwAAAAB4HC4FzAOXAgIAAACQnM8GLn+OFQAAAADAkVOXApYrV67A+6qudvbsWUsFAQAAAICncSpYzZgxw/59YmKiXn75ZXXq1EktW7aUJG3ZskVffPGFxo4dWyxFAgAAAEBp5vI9Vr1791aHDh309NNPOyyfNWuW1qxZo+XLlxdlfW7BPVYAAAAApGK8x+qLL75Q586dcy3v3Lmz1qxZ42p3AAAAAODxXA5W4eHhWrFiRa7lK1asUHh4eJEUBQAAAACexKl7rK42ceJEDR48WBs2bFCLFi0kSdu2bdN//vMfvfPOO0VeIAAAAACUdi4Hq7i4ON1222166623FB8fL0m67bbbtHHjRnvQAgAAAICbCR8QnAcmrwAAAAAgOZ8NXD5jJUnZ2dk6dOiQTp8+rezsbId1bdu2vZ4uAQAAAMBjuRystm7dqocfflhHjx7VtSe7bDabLl26VGTFAQAAAIAncDlYPfnkk7rjjju0cuVKValSRTabrTjqAgAAAACP4XKwOnjwoJYuXapatWoVRz0AAAAA4HFc/hyrFi1a6NChQ8VRCwAAAAB4JJfPWD3zzDN67rnndPLkSTVo0EC+vr4O6xs2bFhkxQEAAACAJ3B5unUvr9wnuWw2m4wxN8zkFUy3DgAAAEAqxunWExISLBUGAAAAADcal4NVdHR0cdQBAAAAAB7ruj4g+PDhw5oxY4b2798vSapbt66effZZ1axZs0iLAwAAAABP4PKsgF988YXq1q2rr7/+Wg0bNlTDhg21bds21atXT6tXry6OGgEAAACgVHN58oomTZqoU6dOeuWVVxyWv/jii/ryyy+1c+fOIi3QHZi8AgAAAIDkfDZw+YzV/v379fjjj+daPmjQIO3bt8/V7gAAAADA47kcrCpUqKDdu3fnWr57925VrFixKGoCAAAAAI/i8uQVQ4YM0dChQ3XkyBG1atVKkrRp0ya9+uqrGjVqVJEXCAAAAAClncv3WBljNGPGDL3++us6fvy4JCkiIkLPP/+8RowYIZvNViyFliTusQIAAAAgOZ8NXA5WV0tNTZUkhYSEXG8XpRLBCgAAAIBUDJNXpKWl6ZNPPrGHKelyoAoJCVFKSoo++eQTXbx40VrVAAAAAOCBnA5Wc+fO1d///vc8z06Fhobqrbfe0rvvvlukxQEAAACAJ3A6WC1evFgjR47Md/3IkSO1aNGioqgJAAAAADyK08Hq4MGDatSoUb7rGzZsqIMHDxZJUQAAAADgSZyebj0rK0u//faboqKi8lz/22+/KSsry6Unj4uLs5/l8vX1VVRUlPr376+//OUv8vFxeSb4G86xcxeUmp6llLRMhQX6KjjAR9XKlXF3WZKoDcCNjeMIcAXjASXNU/c5p9NLvXr1tGbNGjVt2jTP9V9++aXq1avncgGdO3fWggULdPHiRX3++ecaPny4fH19NWbMGJf7KkxGRob8/PyKvN/icDTxvP6y7DttOpRoX3ZXrXBN6dlA0eFBbqyM2gDc2DiOAFcwHlDSPHmfc/pSwEGDBmny5Mn67LPPcq379NNPNWXKFA0aNMjlAvz9/VW5cmVFR0dr2LBh6tixoz755BOdO3dO/fv3V7ly5VSmTBl16dIl16WG//73v1WvXj35+/srJiZGr7/+usP6mJgYTZ48Wf3791doaKiGDh3qcn3ucOzchVw7lCRtPJSol5Z9p2PnLripMmoDcGPjOAJcwXhASfP0fc7pM1ZDhw7Vf//7X3Xv3l2xsbGqU6eOJOmHH37Qjz/+qL59+xZJcAkMDFRiYqLi4uJ08OBBffLJJwoNDdXo0aP1hz/8Qfv27ZOvr6927Nihvn37asKECerXr582b96sp556SuHh4YqLi7P399prr2ncuHEaP358vs958eJFh6niU1JSLL8OK1LTs3LtUDk2HkpUarprl1wWJWoDcCPjOAJcwXhASfP0fc6lG5nee+89de/eXe+//75+/PFHGWNUp04dTZw4UX379rVUiDFGa9eu1RdffKEuXbpo+fLl2rRpk1q1aiXp8qyEkZGRWr58ufr06aM33nhD99xzj8aOHStJuvXWW7Vv3z5Nnz7dIVjdfffdeu655wp87qlTp2rixImW6i9KKWmZBa5PTS94fXGiNgA3Mo4jwBWMB5Q0T9/nXJ4hom/fvpZD1NU+++wzBQcHKzMzU9nZ2Xr44YfVq1cvffbZZ2rRooW9XXh4uOrUqaP9+/dLkvbv36/777/foa/WrVtrxowZunTpkry9vSVJd9xxR6E1jBkzRqNGjbL/nJKSosjIyKJ4edclNNC3wPUhAQWvL07UBuBGxnEEuILxgJLm6fuc0/dYFZcOHTpo9+7dOnjwoNLS0rRo0SLZbLYi6z8oqPCb3Pz9/RUaGurw5U4hAT66q1Z4nuvuqhWukAD3zZhIbQBuZBxHgCsYDyhpnr7PuT1YBQUFqVatWoqKirJPsX7bbbcpKytL27Zts7dLTEzUgQMHVLduXXubTZs2OfS1adMm3XrrrfazVZ6qWrkymtKzQa4dK2dGFHdON0ltAG5kHEeAKxgPKGmevs/ZjDHGXU8eFxenpKQkLV++PNe6Hj166ODBg/rnP/+pkJAQvfjiizp06JB98oqdO3eqWbNm9skrtmzZomHDhmn27Nn2e6xiYmI0cuRIjRw50qW6UlJSFBYWpuTkZLeevcqZwz81PVMhAb4KKUVz+FMbgBsZxxHgCsYDSlpp2+eczQal9nzaggUL9Oyzz+qPf/yjMjIy1LZtW33++efy9b18beXtt9+ujz/+WOPGjdPkyZNVpUoVTZo0yWHiCk9Xmg9a1AbgRsZxBLiC8YCS5qn7nFvPWJVWpeWMFQAAAAD3KtIzVr169XL6iePj451uCwAAAAA3AqeCVVhYWHHXAQAAAAAey6lgtWDBguKuAwAAAAA8ltunWwcAAAAAT3ddswIuXbpUH3/8sX7++WdlZGQ4rNu5c2eRFAYAAAAAnsLlM1ZvvfWWBg4cqEqVKmnXrl1q3ry5wsPDdeTIEXXp0qU4agQAAACAUs3lYDV79mzNnTtXM2fOlJ+fn1544QWtXr1aI0aMUHJycnHUCAAAAAClmsvB6ueff1arVq0kSYGBgUpNTZUkPfbYY/rggw+KtjoAAAAA8AAuB6vKlSvr7NmzkqSoqCht3bpVkpSQkCA+axgAAADAzcjlYHX33Xfrk08+kSQNHDhQf/rTn3TvvfeqX79+6tmzZ5EXCAAAAAClnc24eJopOztb2dnZ8vG5PKHghx9+qM2bN6t27dp64okn5OfnVyyFlqSUlBSFhYUpOTlZoaGh7i4HAAAAgJs4mw1cDlY3A4IVAAAAAMn5bODU51jt2bNH9evXl5eXl/bs2VNg24YNG7pWKQAAAAB4OKeCVePGjXXy5ElVrFhRjRs3ls1my3OiCpvNpkuXLhV5kQAAAABQmjkVrBISElShQgX79wAAAACAK5wKVtHR0Xl+DwAAAAC4junWp06dqvnz5+daPn/+fL366qtFUhQAAAAAeBKXg9U///lPxcbG5lper149zZkzp0iKAgAAAABP4nKwOnnypKpUqZJreYUKFXTixIkiKQoAAAAAPInLwSoyMlKbNm3KtXzTpk2KiIgokqIAAAAAwJM4NXnF1YYMGaKRI0cqMzNTd999tyRp7dq1euGFF/Tcc88VeYEAAAAAUNq5HKyef/55JSYm6qmnnlJGRoYkKSAgQKNHj9aYMWOKvEAAAAAAKO1sJq9P+nXC77//rv379yswMFC1a9eWv79/UdfmNikpKQoLC1NycrJCQ0PdXQ4AAAAAN3E2G7h8xipHcHCwmjVrdr0PBwAAAIAbhsvB6vz583rllVe0du1anT59WtnZ2Q7rjxw5UmTFAQAAAIAncDlYDR48WF999ZUee+wxValSRTabrTjqAgAAAACP4XKwWrVqlVauXKnWrVsXRz0AAAAA4HFc/hyrcuXK6ZZbbimOWgAAAADAI7kcrCZPnqxx48bpwoULxVEPAAAAAHgcly8FfP3113X48GFVqlRJMTEx8vX1dVi/c+fOIisOAAAAADyBy8GqR48exVAGAAAAAHiu6/6A4BsZHxAMAAAAQHI+G7h8jxUAAAAAwJHLlwJeunRJb775pj7++GP9/PPPysjIcFh/9uzZIisOAAAAADyBy2esJk6cqDfeeEP9+vVTcnKyRo0apV69esnLy0sTJkwohhIBAAAAoHRzOVgtXrxY77zzjp577jn5+PjooYce0rvvvqtx48Zp69atxVEjAAAAAJRqLgerkydPqkGDBpKk4OBgJScnS5L++Mc/auXKlUVbHQAAAAB4AJeDVbVq1XTixAlJUs2aNfXll19KkrZv3y5/f/+irQ4AAAAAPIDLwapnz55au3atJOmZZ57R2LFjVbt2bfXv31+DBg0q8gIBAAAAoLSz/DlWW7Zs0ZYtW1S7dm1169atqOpyKz7HCgAAAIDkfDZwebr1a7Vs2VItW7a02g0AAAAAeKzrClYHDx7U+vXrdfr0aWVnZzusGzduXJEUBgAAAACewuVg9c4772jYsGEqX768KleuLJvNZl9ns9kIVgAAAABuOi4Hq5dffllTpkzR6NGji6MeAAAAAPA4Ls8KeO7cOfXp06c4agEAAAAAj+RysOrTp4/9s6sAAAAAANdxKWCtWrU0duxYbd26VQ0aNJCvr6/D+hEjRhRZcQAAAADgCVz+HKvq1avn35nNpiNHjlguyt34HCsAAAAAUjF+jlVCQoKlwgAAAADgRuPyPVYAAAAAAEdOnbEaNWqUJk+erKCgII0aNarAtm+88UaRFAYAAAAAnsKpYLVr1y5lZmbav8/P1R8WDAAAAAA3C6eC1fr163XkyBGFhYVp/fr1xV0TAAAAAHgUp++xql27tn777Tf7z/369dOpU6eKpSgAAAAA8CROB6trZ2X//PPPdf78+SIvCAAAAAA8DbMCAgAAAIBFTgcrm82Wa3IKJqsAAAAAABc+INgYo7i4OPn7+0uS0tPT9eSTTyooKMihXXx8fNFWCAAAAAClnNPBasCAAQ4/P/roo0VeDAAAAAB4IqeD1YIFC4qzDgAAAADwWExeAQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAolIfrH766SfZbDbt3r3bqfZxcXHq0aNHsdYEAAAAAFfzcXcBcXFxWrRokSTJx8dH1apVU58+fTRp0iQFBAQoMjJSJ06cUPny5d1cack7npSm5LRMpaRlKizQV6GBvoooG+jusiRJx85dUGp6lr224AAfVStXxt1lSSrdtQHwDBxHgCsYDyhpnrrPuT1YSVLnzp21YMECZWZmaseOHRowYIBsNpteffVVeXt7q3Llyu4uscQdTTyvvyz7TpsOJdqX3VUrXFN6NlB0eJAbK6M2ADc2jiPAFYwHlDRP3udKxaWA/v7+qly5siIjI9WjRw917NhRq1evlpT3pYDff/+9/vjHPyo0NFQhISFq06aNDh8+7NDna6+9pipVqig8PFzDhw9XZmZmSb4kS44npeXaoSRp46FEvbTsOx1PSnNTZZf/g1BQbcfOXXBTZaW7NgCegeMIcAXjASXN0/e5UnHG6mp79+7V5s2bFR0dnef6X3/9VW3btlX79u21bt06hYaGatOmTcrKyrK3Wb9+vapUqaL169fr0KFD6tevnxo3bqwhQ4bk2efFixd18eJF+88pKSlF+6JclJyWmWuHyrHxUKKS0zLddklganpWgbWlpmflua4klObaAHgGjiPAFYwHlDRP3+dKRbD67LPPFBwcrKysLF28eFFeXl6aNWtWnm3/8Y9/KCwsTB9++KF8fX0lSbfeeqtDm3LlymnWrFny9vZWbGysunbtqrVr1+YbrKZOnaqJEycW7YuyICWt4LNrqenuO/tGbQBuZBxHgCsYDyhpnr7PlYpLATt06KDdu3dr27ZtGjBggAYOHKjevXvn2Xb37t1q06aNPVTlpV69evL29rb/XKVKFZ0+fTrf9mPGjFFycrL965dffrn+F1MEQgPzf22SFBJQ8PriRG0AbmQcR4ArGA8oaZ6+z5WKYBUUFKRatWqpUaNGmj9/vrZt26Z58+bl2TYwsPBL4K4NXTabTdnZ2fm29/f3V2hoqMOXO4UF+uquWuF5rrurVrjCCtnpilNIgE+BtYUEuO8kaGmuDYBn4DgCXMF4QEnz9H2uVASrq3l5eekvf/mL/vrXvyotLfckDQ0bNtT//vc/j5qMwlURZQM1pWeDXDtWzowo7pxyvVq5MgXW5s6pMEtzbQA8A8cR4ArGA0qap+9zNmOMcWcBcXFxSkpK0vLly+3LsrKyFBMTo5EjR+qBBx5Q9erVtWvXLjVu3FiJiYmqU6eO2rVrpzFjxigsLExbt25V8+bNVadOnTz7GzlypHbv3q0NGzY4VVNKSorCwsKUnJzs1rNXOZ9jlZqeqZAAX4WVws+xyqktpBR9vkBprg2AZ+A4AlzBeEBJK237nLPZoFSeT/Px8dHTTz+tadOmqUuXLg7rwsPDtW7dOj3//PNq166dvL291bhxY7Vu3dpN1RafiLKBpSZIXas0H1BLc20APAPHEeAKxgNKmqfuc24/Y1UalZYzVgAAAADcy9lsUOrusQIAAAAAT0OwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFPu4uoDQyxkiSUlJS3FwJAAAAAHfKyQQ5GSE/BKs8pKamSpIiIyPdXAkAAACA0iA1NVVhYWH5rreZwqLXTSg7O1vHjx9XSEiIbDabu8sptVJSUhQZGalffvlFoaGh7i7npsV2KB3YDqUD26F0YDuUDmwH92MblA5Wt4MxRqmpqYqIiJCXV/53UnHGKg9eXl6qVq2au8vwGKGhoRwsSgG2Q+nAdigd2A6lA9uhdGA7uB/boHSwsh0KOlOVg8krAAAAAMAighUAAAAAWESwwnXz9/fX+PHj5e/v7+5Sbmpsh9KB7VA6sB1KB7ZD6cB2cD+2QelQUtuBySsAAAAAwCLOWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUkSf/973/VrVs3RUREyGazafny5Q7rjTEaN26cqlSposDAQHXs2FEHDx4stN9//OMfiomJUUBAgFq0aKGvv/66mF7BjaGg7ZCZmanRo0erQYMGCgoKUkREhPr376/jx48X2OeECRNks9kcvmJjY4v5lXi2wsZDXFxcrve0c+fOhfbLeHBNYdvh2m2Q8zV9+vR8+2Q8uGbq1Klq1qyZQkJCVLFiRfXo0UMHDhxwaJOenq7hw4crPDxcwcHB6t27t06dOlVgv9f7O+VmVdh2OHv2rJ555hnVqVNHgYGBioqK0ogRI5ScnFxgv9d7LLtZOTMe2rdvn+s9ffLJJwvsl/HgmsK2w08//ZTv74clS5bk229RjAeCFSRJ58+fV6NGjfSPf/wjz/XTpk3TW2+9pTlz5mjbtm0KCgpSp06dlJ6enm+fH330kUaNGqXx48dr586datSokTp16qTTp08X18vweAVthwsXLmjnzp0aO3asdu7cqfj4eB04cEDdu3cvtN969erpxIkT9q+NGzcWR/k3jMLGgyR17tzZ4T394IMPCuyT8eC6wrbD1e//iRMnNH/+fNlsNvXu3bvAfhkPzvvqq680fPhwbd26VatXr1ZmZqbuu+8+nT9/3t7mT3/6kz799FMtWbJEX331lY4fP65evXoV2O/1/E65mRW2HY4fP67jx4/rtdde0969e7Vw4UL95z//0eOPP15o364ey25mzowHSRoyZIjDezpt2rQC+2U8uKaw7RAZGZnr98PEiRMVHBysLl26FNi35fFggGtIMsuWLbP/nJ2dbSpXrmymT59uX5aUlGT8/f3NBx98kG8/zZs3N8OHD7f/fOnSJRMREWGmTp1aLHXfaK7dDnn5+uuvjSRz9OjRfNuMHz/eNGrUqGiLu4nktR0GDBhg7r//fpf6YTxY48x4uP/++83dd99dYBvGgzWnT582ksxXX31ljLn8u8DX19csWbLE3mb//v1GktmyZUuefVzv7xRcce12yMvHH39s/Pz8TGZmZr5trudYhivy2g7t2rUzzz77rNN9MB6sc2Y8NG7c2AwaNKjAfopiPHDGCoVKSEjQyZMn1bFjR/uysLAwtWjRQlu2bMnzMRkZGdqxY4fDY7y8vNSxY8d8HwPXJScny2azqWzZsgW2O3jwoCIiIlSjRg098sgj+vnnn0umwBvYhg0bVLFiRdWpU0fDhg1TYmJivm0ZD8Xv1KlTWrlypVP/oWc8XL+cS8tuueUWSdKOHTuUmZnpsG/HxsYqKioq3337en6nwNG12yG/NqGhofLx8SmwL1eOZXCU33ZYvHixypcvr/r162vMmDG6cOFCvn0wHqwrbDzs2LFDu3fvdur3g9XxUPBoAySdPHlSklSpUiWH5ZUqVbKvu9aZM2d06dKlPB/zww8/FE+hN5n09HSNHj1aDz30kEJDQ/Nt16JFCy1cuFB16tSxnw5v06aN9u7dq5CQkBKs+MbRuXNn9erVS9WrV9fhw4f1l7/8RV26dNGWLVvk7e2dqz3jofgtWrRIISEhhV6Cxni4ftnZ2Ro5cqRat26t+vXrS7r8+8HPzy/XP3cK+v1wPb9TcEVe2+FaZ86c0eTJkzV06NAC+3L1WIYr8tsODz/8sKKjoxUREaE9e/Zo9OjROnDggOLj4/Psh/FgjTPjYd68ebrtttvUqlWrAvsqivFAsAI8UGZmpvr27StjjN5+++0C2159PXHDhg3VokULRUdH6+OPP3bqvzfI7cEHH7R/36BBAzVs2FA1a9bUhg0bdM8997ixspvX/Pnz9cgjjyggIKDAdoyH6zd8+HDt3buXe9LcrLDtkJKSoq5du6pu3bqaMGFCgX1xLLt++W2Hq8NsgwYNVKVKFd1zzz06fPiwatasWdJl3vAKGw9paWl6//33NXbs2EL7KorxwKWAKFTlypUlKdcsT6dOnbKvu1b58uXl7e3t0mPgnJxQdfToUa1evbrAs1V5KVu2rG699VYdOnSomCq8+dSoUUPly5fP9z1lPBSv//3vfzpw4IAGDx7s8mMZD855+umn9dlnn2n9+vWqVq2afXnlypWVkZGhpKQkh/YF7dvX8zsFl+W3HXKkpqaqc+fOCgkJ0bJly+Tr6+tS/4Udy3BZYdvhai1atJCkfN9TxsP1c2Y7LF26VBcuXFD//v1d7v96xgPBCoWqXr26KleurLVr19qXpaSkaNu2bWrZsmWej/Hz81PTpk0dHpOdna21a9fm+xgULidUHTx4UGvWrFF4eLjLffz+++86fPiwqlSpUgwV3pyOHTumxMTEfN9TxkPxmjdvnpo2bapGjRq5/FjGQ8GMMXr66ae1bNkyrVu3TtWrV3dY37RpU/n6+jrs2wcOHNDPP/+c7759Pb9TbnaFbQfp8nt43333yc/PT5988kmhZ2/zUtix7GbnzHa41u7duyUp3/eU8eA6V7bDvHnz1L17d1WoUMHl57mu8WBp6gvcMFJTU82uXbvMrl27jCTzxhtvmF27dtlnm3vllVdM2bJlzYoVK8yePXvM/fffb6pXr27S0tLsfdx9991m5syZ9p8//PBD4+/vbxYuXGj27dtnhg4dasqWLWtOnjxZ4q/PUxS0HTIyMkz37t1NtWrVzO7du82JEyfsXxcvXrT3ce12eO6558yGDRtMQkKC2bRpk+nYsaMpX768OX36tDteokcoaDukpqaaP//5z2bLli0mISHBrFmzxtx+++2mdu3aJj093d4H48G6wo5LxhiTnJxsypQpY95+++08+2A8WDNs2DATFhZmNmzY4HDMuXDhgr3Nk08+aaKiosy6devMN998Y1q2bGlatmzp0E+dOnVMfHy8/WdnfqfgisK2Q3JysmnRooVp0KCBOXTokEObrKwsez9Xbwdnj2W4orDtcOjQITNp0iTzzTffmISEBLNixQpTo0YN07ZtW4d+GA/WOHNcMsaYgwcPGpvNZlatWpVnP8UxHghWMMYYs379eiMp19eAAQOMMZenAx07dqypVKmS8ff3N/fcc485cOCAQx/R0dFm/PjxDstmzpxpoqKijJ+fn2nevLnZunVrCb0iz1TQdkhISMhznSSzfv16ex/Xbod+/fqZKlWqGD8/P1O1alXTr18/c+jQoZJ/cR6koO1w4cIFc99995kKFSoYX19fEx0dbYYMGZIrIDEerCvsuGSMMf/85z9NYGCgSUpKyrMPxoM1+R1zFixYYG+TlpZmnnrqKVOuXDlTpkwZ07NnT3PixIlc/Vz9GGd+p+CKwrZDfmNFkklISHDoJ+cxzh7LcEVh2+Hnn382bdu2Nbfccovx9/c3tWrVMs8//7xJTk7O1Q/j4fo5c1wyxpgxY8aYyMhIc+nSpXz7KerxYPv/HQMAAAAArhP3WAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwDATWfLli3y9vZW165d3V0KAOAGYTPGGHcXAQBASRo8eLCCg4M1b948HThwQBEREe4uCQDg4ThjBQC4qfz+++/66KOPNGzYMHXt2lULFy50WP/JJ5+odu3aCggIUIcOHbRo0SLZbDYlJSXZ22zcuFFt2rRRYGCgIiMjNWLECJ0/f75kXwgAoFQhWAEAbioff/yxYmNjVadOHT366KOaP3++ci7eSEhI0AMPPKAePXro22+/1RNPPKGXXnrJ4fGHDx9W586d1bt3b+3Zs0cfffSRNm7cqKefftodLwcAUEpwKSAA4KbSunVr9e3bV88++6yysrJUpUoVLVmyRO3bt9eLL76olStX6rvvvrO3/+tf/6opU6bo3LlzKlu2rAYPHixvb2/985//tLfZuHGj2rVrp/PnzysgIMAdLwsA4GacsQIA3DQOHDigr7/+Wg899JAkycfHR/369dO8efPs65s1a+bwmObNmzv8/O2332rhwoUKDg62f3Xq1EnZ2dlKSEgomRcCACh1fNxdAAAAJWXevHnKyspymKzCGCN/f3/NmjXLqT5+//13PfHEExoxYkSudVFRUUVWKwDAsxCsAAA3haysLP3f//2fXn/9dd13330O63r06KEPPvhAderU0eeff+6wbvv27Q4/33777dq3b59q1apV7DUDADwH91gBAG4Ky5cvV79+/XT69GmFhYU5rBs9erTWrVunjz/+WHXq1NGf/vQnPf7449q9e7eee+45HTt2TElJSQoLC9OePXt05513atCgQRo8eLCCgoK0b98+rV692umzXgCAGw/3WAEAbgrz5s1Tx44dc4UqSerdu7e++eYbpaamaunSpYqPj1fDhg319ttv22cF9Pf3lyQ1bNhQX331lX788Ue1adNGTZo00bhx4/gsLAC4yXHGCgCAAkyZMkVz5szRL7/84u5SAAClGPdYAQBwldmzZ6tZs2YKDw/Xpk2bNH36dD6jCgBQKIIVAABXOXjwoF5++WWdPXtWUVFReu655zRmzBh3lwUAKOW4FBAAAAAALGLyCgAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBF/w9QGXIrORoYpAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Scatter