diff --git a/HW5/code/Ensembles.ipynb b/HW5/code/Ensembles.ipynb new file mode 100644 index 0000000..8860a3b --- /dev/null +++ b/HW5/code/Ensembles.ipynb @@ -0,0 +1,6735 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "9b21216d-37c8-406a-a8bf-5642cff16bb7", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import warnings\n", + "import random\n", + "import math\n", + "import pandas as pd\n", + "import xgboost\n", + "import lightgbm\n", + "import catboost\n", + "\n", + "#from math import comb\n", + "from matplotlib.colors import ListedColormap\n", + "from scipy.stats import pearsonr\n", + "from itertools import combinations\n", + "from sklearn.base import BaseEstimator\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import (RandomForestClassifier,\n", + " ExtraTreesClassifier,\n", + " VotingClassifier)\n", + "from sklearn.tree import (DecisionTreeRegressor,\n", + " DecisionTreeClassifier)\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.model_selection import cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e0cbb7f2-2038-4e1b-8c39-ca20b7b332a2", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = 12, 9\n", + "sns.set_style(\"whitegrid\")\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "SEED = 111\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "5ab161b1-2231-4464-96f9-bb9ae0c3d1ed", + "metadata": {}, + "source": [ + "### Задание 1. Bias-variance trade-off\n", + "\n", + "**2 балла**\n", + "\n", + "Продемонстрируйте bias-variance trade-off для `DecisionTreeRegressor` при изменении глубины дерева. Постройте регрессионную модель функции от одной независимой переменной, представленной в ячейке ниже, используя функцию `plot_regression_predictions` (можете ее как-то поменять, если захочется). Попробуйте разные значения глубины деревьев, при каком значении, на ваш взгляд, модель оптимальна, при каком variance становится слишком большим?