{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "q0PjiV7v7aXC" }, "source": [ "# \n", "\n", "# Машинное обучение. ВМК МГУ" ] }, { "cell_type": "markdown", "metadata": { "id": "uvZhhOzlKCMH" }, "source": [ "# Практическое задание 3: Основы sklearn на примере KNN. Нормализация признаков и Кросс-валидация\n", "\n", "## Уровень: **Базовый (Base)**" ] }, { "cell_type": "markdown", "metadata": { "id": "uqAqpeYFxg5T" }, "source": [ "# О формате сдачи\n", "\n", "🔷 **При решении ноутбука используйте данный шаблон**\n", "\n", " ✅ Можно добавлять новые ячейки любых типов\n", " ❌ Не нужно удалять текстовые ячейки c разметкой частей ноутбука и формулировками заданий\n", "\n", "\n", "🔷 **При оценивании задач учитывается код**\n", "\n", " ✅ Задания, в которых необходим код, обычно помечаются фразами \"Your code here\"/\"Ваш код\" и аналогичными\n", " ❌ Ответы на вопросы без сопутствующего кода оцениваются в 0 баллов\n", " ❌ Наличе работоспособного кода в ноутбуке, если на сказано иного, обязательно\n", "\n", "🔷 **При оценивании задач учитываются выводы**\n", "\n", " ✅ Задания, в которых необходимы выводы, обычно помечаются фразами Вывод\"/\"Ответ на вопрос\"/\"Ваш текст\" и аналогичными\n", " ✅ Обычно выводы подразумевают под собой текстовый ответ (можно писать markdown, latex).\n", " ✅ Сопутствующие изображения, графики, таблички - приветствуются!\n", " ❌ При отсутствии выводов задание не засчитается на полный балл\n", "\n", "-----------\n", "\n", "\n", "* Многие из заданий можно выполнить несколькими способами. Не существуют единственно верного, но попробуйте максимально задействовать арсенал pandas и ориентируйтесь на простоту и понятность вашего кода. Не забывайте, что можно гуглить и что-то искать на stackoverflow, например.\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "exCE5gc27aXG" }, "source": [ "**Примерное время выполнения (execution time/время выполнения, если нажать run all) всех ячеек ноутбука при правильной реализации: 10 минут **" ] }, { "cell_type": "markdown", "metadata": { "id": "ciHgl9KGlJua" }, "source": [ "# Подготовка рабочей среды\n", "\n", "Сначала установим нужные нам версии библиотек. Мы гарантируем, что в данных версиях задание будет корректно отрабатывать.\n", "\n", "После установки нужных версий, **возможно,** нужно перезагрузить среду (runtime), но скорее всего вам это не понадобится\n", "\n", "\n", "На скачивание файла и установку понадобится не более 5 минут.\n", "\n", "**Важно!**\n", "\n", "Устанавливать нужные версии нужно каждый раз, когда создается новый рантайм. Например, если вы 2 часа подряд делаете это задание, то подготовить библиотеки достаточно 1 раз. Но если вы, например, начали в понедельник, затем закрыли/выключили ноутбук, то при продолжении в среду, вам нужно будет запустить рантайм заново и следовательно заново установить библиотеки.\n", "\n", "**Важно!**\n", "Если вы предпочитаете делать практические задания на своем личном ноутбуке, то проверьте, что вы установили рабочее окружение в [соответствии с гайдом](https://github.com/MSU-ML-COURSE/ML-COURSE-24-25/blob/main/tutorials/%D0%A2%D1%83%D1%82%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%20%D0%BF%D0%BE%20%D1%83%D1%81%D1%82%D0%B0%D0%BD%D0%BE%D0%B2%D0%BA%D0%B5%20%D1%80%D0%B0%D0%B1%D0%BE%D1%87%D0%B5%D0%B3%D0%BE%20%D0%BE%D0%BA%D1%80%D1%83%D0%B6%D0%B5%D0%BD%D0%B8%D1%8F%20%D0%B2%20Python%20%D0%B4%D0%BB%D1%8F%20%D1%80%D0%B5%D1%88%D0%B5%D0%BD%D0%B8%D1%8F%20%D0%B7%D0%B0%D0%B4%D0%B0%D1%87%20(2).pdf)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "fMgnzKcqlIen" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 360 100 360 0 0 696 0 --:--:-- --:--:-- --:--:-- 697\n", "Collecting catboost==1.2.8 (from -r ./requirements_2025_26_for_colab_small.txt (line 1))\n", " Downloading catboost-1.2.8-cp312-cp312-manylinux2014_x86_64.whl.metadata (1.2 kB)\n", "Collecting gdown==5.2.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 2))\n", " Downloading gdown-5.2.0-py3-none-any.whl.metadata (5.8 kB)\n", "Collecting h5py==3.14.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 3))\n", " Downloading h5py-3.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.7 kB)\n", "Collecting hyperopt==0.2.7 (from -r ./requirements_2025_26_for_colab_small.txt (line 4))\n", " Downloading hyperopt-0.2.7-py2.py3-none-any.whl.metadata (1.7 kB)\n", "Collecting ipympl==0.9.7 (from -r ./requirements_2025_26_for_colab_small.txt (line 5))\n", " Downloading ipympl-0.9.7-py3-none-any.whl.metadata (8.7 kB)\n", "Collecting ipywidgets==7.7.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 6))\n", " Downloading ipywidgets-7.7.1-py2.py3-none-any.whl.metadata (1.9 kB)\n", "Collecting lightgbm==4.6.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 7))\n", " Downloading lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl.metadata (17 kB)\n", "Requirement already satisfied: matplotlib-inline==0.1.7 in /nix/store/xll44q41bdn0i1zcphwj7p95j5arz356-python3.12-matplotlib-inline-0.1.7/lib/python3.12/site-packages (from -r ./requirements_2025_26_for_colab_small.txt (line 8)) (0.1.7)\n", "Collecting matplotlib==3.10.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 9))\n", " Downloading matplotlib-3.10.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n", "Collecting numpy==2.0.2 (from -r ./requirements_2025_26_for_colab_small.txt (line 10))\n", " Downloading numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n", "Collecting pandas==2.2.2 (from -r ./requirements_2025_26_for_colab_small.txt (line 11))\n", " Downloading pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n", "Collecting pep8==1.7.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 12))\n", " Downloading pep8-1.7.1-py2.py3-none-any.whl.metadata (22 kB)\n", "Collecting plotly==5.24.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 13))\n", " Downloading plotly-5.24.1-py3-none-any.whl.metadata (7.3 kB)\n", "Collecting pycodestyle==2.14.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 14))\n", " Downloading pycodestyle-2.14.0-py2.py3-none-any.whl.metadata (4.5 kB)\n", "Collecting pytest==8.4.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 15))\n", " Downloading pytest-8.4.1-py3-none-any.whl.metadata (7.7 kB)\n", "Collecting scikit-image==0.25.2 (from -r ./requirements_2025_26_for_colab_small.txt (line 16))\n", " Downloading scikit_image-0.25.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n", "Collecting scikit-learn==1.6.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 17))\n", " Downloading scikit_learn-1.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n", "Collecting scipy==1.16.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 18))\n", " Downloading scipy-1.16.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (61 kB)\n", "Collecting seaborn==0.13.2 (from -r ./requirements_2025_26_for_colab_small.txt (line 19))\n", " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", "Collecting tqdm==4.67.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 20))\n", " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", "Collecting umap-learn==0.5.9.post2 (from -r ./requirements_2025_26_for_colab_small.txt (line 21))\n", " Downloading umap_learn-0.5.9.post2-py3-none-any.whl.metadata (25 kB)\n", "Collecting xgboost==3.0.4 (from -r ./requirements_2025_26_for_colab_small.txt (line 22))\n", " Downloading xgboost-3.0.4-py3-none-manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n", "Collecting graphviz (from catboost==1.2.8->-r ./requirements_2025_26_for_colab_small.txt (line 1))\n", " Downloading graphviz-0.21-py3-none-any.whl.metadata (12 kB)\n", "Requirement already satisfied: six in /nix/store/vfy6pmqhgw9kaxiqyhmlg8rmn2aaw6fd-python3.12-six-1.17.0/lib/python3.12/site-packages (from catboost==1.2.8->-r ./requirements_2025_26_for_colab_small.txt (line 1)) (1.17.0)\n", "Requirement already satisfied: beautifulsoup4 in /nix/store/bwljsfpk78gbaq7rvm3wr3jyrqbwb8i9-python3.12-beautifulsoup4-4.12.3/lib/python3.12/site-packages (from gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (4.12.3)\n", "Requirement already satisfied: filelock in /nix/store/zm1ag99a91zq3h525fbpka5s4yvnjx6c-python3.12-filelock-3.18.0/lib/python3.12/site-packages (from gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (3.18.0)\n", "Requirement already satisfied: requests[socks] in /nix/store/7i5sy2qyz79gjkdlcrmdlgzmjlimq7sk-python3.12-requests-2.32.3/lib/python3.12/site-packages (from gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (2.32.3)\n", "Collecting networkx>=2.2 (from hyperopt==0.2.7->-r ./requirements_2025_26_for_colab_small.txt (line 4))\n", " Downloading networkx-3.5-py3-none-any.whl.metadata (6.3 kB)\n", "Collecting future (from hyperopt==0.2.7->-r ./requirements_2025_26_for_colab_small.txt (line 4))\n", " Downloading future-1.0.0-py3-none-any.whl.metadata (4.0 kB)\n", "Collecting cloudpickle (from hyperopt==0.2.7->-r ./requirements_2025_26_for_colab_small.txt (line 4))\n", " Downloading cloudpickle-3.1.2-py3-none-any.whl.metadata (7.1 kB)\n", "Collecting py4j (from hyperopt==0.2.7->-r ./requirements_2025_26_for_colab_small.txt (line 4))\n", " Downloading py4j-0.10.9.9-py2.py3-none-any.whl.metadata (1.3 kB)\n", "Requirement already satisfied: ipython<10 in /nix/store/sx6qmhz6rv8fdx0z2jym3kg7pb4ihi9l-python3.12-ipython-9.2.0/lib/python3.12/site-packages (from ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (9.2.0)\n", "Collecting pillow (from ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5))\n", " Downloading pillow-12.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (8.8 kB)\n", "Requirement already satisfied: traitlets<6 in /nix/store/5fkvg2aiqnv0v6r3k9f4x41qbql50f78-python3.12-traitlets-5.14.3/lib/python3.12/site-packages (from ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (5.14.3)\n", "Requirement already satisfied: ipykernel>=4.5.1 in /nix/store/pzz4808nm0p3lmjpzxnfyb7d3xb6csby-python3.12-ipykernel-6.29.5/lib/python3.12/site-packages (from ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (6.29.5)\n", "Collecting ipython-genutils~=0.2.0 (from ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6))\n", " Downloading ipython_genutils-0.2.0-py2.py3-none-any.whl.metadata (755 bytes)\n", "Collecting widgetsnbextension~=3.6.0 (from ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6))\n", " Downloading widgetsnbextension-3.6.10-py2.py3-none-any.whl.metadata (1.3 kB)\n", "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /home/krosh/Documents/Github/ML/.venv/lib/python3.12/site-packages (from ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.0.16)\n", "Collecting contourpy>=1.0.1 (from matplotlib==3.10.0->-r ./requirements_2025_26_for_colab_small.txt (line 9))\n", " Downloading contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (5.5 kB)\n", "Collecting cycler>=0.10 (from matplotlib==3.10.0->-r ./requirements_2025_26_for_colab_small.txt (line 9))\n", " Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n", "Collecting fonttools>=4.22.0 (from matplotlib==3.10.0->-r ./requirements_2025_26_for_colab_small.txt (line 9))\n", " Downloading fonttools-4.60.1-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl.metadata (112 kB)\n", "Collecting kiwisolver>=1.3.1 (from matplotlib==3.10.0->-r ./requirements_2025_26_for_colab_small.txt (line 9))\n", " Downloading kiwisolver-1.4.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (6.3 kB)\n", "Requirement already satisfied: packaging>=20.0 in /nix/store/h1qsc2cb41r5iaix9n7s3gn4r5s2pdfw-python3.12-packaging-24.2/lib/python3.12/site-packages (from matplotlib==3.10.0->-r ./requirements_2025_26_for_colab_small.txt (line 9)) (24.2)\n", "Collecting pyparsing>=2.3.1 (from matplotlib==3.10.0->-r ./requirements_2025_26_for_colab_small.txt (line 9))\n", " Downloading pyparsing-3.2.5-py3-none-any.whl.metadata (5.0 kB)\n", "Requirement already satisfied: python-dateutil>=2.7 in /nix/store/dn6x1maxc5cxyq84fc51z8pzjk2f4wq6-python3.12-python-dateutil-2.9.0.post0/lib/python3.12/site-packages (from matplotlib==3.10.0->-r ./requirements_2025_26_for_colab_small.txt (line 9)) (2.9.0.post0)\n", "Collecting pytz>=2020.1 (from pandas==2.2.2->-r ./requirements_2025_26_for_colab_small.txt (line 11))\n", " Downloading pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\n", "Collecting tzdata>=2022.7 (from pandas==2.2.2->-r ./requirements_2025_26_for_colab_small.txt (line 11))\n", " Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n", "Collecting tenacity>=6.2.0 (from plotly==5.24.1->-r ./requirements_2025_26_for_colab_small.txt (line 13))\n", " Downloading tenacity-9.1.2-py3-none-any.whl.metadata (1.2 kB)\n", "Collecting iniconfig>=1 (from pytest==8.4.1->-r ./requirements_2025_26_for_colab_small.txt (line 15))\n", " Downloading iniconfig-2.3.0-py3-none-any.whl.metadata (2.5 kB)\n", "Collecting pluggy<2,>=1.5 (from pytest==8.4.1->-r ./requirements_2025_26_for_colab_small.txt (line 15))\n", " Downloading pluggy-1.6.0-py3-none-any.whl.metadata (4.8 kB)\n", "Requirement already satisfied: pygments>=2.7.2 in /nix/store/hdikjhn6cic0ibvb3j8cghy7lg3kv6yy-python3.12-pygments-2.19.1/lib/python3.12/site-packages (from pytest==8.4.1->-r ./requirements_2025_26_for_colab_small.txt (line 15)) (2.19.1)\n", "Collecting imageio!=2.35.0,>=2.33 (from scikit-image==0.25.2->-r ./requirements_2025_26_for_colab_small.txt (line 16))\n", " Downloading imageio-2.37.2-py3-none-any.whl.metadata (9.7 kB)\n", "Collecting tifffile>=2022.8.12 (from scikit-image==0.25.2->-r ./requirements_2025_26_for_colab_small.txt (line 16))\n", " Downloading tifffile-2025.10.16-py3-none-any.whl.metadata (31 kB)\n", "Collecting lazy-loader>=0.4 (from scikit-image==0.25.2->-r ./requirements_2025_26_for_colab_small.txt (line 16))\n", " Downloading lazy_loader-0.4-py3-none-any.whl.metadata (7.6 kB)\n", "Collecting joblib>=1.2.0 (from scikit-learn==1.6.1->-r ./requirements_2025_26_for_colab_small.txt (line 17))\n", " Downloading joblib-1.5.2-py3-none-any.whl.metadata (5.6 kB)\n", "Collecting threadpoolctl>=3.1.0 (from scikit-learn==1.6.1->-r ./requirements_2025_26_for_colab_small.txt (line 17))\n", " Downloading threadpoolctl-3.6.0-py3-none-any.whl.metadata (13 kB)\n", "Collecting numba>=0.51.2 (from umap-learn==0.5.9.post2->-r ./requirements_2025_26_for_colab_small.txt (line 21))\n", " Downloading numba-0.62.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.8 kB)\n", "Collecting pynndescent>=0.5 (from umap-learn==0.5.9.post2->-r ./requirements_2025_26_for_colab_small.txt (line 21))\n", " Downloading pynndescent-0.5.13-py3-none-any.whl.metadata (6.8 kB)\n", "Collecting nvidia-nccl-cu12 (from xgboost==3.0.4->-r ./requirements_2025_26_for_colab_small.txt (line 22))\n", " Downloading nvidia_nccl_cu12-2.28.7-py3-none-manylinux_2_18_x86_64.whl.metadata (2.0 kB)\n", "Requirement already satisfied: comm>=0.1.1 in /nix/store/vav4k2c299vg67mk0x1pgzs50mv09p6q-python3.12-comm-0.2.2/lib/python3.12/site-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.2.2)\n", "Requirement already satisfied: jupyter-client>=6.1.12 in /nix/store/ii8mmw13zhpg4zdwx6c5sp3qi5g9g0iv-python3.12-jupyter-client-8.6.3/lib/python3.12/site-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (8.6.3)\n", "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /nix/store/ppsp7zdbnivzw04r8rrwr30rwkahxy6k-python3.12-jupyter-core-5.7.2/lib/python3.12/site-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (5.7.2)\n", "Requirement already satisfied: nest-asyncio in /nix/store/ah04qkf51fnsq76dkk86gzhm059lbaqr-python3.12-nest-asyncio-1.6.0/lib/python3.12/site-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.6.0)\n", "Requirement already satisfied: psutil in /nix/store/3hsnbwhiflh31c7b808nbai5bwm55ddj-python3.12-psutil-7.0.0/lib/python3.12/site-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (7.0.0)\n", "Requirement already satisfied: pyzmq>=24 in /nix/store/h9nq7xr7pfwdksqlx1givw25fiv1fflh-python3.12-pyzmq-26.3.0/lib/python3.12/site-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (26.3.0)\n", "Requirement already satisfied: tornado>=6.1 in /nix/store/ay14g4n81v3p75rdibn9ddp7m4idazgv-python3.12-tornado-6.5.1/lib/python3.12/site-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (6.5.1)\n", "Requirement already satisfied: decorator in /nix/store/gkh0fg3v2zs85v3h3cw1nh2d6ncdzki6-python3.12-decorator-5.2.1/lib/python3.12/site-packages (from ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (5.2.1)\n", "Requirement already satisfied: ipython-pygments-lexers in /nix/store/qqvbgszbfdlxpzfd41ggzxmh0p5wq0rj-python3.12-ipython-pygments-lexers-1.1.1/lib/python3.12/site-packages (from ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (1.1.1)\n", "Requirement already satisfied: jedi>=0.16 in /nix/store/0g96bmz4qlk00rd58lsivm7xir6ljg0m-python3.12-jedi-0.19.2/lib/python3.12/site-packages (from ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.19.2)\n", "Requirement already satisfied: pexpect>4.3 in /nix/store/hw5i2kd9750fndnb9gdl3x5prxx7mj5d-python3.12-pexpect-4.9.0/lib/python3.12/site-packages (from ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (4.9.0)\n", "Requirement already satisfied: prompt_toolkit<3.1.0,>=3.0.41 in /nix/store/wzmw3slxlzycs29w5hjjc6a1529d0s6d-python3.12-prompt-toolkit-3.0.50/lib/python3.12/site-packages (from ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (3.0.50)\n", "Requirement already satisfied: stack_data in /nix/store/61k17cinvnvwvyr0hxmf2c28407k9q2l-python3.12-stack-data-0.6.3/lib/python3.12/site-packages (from ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.6.3)\n", "Collecting llvmlite<0.46,>=0.45.0dev0 (from numba>=0.51.2->umap-learn==0.5.9.post2->-r ./requirements_2025_26_for_colab_small.txt (line 21))\n", " Downloading llvmlite-0.45.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (4.9 kB)\n", "Requirement already satisfied: notebook>=4.4.1 in /home/krosh/Documents/Github/ML/.venv/lib/python3.12/site-packages (from widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (7.4.7)\n", "Requirement already satisfied: soupsieve>1.2 in /nix/store/j9ndbf8laq3gki56i3f748k0l66s7qzd-python3.12-soupsieve-2.6/lib/python3.12/site-packages (from beautifulsoup4->gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (2.6)\n", "Requirement already satisfied: charset_normalizer<4,>=2 in /nix/store/znsiffn7xpr0bfmmbvh7j9dn0z815pf6-python3.12-charset-normalizer-3.4.1/lib/python3.12/site-packages (from requests[socks]->gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (3.4.1)\n", "Requirement already satisfied: idna<4,>=2.5 in /nix/store/80zr7m04sb6a5kfaq55nd1rf2425gi7r-python3.12-idna-3.10/lib/python3.12/site-packages (from requests[socks]->gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /nix/store/d3n2v8v8m2874vn7gw7zrdggj86dincs-python3.12-urllib3-2.3.0/lib/python3.12/site-packages (from requests[socks]->gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (2.3.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /nix/store/bk8dgkmkprc9wc6wcwc75bdyjk1ndbjk-python3.12-certifi-2025.01.31/lib/python3.12/site-packages (from requests[socks]->gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2)) (2025.1.31)\n", "Collecting PySocks!=1.5.7,>=1.5.6 (from requests[socks]->gdown==5.2.0->-r ./requirements_2025_26_for_colab_small.txt (line 2))\n", " Downloading PySocks-1.7.1-py3-none-any.whl.metadata (13 kB)\n", "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /nix/store/sw3hp8g6i6n5hcj0zk3dvdm8v7k67c6h-python3.12-parso-0.8.4/lib/python3.12/site-packages (from jedi>=0.16->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.8.4)\n", "Requirement already satisfied: platformdirs>=2.5 in /nix/store/ay5c0v86xdaq1w2x8l8z88kkiwv7i2cl-python3.12-platformdirs-4.3.7/lib/python3.12/site-packages (from jupyter-core!=5.0.