plot для столбцов 'Age' и 'Financial Condition'\n", - "plt.figure(figsize=(10, 6))\n", - "sns.scatterplot(x='Age', y='Financial Condition', data=df)\n", - "plt.title('Scatter Plot для Age и Financial Condition')\n", - "plt.xlabel('Age')\n", - "plt.ylabel('Financial Condition')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Удаление строк с пустыми значениями" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "df_cleaned = df.dropna()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Разбиение набора данных на обучающую, контрольную и тестовую выборки" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки: 723\n", - "Размер контрольной выборки: 241\n", - "Размер тестовой выборки: 241\n" - ] - } - ], - "source": [ - "# Разделение на обучающую и тестовую выборки\n", - "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную\n", - "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", - "\n", - "print(\"Размер обучающей выборки:\", len(train_df))\n", - "print(\"Размер контрольной выборки:\", len(val_df))\n", - "print(\"Размер тестовой выборки:\", len(test_df))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Применение методов приращения данных (аугментации)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение Gender в обучающей выборке после oversampling:\n", - "Gender\n", - "Male 397\n", - "Female 397\n", - "Name: count, dtype: int64\n", - "\n", - "Распределение Gender в контрольной выборке после oversampling:\n", - "Gender\n", - "Male 140\n", - "Female 140\n", - "Name: count, dtype: int64\n", - "\n", - "Распределение Gender в тестовой выборке после oversampling:\n", - "Gender\n", - "Female 126\n", - "Male 126\n", - "Name: count, dtype: int64\n", - "\n", - "Распределение Gender в обучающей выборке после undersampling:\n", - "Gender\n", - "Female 326\n", - "Male 326\n", - "Name: count, dtype: int64\n", - "\n", - "Распределение Gender в контрольной выборке после undersampling:\n", - "Gender\n", - "Female 101\n", - "Male 101\n", - "Name: count, dtype: int64\n", - "\n", - "Распределение Gender в тестовой выборке после undersampling:\n", - "Gender\n", - "Female 115\n", - "Male 115\n", - "Name: count, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "# Разделение на обучающую и тестовую выборки\n", - "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную\n", - "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", - "\n", - "def check_balance(df, name):\n", - " counts = df['Gender'].value_counts()\n", - " print(f\"Распределение Gender в {name}:\")\n", - " print(counts)\n", - " print()\n", - "\n", - "def oversample(df):\n", - " X = df.drop('Gender', axis=1)\n", - " y = df['Gender']\n", - " \n", - " oversampler = RandomOverSampler(random_state=42)\n", - " X_resampled, y_resampled = oversampler.fit_resample(X, y)\n", - " \n", - " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", - " return resampled_df\n", - "\n", - "train_df_oversampled = oversample(train_df)\n", - "val_df_oversampled = oversample(val_df)\n", - "test_df_oversampled = oversample(test_df)\n", - "\n", - "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", - "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", - "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")\n", - "\n", - "def undersample(df):\n", - " X = df.drop('Gender', axis=1)\n", - " y = df['Gender']\n", - " \n", - " undersampler = RandomUnderSampler(random_state=42)\n", - " X_resampled, y_resampled = undersampler.fit_resample(X, y)\n", - " \n", - " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", - " return resampled_df\n", - "\n", - "train_df_undersampled = undersample(train_df)\n", - "val_df_undersampled = undersample(val_df)\n", - "test_df_undersampled = undersample(test_df)\n", - "\n", - "check_balance(train_df_undersampled, \"обучающей выборке после undersampling\")\n", - "check_balance(val_df_undersampled, \"контрольной выборке после undersampling\")\n", - "check_balance(test_df_undersampled, \"тестовой выборке после undersampling\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "aimenv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/lab_3/lab3.ipynb b/lab_3/lab3.ipynb deleted file mode 100644 index 27b21b3..0000000 --- a/lab_3/lab3.ipynb +++ /dev/null @@ -1,1408 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Набор данных с ценами на мобильные устройства" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Вывод всех столбцов" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['Unnamed: 0', 'Name', 'Rating', 'Spec_score', 'No_of_sim', 'Ram',\n", - " 'Battery', 'Display', 'Camera', 'External_Memory', 'Android_version',\n", - " 'Price', 'company', 'Inbuilt_memory', 'fast_charging',\n", - " 'Screen_resolution', 'Processor', 'Processor_name'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "import pandas as pd \n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "print(df.columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Бизнес-цели:\n", - "1. Классифицировать мобильные устройства по ценовым категориям (например, бюджетные, средний класс, флагманы).\n", - "2. Определить, какие характеристики мобильных устройств наиболее сильно влияют на их рейтинг." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Выполним разбиение на 3 выборки: обучающую, контрольную и тестовую" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки: 671\n", - "Размер контрольной выборки: 288\n", - "Размер тестовой выборки: 411\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Вывод размеров выборок\n", - "print(\"Размер обучающей выборки:\", len(train_df))\n", - "print(\"Размер контрольной выборки:\", len(val_df))\n", - "print(\"Размер тестовой выборки:\", len(test_df))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение классов в company:\n", - "company\n", - "Vivo 186\n", - "Realme 186\n", - "Samsung 181\n", - "Motorola 127\n", - "Xiaomi 90\n", - "Honor 88\n", - "Poco 75\n", - "OnePlus 75\n", - "Huawei 62\n", - "iQOO 57\n", - "OPPO 38\n", - "Oppo 27\n", - "TCL 26\n", - "Google 23\n", - "Asus 21\n", - "POCO 19\n", - "Lava 19\n", - "Nothing 15\n", - "Lenovo 14\n", - "Tecno 13\n", - "itel 12\n", - "LG 6\n", - "Gionee 5\n", - "Itel 3\n", - "IQOO 1\n", - "Coolpad 1\n", - "Name: count, dtype: int64\n" - ] - }, - { 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение классов в Обучающей выборке:\n", - "company\n", - "Vivo 138\n", - "Samsung 128\n", - "Realme 125\n", - "Motorola 89\n", - "Xiaomi 66\n", - "Honor 59\n", - "OnePlus 56\n", - "Poco 52\n", - "Huawei 46\n", - "iQOO 37\n", - "Oppo 21\n", - "OPPO 20\n", - "Google 16\n", - "Lava 16\n", - "POCO 14\n", - "TCL 14\n", - "Asus 12\n", - "Lenovo 12\n", - "itel 10\n", - "Nothing 8\n", - "Tecno 8\n", - "LG 5\n", - "Gionee 4\n", - "IQOO 1\n", - "Itel 1\n", - "Coolpad 1\n", - "Name: count, dtype: int64\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение классов в Контрольной выборке:\n", - "company\n", - "Realme 26\n", - "Samsung 26\n", - "Vivo 22\n", - "Motorola 18\n", - "Honor 15\n", - "OPPO 13\n", - "Poco 12\n", - "Xiaomi 11\n", - "iQOO 11\n", - "OnePlus 8\n", - "Huawei 7\n", - "Asus 7\n", - "TCL 6\n", - "POCO 5\n", - "Oppo 4\n", - "Google 4\n", - "Tecno 3\n", - "Nothing 3\n", - "itel 2\n", - "Lava 1\n", - "Lenovo 1\n", - "Name: count, dtype: int64\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Распределение классов в Тестовой выборке:\n", - "company\n", - "Realme 35\n", - "Samsung 27\n", - "Vivo 26\n", - "Motorola 20\n", - "Honor 14\n", - "Xiaomi 13\n", - "Poco 11\n", - "OnePlus 11\n", - "Huawei 9\n", - "iQOO 9\n", - "TCL 6\n", - "OPPO 5\n", - "Nothing 4\n", - "Google 3\n", - "Lava 2\n", - "Asus 2\n", - "Oppo 2\n", - "Tecno 2\n", - "Itel 2\n", - "Gionee 1\n", - "Lenovo 1\n", - "LG 1\n", - "Name: count, dtype: int64\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Проверка распределения классов в столбце company\n", - "class_distribution = df['company'].value_counts()\n", - "print(\"Распределение классов в company:\")\n", - "print(class_distribution)\n", - "\n", - "# Визуализация распределения классов\n", - "sns.countplot(y='company', data=df, order=class_distribution.index)\n", - "plt.title('Распределение классов в company')\n", - "plt.show()\n", - "\n", - "# Проверка сбалансированности для каждой выборки\n", - "def check_balance(df, title):\n", - " class_distribution = df['company'].value_counts()\n", - " print(f\"Распределение классов в {title}:\")\n", - " print(class_distribution)\n", - " sns.countplot(y='company', data=df, order=class_distribution.index)\n", - " plt.title(f'Распределение классов в {title}')\n", - " plt.show()\n", - "\n", - "# Разделение данных на обучающую, контрольную и тестовую выборки\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "train_df, temp_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n", - "\n", - "# Проверка сбалансированности для обучающей, контрольной и тестовой выборок\n", - "check_balance(train_df, 'Обучающей выборке')\n", - "check_balance(val_df, 'Контрольной выборке')\n", - "check_balance(test_df, 'Тестовой выборке')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Данные по столбцу company являются несбалансированными. Некоторые компании, такие как Vivo, Realme, и Samsung, имеют значительно больше устройств, чем другие, такие как LG, Gionee, и Itel." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки до upsampling: 671\n", - "Размер контрольной выборки: 288\n", - "Размер тестовой выборки: 411\n", - "\n", - "Распределение классов в всем датасете:\n", - "Класс Vivo: 186 (13.58%)\n", - "Класс Realme: 186 (13.58%)\n", - "Класс Samsung: 181 (13.21%)\n", - "Класс Motorola: 127 (9.27%)\n", - "Класс Xiaomi: 90 (6.57%)\n", - "Класс Honor: 88 (6.42%)\n", - "Класс Poco: 75 (5.47%)\n", - "Класс OnePlus: 75 (5.47%)\n", - "Класс Huawei: 62 (4.53%)\n", - "Класс iQOO: 57 (4.16%)\n", - "Класс OPPO: 38 (2.77%)\n", - "Класс Oppo: 27 (1.97%)\n", - "Класс TCL: 26 (1.90%)\n", - "Класс Google: 23 (1.68%)\n", - "Класс Asus: 21 (1.53%)\n", - "Класс POCO: 19 (1.39%)\n", - "Класс Lava: 19 (1.39%)\n", - "Класс Nothing: 15 (1.09%)\n", - "Класс Lenovo: 14 (1.02%)\n", - "Класс Tecno: 13 (0.95%)\n", - "Класс itel: 12 (0.88%)\n", - "Класс LG: 6 (0.44%)\n", - "Класс Gionee: 5 (0.36%)\n", - "Класс Itel: 3 (0.22%)\n", - "Класс IQOO: 1 (0.07%)\n", - "Класс Coolpad: 1 (0.07%)\n", - "\n", - "Распределение классов в Обучающей выборке до upsampling:\n", - "Класс Vivo: 94 (14.01%)\n", - "Класс Samsung: 89 (13.26%)\n", - "Класс Realme: 82 (12.22%)\n", - "Класс Motorola: 66 (9.84%)\n", - "Класс Xiaomi: 46 (6.86%)\n", - "Класс Honor: 40 (5.96%)\n", - "Класс OnePlus: 40 (5.96%)\n", - "Класс Poco: 37 (5.51%)\n", - "Класс Huawei: 35 (5.22%)\n", - "Класс iQOO: 28 (4.17%)\n", - "Класс OPPO: 15 (2.24%)\n", - "Класс Oppo: 14 (2.09%)\n", - "Класс Lava: 12 (1.79%)\n", - "Класс Google: 12 (1.79%)\n", - "Класс TCL: 10 (1.49%)\n", - "Класс Lenovo: 9 (1.34%)\n", - "Класс POCO: 9 (1.34%)\n", - "Класс Asus: 8 (1.19%)\n", - "Класс itel: 7 (1.04%)\n", - "Класс Nothing: 5 (0.75%)\n", - "Класс Tecno: 5 (0.75%)\n", - "Класс LG: 3 (0.45%)\n", - "Класс Gionee: 3 (0.45%)\n", - "Класс Coolpad: 1 (0.15%)\n", - "Класс Itel: 1 (0.15%)\n", - "Размер обучающей выборки после upsampling: 2350\n", - "\n", - "Распределение классов в Обучающей выборке после upsampling:\n", - "Класс Realme: 94 (4.00%)\n", - "Класс Motorola: 94 (4.00%)\n", - "Класс Vivo: 94 (4.00%)\n", - "Класс Lava: 94 (4.00%)\n", - "Класс Lenovo: 94 (4.00%)\n", - "Класс TCL: 94 (4.00%)\n", - "Класс OPPO: 94 (4.00%)\n", - "Класс Honor: 94 (4.00%)\n", - "Класс Poco: 94 (4.00%)\n", - "Класс itel: 94 (4.00%)\n", - "Класс Oppo: 94 (4.00%)\n", - "Класс iQOO: 94 (4.00%)\n", - "Класс Samsung: 94 (4.00%)\n", - "Класс Xiaomi: 94 (4.00%)\n", - "Класс LG: 94 (4.00%)\n", - "Класс Huawei: 94 (4.00%)\n", - "Класс OnePlus: 94 (4.00%)\n", - "Класс Google: 94 (4.00%)\n", - "Класс Tecno: 94 (4.00%)\n", - "Класс Asus: 94 (4.00%)\n", - "Класс Gionee: 94 (4.00%)\n", - "Класс POCO: 94 (4.00%)\n", - "Класс Nothing: 94 (4.00%)\n", - "Класс Coolpad: 94 (4.00%)\n", - "Класс Itel: 94 (4.00%)\n", - "\n", - "Распределение классов в Контрольной выборке:\n", - "Класс Vivo: 44 (15.28%)\n", - "Класс Realme: 43 (14.93%)\n", - "Класс Samsung: 39 (13.54%)\n", - "Класс Motorola: 23 (7.99%)\n", - "Класс Xiaomi: 20 (6.94%)\n", - "Класс Honor: 19 (6.60%)\n", - "Класс OnePlus: 16 (5.56%)\n", - "Класс Poco: 15 (5.21%)\n", - "Класс Huawei: 11 (3.82%)\n", - "Класс iQOO: 9 (3.12%)\n", - "Класс Oppo: 7 (2.43%)\n", - "Класс POCO: 5 (1.74%)\n", - "Класс OPPO: 5 (1.74%)\n", - "Класс Google: 4 (1.39%)\n", - "Класс Asus: 4 (1.39%)\n", - "Класс TCL: 4 (1.39%)\n", - "Класс Lava: 4 (1.39%)\n", - "Класс itel: 3 (1.04%)\n", - "Класс Nothing: 3 (1.04%)\n", - "Класс Tecno: 3 (1.04%)\n", - "Класс Lenovo: 3 (1.04%)\n", - "Класс LG: 2 (0.69%)\n", - "Класс Gionee: 1 (0.35%)\n", - "Класс IQOO: 1 (0.35%)\n", - "\n", - "Распределение классов в Тестовой выборке:\n", - "Класс Realme: 61 (14.84%)\n", - "Класс Samsung: 53 (12.90%)\n", - "Класс Vivo: 48 (11.68%)\n", - "Класс Motorola: 38 (9.25%)\n", - "Класс Honor: 29 (7.06%)\n", - "Класс Xiaomi: 24 (5.84%)\n", - "Класс Poco: 23 (5.60%)\n", - "Класс iQOO: 20 (4.87%)\n", - "Класс OnePlus: 19 (4.62%)\n", - "Класс OPPO: 18 (4.38%)\n", - "Класс Huawei: 16 (3.89%)\n", - "Класс TCL: 12 (2.92%)\n", - "Класс Asus: 9 (2.19%)\n", - "Класс Google: 7 (1.70%)\n", - "Класс Nothing: 7 (1.70%)\n", - "Класс Oppo: 6 (1.46%)\n", - "Класс POCO: 5 (1.22%)\n", - "Класс Tecno: 5 (1.22%)\n", - "Класс Lava: 3 (0.73%)\n", - "Класс Lenovo: 2 (0.49%)\n", - "Класс itel: 2 (0.49%)\n", - "Класс Itel: 2 (0.49%)\n", - "Класс LG: 1 (0.24%)\n", - "Класс Gionee: 1 (0.24%)\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from imblearn.over_sampling import RandomOverSampler\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Вывод размеров выборок\n", - "print(\"Размер обучающей выборки до upsampling:\", len(train_df))\n", - "print(\"Размер контрольной выборки:\", len(val_df))\n", - "print(\"Размер тестовой выборки:\", len(test_df))\n", - "\n", - "# Функция для проверки балансировки данных\n", - "def check_balance(df, title):\n", - " class_distribution = df['company'].value_counts()\n", - " print(f\"\\nРаспределение классов в {title}:\")\n", - " for cls, count in class_distribution.items():\n", - " print(f\"Класс {cls}: {count} ({count / len(df) * 100:.2f}%)\")\n", - "\n", - "# Проверка балансировки для всего датасета\n", - "check_balance(df, 'всем датасете')\n", - "\n", - "# Проверка балансировки для обучающей выборки до upsampling\n", - "check_balance(train_df, 'Обучающей выборке до upsampling')\n", - "\n", - "# Применение upsampling к обучающей выборке\n", - "X_train = train_df.drop('company', axis=1) # Отделяем признаки от целевой переменной\n", - "y_train = train_df['company'] # Целевая переменная\n", - "\n", - "# Инициализация RandomOverSampler\n", - "ros = RandomOverSampler(random_state=42)\n", - "\n", - "# Применение upsampling\n", - "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n", - "\n", - "# Создание нового DataFrame с балансированными данными\n", - "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n", - "\n", - "# Вывод размеров выборок после upsampling\n", - "print(\"Размер обучающей выборки после upsampling:\", len(train_df_resampled))\n", - "\n", - "# Проверка балансировки для обучающей выборки после upsampling\n", - "check_balance(train_df_resampled, 'Обучающей выборке после upsampling')\n", - "\n", - "# Проверка балансировки для контрольной и тестовой выборок (они не должны измениться)\n", - "check_balance(val_df, 'Контрольной выборке')\n", - "check_balance(test_df, 'Тестовой выборке')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Данные были сбалансированы. Теперь можно перейти к конструированию признаков. Поставлены следующие задачи:\n", - "1. Классифицировать мобильные устройства по ценовым категориям (например, бюджетные, средний класс, флагманы).\n", - "2. Определить, какие характеристики мобильных устройств наиболее сильно влияют на их рейтинг." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from imblearn.over_sampling import RandomOverSampler\n", - "\n", - "# Определение категориальных признаков\n", - "categorical_features = [\n", - " 'Rating', 'Ram',\n", - " 'Battery', 'Display', 'Camera', 'External_Memory', 'Android_version',\n", - " 'Price', 'company', 'Inbuilt_memory', 'fast_charging',\n", - " 'Screen_resolution', 'Processor'\n", - "]\n", - "\n", - "# Применение one-hot encoding к обучающей выборке\n", - "train_df_resampled_encoded = pd.get_dummies(train_df_resampled, columns=categorical_features)\n", - "\n", - "# Применение one-hot encoding к контрольной выборке\n", - "val_df_encoded = pd.get_dummies(val_df, columns=categorical_features)\n", - "\n", - "# Применение one-hot encoding к тестовой выборке\n", - "test_df_encoded = pd.get_dummies(test_df, columns=categorical_features)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Дискретизация числовых признаков" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки после балансировки: (5600, 22)\n", - "Размер контрольной выборки: (288, 22)\n", - "Размер тестовой выборки: (411, 22)\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from imblearn.over_sampling import RandomOverSampler\n", - "import re\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Извлечение числовых значений из столбца Battery\n", - "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "df['Ram'] = df['Ram'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "df['Camera'] = df['Camera'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "\n", - "# Удаление запятых из столбца Price и преобразование в числовой формат\n", - "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n", - "\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Применение upsampling к обучающей выборке (если это необходимо)\n", - "X_train = train_df.drop('Price', axis=1) # Отделяем признаки от целевой переменной\n", - "y_train = train_df['Price'] # Целевая переменная\n", - "\n", - "# Инициализация RandomOverSampler\n", - "ros = RandomOverSampler(random_state=42)\n", - "\n", - "# Применение upsampling\n", - "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n", - "\n", - "# Создание нового DataFrame с балансированными данными\n", - "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n", - "\n", - "# Определение числовых признаков для дискретизации\n", - "numerical_features = ['Spec_score', 'Battery', 'Ram', 'Camera' ]\n", - "\n", - "# Функция для дискретизации числовых признаков\n", - "def discretize_features(df, features, bins=5, labels=False):\n", - " for feature in features:\n", - " try:\n", - " # Заполнение NaN значений, если они есть\n", - " df[feature] = df[feature].fillna(df[feature].median())\n", - " df[f'{feature}_bin'] = pd.cut(df[feature], bins=bins, labels=labels)\n", - " except Exception as e:\n", - " print(f\"Ошибка при дискретизации признака {feature}: {e}\")\n", - " return df\n", - "\n", - "# Применение дискретизации к обучающей, контрольной и тестовой выборкам\n", - "train_df_resampled = discretize_features(train_df_resampled, numerical_features)\n", - "val_df = discretize_features(val_df, numerical_features)\n", - "test_df = discretize_features(test_df, numerical_features)\n", - "\n", - "# Вывод размеров выборок\n", - "print(\"Размер обучающей выборки после балансировки:\", train_df_resampled.shape)\n", - "print(\"Размер контрольной выборки:\", val_df.shape)\n", - "print(\"Размер тестовой выборки:\", test_df.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ручной синтез. Создание новых признаков на основе экспертных знаний и логики предметной области." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки после балансировки: (5600, 19)\n", - "Размер контрольной выборки: (288, 19)\n", - "Размер тестовой выборки: (411, 19)\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from imblearn.over_sampling import RandomOverSampler\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Преобразование столбца Battery в числовой формат\n", - "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "\n", - "# Преобразование столбцов Camera и Display в числовой формат\n", - "df['Camera'] = pd.to_numeric(df['Camera'], errors='coerce')\n", - "df['Display'] = pd.to_numeric(df['Display'], errors='coerce')\n", - "\n", - "# Удаление запятых из столбца Price и преобразование в числовой формат\n", - "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n", - "\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Применение upsampling к обучающей выборке (если это необходимо)\n", - "X_train = train_df.drop('Price', axis=1) # Отделяем признаки от целевой переменной\n", - "y_train = train_df['Price'] # Целевая переменная\n", - "\n", - "# Инициализация RandomOverSampler\n", - "ros = RandomOverSampler(random_state=42)\n", - "\n", - "# Применение upsampling\n", - "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n", - "\n", - "# Создание нового DataFrame с балансированными данными\n", - "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n", - "\n", - "# Создание нового признака \"Camera_to_Display_Ratio\" на основе признаков \"Camera\" и \"Display\"\n", - "train_df_resampled['Camera_to_Display_Ratio'] = train_df_resampled['Camera'] / train_df_resampled['Display']\n", - "val_df['Camera_to_Display_Ratio'] = val_df['Camera'] / val_df['Display']\n", - "test_df['Camera_to_Display_Ratio'] = test_df['Camera'] / test_df['Display']\n", - "\n", - "# Вывод размеров выборок\n", - "print(\"Размер обучающей выборки после балансировки:\", train_df_resampled.shape)\n", - "print(\"Размер контрольной выборки:\", val_df.shape)\n", - "print(\"Размер тестовой выборки:\", test_df.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки после балансировки: (5600, 19)\n", - "Размер контрольной выборки: (288, 19)\n", - "Размер тестовой выборки: (411, 19)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\sklearn\\utils\\extmath.py:1137: RuntimeWarning: invalid value encountered in divide\n", - " updated_mean = (last_sum + new_sum) / updated_sample_count\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\sklearn\\utils\\extmath.py:1142: RuntimeWarning: invalid value encountered in divide\n", - " T = new_sum / new_sample_count\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\sklearn\\utils\\extmath.py:1162: RuntimeWarning: invalid value encountered in divide\n", - " new_unnormalized_variance -= correction**2 / new_sample_count\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from imblearn.over_sampling import RandomOverSampler\n", - "from sklearn.preprocessing import StandardScaler\n", - "import re\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Преобразование столбца Battery в числовой формат\n", - "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "\n", - "# Преобразование столбцов Camera и Display в числовой формат\n", - "df['Camera'] = pd.to_numeric(df['Camera'], errors='coerce')\n", - "df['Display'] = pd.to_numeric(df['Display'], errors='coerce')\n", - "\n", - "# Удаление запятых из столбца Price и преобразование в числовой формат\n", - "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n", - "\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Применение upsampling к обучающей выборке (если это необходимо)\n", - "X_train = train_df.drop('Price', axis=1) # Отделяем признаки от целевой переменной\n", - "y_train = train_df['Price'] # Целевая переменная\n", - "\n", - "# Инициализация RandomOverSampler\n", - "ros = RandomOverSampler(random_state=42)\n", - "\n", - "# Применение upsampling\n", - "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n", - "\n", - "# Создание нового DataFrame с балансированными данными\n", - "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n", - "\n", - "# Создание нового признака \"Camera_to_Display_Ratio\" на основе признаков \"Camera\" и \"Display\"\n", - "train_df_resampled['Camera_to_Display_Ratio'] = train_df_resampled['Camera'] / train_df_resampled['Display']\n", - "val_df['Camera_to_Display_Ratio'] = val_df['Camera'] / val_df['Display']\n", - "test_df['Camera_to_Display_Ratio'] = test_df['Camera'] / test_df['Display']\n", - "\n", - "# Определение числовых признаков для масштабирования\n", - "numerical_features_to_scale = ['Spec_score', 'No_of_sim', 'Ram', 'Battery', 'Display', 'Camera', 'Inbuilt_memory', 'Screen_resolution', 'Camera_to_Display_Ratio']\n", - "\n", - "# Удаление строковых значений из числовых признаков\n", - "for feature in numerical_features_to_scale:\n", - " train_df_resampled[feature] = pd.to_numeric(train_df_resampled[feature], errors='coerce')\n", - " val_df[feature] = pd.to_numeric(val_df[feature], errors='coerce')\n", - " test_df[feature] = pd.to_numeric(test_df[feature], errors='coerce')\n", - "\n", - "# Инициализация StandardScaler\n", - "scaler = StandardScaler()\n", - "\n", - "# Масштабирование числовых признаков в обучающей выборке\n", - "train_df_resampled[numerical_features_to_scale] = scaler.fit_transform(train_df_resampled[numerical_features_to_scale])\n", - "\n", - "# Масштабирование числовых признаков в контрольной и тестовой выборках\n", - "val_df[numerical_features_to_scale] = scaler.transform(val_df[numerical_features_to_scale])\n", - "test_df[numerical_features_to_scale] = scaler.transform(test_df[numerical_features_to_scale])\n", - "\n", - "# Вывод размеров выборок\n", - "print(\"Размер обучающей выборки после балансировки:\", train_df_resampled.shape)\n", - "print(\"Размер контрольной выборки:\", val_df.shape)\n", - "print(\"Размер тестовой выборки:\", test_df.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Конструирование признаков с применением фреймворка Featuretools" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n", - " warnings.warn(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " pd.to_datetime(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n", - " warnings.warn(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Обучающая выборка после конструирования признаков:\n", - " Unnamed: 0 Rating Spec_score No_of_sim Ram \\\n", - "id \n", - "0 305 4.70 86 Dual Sim, 3G, 4G, 5G, VoLTE, 12 GB RAM \n", - "1 941 4.45 71 Dual Sim, 3G, 4G, VoLTE, 4 GB RAM \n", - "2 800 4.20 68 Dual Sim, 3G, 4G, VoLTE, 4 GB RAM \n", - "3 97 4.25 69 Dual Sim, 3G, 4G, VoLTE, 4 GB RAM \n", - "4 1339 4.30 74 Dual Sim, 3G, 4G, VoLTE, 6 GB RAM \n", - "\n", - " Battery External_Memory Android_version Price \\\n", - "id \n", - "0 5000 Android v12 NaN 30999.0 \n", - "1 5000 Memory Card Supported, upto 1 TB 12 6999.0 \n", - "2 5000 Memory Card Supported 12 8999.0 \n", - "3 5000 Memory Card Supported 12 9999.0 \n", - "4 5000 Memory Card Supported, upto 256 GB 12 8499.0 \n", - "\n", - " company Inbuilt_memory fast_charging \\\n", - "id \n", - "0 Realme 256 GB inbuilt 65W Fast Charging \n", - "1 Motorola 64 GB inbuilt 10W Fast Charging \n", - "2 Vivo 64 GB inbuilt 10W Fast Charging \n", - "3 Vivo 128 GB inbuilt 10W Fast Charging \n", - "4 Lava 128 GB inbuilt NaN \n", - "\n", - " Screen_resolution Processor \n", - "id \n", - "0 1080 x 2400 px Octa Core \n", - "1 720 x 1600 px Octa Core \n", - "2 720 x 1600 px Display with Water Drop Notch Octa Core \n", - "3 720 x 1600 px Display with Water Drop Notch Octa Core \n", - "4 1600 x 720 px Octa Core \n", - "Контрольная выборка после конструирования признаков:\n", - " Unnamed: 0 Rating Spec_score No_of_sim Ram \\\n", - "id \n", - "1028 NaN NaN NaN \n", - "825 NaN NaN NaN \n", - "900 NaN NaN NaN \n", - "702 NaN NaN NaN \n", - "230 1050 4.05 90 Dual Sim, 3G, 4G, 5G, VoLTE, 8 GB RAM \n", - "\n", - " Battery External_Memory Android_version Price company \\\n", - "id \n", - "1028 NaN NaN NaN NaN \n", - "825 NaN NaN NaN NaN \n", - "900 NaN NaN NaN NaN \n", - "702 NaN NaN NaN NaN \n", - "230 4500 Android v12 NaN 62990.0 Motorola \n", - "\n", - " Inbuilt_memory fast_charging Screen_resolution Processor \n", - "id \n", - "1028 NaN NaN NaN NaN \n", - "825 NaN NaN NaN NaN \n", - "900 NaN NaN NaN NaN \n", - "702 NaN NaN NaN NaN \n", - "230 128 GB inbuilt 125W Fast Charging 1080 x 2400 px Octa Core \n", - "Тестовая выборка после конструирования признаков:\n", - " Unnamed: 0 Rating Spec_score No_of_sim \\\n", - "id \n", - "427 187 4.40 91 Dual Sim, 3G, 4G, 5G, VoLTE, \n", - "1088 NaN NaN \n", - "668 592 4.45 91 Dual Sim, 3G, 4G, 5G, VoLTE, \n", - "572 1130 4.60 75 Dual Sim, 3G, 4G, VoLTE, \n", - "115 117 4.60 72 Dual Sim, 3G, 4G, VoLTE, \n", - "\n", - " Ram Battery External_Memory Android_version \\\n", - "id \n", - "427 12 GB RAM 5000 Memory Card Not Supported 14 \n", - "1088 NaN NaN NaN \n", - "668 12 GB RAM 4500 Android v12 NaN \n", - "572 6 GB RAM 5000 Memory Card Supported, upto 1 TB 13 \n", - "115 4 GB RAM 5000 Memory Card Supported, upto 1 TB 12 \n", - "\n", - " Price company Inbuilt_memory fast_charging \\\n", - "id \n", - "427 63999.0 Vivo 256 GB inbuilt 120W Fast Charging \n", - "1088 NaN NaN NaN NaN \n", - "668 54990.0 Honor 256 GB inbuilt 100W Fast Charging \n", - "572 8499.0 Xiaomi 128 GB inbuilt 18W Fast Charging \n", - "115 11580.0 Vivo 64 GB inbuilt 18W Fast Charging \n", - "\n", - " Screen_resolution Processor \n", - "id \n", - "427 1260 x 2800 px Octa Core \n", - "1088 NaN NaN \n", - "668 1200 x 2652 px Octa Core \n", - "572 720 x 1600 px Octa Core \n", - "115 720 x 1612 px Display with Water Drop Notch Octa Core \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "import featuretools as ft\n", - "import re\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Преобразование столбца Battery в числовой формат\n", - "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "\n", - "# Преобразование столбцов Camera и Display в числовой формат\n", - "df['Camera'] = pd.to_numeric(df['Camera'], errors='coerce')\n", - "df['Display'] = pd.to_numeric(df['Display'], errors='coerce')\n", - "\n", - "# Удаление запятых из столбца Price и преобразование в числовой формат\n", - "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n", - "\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Создание нового признака \"Camera_to_Display_Ratio\" на основе признаков \"Camera\" и \"Display\"\n", - "train_df['Camera_to_Display_Ratio'] = train_df['Camera'] / train_df['Display']\n", - "val_df['Camera_to_Display_Ratio'] = val_df['Camera'] / val_df['Display']\n", - "test_df['Camera_to_Display_Ratio'] = test_df['Camera'] / test_df['Display']\n", - "\n", - "# Определение сущностей\n", - "es = ft.EntitySet(id='mobile_data')\n", - "es = es.add_dataframe(dataframe_name='train', dataframe=train_df, index='id')\n", - "\n", - "# Генерация признаков\n", - "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='train', max_depth=2)\n", - "\n", - "# Преобразование признаков для контрольной и тестовой выборок\n", - "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df.index)\n", - "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df.index)\n", - "\n", - "# Вывод первых нескольких строк для проверки\n", - "print(\"Обучающая выборка после конструирования признаков:\")\n", - "print(feature_matrix.head())\n", - "print(\"Контрольная выборка после конструирования признаков:\")\n", - "print(val_feature_matrix.head())\n", - "print(\"Тестовая выборка после конструирования признаков:\")\n", - "print(test_feature_matrix.