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "563b781e-78dd-4679-b0b0-3e70a064cfd7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Сгенерируем какую-нибудь необычную зависимость и научимся ее предсказывать\n", + "np.random.seed(42)\n", + "m = 300\n", + "X = np.linspace(-3, 3, m).reshape(-1, 1)\n", + "y = (3 + 2/np.pi * np.arcsin(np.cos(10 * X))) * X\n", + "y = y + np.random.randn(m, 1) / 3\n", + "plt.plot(X.reshape(-1), y.reshape(-1), \"b.\");" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d7c9c3d-5d4e-43a8-9946-3bde8c5b6e85", + "metadata": {}, + "outputs": [], + "source": [ + "# Функция для отрисовки предсказаний деревьев решений в случае регрессии\n", + "def plot_regression_predictions(tree_reg, X, y, axes=[-3, 3, -10, 10], ylabel=\"$y$\"):\n", + " x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n", + " y_pred = tree_reg.predict(x1)\n", + " plt.axis(axes)\n", + " plt.xlabel(\"$x_1$\", fontsize=18)\n", + " if ylabel:\n", + " plt.ylabel(ylabel, fontsize=18, rotation=0)\n", + "\n", + " plt.plot(x1, y_pred, linewidth=2, label=r\"$\\hat{y}$\")" + ] + }, + { + "cell_type": "markdown", + "id": "243e15dc-2e23-42dc-b252-828c9a6b1e00", + "metadata": {}, + "source": [ + "Изобразите, как меняются предсказания дерева при увеличении максимальной глубины (можно взять что-то в диапазоне от 1 до 15):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a8191bd8-71c5-4902-9f17-5b3b46943074", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ccK/stPBzGYMG9PxFsyLR1RXYfxcC03RK/HYfrPDvqCgn6M4gN8PHuIEbKS4+uPNJ4BfMnz+f888/nylTpnD88cdz7bXXsnHjxmQ3SwghhBBCCNELWR1U+JVODz83YvzhPX7NjKL88LZpZex3X9PtxNy+2KX/808/Dm8r3XDQ55PAL/jggw/4xje+wbPPPssTTzxBMBhk7ty5NDc3H/hgIYQQQgghRL8SXeH3pjrVdq1CgV/bTJp8XI9fc8yUY6hp9LK+IoeaxsEEAoF97muYTsy1+2CX/ppdu8LbPbG8oYzhFyxcuDDm8d13383xxx/Pp59+yrHHHpukVgkhhBBCCCGSzaqtpe5fL2HV14WfC9TVhLeb//M6uz5oRhtON35XsAnfU09T1cPtePvtMfzsL6VorVBqCvY33+Rb09Z1vHPzKMCDFdj35H69ga8liN7rpkSwvhUztG2ZTbQ2Ojc2LLt770UCf7x9+gIsuRN8jYm7pjcDZv4YDj+nW4c3NDhdR7Kzs3uwUUIIIYQQQoi+Zuddd1H3j3/GPBc8YgJOKnXR9MLf2N24jWDpnQAYVhO773+gR9uwIzCQWzf+B40zNl9rxW3PnMSX3v9/DHLvbLe//tJPIW0QwebWHm1HT3rjic9Y9/6Ods+3rTAAYLl8LLzlbQBcXsUxl2V1+ToS+OPt3fth9/rEXrMBWHZ/twK/bdvceeedHHXUUYwZM6bHmyaEEEIIIYToO1rXl7V7zg4tGYdyYdqtVA3MwzZTADB0zxc6twSGY4fr3qE2YLI1MKzDwK9C1fBuFsXjztcc6DDsA7gCHuxQSrfcQYzQe9B294YnSOCPtxNugCW/SnyFv/S73Tr09ttvp6ysjKeffrqHGyWEEEIIIYToc4JBAJTHQ/ED9zvPPfYwBPyAi6Kf/JAPNi6Htt71uomh8x/t0SYYuzwYF2psO1L+Vthwxa0YR9QzpDAyp4DWmhULNgFg99Ip6wK+yGSCaVkeCoZnhh9v/zA1HPjtFIORo5zJCj0pCtj3vAX7IoE/3g4/p9td6xPtjjvu4K233uKpp55i0KBByW6OEEIIIYQQIsm05ZSYlddLxowZznMLHnSeUy4yj/sStZ+/FdlfNYX36yljgQX1cNWVNrY2UGhAcfkdh6OUzSVnvcCT/zg/vL+x4ItQWwxsW2MYquMTJ0n06gFDxuZyytzIqgaPf/OV8Hb20ELOvO5IAPx+P6tXr+7ytXrnLQ+RUFpr7rjjDv7973/zpz/9iaFDhya7SUIIIYQQQoheQFuhCr8Z6VJv206lubalkGXvu9ixNeo1syUu7Zg7F/7fhb/kipOd0BsZz2/w5xfP4747Hg7vW9OSGprN34tt9b6l+aIDv+mKvRmhdKTaP2rCwS9vKBV+we23386//vUvHn74YdLT06mqcubUzMzMJCUlJcmtE0IIIYQQQiRNMDSI3O1ER9u20HaQ9zZ9hec//A76HwZK/ZiLpq+jdFwllsu/n5MdnPzxLeyq94XDfhtbKz5/YzTXfvI7dm05guc/uhCtDZSyKThBM++auDWpW2IDf2wNXqsMAJQd5NhpJx/0tSTwC5555hkALr300pjn77rrLs4777xkNEkIIYQQQgjRC+i2MfymEx0tf4Bq30Ce//C7aB2psi9eOo7xxXvIzQ3GrS3Tpp/M1rInUeootI4EZYVmwdtfQXMKhLr7t7Xruu9ozjgLiovj1qwu21/gt02nwu8KNpCalnbQ15LAL1i3bh/rVwohhBBCCCH6tfAY/lCX/oDfR60xIRz229haUVWfyuCJOXFry5ipJ3D0sqVcPG0hz7zzLWxtAhYaA8JV/9h2WZZiw4beFfjtfQT+poYGAm4n8BtWQ49cS8bwCyGEEEIIIYToWKjCj8sJ/EG/n8KsapSKXSbOUDYF+f9k7q0/j1tT3CkpnPej23lq6ZVs2WqyZAn85S8me4f8aKapGT06bk3qlmAgKvC7I5H8f++/Bcr5nFUPLW8ogV8IIYQQQgghRIciFX6nc3jQ7yc/tZGLpq/FCIV+04QFjxn85IlfJKxdxcVw0klQWgqGsfca9c5jQ9nc+8vGXlXdB7CCkfZGV/g3rYnMwq9VzwR+6dIvhBBCCCGEEKJDe3fpD/p9mEE3peMqGV+8h6qmj/n+A9cnLVQXF8OCBYp588CynJsPXz3qU4oH+SlK2c7XT58EZB7wPIlkBTru0r+tzKKqIoeC7BYK3U09ci0J/EIIIYQQQgghOhbu0t9W4fdhWh6CJuRm+MgdvCnpFfS5c2H2bNiwAUaPhld++il+bwGugIWvvj65jetAzKR9bmc4wnXf/i+P/OHHodUFNBdPq+CqHriWdOkXQgghhBBCCNGh9hV+P4btDb/uyvB2eFyitXXxLy4GpZ2lAW3DS3NtbVLb1ZG9Z+l/4M75PPKH6eGVB7RWPP3OBZSXH/y1JPALIYQQQgghhGhHa+30kyc28CudEt4nLT8nGU3bv7bAb3rYs2tHkhvT3t6z9G96h3arHmhtsGHDwV9LAr8QQgghhBBCiPZCYR8Id+n3t7aidGr46UHDRiS4UQemtC+8vbNmWxJb0rHgXmP4B6e62696YNg9srqABH4hhBBCCCGEEO3oqMDfVuH3NbcCkcB/xFFTE92sTvCHt2obapLYjo5ZQRutbezgDiq3rCIzZ0Bo1QPnRoBSFj+94dMemRtBJu0TQgghhBBCCNFe24R9gHI5gd/f0oo20gAwLB+Di