*,>=4.12->ipykernel>=4.5.1->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (4.3.7)\n", "Requirement already satisfied: jupyter-server<3,>=2.4.0 in /nix/store/zj57c9yx8bkqg83xjxz6bjwnxg65dk5c-python3.12-jupyter-server-2.15.0/lib/python3.12/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.15.0)\n", "Requirement already satisfied: jupyterlab-server<3,>=2.27.1 in /nix/store/pd8cbi214wii25llx5g2ismw6pmvgkf5-python3.12-jupyterlab-server-2.27.3/lib/python3.12/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.27.3)\n", "Collecting jupyterlab<4.5,>=4.4.9 (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6))\n", " Using cached jupyterlab-4.4.10-py3-none-any.whl.metadata (16 kB)\n", "Requirement already satisfied: notebook-shim<0.3,>=0.2 in /nix/store/agvxbdh9lb9i7gm5v1qjx7a0bv22am8n-python3.12-notebook-shim-0.2.4/lib/python3.12/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.2.4)\n", "Requirement already satisfied: ptyprocess>=0.5 in /nix/store/rz2ygcrnc8wx1n3pz0slcah47s71havg-python3.12-ptyprocess-0.7.0/lib/python3.12/site-packages (from pexpect>4.3->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.7.0)\n", "Requirement already satisfied: wcwidth in /nix/store/82wc5sicriaacpnavd97c9b2b3l88d1j-python3.12-wcwidth-0.2.13/lib/python3.12/site-packages (from prompt_toolkit<3.1.0,>=3.0.41->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.2.13)\n", "Requirement already satisfied: executing>=1.2.0 in /nix/store/vryr7qfzh6qp2v8whvc5357hz3f3xxpc-python3.12-executing-2.2.0/lib/python3.12/site-packages (from stack_data->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (2.2.0)\n", "Requirement already satisfied: asttokens>=2.1.0 in /nix/store/lh5is09vgpvw5rnvm9bgcxiasm7pidw1-python3.12-asttokens-3.0.0/lib/python3.12/site-packages (from stack_data->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (3.0.0)\n", "Requirement already satisfied: pure_eval in /nix/store/lab1jiw46l09zci0r8h1cl2wnb04w1s5-python3.12-pure-eval-0.2.3/lib/python3.12/site-packages (from stack_data->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.2.3)\n", "Requirement already satisfied: anyio>=3.1.0 in /nix/store/slsfw31xlwaczcnqgg4r5qw3xpwm0nbq-python3.12-anyio-4.9.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (4.9.0)\n", "Requirement already satisfied: argon2-cffi>=21.1 in /nix/store/1clmzlxvasiidjs72h4vx4pqcrkwg3zf-python3.12-argon2-cffi-23.1.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (23.1.0)\n", "Requirement already satisfied: jinja2>=3.0.3 in /nix/store/svijzcfkrsxb8jripykx1d2krzi2q0fq-python3.12-jinja2-3.1.6/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.1.6)\n", "Requirement already satisfied: jupyter-events>=0.11.0 in /nix/store/4jydas1rslfay93x8h61s7a16023pcql-python3.12-jupyter-events-0.11.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.11.0)\n", "Requirement already satisfied: jupyter-server-terminals>=0.4.4 in /nix/store/ly5g70s9xpnm9piajwcmrlrd8maixfh4-python3.12-jupyter-server-terminals-0.5.3/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.5.3)\n", "Requirement already satisfied: nbconvert>=6.4.4 in /nix/store/5h2jqcxjgkyrp8yy28s1cxbmlps9llbr-python3.12-nbconvert-7.16.6/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (7.16.6)\n", "Requirement already satisfied: nbformat>=5.3.0 in /nix/store/mha5x4r94hi8278p347v2ig89r7pg37i-python3.12-nbformat-5.10.4/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (5.10.4)\n", "Requirement already satisfied: overrides>=5.0 in /nix/store/8hnr7s08x0zglraaqhsvxw9cxfb4m5vf-python3.12-overrides-7.7.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (7.7.0)\n", "Requirement already satisfied: prometheus-client>=0.9 in /nix/store/hi0lchzh5wd2mima5ryzxv1sp8avpixv-python3.12-prometheus-client-0.21.1/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.21.1)\n", "Requirement already satisfied: send2trash>=1.8.2 in /nix/store/qaybrn941lhzv68mmly22vml2bi9biln-python3.12-send2trash-1.8.3/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.8.3)\n", "Requirement already satisfied: terminado>=0.8.3 in /nix/store/r0c0p4ycha98vxggn3ib9bmrsma9a5gq-python3.12-terminado-0.18.1/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.18.1)\n", "Requirement already satisfied: websocket-client>=1.7 in /nix/store/maij4ca0hdq51yh6sc8ha0dbfksfvbs2-python3.12-websocket-client-1.8.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.8.0)\n", "Requirement already satisfied: async-lru>=1.0.0 in /nix/store/appfbz4pp2rq674miy7y35llz30382ab-python3.12-async-lru-2.0.5/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.0.5)\n", "Requirement already satisfied: httpx<1,>=0.25.0 in /nix/store/83krrvn5s26brnzqfqb9w7cvsa94b9w6-python3.12-httpx-0.28.1/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.28.1)\n", "Requirement already satisfied: jupyter-lsp>=2.0.0 in /nix/store/w0fm4viprsj0s6vdymp1grlgq9gg24pp-python3.12-jupyter-lsp-2.2.5/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.2.5)\n", "Requirement already satisfied: setuptools>=41.1.0 in /nix/store/jahiclal74yv1j7py0jnlm30ji3wa1wr-python3.12-setuptools-78.1.1/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (78.1.1.post0)\n", "Requirement already satisfied: babel>=2.10 in /nix/store/0k8xz970r9zwm7c0bbmnps9ppya32jyx-python3.12-babel-2.17.0/lib/python3.12/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.17.0)\n", "Requirement already satisfied: json5>=0.9.0 in /nix/store/m67k0bjvzldj8fg862y9jaqdn14fav0r-python3.12-json5-0.10.0/lib/python3.12/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.10.0)\n", "Requirement already satisfied: jsonschema>=4.18.0 in /nix/store/cbv4a3ncj97dj8a7sz4ldw5hpajhnrgd-python3.12-jsonschema-4.23.0/lib/python3.12/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (4.23.0)\n", "Requirement already satisfied: sniffio>=1.1 in /nix/store/6jdf3szr6xcvzdg3cdzrgvhy6dc6ppdp-python3.12-sniffio-1.3.1/lib/python3.12/site-packages (from anyio>=3.1.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.3.1)\n", "Requirement already satisfied: typing_extensions>=4.5 in /nix/store/lpap2xy7m2z6qs3pjgx57whdvn0gmrld-python3.12-typing-extensions-4.13.2/lib/python3.12/site-packages (from anyio>=3.1.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (4.13.2)\n", "Requirement already satisfied: argon2-cffi-bindings in /nix/store/xs11y0i84x084h3f9va5xn3vw71g9sn5-python3.12-argon2-cffi-bindings-21.2.0/lib/python3.12/site-packages (from argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (21.2.0)\n", "Requirement already satisfied: httpcore==1.* in /nix/store/45mbjl9s9pv0libawf8ikmh588k831zz-python3.12-httpcore-1.0.9/lib/python3.12/site-packages (from httpx<1,>=0.25.0->jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.0.9)\n", "Requirement already satisfied: h11>=0.16 in /nix/store/ndg6nlcirvbl8w5sdz49r0x28x4hnfr7-python3.12-h11-0.16.0/lib/python3.12/site-packages (from httpcore==1.*->httpx<1,>=0.25.0->jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.16.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /nix/store/s1s7cngw3wc3wm6ysqyjkwq8nq4cz4vl-python3.12-markupsafe-3.0.2/lib/python3.12/site-packages (from jinja2>=3.0.3->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.0.2)\n", "Requirement already satisfied: attrs>=22.2.0 in /nix/store/aj8dzb2ampzwwmjy7i47ypbp9vdcb4zy-python3.12-attrs-25.3.0/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (25.3.0)\n", "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /nix/store/vd97fs6kjvygdndlsnh2c9166z4d0xh3-python3.12-jsonschema-specifications-2024.10.1/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2024.10.1)\n", "Requirement already satisfied: referencing>=0.28.4 in /nix/store/n7knhq0j8jmls8dckxyim8qhslw399ng-python3.12-referencing-0.36.2/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.36.2)\n", "Requirement already satisfied: rpds-py>=0.7.1 in /nix/store/8a2h77svk68xqlhbp0bi2xk8rwj9j50j-python3.12-rpds-py-0.24.0/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.24.0)\n", "Requirement already satisfied: python-json-logger>=2.0.4 in /nix/store/p56rsir3w5f044svipd9mpa6qqxgsa78-python3.12-python-json-logger-3.2.1/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.2.1)\n", "Requirement already satisfied: pyyaml>=5.3 in /nix/store/9ap0fph2ybcfnl0f1rsklj990n25ll84-python3.12-pyyaml-6.0.2/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (6.0.2)\n", "Requirement already satisfied: rfc3339-validator in /nix/store/14rqw00zqxk6jcslmab0skzzf3qcp5xw-python3.12-rfc3339-validator-0.1.4/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.1.4)\n", "Requirement already satisfied: rfc3986-validator>=0.1.1 in /nix/store/zqnambrrca5dzmfiizygsslmgakhiigc-python3.12-rfc3986-validator-0.1.1/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.1.1)\n", "Requirement already satisfied: bleach!=5.0.0 in /nix/store/0svbzbngdvar6sdz7p4iscpjhcp5i7aq-python3.12-bleach-6.2.0/lib/python3.12/site-packages (from bleach[css]!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (6.2.0)\n", "Requirement already satisfied: defusedxml in /nix/store/4l429an1q3m5m7gbmr004zi4mzhbksn1-python3.12-defusedxml-0.8.0rc2/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.8.0rc2)\n", "Requirement already satisfied: jupyterlab-pygments in /nix/store/fm0shwwm682kdk11hx331ya0axmx6357-python3.12-jupyterlab-pygments-0.3.0/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.3.0)\n", "Requirement already satisfied: mistune<4,>=2.0.3 in /nix/store/9mln2k5sj8y5jr1mggmd55zbj6m5i4vb-python3.12-mistune-3.1.2/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.1.2)\n", "Requirement already satisfied: nbclient>=0.5.0 in /nix/store/yhm4rkd5j81xfwmcr8s749m67z9099pa-python3.12-nbclient-0.10.2/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.10.2)\n", "Requirement already satisfied: pandocfilters>=1.4.1 in /nix/store/n76h7wginvrsqaq8ngnmf259fagz6dyp-python3.12-pandocfilters-1.5.1/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.5.1)\n", "Requirement already satisfied: fastjsonschema>=2.15 in /nix/store/yh2gsm8j4cnx422kxm69bx0k7c54n6b4-python3.12-fastjsonschema-2.21.1/lib/python3.12/site-packages (from nbformat>=5.3.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.21.1)\n", "Requirement already satisfied: webencodings in /nix/store/n8jzk1lyamvlj6rw9pyx88ai0rhcmfgm-python3.12-webencodings-0.5.1/lib/python3.12/site-packages (from bleach!=5.0.0->bleach[css]!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.5.1)\n", "Requirement already satisfied: tinycss2<1.5,>=1.1.0 in /nix/store/lrxfr5x30dinqpw0vg2zvdvh6jfy4kxn-python3.12-tinycss2-1.4.0/lib/python3.12/site-packages (from bleach[css]!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.4.0)\n", "Requirement already satisfied: fqdn in /nix/store/c676msxyjp0k5r70s33p040rk6fxii8d-python3.12-fqdn-1.5.1/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.5.1)\n", "Requirement already satisfied: isoduration in /nix/store/4mnklym4fix9xi1i4ppknqk5fcpljfga-python3.12-isoduration-20.11.0/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (20.11.0)\n", "Requirement already satisfied: jsonpointer>1.13 in /nix/store/9lhd1pal6j928py2p7x6g5xs2fyqjfzf-python3.12-jsonpointer-3.0.0/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.0.0)\n", "Requirement already satisfied: uri-template in /nix/store/g3g6pdfkd8xlnram70kml18wxdl1zyl2-python3.12-uri-template-1.3.0/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.3.0)\n", "Requirement already satisfied: webcolors>=24.6.0 in /nix/store/vffapvc0y8z4y3x1kq1yahyrk203z60v-python3.12-webcolors-24.11.1/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (24.11.1)\n", "Requirement already satisfied: cffi>=1.0.1 in /nix/store/xmrm1mvjf8kp4nl1im58apax0n0pgb81-python3.12-cffi-1.17.1/lib/python3.12/site-packages (from argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.17.1)\n", "Requirement already satisfied: pycparser in /nix/store/zvrx9j7dsh4h5ryn9nn2vs9v7i7lslc0-python3.12-pycparser-2.22/lib/python3.12/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.22)\n", "Requirement already satisfied: arrow>=0.15.0 in /nix/store/xkn28vnhxy42lz0039qqpxhf1dplifb6-python3.12-arrow-1.3.0/lib/python3.12/site-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.3.0)\n", "Requirement already satisfied: types-python-dateutil>=2.8.10 in /nix/store/4rbnpq6m8shi5h2s2l3kjn86f157gh2d-python3.12-types-python-dateutil-2.9.0.20241206/lib/python3.12/site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.9.0.20241206)\n", "Downloading catboost-1.2.8-cp312-cp312-manylinux2014_x86_64.whl (99.2 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.2/99.2 MB\u001b[0m \u001b[31m486.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:06\u001b[0m\n", "\u001b[?25hDownloading gdown-5.2.0-py3-none-any.whl (18 kB)\n", "Downloading h5py-3.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m314.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m:04\u001b[0m\n", "\u001b[?25hDownloading hyperopt-0.2.7-py2.py3-none-any.whl (1.6 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m544.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m:02\u001b[0m\n", "\u001b[?25hDownloading ipympl-0.9.7-py3-none-any.whl (515 kB)\n", "Downloading ipywidgets-7.7.1-py2.py3-none-any.whl (123 kB)\n", "Downloading lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl (3.6 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m636.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m:04\u001b[0m\n", "\u001b[?25hDownloading matplotlib-3.10.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.6 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m202.3 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[36m0:00:02\u001b[0mm eta \u001b[36m0:00:03\u001b[0m\n", "\u001b[?25hDownloading numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (19.2 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.2/19.2 MB\u001b[0m \u001b[31m465.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:02\u001b[0m\n", "\u001b[?25hDownloading pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.7 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.7/12.7 MB\u001b[0m \u001b[31m314.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:02\u001b[0m\n", "\u001b[?25hDownloading pep8-1.7.1-py2.py3-none-any.whl (41 kB)\n", "Downloading plotly-5.24.1-py3-none-any.whl (19.1 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.1/19.1 MB\u001b[0m \u001b[31m226.8 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:04\u001b[0m\n", "\u001b[?25hDownloading pycodestyle-2.14.0-py2.py3-none-any.whl (31 kB)\n", "Downloading pytest-8.4.1-py3-none-any.whl (365 kB)\n", "Downloading scikit_image-0.25.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.0 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.0/15.0 MB\u001b[0m \u001b[31m450.7 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m0:02\u001b[0m:03\u001b[0m\n", "\u001b[?25hDownloading scikit_learn-1.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m569.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m0:01\u001b[0m:02\u001b[0m\n", "\u001b[?25hDownloading scipy-1.16.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (35.2 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.2/35.2 MB\u001b[0m \u001b[31m943.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:02\u001b[0m\n", "\u001b[?25hDownloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", "Downloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", "Downloading umap_learn-0.5.9.post2-py3-none-any.whl (90 kB)\n", "Downloading xgboost-3.0.4-py3-none-manylinux_2_28_x86_64.whl (94.9 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.9/94.9 MB\u001b[0m \u001b[31m650.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:05\u001b[0m\n", "\u001b[?25hDownloading contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (362 kB)\n", "Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n", "Downloading fonttools-4.60.1-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl (4.9 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m685.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m:02\u001b[0m\n", "\u001b[?25hDownloading imageio-2.37.2-py3-none-any.whl (317 kB)\n", "Downloading iniconfig-2.3.0-py3-none-any.whl (7.5 kB)\n", "Downloading ipython_genutils-0.2.0-py2.py3-none-any.whl (26 kB)\n", "Downloading joblib-1.5.2-py3-none-any.whl (308 kB)\n", "Downloading kiwisolver-1.4.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.5 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hDownloading lazy_loader-0.4-py3-none-any.whl (12 kB)\n", "Downloading networkx-3.5-py3-none-any.whl (2.0 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m985.8 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m:01\u001b[0m\n", "\u001b[?25hDownloading numba-0.62.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.8 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hDownloading pillow-12.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (7.0 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hDownloading pluggy-1.6.0-py3-none-any.whl (20 kB)\n", "Downloading pynndescent-0.5.13-py3-none-any.whl (56 kB)\n", "Downloading pyparsing-3.2.5-py3-none-any.whl (113 kB)\n", "Downloading pytz-2025.2-py2.py3-none-any.whl (509 kB)\n", "Downloading tenacity-9.1.2-py3-none-any.whl (28 kB)\n", "Downloading threadpoolctl-3.6.0-py3-none-any.whl (18 kB)\n", "Downloading tifffile-2025.10.16-py3-none-any.whl (231 kB)\n", "Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)\n", "Downloading widgetsnbextension-3.6.10-py2.py3-none-any.whl (1.6 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m980.3 kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hDownloading cloudpickle-3.1.2-py3-none-any.whl (22 kB)\n", "Downloading future-1.0.0-py3-none-any.whl (491 kB)\n", "Downloading graphviz-0.21-py3-none-any.whl (47 kB)\n", "Downloading nvidia_nccl_cu12-2.28.7-py3-none-manylinux_2_18_x86_64.whl (296.8 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 MB\u001b[0m \u001b[31m561.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:20\u001b[0m\n", "\u001b[?25hDownloading py4j-0.10.9.9-py2.py3-none-any.whl (203 kB)\n", "Downloading llvmlite-0.45.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (56.3 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m551.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:03\u001b[0m\n", "\u001b[?25hDownloading PySocks-1.7.1-py3-none-any.whl (16 kB)\n", "Using cached jupyterlab-4.4.10-py3-none-any.whl (12.3 MB)\n", "Installing collected packages: pytz, py4j, pep8, ipython-genutils, tzdata, tqdm, threadpoolctl, tenacity, PySocks, pyparsing, pycodestyle, pluggy, pillow, nvidia-nccl-cu12, numpy, networkx, llvmlite, lazy-loader, kiwisolver, joblib, iniconfig, graphviz, future, fonttools, cycler, cloudpickle, tifffile, scipy, pytest, plotly, pandas, numba, imageio, h5py, contourpy, xgboost, scikit-learn, scikit-image, matplotlib, lightgbm, hyperopt, gdown, seaborn, pynndescent, catboost, umap-learn, jupyterlab, widgetsnbextension, ipywidgets, ipympl\n", " Attempting uninstall: jupyterlab\n", " Found existing installation: jupyterlab 4.4.1\n", " Not uninstalling jupyterlab at /nix/store/jzhscs6dw96a836cvv12s271j59a5lmv-python3.12-jupyterlab-4.4.1/lib/python3.12/site-packages, outside environment /home/krosh/Documents/Github/ML/.venv\n", " Can't uninstall 'jupyterlab'. No files were found to uninstall.