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Оценка качества каждого набора признаков\n", - "\n", - "Предсказательная способность Метрики: RMSE, MAE, R²\n", - "\n", - "Методы: Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.\n", - "\n", - "Скорость вычисления Методы: Измерение времени выполнения генерации признаков и обучения модели.\n", - "\n", - "Надежность Методы: Кросс-валидация, анализ чувствительности модели к изменениям в данных.\n", - "\n", - "Корреляция Методы: Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.\n", - "\n", - "Цельность Методы: Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n", - " warnings.warn(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки: 671\n", - "Размер контрольной выборки: 288\n", - "Размер тестовой выборки: 411\n", - "Feature Importance:\n", - " feature importance\n", - "4 Price 0.999443\n", - "2 Spec_score 0.000227\n", - "3 Battery 0.000146\n", - "0 Unnamed: 0 0.000146\n", - "1 Rating 0.000039\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from imblearn.over_sampling import RandomOverSampler\n", - "import featuretools as ft\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "import re\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Преобразование столбца Battery в числовой формат\n", - "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "\n", - "# Преобразование столбца Display в числовой формат\n", - "df['Camera'] = pd.to_numeric(df['Camera'], errors='coerce')\n", - "df['Display'] = pd.to_numeric(df['Display'], errors='coerce')\n", - "df['Inbuilt_memory'] = pd.to_numeric(df['Inbuilt_memory'], errors='coerce')\n", - "df['fast_charging'] = pd.to_numeric(df['fast_charging'], errors='coerce')\n", - "\n", - "# Удаление запятых из столбца Price и преобразование в числовой формат\n", - "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n", - "\n", - "# Удаление столбцов с текстовыми значениями, которые не могут быть преобразованы в числа\n", - "df = df.drop(columns=['Name', 'company', 'Android_version', 'Processor_name', 'External_Memory', 'No_of_sim', 'Ram', 'Screen_resolution', 'Processor' ])\n", - "\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Вывод размеров выборок\n", - "print(\"Размер обучающей выборки:\", len(train_df))\n", - "print(\"Размер контрольной выборки:\", len(val_df))\n", - "print(\"Размер тестовой выборки:\", len(test_df))\n", - "\n", - "# Применение upsampling к обучающей выборке (если это необходимо)\n", - "X_train = train_df.drop('Price', axis=1) # Отделяем признаки от целевой переменной\n", - "y_train = train_df['Price'] # Целевая переменная\n", - "\n", - "# Инициализация RandomOverSampler\n", - "ros = RandomOverSampler(random_state=42)\n", - "\n", - "# Применение upsampling\n", - "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n", - "\n", - "# Создание нового DataFrame с балансированными данными\n", - "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n", - "\n", - "# Определение сущностей\n", - "es = ft.EntitySet(id='mobile_data')\n", - "es = es.add_dataframe(dataframe_name='mobile', dataframe=train_df_resampled, index='id')\n", - "\n", - "# Генерация признаков\n", - "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='mobile', max_depth=2)\n", - "\n", - "# Преобразование признаков для контрольной и тестовой выборок\n", - "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df.index)\n", - "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df.index)\n", - "\n", - "# Оценка важности признаков\n", - "X = feature_matrix\n", - "y = train_df_resampled['Price']\n", - "\n", - "# Разделение данных на обучающую и тестовую выборки\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - "# Обучение модели\n", - "model = RandomForestRegressor(n_estimators=100, random_state=42)\n", - "model.fit(X_train, y_train)\n", - "\n", - "# Получение важности признаков\n", - "importances = model.feature_importances_\n", - "feature_names = feature_matrix.columns\n", - "\n", - "# Сортировка признаков по важности\n", - "feature_importance = pd.DataFrame({'feature': feature_names, 'importance': importances})\n", - "feature_importance = feature_importance.sort_values(by='importance', ascending=False)\n", - "\n", - "print(\"Feature Importance:\")\n", - "print(feature_importance)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Размер обучающей выборки: 671\n", - "Размер контрольной выборки: 288\n", - "Размер тестовой выборки: 411\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n", - " warnings.warn(\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " df = pd.concat([df, default_df], sort=True)\n", - "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error: 53834536.21488374\n", - "R2 Score: 0.9445638071244045\n", - "Cross-validated Mean Squared Error: 311290473.964474\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train Mean Squared Error: 40281623.425488226\n", - "Train R2 Score: 0.9581963040734582\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "from sklearn.model_selection import cross_val_score\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import featuretools as ft\n", - "import re\n", - "\n", - "# Загрузка данных\n", - "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n", - "\n", - "# Преобразование столбца Battery в числовой формат\n", - "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n", - "\n", - "# Преобразование столбца Display в числовой формат\n", - "df['Camera'] = pd.to_numeric(df['Camera'], errors='coerce')\n", - "df['Display'] = pd.to_numeric(df['Display'], errors='coerce')\n", - "df['Inbuilt_memory'] = pd.to_numeric(df['Inbuilt_memory'], errors='coerce')\n", - "df['fast_charging'] = pd.to_numeric(df['fast_charging'], errors='coerce')\n", - "\n", - "# Удаление запятых из столбца Price и преобразование в числовой формат\n", - "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n", - "\n", - "# Удаление столбцов с текстовыми значениями, которые не могут быть преобразованы в числа\n", - "df = df.drop(columns=['Name', 'company', 'Android_version', 'Processor_name', 'External_Memory', 'No_of_sim', 'Ram', 'Screen_resolution', 'Processor' ])\n", - "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n", - "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n", - "\n", - "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n", - "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n", - "\n", - "# Вывод размеров выборок\n", - "print(\"Размер обучающей выборки:\", len(train_df))\n", - "print(\"Размер контрольной выборки:\", len(val_df))\n", - "print(\"Размер тестовой выборки:\", len(test_df))\n", - "\n", - "# Определение сущностей\n", - "es = ft.EntitySet(id='mobile_data')\n", - "es = es.add_dataframe(dataframe_name='mobile', dataframe=train_df, index='id')\n", - "\n", - "# Генерация признаков с уменьшенной глубиной\n", - "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='mobile', max_depth=1)\n", - "\n", - "# Преобразование признаков для контрольной и тестовой выборок\n", - "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df.index)\n", - "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df.index)\n", - "\n", - "# Удаление строк с NaN\n", - "feature_matrix = feature_matrix.dropna()\n", - "val_feature_matrix = val_feature_matrix.dropna()\n", - "test_feature_matrix = test_feature_matrix.dropna()\n", - "\n", - "# Разделение данных на обучающую и тестовую выборки\n", - "X_train = feature_matrix.drop('Price', axis=1)\n", - "y_train = feature_matrix['Price']\n", - "X_val = val_feature_matrix.drop('Price', axis=1)\n", - "y_val = val_feature_matrix['Price']\n", - "X_test = test_feature_matrix.drop('Price', axis=1)\n", - "y_test = test_feature_matrix['Price']\n", - "\n", - "# Выбор модели\n", - "model = RandomForestRegressor(random_state=42)\n", - "\n", - "# Обучение модели\n", - "model.fit(X_train, y_train)\n", - "\n", - "# Предсказание и оценка\n", - "y_pred = model.predict(X_test)\n", - "\n", - "mse = mean_squared_error(y_test, y_pred)\n", - "r2 = r2_score(y_test, y_pred)\n", - "\n", - "print(f\"Mean Squared Error: {mse}\")\n", - "print(f\"R2 Score: {r2}\")\n", - "\n", - "# Кросс-валидация\n", - "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n", - "mse_cv = -scores.mean()\n", - "print(f\"Cross-validated Mean Squared Error: {mse_cv}\")\n", - "\n", - "# Анализ важности признаков\n", - "feature_importances = model.feature_importances_\n", - "feature_names = X_train.columns\n", - "\n", - "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n", - "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n", - "\n", - "plt.figure(figsize=(10, 6))\n", - "sns.barplot(x='Importance', y='Feature', data=importance_df)\n", - "plt.title('Feature Importance')\n", - "plt.show()\n", - "\n", - "# Проверка на переобучение\n", - "y_train_pred = model.predict(X_train)\n", - "\n", - "mse_train = mean_squared_error(y_train, y_train_pred)\n", - "r2_train = r2_score(y_train, y_train_pred)\n", - "\n", - "print(f\"Train Mean Squared Error: {mse_train}\")\n", - "print(f\"Train R2 Score: {r2_train}\")\n", - "\n", - "# Визуализация результатов\n", - "plt.figure(figsize=(10, 6))\n", - "plt.scatter(y_test, y_pred, alpha=0.5)\n", - "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n", - "plt.xlabel('Actual Price')\n", - "plt.ylabel('Predicted Price')\n", - "plt.title('Actual vs Predicted Price')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "aimenv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/lab_7/lab7.ipynb b/lab_7/lab7.ipynb new file mode 100644 index 0000000..2658522 --- /dev/null +++ b/lab_7/lab7.ipynb @@ -0,0 +1,2376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Начало лабораторной работы №7 #" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Unnamed: 0', 'Name', 'Brand', 'Model', 'Battery capacity (mAh)',\n", + " 'Screen size (inches)', 'Touchscreen', 'Resolution x', 'Resolution y',\n", + " 'Processor', 'RAM (MB)', 'Internal storage (GB)', 'Rear camera',\n", + " 'Front camera', 'Operating system', 'Wi-Fi', 'Bluetooth', 'GPS',\n", + " 'Number of SIMs', '3G', '4G/ LTE', 'Price'],\n", + " dtype='object')\n" + ] + }, + { + "data": { + "text/html": [ + "