4cnpWn7o1Uk8Lf4e2a2+54UDNj4G55FW9tZ/vYg0t0XUzquksn577JxzxcMyNjOhed/q0euJYFfCCGEEEIIIUQ70RV+zFCX/hYfdijwm1ZzMprVCYHwlhWI3LQoL4eyMigpIakrC/iaG9DWdgDSfRnh5zNz1zPa9RkAA4b2zI0UCfyCp59+mmeeeYaKigoASkpKuPbaa5kxY0aSWyaEEEIIIYRIFh1T4XeiY6C1FcscAIBh987AH13ht0M3LRYuhCuvtNHawDBgwQJnOb9kCPgi7WvaPZoKXw4F2S1kDfFx9PRzKB5/BAN6aG4ECfyCQYMGccsttzB8+HC01vz973/nuuuu44UXXqCkpCTZzRNCCCGEEEIkge6gS/+euj3YZpHznG5JSrsOxDYCUQ805eWRsA9g2zBvHsyenZxKv+XzE9RBVlR/neeXXI7WCqU0v/p/IzjpsiE9ei2ZtE8wa9YsZsyYwYgRIxg5ciQ33XQTaWlprFy5MtlNE0IIIYQQQiRLB136q+t2RZ7TvbTCb0TdqAgqysoIh/02lkWPLHvXHUG/H8P6Wjjsg7Ms309/PoTy8p69llT44+y1za/x0MqHaAokbrKIdHc610++nlNGnNLlYy3L4tVXX6W5uZkpU6bEoXVCCCGEEEKIvqCjWfpbWpox215XrUlo1YFpI9JuwzIoKQGl7JjQb5r0yLJ33VG+ZyXl/hnhsN+m7SZET/Y6kMAfZ39c80c21W1K/HU//WOXAv+6deu48MIL8fl8pKWl8dBDDzE6Wb8BQgghhBBCiOTroEu/7Q9EBf5e2qXftCG0rL1hmxQXw6XHvcZTy0/F1gpD2cyfbyRt4j6fr5qC7BaU0jGhPx43ISTwx9kVR1zBgysfTHiF//LDL+/SMSNHjuTvf/87DQ0NvPbaa9x666089dRTEvqFEEIIIYTopzqapV/5dfgp2/TvfUivoF0aFRrGb9jO7Ynpo8soGZVFVX0qhVn/Yu7cnyWtfVawldwMHxdNX8vipWOxtYFpwvz5PT+ngAT+ODtlxCnd6lqfaB6Ph+HDnaUfjjjiCFavXs2TTz7JHXfckeSWCSGEEEIIIZJBByOBf6dvN5sq3sXwm+HnLNPPuxXvArBru4fyTakUj2yhsCi5NwJsl8YMB36X00aVSm6Gj9wMH4ZVH253Mmi/07jScZVMKvwXaXNOpXhEK4VFft6t6PiYFJXSrWtJ4Bcdsm0bv7933rETQgghhBBCJIAV6dL/3+1vs/CNZVxjfYW2ofCWK8jVb1zNnv+ey/Y//hy0Ccqi6PLbyZvxQpIaDZcYJ9C2ur2y3Vz976u5Qf0w/LqyTa5+4+rkNA74VjCy/HlOZjUPNl8Mn+H82YcUI4VHJzza5WvJLP2Ce++9lxUrVlBeXs66deu49957+eCDDzjrrLOS3TQhhBBCCCFEkkR36bdDydEMesPP+d1BAnsGRsI+gDbZ/qefE9gzMJFNjRF02eFtpd2YtsI2UqOeMzs6LGFMK6qXRPQSgnEgFX5BdXU1t956K7t27SIzM5OxY8eycOFCTjjhhGQ3TQghhBBCCJEkOmrSPkyTa468BmPJzvBTKtXFqRnXs27vAG2bnJp5PWOO3J6glsZq3rYR6pxtpd1cWXI51ntp4deVdnHNkdckpW0A+r2yyLbL6lRb0ow06Ma9AQn8gjvvvDPZTRBCCCGEEEL0NlEVfsPl5trJ1/K4vjv8XFZuIbecdg4PGGBHiuqYJtxy6jlJmwX/2RWPUBVaz15pDzO8x/JfM5KWlTa5dvK1yWkc8Ejw+9ihJK7dmus60Ra/38/q1au7fC3p0i+EEEIIIYQQop3oSfswnSq+0pHJ43ILB1NcDAsWhF+O22zzXZEzcHDUIw9la2ODstLuxDZoL4Ydub52q/3sefCkwi+EEEIIIYQQop2YLv2uUKJXka7xI0smAzB3LsyeDRs2OOvIJzPsAwwbMZYyKkOPPOzZUQmMi9ojuTE4OvAbKfFtiwR+IYQQQgghhBDtRc3Sr0KBXytn8jvD8jN85Jjw68XFyQ/6bQoHFoEuB2WilQdfXUPM60onNwYr7Qlvu1K7t9xeZ0mXfiGEEEIIIYQQ7UTP0o/phOTqlnzWV+RQX68x3b0zTqZlpGFazhLjWnmxW4J77ZHcLv1KR1Y6yMjOjuu1pMIvhBBCCCGEEKIdHYiq8JsuHnvM5kd/PQ2tFUppMk4Ict13k9jAfTBdBobtwyIVbXjBZ++1R7JjcCTwDygcFNcr9c5bMkIIIYQQQgghksoK+sPbdS2DuPpqhdbOJHNaK274novy8mS1bt8Ml0JpHwC24cEI7FXRV8kL/FprtIp04x86oiSu15PAL4QQQgghhBCinWDAF97eXTcU246dUd6yFBs2JLpVB2YYCmU7Nytsw4NpxQZ+ncQu/XZQ01bhNyw/gwYNiev1JPALIYQQQgghhGgnGFXhLyzciVKxXeNNUzN6dKJbdWBKKSA0ht9wY1qxE+NplbzAbwVttOG0x7B9mGneAxxxcCTwCyGEEEIIIYRoJxiIBP683DouOvkvGEoDYCib+fNVr5mZf29tXfoBDDtrrxeTG/jtcOBvxe2Nb+BP9mwFQgghhBBCCCF6ISuqS7/hclN62AeMLxxGVX0qBZkvMnfuz5PYugOJ3KxAxQZ+O8mB3zKdkK/sVlzu+LZFKvyinQULFjB27Fh+9atfJbspQgghhBBC9JjycliyhF450VxvFF3hN1xuXH4PuRk+xhTVkplXncSWHZhWkbZbRm7sa4YbK3rJwQTaVr4ZbTghX+HDdHviej0J/CLGqlWrWLx4MWPHjk12U4QQQgghhOgx3zznBYYNs5k1C4YNs/nVL7cmu0m9XvQs/YbLjWGlR17zmMloUqdpAuHtgCcn9jXlIhgMkgxbNn4e1RAfhhnfz1ECvwhramri+9//Pr/85S/Jzs5OdnOEEEIIIYToEW+/vZZF/zwHrZ34o7XBz35WLJX+AwjGBH4Xhp0ReezNS0aTOk0bkcCPio29tumhqbExwS1yVO+sCG9rWkMTDMaPjOGPs/pXX6Xq/gewm5oSdk0jPZ2C736XrFNnd+m4O+64gxkzZlBaWsojjzwSp9YJIYQQQgiRWK/+bSVaj4t5ztYGGzbQayed6w1svz9cITbdHmyVGX4tyzUpOY3qJK0C+319564K8vLzE9SaiOaaqKEQUcMO4kUCf5xVL/wD/o0bE3/dP/yhS4H/pZde4rPPPuO5556LY6uEEEIIIYRIvEz/Fyil0TpSTTWUzejR0uF5f6xgAEOZ7MkbT7BuJLbh9AI2A43kZPXuOyW2sf8u+7urtsP4xN+08Dc0h7dtw7efPXuGBP44y587l6r77094hT//W9/q9P6VlZX86le/4g9/+APeOC8LIYQQQgghRKLl6Foumr6WxUvHYWuFoTSXHPcexcUnJLtpvZoV9LN59AVUDJlOYKMPr8u5YeKyGnC5e/fNEm3a+329troqQS2JpVsDtN12OlAvhJ7QZwP/rFmzqKioaPf8xRdfzM9/3n55iOeff54f/ehHMc95PB5Wr14dtzYCZJ06u8td6xPt008/pbq6mvPOOy/8nGVZrFixgj//+c+sXr0aM86TSQghhBBCCBEvZmsapeMqGV+8x1lSLquFQk9lspvV69nBALXZRwBgeTdhB5xhEcquo3BE1v4OTTrbsFB636831tQmrC0x/JEbEbYpgX+fnnvuuZilFMrKyrjiiis49dRT93lMRkYGr776avhxvCdI6CuOO+44XnzxxZjnfvSjHzFq1CiuvPJKCftCCCGEEKJPMyynK3puho/81Hps00vQzsQKBjFdfTYSxZ0dCITXjNeerRAK/JoGjj1jZDKbdkC2qTGDUNPopaoulYLsFnIzIl3oa1d8yLqHj014u9TRJ9BW4rfNIOuO6WQbcnPh7ru6fL0+++3Oy4udFXLBggUMGzaML33pS/s8RilFQUFBj1zfsqyYGw6WZaG1Dv/pS9LT0ykpKYl5LjU1lZycHEpKSrr1fto+h70/J9F5bZ+bfH4i3uS7JhJFvmsiUeS7JvamVWgddm3j8W2jNW002nBTuelzBo+a0O3zHurfNSvoDwd+02oJP2+bTaB0r37f2qVYtmYwzywdh9YKpTQXTV9L6TinZ4elFHYSZuo3bBd2qJ5qm1an26C7+Vn32cAfze/3889//pMrrrhiv1X75uZmZs6ciW3bTJgwge9973vtgm5nffbZZ+2ec7lctLS0YNv7Hy/SF9i2TSAQoLm5+cA7d8Dn8xEIBFi7dm0Pt6z/ifewEyHayHdNJIp810SiyHdNAKxbtw7L5RQL3YE6tKoPv7b8P68wsv7gZ0o/VL9rdTU14cDviup9brmaWblyZXIa1Uk1LXnhsA+gtWLx0nGML95DboYP2+XGLipKeLsMyx0J/IbudBvsnJxuXe+QCPxvvPEGDQ0NnHvuufvcZ+TIkdx5552MHTuWhoYG/vCHP3DhhRfy0ksvMWjQoC5fc8KECXg8nvDj1tZWtmzZQmpqKikpKd16H73Jn//854M63jAM3G43o0ePPiQ+j2SwLIvVq1czceJEGVYh4kq+ayJR5LsmEkW+ayLaSy89T777ywCYwWpsI1LQslp8TJ48udvnPtS/a++mpGK3OJnH5Y8EVcsTPKjPLRH+oitjVmUAsLWiqj6V3AwfZnoW4/79esLb9e63b488cKtOt8Hv93dYdD6QQyLw/+1vf2P69OkMHDhwn/tMmTKFKVOmxDw+/fTTWbx4MTfeeGOXr2maZswvtWmaKKXCf/q7ts9h789JdJ18hiJR5LsmEkW+ayJR5LsmAoEAZnNd+LGmBsuMdE1v2d3YI9+RQ/W7ZlmRXGMGU8OBnwyj17/fKePGoJSN1pHVBAxlU5Dl/Px1UCflPRh2pGisXZ3/HLvb1t69lkInVFRUsGzZMi644IIuHed2uxk/fjxbt26NU8uEEEIIIYQQydTS0kJKS2Q+KtvdQMAdmbgt2NC1cdHl5bBkifPf/iAYjArLVkZ4O31gbjKa0yWDCiwumr4OIzRVv2nCxSc8F5m4L5CkYdjaS02jl/UVOTQ198z8cvvT5yv8zz//PPn5+Zx00kldOs6yLNavX8+MGTPi0zAhhBBCCCFEUrW0tJDS6o3Mip7ait+wSAkN29etnY9DCxfClVc6FWPD0CxYoLj88p5vc29i2c4HV9PopW7XWLILveRm+CiZfHSSW3ZghsugdFwF44v3cMRZUzjmhDRevON9bM4AQAWT0653yqbw5PuloYkEJzNoCsydG7/r9enAb9s2zz//POeccw6uvZbT+MEPfsDAgQO5+eabAXjwwQeZPHkyw4cPp76+noULF7J9+3bmzJmTjKYLIYQQQggh4qy5uRm3P42AM+8cRr4bf2sk8Ku2Fw6gvDwS9gFsWzFvHpx8cjxanTzl5VBWBiUlUFwMVtDgv1v38Nwb56G1gVKai6et4VvHHZbsph6QaTo/q9wMH8cdG2BQMdhGVFXfSvww7PJyeHL5LDRtEwkazJsHs2c7n3c89OnAv2zZMrZv387555/f7rXKykoMI9IFpb6+np/+9KdUVVWRnZ3N4YcfzuLFixk9enQimyyEEEIIIYRIkJaWFmobiqnYnUNBdgsTpg2ncksFhIb1KzutU+cpKyNmLDiAZcGGDdDNydN7nUcf8XPtda6YHgyqIY/n/n0eOjQSXGvFM+8czt17DIo799EljemOBHorGAr6pg2hURzKTuzo9vJyePZZwp9luG2h75EE/g5MmzaNdevWdfjaokWLYh7fdttt3HbbbYlolhBCCCGEEKIXeOaZNH71/FXh6vSvjxyDUfgsbAvtoDP2e3ybkhLaTQBnmjB6NOzeHYeGJ1h5OeGwD5EeDDeWlrQLqLY24hpQe4phRtptB51x/NrQqAQH/s1byvjx3P/xzJtfC32+mvAYEyLfo3jp85P2CSGEEEIIIcTeysvhzjuHh0Os1oof/agA7ZmAsp3Up1V6p85VXAyXlL4YngDOUDbz5/f+0NtZ++rBYGtQSsc8H++A2lNMV1SF32qr8EeeM+zEzNC/+AfP88ybX4/6fBVO6AfDiP/3