\n", " Attempting uninstall: widgetsnbextension\n", " Found existing installation: widgetsnbextension 4.0.15\n", " Uninstalling widgetsnbextension-4.0.15:\n", " Successfully uninstalled widgetsnbextension-4.0.15\n", " Attempting uninstall: ipywidgets\n", " Found existing installation: ipywidgets 8.1.8\n", " Uninstalling ipywidgets-8.1.8:\n", " Successfully uninstalled ipywidgets-8.1.8\n", "Successfully installed PySocks-1.7.1 catboost-1.2.8 cloudpickle-3.1.2 contourpy-1.3.3 cycler-0.12.1 fonttools-4.60.1 future-1.0.0 gdown-5.2.0 graphviz-0.21 h5py-3.14.0 hyperopt-0.2.7 imageio-2.37.2 iniconfig-2.3.0 ipympl-0.9.7 ipython-genutils-0.2.0 ipywidgets-7.7.1 joblib-1.5.2 jupyterlab-4.4.10 kiwisolver-1.4.9 lazy-loader-0.4 lightgbm-4.6.0 llvmlite-0.45.1 matplotlib-3.10.0 networkx-3.5 numba-0.62.1 numpy-2.0.2 nvidia-nccl-cu12-2.28.7 pandas-2.2.2 pep8-1.7.1 pillow-12.0.0 plotly-5.24.1 pluggy-1.6.0 py4j-0.10.9.9 pycodestyle-2.14.0 pynndescent-0.5.13 pyparsing-3.2.5 pytest-8.4.1 pytz-2025.2 scikit-image-0.25.2 scikit-learn-1.6.1 scipy-1.16.1 seaborn-0.13.2 tenacity-9.1.2 threadpoolctl-3.6.0 tifffile-2025.10.16 tqdm-4.67.1 tzdata-2025.2 umap-learn-0.5.9.post2 widgetsnbextension-3.6.10 xgboost-3.0.4\n" ] } ], "source": [ "! curl https://raw.githubusercontent.com/MSU-ML-COURSE/ML-COURSE-25-26/refs/heads/master/requirements/requirements.txt -o ./requirements_2025_26_for_colab_small.txt\n", "! pip install -r ./requirements_2025_26_for_colab_small.txt" ] }, { "cell_type": "markdown", "metadata": { "id": "-0DgvOqZix4h" }, "source": [ "Проверим версию библиотеки:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "QwWXXElyiRYq" }, "outputs": [], "source": [ "import catboost\n", "assert(catboost.__version__ == '1.2.8')" ] }, { "cell_type": "markdown", "metadata": { "id": "53PQKGPVizrv" }, "source": [ "Теперь можно приступать к выполнению задания! :)" ] }, { "cell_type": "markdown", "metadata": { "id": "7Z8vzxJLyEDk" }, "source": [ "-----------\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "6MBU1hOZ7aXH" }, "source": [ "# Часть 0. Знакомство с библиотекой scikit-learn" ] }, { "cell_type": "markdown", "metadata": { "id": "_ZjnB4VJJuR6" }, "source": [ "`Scikit-learn` - это библиотека машинного обучения с открытым исходным кодом, которая предоставляет различные инструменты для предобработки данных и обучения моделей.\n", "\n", "Подробную документацию с обширным количеством теоретических и практических примеров можно найти на [сайте библиотеки](https://scikit-learn.org/stable/index.html)" ] }, { "cell_type": "markdown", "metadata": { "id": "m-BabH7eK9GP" }, "source": [ "## Импорт библиотеки" ] }, { "cell_type": "markdown", "metadata": { "id": "Um1PZOAzLCyv" }, "source": [ "Можно импортировать библиотеку целиком:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "U3dp8WbZLCG7" }, "outputs": [], "source": [ "import sklearn" ] }, { "cell_type": "markdown", "metadata": { "id": "sf0_wD24LBNE" }, "source": [ "Однако, так как библиотека очень обширная и [содержит большое количество модулей](https://scikit-learn.org/stable/api/index.html), то импортируют либо модуль, либо классы/методы точечно и целенаправленно:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "DVpuJQwMLO2m" }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier # класс, с помощью которого мы в дальнейшем будем обучать kNN" ] }, { "cell_type": "markdown", "metadata": { "id": "ALAVj82zLhrn" }, "source": [ "**Подсказка** обычно по названию модуля интуитивно понятно, какие методы и функции в нем содержатся. С увеличением практики использования библиотеки вы сможете лучше и быстрее навигироваться в ней. Например, модуль neighbors содержит различные методы поиска ближайших соседей. Помните, что есть как версии для классификации, так и для задач регрессии? Вот версию для классификации мы выше как раз и импортировали. Однако если провалиться в [описание модуля](https://scikit-learn.org/stable/api/sklearn.neighbors.html) можно увидеть еще много разных классов и функций, которые так или иначе связаны с поиском соседей." ] }, { "cell_type": "markdown", "metadata": { "id": "OsJ49yZjMXux" }, "source": [ "## Общее устройство моделей\n", "\n", "Одним из несравненных плюсов библиотеки является то, что она предоставляет максимально общий и максимально понятный интерфейс взаимодействия с различными алгоритмами машинного обучения.\n", "\n", "Обычно, чтобы решить задачу машинного обучения, нам нужно (как минимум):\n", "* Знать, какой моделью (алгоритмом) хотим воспользоваться\n", "* Обучить ее на некоторое обучающей выборке\n", "* Уметь узнавать предсказания модели на новой выборке\n", "\n", "Вам не нужно запоминать для каждой конкретной модели, а как она вообще обучается и как это написать: библиотека все делает за вас." ] }, { "cell_type": "markdown", "metadata": { "id": "Gi32BlL6NFzJ" }, "source": [ "\n", "Каждая **модель машинного обучения** имеет два метода: `fit` - метод ответственный за обучения модели и `predict` - метод ответственный за предсказание целевой переменной.\n", "* Как правило, метод `fit` принимает на вход два аргумента: $X_{train}$ - **обучающая** выборка, $y_{train}$ - значения целевых переменных на объектах обучающей выборки.\n", "* Метод `predict` принимает на вход набор данных $X_{test}$ и выдает предсказанные значения целевых переменных $y_{test}$ на этом наборе данных.\n", "* В моделях классификации бывает метод `predict_proba`, который принимает на вход набор данных и выдает вероятности принадлежности классам.\n", "\n", "\n", "\n", "- Если нами решается задача *классификации*, то вектор целевых переменных $y$ содержит целые числа (или другое множество дискретных величин).\n", "- При решении задачи *регрессии* вектор $y$ содержит вещественные числа.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "VSYINz7HN93c" }, "source": [ "\n", "**Выборка (данные)** $X$ представляется в виде матрицы размера `(n_samples, n_features)`, то есть каждому *объекту* соответствует отдельная строка, а каждому *признаку* - отдельный столбец.\n", "\n", " **Целевая переменная** обычно представляется одномерным вектором размера `(n_samples)` - предсказаниями для каждого объекта, в случае предсказания вероятностей классов - матрицей размера `(n_samples, n_classes)` - значения вероктностей принадлежности объекта к тому или иному классу\n" ] }, { "cell_type": "markdown", "metadata": { "id": "MzWYc7HPSFSh" }, "source": [ "Приведем пример стандартной работы с библиотекой `scikit-learn`\n", "\n", "**Подсказка** Это совсем-совсем базовый способ обучения моделей, на практике используют много дополнительных приемов, постепенно которые мы будем с вами изучать" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9LX98EkWRXDH", "outputId": "3015ab1a-f0de-4e07-db2e-b69fd05c469f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1]\n" ] } ], "source": [ "#Создаем обучающую выборку\n", "X_train = [[0], [1], [2], [3]] # матрица размера 4 x 1, 4 - объекта, 1 признак\n", "y_train = [0, 0, 1, 1] # обучающий вектор целевых переменных\n", "\n", "#Создаем тестовую выборку\n", "X_test = [[1.1], [2.8]] # матрица размера 2x1, 2 - объекта, 1 признак\n", "\n", "#Создаем объект класса 3-NN классификатора\n", "neigh = KNeighborsClassifier(n_neighbors=3)\n", "\n", "#Обучаем классифкатора на созданной ранее выборке\n", "neigh.fit(X_train, y_train)\n", "\n", "#Предсказываем метку класса нового объекта с помощью метода predict\n", "y_test = neigh.predict(X_test)\n", "print(y_test) # y_test - вектор размера 2 (в тестовой выборке 2 объекта)" ] }, { "cell_type": "markdown", "metadata": { "id": "XsYzlFYAQSY_" }, "source": [ "## Общее устройство предобработки данных\n", "\n", "На практике почти никогда вы не встретите идеальных данных :) Вам всегда понадобится на них посмотреть, проанализировать, где-то почистить, где-то преобразовать и пр.\n", "\n", "Упрощая, все работу с данными можно проделать с помощью 2 библиотек:\n", "\n", "- `pandas` - с помощью этой библиотеки проанализировать данные, посмотреть на то, что в них хранится, принять решение о удалении мусора, замены каких-то значений и прочего. Данные техники вы совсем немного затронули в предыдущем домашнем задании, но подробнее мы к ним вернемся в следующем домашнем задании\n", "\n", "- `skelarn` - технические способы обработки данных, особенности которых связаны непосредственно с моделью или способом обучения\n", "\n", "**Пример** Данная библиотека поможет решить вопрос: а как нам признаки с типом строка (например, название видео) \"запихнуть\" в kNN? Он же только чиселки умеет понимать?\n", "\n", "**Пример**\n", "Или на следующий вопрос: с лекции немного припоминаю, что для более хорошей работы метрических методов хорошо бы данные привести в один масштаб. Как это можно быстро и безболезненно сделать?\n", "\n", " Например, есть 2 признака:\n", "- вероятность покупки $p$ - лежит на отрезке [0, 1]\n", "- стоимость покупки $s$ - лежит на отрезке [0, 100000500000]\n", "\n", "Хотелось бы чтобы оба признака были на отрезке [0, 1], потому что покупка, кажется, может вносить гораздо больший вклад по расстоянию в обучении модели..\n" ] }, { "cell_type": "markdown", "metadata": { "id": "41UPaR_b7aXH" }, "source": [ "\n", "В `sklearn` способы обработки данных также предоставляются через различные классы, которые имеют одинаковый интерфейс:\n", "\n", "Каждый **модуль предобработки** данных имеет два метода: `fit` и `transform`.\n", "\n", "* Как правило, метод `fit` принимает на вход **обучающую** выборку $X_train$ и считает по ней необходимые для заданного типа преобразования статистики.\n", "* Метод `transform` преобразует входные данные, используя статистики, посчитанные при вызове метода `fit` и возвращает преобразованные данные.\n", "\n", "\n", "**Пояснение:** Когда не хватает `pandas` или `skelarn`, можно обратиться к другим библиотекам, например, `numpy` :) Sklearn обычно одинаково хорошо работает с данными, представленным как и pd.DataFrame, так и numpy-array или обычными вложенными питоновскими списками\n", "\n", "------\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WHa66l-oSzgb", "outputId": "7fa5b729-3a40-434f-94e6-6b6046352353" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.000000e+00 0.000000e+00]\n", " [5.000000e-01 1.000000e+02]\n", " [1.000000e+00 1.000005e+09]]\n" ] } ], "source": [ "import numpy as np\n", "X_train = np.array([[0, 0], [0.5, 100], [1, 1000005000]]) # numpy используем для удобства отображения\n", "# выборка из 3 примеров, 1й признак - вероятность покупки p\n", "# 2й признак - стоимость покупки s\n", "\n", "print(X_train)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 74 }, "id": "EHH79PIHTUI4", "outputId": "a443bccd-5eb7-4dce-c9dc-e73311c861e4" }, "outputs": [ { "data": { "text/html": [ "
MinMaxScaler()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "MinMaxScaler()" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import MinMaxScaler # штука, которая приводит все значения признаков к отрезку [0, 1]\n", "# В части 1 подробнее разберем\n", "\n", "scaler = MinMaxScaler() # создаем экземпляр класса\n", "\n", "scaler.fit(X_train) # \"обучаем\" преобразователь, на самом деле под капотом считаются статистики\n", "# X_train тут не меняется!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YfGruLIOTvFx", "outputId": "9c5cf042-62e0-4ccf-abae-6dace6e07e12" }, "outputs": [ { "data": { "text/plain": [ "(array([0.]), array([3.]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Теперь метод для каждого признака посчитал его минимальные и максимальные значения\n", "# Но это под капотом, сюда можно вообще не лезть\n", "scaler.data_min_, scaler.data_max_" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MDJNlPk4URM5", "outputId": "0c23b481-e84a-479e-d07e-44140bffe046" }, "outputs": [ { "data": { "text/plain": [ "array([[0. ],\n", " [0.33333333],\n", " [0.66666667],\n", " [1. ]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_new = scaler.transform(X_train)\n", "X_new\n", "# Значения в 1й признаке не поменялись, так как они уже удовлетворяли условию\n", "# А во втором - стали на отрезке [0, 1]" ] }, { "cell_type": "markdown", "metadata": { "id": "qDP-Ptto7aXI" }, "source": [ "# Часть 1. Нормализация признакового пространства" ] }, { "cell_type": "markdown", "metadata": { "id": "vWcywKKu7aXI" }, "source": [ "В задачах машинного обучения важную роль играет нормализация данных. Ее мы затронули как раз выше. Для числовых признаков, как правило, используют два типа нормализации:\n", "\n", "* Нормализация к распределению с нулевым матожиданием и единичной дисперсией, $x := \\frac{x - E(x)}{\\sigma (x)}$, где матожидание $E(x)$ и стандартное отклонение $\\sigma (x)$ считаются **по каждому признаку (столбцу) отдельно**\n", "* Нормализация в отрезок [0, 1], $x := \\frac{x - \\min(x)}{\\max(x) - \\min(x)}$, где минимум и максимум берется **по каждому признаку (столбцу) отдельно** (как в примере выше)" ] }, { "cell_type": "markdown", "metadata": { "id": "dVycAwTI7aXJ" }, "source": [ "**Пример**\n", "\n", "Ниже приведены: таблица до нормализации признаков, таблица после нормализации к нулевому матожиданию и единичной дисперсии, таблица после нормализации в отрезок [0, 1]\n", "\n", "**До нормализации**\n", "\n", "| Признак 1 | Признак 2 |\n", "| ----------- | ----------- |\n", "| 1 | 2 |\n", "| 2 | -1 |\n", "| 3 | 0 |\n", "\n", "**Нормализация к нулевому матожиданию и единичной дисперсии**\n", "\n", "| Признак 1 | Признак 2 |\n", "| ----------- | ----------- |\n", "| -1.225 | 1.336 |\n", "| 0 | -1.069 |\n", "| 1.225 | -0.267 |\n", "\n", "**Нормализация в отрезок [0, 1]**\n", "\n", "| Признак 1 | Признак 2 |\n", "| ----------- | ----------- |\n", "| 0 | 1 |\n", "| 0.5 | 0 |\n", "| 1 | 0.333 |" ] }, { "cell_type": "markdown", "metadata": { "id": "drALXD4rz5N2" }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "id": "95AEYuq47aXJ" }, "source": [ "### **Задание 1.1 (юнит-тесты, 4 баллa)**\n", "\n", "В модуле `scalers.py` реализуйте два вида нормализации признакового пространства. `StandardScaler` - нормализация к распределению с нулевым матожиданием и единичной дисперсией, `MinMaxScaler` - нормализация в отрезок [0, 1].\n", "\n", "Нормализаторы имеют два метода - метод `fit` и метод `transform`, что максимально повторяет концепцию `sklearn`. Метод `fit `получает на вход **обучающую** выборку и считает по ней все необходимые для заданного преобразования статистики, которые сохраняет во внутренние поля класса. Метод `transform` преобразует данные, используя статистистики, посчитанные при вызове метода `fit`.\n", "\n", "Примеры запусков функций можно увидеть в открытых тестах." ] }, { "cell_type": "markdown", "metadata": { "id": "-ZHzsl0b7aXK" }, "source": [ "**Внимание!** В текущую директорию необходимо загрузить файл scalers.py с реализованными классами. В Google Colab это можно сделать, нажав на значок \"папки\" слева, дальше на \"загрузить\" (значок листа со стрелкой вверх). Загружать нужно в папку \"sample_data\" (она у вас откроется по умолчанию)" ] }, { "cell_type": "markdown", "metadata": { "id": "g498ihOBWqnx" }, "source": [ "-----------\n", "\n", "**Внимание!** Перед тем, как выполнять задание дальше, убедитесь, что ваша реализация проходит тесты с системе проверке заданий, и вы загрузили сюда вашу реализацию\n", "\n", "Теперь посмотрим работу \"скейлеров\" на примере данных:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "lTA4jWKs7aXK" }, "outputs": [], "source": [ "from scalers import StandardScaler, MinMaxScaler" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "h7aZFM4A7aXL" }, "outputs": [], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import pickle\n", "\n", "from matplotlib import pyplot as plt\n", "\n", "\n", "plt.rcParams[\"figure.figsize\"] = (5,5)" ] }, { "cell_type": "markdown", "metadata": { "id": "EmhwCAA08bIf" }, "source": [ "Загрузим файл с данными data.pkl:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZSX7xhNEYbEM", "outputId": "7f6b0897-9786-46d5-a2d3-81672995237a" }, "outputs": [], "source": [ "import gdown\n", "\n", "gdown.download(id='1cGV6SvpJuP_pa1mLCnI_SvTafEtW2sSO')" ] }, { "cell_type": "markdown", "metadata": { "id": "ywxtVnMY7aXM" }, "source": [ "Загрузим данные в память. В переменной $X$ будут храниться признаковые описания объектов, в переменной $y~-$ метки классов" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Sobm6X-o7aXM" }, "outputs": [], "source": [ "with open('/content/data.pkl', 'rb') as file:\n", " X, y = pickle.load(file)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "yCVPp1oY7aXN" }, "outputs": [], "source": [ "def plot_data_points(X, labels, xlim, ylim):\n", " g = sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=labels)\n", " g.set(xlim=xlim, ylim=ylim)\n", " plt.xlabel('x_1')\n", " plt.ylabel('x_2')\n", " plt.grid()" ] }, { "cell_type": "markdown", "metadata": { "id": "yKx2F8Zt7aXN" }, "source": [ "Визуализируем наши данные" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 470 }, "id": "5AFXiGvE7aXO", "outputId": "9ba654f8-ecec-4600-d072-345287ac8dcf" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_data_points(X, y, xlim=(-15, 15), ylim=(-15, 15))" ] }, { "cell_type": "markdown", "metadata": { "id": "Ugopb03e7aXP" }, "source": [ "Как видно, наши данные идеально разделяются прямой $x=0$" ] }, { "cell_type": "markdown", "metadata": { "id": "HCCnz9AM7aXP" }, "source": [ "Функция $plot\\_knn\\_bound$ принимает на вход объекты $X$, метки классов $y$, метод нормализации признаков $scaler$, число соседей $n\\_neighbors$ и границы рисунка $xlim$, $ylim$. Функция обучает $KNN$ классификатор с числом соседей $n\\_neighbors$ и визуализирует разделяющую поверхность для классов, полученную с помощью обученного $KNN$ классификатора.\n", "\n", "* Если вы не можете различить _выбранные нами цвета_, то измените список цветов в переменных cmap_light и cmap_bold. Актуальный список возможных цветов находится [здесь](https://matplotlib.org/stable/tutorials/colors/colormaps.html)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "0rkVIYjn7aXQ" }, "outputs": [], "source": [ "from matplotlib.colors import ListedColormap\n", "from sklearn import neighbors, datasets\n", "\n", "def plot_knn_bound(X, y, scaler=None, n_neighbors=10, xlim=(-15, 15), ylim=(-20, 20)):\n", " # step size in the mesh\n", " h = 0.05\n", "\n", " # Create color maps\n", " cmap_light = ListedColormap(['plum', 'blue', 'plum', 'green'][:np.unique(y).shape[0]])\n", " cmap_bold = ['plum', 'blue', 'plum', 'darkgreen'][:np.unique(y).shape[0]]\n", "\n", " x_min, x_max = xlim\n", " y_min, y_max = ylim\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", " grid = np.c_[xx.ravel(), yy.ravel()]\n", "\n", " X_scaled = X # if scaler is None\n", " if scaler is not None:\n", " grid = scaler.transform(grid)\n", " X_scaled = scaler.transform(X)\n", "\n", " # we create an instance of Neighbours Classifier and fit the data.\n", " clf = neighbors.KNeighborsClassifier(n_neighbors, algorithm='brute')\n", " clf.fit(X_scaled, y)\n", "\n", " Z = clf.predict(grid)\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " plt.contourf(xx, yy, Z, cmap=cmap_light)\n", "\n", " # Plot also the training points\n", " sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y,\n", " palette=cmap_bold, alpha=1.0, edgecolor=\"black\")\n", " plt.xlabel('x_1')\n", " plt.ylabel('x_2')\n", " plt.title('Разделющие поверхности алгоритма {}-NN'.format(n_neighbors))\n", " plt.grid()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "tbEC-a8S7aXQ" }, "source": [ "Нарисуем разделющие поверхности $1$-$NN$ и $10$-$NN$" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "eC13zGrp7aXQ" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_knn_bound(X, y, n_neighbors=1)\n", "plot_knn_bound(X, y, n_neighbors=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "XUU46vi17aXR" }, "source": [ "### **Задание 1.2 (кросс-проверка, 2 балла)**\n", "\n", "Чем отличаются поверхности, полученные при числе соседей 1 и 10? Объясните, чем вызваны данные отличия" ] }, { "cell_type": "markdown", "metadata": { "id": "wvDn9mO17aXR" }, "source": [ "**Ваши выводы тут:**\n", "\n", "При 1 соседе поверхность имеет выраженные границы между классами. Наблюдается высокая вариативность предсказаний даже при небольших изменениях входных данных.\n", "\n", "При 10 соседях поверхность имеет более гладкие границы, но смещение может увеличивается" ] }, { "cell_type": "markdown", "metadata": { "id": "1jkeEeZM0Qkh" }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "id": "YmnJpzNs7aXS" }, "source": [ "### **Задание 1.3 (кросс-проверка, 1 балл)**\n", "\n", "Данную проблему может решить нормализация признакового пространства.