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Unnamed: 0NameBrandModelBattery capacity (mAh)Screen size (inches)TouchscreenResolution xResolution yProcessor...Rear cameraFront cameraOperating systemWi-FiBluetoothGPSNumber of SIMs3G4G/ LTEPrice
00OnePlus 7T Pro McLaren EditionOnePlus7T Pro McLaren Edition40856.67Yes144031208...48.016.0AndroidYesYesYes2YesYes58998
11Realme X2 ProRealmeX2 Pro40006.50Yes108024008...64.016.0AndroidYesYesYes2YesYes27999
22iPhone 11 Pro MaxAppleiPhone 11 Pro Max39696.50Yes124226886...12.012.0iOSYesYesYes2YesYes106900
33iPhone 11AppleiPhone 1131106.10Yes82817926...12.012.0iOSYesYesYes2YesYes62900
44LG G8X ThinQLGG8X ThinQ40006.40Yes108023408...12.032.0AndroidYesYesYes1NoNo49990
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" + ], + "text/plain": [ + " Unnamed: 0 Name Brand \\\n", + "0 0 OnePlus 7T Pro McLaren Edition OnePlus \n", + "1 1 Realme X2 Pro Realme \n", + "2 2 iPhone 11 Pro Max Apple \n", + "3 3 iPhone 11 Apple \n", + "4 4 LG G8X ThinQ LG \n", + "\n", + " Model Battery capacity (mAh) Screen size (inches) \\\n", + "0 7T Pro McLaren Edition 4085 6.67 \n", + "1 X2 Pro 4000 6.50 \n", + "2 iPhone 11 Pro Max 3969 6.50 \n", + "3 iPhone 11 3110 6.10 \n", + "4 G8X ThinQ 4000 6.40 \n", + "\n", + " Touchscreen Resolution x Resolution y Processor ... Rear camera \\\n", + "0 Yes 1440 3120 8 ... 48.0 \n", + "1 Yes 1080 2400 8 ... 64.0 \n", + "2 Yes 1242 2688 6 ... 12.0 \n", + "3 Yes 828 1792 6 ... 12.0 \n", + "4 Yes 1080 2340 8 ... 12.0 \n", + "\n", + " Front camera Operating system Wi-Fi Bluetooth GPS Number of SIMs 3G \\\n", + "0 16.0 Android Yes Yes Yes 2 Yes \n", + "1 16.0 Android Yes Yes Yes 2 Yes \n", + "2 12.0 iOS Yes Yes Yes 2 Yes \n", + "3 12.0 iOS Yes Yes Yes 2 Yes \n", + "4 32.0 Android Yes Yes Yes 1 No \n", + "\n", + " 4G/ LTE Price \n", + "0 Yes 58998 \n", + "1 Yes 27999 \n", + "2 Yes 106900 \n", + "3 Yes 62900 \n", + "4 No 49990 \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"..//static//csv//ndtv_data_final.csv\")\n", + "print(df.columns)\n", + "display(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**Создание лингвистических переменных**\n", + "\n", + "Входные переменные: Battery capacity (mAh) (емкость батареи) и RAM (MB) (объем оперативной памяти). \\\n", + "Выходная переменная: Price (цена)." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from skfuzzy import control as ctrl\n", + "\n", + "data = pd.read_csv(\"..//static//csv//ndtv_data_final.csv\")\n", + "\n", + "# Инициализация лингвистических переменных\n", + "battery_capacity = ctrl.Antecedent(np.arange(data['Battery capacity (mAh)'].min(), data['Battery capacity (mAh)'].max(), 100), \"battery_capacity\")\n", + "ram = ctrl.Antecedent(np.arange(data['RAM (MB)'].min(), data['RAM (MB)'].max(), 100), \"ram\")\n", + "price = ctrl.Consequent(np.arange(data['Price'].min(), data['Price'].max(), 10), \"price\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Формирование нечетких переменных для лингвистических переменных и их визуализация**" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import skfuzzy as fuzz\n", + "\n", + "battery_capacity['low'] = fuzz.zmf(battery_capacity.universe, 2000, 3000)\n", + "battery_capacity['average'] = fuzz.trapmf(battery_capacity.universe, [2000, 3000, 4000, 5000])\n", + "battery_capacity['high'] = fuzz.smf(battery_capacity.universe, 4000, 5000)\n", + "\n", + "ram['low'] = fuzz.zmf(ram.universe, 1000, 2000)\n", + "ram['average'] = fuzz.trapmf(ram.universe, [1000, 2000, 4000, 6000])\n", + "ram['high'] = fuzz.smf(ram.universe, 4000, 6000)\n", + "\n", + "price['low'] = fuzz.zmf(price.universe, 10000, 20000)\n", + "price['average'] = fuzz.trapmf(price.universe, [10000, 20000, 40000, 60000])\n", + "price['high'] = fuzz.smf(price.universe, 40000, 60000)\n", + "\n", + "battery_capacity.view()\n", + "ram.view()\n", + "price.view()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Формирование и визуализация базы нечетких правил**" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Нечеткие правила\n", + "rule1 = ctrl.Rule(battery_capacity[\"low\"] & ram[\"low\"], price[\"low\"])\n", + "rule2 = ctrl.Rule(battery_capacity[\"low\"] & ram[\"average\"], price[\"low\"])\n", + "rule3 = ctrl.Rule(battery_capacity[\"low\"] & ram[\"high\"], price[\"average\"])\n", + "rule4 = ctrl.Rule(battery_capacity[\"average\"] & ram[\"low\"], price[\"low\"])\n", + "rule5 = ctrl.Rule(battery_capacity[\"average\"] & ram[\"average\"], price[\"average\"])\n", + "rule6 = ctrl.Rule(battery_capacity[\"average\"] & ram[\"high\"], price[\"high\"])\n", + "rule7 = ctrl.Rule(battery_capacity[\"high\"] & ram[\"low\"], price[\"average\"])\n", + "rule8 = ctrl.Rule(battery_capacity[\"high\"] & ram[\"average\"], price[\"high\"])\n", + "rule9 = ctrl.Rule(battery_capacity[\"high\"] & ram[\"high\"], price[\"high\"])\n", + "rule1.view()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Создание нечеткой системы и добавление нечетких правил в базу знаний нечеткой системы**" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "price_ctrl = ctrl.ControlSystem(\n", + " [\n", + " rule1,\n", + " rule2,\n", + " rule3,\n", + " rule4,\n", + " rule5,\n", + " rule6,\n", + " rule7,\n", + " rule8,\n", + " rule9,\n", + " ]\n", + ")\n", + "\n", + "# Создание симулятора нечеткой системы\n", + "price_sim = ctrl.ControlSystemSimulation(price_ctrl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Пример расчета выходной переменной influx на основе входных переменных level и flow** \\\n", + "Система также формирует подробный журнал выполнения процесса нечеткого логического вывода" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: battery_capacity = 4000\n", + " - low : 0.0\n", + " - average : 0.991\n", + " - high : 0.00017999999999999998\n", + "Antecedent: ram = 2000\n", + " - low : 0.0016588799999999997\n", + " - average : 0.9769599999999999\n", + " - high : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF battery_capacity[low] AND ram[low] THEN price[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[low] : 0.0\n", + " - ram[low] : 0.0016588799999999997\n", + " battery_capacity[low] AND ram[low] = 0.0\n", + " Activation (THEN-clause):\n", + " price[low] : 0.0\n", + "\n", + "RULE #1:\n", + " IF battery_capacity[low] AND ram[average] THEN price[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[low] : 0.0\n", + " - ram[average] : 0.9769599999999999\n", + " battery_capacity[low] AND ram[average] = 0.0\n", + " Activation (THEN-clause):\n", + " price[low] : 0.0\n", + "\n", + "RULE #2:\n", + " IF battery_capacity[low] AND ram[high] THEN price[average]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[low] : 0.0\n", + " - ram[high] : 0.0\n", + " battery_capacity[low] AND ram[high] = 0.0\n", + " Activation (THEN-clause):\n", + " price[average] : 0.0\n", + "\n", + "RULE #3:\n", + " IF battery_capacity[average] AND ram[low] THEN price[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[average] : 0.991\n", + " - ram[low] : 0.0016588799999999997\n", + " battery_capacity[average] AND ram[low] = 0.0016588799999999997\n", + " Activation (THEN-clause):\n", + " price[low] : 0.0016588799999999997\n", + "\n", + "RULE #4:\n", + " IF battery_capacity[average] AND ram[average] THEN price[average]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[average] : 0.991\n", + " - ram[average] : 0.9769599999999999\n", + " battery_capacity[average] AND ram[average] = 0.9769599999999999\n", + " Activation (THEN-clause):\n", + " price[average] : 0.9769599999999999\n", + "\n", + "RULE #5:\n", + " IF battery_capacity[average] AND ram[high] THEN price[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[average] : 0.991\n", + " - ram[high] : 0.0\n", + " battery_capacity[average] AND ram[high] = 0.0\n", + " Activation (THEN-clause):\n", + " price[high] : 0.0\n", + "\n", + "RULE #6:\n", + " IF battery_capacity[high] AND ram[low] THEN price[average]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[high] : 0.00017999999999999998\n", + " - ram[low] : 0.0016588799999999997\n", + " battery_capacity[high] AND ram[low] = 0.00017999999999999998\n", + " Activation (THEN-clause):\n", + " price[average] : 0.00017999999999999998\n", + "\n", + "RULE #7:\n", + " IF battery_capacity[high] AND ram[average] THEN price[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[high] : 0.00017999999999999998\n", + " - ram[average] : 0.9769599999999999\n", + " battery_capacity[high] AND ram[average] = 0.00017999999999999998\n", + " Activation (THEN-clause):\n", + " price[high] : 0.00017999999999999998\n", + "\n", + "RULE #8:\n", + " IF battery_capacity[high] AND ram[high] THEN price[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[high] : 0.00017999999999999998\n", + " - ram[high] : 0.0\n", + " battery_capacity[high] AND ram[high] = 0.0\n", + " Activation (THEN-clause):\n", + " price[high] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: price = 32933.17210826396\n", + " low:\n", + " Accumulate using accumulation_max : 0.0016588799999999997\n", + " average:\n", + " Accumulate using accumulation_max : 0.9769599999999999\n", + " high:\n", + " Accumulate using accumulation_max : 0.00017999999999999998\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "np.float64(32933.17210826396)" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "price_sim.input['battery_capacity'] = 4000\n", + "price_sim.input['ram'] = 2000\n", + "price_sim.compute()\n", + "\n", + "price_sim.print_state()\n", + "\n", + "price_sim.output[\"price\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Визуализация функции принадлежности для выходной переменной influx** /\n", + "Функция получена в процессе аккумуляции и используется для дефаззификации значения выходной переменной influx" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Unnamed: 0NameBrandModelBattery capacity (mAh)Screen size (inches)TouchscreenResolution xResolution yProcessor...Rear cameraFront cameraOperating systemWi-FiBluetoothGPSNumber of SIMs3G4G/ LTEPrice
00OnePlus 7T Pro McLaren EditionOnePlus7T Pro McLaren Edition40856.67Yes144031208...48.016.0AndroidYesYesYes2YesYes58998
11Realme X2 ProRealmeX2 Pro40006.50Yes108024008...64.016.0AndroidYesYesYes2YesYes27999
22iPhone 11 Pro MaxAppleiPhone 11 Pro Max39696.50Yes124226886...12.012.0iOSYesYesYes2YesYes106900
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Unnamed: 0NameBrandModelBattery capacity (mAh)Screen size (inches)TouchscreenResolution xResolution yProcessor...Rear cameraFront cameraOperating systemWi-FiBluetoothGPSNumber of SIMs3G4G/ LTEPrice
00OnePlus 7T Pro McLaren EditionOnePlus7T Pro McLaren Edition40856.67Yes144031208...48.016.0AndroidYesYesYes2YesYes58998
11Realme X2 ProRealmeX2 Pro40006.50Yes108024008...64.016.0AndroidYesYesYes2YesYes27999
22iPhone 11 Pro MaxAppleiPhone 11 Pro Max39696.50Yes124226886...12.012.0iOSYesYesYes2YesYes106900
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" + ], + "text/plain": [ + " Unnamed: 0 Name Brand \\\n", + "0 0 OnePlus 7T Pro McLaren Edition OnePlus \n", + "1 1 Realme X2 Pro Realme \n", + "2 2 iPhone 11 Pro Max Apple \n", + "\n", + " Model Battery capacity (mAh) Screen size (inches) \\\n", + "0 7T Pro McLaren Edition 4085 6.67 \n", + "1 X2 Pro 4000 6.50 \n", + "2 iPhone 11 Pro Max 3969 6.50 \n", + "\n", + " Touchscreen Resolution x Resolution y Processor ... Rear camera \\\n", + "0 Yes 1440 3120 8 ... 48.0 \n", + "1 Yes 1080 2400 8 ... 64.0 \n", + "2 Yes 1242 2688 6 ... 12.0 \n", + "\n", + " Front camera Operating system Wi-Fi Bluetooth GPS Number of SIMs 3G \\\n", + "0 16.0 Android Yes Yes Yes 2 Yes \n", + "1 16.0 Android Yes Yes Yes 2 Yes \n", + "2 12.0 iOS Yes Yes Yes 2 Yes \n", + "\n", + " 4G/ LTE Price \n", + "0 Yes 58998 \n", + "1 Yes 27999 \n", + "2 Yes 106900 \n", + "\n", + "[3 rows x 22 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "data_train = pd.read_csv(\"..//static//csv//ndtv_data_final.csv\", sep=\",\")\n", + "data_test = pd.read_csv(\"..//static//csv//ndtv_data_final.csv\", sep=\",\")\n", + "\n", + "display(data_train.head(3))\n", + "display(data_test.head(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Инициализация лингвистических переменных и автоматическое формирование нечетких переменных**" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\ULSTU\\AIM2\\AIM-PIbd-32-Puchkina-A-A\\aimenv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = pd.read_csv(\"..//static//csv//ndtv_data_final.csv\")\n", + "\n", + "# Инициализация нечетких переменных\n", + "battery_capacity = ctrl.Antecedent(data[\"Battery capacity (mAh)\"].sort_values().unique(), \"battery_capacity\")\n", + "ram = ctrl.Antecedent(data[\"RAM (MB)\"].sort_values().unique(), \"ram\")\n", + "price = ctrl.Consequent(np.arange(0, 100001, 1000), \"price\")\n", + "\n", + "battery_capacity.automf(3, variable_type=\"quant\")\n", + "battery_capacity.view()\n", + "ram.automf(3, variable_type=\"quant\")\n", + "ram.view()\n", + "price.automf(5, variable_type=\"quant\")\n", + "price.view()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Нечеткие правила**" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "rule1 = ctrl.Rule(\n", + " battery_capacity[\"low\"] & ram[\"low\"],\n", + " price[\"low\"],\n", + ")\n", + "rule2 = ctrl.Rule(\n", + " battery_capacity[\"average\"] & ram[\"low\"],\n", + " price[\"low\"],\n", + ")\n", + "rule3 = ctrl.Rule(\n", + " battery_capacity[\"high\"] & ram[\"low\"],\n", + " price[\"average\"],\n", + ")\n", + "\n", + "rule4 = ctrl.Rule(\n", + " battery_capacity[\"low\"] & ram[\"average\"],\n", + " price[\"low\"],\n", + ")\n", + "rule5 = ctrl.Rule(\n", + " battery_capacity[\"average\"] & ram[\"average\"],\n", + " price[\"average\"],\n", + ")\n", + "rule6 = ctrl.Rule(\n", + " battery_capacity[\"high\"] & ram[\"average\"],\n", + " price[\"high\"],\n", + ")\n", + "\n", + "rule7 = ctrl.Rule(\n", + " battery_capacity[\"low\"] & ram[\"high\"],\n", + " price[\"average\"],\n", + ")\n", + "rule8 = ctrl.Rule(\n", + " battery_capacity[\"average\"] & ram[\"high\"],\n", + " price[\"high\"],\n", + ")\n", + "rule9 = ctrl.Rule(\n", + " battery_capacity[\"high\"] & ram[\"high\"],\n", + " price[\"high\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Создание нечеткой системы**" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[IF battery_capacity[low] AND ram[low] THEN price[low]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[average] AND ram[low] THEN price[low]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[high] AND ram[low] THEN price[average]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[low] AND ram[average] THEN price[low]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[average] AND ram[average] THEN price[average]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[high] AND ram[average] THEN price[high]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[low] AND ram[high] THEN price[average]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[average] AND ram[high] THEN price[high]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax,\n", + " IF battery_capacity[high] AND ram[high] THEN price[high]\n", + " \tAND aggregation function : fmin\n", + " \tOR aggregation function : fmax]" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fuzzy_rules = [\n", + " rule1,\n", + " rule2,\n", + " rule3,\n", + " rule4,\n", + " rule5,\n", + " rule6,\n", + " rule7,\n", + " rule8,\n", + " rule9,\n", + "]\n", + "\n", + "price_cntrl = ctrl.ControlSystem(fuzzy_rules)\n", + "\n", + "sim = ctrl.ControlSystemSimulation(price_cntrl)\n", + "\n", + "fuzzy_rules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Пример использования полученной нечеткой системы**" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: battery_capacity = 3110\n", + " - low : 0.15831663326653306\n", + " - average : 0.8416833667334669\n", + " - high : 0.0\n", + "Antecedent: ram = 4000\n", + " - low : 0.34048257372654156\n", + " - average : 0.6595174262734584\n", + " - high : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF battery_capacity[low] AND ram[low] THEN price[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[low] : 0.15831663326653306\n", + " - ram[low] : 0.34048257372654156\n", + " battery_capacity[low] AND ram[low] = 0.15831663326653306\n", + " Activation (THEN-clause):\n", + " price[low] : 0.15831663326653306\n", + "\n", + "RULE #1:\n", + " IF battery_capacity[average] AND ram[low] THEN price[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[average] : 0.8416833667334669\n", + " - ram[low] : 0.34048257372654156\n", + " battery_capacity[average] AND ram[low] = 0.34048257372654156\n", + " Activation (THEN-clause):\n", + " price[low] : 0.34048257372654156\n", + "\n", + "RULE #2:\n", + " IF battery_capacity[high] AND ram[low] THEN price[average]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[high] : 0.0\n", + " - ram[low] : 0.34048257372654156\n", + " battery_capacity[high] AND ram[low] = 0.0\n", + " Activation (THEN-clause):\n", + " price[average] : 0.0\n", + "\n", + "RULE #3:\n", + " IF battery_capacity[low] AND ram[average] THEN price[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[low] : 0.15831663326653306\n", + " - ram[average] : 0.6595174262734584\n", + " battery_capacity[low] AND ram[average] = 0.15831663326653306\n", + " Activation (THEN-clause):\n", + " price[low] : 0.15831663326653306\n", + "\n", + "RULE #4:\n", + " IF battery_capacity[average] AND ram[average] THEN price[average]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[average] : 0.8416833667334669\n", + " - ram[average] : 0.6595174262734584\n", + " battery_capacity[average] AND ram[average] = 0.6595174262734584\n", + " Activation (THEN-clause):\n", + " price[average] : 0.6595174262734584\n", + "\n", + "RULE #5:\n", + " IF battery_capacity[high] AND ram[average] THEN price[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[high] : 0.0\n", + " - ram[average] : 0.6595174262734584\n", + " battery_capacity[high] AND ram[average] = 0.0\n", + " Activation (THEN-clause):\n", + " price[high] : 0.0\n", + "\n", + "RULE #6:\n", + " IF battery_capacity[low] AND ram[high] THEN price[average]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[low] : 0.15831663326653306\n", + " - ram[high] : 0.0\n", + " battery_capacity[low] AND ram[high] = 0.0\n", + " Activation (THEN-clause):\n", + " price[average] : 0.0\n", + "\n", + "RULE #7:\n", + " IF battery_capacity[average] AND ram[high] THEN price[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[average] : 0.8416833667334669\n", + " - ram[high] : 0.0\n", + " battery_capacity[average] AND ram[high] = 0.0\n", + " Activation (THEN-clause):\n", + " price[high] : 0.0\n", + "\n", + "RULE #8:\n", + " IF battery_capacity[high] AND ram[high] THEN price[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - battery_capacity[high] : 0.0\n", + " - ram[high] : 0.0\n", + " battery_capacity[high] AND ram[high] = 0.0\n", + " Activation (THEN-clause):\n", + " price[high] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: price = 40755.45920618248\n", + " lower:\n", + " Accumulate using accumulation_max : None\n", + " low:\n", + " Accumulate using accumulation_max : 0.34048257372654156\n", + " average:\n", + " Accumulate using accumulation_max : 0.6595174262734584\n", + " high:\n", + " Accumulate using accumulation_max : 0.0\n", + " higher:\n", + " Accumulate using accumulation_max : None\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "np.float64(40755.45920618248)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "example_row = data.iloc[3] # Используем строку в качестве примера\n", + "\n", + "sim.input[\"battery_capacity\"] = example_row[\"Battery capacity (mAh)\"]\n", + "sim.input[\"ram\"] = example_row[\"RAM (MB)\"]\n", + "\n", + "sim.compute()\n", + "\n", + "sim.print_state()\n", + "display(sim.output[\"price\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Функция для автоматизации вычисления целевой переменной Y на основе вектора признаков X**" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Unnamed: 0', 'Name', 'Brand', 'Model', 'Battery capacity (mAh)',\n", + " 'Screen size (inches)', 'Touchscreen', 'Resolution x', 'Resolution y',\n", + " 'Processor', 'RAM (MB)', 'Internal storage (GB)', 'Rear camera',\n", + " 'Front camera', 'Operating system', 'Wi-Fi', 'Bluetooth', 'GPS',\n", + " 'Number of SIMs', '3G', '4G/ LTE', 'Price'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(data_train.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "def fuzzy_pred(row):\n", + " sim.input[\"battery_capacity\"] = row[\"Battery capacity (mAh)\"]\n", + " sim.input[\"ram\"] = row[\"RAM (MB)\"]\n", + " sim.compute()\n", + " return sim.output[\"price\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Тестирование нечеткой системы на обучающей выборке**" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameBrandModelBattery capacity (mAh)Screen size (inches)TouchscreenResolution xResolution yProcessor...Front cameraOperating systemWi-FiBluetoothGPSNumber of SIMs3G4G/ LTEPricePredicted Price