qE9X+IUQQgghhBCiI2VlTqU6mmWBq7kIM/gZQU82tpkZfm3v8et7m37Y54wZMYCq+lSGeNYzd+6FWF2b5L/X2lcPhkGFZVw0fS2Ll47D1grTpM/c6Oiowk/UTQClExP4d9dORuu95wtQfO2YN7jgkgbmzD03rteXCr8QQgghhBDikNMWYqOZJkyZ4MG0GgAIujKwgkEWLoThw2HWLOe/Cxe2P5/S6eRm+BhTVEtOZu9eg76riovhkhNeaNeDwTO4ntJxldx+8TJu+cpzbN4c3xnle5LpikTdtjH8RtRzyo5/7Xv9+tXkDEht10tCKZtvHR1gyJD4Lw0ogV8IIYQQQghxyCkuhq+d/td2IXbyuDSUbgRAG27WrNjCVVeBHcpetg3z5jkV/zZ+20aryHh/y9W5sf99ybTRn3D7xcv47lkfcfe5f2DuXLDdzmu5GT7GDl7fJyr7bTrq0m96okN+/G/avLn4aXIzg1w0fS1G6OaToTT3zN7A6EwTt9cT9zZIl37BAw88wIMPPhjz3MiRI3n11VeT1CIhhBBCCCEOXumoZRxxcTFV9akMTn+VS2d/h5ZGA01TeJ9l/yrDtmOXmbMs+Py9ZgqPd/rsb/YHYrr/26aX9ctXM6xomDPQ/RCgtIvcDB+5GT5SWpz3pKKGRNhGS7Ka1i3R1fy2Lv0ebwrB0HNKu+N6/cDOJtgWBBNKx1UyofgvjLHPY0ROC4Oz/DQFA3hSOrcs5MGQwC8AKCkp4Yknngg/Ns1Dq5uSEEIIIYTof1ytkRCr1G52PbgSjcY2mqlp9FJVl8qQmncw1GzsqHHWptLkLFnNrv/5Afgkyyboyow59yfPLSHNNYX8dIWeZDvjBfowpSPVZttwejCYwajQ7GpNeJsOhmm2X5bPk5ZGc+g5peMbhZs/2oXbNxArtHzhgMGtHG/WRV4P1uNNKYprG0ACvwgxTZOCgoJkN0MIIYQQQoge4wqkEggVUVXbf1Es/eIInn63FK0VismcMm4Vb6ybhKUVptLcPbuMwVn+8HkqjSpMIy/m3HXBanCBq0kT2N6Ea2ROgt5VfERXvC0zE23bmAF3eBC45Q7u48jeKabCbzkV/rS0DGrDz8Y3ClsNfmxzmNMWy8fMUWfg9eRQsW4Lzc2VrK17nxNT4j8hggT+ONvwv1188OJG/K2Jm8LTk2LypbNGMfrowk4fs2XLFqZNm4bX62Xy5MncfPPNFBXF/46TEEIIIYQQ8aC1xrAiVfmMjBzSjx1MxW6Tp+85ITxzusbg3+sm8bsrH2bQiCMZMbiJwQNaie6o37B+MznEBn5L+yLX8h8K0/VHKvyWK42Na9fgsryEi/wpyWlVd3U0aV9WVi7bw8/Gt0v/si1vsTNwBFW7Uyn2fs6Ya04D4IXvvEJD1VKnBW4Zw9/nffz6Fmp2NB94xx7UBHz8762dDvyTJk3irrvuYuTIkVRVVfHQQw/xjW98gxdffJGMjENvQhIhhBBCCJFEX7wJb/8W/I1xvYzfNlFcEH48ygW5O7/NynWT0fr3MfvaWrFt83BeGbkMVWXSUpZBoCaDMyb/iy+N/oBA3ZnAUTHHaJ8PUkMPXvoJ/PfzuL6f+Lsw5tHHi+/GsMaEc7HpaoYFJyW+Wd1k1I0GLgbAWv44bHmb3J1FwLdDe7jC76e8poCyXUMoKaygOLeqR67/zGtX8syboV4kajJ5l93D3GkvYzdNDe9jvvZ9+KBzM/UbKflw+G1dbocE/jibcsrwpFT4p3xlWKf3nzFjRnh73LhxHHnkkcycOZNXXnmFOXPmxKOJQgghhBCiv3r1NqiKfzhuJgvbyAacLtUlrXtg+8eU6J2gbIhac95QmuyCXGZ9sZOFW85i3RvTQRu8qy7i5u/dxgDb1+78KuDGTxAPLnTNdqj/OO7vKZ60uizm8a5mNx47Nfw406yB7X3nPZq+SP6y66tAf0xhc234Oa3csP1jrlv+Cx55/Qa0NlDK5uZTf83/fenug7p2eX0Rz7x5UaQXiTaY99T3mD3gCWwrMjTC3LMeWho6dU