\n", "\n", "Начертите разделяющие поверхности для $KNN$, обученного на нормализованных признаках с помощью реализованных вами нормализаторов. Используйте функцию $plot\\_knn\\_bound$. Менять функцию $plot\\_knn\\_bound$ нельзя." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "QQtyZezc7aXT" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAHWCAYAAADD8QggAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAqjlJREFUeJztnQWcFHUbx5/jgOPo7gYFaZAO6RJQQBrp7pQWCenukFRBQsIXRAGRUAlBpKS7Ow8u4Nj38/vfM8fs3sze3u3F7t3z9bMetzM3O7M7O8889Xs8LBaLhQRBEARBCBdxwvdngiAIgiAAMaSCIAiC4ARiSAVBEATBCcSQCoIgCIITiCEVBEEQBCcQQyoIgiAITiCGVBAEQRCcQAypIAiCIDiBGFJBEARBcAIxpIIgCILgBGJIw8iKFSvIw8Mj+JEgQQJ6//33qWfPnnTv3r3o3j1BEASXY8GCBdS4cWPKmjWrum62bds2TH+P9fF3hQoVIiNVWyzDNVjj6tWrwdfoDRs2hFh/1KhRatnDhw8pIhBDGk7GjBlD3333Hc2dO5fKli2rTpQyZcrQq1evonvXBEEQXIpJkybR77//Tvnz56e4ceOGezsnT56kjRs3hvlaHdmS8mJIw0nt2rXp888/p44dOyovtW/fvnTlyhX66aefonvXBEEQXIq9e/cq7++XX34hLy+vcG3D29tbRf/CYhiLFClCJ06coE2bNlFkIoY0gqhSpYr6CWMKHj9+TAMHDqSCBQtS4sSJKWnSpMr4Hj9+3Orv/vzzTypfvjylTp1ahYlz5sxJgwcPJj8/P9Pwhu3DKEyCE7ZChQqUKFEiSpIkCdWpU4f+++8/w3032iYeCI9oZM+e3ep1Xrx4oUIpmTJlUl+M9957jyZOnEhv3761G3IBdevWVdvT8+OPP6p19+zZEyKMrt8PbB/hHTyP5XrOnj1LjRo1opQpU6r3snjx4vS///2PQkMfBjJ6VKpUyWr9+/fvU4cOHShdunTqdQoXLkwrV660u8148eKpY/7iiy8oICDAat2nT5+qG7EsWbKo9zJ37tzqDl7/Xmrbmzp1Ks2YMYOyZcumLiwVK1akU6dOWe1bmjRp1D7rLzYXL15U50LTpk2Dn8M6BQoUCPF+4DVs33cwf/585VFgHzNmzEg9evRQ+27LoUOH6OOPP6YUKVKo18TnNWvWLLvnsNF5Z3vOgfXr16t1bM8fI3BTi/Me+4p9zpUrF40dO5YCAwPDdA44ss73338fvN7ly5dVGBPnYcKECal06dL0888/W70eznP83dq1a2nYsGGUPn169V598skndOPGDat18TnZnoOHDx8OsX+hva/aNrTXxuPYsWNW27116xZ5enqqZfhOasAY4bPA9QnnPPa3ffv29OjRI3IEnK/6fQ0PceLEoREjRoTJMDZr1izMxjc8hN/HFqy4dOmS+pkqVargL9PmzZvVFypHjhwqf7po0SJ14Tt9+rT6cmsG6YMPPqAmTZqoL92BAwdo8uTJKkQ8Z86cEK+DC8KSJUuCf4dHbAtCzm3atKGaNWuqCzK2hdAzDPa///5reBFq0KABNWzYUP37jz/+oMWLF9s93s8++4x27txJrVu3ppIlS9Lu3btp6NCh6mKzcOFCiixwbAjv2IKbhHLlyinDPmTIEHVRWrduHdWvX1/lSHB8odG8eXNlAPTgmPT4+vqqCxIME24Q8Nni4o6LDIxKnz59rNbv3LmzuqHx9/en7du3KyOFCxEu6ACfDc4JXMC6dOmickj79+9Xr3vnzh2aOXOm1fa+/fZbdc7AiOFmCwYKN3F4T2DY06ZNG5yPwvnTu3dvZZCxf7ihgjEMD8gpjR49mqpVq0bdunWjc+fOqdfBBf2vv/5SNwoA5wRulDJkyKDeC1xwz5w5Q1u3blW/4xixDY1WrVpZnXsANwJGvHnzhoYPH+7wPuNGCzex/fv3Vz8RWhw5ciQ9f/6cpkyZYvg32ucFEEI0umAbnSc49wC+50j14HPFe4/rAW6yYCBhmGzPw3HjxikDg5tn3ATh88b7AwOHGyUzsL7Rd0ND+w7jpgs36QDnhx6ch8uXLw++yQHY1/jx44e4kcfnimtau3bt1GeK7xu2j58HDx502kg6SosWLdR3B4YR72Vor4ubAhhfXKfwWerPswgF80gFx1m+fDluayy//fab5cGDB5YbN25Y1qxZY0mVKpXF29vbcvPmTbWen5+fJTAw0Opvr1y5YvHy8rKMGTPG7mt8/PHHlgIFCoR4vkWLFpbEiRNbPZcoUSJLmzZtgn9/8eKFJXny5JZOnTpZrXf37l1LsmTJQjz/+vVrdTyjR48OcYzYX41s2bIFv86WLVvU8iFDhlhtq23btur5kydPBj+H33v06GG1Xp06ddT29Kxfv16tu3v3btP9wHuaNWtWS+3atdXzWK5RtWpVS8GCBdU6Gm/fvrWULVvW8t5771nsge1je1OmTAmxLH/+/JaKFSsG/z5z5ky17vfffx/8XEBAgKVMmTLqs3n+/LnVNvX7CDJmzKg+X42xY8eqz/D8+fNW6+G99fT0tFy/ft1qe/pzDBw6dEg9369fP6u/b968uSVhwoRquzgurLN582ardXBcOD5btPW19/3+/fuW+PHjW2rUqGF1Ts+dO1ett2zZMvX7mzdvLDly5FCf7ZMnT6y2ic/CCPz9V199ZbhMf86B+fPnq+9P5cqVQ5w/Rrx69SrEc126dFHvi/48ARcuXFD7snLlyuDnsF/6S6S980Sjb9++ap0//vjD6juJ9yV79uzB7x/Oc6yXKVOm4HMGrFu3Tj0/a9Ysq89Jfw5u27ZNrVOrVi2r/dNj9B3W0F4b5wiuW/7+/sHL8F3BdQbL8Z20917+8MMPar19+/ZZwoLtNcsRsD7+DuAzwutu3LjR9Dqj/6xwXuK4ChcuHHweap8truERgYR2wwnuGnHnjHAcwge448UdDzwizXNEKAIglIQQCNbJkycPHT16NMT2EAqGBwIvFl7pRx99FGId3CXiLtIeuHOEZ4S7ZuQktAfuzEqVKqU8Rz1amNGRvAW8KmxLywP369fPavmAAQPUT9swVkQxb9489T5+9dVXId47eBvw6uGtaceMdeGVX7hwQXl8EcG2bdvUHTneXw14Y/A+fHx8VC5ID57DvuD1cQd/9+5dqlq1avByeLPwgBAG1X9eOL9w3uzbt89qe/CwtXMMIBqAzxX7pQdFcMmSJVOh7i+//FJ5fp9++mmI48Fr6F8XD9uCud9++02dJwg/a+c06NSpk0pZaJ83oh1IbWC95MmTW23DWY8F+wQvBFEAeO2OoPfotPMC7zW2hTRAeL8H9sDngM8E0R8NfO/h6SJag2iUHnhKiBRo4POCN2/7eWrAZiBagYgQPndnqFevnvpctPQHvNibN29ahf+N3ktch/BeImQNjhpczyKTli1bqlSSo+FazStFWg3X18hAQrtOXNQRe0cFGkImMJD6iwzCaQiZIJSGi4s+L6OFf/Xky5cvuH0GYTh9uEUDJy8ujvaA0dDnbG3BhU+PluPClz001qxZox4AF36EEfVo74Ftbi0iePbsGY0fP16F6WxDVAiz4gsFg4GHEQib6Q1QeLl27Zr6Eus/a4DwvLZcT69evdRDA6Ex/Q0IPi/kfMzCmdhvPXhtW3AeIoytB/m52bNnqxAv3i/82wgYFLPX1tCOCZ+vHoQAkTPTlmvpDaO8q7NMnz5dXcCRT8Q54AgIO+ICipsshHNtz6fwfg/sgffCyMDpzw/9+2P7ecKwIUdu9h1atWqVOi583qtXr3ZqX3EDiILJZcuWKQOOnzDQttcI7WYVoX18/23PyWc272V4QdrEdlu4aTUzjEhfwTA6kraB8dVCwrgZjWjEkIYT3HWimMUMXPRxUUdCHh8gLmy4+OJu3bYgR/NM8GX/559/VNEOLvpff/211Tr4chldSPVo20a+xOgktC09h4cEjNa1pUaNGqpYBl8os8KlyAK5Xrx/eH3bAgftmFHcBQ/UCFycogPsL9433EjhPdPuopGb0va9evXqNGjQIMO/h5EML8jJgidPnihPw9ZLBMiXf/PNNyHOxdBy5FEJbiCR04Qnhu+RI8AwIvcMo4D3HIVGiObAe0J+0fY7GJbvQXQBrxnXFBS6OXNe6MH1qWjRoirnjc/drDgP0R7k7nE+oxIWNxx4D2vVqmV4PQsPKLzCjaYeM48zrIZRM75wUiKjs0IMaSSBwoLKlSvT0qVLQ3zBteS/Hq3AAVWGuCtFcQeKZrQ75AcPHtD169etQopG4IIB4C3qizrM0EJN2h2zPRBywjYRwka1MfZJ782cP39efakcqagMC7dv31Ye+oQJE1QYzNaQwivS7rAdOWZnQPUhPEgcp94r1UKFWG4badD2CUYe4XF4VSgyQcEZPi+Efx3dby3ioAfvu+17/uuvv6qiNBhoeDG4e0c1re2NFIqybF/btpJTOyZcbLX3WruwI9qi/b127qGKOCI/B9xQ4nO3LeSyBypTcZ6gYEifJtGq6o2+B/je2XrdYQXvFd4nW8zOD9vPE4YDERZUOtuC6Ba8QVwbIgp0FcCQwlDiu4xrlm16Ajdiu3btUjfQKNYy23dnwfcDqSlHCI9hhPeNcwnHgeKviERypJEEPmjbuync8TmSq8MdOC7Ur1+/tvpbYJTnsj0ZcRcOj1j/9xowfrZ3gTCQjhhSDdyFAtuKUoTftJuBiAQnPsKTXbt2NVyOmwZU0qIqGnnm0I7ZGVCtCe8F75u+mhQVsrjpgRcUWvhKn5PDBQw5cc17tL3pwrb1IJSlP4f+/vtvZSDRWqX/O1RzI2qC8wAGFZ4Y/h0eYBQRxkV4WH9O4yYRoTjt8y5WrJiqYsZ5YdsWE97WA0RhUB0M42GvitXo+2f7unjPjaqW8R6jshvvl7OhXZwf+EzwmWq8fPlSefi42cGNlVEVtv4GHOew/vMEWAc3X0gLRLTXDK9Ua28xymUbvZdG339n0W7U9Y/QDCMiTbg+hMX44kbRkba4sCAeaSSBFgCEHRCqQDk82hPgGejv6EH37t2VJ6XlF+HpIfeBv0ceUsvH4gTAHSPyUFouSrsIoCwdd3IIEcKI4sKD4hJc2FAIhb+DN4uiEJTpoxDlyJEjKkwEzwXtKmEpBsG+IQeLCzMu6rgAwQOAsUd7g22ODK+N19EbNhgU/XOaF4SLEMLX+nzmjh071HuHi7kZeI9Q4IE7bBTB4H1GzhkXNIQ1bft3wwuKRmCwcdFBGB4XR1z80AKCC4u+cATg9eEFaqFdGFx4AJoHiVAZvtR4T7HNDz/8UF14cb5guzAk+ggGLhw4TrSgwLvFayLnrg8Nw3ODN4YiIVw8cOMDw4q7cdyIoe81LOD8QVgVFyxsC3fz8LpglEqUKKEuaADnL849FLEg/IdzHxdHeGM4dqObhdCAd4SbPNuQX2jgO4fvDzxxFILh/Ea6w9YY4D3C9wCGZMuWLeQsiCL98MMPyhDidRGKRksJPGEYa9vcOpbj88Tx4XzF54nPGOewHtwI4TwwSwE4A14LuXSz+gtcU+DVoy0PN+f4buI7aebdG4H3VvsOYht4v7XUFc4nIw88NHBuox0qLOeGFhK2jbo4TYTU/sYitLLyw4cP210P5fUDBgywZMiQQbUslCtXznLgwIEQpewLFixQbRso7Ub7RL58+VQrio+PT/A6eL3QHvptaiXuNWvWVC0vCRIksOTKlUu1pxw5ckQtnzRpkqVEiRKWVatWmR6jWfuLVtLfp08f1c4RL148tf3x48erUnM9juy77UNrGdH2o0iRIlbtE2atJZcuXbK0bt3akj59erVPaC2oW7eu5ccff4yw9hdw7949S7t27SypU6dWbSH4/Gz3Rdum9ogTJ44lc+bM6j3Ut69o7+XQoUMtuXPnVtvDdtG2M3XqVNVaY7uP06ZNs2TJkkW1glSoUMFy/Pjx4G399NNPaj2sowctFvgM0QKgbdPR9hd9u0vevHnVe5suXTpLt27dQrS5gD///NNSvXp1S5IkSdR5XahQIcucOXPC1f6C5Zs2bbJ6Hu+hI+0vf/31l6V06dLq+4fzdNCgQZbt27dbtVn16tXL8tFHH1l+/fXXEH8fnvYX7Txs1KiRakPDd69kyZKWrVu3GragoIUEn33atGnVfqI17Nq1a1br4nPCujNmzLC7f2Ftf9G3t4S2HOdsgwYN1DHhmtK4cWPL7du37X5+tp9ZaN93R9tfbNv3cO2x1/5i9t5EZPuLB/4XsaZZiGhwN422FVt1E33jOR56VSB3BccIzyysotYxHXimCJui6AZFVYJ7g+8q8pGI4qBiVnBvJEcqCIIgCE4ghtQNQFzftndSD6olkR+NCSDfGhH9noIgCFGFFBu5AXpBbCPQOqO1z7g7KGgQBEFwJyRHKgiCIAhOIKFdQRAEQXACMaSCIAiC4ASSI7UBikKQpENjfVTN2BMEQRBcC2Q9oSgFKU9bIQ1bxJDaACOK0WiCIAiCcOPGDcqcObPddcSQ2qBJvHl5XSF/f8cmTbgSCRK8pmXLdlD79jXIzy8euSNyDO6//8cWOC4fF1m8pbd0NtlZyvssL8Vx0yyWHEP04ePrQ+X7lw8h+2mEGFIbtHCuhwfevJBz+VwdD4/XlDBhQvLwwL673wUcyDG4//4n8Q794hPZBFKgOobEAYnJk4KE190NOYbox5EUn/vcHgiCIAiCCyKGVBAEQRCcQAypIAiCIDiB5EgFQRCEEFjwn2fQw9kcaVzM5I0fSK6ER6BH0IOcb3MUQyoIgiBY8TbuW/JL50dvk751up/eQhZKHyc9BaQNiBCjFZF9onGex6EE9xJQnDfOBWfFkAqCIAjBWDws9Cr7K/JK6kWpkqeieJ7xnDKmFrKQv6c/eQV6uYwhhRF9HfiaHnk/olferyjRpUTkYQn/vokhFQRBEIJ5G+8tUXyidKnSkXd874gJEce1kNcb1zGkIAEloLiecem633V1zJ4B4W/NkWIjQRAEwQoYvDgeMd88xPGIIzlSQRBch0srLkf3LghCtBDzbzkEQRAEIRIRQyoIgiAITiCGVBAEQYhwfHx8aNzEcVTvk3rU8pOWVK9ePfU7no9sFi5dSO8Xe5+SZU5GFWpWoMNHD0fq64khFQRBECIUHx8fatmmJb3n+R4t7LyQ5nSYQwu7LFS/4/nINKbrN62nQSMH0fCBw+ngroNUMH9BqtekHt1/cD/SXlMMqSAIghChzJo7i1qWbknl8pXTTdTyUL/jeSyPLGYvnE3tP29PbVq0oQ/yfEBzp86lhN4JaeXqlZH2mmJIBUEQhAjlwP4DVPaDsobL8DyWRwYBAQF09PhRqlKxSvBzceLEocofVaZDRw5Fymuq14i0LQuCIAixEi9PL1M1JDyP5ZHBw8cPKTAwkNKmSWv1fLq06eje/XsUWYghFQRBECIU/0B/JcNnBJ7H8piEGFJBEAQhQilTtgztP7PfcBmex/LIIHXK1OTp6RmisAjeKLzSyEIMqSAIghCh9OnZh1YdXEV/nf4r2DPFT/yO57E8MogfPz4VK1yMdu/bHfzc27dvac8fe6hU8VIUWYhEoCAIghChJE6cmFatXKWqc79d/C3FjxufAt4EKE8Uz2N5ZNG7a2/q2KsjFStSjEoUK0FzFs2hl69eUuvmrSPtNcWQCoIgCBFO4sSJafiQ4Wr6i19cP0rwJkGUTH9p3KAxPXz0kMZMGqNCuoULFKb/rf1fpIZ2xZAKgiAIMYpuHbupR1QhOVJBEARBcAIxpIIgCILgBGJIBUEQBMEJxJAKgiAIghNIsZEgCE5xacXl6N4FQYhWxCMVBEEQBCcQQyoIgiAITiCGVBAEQRCcQAypIAiCIDiBGFJBEARBcAIxpIIgCEKE4+PjQ4O+nED5S35G+fO3p/wlGqrf8Xxk8cf+P6hhy4aUo0AOSpAmAf1v2/8oKhBDKgiCIEQoPj4+VKVea5q7uBxdvrKVbt/eSJev/qx+x/ORZUxfvXpFBfMXpJmTZlJUIn2kgiAIQoQyZtIcOnW6L719W133rIf6/dTpoOWTxw6N8NetWa2mekQ14pEKgiAIEcrW7Yfp7dtqhsvwPJbHJMSQCoIgCBGKv38C5YEa40EBannMQQypIAiCEKF4efkRkcVkqYXiq+UxBzGkgiAIQoRSt2YJihPnN8NleB7LYxJiSAVBEIQIZeTgXlQg30yKE2enzjO1qN/xPJbHJKRqVxAEQYhQEidOTL9v+VZV527dPpP8A7zIK76/8kRHDv5WLY8M0FZz6cql4N+vXr9Kx08epxQpUlDWzFkpsnArj3Tfvn1Ur149ypgxI3l4eNDmzZutlrdt21Y9r3/UqlUr2vZXEAQhtpI4cWLV4vLf3xvo1Kll6id+jywjCv45/g+VqlJKPcCgLwepf4+ZOIYiE7fySF++fEmFCxem9u3bU8OGDQ3XgeFcvnx58O9eXl5RuIeCIAhCdFGxXEXyexD1hUxuZUhr166tHvaA4UyfPr3D2/T391cPjefPn6uf3t6vycPjNbkb2G/9T3dEjsG99j+QAskV0fbLVffPVY8Br2XR/ecsFt5GRGwrotGO0ej9fUtvY6YhdYQ9e/ZQ2rRpVUy8SpUq9PXXX1OqVKlM158wYQKNHj06xPPz5u2mhAkTkruybBmS/O6NHIN77P9pcm3OJT9H7k5UHkPcuHEpfZz05O/pT5a4EWf8/OO+c1hchYC3AfQ6zmu6kfQGvXnzxmrZq/ivHN6Oh8Vicb3bBAdA/nPTpk1Uv3794OfWrFmjjF+OHDno0qVLNGzYMBWPP3DgAHl6ejrskWbJkoVSpLhDfn7mBthVgQeBi1/79tXJ1zceuSNyDK6//8cXXCVXB14GDFCep3nIk4y//65OdBxDYPxACsgdQNkyZyOveM6nxixkUUbU640XeZiKNEQP/q/96drNaxT/YnzyDLB+f318fahYt2L07NkzSpo0aezxSJs1axb874IFC1KhQoUoV65cykutWrWqaSjYKI+Ki4efn/tdAPX7744XcD1yDK67/+5kmLCv7rS/rnAMHrr/InqbroS2T0bvb5ww1OK6VdVuWMmZMyelTp2aLl68GN27IgiC4Fa4abAyWo4xRhvSmzdv0qNHjyhDhgzRvSuCIAhuQZw3ccjy1kK+Ab4U0/EN8FXHimN2BrcK7aLZVu9dXrlyhY4dO0YpU6ZUDxQNffbZZ6pqFznSQYMGUe7cualmzagfqyMIguCOeLz1IM9HnvQg7gP1u3d8b1WT4kyONOBtAHkEuk5oF54ojOiDRw/UseKYY40hPXLkCFWuXDn49/79+6ufbdq0oQULFtCJEydo5cqV9PTpUyXaUKNGDRo7dqz0kgqCIISBBA8SkB/50f0398kjjnNGxkIWVRkb7208lzGkAJ4ojCiO1VncypBWqlTJbkx7+/btUbo/giAIMREYPO8H3mR5ZKG3cR3vpzSrPEZ7Sa7nuVyq6AvhXGc9Ubc0pIIgCEIUh3lt2kLCw5s3b9R2XMmQRiQxuthIEARBECIbMaSCIAiC4ARiSAVBEATBCcSQCoIgCIITiCEVBEEQBCcQQyoIgiAITiCGVBAEQRCcQAypIAiCIDiBGFJBEARBcAIxpIIghIlLKy5H9y4IgkshhlQQBIcRIyoIIRFDKgiCIAhOIIZUEARBEJxADKkgCIIgOIEYUkEQBEFwAjGkgiAIguAEYkgFQRAEwQnEkAqCIAiCE4ghFQRBEAQnEEMqCIIgCE4ghlQQBEEQnEAMqSAIgiA4gRhSQRAEQXACMaSCIAiC4ARiSAVBEATBCcSQCoIgCIITiCEVBEEQBCcQQyoIgiAITiCGVBAEQRCcQAypIAiCIDiBGFJBEARBcAIxpIIgCILgBGJIBUEQBMEJxJAKgiAIghOIIRUEQRAEJxBDKgiCIAixxZDu27eP6tWrRxkzZiQPDw/avHmz1XKLxUIjR46kDBkykLe3N1WrVo0uXLgQbfsruAMWItpPRAuJaDURPYvuHRIEwc1wK0P68uVLKly4MM2bN89w+eTJk2n27Nm0cOFCOnToECVKlIhq1qxJfn5+Ub6vgjuAm6y6RLSTiN7jr0MHIprCBtZRHhHRAiL6ioi+I6JXkbjPgiC4GnHJjahdu7Z6GAFvdObMmTRixAj69NNP1XPffvstpUuXTnmuzZo1i+K9FVwbeJ492fCl1T2P82QSES0ioq4ObGcOYiVE1JaIKhDRSSJqRETdI3HfBUFwJdzKkNrjypUrdPfuXRXO1UiWLBmVKlWKDhw4YGpI/f391UPj+fPn6qe392vy8HhN7gb2W//THYmaY/iWiHoRUQoisn2dfkTUmI2jp51tbCGi+0S0iog8+Lk8RFSfvL17ENGnbvs5mH0GgRRI7oK2r+60z7bIMUQfb+mtw+t6WODKuSHIkW7atInq16+vft+/fz+VK1eObt++rXKkGk2aNFHrrl271nA7o0aNotGjR4d4fvXq1ZQwYcJIPAJBEATBVXn16hW1aNGCnj17RkmTJo0dHml4GTp0KPXv39/KI82SJQv16FGZ/PxSkbsBD2LZsp3Uvn118vWN58SWThHRz/DZiagkEVUPxTtzxWOwR2siWqnzJG2ZQUSViKgoER0ioonK0yQqRkT3uDDpFhH9argN62O4RkTDeFsl2IvdSkRViKgjuSJmn8HxBVfJXYAHdC75OcrzNA95RtG5G9HIMUQfPr4+Dq8bYwxp+vTp1c979+5ZeaT4vUiRIqZ/5+XlpR624OLh5xdZF/HIB/sfPiPkS0Sd8Y4SUXMiSkREO1SYkmguF+W4+jE4Ql4iOkxE5UyW/8mh37ucM11HRPoIBXL1Y4hoBb9fxvj6+pCvbz/OxaaxMeQoTkKk5HNyVWw/A3e6EOr32R33OyKO4cz1M/TXf3+pf1coUIHyZEHqIXrwdLPPIU4YanHdqmrXHjly5FDGdNeuXVbeJap3y5QpE6375l4M0FWuwvvKwwYFhgB5v3f5ZPemExFN4BsHW35k79FbV41rFOYfQUQbVTYlJJrn9j0RDbQxosRe7CjOrzqeixEER3jx6gX1nt+bNu/fTEVyF1GPDX9tUM9hmRCxuJVH6uPjQxcvXrQqMDp27BilTJmSsmbNSn379qWvv/6a3nvvPWVYv/zyS9VzquVRhdCA9/WSQ5q2oLK1JRFtIKIW5P5kJKLBHK5tx57pQyJaTkQBRKS1WJ0jouJ27kNzENElG089gI1vUw4L97Xz94WJ6Dx7yIIQFFL86cBPdPj8YUqQIAG17NuSXge+Jk9Px725YSuGUdc6XSl/tvzBzxV/rzidunqKhq8YTrO7z46kvY+duJVHeuTIESpatKh6AOQ28W+IMIBBgwZRr169qHPnzlSiRAlleH/99Vd1MgqOgHBmLTvLPyGidx6/+4N2lU1E5Mc5UQh8dGNxBkcvWp78N7jBOEJEy/h9aqPzPO19zXBuumdlrxDx/HftP+o5ryelSZaGJnecTMOaIbdOypN88PSBw9vIkjqLlRHVKJC9AGVImUGFfIVY6pFWqlRJ9YuagercMWPGqIcQHnDBf2NnOcrX3SfH4RgJuc0ltFwqioRsecve6I9skH8jolxE9BO/l9v49385VGwEjG/QxVKI3fgH+NPkdZNpbs+5lMQ7iXrOM27Q921Y82E06vtRNK+nsRiNnj9O/kE1Pqxhurxm8Zq079Q++iDrBxG497Ebt/JIhcimIl/8zYDBMBbEiLlAlGGMiVrRdCJqSETJOTw8hPtP9cVr7YloLId7bVlDRMjfx4/E/Y+54Kb66MWjtOOfHXTuJkLw7s0vR36hBuUaBBtRPfAwM6XKROduhH6cFrJQHA/zS7ta5pZNj66LGFJBB9p9MrN3ZcsV9rTqRcF+aPrIqHadxrnL6CILe4wNiGgxe5e/cJ7Yz4H2lawc+v2UQ8fIQ5/gvOleNr5CWEElaocZHejA6QP0yv8VbfprE3WZ1YUu3HJfbe1jl45RmQ/MCyPL5CtDxy8fD3U75fKXo53/QvbSmJ1Hd6p1hFga2hWigglcubuFvSut/QV9pUsj+ZTBbfIX7L1V5arYU2ysmnPxTnRQhvs+t3JvbUoimmkjLWgP9OCW5grdr9mDRctMvkje75gJvND1f6ynBb0WkFe8d97/E58n1H9Rf5rQfgKlTxHUDudOJIifgF76odjPvAgJ64RGoRyFaNHPi+j8rfP0fqb3rZbBo71676rKlQoRhxhSweCUmMViA1u43eVTNgCRzXwiys3tN9vYQ67GwgXtOF+JKtfoIB57pXiEhyQOavcCVKaf45aZEnZEI2Iny35dpoyl3oiCFIlT0IDPBtC3O7+lQU0GkbtRu0Rt1a7S/7N3AjF6dhzdQV+3cex7OK7tOBq2fBjlzphb5UsR7kUI/NLtSzS+3fgI3nNBQruCCZn4wt/HpNAmonnL3l5nk9MUMo6hF1q4N1dZ8H4WqydtJ6I6/FMAvv6+FDduXEqSMGQeEcDTunQHBWDuBzzJa/ev0d/n/g6xbONfGylXhlyULFEyh7aVPHFyVZhUtWhV2nNiD+09sZeqF61O83vNV8uEiEU8UsFFuMsVrjCaRuLW2XlcmcZLbjXZxd5iXG45qWnjwflwoVAqF684xrF14T5W9LhqvOaCpQRcDBa7CXgTQN7xIZRhTlxP68vaV999RU+ePSHPOJ5Up2QdZVxs13EF0HUwqcMkGr9mPP2w+weVx3xjeUP5P8lPj54/oj71+4R5e4VzFlYPIXJxvbNJiKXAGIY2N1YzsE9YHKIL92968nOzuIBnAhcFTWLDnIKLpcpzDtYVpR8XcVGT3ogS7yv6WluJISWipAmT0r2n9+jt27cUJ07IgNrjF48pftygKuh5W+ZR5VaVqUe9HpQheQaVf9z450bqt7AfTe08NURo2BVADnRM6zHKcKKwKG78oEt0p9qdyENC/C6LhHYFFyENhzPNjCl6ObUG8+FENJ5zt5qXmYIl9zw51zqGQ8Gr+SfCxh9wrtUVJfkQzvvIZFkiPr7orF52DeBlVS5UWRUbGbXDzP3fXGpeubkKZWqkTR5UFJYoQSJqVa0VNSzfUBXjRAdvAt9Q4NvQx4mlSpqKqhSpQuXySXWtOyCGVHAh0CbS38DQPWUj2Z1DtVB4MRtEgLaSb1gbWD+9x4MLhYq4aM4Rnoc9jyOpiS5w7KNllZZKmWfCmgl0/f51ZZyg5jNw8UDKni47lcxTkjb8uYFaV8VggJBUKlSJTlw5of4uqkChD9pz+i3qp1SKes3rRYfP4eZQiAlIaFdwISD28JzbXNrxiLJjRHSAx5ihAOosEVmX9FvzhI1lYpPlqAju7YLCEiieuW0Q2tXagqDH+26qUWwGId1RrUbRyasnafmO5fTw+UPKljYb9WnQh7KmRd8uKa/PrCAJXm3m1JnpyYsnlCa57TCBiGfZ9mUq5Dyr26zg9pXnL5/TuDXj6LHPY6r5IfL6gjsjhlRwMWBE67Lc3mNufxmh89bgZd6x8/c3uWjJjBRR5Nlp0jHwit5wb2xZroROZkdBaYGBZ7qB87vyddVTMHtB9TAC3qY9OdGnL59SwgRGE30ilvtP76tcJ4yonqSJkqo2lE4zO1HlwpWD87qCeyKhXcEF0S4qLVgMQW9Y0nB4F0bWiJOQ7Q6lxSSyvRBcwIfyv1EotJ5VjT7kGwWjXGcp1uNtxV64hW8YRrEQBIqkBEcplrsYHTqHyTshgXeo5UwjG0xxaVapmeEyVBHDG91zfE+k74cQuYghFdyQL7nVxXYaxj5uh4ERumbyt9MdkPVzll91+VnN6/Fg73oaF0sZ0YX7ZTHsuwmPYqvGLTGu4Y3maptTPVyd5pWa08qdK0M8j1mcQ5cNVSPGooJ7T+6psLMZWdJkUV6rK+AX4Ednb5yly3cu2/XmhZC4xrdTEMJEQW5x6c1VuvAwL3PudDXnSduzwfqIjdgTHlaelj3DyASFTgjl7TdYlp+LpzBc2SiHl4uPTXAGhE6hAnSP7tHw5cMpa+qsdOfxHZVP7fVpL8qbJWrmv2ZKnYku3r5IGVMZ5b5JKQ0hXxsa/178l3xe+SjJP7NthZfXb17TrM2zlE4xBC2gXYwhAI3KN6KPS34coa8VUxFDKrgp0Ar9gYiecYESDKTWF5iIly1kDzQ+n+rtWfc2skE+1J56TC4O2xoXw7g6l1bgpsX1QQsJDOkXTb6gOw/uUMokKSPcCIVG/bL1lVRfhQIVVJGTHv/X/rTr2C5a0m+J6d9vO7yNslfPTscuH6OkXklp+5Ht6u+GNx+ujs9Z4HkOWTaEPi7xMQ1sNDD4eRRrTVo7ifxe+1HDcphwJNhDDKng5iQzKd5JZSeEGhVAo9gM6Bg7fxGMDtzFiOpJnig5pUoUPe839H/RD4pw8oBGA9TAbnD70W3VvtPp406mKks///2zauvJTtmpXY125Eme1KRiE+XFDvxmIC3stZC84ns5PXEG49mg9mSbvx3abCh1nNGR6paqK8VQoSCGVBAinPpcYJSSK3b1PaL32GN1T0MqOA5aXOZumUtX7lyhxN6JqdvsbvQ68LXyJNMlT0c9P+lJebLkMfxbKDf9+MePtLjfYjqrWr7ekStjLvq0zKfKW8X80tAm5azbu45e+L5QykgwmHVK1Qk2jP87+D9qXxORmpDAg65YqCIdOntIedSCOWJIBSHc+HJoGQZTf8eO8XM1WFyiE+dD03IrzAQON7sX7uiJRie3H9+m3vN6qxBs0dyoxg7i1NVTNPXHqTS4yWC74vHIUULE3kgGEdQuXlt5pfYM6fwt89VouX4N+1G6FOnIP8Cffj78sxKDmNF1BiX0SqgMbOqkqU23AaP//BVSJ4I9pGpXiIE84urYxqzJu57F3yMKtNC0Z8P4Nb8OjCYuOKh27MlVxcQVtz9xmHkQqzNFTaFLRCFGNGws/XUp9ZjTQ4Vy9UYUoJinb4O+9M0vUN8yxzfAV+kKm4GQrr3KWqgmoVcWhhxGVPsb5Ds7f9yZpm8IuplD8dLRS0fthn5tZ5oKIRFDKsQw/uRezELcRrKYDdynBu0y4TWiaFMZyQZ6LhvKJjx8HJq+2XgfbKfX/MwTa4SYCsKoaCOBl1c6L4a5h6RIriLK47RH7gy5VYGRGfBss6Uzb6tZu28tda5tNJKQ6MP3PqRbj26pkXSflf+MVmxfYaj/iyrnmw9v0nuZ3rO7r4IYUiFG8Zw9xI1cnRuHK3ghCziD22WcZSxXA8Mw6sFFsyt7wmY9iggBp2djLMQ0YIx+OfILda/XXU2Wsa3S1YNRcMiDmi738qb7T+7TPxf+MXydBVsXqF5ZeKUIz2KyjR4YydTJzEO2OdPnpLtP7qrQLQqY+szvQ+dunAve/u/HflcFUiOaQ1VMCA3JkQoxiO85dBqkZ2pNHpYHvBSKhGBolbjIieYwWV6HRRRS2wkla60vtobYNTm+4KqLz3F1HdAvitAtDCjaRsxGvcH4oVfTLP8Jvl79NbWt0Za+//176lSiEz17+YxSJExBRy8cpWkbp1Gb6m1UIRFmrcIYQhIR2/y8yudUvkB5emt5q54zqwiGulMS76D2q+rFqtP7md+nVb+volsPb6n9K5W3FM3pMSd4HcE+YkiFGMRhg5CqnkpEdMTAkCKs9QsR7WGj8bFOyEEPiobM7/KDPGAPHlJuVpV7jmUChZgGDKdWDVs2X1k18aVWiVoh1ttzYo8Kr5px88FNNcC8donaVO3Daqpqd8qPU+jFixeqyjdHuhx08MxB1UqzsPdCihc3aL4uDClaalBgVCF/Bdp5dKfahi3wYGGY9R4r1JeGNcM8XCE8SGhXiEEkZI/RDCyzFSq/zp4kCmp6cBj4L25hsdXzTc49oPaqeDF1ZrbJ8rusahS6ko3gfuTMkJNOXD4RPOoNrSVbDm4JHteGkOnPh36mNXvWmLacgN+P/656N4HmtUKlaW7PuUqVqWnFpqq/FCFkzYgCVOGObjVa6ftCYAEzW09fO221bR9fHxq8ZHCUSSTGFsQjFdwUGMWlrK8blw1kCZbn0wTjbdlGRKt0v7/lwqElNsYNd+anOEy8Rvd8XJb422cyhHsJbw9e7xwbz/ckC89jyLjrV+gGUiBZX4KF0EBeFIVE/zvwP/qkzCc0s9tMWrtnLfWY24PixolL1x5cUyL1s7vNDiGkgHAqKnURioVknzZuzYgEXgkoVwbj9AQML14Dgv0zu8ykyesn07NXz+i9jO/Rg2cPlEQijGihnCjGEyIKMaSCGwLPri0P8cbDwmPX0FJwlUO4mBqjZyoRVbDxSDHgu6aJhwgJwoxsUPFvjRFcnfuAK4HxFXrFr411F/FyVAwTt8GgECQ3G37MVBXckSt3r6g8IlSJ4F3mz5ZfFfzoZ5p2rduVxq4aq/KXDcs3VAIIKZKkUOIKX7b4UuUebQ3oun3raPs/25UKEno9YVChXmQW/kXIFsPLzciYOiNdvXs1SG+47dcqlHv74W31e4aUMtM2MhBDKkQxb7ldZA3nJuNwH2bDMBS1aJ5dLt4OpnyUYwO6jUO0ubly9yVPhMG/e9lsZy+Hcs2ow96n3pAidLuO+0MbEVE8PqYmvH0tW/IZ7wv27V34TXBP9p7YS+v3rVfDw9EOAgMIYzl42WAa3HhwsEIRpPUwdBwTVLYe2qqMWJ7MeWhBrwWqEteW8WvGU9Y0WWlxn8XBhUHIcfZd0FeFhT8ubS0a/+DpA9r17y7q8QnOcWPO3zhPuTPh/A8CBUNmCkpCxCCGVIhCAnmEWWH2zhKzoVvOntsKB07Ju7xOLhamP8z9mZoB68nPfcXeHzzQbjpBez2eLNdnRoCJcffmsC8eMQMRXTAHecUVO1coY6flJFGZC48R4dM+C/vQsv7LrNpdkC/tXd9+uxXylwjjtqpmXSAHzxSv1WBMA3rh/4IKflpQiSv8/s/vSn93bJuxNHvzbKpcqHKIyl+0vew9uZdaV4dYiBBVSLGREIUs5fBqXzaixH2eMH64817gwDYu8Ri1QM6HTjU4jZErrcuGEF6qmbB3PfYuzdgAMTYHj02IqWw5tEUN59YX9mggXFoqTyn6+9zfoW7n4bOHSino2v1rqmp22IphqsXFCORQUXH7yg9pA6KZm2ZSwgQJaXHfxSqkXK90PRq6fKiad6oBL7jfon7U89OeyjMWog7xSIUoZAsRbTJZ1pRzjrbhV1tQsn+bvc7ydu4FW7EniupbM0qxIYZEWjGbZcif4sKZNZT9EWI6F29dpI61zYfBF8xZUOU0bfOfGhjcjZFk8B7Rr3n38V3VvgKtXXuzSDH0O1FC3GgSjfp8lJr+ooGq3qxps9K0DdOUx4ycLfKf0PDNkd6sz1mILMSQClGIl51TzpNDpuZqL0Hk4V7MR6wUZEbiUEaZAQ/2knvwKLZ6LKQAY4+qyZkU05GQbujA4CE3aVaoAwWiZImNRvmR8jwHLRlEo1uPVr2aGlAiajGxhRJxMBsyfv7meapXrh69UROEQgJR+8kdJ4fpWBBORugXhrf0B6Xpw9wf2lVgEhxDQrtCFOLvwHJHTsnBXB273846h4gonwPbSsaKSH2I6AZX447B7Aw7IWEhNoEwKiprjUDR0a9HfqXKhSsHP3f13lVVpfvT/p9o2fZl1Kl2JysjChIlSKR6QxGyNTPAF25diDDBeHitfRb0Ub2tEIvA/h44fYC6zu6qQs6Cc4hHKkQhH3AYFT+Ps9EswiPI/rMjvWdLeT51Ebo9xtvQg/zpZO7p7MMFStm4QjcPL4fown0ieo+Ln3DBkikXQkiyp8uu8pMQUoAYgubBQWhhxsYZVLFgxaCRZK9e0KjvRlEi70TqOagTQcUI016MKJijID158URV7vao14OSJQryas/fOk8jV45UXmxEASlBiEAUzolzPQjkWmH0oamL3Kt4puFHDKkQhfTllhJU01bkode4WJRkw4bqXUeBSPxuzq2i1eRzDg0f5pmf8Vj2DznXnAhqsXH1ZM+zCrJQ3D4zgpfBwMd8XC2cC2m9A2cPqFwkRodVK1qNkiR0LY3XoU2HqsrdDjM6KC8R1bYwQugVrVOyjvJMBy0dpIZ1w0AB9JumTZ7WroHKly2fCtGOWDlCvQ8wzpj/CdUitM1AGCMi+l/RAqM3ovqbBFQfHzx7kMp8YNt7LTiKGFIhChlHRENstGYhpjCFx56FVawgORcwbea2GuQ383JlME5tfftBAc6H9uKcaJAEG1Ez1FNycdJSFmEQooqzN87SxLUTqUy+MlQ0V1GlvjN46WAq/n5xuzJ6UQ2MYbsa7ah1tdZqtJinhydlSp0p2EgePn9YGVA8MAt0ya9LKH2K9PTU56nqC0VLiy0wvjce3FAzQzVJQLwfGMj9UUEj5azwsf/0fnVzYgZE6zf+tVEMqROIIRWiCG3+oq1guwcPvG7GBURmYu9mxGcxhOKc1zzDLTINOXSb1mb9r9noaoZUqwQeyyIPMPZCVIDc3KR1k5RkHtpINODhzflpjsozNqoA0QvXAW0ltvlOAJGE5pWbqzYYqB/N7DpTCTD8dfov+mbbNzSoCc5xa3Yc3aG8UBQcofgHudYrd67Q+HbjI3SfPciDLOqG1Rgsk7Cuc0ixkRBFrGfRBTNgYP8Xzm3vYLUjNKH/yHnYivx6yL3qMa6uDDLEtusKkcmavWuoe93uVkZUAznDbYe32Z3Z6UogJIvJLzCakzpMClYxKpevnCosGv39aBXqBcilLt+xXAnYd/64M20/sl3N/6xetLoSpjd6P5yhbP6ySlbQDLx+RHrAsZEY5ZGOGjWKRo+2TtDnyZOHzp49G237JOhHkNnzNrHsZji268NDu2GEtYZ53F1/xOHcz1n5SH/HbTYrVKT8ohJMMIHBNAI9l8gRQrzAHfoiS7xfQoVHs6XLFkJwHnJ+J66cUF422k+geoRQLsLE8HDRhhKZIA/qF+BHRy4coeLv4YbxHeh/xb4hJyuEnxhlSEH+/Pnpt98gYB5E3Lgx7hDdFFTW/mlnqPYungcK/du37E0idxmayDZkAjuZGMGUrMH7J+dNiUO/IUNzQcO2bUesxTxcrdDIXkgRRgYhT3egWrFq9M34b6hqkaqGy1FQhAcmwczoihu/qAMTXz4r/xkt/WWp8oIxYg26vruO7VJqSBPaT5DQrpPEOCsDw5k+ffro3g1BcZnHlsEg5mCxeuS8gtRa3nGfK2x387IA/r0T94vaK0JCOHagneUleR0Y0ifcMgNNXz0WLoKK2fqkrmZEc6bPSaeunqIC2fVDAd4V4py+ftq0dcTVgGEa2XIkTf1xqql3d/3+dUqVNCgqgykvG/dvpD3H96i/RR4TXmrN4jUjzKhBPhA5aNyQ5M6YW1UQo9IYuVkMBa/xYQ1l3AXniXGG9MKFC5QxY0ZKkCABlSlThiZMmEBZs5rLvPn7+6uHxvPnz9VPb+/X5OFhFgJ0XbDf+p/Rg4WLdyBu0JYN4XH2+GqzoHwF9jx/ZZm+lVw49Jq8vYOUXLy9p3FrymI7r4ULE3JP6UyWYxD3Wa7Wvc4tLyhKas//PsFFRmiPWcbau1/a2Z47fQ7WhKWVQls3ItovzGhepTlNXDeRJneaTF5xrcUvNu3fRBUKVSAPT49w70NUHIOeAjkLULYM2ej0zdMqLG3Lst+WUdPKTcknwIeGLB2itHRn9ZxF8Tzj0Sv/V6q4asyaMTS82fBgYxreY4DI/Zfff0kjWoygTKkyWQkzDF8xnD6r+JmaURoV701gFH8OEcXbUFXW3uFhwa1fDOGXX34hHx8flRe9c+eOypfeunWLTp06RUmSJHE4rwpWr15NCRPG/FCfIAiCEJJXr15RixYt6NmzZ5Q0adLYY0htefr0KWXLlo2mT59OHTp0cNgjzZIlC6VIcYf8/MLaihH9wANatmwntW9fnXx9o6N4RpvNuc6kKPwaix8gJ9mfc5i4+37Nw7D3kbf3HFq2bBcfAyTUavHEFyO+5/BvRw7NamGxQBZmSMch4uacTzWjDQ/nhlf8DxcojXTjzyEkxxdg6LljwHs4l/wc5Xmax0osPTJAIQzCjSh8QcVqreK1rLyo8BKVx6DH77Ufbft7G/156k/lWUJconGFxqrH9M3bN9R/YX+a3X228d8G+NGw5cNoepfpVsewbdk2uvXgFsXxiKNUlJpVbEb5swcJP2j4+PnQj/t+pH8v/auE8lcPWW0aJv5iyRdqHFuCeNaFUZFBYDR9Ds4C771Yt2IOGdIYF9rVkzx5cnr//ffp4sWLput4eXmphy24+Pn5ucYFMDxg/6PnAn6HQ6ZmOrW5OYw610baD/vajvV2kUtNzMdwhYiQ8zY6li0cmoUqEgo4GvA2fbl4CdvrrnvdkwZTXohD0B663G1p7ieN58afQ0jCcxHD30T2xS9R/ETUoDQ+u8ghKo5BT6J4iahxucbqYaShmyJhCtP9wXsR+DowePmJyycobrG49FnZzyh3+tzBuc/pG6dTufzl6JPSn6jnHr94TP0X9VciFu2qtaNe83tRXA/zy7t3XG8K8A9Q+xpVeEbx5+AsccLQHRqj+0gR5r106RJlyBBa5acQccRzUJzeVh9X7xlq/aQQaHhqR20IudOJLPs3kA0wvNIeXOSEnKgGNHeH4TJks41XrMmLPlQ9XjGqyMjVCo0iA/ScQtt2wKIBqjp2zGoMH3AtEnklUkpH9o4BGr3avxf8HDSjV98ClC5FOprQboKqwNW2hSInKCShHxStQ/BcX/ii5SwkCELeeXyHkiU066kWwkqMMqQDBw6kvXv30tWrV2n//v3UoEED8vT0pObNEdYToobUbKzMjOm/obSZoJFdu2uFgRtlst4jDtvqDR7uwPNwiw0qQeHNamRgL7g7DxKfwVNkmrIR1Rv21w7cDLgHscGAaoIIX3zzhRo9NvLzkTSv5zzqVAshfaIFWx0ZGB81YGB3Yu/EdOshiuBCsvfE3uC5ptAfLp3XuMcUxrJllZa0ef9mZTARhnwvEwYwBAEN4GW/onguJBBnQD8ptiFEDDHqnbx586Yymig2atKkCaVKlYoOHjxIadKkie5di2V0ZuNkW/X2nKtwMSvUDBgx7eIPb9M6D/SOtw5kJmzzQwiNbeAc7lruW/XiCTL6u/fviCgoZCZELmjHUNNP5vZQj7V715KvP0LzYWPJL0tULyeUgrQpKtr8UPSiwkC5ChC2R+WspnSkcfLqSfru9++oWaVmwe0y72V+ZxxtQWUw1sGgcIgu6MGYNORjJ6yZoELBwYpK25crxSiMdhMijhiVI12zBpM8hOgF4dQcXBz0CWveZmFP9HfOPS5EoxKPMLNlLec2QVBOyNzzxRSXNyanMbxRoxuoXzkkDO+0OP89cq3IZ81kucGjLGAvRCa/HP5F6ctqk04wUQVFR93mdKOpnaZS6mT4jEMHIdC/z/9NXeogghGSNtXa0JcrvqSKhSAbGblotZv2ekGzpMmiRqRhBJv/a3/V03n78W1l+Gd2makkBUHqpKnpzqM7lFwNZwgJDDHWSZEkBd17apuyIBrw2QA6evEoTdswjc7dPKf6dpWiUvUgRSUh4ohRhlSITuDdodIQPbupuQgoJ4dxr7AwArxUDw7XtuPwquZx4gK0jYg2skf4Tp3KGA/2LKdxiFbPG/Z8IbKgB/mkOTwtJp7uK9CAjWpd7iFFhfe3RHSE978R77/7qL+4ekgXObotB7fQ3B5zg0OM8eLGU4L1qG79+oevlfC7Izx79YwypsxoarxQNWt5G7nNCQiXbvhzgzJQCDPjNTvU7EAfZDUezQfh+ymdptBLv5dqbFrKxClV2FcP8p19F/elD+oab+OHPT9Qn/p9lDF95ffKcMpMsdzF1Hi68vnL0ydlJMoSWYghFSKAXTxLFN5kkFh3EH+wsVxnc6plYnWhiWxkEYp7DHltdPDqcqSh0ZqnubRm45eVw7RLOL9q2zKzkoUZjKpo4TVXYyMMw9qCPWvcIPTj57HP+cLx/gi2rNu7jjrW7miYp1ODtL0SqjwiRpWFhnd8b2WM7HmJrwMjTxhj0bZF9NL3Jc3oMiNYrB7tJ2NXjVV5THtauvA+NQ/UFq94XlSvdD31b98AX0ocPyglAs992fZllDl15uD3p1f9XipHjCKkNMnfRWLQgoMCrFndZkXoMQvWiCEVnMTCnuhGg0pXqBed4tCpbXsDKnFn898H2Pytoxc9D/Ygb7EB/pnDwQjxGwlwwEu2N+OyMKscLeeCJOR6+/LX5Bj/G8/Lnb2zXLh1gXrX18+LtaZIziJ0/tZ5hwwpROJhkB89fxQswafn2KVjhjKEEcHNBzeVxwfvUg/k+BCe7jqnK5XMUzLchT01P6xJp+m06i2Nh//ixlMzWxGirV+2fvB6GDY+pOkQNdsVNw3JEydXod98WfMpA4+/EyIPMaSCk0AEvoCddhEMzEZhQwM7xtDZVpNMBu0rRiRl3V+z5mrkRwdxmwzyuPoCjiKs//sp95nazjkVwkLCBAmVjJ3RwGutLzJnRqQGHKNrna7K2EzrPE1VxepZ/MtimtgW0Y+IA/2dmPaCySmjPjeuLEeoFsOyMaPU2QkvMIZ+fn4UGBiowsZGYWzo6U7rMk3JDSLUC2MKHV8h8olRVbtCdPA0FF1aXNRcRW8WbVDGLQFBnvEJ9mhxPNZVkEF4cpgXHqvgDAhZQlvWXvFQ0VxFHd5e3ix51Ui2AYsHqJDq0l+X0lffQdOZ6KvPv6KUSTAJyHmwbyNWjKDt/2ynDrU6qJYTe2PeEKa+++RuhLw2QsBQfgpN1B5hcRRqiRGNOsSQCk7yHoc9zTgXyvSWqOIaG/2jHAK2baVBcVIqPpYydrZTng2u4AwYeH380nE6dPaQ1fNoVRmzaowa+xXWytJCOQvRoj6LqFW1VlQkV5HgWafpU0TcNKil25eq2aNfNP5CVd9CyvDyXfPCriv3rgS34QgxF7llEZwEhQ2eHOL9wMDLm4RCfIo+brLqEVoI4OGU4KrhyTyJ5iVX57bkEPVRLjwyA3KCogjjLMgZTu08VbVmrNy5kgrmKKj6HM/ePEtNP2qqxomFF3iBeEDj9aEK5UcMMPIHzxykJf1QzBYEDD7k+hBSNtLN3f/ffpW3PbvzrPKakS+V2Z8xDzGkQgSAQovPWTgeOUQUNkAgfTwbLjNRhcjmKVfzLrYZ5j2Ci46Qn/uYiEZzcCaAw7YnuLrX6IKH5aKUFRGgSAiydlDlgVeH6lvk+SLb0EAJ6I+Tf6hcYsHsBSlPlpAjz4xAJW62dNms9g/FUB9k+YCmrJ+i+mG1Cly09wxbNkzlNCELmCV1Fvrnwj+08OeFylOGUbXN5QruixhSIQJIzVW7mMTSjI1SSh6i7XieK+JZyhNm9EaU2NAvYoOob+KPzy0yTVhKcJ5N9mMHC+7DEAsRBQxKVAyYRhvMop8XKQWh6sWqq7wpCoZuPLihZAVDCwHD8KOIxxa08ew+tlu1n6BICspKUGeCyMTwFsODvdmTV06qfVi+Y7nKXyLfir8tnBPV4oI7I4ZUiCAScrsIHq7CH1wcZLa/KCpCIYj+ApqB/w6huo+IqBTfFPzDhUjoJZXQnDsCA4ZKWujwalQpUkW1sGDQ9qLei0KIIuhBhTFED2AktX5RjcpFKlOSRElUKLfHJz2o08xONKz5sGADPnz5cCqbrywtH7A82KN9/vI5DV85nD6v8nmwvq7gnkixkRDD7xPtneIpOEdqxAA2qAgB12S1pckxaipMbAJSfAdOH6B2NTT5yXdkTpNZ9WT+cgTtTfZpW6OtqgaGepGeh88f0tyf5lKLKi1Ujyw8bM1gHjl/hDKmyqiUhfRhYVTgTu04lb755ZtgaUHBPRGPVIjBJGc9XrPiof9CKSzyiMb8rnPkapvTLaQCI0q/NjSOXz5ut5ezdvHaati1XuTACPSFwiPtPKuz8iLTJ09P/13/j67du6babCDXB5F4fesJwsf9GyLFEBJ4wMiXnrl+hvJlE9Usd0UMqRCD6coi+RijZXsR/pmHfCMvKrgiv/37G63ft57iecZTk0zQH9muZjtVIBRWoPYDyT0zoPyDPKYjIBxcqVAlVTz02OcxNarQSCkLaaBgaubGdzrBkC/Uy/bZgvaYRy8wFlBwV8SQCjGY4tzO0oZbYFDQgnaIJVyZi8KimI3mmbqbd4pw57OXz5RwvZaPRPh0zPdjqMlHTah8AfTzOk6BbAVozZ419HlVVJeH5K/Tf4VJAALtOyXyoCI9JDDYhXMVpv8d+J8K5yKse/H2RWVgjTh19VSYj0dwLSRHKsRwUPw0kiuKG7GUIC6Aq0zE64XoBhqxZ6+fpYGNBloV9SBsCk3bJb8ucdh71EAlbdpkaZXBtAWhWhQifVbhM4ooutftTv9e+pcGfjOQCucoTIu3oQXLuKXm4bOHahqM4L6IIRViAbm5UOhH7gOtGisrb+Gd6j1UVwU5xZZVIZBh7O1VLFhRFQ6FlUGNB9GP+35UIhCX71xWHu7WQ1vV/NNen/Qy1f0ND/BYkTNtVbWV0vqFwUSREtpjtLwvQsMdZ3SkL1ti8ILgzkhoVxAEl+Lu47uRol+Lwp4ZXWcowfl1+9YpTxRTYRb0WhCinSWiQI/osGbDVKgacoIITaOCGLlVXz9fmtNjDmVNi/F/gjsjhlQQYhnwSl05X4qc4qU7l6j4e8hxhwTLPshmPOzaUU1ePKIKVAtDPWnDHxvoreWtEqBo/FFjqlioYpj1hAXXRAypIMRCXLk9pkG5Bmqu5oe5PwzR8uIf4E9//vcnta9pb66s64GwMVSMwgJaYq49vEaZqmRSKkiOGl14vL8c/oV2/btLGe40ydJQ80rNHZZCFMKO5EgFQXAp0A5SNHdRmrB2gtLF1YB+LcakYfZoeAdlRycwhqGB3Onm/ZupwegGNHn9ZKW6BHot6EW7j+8O9e8h/N9jbg9VjIXB4lBxgogEiqlW714dIcchhEQ8UkGIxbiqZ9q2elvad3Kfku4DMAyovO31aS+38qwePX9Ei7Ytoit3rqhCKd8AXyXkAOMWP651D/ODpw9o8NLBasbp2DZjlXrSjmPQdyYa33Y8jVs1ToniY4KMGZPWTaK+Dfqq3K8+pzyh/QQ1+Pxc7nNu9f65C2JIBUFwST4q+JF6uCtoa0H7C2aX5s+WP9jj3Hl0J/Vf2J+md50ebEzhrQ5ZNoS+avWVVStMwVwF6TSdpqk/TqUxrccoYXwzQwrtXog/6I2oBkLkXep0oRU7VtCoVhgjKEQk7hcfEQQh1rbGuBOzNs+iES1GBBtRzaDV+LAGVS1alTb/tTn4+QNnDihP1ayfFBXHaNdBdbE+3K3n6v2rlC+rucwgPNMHzzBPV4hoxJAKgsKfiDYQ0QwiWoexzNG9Q4Ibg6IohHXN1IzqlKpDvx//Pfh3FFBhtJsZkCTcf3q/kklEMZERSbyTBPepmu1THA+55EcG8q4KgtLd/ZQHgSNs5ktEDdmwxi7EM40YEGJNmzyt6XKEdMPa+mJ5a6Fbj25RysQY62fscWJAupmh3fr3VqpWtFqYXlNwDDGkQiwHc0ZRzbiFiDoQUTnW5t3CBvZPio0U7pY9unfBrcGINHuiETB2eplDaO3u+CeosMgIeK8wzlB1MqtYRtgYov7Dlg0LYUwxzPzXw79SrRK1wnU8gn2k2EgIB8+JCCGktDwg24g7vA5UW5KQ6zKLh3jb6u7CW5hKRN1wmaPYCgzqqQUYRSeEBVTopkuejs7dMK6ShaC93jss+0FZWrF9hcqfGqk6Xbh5gTwyeNDIltCNNqdcvnJEFlItMNhOyiQp6fT105Q8UXJV3GRvAo4QfsSQCmHgEhGh4g930hlR3sDG9Gsi0sJNR3l0GX7PQERniSgVEU3g+aCuhg8RpTdZhmPAAGdLrNTmFZyjd/3equ8VP4vlLhbcxvPz3z/TH6f+oOldplt5kxM6TKARK0aokWwY1Yb2l40HNlLzPs1p4GcDqViuoG2ERrn85ahsvrJ09d5VVZjUsnJL5SELkYcYUsFBrhBRDx5Blln3/HEiasEFOjC0Y3g8WTLdOsd4nfVElIhcCw8HloshFUIS8CZA/bTtB9VIlTSVGgO3bPsyWrBlAcWPF1+FXCsUqEDTOk+zGv6tTbeB7i+GkB86d4g8PTxVqNaP/NRYtrAAw2xPr1iIWMSQCg4CL3OhjREF+IIP4GWHebqK3oiCIpx//JZDpa5EPA5BGxVwvGAjGrtLCdx1pmlkAaEIqARhGDh4/ea1kuCDdq4tyRMnp/6f9Q+TASySq4h6gEAKVH2kgmsjhlRwANx5P0FdoMnyapxnRCjXbBQVqmKbuKAh7U5Ew4hogY3XCQM6kueZCkIQ6/etpzM3zqiwLFpRwCv/VzR9w3S69+QeNamIc1yIbcTuW23BQV7aMZDEBgiPtKHcs7nipAso55QlovpoECCii0T0Cw8Bz0tENaN7B12K2Nweg3zjb//+Rl+2+DLYiAL8e3jz4bTr2C6ldSvEPsQjFRwgGVfhmuUK0XeJPNF5O9uAokqCCNqfU0S0mIhuc861JXvF4b0vbE1E9YhoDRH9RUSZePsokhKEINA+0qBsgxATaQCea1i+IW07vI2aVmxqdzt+AX605/geNZMUgg0l3i9huE3BfQjTlef48eP09ddf0/z58+nhw4dWy54/f07t27vXaCMhLKcJZkPuNFkOo9OciHKwITICFYoRcX4ghDwb8zCI6EdWIkIxUytkq5zYbgoOO6O6uKcYUQc909jknd57eo+ypM1iujxrmqx0/+n9UEPDvef3Jh8/H8qSJgv9c+Ef6jCjA529gep2IcYb0h07dlDJkiVpzZo1NGnSJMqbNy/t3v1urI+vry+tXIlqTSFmMoTziN9xzhQgjDUFkxOJCHfhY4loMhcVaQ3haEofxN5oZSf34Q8iusmG+z1+LiVvvzERTXRy+4JgTubUmeniLYT+jblw+4Jax4ztR7ardRb1WUSNKjRS1bvd6najmV1m0pT1U0I1wkIMMKSjRo2igQMH0qlTp+jq1as0aNAg+uSTT+jXX3+N3D0UXARvIlrLfZXN2HB1ZIOmFeok4jYY4nBrIzbACJt+FQH7AAM61E4x00HePyEqiS1eaa3iteingz+p/k5b8BzmiGIdIzD1Zf0f6+mLRl+ECOOixxPj4WReaCzIkf7333/03XffBecDYEgzZ85MjRo1Ul5qiRIlInM/BZcAedB2/DDDi3OOeEQ0fnYKmnBxgkD4Pc5xClFtTGNqa4yvv69qeXn28hmVylOKBn0ziAY3HUzpUqRTy1GtiyHcaIHBdBazsDC8Va1lxpbCOQvTwp/RQibEaEPq5eVFT59C1PsdLVq0ULqPTZs2pWnTkLtyDebNm0dTpkyhu3fvUuHChWnOnDkqLC24O69DEUd4akeyUBDCzg97flCFQZjMkjZFWqWfe/fpXeo2u5sSZMD1D4IMnet0tju95c2bN6ZGVHNOZDJLLDCkRYoUUTnRDz/80Or5Zs2aqbBFmzYQ+o5+1q5dS/3796eFCxdSqVKlaObMmVSzZk06d+4cpU1rrz1DcG3uc5UuRk9VNVj+zIE2HSEyiWnCDVsObqGbD27Swt4Lg8OxGGf2+YvPlSGd32s+pU+Zns5cP0Pzt8ynwMBAql2ituG2MqTKQJfuXFLXSqMK3ct3LtudFiO4Ng7fAnXr1o1u3bpluKx58+a0YsUK+uij6J9mP336dOrUqRO1a9eO8uXLpwxqwoQJadmyZdG9a0K4ecsVv3O5mOmaQcgXwglfRNP+CTENGLxN+zcpVSJbwwch+L4N+6qcKPgg6wc0q9ssZXjvPEabWEgwMg2TW+DhGuVXZ26aSa2qovJciNEeaYMGDdTDDIR58dD44YcfVDFSokRRp60aEBBA//zzDw0d+q4gBaGXatWq0YEDBwz/xt/fXz30bTzA2/s1eXg4004RPWC/9T/dkZDHoHmhRbngaARPlMnPXuourtyFqPdrN/0c0MKzlD1rDAVAzUGbaPOwnT2PCnQLahM5vgCDDaIHyOvpf4aFG49uUN5seZWrYfT3JfKWoB/2/fBuWRyiTnU60Y/7f6TudaGWFZKW1VrS9I3TadTqUdSwXENKkyyN8mZRhNS4QmPKnjF7iNdy5hhchUA3PYa36gbeMTwsuPWKBJImTUrHjh2jnDmjrqLv9u3blClTJtq/fz+VKVMm+HkURu3du5cOHTpkWI08evToEM+vXr1aebKCIAhC7OPVq1fKOXz27JmyZ9GibBRJ9jnCgfeKnKreI82SJQv16FGZ/PzcrykfHsSyZTupffvq5OtrXtzgXscwGEOp7FTjbmMtYLTcRDWBnCHxCOfncJXH0H1jUEQFbxvRlZVufx5Fh2cKD+hc8nOU52ke8gyjPOWpq6do3A/j1MDt4u8Xp5Lvl7QK8V64dUHNFB3QCAMbgsDg7QlrJlC1YtWUkD0qcTOkzBBtx+AqBLrpMfj4YsRiLJQITJ06NXl6etK9e2iBeAd+T58+vWk1Mh624OLh5+eehkjbf3c1pCGPoSIbS+OQGdHPRDTcYDh3ZBrPhbxPiThHm5kNfrYwfg6YltOXW4tsycaCE9dRykPufB5F5wUUr+3o67/0e0nDlg+jTKky0cjmI1Wl7Z4Te2jpz0vp67ZfK8MIIzl301wa2Hig1XbX/r6Wrt+9Ti98XlCC+Alo/k/z1boYxp0kYZIoOwZXxdPNjiFOGIT/YpQhjR8/vqoq3rVrF9WvDxFyordv36rfe/aE7JvgntQhorpE9InBGDfkvt+EMGCRx1sWooBK0/90QvwXiKgryxdqqkuOAH3ioJFZQRxBAxf8G271gZH+LdoMaWxj9PejqV2NdsFjzECezHmoXql6NHjpYGpRqQUt+XUJda/XnbKnezcN6e9zf9Pu47vph6E/qLoMUKdkHeXZDvxmoFXlrxDziFGGFCBMi1ac4sWLq95RtL+8fPlSVfEK7kpcLjJCZW4VIvqYDQyUlm7wsqhiGxc52QpOvKfzLiF+7ygoJkIEJT0PPod3PZ6IMvLyS7zNPGi+iMDjEE5cOUFr9qxRQgsAxhM9oXojqpExVUYq+0FZOnDmALWs0pK+2/Ud/XvpX0rinYSOXTpGtx7eojXD1gQbUY0C2QtQ0VxF1d+VzYcpQ0JMJMYZUohDPHjwgEaOHKkEGdD/ChnDdOmCVEgEdyUrjzlDhe5qDoU240reqGQ1SyIaAWOYkA2j0aBwI1pxfrQ3axSjpUIf/srFBhYSiOVj4lc2Wlj661K6fv869fykpzKS/q/9lTdavai5qELdUnVp5W8rqUG5BlS/bH06f+u8Uj1KnTS1MqBmqkaflPmEvvnlGzGkMZgwS2noheptWbRoUfC/s2XLRvHiRU+ODmHca9euqbYWVOpCmEGIKadrdS7OGWlgRCFov5+IrkTiPgTwWDkzcrBQv6NU4BmowzlkbJRDSsCG1J6u9Utun4FB/hKinmHYh9jFicsnlBEd3Xq0MqLAK54XFctdjN5azFseAi2BwUWUCNMi5AvvNa5nXPKOb2xEAQwsVJBcBeyLf8C7lj8hGgxprVq16IsvvqDXr9/1l2GkWr169WjIEAiUBwFxe1S/CkLkc0k3fQbC9ZCrbEhEJyPhtWDUHttZflEXlnUEDzaAyLEWtLNeAejfmCzbS0SfoemMhwRgWMASztm6V+9eVABRhC51uoR4Hh7jzqNmowKJth7cqmaJ2lI4V2Had2qf6d/9fux3KpIzZLg4qjl45iD1mNuDBiweQEOWDaFOMzqpGauC88QNj0faunVr2rlzp+q1vHLlCnXo0IHy5Mmj+kYFIWpBiwguihiooG81eMJhU8xBfT8CX68Vh3bhQdpygwuf0oRRGAJfw5rsSZv1XaN9xCg9cYtvHH7igQHEhhxzWjfwzcWoMOxLzOfFqxfBnqgePAfv8o+Tf1CFgogUvOPK3St07PIxJQNoS7a02ej01dN09vpZyps1b4jXWvX7KiVoH538cvgXJbw/scNEldcF8Ernb51PNx7eoE61O0Xr/sU6j7Rs2bLKYBYoUICKFSum1I769etHe/bsUeFcQYg6XnOeFIYkg0ERDwzeJDt/f4xbahrzdtY6YABrcAh5vm4uq7atjlwoFB6gCmYmY/mW86SoWrYFhVbDdEZUD7zUf7kwK3qA5q6r6e5ayGIaav2yxZdKrg8tMH+d/ouOnD+iZoXiMbH9RErolTBEjzzyqymTplSqRci9Yq7o85fPlWRgz3k9VfvL78ehzhU9YP+gnoT2Hc2IAq/4XtSvYT/679p/MgvVScI1buD8+fN05MgRNUYtbty4ShAeKhCCELWgbxN5+MImy5FaeMiGyJZZbGgHspFaxi0njTnfaC8UO4/7RxvxA/NWVxHRCifacNJzzhdzW/UXeexLLzb0Rnm4U0RkrwagFK8jaFQuXJl++fsX0zxo6mSpqWudrmqI9/HLx5UQPQTqUyVNpYySbRsLqn6zpMmiWlzey/QezflpDo1fM16FgfEc8qjROdkF02tqF6+t9H6NaFaxGf10ABENIcpCuxMnTqSvvvqKOnfurEaVXbx4kVq1akWFChWi77//3kqaT3BH3nC4ELlAV650vs+P0LRoE/Ex6QUPDnP/5rviuKBqW4S3PmAtX4RGzYjDOrgRPfFIM+qf8XEhjIgb1G7sCZuBGwWzRncUlbi3MEdEg57QHvN6UM6MOalg9nd5aVTgIodYo1gNypo2K7Wpbv35oic0Z4aQofekCZPSg2cPVOXuRwU/Ug89EGUwGgYeVcDbzJ0Js3qNwbEijytEoSGdNWsWbd68mWrXDhoXhBDv33//TcOGDaNKlSpZCcAL7kQgh0j3EFFe9oRQfQohC/OWgOjjNw51oh3F18RbwzE9M1ANWmQnb1ie86o4/qgbuPCOxvzwYYMdmt4zLtrbubfWFgvfNKDCWdCHNGd0mUFTfpxCi7ctpvczvU+Pnj9Sk1taV2tNK3eupGLvFVNVuRoXb1+kqT9Opamdp4bYHlSMkiVKpvKoOdKjatuabX9vU15wdJE5TWYlaVjmA2Mn59zNc2odIQoN6cmTJ5UUnx60ucA7rVsX6jOCe9KNG/4hCKCFrny5nQIXdfPJP9FDIBvPljxezWiE2hJWRTIaAG7vwpGfR7Xlo+gjsYPrteGK5eJEZDvPcgJXL7uPLFtUAcm+Ma3H0AvfF2rmKLzKTKmDtJwL5ShEszfPpvvP7lO65OmUR4ew7pSOU1TPqBG96/emIUuH0NBmQ4MNMHKpCKtu/2c7zehqL8IRuVQoUIE6zuhITT9qqm4i9ED5DaIUkzrYqyUQItyQ2hpRPRUrQhNVcD+OsIDAuzF4QXizpmw99v5c6YJcgQ3FQh6hNoSNfkbOi85hbwwiDkah0Nd2Qp4PQ+kVdSWSc84W81o/RDkg7/8Gfo9QACWYgeIbzBPVA6OJHlPkQ1E0BCNra4BsSZ8iPU3uOJkWbF2gDHNi78T07NUz+jD3h8r7RZ9qdIFKZOR8+y/uT0OaDlH5XPDw2UOatmEafVziY0qeGOeREF5EJkXg1pE+Jss8eRboHy4mUZedi3JwEzCFiP7iEOZTNpAX+TmjIo9a3C6CQiFbENK9Y2fSjCuC/NcWKL5izgrfBGBijHNC6c7iatW6ZsBzvHrvqmpVyZYumwrTAhi/NMnRyuQY8FY71OygipFQsBSdxtOW0h+UVjcIC39eqMLYKDzC/n1e9XMlRCE4hxhSgT0YeyICmpfnakAgvgPndJtxb+c2FpP/wWSiCvicjWheFjogXSi7Axf92OLPfZ4JuDLX1QTIPbhC1zVUvNzFiP556k9asXMF5cqQS7WwLN2+VHmTQ5oMcXhiCwwxRB4g5pA7Y24VLr1y7wrVL1NfyQO6CqgoHtd2XHTvRoxEDKnABuUohwWNOBoJFar2QO8j8WtCraediYFIwr2f8MS+Zw+1AhtTe2HohLz+IDaehbiw6iLnWlFwpIFqS1x8/uH1XnDFL3LKrnORFMJnRDcf2KxaWyBWr6/O7beoH83rOc8hr3LW5lmULGEyWtpvabBoPap05/5vrjLSbau3jdTjEKIfMaQC59cQ2i1j4GndY/k6vecWWVhYXOANG7OVbODm8ESV6Qb7F15PLCUXIz1miUG0m+Q22J9ObDDR36nxmo37Vs7RRl+PoBA+4EXCyNkaUW1iC3pHtx/ZHqpHiUrfmw9vUt9OmNBjnZfsU7+Paqf5rNxnTs8jFVwbuQIInA/8lMOaqFbVjMgf7BUiBxkVrGeDplcHQmHEZBaDRy43ooFBLWFgRMEhfm9sK5bj8aSWM6yMZK10I7g+yImiJ9TWiGrULVnXru6uxtZDW6lReaNce5Cwfb3S9WjH0R1O76/g2oghFXQasr04jNmY2yb2svEKy6BqZ1jNlbdGdGVDGxXAMD7i+aKd7Hx1GrIaEgyu4E6gsChVklSmy1Gla28SjMZTn6eULoW5cEna5GnVOkLMRgypoKMoa7fCYG1ihR/HqxYjJtOAYh4j4vOy0C9uzhnQRdx7OpQ1dfvyDYWZrF8lNriCq4RsbbVwjcieLrvSmDUD4gppkoV+7ufKmItOXjlpd3g41hFiNpIjFVyI0ATjA8J574cL61mW28tjR+wAhUY5uZVEK1byYS/5OffT6jnMHmvoIcDYQHRW6u45sYfW7l1LHuShPEnMB21Xqx15FDGurk6aKKnqDz159aSVTCCAIUY/aMfaoffgogez6+yuVKtErRBh4pd+L2nvib1KLUm/7Ut3LqllOdPnDFNl8PErx+m/q/9RwgQJlVKS1qYjRD9iSAUXIjMXNqFH1JZrBso9jvATe5m4WCZnEYcsnIfVywqe5hYX5Dz1JGYv/ROW4dMM7C1uh8GFWuTVopNvf/uWbj28RVM7TaVECYJkHR8+f0iTf5xMrYogZWHM4KaDqf/C/lSzeE2qW6qukvqDJwojit5KSAeGBv6m08edqM+CPtS3Qd9gVSN4onM2z6H+DfsHi8VDz/b737+nvJnzUrLEyWjJr0soReIUNLjJ4OD9NgPbz5UuF5XKW4qevnxKI1aMoDxZ8lCPej1CiOgLUY8YUsGFGMjavrahUujl9uCq3bAAdZ9d3FeqP9URqsUFdp3Ow/2W87BG4G+rYBov/9zJ+7KIDbJ1xaYQdTx4+oAOnz9Ms7vNtjIoEEcY02YMXaAL9DrwNXl6ehqqGs3rNU8Nt/5iyRdkeWtRQgodanWw0tkNjXL5ylHm1Jnp+13fK4OOMW3oS8XrZ0gZNN5v17+76Ld/f6NFvRdRvLjvFLWOXTpG/Rf1V602qPS1xTcA7VlEI1qMoAzJ340KrFOyjtIEXr5jObWviap7IToRQyq4EKjMncReYSsebXaevdSxYRzQjVzqUpswrUZF7gvdwSpHWptPVjvby8l9p/CWy7ERRetLrmjW5I3dbNq/iVpWbmnolcXzDDJYo78dTd3qdFNTTmxBOBYtLs4KJ2C49/Dmw03DsvBEv+n7TQhjiRFrmBaz69guqvkhhruHHMidt05eVbRkC6bTdJrRSakTmVUfC1GDFBsJLkYBViUiNlKd2KNEIVRY+Jt7Uc2EGdpyb6pGbpbXsycSUYAN9EGehVqFfwrRxd0ndw0nruhJkyKNmvIy7odxSnUoqjlz/YwSwjfyOEH9svWVwTRi/3/77W67RJ4SKowckcDwoxDrr//+otuPbkfotmMq4pEKLkxFJ2ZpIhxsr+oyhc0A73YsSgFv09a7ecqGFN5tZOajLNxKc5Rzs/UcmLcau8mYMiNdvnvZbgsK8p2VClSiH//4UUkAdqpt1tIUOWDCTMok6Fc2BvnRS7cvKfEGKCLhWBBehpeLMLE9ECaOyJsDGM9l25cpUQp4wdsOb1OtQphqo4WphZCIIRViKAi3/minDxQVtx/Y6AnX5jwpZpVqFw14qcM5FxqZRvQyVwcX5xmjj1mGMD+3IbluQUl0VuvCmxv9/Wgqnbd0iPBuQCCqvInK5Auaw/lZ+c/UODFI9unzlJEN2l/W7UM+3pizN86qIiLkQQEKnr5e9TX1bdhXebL2OHzuMDWu0FgZYlQuB74NVNsqnLNwmPfz73N/q5uNBb0XWIWKkffFiLjZ3WdLpbAJEtoVYihZuN0FOVZb3rJak217Q3v2TDGSDWo19YloFREtYJ3dyMKHDfg3bMSr8OuvYY90ZiS+tnuD4iAMrEbYFp6fBmaIfrniS/XvuHGC/AUYWnhaEJSP0n1MmlqFdTFA2xat1aZVtXfVxQhVT+8ynWZunEmflv3UquhIz+5ju9UM1VHfj1I5WBjdEu+XUEVNaMlB5XJYWPrrUhrXblyIfCteA61A9m4GYjvikQpuzp9c9OPHGr15WaEJ0n4YptyaDSRk/uCF/MfqTZ8ZFBcFchsMQrnx+fdEYRiyHV5WsSE1Cp315BAvqpaloMSIFlVaKAH6oUuHKuMBrwn9mW1rtQ0RGkXo1NMjcubqIp8II+YX4Kc8wpJ5SgZ7ycOaDVNC+PVK1VO9p1BOOn/rvGqRqVq0qgrj6sH+o73l7uO76lTut7Afffzhx8rbfPbyGW36a5Oad4qwLgZ2I1eqUTR3Ubp857J6Pxb3XexQewzeN4SfE3phoINxZfKq31dFeVjcXRBDKrgxC9gwohVFq2r8hz3N6Ry63cjFS811PZ+jDCqA37LWcDEWwEdBESZ/HGBx+p/D2cfqCL/pCqyMqMBzV82m8wjlC5RXD60HFAYnkALptLoxCgJG5/T10zTgswER+towzuPXjKdXfq9UPypyngfOHFAFTqNajVKDtGHcF/RcoHKOA78ZqDxRiN1P6TRFjTczAi00EMVPR+loVrdZtP3v7ardBcYOYWpvL2/1GnojqgEd4Q/f+5AOnj2oPPbQ8PX3VQIVZmCqjebZCyGRd0ZwU66yN4pxaPo77g/5uXZc7ZuQDSQe9ljPxnUb97MO5e2+4NBqQxbxj4xcJbwme15SPPaOhdBo8lETZajyZslLiRNZRxJm/zSbsqfNThv/2qjmhsJrjAgxg5mbZqqQKibG6L3Ce0/u0eClg1XvKDxQPBqUa6Ae4OvVX5tW8oJr969RlcxVgoeM6/8WrNmzhqoVq2b69xCaWL9vvUOGFAVO8GLtVUcn8rYvGhGbkRyp4KagR7SfiWFLxd5oWNoC1nM+FeHgarrtQsINubaS6OqjyKEkC0eY8Uc42n9iJ6mSpqIhTYdQ34V9afn2IGGP7f9spyZfN6GDZw7SB1k/UGL1kO7rNLMTXbyNGbRE526co6HLhlL3Od3VA/9G20pooKL16t2rVkZUb5w+LfMp/XLE+LxBkRBUmcw8RGj45stq3qOMsHUcD/uX8NCqfjWQF82fPb8SjgixDYuF5m+ZT80rIaojGCEeqeCm4AJorzKxEM8ZdbRI6A1LBOorefUMY6EIyARGNPCWP+d5sLZ3/f8LRR84dlbr2gOqRMv6L6ODFxCeJzp+6TgVyV2EhjYdGuyBVi5SmR6/eEwDFg9Q80K3H91OQ5oMUYU1Wr5z8vrJ9EnpT6hKkSCv0Ih/Lv6jQspm1Cpei4avGK6qi0PsZ5Y8SmJwyS9LqHX11sFFPlBr+uq7r6hbvW52PWZ4mmhVgaCDERgDVz6/+b7ZArnBQUsGqZuLJhWbKPlC5HG/2faN8t4hHiEYI4ZUcFOQr7zOog1GYJnxBca8ctae0U0dieFVeNCjuSDqc+6fRfvLt7xfyAW7Fq5qRDVggEq+X1LlSK8/uE5zus4JYZRQXAN5vVmbZtEPQ3+waonJmCojTek4hTrP6kxl85VVBs8IeGv2jB2W2ZtGM6jxINq8fzP1mNND5TwD3gSoHGvv+r1VeBp5XnsTbF75v6J/L/6rQsm2YeFDZw9Rx1qhC+9r4PindZ6m8rvjfxivvOKMqTNSt7rdVM5VMEcMqeCmtGEDM9VgGfoH94VRdQiVsUEejDFopYmcas934d0t3Ps6hz3QjtxHKjgDKmLNcpHPfJ5RowqNDPtK8VyDsg3o1yO/GnqUAIVNX678kppWbGrqFZbLD5EPc0Or5T5VRXEczzDlbUe3Gq0E7BE+hv4ujhPi+AgLT2w/URUJhQWsj/21t89CSCRHKrgpxTgUu8RmRulzbnfpG8bTG2HbMzzQ24jvuOAoMkGVcEu+OUBlsRjRiCB+PPO2IXir6C21Fya+8eCG6XJU46ZPmV71dNqC0PGGPzcoA+cIMIJhLX6C9zqj6wxqWK4h7T+9n3Yf363EKRb1WURpkkflLOHYjXikghszi73SuizA8JKNK4xoWO+o47FIPsKra3U9nQjL/coTX+y1qAiuyoVbF0KEYDEP9H8H/0d/nPyDjl48qoTfPyrwUQgPDi0qKGCyxxeNvlA5zb0n91K90vUosXdiZdRQ0PTV51+pkG1kgzAwHpHBw2cPVcgZxVPaSDjBGjGkghsTh4UKurMnCY/OsUHJxhTjqS6DWeAB+qg3uaUGoglRJysnRBzop0QeUmsdOXX1FE39caoKx37T7xsloLDl4BZatWsVTe08NVgGD8b3xz9/VCFSeyAEPL7deCXtt+OfHUqFCMU5bfq3CXNo1ZXAjQBEGKC5i5sBDCRHgVOHmh3c+rgiAzGkQgzAg4uBIoIPuMjHl3tIMQxcFIXcmTbV2tC4VePo5NWTqgp32oZptKDXAuU5AvxsV7OdMhJjvh9D07pMI/8Af5qxaYZS9HFUXxbSfl3qdKGYAKbRQMB+Tvc5qv9Vu7HADQkkCce0HhPdu+hSiCEVhBBAqH4aG9L4XGjUXKeOJLgT8J6gMIQQLzxRFBdpRlRP3qx51RDwrrO6qv5LrFe9WHWKbaDoCeL1kBfUh3K1wij0156+dpryZZM5vBpiSAUhhPgBcq9ziSi9rgp4JgszfB3N+yeEF0jxoTeycqHKputAWAEtJZDgi61AuAK9sWb50GaVmilVJTGk7xBDKkQBv3B1Lbw59MUhVNY/kieqhIXnnAPFzNH9LIKvGVFir3QQ52Kh3Rp7LyCu3j8aGpDae+n/kpImMtaVhRE18lZjE499HlP6FPrz3xrkTJ/4PInSfXJ1YlTGOHv27Cr8oH9MnGi/UECIbODJoTVgBfdIbuLpK2OJaE907xwR/Q6FVp5HOoFF7n/mPtWgeZbWk1iCZOesQfvNdja0HflYUawkuBo1PqyhCovMQA8m8qJ6MOPzyIUjahmEDmIKZkIRuTPkpv+uYRiEMVgmAg0x3CMdM2YMder0btRPkiTOVHEKjnMB4mYoudC1jtzkAdq2wvKZuC+zPqsPOXI/h9aWs0TkzQVBZrnK10S0mFtWvNigleXRarbnwm029D/xuiAVG9RtHMqdpFs/N8Yc22zjCRvdSjyA25tfuxkRdXbguISIAkbu0fNHlCV1FtMeShQUQd8Wlbu2/aM/7PlBtZCgNxNFNSi4ef3mteoHRcFRxUIVad/JfaodZFjzYUr9yN3ACLZvfvlGDROHd46K5ZJ5S1Lbam2Di4oQsoUQP95L29YfTNBZsXMFjWklxUYx2pDCcKZPbx6WECIaqAGNZ6m+LBzC9ePB2Su5PcXI6EFyrTp7pXot0wD2Cr3Z+H3EI8SucRvKCxaj72AgkPCaJfbQ5rCZlYhw172LDdtqDitrfMNDvDUjqudjPhZI9GmhPgg2WM+NDNpHeNgFdc+hKKkGT6fx57YZeK1/EdENnoMKr0cKlyKCE5dP0Lwt85RObsaUGWnt3rXk/9qfBjUbFFR0bVN4hBaXsavGKsH3svnLkp+/H+05sYfyZ8uv9GbHrBpD2dJlo9ndZytjA2B4Jq6dSGPbjFW5w2HLh6mKVswN1YNq30BLoOlcT23oOFpLUNhU/P3i9H4m25F+IcHxPXj8QMkCouAH8oZh5anPUyXmD/nBgY0GBnulu47tor6L+tLMrjODjxfC/5iig/cD7UOI7kGYYtbmWVS7eG0Re4jphhSh3LFjx1LWrFmpRYsW1K9fP4ob1/ww/f391UPj+XPky4i8vV+ThwcuzO4F9lv/M/KrW2eyd5nQRucWbQDpiAjRAbN9eZ8Ni7YcxrIveXuj0MObvL1Hcij4OBs1zBIlzrMOY6OrLwpZzlJ/n7Hh0hSPKrLhnsAhZY2zPC7NbP/K82tDvg9MJqKRuvWvEFEKHiZuvQ1v76AmfG/vjWx8p/H2crNBxY0GLmalydXPo+MLMLIuCFcb5gYDt3j7YprSZYryJDUgOj9uzTjqOqRrCL3ahN4JaULHCWrWJzzTxAkT06TOk5TxO3juIKVKnopaVW+l1tX+9r0s76m/QevMpA6TqF3tdrTxwEYlSo8B27ce3lJeLHpKoVD0/NVzNcBbPxUGhnP6hunk4+ujhnlDcWnTgU107+k9Gt58OCVPZG31YeTm/jyXqn5elSoXrUzpk6WnczfP0cjvR6rXNZo4Y49Fvy6iAU0GqIkywe+JB6ltv377mr7f/T2lSJJC7TtCtzhejJxb+ftKdfOQLHEyal+7vVJ7sqcBbIu2blj+xhV4a6WYZh8Piz1FZTdj+vTpVKxYMUqZMiXt37+fhg4dSu3atVPPmzFq1CgaPRqC4dasXr2aEiY0v6sUBEEQYi6vXr1SztizZ88oaVLzoeduYUiHDBlCkybp81QhOXPmDOXNG1Iea9myZdSlSxfy8fEhLy8vhz3SLFmyUIoUd8jPz740mCsCD2LZsp3Uvn118vWNTCWeF1x5i/CoETitGrM6EHKWtgRwuHU9h2D3cMi2t8kxTOFwKPKdGnM43Ks915rFFMzozq+FeaOfsgebmr1Yo/3/iHO+gZwb3WEjzrCZjwPFSta8O4a55Ou7nL1zW26xl4xWG9dC/xkcnG6bF3YNoCA06rtRykM04o3lDZ1PcZ7yPM1Dng4OHMBYNUxAMQNjy0p/UFp5dQiTTuo4ifov6k+zu802bBfBCLVen/YKmqzy4zSlgGTEih0rVB8rdHIBLss95/ekmd1m0oWUF0IcA7zpRdsW0ajPockcOj5+PjRl/RQlcm8LpBIxuLtz7c4h/mbI0iFK5jBNsvCHcgMpkM4lPxemz8EVQOSgWLdiDhlSlw/tDhgwgNq2bWt3nZw5jSvISpUqRW/evKGrV69SnjyY6RgSGFgjI4sLuJ+f+0rCYf8j15Di5gP5IXuvEZ+F17/CJUU3a/MuEfVhTdwEulxrPavtWR9Dda6wRZhWAzc6z/hvYOyesqE0K166x5XD9Tm/2oJ/ltSFjTUjOpbzsMh9juewru2s0OocwobQvDG+vgnI11e/bT3ZeZ9c9zzD+++qFz9/P39KGC+h+f5xChrLHT0GT4sn+bz0MVUzunL7CtUrWY9u3LtBKRKmoD+P/0mVC1Sm+HGM1a8+K/MZzfxxpgoh9/ikh+l+1Cxak5btWEbl8gZVDD98/pDSJklL8eLEMzyGzCkz0+Onjx0+rsTxE9O9h/dCrI/ioS37t9CSvksojs33JlmCZNSxRkdat3udyqs6i2cYPgdXwPb9cGtDmiZNGvUID8eOHVPFBWnTYnalELHgM7nMRseoaAbat/E4D/g/LgLyZIOXmIt89DMUvbgy1wws0254zrEm7l9cIbyZK3ABcpKNDP7+BBsuXPAqENEhHqS9hAuGcvPzD9mr9edt32Bh/KCBz9Yk5zzvSq7c1YPcKgitJ9G1LyxB+VHX3MfkiZMrUXmzmaDI9dkWG4XGJ2U+oR92/0Bd63YNsQxVrM9fPqd0ydPRF0u+oE61OtE/F/5RhUlmpEuZjk5dO6X+RivkMQL5Uhg1gJ9QFrr3BDdZ5qAtx1HgLUPC8MSVE1QoRyGr6Te5M+Y21c7FmDhU+QqxpI/0wIEDNHPmTDp+/DhdvnyZVq1apQqNPv/8c0qRAgUhQsSCezCEocx68qawRwc+4aKh9fwTxUnWg4iDQq1r7LzeGl5nBasLdWRjiErc4my0v+UH2k/0GQsILXzB7SnaTQA8WeIKWuxTK34uJbe+HGCjPMbEiGqM5huKxvzaOMbOLOoA3rBurxGoCHbpzIpLA+NQ/L3itOMoQu4hQZtGWPmo4Ed09d5VZcj0hgqtNahirVWiFrWf1p7K5y9PebLkoSxps9D5m+dNtzdxzUQa0mQIjW07lrYfQa+xMehRLZW3lPo3qoZRlYsbBL/Xxv3I8HBzpg9bL2f3et1VsRN6YrWMXmBgoF2DrPXjC27ukToKwrNr1qxRxUPIeebIkUMZ0v79kccTIoch7IldYKOJ2394MMgxoQWpqs369r6Qedjg4KJoK+EGjxPV1H7cyvKtblsJOfdZgmd4ruFw7RTOS75grxG9rFpk4xC3p+gpFE6lpThsTJ/xvgVwGDsjG2SEj5HDM8plQSykXTheU9DoVLsTDVoyiG7cv0FNPmqiFItQsbv016WUMnnYW0RgNMa1G6ck8DrP7KwqeeHZIl8GDxhVwtDh1QZ9l81XVr1W88rNKUF8LU0RxP0n95UhRP8pBnxjOgxadQrltD7PEMbdemgrLR+wXAnrw3NtUrGJmnMKbeCG3azbvF68eqH6PMMqHI/9n9VtlsrzLty6MLiPFFXHZl49xBdypEOdgDXopf3pwE8qV5shZQb6tOynlDppRA2OcD9ijCFFte7Bg8izCVFHPDZQv3J41JeNVSceSRZW5rEU32rdXNBtbCzTcX9mA+7bzMMeqpabKsEDsQO4uKk/zxdFOE1/gTjK/aBDeVkD7hl1NnyJnJr+gqe1w9Tl12vPykjvEdF5LpTKZ1LoJDiKKuLpPE3NAsVUkoDXAapNo+lHTSl/zvx0Wkk6Og6MHbxDCLcjp4kwKDxfvZEZ98M4FdJFfyXCsJgu03tBbxrWbJjq89QMTfe53ZUuLWaTLti6QPVpTl43WWn+1ilVRxky9JP+fOhn+iDrB+pYNv65kdrVaBfsHb/wx40g0fp96yld0nT076V/lUc5ovmIcAlCIPfbr2E/q+dW7lxJq3evppZVrHP96MWd89Mc9Vp6MFrtwOkD1LxKc/q45Md0+e5lGvXtKHVT0aIKbhxjHzHGkArRRRw2RHg4ixcLxt8hor9ZlGE2V9ZO458I42bkcG097ifVCpCKsXdcgkOtuDCM4YKn12xo17N3WIOnuvzABhlebtg9GMfAPl7isPRNFq5AAVbIO30h7CC/V7lwZfXQE5a+RXhmGM7tHd+b6pSso3KWe47voUU/L1IiDNCX1ejboC/1nNtTGT6AZVBAwtQY9LLCk4SRhOf4vwP/U/uFbU5aN4mypMlCZfKVoU1/bVJ/UyJPCfp20LfUa36v4Dys3kCiXxQ3Axiq/ejpIyUi0bpaa0PBeLzepv2b6PD5w2q9SoUqqV5WTbHIDGwP/bGDlw5WYv2o0IXnjG11rdOVMqd5VywHZSeEvuf0mBN8c5EhZQYq+0FZGr9mPP1x6g+qUAC1BrELMaSCC5Jal1vVvF4UG/2mWwc51hbspcIw5eQCJ01ppilPchnGRjQOy/lBbUjrD07AykuQ9+vNrxNZ5LIRgxDCCgzFun3rlKGAl5jEOwm1qNwiRKg0PIxdPVaFayEhqIHh3NfvX6ehy4bSN32/CS7IgbeKZv0+DfooNSQNiCVMWDNBGV6Ee5HzxPBwjQ7UgXb9u0sJN9i2wWitMyhcOnPjDBXMrlfKCvJOUfEKdSWjUCvGmk1eP5k+r/o5zegyQ+U+fznyC3WZ3UW9lj3vFQYRSkcIiSPEDBlBeM2Lei8KYYShGoV2I9swsIeHh6rsRbtMbDSkMabYSIipWDjUi7yjLQnYOM1lYwmhba3N6TH3phZnCcKH3G5jJLKRn1tboFQkuCLoc4TXhgrT+T3n07ye85QhW//HehVqdAbk+WB49EZUI2varCpk+ed/uAELAjnGXp/0sjKiAIo//T/rr2T07j6+a2VENaBoBG/18p3LVuFkLb+IkPSyX5cZCspD8xfroshJT8CbAOXtwkusVrSaUlaCAcSNAYzo6O9D9o4aAWPb+ePO9EXjL9TfGnmy2C+z6ThJvJOYCuHHdMSQCi7OHfY2zXKYBdgAduQcJPEotHpcePQlt+BA6L6mndepyuFkwRWBp/d1m69V4Y7mGSKkiIKbY5eOWRmmsIIcJgycGTU/rKlCmvoCHORHjUBOFfuDHKgZMFIQxAeomIXhhScJEEaF/i6MH/Ks+teEWMSgxqghsGbHPzuobqm6ypAZGUd4l2h7iQjeWt5GWEtOTEIMqRADuM5iDEtZbAFh2g1sZDW8ud3EDBR1BOnjCq4FQo7IPepzdfqQYqePO6kq2/ASmheF19CvA4/SXktIPM94lDShuRIOlmGeJwqNkFeFZi6MnQaKfuqWrqtuHgYuDhKXh7D8xPYTKVdGpAisgZGE2pIZyMki5xkRoIoZVcZGPHz2kBIniJ2zXMWQCi4ORBHgbZjd6Z5iKb+fWLWoNXumthOAUFFrLwSIGaTmXokQfVy5d0VJ8pmB6Sm3HoVfxhChW1TqmoE+1fIFMHDgXTWrmecFg4v/Dp417yCAdwsPGtWuUzpNUR6vLeiPndZlmppUA3p/2lsVHBmBAim055jh88onRGtOeGlboy2NWz1OFUrpef3mtapmblPDVpgkdiCGVHBxPLioCJq0tvjxNBYtpKt5lkYayVU45Iv5qLagFaWUgQSgULhbUDtHdJIiUQo1eswMeHfeXuGPJmiFOIfPhTw3MNUFlago9tEol78c/XoELV8hgedYvWh1+vfiv4b7DGUkGO3FfRerNhf0djoLPFpU2JqBoiN7oeuwgNA11J86zeykctMIi6/6fZX6Hb2ketWk2IRU7QpuwOcssNCUPc5M3P6yhvtBc9lU86426M+Mwy0uzbjgqB5XAkNtBvqmIXNPgmuAHsupG6aqopr4cUNq2qLH8tMyaGEKPyNbjqQRK0fQ9n+2W7W/IN+Jgh29ID1Cr8hXwjOtV6qeaoNBJe+2w9tU7hP9ouiv/OKbL6hRhUZUs3hN9fe7j++m73d9T0ObDVUFQREFhpE/efHEsPVk3d51quUmReKIU3dDOw+UneBZo/gpQ8oMqqpZaweKjYghFSKRQM5bQkbQi/Vri3L1bFh7NiHx95D7QCGqkIe3a3thLcx9oujXtM2pxed9GsciCWm5j1Ryo64M8pEdanZQrRXj2o6z8j4xkBuFOB1rodgs/GCbEHY4f+s87T62WxlttK9AVs82HwpjPr3zdDWrs+e8nsoowpCiEEobjo1q34W9F6p2EuQ5EfItmaekqqw1KgoKLxCEeOX/ika3Hq3k/2A4kRPF/vz131+qhadfA2sBhogARjOivNyYgBhSIZKAwWrHIdVNfKpZWMO2BXuHYR0mgBaBbg6sB1GHjqyw9Am/9l8s1TeEJQPxENwFhFNhsDC6LFXSVEqh59LtS8pbRZ7RTHQ9rCDfiocjhgTtLUYtLnrj3PijxuoR0fj6+9LCnxcqzd3UyVLTU5+nSkih56c9VUgZ70fjCo2dCnkLjiOGVIgk1vF0Ff0IPA+eHTqdlX0wVSUyyMqFR6vZaL9lT3UxFy8J7gg8RDwePH1ArwJeqZCiUag3puMf4K9uKKBIBJUlzWNGmBVtM6EJMAgRjxQbCZEE2k/M5siiAvMBS/RFFol5Cst63hcUJYkRjQmkSZ6GsqXNFiuNKNjw5wZV8AMvXR92xpg09NVCH1eIWsSQCpGIvbBSFpb0cwSEhH14JJkr8Ir7VmOHikuutjmDH0L0A4F+o5YZgNwspBShHSxEHRLaFSKJt1xcZCaYfcuBgiNMcpnKmrlpeVRZKtbPjY6L+iEWz/fkVpnbPN0FeVuZ2RhTgQABCpBgnArmKKgKeCJqRic0ctG6AjED6Owir5kptb35t6RyxfaqfpEzhTGNqN5RIXTEkAqRxKdcEWsU3sUklCQO9G2iWKgpFwhpF66rbLhmRXHBEGaNfkNEi9DZqLtZWMpKShJOi2mgInb6xumqlxTtLNCYRQvL/C3zaVSrUU7lIVHFC6F8tMVAeB/bOnfjHE35cYqq7sVz9v4WRt3MUEI7GKPkhKhDQrtCJNGCZ4lutgmBnuJB3I4IaVdig6y/+8/Oc0aNBmVHFhb2RFfqjKj29YGxxwXtnyjcHyEqmLdlnpovOqPrDKperLoStccsTxjRYcuHqaKf8AIRA8gCDm8+XOU20TKDKTaY3HL+5nk1c9SMWiVq0Y9/oJiODOUCM6fOHGvzx9GFGFIhktDGn53n8GczFkH4hudyhtRNDQn+xogMvH1Hc6wREdItaydM3Y2PSXAXoIa0cOtC6janG/WY24NGfTeKLtzCLNsg0JuJ/lQIKtgC7xECEFAMCg/wKH8//rua9GILQsZoYfnhd0RzjKlbsi6dvHpSDeOGKIS2TQgkzNg4g/rU7xOu/RLCj4R2hUgkPisGDeJCIeQWPSKwWOmhiRxgRHMPkyLtLM/G64SHk9ymg1mpGMvVioicl42LLFBwdGlF+CetuAIYTA3DiZFhXep0UcYLrSOzN8+mGh/WUJJ7kPgrlw+KV8ZgnSHLhqhJLmHl6cunlD5FetPeV4xU83ttXiyEv5vQboLKrfae31uJ5ENA4sP3P6Q53eeYjjkTIg8xpIILn2rPdEO+jfKsUdXOkosFJOwZw5BTOUIXrOjFXm4HFtn/m+UQ4eGaj+GKbtzdmGKqytROU1VRjgbCq1A2godaIk8JJUofN675OYvcJnKo4QFh19CqaqFMZA8Y08/Kf6YeQvQjhlRwMEe4j3OEz4koGasWlYvkalUU8gw2eP4c6+Waj6qKWArwaz4y8YBncJ9qaLzgXKonDx2HLmpz3fKPiagGh7QxPeQie/X1OSwuX1dnOXvjLOXMkNPKiOqNE6aboE+zWcVmamh480r6z+cdCM2iKCg8YCQcQrIvfF8YygUipJwrQ1hvzMxByBoi+y/9XlKB7AWU1y051IhFcqSCA0YUntPvPIEFRQ5fY6YEEfWL5F7KW1yd62tj0FElO56ilgkcdj2mew6GtT8RYcizvQvfa648bsfe6wHWCTbKAcflQqrz7AXPJqL7LHVoPAdSCJtRsTehBMsu3rqoJAjTJU9nNdBbP8Fl9e+rwxTWRU528bbF1H1Od/VIljAZfbnyyxDjyCD1N23DNGpVDeeac2DbQ5cNVbNaKxWuRM0rN1fSgp1mdFLGWog45BZXCIXlnLvTa9xmYOH3WTzjE+HIyGAqqxK1YC8OfaUluK0mrKL3zpKPC6XmsLwhip3is3oSqovt0Z0N4UT+/Qx79h52POA3/Brw/rvwzFXc0JgXoQihg7FlmAVqBEK1526eC9an/aLxF8rY/XnqTzUiDN7jgTMHaNvf29QEF0dzkdgmwsmdandS4vrIyf5z4R+auHYitZnahioWrKgKmOAto2J3SNMhSv7QWdC6U61oNStxeShCoeq317xeNLvbbEqaKKqiOjEbMaRCKEBwfqPJsq5E1DgSDSkMDUJrzXUe6W4imslGBuHOIkT0mIiWsKcXl8UbuvMNQESSSWcMHeUs98vqx7ph3+3lyHCctvm3D/jmAeHe3GHcB0GjdN7StGLHCmpVtVVwsQ8qXlEBi1miSRMGGZaus7sqLdvJHSer/k6ERn0DfJXHuqTfklBHhkEQYd2+dWrG6b2n91QbjVd8r+DXLP5+cfpu0HfUZVYXFW7F+hjfBuMdESBsfP3+dRrcJGRqBDcEbaq1oQ1/bVAzUQXnEUMqhHJBR4+k2UUDhTJRlWu5yZ5ZRSJqwt7pGg6DvmC1oy/Y+F7kPlW03ZhP54ga0EdrG6bLxV4pCo7ezbl8x+8s+G8LJulg+LQY0vACAwjvcsyqMTSs+TCVK4S3CEO3pO+SYEOH9pexq8aSj6+PyinmyYKxfY4BUf0vlnyh8q0wVNgmnlvw8wI6fuk4dajVIXg6DDRzH794TPVK287PdQ5MhYHAvxkfFfqINswXQxpRSI5UsAOMUmhN5zBokU0gCx8s4FYahD6LsXeI39MRUVVdqDQ3F0atJaK7FL1geLjtUGUPHlA+zCDH/ICHmAddbK1BOFjGYjnLJ6U/ofIFylO32d2o74K+9NjnMbWo0sKqHSWhV0Ia22Ysrdm7xm4FLcLBaJ25ePti8HoI2UK0oVKhSsHbhNA+hodfvntZhXA1PnzvQxX6DQ83H9xU80fxgPqSbVVxYCC+N8agKtkjjshaRhRiSIVQyMitJkacZaWhyOZXdO7xeDRbIJSAcNwVg1O7L6sgRSfxuDhrk004F57ye5z7XMzFR0PYi55tUh2MdapF4b7HXJA7nN19Nl26c0n1kxoBPdsKBSrQwTOooA4J1IU6zuyoQrib929WYVr0osJ4wsM1AnlSFP9oPHrxKMyDvlEwhCHn8HBRfYwHVJhQWIRloHCOwnTwrPF+g51Hd1KF/KgaFyICCe0KoTCIi1y+tbm4P+Cq3YVRpHOLSl0z6nE41NaLK8FGKjpACJbYG63IGsGfcZhXq9atw8VUabkCuCFX6xopNsG4ot0iepvtXal/9N6Te3TjwQ1KkTgF5coY9nYReI4QiLdX2JMxZUYVerVl6a9LVc5UHw5GrnXy+smqJ9UMGNgHz/DdCWLjnxupW11HhtW/Y8SKEdS8SnMq/l7x4OeqFKlCf5/7m0Z+O1INOkc+tmiuorR279oQw8cx+Hv9vvU0v9f8ML2uYI4YUiEUcnKoEXftuODggnWBWzJmhKL4E1HgQmWv+T3QJLhyNwzVvWgtucZGDapJznCJh5e34psQLcfcnougkrDYxEq+EdFfeL/llpqvuZALoeEd3HNq1FMb+7j75K4ygt7xven9zO8rkfYrd65Ql7pdrIyLPWAckf98P9P7qhUERUhGnLlxhioXrmz1HAqDjl48SvN6zrN6HtW4jco3omU7zKMg+FvkZWF0N/61UfWUZknj+PmGMHDKpCkNjxN9rb8c/oUu3b6kbiyg2gTJwH4L+yklJrT0HDp7SOnxImyN8LUQMYghFRxs/djA4dPb7DlFhQHVqEVE6zj3uZdP27pcfOPBBT1jDP4Olbyh9ePdZQNl4crYG/zcl0RUNJz7O4uLnVD0pCcOG9gy3Nbyi8EEnMTcWtSDVZ0ysgGW3Ch49vKZCmt+3eZrypzmnV4zQpoDvxlI8T3jK/H30Dhz/YwSZjhw+oAylqXylAoxGg2vdfLKyRDatTv/3Ul1S+H8CwkM2NW7V02ns/yw+wf1Op1mdlLGcGjToWE4eqI9x/fQxyUg3GEMKn8xoQb7gdfp/1l/ddPw+7Hf1Q0HcsO9Pu0VYWPghCAkRyqEgRysZhSVRpQ4N/o9hzxx4enDCkGfskDEGfYkNSxs+O9yeNcM6Nu2YaP5LW97PvfOfsXiCeHhhp0Rb/ACCvMxWReIWIdxEe6ry0pHYkT1hgg5Tb0R1SpgoT/7zS/o9Q0dTFvZe2Ivzeo2i2oVr6WmucDT1Ve9tp3algY1HmRoYKGHa0bTSk2p+9zuyuPVg9c7dO4Q1ShWQ4WTj10+Rr3m96LBSwer13MEFDTZa73BMtviqJRJUirx/TbV21Cx3MXEiEYC4pEKUcRLrgBGzjAsX+Q3HB7dxdW5+twtPFWU7w9nLzkHe3QwgKV5dqg9FvJ2bNtJkrE324srf8OKUUuLkZh/L/aGK/N78oKNqJ8DIg+xk38v/atClkZAXCB+vPhKeSg0oQGEVOGRopoWerXvZXyPpm+YrmT0YIgg3ABlI6O2F4SDYQShyWuIhdQAcLTAQMEIIVVU9mJbHWp2oG93fav6O7VcKjxFqBlVLVJVhWDtgZYWeJzoPTUCyz4qiAI2ISoRQypEMgc4lxqXw5i3uMimm4MBEVSqNrAxohoI4ZXnkOwW7jVF1WJ2B/tb93OlLAwYpOBeswFOzx7uY74BCG0AuS3Igd4xWfaaw+Oo2F3DxnyWbn8/ZxUk8RrMKmnteVQQVEARUGiG9PqD61T6g3d5UYSDJ+ecbCWv138RctUhwVxSeL4Yg2b7OtDQRe5zYe+FKrT76PkjFXZOmyKtaknpML1D8DINeKeTOkyirrO6Uvn85SlJQvMqXrTLQGoQYgtZ01pXsV+7f02FrGWMWtQjhlSIRH7jMOk37OURFw2hGKMPy+2Fxh+8rhm1eZ0PwlEkFMC51RPs3Xqxp5iQc5lpWFvXfJyWMT15u0ZN9tNZqcmDvfOw5chie7UuQrLI+SFcaQsKeFDFmypp6KP1UOmLXKYZqGw1eg2AKl14lH0W9qHudbsrlSIYd4Rn5/w0h7rW6RpsKPX7Am8RhUtGuVMYWYSE/3fwf9SySkvTQiMoJcETRqsLKnUhFoHj3v7PdtWmM7H9RAndRgNiSIVIAgZzGhH9ZOMdwgvtyCpER1lYwR6hyen5OaGuBA82K+dDNSCU8CcX+NzlMHE5k9ddx55sPPYia/LxQcS+Oq+3k3Oi17gVJ2ckSirGbCMKmnzUhBb9vEhp3doCib/COQsrrzU04Nkt2LpAqQwZrQ8hhgblEAkx5oOsH9CUjlPUeqjS9SAPFar96vOvlG6uEfAi82bJa7rNvJnzqhYWIxAmTpUolRJ5CHgdoAQgoNd7+9FtZYQrFKygwsZmM06FyEUMqRBJHOSwq5mR68YhzdAMaT3OU5qNKYMO8LuQnOM8Yu1cI4m08hwqRrGIUWHHf9xD24Zf25+N6jxuaUnFOdttRHSZ21fSspdqJCoh2AMeF6aw/HTgJ2VEHr54qCaYjGgxgrKly6aKf9AXeeraKWXcHAHGp0XlFqonEypEmpeI19p2eJuS9INRtgeEEHp+guiDY2D9W4/MCsxILbMtYsL+AHi572d4V8BWuUhl5eGigOnLliiWE6ITMaRCJHEvlOrerLxOaJTjHKuR97qZjRZymuFRSzKS4dNox14pvEzbcHA/Nu566b8+XGHb02ZCCwpj7AucC2RXgm/EyhGq1xLeHgp3YDi/2/WdymGmS5FO9ZNCs7Z9zfZh8siqF6uueikx1gzVrlAYQtgY/Zjj2o2L8BApiokwdaV+mfqG+wmpv0FNkFp4x9/n/6YkpZIYzidFmBiTaBCGTptcX7UePgLeBND2I9uV6hHe9/Qp06t5rOERu4htiCEVIglUwn5nZzlCpo58QT04p9qLi34+Zg9wM+cww+ONEhclwSM1IwlX32Y28ICbG+jnEudp07J0Yi4D0YjfuRc3AxtoGa4cGhiujQpY/YBtGFN4gpr31qyS0VxXxyak/Pz3z6q4B1WwMKKQ7MuTOY/yWCMaeL11StVRgvnIsWrj2lDYtGjbIsqdKbe6MdDz58k/qXap2nZvBv449YeqPHYGvBf9F/ZX20PhE/YNs1vnb5lPZfKVUe0zQgwwpOPGjaOff/6Zjh07RvHjx6enT5+GWOf69evUrVs32r17NyVOnJjatGlDEyZMoLhx3eYwYxAF2aAghGpb/GHhohsjEQUjknEry0I2zilY8MCZO+XSXAhldpH6mdWczCp9zajB6+j37S8+1hr8viDcW59zxQgBC2b8dvQ3Uyk7XNwx7iw8hhTeFwxHnwZ9rFpJYNQgs2chiyrmiWgwDByFTn0X9qXECRKra9PDZw/VRBqI6RuJy9sjnmc8Fe6GB+lMfnTS2knUu0FvKpgd52cQ72V6j6Z2nqqELiA3KJ6pOW5jYQICAqhx48ZUpkwZWrp0aYjlmHRQp04dSp8+Pe3fv5/u3LlDrVu3pnjx4tH48eOjZZ+FSawsNE6nEnSHp7ZgxJO5Jqk1W1kooQUbMXh1A1iQIbxjoHDxvG4y3/MZCz0gT2oLvMhXdrb7iqt/NbD98Sxar5dk68SGOhVr8QpGoC/UTIAARUIJ4oWsgHUEhDBR8Wrbj4nXgnweBOgROo2MCtiKhSqqB3pWYQAxINzsdcx6VfF3P+z5QWnpog1mz4k9qve1y8ddVH9sWECo/Pmr51ZGVAP7BS1gzGuVXGwMMKSjR8MDIVqxYoXh8h07dtDp06fpt99+o3Tp0lGRIkVo7NixNHjwYBo1apTyYoWo5gNufZnBRiOQDYeFjY2Rt2rLv9xv+T/d6VqMPTlMd9lukMd0lLlsiOEJNOV9+pnFGKbaGERwiw0lbuQmmGxzk01bz1gWWbDVNfXk9wWvL4bUDHiIoXmW4QF5QIQwzQw0qnLP3zqvwryRBUQhQgPVuOfpvBKLuHI7aMIRiqAu3L6gJAY3fbUpOAyNCl540/0b9qd82SDr6RgQizATeAC5M+a2Un0S3NiQhsaBAweoYMGCyohq1KxZU4V6//vvPypa1Fg31d/fXz00nj/HzEcib+/X5OFh/0vsimC/9T+jHzSsX2URdk3Bh7h4qC0b2lR2jmEBe3QwvrbHNIYNdHhDcClZShCFR4N4++W5Ajehzeuhlecct8fMZqEJW+Hw9dzeklR3DPe4sMro8/Dm9+Ml//yO22XggcFAfMQFT+HzupxB/xkEqhug6CFbhmx0/s55w2IbzPbMnjG76f5pzxstjxM3DsX3im/6t6mSpaKXAS+j9djBiasnKEHKBFT1w6pUqGGQhvDBcwdpz6k91DlPZ9Vtpe1julTpaGqXqfTlt1/SzK4zHX6NRIkS0Qv/F6bH6vfaj7wSeIX7vQi08zm4Mm/tDsqwxsOi1Ve7CfBI+/btGyJH2rlzZ7p27Rpt3w4PJYhXr16pk2Tbtm1Uu7ZxLgzequbt6lm9ejUlTCjTEQRBEGIjr169ohYtWtCzZ88oadKkruuRDhkyhCZNMg6vaJw5c4by5jVvYnaWoUOHUv/+/a080ixZslCPHpXJzy90hRRXAx7EsmU7qX376uTr6wptFygEWW1HDlBb/pTbUUaQt3chPoZq5Ou7j0Oty03E29txIZJt6N7CfwdvE+G51+wZt+RCH0fBdprwPsYzEHTYxjnc73TqTfrP4Rvy9V3MFca2BLCX3pYLqIwqI/ew9zs02s6jg9PNex+jghsPb9Dcn+aqqtfMqTPTzYc3lexer/q9KEtqczUreEDnkp+jPE/zkKeN/jHk9BZuXUjj240PkZ/ENBgM6/6qpV6oI+rZcmgLecb1pOxVsxsew7Lty1RFc4n3rfOo3+/6nvJny09Fczs+vejP//5U4W705qKASePsjbNqaDiKjrzi2qY6HCPQzufgytgOHXBZQzpgwABq2xYXEXNy5nQscY4io7//tlYFuXfvXvAyM7y8vNTDFhghPz9XMEThA/vvGoYUFymvUNpM3nAby2hWBQoKK/r6xidf39pscGAUbbVPERLFyW6UaxrKrSubdUYc2/2Sp758zKIR8dmYmembYl20WRhFJ3LwuDMI6htPA/H19SZf31Fs7G0LShCyTs/j1LCfRheZakSEMF30fJY4h6L74pc9dXaa2mGqqm7VeiYhbuAo2H/bY8iZNieVzlOavlj0BXWr100J0cM4bz20VcntzegyI9qP++zVs9SiWgt6Rs8Mj6FQtkJ09tpZKv2+9SzVizcvUvUi1cO0/xXzVyRLoIV6zOqh1JdQWYw5rWiDmdR2EiWM63x0ztPgGFyZOGEYjhathjRNmjTqERGgmhctMvfv36e0aYOak3fu3Klc8nz5HE+8CxFNABvKuHYGaiMHeJ6NqJm60Sc2htTCRtGoRQVFGQ8MCoJgjIZx7nE/t6M8Za8W+acRBp4zfrceSxUSe9kR3ETk5+IoDPUuwPu3iD3keXwDYXaBgfFNzu9j7C6Yg/EMiwENjYblGqq2DlSk3nl0R7WPoI8S7TYYvh3dKIGI549NjQ8E8W0F7nGz8eTFE1OZQntAfrBiwYpKfhDeGNqLUAksxKBiI/SIPn78WP1Eqwv6SUHu3LlVz2iNGjWUwWzVqhVNnjyZ7t69SyNGjKAePXoYepxCVFGLW0mMev0OsJHRxquZEYcNHqp387EhQvU2vFWjIceruUfTyOC14n5U/V081l3MxUvwHvUkY6/U1yS0fDYU2b+vWd2oO4tQoKUG+Zb4fHNQgHV7sW9mrRYogHOF6ELMA/q4w5tjDJ/rAfGGlb+vpIZFjXuN1+5bS5M7TLYKw05cO1FJHoYXhLnRPyrEUEM6cuRIWrkSOqZBaFW4EF+oVKkSeXp60tatW1WVLrxTFBlBkGHMGEeb/oXIoSP3f+KuuiH/tLDKzzSW0/PmkWVmoNovOYdmN7MyEHKmZnfLj3gdW35nw20dCguiM3u+z6xynUF05bmhs2yM3Sv2cO2pK2Viww5DfYgN4hPuI9XmRpbhal14yLaguhkXNpnoEdvIni47xfcM8oxVTSifAvg3hPsxL/Wr776iuHHi0uvA10p3eGKHiZQ+RXgkM4VYYUhRrWvWQ6qRLVs2VaEruBLx2ZAs4CIfL/ZAS7GHqVXDFWQFIKNJKz+yZwtv0hFQnPaPgdbvAu4PNQPFPtu5uEhPbQ4Vw4NszNs9wUVGIw0EHWxJFUqxUC/uY8VQ8rK654/z330fyvaFmMrARgPpLJ2l3gt6U96MedXQcYjz1yhWgyZ3DK88phBrDang7sa0TyhzRYdy+LefTqAgkMewrWdj7CjN2TDVswmJXmdjZQY80TMmy1qzod3CRhTGEzdtEVE8kYQ98wn8SM1edQ42ohFTRxBecrXN6ZIj1WIDmuzftC7T6Oqtq+p3yBpGhhawEH7EkAouQlKeqDKHPcc2HBL+iI1o/DAapp7892NYYQnh5Lc80gzPG7HZJLyqkZANdGSQjKUT33LIGK8lsyWFIBDihdqS4JqIIRVciCScc3zN3t5aJ4psUISUi6tib7GBSsy5yrq6kLLGafY0oztcFicUr1kQBFdDDKkQg8nDcn4a57mFphEXQdXmalxNyxfrOz/XURCE2IUYUiEW8T5XDu/iqSy9+CuQin9CxF4QBCFsSBJGcEOesioRQrFhlYpuxa0sd1gV6Sl7opja4n6SkIIgRD/ikQpuxEue0gI1pJJsBAdzT6a9Adkwtit40ktiFkBADyoa183HRwmCIDiCGFLBTUArzOdsSMvYPN+HC5Tq6UaT6RnArSSbdMVLt1ltaCj3tAqCIIQPMaSCm7CNxeX1RhR4ckFRMR7K/ZD7UPuz0TzCGQzkQ/VAi3QVt7OgN1SUgwRBCB+SIxXchHXcW2p2GjfmSSw/s6ReKxabX25gRDUSsVg9NHAFQRDChxhSwU3wM9DA1QM1oBfsWTZkrxSh3HsGUoF60GsavfM2BUFwb8SQCm4C9HOt581agyrefDaSfut5JukFO38HScDsEbifgiDENsSQCm5CJ1YdgkIRGagSoegoo03Y1sLCCxiMbcQTNrIivSYIQvgRQyq4CVm5MKg5T0XRwr3fcVXuDAMjGZ/bW9Ly8Gz0jerniKIKeFwUHoMgCDERqdoV3AgUFH3Ig7m/5gHfzTgXmsBmXQjft+R/f8VTZFrzveNrzptCh1fCuoIgOIcYUsHNyKkTlr/LlbxleI6nB3upi7iASD8D9FN+CIIgRCxiSAU3Jj2PWMPotUk8NPwNe669pTdUEIQoQQyp4OakYqk/QRCE6EGKjQRBcJhcbXOqhyAI7xBDKgiCIAhOIIZUEARBEJxADKkgCIIgOIEYUkEQBEFwAjGkgiAIguAEYkgFQRAEwQnEkAqCIAiCE4ghFQRBEAQnEEMqCIIgCE4ghlQQBEEQnEAMqSAIgiA4gRhSQRAEQXACMaSCIAiC4ARiSAVBEATBCcSQCoIgCIITiCEVBEEQBCcQQyoIgiAITiCGVBAEQRCcIK4zfywIQuwkV9ucwf++tOJytO6LIEQ3buORjhs3jsqWLUsJEyak5MmTG67j4eER4rFmzZoo31dBEAQh9uA2HmlAQAA1btyYypQpQ0uXLjVdb/ny5VSrVq3g382MriAIgiDEKkM6evRo9XPFihV214PhTJ8+fRTtlSAIghDbcRtD6ig9evSgjh07Us6cOalr167Url07FeI1w9/fXz00nj9/rn56e78mD4/X5G5gv/U/3RE5Bvfa/0AKJFdE2y9X3T9HkGOIPt7S29hpSMeMGUNVqlRRedQdO3ZQ9+7dycfHh3r37m36NxMmTAj2dvXMm7dbbcddWbZsJ7k7cgzusf+nybU5l/wcuTtyDFHPq/ivHF7Xw2KxWCiaGDJkCE2aNMnuOmfOnKG8efMG/47Qbt++fenp06ehbn/kyJEqZ3rjxo0weaRZsmShFCnukJ9fKnI34EHg4te+fXXy9Y1H7ogcg3vt//EFV8kVgQeEi3eep3nIkzzJHZFjiD58fH2oWLdi9OzZM0qaNKnreqQDBgygtm3b2l0HIdrwUqpUKRo7dqwylF5eXobr4HmjZbh4+Pm53wVQv//ueAHXI8fgHvvv6hdH7J+r72NoyDFEPXHC0NQSrYY0TZo06hFZHDt2jFKkSGFqRAVBEATBWdwmR3r9+nV6/Pix+hkYGKiMJMidOzclTpyYtmzZQvfu3aPSpUtTggQJaOfOnTR+/HgaOHBgdO+6IAiCEINxG0OKfOfKlSuDfy9atKj6uXv3bqpUqRLFixeP5s2bR/369SOkfWFgp0+fTp06dYrGvRYEQRBiOm5jSFFkZK+HFCIMeiEGQRAEQYgK3EYiUBAEQRBcETGkgiAIguAEYkgFQRAEwQnEkAqCIAiCE4ghFQRBEAQnEEMqCIIgCE4ghlQQBKfI1TaneghCbEUMqSAIgiA4gRhSQRAEQXACMaSCIAiC4ARiSAVBEATBCcSQCoIgCIITiCEVBEEQBCcQQyoIgiAITiCGVBAEQRCcQAypIAiCIDiBGFJBEARBcAIxpIIgCILgBGJIBUEQBMEJxJAKgiAIghOIIRUEQRAEJxBDKgiCIAhOIIZUEARBEJxADKkgCIIgOIEYUkEQIoRcbXNG9y4IQrQghlQQhAg1pmJQhdiGGFJBEARBcAIxpIIgCILgBGJIBUEQBMEJxJAKgiAIghOIIRUEQRAEJxBDKgiCIAhOIIZUEARBEJxADKkgCIIgOIEYUkEQBEFwAjGkgiAIguAEYkgFQRAEIaYb0qtXr1KHDh0oR44c5O3tTbly5aKvvvqKAgICrNY7ceIEVahQgRIkSEBZsmShyZMnR9s+C4IgCLGDuOQGnD17lt6+fUuLFi2i3Llz06lTp6hTp0708uVLmjp1qlrn+fPnVKNGDapWrRotXLiQTp48Se3bt6fkyZNT586do/sQBEEQhBiKWxjSWrVqqYdGzpw56dy5c7RgwYJgQ7pq1SrloS5btozix49P+fPnp2PHjtH06dPFkAqCIAix25Aa8ezZM0qZMmXw7wcOHKCPPvpIGVGNmjVr0qRJk+jJkyeUIkUKw+34+/urh367IEGCx+SOJEjwml69ekUJEjwiiyUeuSNyDO6//898g75H0UkgBdKr+K/UvniSJ7kjcgzRh4+vj/ppsVhCX9nihly4cMGSNGlSy+LFi4Ofq169uqVz585W6/333394ByynT5823dZXX32l1pGHPOQhD3nIg2weN27cCNUmRatHOmTIEOUx2uPMmTOUN2/e4N9v3bqlwryNGzdWeVJnGTp0KPXv3z/496dPn1K2bNno+vXrlCxZMnI3kCtGodWNGzcoadKk5I7IMUQ/7r7/QI7BNXjupscAT/TFixeUMWPGUNeNVkM6YMAAatu2rd11kA/VuH37NlWuXJnKli1Lixcvtlovffr0dO/ePavntN+xzAwvLy/1sAVG1J0+dFuw7+68/0COIfpx9/0HcgyuQVI3PAZHnaloNaRp0qRRD0eAJwoj+uGHH9Ly5cspThzrzp0yZcrQ8OHD6fXr1xQvXlBOZ+fOnZQnTx7T/KggCIIgxIo+UhjRSpUqUdasWVWV7oMHD+ju3bvqodGiRQtVaIR+0//++4/Wrl1Ls2bNsgrbCoIgCEKsrNqFZ3nx4kX1yJw5s9UyraIKLviOHTuoR48eymtNnTo1jRw5MsytLwjzQuzBKNzrDrj7/gM5hujH3fcfyDG4Bl4x4BhCwwMVR9G9E4IgCILgrrhFaFcQBEEQXBUxpIIgCILgBGJIBUEQBMEJxJAKgiAIghOIIXUAaPEWKVKEPDw8lBC+O/HJJ5+otiGMlsuQIQO1atVKCVvEpPF5rs64ceOUiEjChAnVNCJ3YN68eZQ9e3Z13pQqVYr+/vtvchf27dtH9erVU4o0+M5u3ryZ3I0JEyZQiRIlKEmSJJQ2bVqqX7++GtThLixYsIAKFSoULMKAPv9ffvmFYipiSB1g0KBBDslEuSIQsVi3bp36Em7YsIEuXbpEjRo1Incbn4fe4BkzZqgRecOGDSN3AoYfkpbdunUjdwA92Oi/xk3L0aNHqXDhwmoAxP3798kdwHhF7DNuBtyVvXv3qla+gwcPqvY/CM1gTCSOzR3InDkzTZw4kf755x86cuQIValShT799FP1PY6ROCcfH/PZtm2bJW/evMEC+P/++6/Fnfnpp58sHh4eloCAAIs7MnnyZEuOHDks7sjy5cstyZIls7g6JUuWtPTo0SP498DAQEvGjBktEyZMsLgb+M5u2rTJ4u7cv39fHcvevXst7kqKFCksS5YsscRExCO1A7R6IYz/3XffqbCcu/P48WM1txVhRk1G0d3H5wkR7z3Di6hWrVrwc5DjxO8YVShED9p4R3c89wMDA2nNmjXKm0aINyYihtQE3MxCUL9r165UvHhxcmcGDx5MiRIlolSpUqmpNj/99BO5I1C2mjNnDnXp0iW6dyXG8vDhQ3XhS5cundXz+F0vySlEHUhv9O3bl8qVK0cFChQgd+HkyZOUOHFipWiE6+imTZsoX758FBOJdYYUo9tQgGDvgdwcLtgYoYMxa+56DBpffPEF/fvvv0pC0dPTk1q3bu3YsFoX2f/IGJ8XHccgCOEBudJTp04pr86dyJMnjyrOPHTokKoPaNOmDZ0+fZpiIrFOIhCC948ePQp1dFuTJk1oy5Yt6oKogTt1GKKWLVvSypUrydWPASL+tty8eVPNBty/f3+0hVnCuv+oMsbQgtKlS9OKFStCTP5xl88A+w7PAjNvXTm0izTGjz/+qCpFNXARxH67WzQD3194QvpjcSd69uyp3nNUIqN63Z2pVq2aqrxH8WBMwy1E66NjdNvs2bPp66+/Dv4dF3NULqKiEe0A7jJ+zihMpLX0xITxee74GbgyMPx4r3ft2hVsfHDO4Hdc1IWoAf5Nr1691E3Anj173N6IaudRdF53IpNYZ0gdBb2XehDrB7ijsp1A46ogpHL48GEqX768msmK1pcvv/xSHYM7JP218XnZsmULHp+nYW9Yu6uBvDQKvfATUQ2tFzl37tzB55UrgdYXeKCoDShZsiTNnDlTFYq0a9eO3AEfHx+VT9e4cuWKes9RqGP7vXblcO7q1auVN4peUi0/jSlX6Kl2dYYOHUq1a9dW7zdSZDgW3BBs376dYiTRXTbsLly5csXt2l9OnDhhqVy5siVlypQWLy8vS/bs2S1du3a13Lx50+Iu7SJ4z40e7kSbNm0Mj2H37t0WV2XOnDmWrFmzWuLHj6/aYQ4ePGhxF/C+Gr3f+BzcBbPzHt8Jd6B9+/aWbNmyqfMnTZo0lqpVq1p27NhhianEuhypIAiCIEQkrpFwEgRBEAQ3RQypIAiCIDiBGFJBEARBcAIxpIIgCILgBGJIBUEQBMEJxJAKgiAIghOIIRUEQRAEJxBDKgiCIAhOIIZUEARBEJxADKkgCKFy584datGiBb3//vtqcACm2AiCEIQYUkEQQgVTOzDtZsSIEVS4cOHo3h1BcCnEkApCLAHTczA1Z/z48cHPYS4tRqdhTJo9smfPTrNmzVJD4TGBRBCEd8gYNUGIJcCjXLZsmZozWqNGDcqTJw+1atVKzRmtWrVqdO+eILgtYkgFIRbx8ccfU6dOnahly5Zq3miiRIlowoQJ0b1bguDWSGhXEGIZGJL+5s0bWr9+Pa1atYq8vLyie5cEwa0RQyoIsYxLly7R7du36e3bt3T16tXo3h1BcHsktCsIsYiAgAD6/PPPqWnTpipH2rFjRzp58iSlTZs2undNENwWMaSCEIsYPnw4PXv2jGbPnk2JEyembdu2Ufv27Wnr1q2h/u2xY8fUTx8fH1UBjN9R8ZsvX74o2HNBcF08LBaLJbp3QhCEyGfPnj1UvXp12r17N5UvX149h9Au+kInTpxI3bp1s/v3Hh4eIZ7Lli2bhIeFWI8YUkEQBEFwAik2EgRBEAQnEEMqCALlz59f5UyNHmiREQTBHAntCoJA165do9evXxsuS5cuHSVJkiTK90kQ3AUxpIIgCILgBBLaFQRBEAQnEEMqCIIgCE4ghlQQBEEQnEAMqSAIgiA4gRhSQRAEQXACMaSCIAiC4ARiSAVBEASBws//AX00vpKSolw8AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scalers import StandardScaler, MinMaxScaler\n", "\n", "scaler_std = StandardScaler()\n", "scaler_std.fit(X)\n", "plot_knn_bound(X, y, scaler=scaler_std, n_neighbors=1, xlim=(-4, 4))\n", "plot_knn_bound(X, y, scaler=scaler_std, n_neighbors=10, xlim=(-4, 4))\n", "\n", "scaler_mm = MinMaxScaler()\n", "scaler_mm.fit(X)\n", "plot_knn_bound(X, y, scaler=scaler_mm, n_neighbors=1, xlim=(-4, 4))\n", "plot_knn_bound(X, y, scaler=scaler_mm, n_neighbors=10, xlim=(-4, 4))" ] }, { "cell_type": "markdown", "metadata": { "id": "q_Ddj4E97aXW" }, "source": [ "# Часть 2. Кросс-валидация на задаче регрессии" ] }, { "cell_type": "markdown", "metadata": { "id": "BJBwxMCvZBo8" }, "source": [ "Часто модели в машинном обучении имеют вид не просто какой-то функции, а какой-то функции с некоторыми, так называемыми **гиперпараметрами** - это такие переменные величины, которые могут существенно повлиять на качество и вид модели, но напрямую обучить их нельзя (то есть в методе fit они вообще не обучаются). Одним из примеров таких гиперпараметров является:\n", "\n", "`n_neighbors` - кол-во соседей в методах kNN. Если вернуться в начало ноутбука, то можно увидеть, что имено это значение мы явно задавали, когда создавали модель с помощью класса `KNeighborsClassifier.