00OnePlus 7T Pro McLaren EditionOnePlus7T Pro McLaren Edition40856.67Yes144031208...16.0AndroidYesYesYes2YesYes5899875000.000000
11Realme X2 ProRealmeX2 Pro40006.50Yes108024008...16.0AndroidYesYesYes2YesYes2799955792.501783
22iPhone 11 Pro MaxAppleiPhone 11 Pro Max39696.50Yes124226886...12.0iOSYesYesYes2YesYes10690046614.283392
33iPhone 11AppleiPhone 1131106.10Yes82817926...12.0iOSYesYesYes2YesYes6290040755.459206
44LG G8X ThinQLGG8X ThinQ40006.40Yes108023408...32.0AndroidYesYesYes1NoNo4999055792.501783
55OnePlus 7TOnePlus7T38006.55Yes108024008...16.0AndroidYesYesNo2YesYes3493059015.272109
66OnePlus 7T ProOnePlus7T Pro40856.67Yes144031208...16.0AndroidYesYesYes2YesYes5299059015.272109
77Samsung Galaxy Note 10+SamsungGalaxy Note 10+43006.80Yes144030408...10.0AndroidYesYesYes2YesYes7969975000.000000
88Asus ROG Phone 2AsusROG Phone 260006.59Yes108023408...24.0AndroidYesYesYes1YesYes3799975000.000000
99Xiaomi Redmi K20 ProXiaomiRedmi K20 Pro40006.39Yes108023408...20.0AndroidYesYesYes2NoNo2319055792.501783
1010Oppo K3OppoK337656.50Yes108023408...16.0AndroidYesYesYes2YesYes2399053251.486942
1111Realme XRealmeX37656.53Yes108023408...16.0AndroidYesYesYes2YesYes1499944322.761836
1212Xiaomi Redmi K20XiaomiRedmi K2040006.39Yes108023408...20.0AndroidYesYesYes2YesYes1928255792.501783
1313OnePlus 7 ProOnePlus7 Pro40006.67Yes144031208...16.0AndroidYesYesYes2YesYes3999555792.501783
1414Oppo Reno 10x ZoomOppoReno 10x Zoom40656.60Yes108023408...16.0AndroidYesYesYes2YesYes3699056431.899431
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15 rows × 23 columns

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" + ], + "text/plain": [ + " Unnamed: 0 Name Brand \\\n", + "0 0 OnePlus 7T Pro McLaren Edition OnePlus \n", + "1 1 Realme X2 Pro Realme \n", + "2 2 iPhone 11 Pro Max Apple \n", + "3 3 iPhone 11 Apple \n", + "4 4 LG G8X ThinQ LG \n", + "5 5 OnePlus 7T OnePlus \n", + "6 6 OnePlus 7T Pro OnePlus \n", + "7 7 Samsung Galaxy Note 10+ Samsung \n", + "8 8 Asus ROG Phone 2 Asus \n", + "9 9 Xiaomi Redmi K20 Pro Xiaomi \n", + "10 10 Oppo K3 Oppo \n", + "11 11 Realme X Realme \n", + "12 12 Xiaomi Redmi K20 Xiaomi \n", + "13 13 OnePlus 7 Pro OnePlus \n", + "14 14 Oppo Reno 10x Zoom Oppo \n", + "\n", + " Model Battery capacity (mAh) Screen size (inches) \\\n", + "0 7T Pro McLaren Edition 4085 6.67 \n", + "1 X2 Pro 4000 6.50 \n", + "2 iPhone 11 Pro Max 3969 6.50 \n", + "3 iPhone 11 3110 6.10 \n", + "4 G8X ThinQ 4000 6.40 \n", + "5 7T 3800 6.55 \n", + "6 7T Pro 4085 6.67 \n", + "7 Galaxy Note 10+ 4300 6.80 \n", + "8 ROG Phone 2 6000 6.59 \n", + "9 Redmi K20 Pro 4000 6.39 \n", + "10 K3 3765 6.50 \n", + "11 X 3765 6.53 \n", + "12 Redmi K20 4000 6.39 \n", + "13 7 Pro 4000 6.67 \n", + "14 Reno 10x Zoom 4065 6.60 \n", + "\n", + " Touchscreen Resolution x Resolution y Processor ... Front camera \\\n", + "0 Yes 1440 3120 8 ... 16.0 \n", + "1 Yes 1080 2400 8 ... 16.0 \n", + "2 Yes 1242 2688 6 ... 12.0 \n", + "3 Yes 828 1792 6 ... 12.0 \n", + "4 Yes 1080 2340 8 ... 32.0 \n", + "5 Yes 1080 2400 8 ... 16.0 \n", + "6 Yes 1440 3120 8 ... 16.0 \n", + "7 Yes 1440 3040 8 ... 10.0 \n", + "8 Yes 1080 2340 8 ... 24.0 \n", + "9 Yes 1080 2340 8 ... 20.0 \n", + "10 Yes 1080 2340 8 ... 16.0 \n", + "11 Yes 1080 2340 8 ... 16.0 \n", + "12 Yes 1080 2340 8 ... 20.0 \n", + "13 Yes 1440 3120 8 ... 16.0 \n", + "14 Yes 1080 2340 8 ... 16.0 \n", + "\n", + " Operating system Wi-Fi Bluetooth GPS Number of SIMs 3G 4G/ LTE \\\n", + "0 Android Yes Yes Yes 2 Yes Yes \n", + "1 Android Yes Yes Yes 2 Yes Yes \n", + "2 iOS Yes Yes Yes 2 Yes Yes \n", + "3 iOS Yes Yes Yes 2 Yes Yes \n", + "4 Android Yes Yes Yes 1 No No \n", + "5 Android Yes Yes No 2 Yes Yes \n", + "6 Android Yes Yes Yes 2 Yes Yes \n", + "7 Android Yes Yes Yes 2 Yes Yes \n", + "8 Android Yes Yes Yes 1 Yes Yes \n", + "9 Android Yes Yes Yes 2 No No \n", + "10 Android Yes Yes Yes 2 Yes Yes \n", + "11 Android Yes Yes Yes 2 Yes Yes \n", + "12 Android Yes Yes Yes 2 Yes Yes \n", + "13 Android Yes Yes Yes 2 Yes Yes \n", + "14 Android Yes Yes Yes 2 Yes Yes \n", + "\n", + " Price Predicted Price \n", + "0 58998 75000.000000 \n", + "1 27999 55792.501783 \n", + "2 106900 46614.283392 \n", + "3 62900 40755.459206 \n", + "4 49990 55792.501783 \n", + "5 34930 59015.272109 \n", + "6 52990 59015.272109 \n", + "7 79699 75000.000000 \n", + "8 37999 75000.000000 \n", + "9 23190 55792.501783 \n", + "10 23990 53251.486942 \n", + "11 14999 44322.761836 \n", + "12 19282 55792.501783 \n", + "13 39995 55792.501783 \n", + "14 36990 56431.899431 \n", + "\n", + "[15 rows x 23 columns]" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_train = data_train.copy()\n", + "result_train[\"Predicted Price\"] = result_train.apply(fuzzy_pred, axis=1)\n", + "\n", + "result_train.head(15)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**Тестирование нечеткой системы на тестовой выборке**" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameBrandModelBattery capacity (mAh)Screen size (inches)TouchscreenResolution xResolution yProcessor...Front cameraOperating systemWi-FiBluetoothGPSNumber of SIMs3G4G/ LTEPricePredicted Price
00OnePlus 7T Pro McLaren EditionOnePlus7T Pro McLaren Edition40856.67Yes144031208...16.0AndroidYesYesYes2YesYes5899875000.000000
11Realme X2 ProRealmeX2 Pro40006.50Yes108024008...16.0AndroidYesYesYes2YesYes2799955792.501783
22iPhone 11 Pro MaxAppleiPhone 11 Pro Max39696.50Yes124226886...12.0iOSYesYesYes2YesYes10690046614.283392
33iPhone 11AppleiPhone 1131106.10Yes82817926...12.0iOSYesYesYes2YesYes6290040755.459206
44LG G8X ThinQLGG8X ThinQ40006.40Yes108023408...32.0AndroidYesYesYes1NoNo4999055792.501783
..................................................................
13541354Intex Aqua A2IntexAqua A215004.00Yes4808004...0.3AndroidYesYesYes2YesNo259927649.663226
13551355Videocon Infinium Z51 Nova+VideoconInfinium Z51 Nova+20005.00Yes4808544...5.0AndroidYesYesYes2YesNo294030578.211284
13561356Intex Aqua Y4IntexAqua Y417004.50Yes4808542...2.0AndroidYesYesNo2YesNo299927750.249507
13571357iBall Andi4 B20iBallAndi4 B2012504.00Yes4808001...0.3AndroidYesYesYes2YesNo249826169.615853
13581358iBall Andi Avonte 5iBallAndi Avonte 521505.00Yes4808544...0.0AndroidYesYesYes2YesNo399930880.178199
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1359 rows × 23 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Brand \\\n", + "0 0 OnePlus 7T Pro McLaren Edition OnePlus \n", + "1 1 Realme X2 Pro Realme \n", + "2 2 iPhone 11 Pro Max Apple \n", + "3 3 iPhone 11 Apple \n", + "4 4 LG G8X ThinQ LG \n", + "... ... ... ... \n", + "1354 1354 Intex Aqua A2 Intex \n", + "1355 1355 Videocon Infinium Z51 Nova+ Videocon \n", + "1356 1356 Intex Aqua Y4 Intex \n", + "1357 1357 iBall Andi4 B20 iBall \n", + "1358 1358 iBall Andi Avonte 5 iBall \n", + "\n", + " Model Battery capacity (mAh) Screen size (inches) \\\n", + "0 7T Pro McLaren Edition 4085 6.67 \n", + "1 X2 Pro 4000 6.50 \n", + "2 iPhone 11 Pro Max 3969 6.50 \n", + "3 iPhone 11 3110 6.10 \n", + "4 G8X ThinQ 4000 6.40 \n", + "... ... ... ... \n", + "1354 Aqua A2 1500 4.00 \n", + "1355 Infinium Z51 Nova+ 2000 5.00 \n", + "1356 Aqua Y4 1700 4.50 \n", + "1357 Andi4 B20 1250 4.00 \n", + "1358 Andi Avonte 5 2150 5.00 \n", + "\n", + " Touchscreen Resolution x Resolution y Processor ... Front camera \\\n", + "0 Yes 1440 3120 8 ... 16.0 \n", + "1 Yes 1080 2400 8 ... 16.0 \n", + "2 Yes 1242 2688 6 ... 12.0 \n", + "3 Yes 828 1792 6 ... 12.0 \n", + "4 Yes 1080 2340 8 ... 32.0 \n", + "... ... ... ... ... ... ... \n", + "1354 Yes 480 800 4 ... 0.3 \n", + "1355 Yes 480 854 4 ... 5.0 \n", + "1356 Yes 480 854 2 ... 2.0 \n", + "1357 Yes 480 800 1 ... 0.3 \n", + "1358 Yes 480 854 4 ... 0.0 \n", + "\n", + " Operating system Wi-Fi Bluetooth GPS Number of SIMs 3G 4G/ LTE \\\n", + "0 Android Yes Yes Yes 2 Yes Yes \n", + "1 Android Yes Yes Yes 2 Yes Yes \n", + "2 iOS Yes Yes Yes 2 Yes Yes \n", + "3 iOS Yes Yes Yes 2 Yes Yes \n", + "4 Android Yes Yes Yes 1 No No \n", + "... ... ... ... ... ... ... ... \n", + "1354 Android Yes Yes Yes 2 Yes No \n", + "1355 Android Yes Yes Yes 2 Yes No \n", + "1356 Android Yes Yes No 2 Yes No \n", + "1357 Android Yes Yes Yes 2 Yes No \n", + "1358 Android Yes Yes Yes 2 Yes No \n", + "\n", + " Price Predicted Price \n", + "0 58998 75000.000000 \n", + "1 27999 55792.501783 \n", + "2 106900 46614.283392 \n", + "3 62900 40755.459206 \n", + "4 49990 55792.501783 \n", + "... ... ... \n", + "1354 2599 27649.663226 \n", + "1355 2940 30578.211284 \n", + "1356 2999 27750.249507 \n", + "1357 2498 26169.615853 \n", + "1358 3999 30880.178199 \n", + "\n", + "[1359 rows x 23 columns]" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_test = data_test.copy()\n", + "\n", + "result_test[\"Predicted Price\"] = result_test.apply(fuzzy_pred, axis=1)\n", + "\n", + "result_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Оценка результатов на основе метрик для задачи регрессии**" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'RMSE_train': 28568.780129367737,\n", + " 'RMSE_test': 28568.780129367737,\n", + " 'RMAE_test': 165.28156612336826,\n", + " 'R2_test': -3.2533731852277636}" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import math\n", + "from sklearn import metrics\n", + "\n", + "rmetrics = {}\n", + "\n", + "rmetrics[\"RMSE_train\"] = math.sqrt(\n", + " metrics.mean_squared_error(result_train[\"Price\"], result_train[\"Predicted Price\"])\n", + ")\n", + "\n", + "rmetrics[\"RMSE_test\"] = math.sqrt(\n", + " metrics.mean_squared_error(result_test[\"Price\"], result_test[\"Predicted Price\"])\n", + ")\n", + "\n", + "rmetrics[\"RMAE_test\"] = math.sqrt(\n", + " metrics.mean_absolute_error(result_test[\"Price\"], result_test[\"Predicted Price\"])\n", + ")\n", + "\n", + "rmetrics[\"R2_test\"] = metrics.r2_score(\n", + " result_test[\"Price\"], result_test[\"Predicted Price\"]\n", + ")\n", + "\n", + "rmetrics" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aimenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}