`\n", "\n", "В этот момент возникает **закономерный вопрос:** а как нам найти то значение гиперпараметров, при которых наша модель будет показывать более высокие результаты решения задачи?\n", "\n", "-----------\n", "\n", "Продолжаем проваливаться в \"кроличью нору\" дальше. Для того, чтобы ответить на предыдущий вопрос, нужно сначала понять **следующее:** а как оценивать-то собственно качество моделей? Пусть даже мы зафиксировали какой-то гиперпараметр, как нам оценивать, насколько наша модель хороша?\n", "\n", "Чтобы уметь оценивать качество моделей (\"насколько вообще хорошо мы решили задачу\") хочется иметь некий \"стандарт качества\" - данные, которые вообще не участвовали в обучении (\"модель их не видела\") и верные ответы на этих данных (которые модель также не видела)\n", "\n", "При решении прикладных задач, как правило, доступна лишь обучающая выборка, а \"стандарт качества\" зачастую отсутствует. Решить данную проблему нам поможет техника, называемая **кросс-валиация**. Данная техника позволяет создавать \"стандарты качества\" (тестовые наборы, выборки) используя только обучающую выборку, но не позволяя модели \"подглядывать\" в создаваемые тестовые наборы.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "LGhcFh_ZbS2m" }, "source": [ "**Алгоритм применения кросс-валидации**\n", "\n", "Пусть зафиксирован некоторый набор значений гиперпараметров модели. В $KNN$ под гиперпараметрами могут пониматься число соседей n_neighbors, метрика близости $\\rho$, стратегия выбора весов $w_i$.\n", "\n", "* При кросс-валидации **обучающая** выборка делится на $n$ равных частей (folds, фолды).\n", "* Затем обучаются $n$ моделей с заданными гиперпараметрами (у всех моделей гиперпараметры одинаковые и фиксированы) - $i$-ая модель обучается на всей обучающей выборке, **кроме объектов, которые попали в $i$-ый фолд (out-of-fold).**\n", "* $i$-й фолд объявляется \"стандартом качества\" (тестовым). Затем измеряется качество $i$-ой модели на $i$-ом фолде. Так как он не участвовал в обучении этой модели, то полученная оценка качества модели будет являться корректной.\n", "* Финальным значением метрики качества для модели с заданными гиперпараметрами является **среднее полученных нами значений** на $n$ фолдах.\n", "\n", "**Идея:** Подумайте, как в описанную выше схему корректно добавить обучение скейлеров. Корректно ли будет масштабировать сначала все данные и потом делать кросс-валидацию или все-таки нужно как-то действовать хитрее?\n", "\n", "\n", "Собственно, теперь мы можем выбрать наилучшие гиперпараметры по следующему алгоритму:\n", "* Фиксируем, какие значения гиперпараметров хотим перебрать\n", "* Для каждого набора значений проводим кросс-валидацию\n", "* Для каждого набора значений сохраняем качество на кросс-валидации\n", "* Выбираем тот набор значений гиперпараметров, где качество оказыается наилучшим!\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "Принцип работы кросс-валидации схематично изображен на рисунке." ] }, { "cell_type": "markdown", "metadata": { "id": "SMZYgnu6Xe3U" }, "source": [ "\n", "\n", "![cross-val-2.png](data:image/png;base64,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)\n", "\n", "Небольшое видео объяснение о том, как работает кросс-валидация, можно посмотреть по [ссылке](https://www.youtube.com/watch?v=fSytzGwwBVw)" ] }, { "cell_type": "markdown", "metadata": { "id": "IEj2xi8T0iWh" }, "source": [ "-----------\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "3dhlgeuoXe3V" }, "source": [ "Теперь применем все полученные знания на практике на примере тренировочного датасета!\n", "\n", "Далее будем решать задачу предсказания цены дома в Калифорнии с помощью $KNN$-регрессии. Цена дома - вещественное число, поэтому наша задача - задача регрессии. В качестве метрики качества будем использовать $R^2-$score.\n", "\n", "$R^2(y\\_true, y\\_predict) = 1 - \\frac{\\sum_{i=1}^{n} (y\\_true_i - y\\_predict_i)^2}{\\sum_{i=1}^{n} (y\\_true_i - E(y\\_true_i))^2}$\n", "\n", "**Примечание** вы же помните, что предсказания и верные ответы можно оценивать по-разному и это называется метриками качества? Если данная теория подзабылась, то освежите, пожалуйста, знания лекционным материалом" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "a41xTGHjXe3W" }, "outputs": [], "source": [ "from sklearn.datasets import fetch_california_housing # Да, из sklearn даже можно импортировать данные\n", "from sklearn.model_selection import train_test_split # Вспомогательная функцию которая разобьет нам датасет на 2 части" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "kh3pM-ULXe3X" }, "outputs": [], "source": [ "X, y = fetch_california_housing(return_X_y=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "DZEL2B5deJbf" }, "source": [ " Мы разобьем выборку на 2 части: обучающую (75%) и тестовую (25%) с помощью функции $train\\_test\\_split$. По умолчанию данная функцию перемешивает данные, что часто оказывается полезно в задачах подобного рода.\n", "\n", " Зачем мы разбиваем? Мы пытаемся \"эмулировать\" жизеннную ситуацию. ML-разработчику приносят данные, говорят \"обучи как можно лучше\", потом мы протестируем твою модель на новых данных. Вот та тестовая выборка, которую мы выделили (25%) - это те данные, **в которые ни в коем случае во время обучения подглядывать нельзя**, нас потом на них будут проверять!" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "TcVBAjeUeMbq" }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)" ] }, { "cell_type": "markdown", "metadata": { "id": "99fypInUjkG5" }, "source": [ "### Pipeline и перебор гиперпараметров" ] }, { "cell_type": "markdown", "metadata": { "id": "ESf0zTIgfQQ-" }, "source": [ "Кросс-валидация в `skelarn`, как нетрудно догадаться, уже реализована, остается научиться ее запускать :)\n", "\n", "Но перед этим изучим еще парочку джедайских техник работы с `sklearn`, которые облегчат жизнь при написании моделей\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "fE-FiYsnfWtM" }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsRegressor # kNN - регрессия\n", "from sklearn.pipeline import Pipeline # разберем ниже\n", "from sklearn.model_selection import GridSearchCV # класс, выполняющий кросс-валидацию\n", "from sklearn.preprocessing import MinMaxScaler, StandardScaler # будем здесь использовать уже реализованные скейлеры" ] }, { "cell_type": "markdown", "metadata": { "id": "yXJ_a_Omfxjz" }, "source": [ "Что нам собственно нужно при решении задачи с учетом кросс-валидации?\n", "\n", "* Понять, предобрабатываем ли мы как-то данные или нет\n", "* Зафиксировать модель\n", "* Зафиксировать метрику качества\n", "* Понять, какие гиперпараметры мы хотим перебрать\n", "* Собственно выбрать лучший набор с помощью кросс-валидации\n", "* И оценить, а что на тестовой выборке-то получилось!\n", "\n", "Шаг 1 и шаг 2 - объединяются логически - часто бывает так, что нам сначала нужно применить логику предобработки данных, а затем уже применить и обучить модель.\n", "\n", "Логическая цепочка в таком случае будет выглдять так:\n", "\n", " X -> предобрабатываем X -> X уходит в fit некоторой модели\n", "\n", "`sklearn` Предоставляет удобное решение для составление таких цепочек-шагов обработки результатов, полученных на предыдущем шаге. Инструмент назвается Pipeline (пайплайны):\n", "\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "ieIKDcDCfwrF" }, "outputs": [], "source": [ "pipeline = Pipeline([\n", " ('normalizer', MinMaxScaler()),\n", " ('classifier', KNeighborsRegressor())\n", "])" ] }, { "cell_type": "markdown", "metadata": { "id": "OJoCXHe1g95y" }, "source": [ "В данном примере вы говорим, что хотим, чтобы сначала данные отскелились, а потом, что получилось, подалось бы модели. Причем шаги этой цепочки обозначены строковыми и понятными обозначениям, которые нам скоро понадобятся\n", "\n", "`Pipeline` наследует абсолютно то же интерфейс, что и остальные модули обучения в `sklearn` - имеет методы `fit` и `predict`! То есть получили некоторый \"сборный конструктор\" из различных частей." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "L_W-zQ4Hg9VA", "outputId": "dfb7f73f-e1b4-498d-c626-d1c07d39b455" }, "outputs": [ { "data": { "text/plain": [ "array([0.5482 , 0.7506 , 4.628604, ..., 1.328 , 2.4206 , 4.089404],\n", " shape=(5160,))" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline.fit(X_train, y_train)\n", "pipeline.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": { "id": "j6NOGtDhhkU1" }, "source": [ "Далее встает вопрос: а как этот процесс обучения можно варьировать? В самом `KNeighborsRegressor` много гиперпараметров (число соседей, метрика расстояния и пр.), которые задаются при создании класса, а мы в `Pipeline` создали дефолтный экземпляр класса.. И вообще, можно же еще и перебирать, хотим ли мы использовать скейлер или нет, вдруг будет лучше без него? (а такое тоже бывает!)\n", "\n", "И здесь `sklearn` нас снова выручает и дает возможность варьировать создание моделей, которые мы используем в `Pipeline`:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "07ww_pFFiOmC" }, "outputs": [], "source": [ "parameters = {\n", " 'classifier__n_neighbors': [1, 5, 10],\n", " 'classifier__metric': ['euclidean', 'cosine'],\n", " 'classifier__weights': ['uniform', 'distance'],\n", " 'normalizer': ['passthrough', MinMaxScaler(), StandardScaler()]\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "1gCIss5uiQzt" }, "source": [ "Мы можем задать такой словарик типа `str` -> `list`, который будет описывать все наши изменения, которые должны будут примениться в `Pipeline` (применяться они будут через класс кросс-валидации)\n", "\n", "Если в качестве ключа написать ровно то название, которым мы обозначили шаг в `Pipeline`, то потом появляется возможность программно сопоставить эти два шага и заменить значения в `Pipeline`:\n", "\n", " 'normalizer': ['passthrough', MinMaxScaler(), StandardScaler()]\n", "\n", "В данном случае у нас в `Pipeline` значения в названии шага `normalizer` будут меняться в соответствии с предложенными вариантам в списке\n", "\n", "**Пояснение:** `passthrough` - ключевое слово, которые говорит `sklearn` \"пропусти этот шаг\"\n", "\n", "А здесь уже логика чуть посложнее: у класса `KNeighborsRegressor`, который лежит в шаге classifier, при создании можно указать различный набор гиперпараметров, например, тот же n_negihbors. Обращаясь через двойное нижнее подчеркивание появляется возможность на лету изменять значения этих гиперпараметров в классе:\n", "\n", " 'classifier__n_neighbors': [1, 5, 10]\n", "\n", "Здесь мы говорим \"посмотри, что за модель лежит на шаге classifier, и замени у нее гиперпараметр n_neighbors в соответствии с сеткой перебора\"" ] }, { "cell_type": "markdown", "metadata": { "id": "GIFnL73G7aXZ" }, "source": [ "### **Задание 2.1 (кросс, 2 балла)**\n", "\n", "\n", "Запустите кросс-валидацию на 3 фолдах с помощью класса `GridSearchCV` и метода $fit$ этого модуля. В качестве метрики используйте $R^2$-score (строкое представление в `sklearn` \"r2\"). Параметры для перебора описаны ниже" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "id": "XjbNVqSY7aXa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n", "[CV 1/3; 2/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 2/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 1/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 1/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough\n", "[CV 3/3; 1/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough;, score=-0.275 total time= 0.0s\n", "[CV 1/3; 3/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 2/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.509 total time= 0.0s\n", "[CV 2/3; 1/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough;, score=-0.250 total time= 0.0s\n", "[CV 3/3; 2/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.545 total time= 0.0s\n", "[CV 2/3; 2/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 3/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 4/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 3/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 4/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough;, score=-0.285 total time= 0.0s\n", "[CV 2/3; 4/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough\n", "[CV 3/3; 4/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough\n", "[CV 1/3; 1/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 4/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough;, score=-0.250 total time= 0.0s\n", "[CV 1/3; 5/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 2/3; 2/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.544 total time= 0.0s\n", "[CV 2/3; 5/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 4/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough;, score=-0.275 total time= 0.0s\n", "[CV 3/3; 5/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 1/3; 1/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough;, score=-0.285 total time= 0.0s\n", "[CV 1/3; 6/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 1/3; 3/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler();, score=0.516 total time= 0.1s\n", "[CV 2/3; 6/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 1/3; 5/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.509 total time= 0.0s\n", "[CV 3/3; 5/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.545 total time= 0.0s\n", "[CV 3/3; 6/36] START classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 1/3; 7/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 5/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.544 total time= 0.0s\n", "[CV 2/3; 7/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough\n", "[CV 1/3; 7/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough;, score=0.100 total time= 0.0s\n", "[CV 3/3; 7/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 7/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough;, score=0.096 total time= 0.0s\n", "[CV 3/3; 3/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler();, score=0.490 total time= 0.1s\n", "[CV 1/3; 8/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 7/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough;, score=0.098 total time= 0.0s\n", "[CV 2/3; 8/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 8/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 1/3; 9/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 2/3; 3/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler();, score=0.536 total time= 0.1s\n", "[CV 2/3; 6/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler();, score=0.536 total time= 0.1s\n", "[CV 2/3; 9/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 3/3; 8/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.691 total time= 0.0s\n", "[CV 2/3; 8/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.692 total time= 0.0s\n", "[CV 1/3; 8/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.691 total time= 0.0s\n", "[CV 3/3; 9/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 10/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 10/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough\n", "[CV 3/3; 6/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler();, score=0.490 total time= 0.1s\n", "[CV 1/3; 6/36] END classifier__metric=euclidean, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler();, score=0.516 total time= 0.1s\n", "[CV 3/3; 10/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough\n", "[CV 1/3; 11/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 1/3; 10/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough;, score=0.120 total time= 0.0s\n", "[CV 2/3; 11/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 11/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 2/3; 10/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough;, score=0.120 total time= 0.0s\n", "[CV 1/3; 12/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 3/3; 10/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough;, score=0.119 total time= 0.0s\n", "[CV 2/3; 12/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 1/3; 11/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.695 total time= 0.0s\n", "[CV 3/3; 12/36] START classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 3/3; 11/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.697 total time= 0.1s\n", "[CV 1/3; 13/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 11/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.698 total time= 0.1s\n", "[CV 2/3; 13/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough\n", "[CV 1/3; 13/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough;, score=0.116 total time= 0.0s\n", "[CV 3/3; 13/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 13/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough;, score=0.114 total time= 0.0s\n", "[CV 1/3; 14/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 13/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough;, score=0.118 total time= 0.0s\n", "[CV 2/3; 14/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 1/3; 9/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler();, score=0.668 total time= 0.2s\n", "[CV 3/3; 14/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 2/3; 9/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler();, score=0.701 total time= 0.1s\n", "[CV 1/3; 15/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 3/3; 9/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler();, score=0.669 total time= 0.2s\n", "[CV 2/3; 15/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 2/3; 12/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler();, score=0.705 total time= 0.1s\n", "[CV 1/3; 14/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.694 total time= 0.1s\n", "[CV 3/3; 15/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 16/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 14/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.698 total time= 0.1s\n", "[CV 2/3; 16/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough\n", "[CV 1/3; 12/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler();, score=0.673 total time= 0.1s\n", "[CV 3/3; 16/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough\n", "[CV 1/3; 16/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough;, score=0.141 total time= 0.0s\n", "[CV 1/3; 17/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 14/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.695 total time= 0.1s\n", "[CV 3/3; 17/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 2/3; 16/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough;, score=0.143 total time= 0.0s\n", "[CV 3/3; 16/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough;, score=0.145 total time= 0.0s\n", "[CV 2/3; 18/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 3/3; 12/36] END classifier__metric=euclidean, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler();, score=0.672 total time= 0.1s\n", "[CV 1/3; 19/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough[CV 3/3; 19/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough\n", "\n", "[CV 1/3; 17/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.702 total time= 0.1s\n", "[CV 2/3; 17/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 17/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.703 total time= 0.1s\n", "[CV 1/3; 18/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 2/3; 17/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.706 total time= 0.1s\n", "[CV 1/3; 15/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler();, score=0.677 total time= 0.2s\n", "[CV 2/3; 15/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler();, score=0.708 total time= 0.2s\n", "[CV 2/3; 20/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 15/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler();, score=0.673 total time= 0.2s\n", "[CV 1/3; 21/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 3/3; 21/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 2/3; 22/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 18/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler();, score=0.713 total time= 0.2s\n", "[CV 3/3; 18/36] START classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 1/3; 18/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler();, score=0.683 total time= 0.2s\n", "[CV 1/3; 23/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 18/36] END classifier__metric=euclidean, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler();, score=0.679 total time= 0.2s\n", "[CV 3/3; 23/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 1/3; 23/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.450 total time= 1.7s\n", "[CV 2/3; 23/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 21/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler();, score=0.458 total time= 1.9s\n", "[CV 1/3; 22/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 20/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.458 total time= 1.9s\n", "[CV 3/3; 20/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 1/3; 21/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler();, score=0.472 total time= 1.9s\n", "[CV 2/3; 21/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 2/3; 22/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough;, score=0.102 total time= 1.9s\n", "[CV 3/3; 22/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough\n", "[CV 1/3; 19/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough;, score=0.078 total time= 2.2s\n", "[CV 2/3; 19/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough\n", "[CV 3/3; 19/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough;, score=0.135 total time= 2.2s\n", "[CV 1/3; 20/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 23/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.470 total time= 1.8s\n", "[CV 1/3; 24/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 2/3; 23/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.458 total time= 0.8s\n", "[CV 2/3; 24/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 1/3; 22/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough;, score=0.078 total time= 0.8s\n", "[CV 1/3; 25/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough\n", "[CV 3/3; 20/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.470 total time= 0.8s\n", "[CV 3/3; 25/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough\n", "[CV 3/3; 22/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=passthrough;, score=0.135 total time= 0.8s\n", "[CV 2/3; 21/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=StandardScaler();, score=0.509 total time= 0.8s\n", "[CV 2/3; 26/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 1/3; 27/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 2/3; 19/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=passthrough;, score=0.102 total time= 0.8s\n", "[CV 3/3; 27/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 20/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.450 total time= 0.9s\n", "[CV 2/3; 28/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough\n", "[CV 1/3; 24/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler();, score=0.472 total time= 0.9s\n", "[CV 1/3; 29/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 2/3; 24/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler();, score=0.509 total time= 0.5s\n", "[CV 3/3; 24/36] START classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 3/3; 24/36] END classifier__metric=cosine, classifier__n_neighbors=1, classifier__weights=distance, normalizer=StandardScaler();, score=0.458 total time= 0.6s\n", "[CV 3/3; 29/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 1/3; 27/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler();, score=0.657 total time= 1.1s\n", "[CV 2/3; 27/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 25/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough;, score=0.382 total time= 1.2s\n", "[CV 2/3; 25/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 26/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.639 total time= 1.2s\n", "[CV 3/3; 26/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 25/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough;, score=0.385 total time= 1.2s\n", "[CV 1/3; 26/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 3/3; 27/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler();, score=0.648 total time= 1.2s\n", "[CV 1/3; 28/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 28/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough;, score=0.390 total time= 1.3s\n", "[CV 3/3; 28/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough\n", "[CV 1/3; 29/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.641 total time= 1.2s\n", "[CV 2/3; 29/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 29/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.649 total time= 1.1s\n", "[CV 1/3; 30/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 2/3; 27/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=StandardScaler();, score=0.687 total time= 1.3s\n", "[CV 2/3; 30/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 3/3; 26/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.647 total time= 1.2s\n", "[CV 1/3; 31/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough\n", "[CV 2/3; 25/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=passthrough;, score=0.367 total time= 1.4s\n", "[CV 3/3; 31/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough\n", "[CV 1/3; 26/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.643 total time= 1.4s\n", "[CV 2/3; 32/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 1/3; 28/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough;, score=0.393 total time= 1.4s\n", "[CV 1/3; 33/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 2/3; 29/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.642 total time= 1.3s\n", "[CV 2/3; 33/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 3/3; 28/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=passthrough;, score=0.404 total time= 1.4s\n", "[CV 3/3; 33/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler()\n", "[CV 1/3; 30/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler();, score=0.660 total time= 1.4s\n", "[CV 1/3; 34/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 30/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler();, score=0.688 total time= 1.7s\n", "[CV 3/3; 30/36] START classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 1/3; 31/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough;, score=0.378 total time= 1.7s\n", "[CV 2/3; 31/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough\n", "[CV 3/3; 31/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough;, score=0.388 total time= 1.7s\n", "[CV 1/3; 32/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 2/3; 32/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.658 total time= 1.7s\n", "[CV 3/3; 32/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler()\n", "[CV 1/3; 33/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler();, score=0.665 total time= 2.0s\n", "[CV 2/3; 34/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough\n", "[CV 3/3; 33/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler();, score=0.664 total time= 1.9s\n", "[CV 3/3; 34/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough\n", "[CV 2/3; 33/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=StandardScaler();, score=0.700 total time= 2.1s\n", "[CV 1/3; 35/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 1/3; 34/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough;, score=0.405 total time= 1.4s\n", "[CV 2/3; 35/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 3/3; 30/36] END classifier__metric=cosine, classifier__n_neighbors=5, classifier__weights=distance, normalizer=StandardScaler();, score=0.650 total time= 1.7s\n", "[CV 3/3; 35/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler()\n", "[CV 1/3; 32/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.660 total time= 1.6s\n", "[CV 3/3; 32/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=MinMaxScaler();, score=0.661 total time= 1.6s\n", "[CV 1/3; 36/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 2/3; 36/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 2/3; 31/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=uniform, normalizer=passthrough;, score=0.366 total time= 1.8s\n", "[CV 3/3; 36/36] START classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler()\n", "[CV 2/3; 34/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough;, score=0.403 total time= 1.6s\n", "[CV 3/3; 34/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=passthrough;, score=0.419 total time= 1.7s\n", "[CV 1/3; 35/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.664 total time= 1.7s\n", "[CV 2/3; 35/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.665 total time= 1.7s\n", "[CV 3/3; 35/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=MinMaxScaler();, score=0.667 total time= 0.9s\n", "[CV 1/3; 36/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler();, score=0.673 total time= 0.9s\n", "[CV 3/3; 36/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler();, score=0.669 total time= 0.9s\n", "[CV 2/3; 36/36] END classifier__metric=cosine, classifier__n_neighbors=10, classifier__weights=distance, normalizer=StandardScaler();, score=0.706 total time= 1.0s\n" ] }, { "data": { "text/html": [ "
GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[('normalizer', 'passthrough'),\n",
              "                                       ('classifier', KNeighborsRegressor())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'classifier__metric': ['euclidean', 'cosine'],\n",
              "                         'classifier__n_neighbors': [1, 5, 10],\n",
              "                         'classifier__weights': ['uniform', 'distance'],\n",
              "                         'normalizer': ['passthrough', MinMaxScaler(),\n",
              "                                        StandardScaler()]},\n",
              "             scoring='r2', verbose=10)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "GridSearchCV(cv=3,\n", " estimator=Pipeline(steps=[('normalizer', 'passthrough'),\n", " ('classifier', KNeighborsRegressor())]),\n", " n_jobs=-1,\n", " param_grid={'classifier__metric': ['euclidean', 'cosine'],\n", " 'classifier__n_neighbors': [1, 5, 10],\n", " 'classifier__weights': ['uniform', 'distance'],\n", " 'normalizer': ['passthrough', MinMaxScaler(),\n", " StandardScaler()]},\n", " scoring='r2', verbose=10)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# задаем нужный пайплайн\n", "pipeline = Pipeline([\n", " ('normalizer', 'passthrough'),\n", " ('classifier', KNeighborsRegressor())\n", "])\n", "\n", "# и сетку перебора параметров\n", "parameters = {\n", " 'classifier__n_neighbors': [1, 5, 10],\n", " 'classifier__metric': ['euclidean', 'cosine'],\n", " 'classifier__weights': ['uniform', 'distance'],\n", " 'normalizer': ['passthrough', MinMaxScaler(), StandardScaler()]\n", "}\n", "\n", "grid_search = GridSearchCV(estimator=pipeline, param_grid=parameters, cv=3, scoring='r2', n_jobs=-1, verbose=10)\n", "grid_search.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "id": "dGGPjHYekQFH" }, "source": [ "Посмотрим на результаты кросс-валидации. Посмотреть результаты можно удобно через аттрибут `cv_results_`:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "CFBVfk2Ff0cb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 1, 'classifier__weights': 'uniform', 'normalizer': 'passthrough'}, np.float64(-0.2698362539419594))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 1, 'classifier__weights': 'uniform', 'normalizer': MinMaxScaler()}, np.float64(0.5329368736058075))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 1, 'classifier__weights': 'uniform', 'normalizer': StandardScaler()}, np.float64(0.5139255367340351))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 1, 'classifier__weights': 'distance', 'normalizer': 'passthrough'}, np.float64(-0.2698362539419594))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 1, 'classifier__weights': 'distance', 'normalizer': MinMaxScaler()}, np.float64(0.5329368736058075))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 1, 'classifier__weights': 'distance', 'normalizer': StandardScaler()}, np.float64(0.5139255367340351))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 5, 'classifier__weights': 'uniform', 'normalizer': 'passthrough'}, np.float64(0.0977684191856583))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 5, 'classifier__weights': 'uniform', 'normalizer': MinMaxScaler()}, np.float64(0.6915621333110309))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 5, 'classifier__weights': 'uniform', 'normalizer': StandardScaler()}, np.float64(0.6793573692425973))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 5, 'classifier__weights': 'distance', 'normalizer': 'passthrough'}, np.float64(0.1199731657678866))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 5, 'classifier__weights': 'distance', 'normalizer': MinMaxScaler()}, np.float64(0.6964752425342952))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 5, 'classifier__weights': 'distance', 'normalizer': StandardScaler()}, np.float64(0.6834229913464561))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 10, 'classifier__weights': 'uniform', 'normalizer': 'passthrough'}, np.float64(0.1157913069850766))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 10, 'classifier__weights': 'uniform', 'normalizer': MinMaxScaler()}, np.float64(0.6955574490923349))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 10, 'classifier__weights': 'uniform', 'normalizer': StandardScaler()}, np.float64(0.6860471330995802))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 10, 'classifier__weights': 'distance', 'normalizer': 'passthrough'}, np.float64(0.14310059566240177))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 10, 'classifier__weights': 'distance', 'normalizer': MinMaxScaler()}, np.float64(0.7035316821435433))\n", "({'classifier__metric': 'euclidean', 'classifier__n_neighbors': 10, 'classifier__weights': 'distance', 'normalizer': StandardScaler()}, np.float64(0.691615057187545))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 1, 'classifier__weights': 'uniform', 'normalizer': 'passthrough'}, np.float64(0.10527040776150857))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 1, 'classifier__weights': 'uniform', 'normalizer': MinMaxScaler()}, np.float64(0.4590323476108448))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 1, 'classifier__weights': 'uniform', 'normalizer': StandardScaler()}, np.float64(0.47958515234726945))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 1, 'classifier__weights': 'distance', 'normalizer': 'passthrough'}, np.float64(0.1052704077615086))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 1, 'classifier__weights': 'distance', 'normalizer': MinMaxScaler()}, np.float64(0.4590323476108448))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 1, 'classifier__weights': 'distance', 'normalizer': StandardScaler()}, np.float64(0.47958515234726945))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 5, 'classifier__weights': 'uniform', 'normalizer': 'passthrough'}, np.float64(0.37805502479959713))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 5, 'classifier__weights': 'uniform', 'normalizer': MinMaxScaler()}, np.float64(0.6429483717302537))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 5, 'classifier__weights': 'uniform', 'normalizer': StandardScaler()}, np.float64(0.6639133943561187))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 5, 'classifier__weights': 'distance', 'normalizer': 'passthrough'}, np.float64(0.3955985736693544))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 5, 'classifier__weights': 'distance', 'normalizer': MinMaxScaler()}, np.float64(0.6439729194747269))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 5, 'classifier__weights': 'distance', 'normalizer': StandardScaler()}, np.float64(0.666103286084983))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 10, 'classifier__weights': 'uniform', 'normalizer': 'passthrough'}, np.float64(0.3775295488771868))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 10, 'classifier__weights': 'uniform', 'normalizer': MinMaxScaler()}, np.float64(0.6595874224717159))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 10, 'classifier__weights': 'uniform', 'normalizer': StandardScaler()}, np.float64(0.6765890035252449))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 10, 'classifier__weights': 'distance', 'normalizer': 'passthrough'}, np.float64(0.4089433531052468))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 10, 'classifier__weights': 'distance', 'normalizer': MinMaxScaler()}, np.float64(0.6650747099452051))\n", "({'classifier__metric': 'cosine', 'classifier__n_neighbors': 10, 'classifier__weights': 'distance', 'normalizer': StandardScaler()}, np.float64(0.6829471618912982))\n" ] } ], "source": [ "for elem in zip (grid_search.cv_results_['params'], grid_search.cv_results_['mean_test_score']):\n", " print(elem)" ] }, { "cell_type": "markdown", "metadata": { "id": "BlJ0TiNE7aXb" }, "source": [ "### **Задание 2.2 (кросс, 1 балл)**\n", "\n", " Какой наибольший $r2\\_score$ удалось достичь на кросс-валидации? Какие закономерности вы видите?\n", "\n", " * Обучите модель с наилучшими параметрами на всей обучающей выборке\n", " * измерьте $r2\\_score$ на тестовой выборке.\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "0R-7usFU7aXc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Максимальный r2_score 0.7035316821435433\n", "Лучшие параметры {'classifier__metric': 'euclidean', 'classifier__n_neighbors': 10, 'classifier__weights': 'distance', 'normalizer': MinMaxScaler()}\n", "r2_score на тестовой выборке 0.7117082182374788\n" ] } ], "source": [ "from sklearn.metrics import r2_score # изучите самостоятельно, как с помощью этой функции измерять качество\n", "\n", "print(\"Максимальный r2_score \", grid_search.best_score_)\n", "print(\"Лучшие параметры \", grid_search.best_params_)\n", "\n", "best_model = grid_search.best_estimator_\n", "best_model.fit(X_train, y_train)\n", "y_pred = best_model.predict(X_test)\n", "\n", "test_r2 = r2_score(y_test, y_pred)\n", "\n", "print(\"r2_score на тестовой выборке \", test_r2)" ] }, { "cell_type": "markdown", "metadata": { "id": "U_Df4Tqsd1p_" }, "source": [ "**Ваши выводы тут:**\n", "\n", "Лучшие параметры модели:\n", "\n", "n_neighbors=10\n", "\n", "weights=distance\n", "\n", "metric=euclidean\n", "\n", "Нормализация с использованием MinMaxScaler\n", "\n", "Модель показала хорошее качество: $R^2$ около 0.7 говорит, что 0.7 дисперсии целевой переменной объясняется моделью.\n", "\n", "Таким образом:\n", "\n", "Увеличение числа соседей до 10 дало лучший баланс.\n", "\n", "Использование весов 'distance' улучшает качество по сравнению с равными весами.\n", "\n", "Евклидова метрика оказалась эффективнее других\n", "\n", "Нормализация данных (MinMaxScaler) важна для метрических методов." ] }, { "cell_type": "markdown", "metadata": { "id": "VYFkpZial3J3" }, "source": [ "Поздравляем с первой обученной моделью машинного обучения!" ] } ], "metadata": { "colab": { "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 0 }