7lSoPDu94WCfxxNvrowi51re8NsrKyGDFiBFu3bk12U4QQQgghxKGmeXdkW8VvlfAWnUbQnQOAK1hLiqsZlEFx9g5OvOzfvP3kKaAVStlcOH0duRk+ahrPY/0bpdA2gZ82+N3vfsVPLr6LzPTY8ysrhSdT/stgK5eLSYvre0kIFdu93BdIxUvkTY90be9T79FUkcp5ZWAcK5vOJmilo+wA2nADbl7d8c1w2AcnmN/7yq3UZ3k4pfQtCvN2AeCqzSRlyyAUqqNLtbOiYnz4nG0s7eLViksIemvDz7kMOv2Zak/mgXfqgAR+0U5TUxPbtm2TSfyEEEIIIUTPs0OV19yRcMPKbp2i/PM1lL2/DNved3foLTU7sU1n4Llh1bE+78vU5Tjd8ieMbeTz0z7F2uZhlPEFs1ZupJXxVNWlhquy4ebaJnV7BlK4V+A3dBrQSqVZw/ajvs/Ys5/p1nvpLfQVz8Y8NoNutAq9aW1z5A9egczuhc5kMDbWwT3/A6DCP4kK/yQ8+DHsAJbhRisXL2w/q10w1xgs+MvNPPaXm7jsuCWcWPIZQc82KBhGsGZCp65d7/aisNHE9iLZ4T6JdP2P8HPm9e/BkKGdOqft98Pq1Z3aN5oEfsGvf/1rZs6cSVFREbt27eKBBx7AMAzOPPPMZDdNCCGEEEIcanQo8BvdW8bO39LM83ffTqB1/+vC+4sKyeJE55KqnrLVa9nevAGAlrFHYc48DLMwSDmDWZxXxdff+oBibyFKTY4JgUrZDE5r3+1a6QzAWaquoa62W++lN7GN2Aq/K+BGG07gdwWbSO9DYR8grygdb5oLX3OkC72NC2U7PzOt3OQP+AilTm53kwec4L/o/VmMPiyFXNfh+Fv+gYfOBf6BmT4uO+5NFr3/Zey9epH4GyNDDVwyaZ9IhB07dvC9732P2tpa8vLyOProo3n22WfJy8s78MFCCCGEEEJ0RVtVXnUv8DfsqT5g2Adw25FZ2G2jEZ8VOWZo5WaUbaFDNx1WD5/E6m9OAiBzZDkN9xajtYGhNHOm/Y38NJu9R/E71W9neEJTXR19nW14Yx6blpegy5nA27CaktGkg+JJcXHxL45je1ktWofWXAjavDX/bQC04WaIZxcXTV/L4qXjsDsI/bZWVNWnkpvhwxsw+cq3OzeIXu1pRTe9yujDUqmqT2Xa+UEmHz4cyOODF5aww7nvhMsjgV8kwO9+97tkN0EIIYQQQvQXB1nhD/oi0XvM1BM49qsXdLjf3373UHjbcjUz+zs3orIj8ee8oM1n/g6GBFzWxK60R9jy8fEUZLUw2NgMRFauMqxWbDMF24xUvH2+1m69l96itaUF24hdps4MZhJMDQ2JsOO7okK8pGV5YuZT07bmrUedir9WbjwtmZSOq2R88R7W1X7Kn1/+drveHQVZzo0iZRmUHDOwU9dtWbcHrZwbBXlpjZz7jS/jcjnfvVX/jpzfdMd3aUCQwC+EEEIIIYRIJDvUxbqbFf5AayRcZxUOZNBhJR3uZ7Z6sUJpx/YGGTihBCMlEn8GsfdCexGtIwbzxPfewza9WL4RGNq5yWBYPtyBanzmECwzA8uyME0Tn8/frffSW5Rv+6KDyeOi5/PqexX+jihDoXQAANtw4bYHAZCb4WPs6NWcabzIv/51VnjG/jkznyE3YzAARrDz31e7MYBtOCscmFZLOOwDWP7Id0W69AshhBBCCCEOLW2T9hndm/E9EFVNd3u9He5TXg5l2yaQW+AlN8OH4XWhvJ0PbCmZOXj8W2hNHcOuQBFVdakUZLcw0L0bpZ1qt2160HYATBN/MNCt99JbbN+6CYidlTDoilSztWpOcIviyflZacNNwDMEAJe/nnMumMu5c9zccEMZW7d6GDbMz6f//h/UOvOaKbvz0dluCmCbTuA3rNjhJ8FAJPBLhV8IIYQQQghxaAl36e9eFAn4I136N6+uo7nxs5jXF7/WyB+ePQatv4lSmoumr+X0Ewbwnz9+3rULGRUsWzuDZ5aOQ2uFUppLj3uHE0u2AFDT6GVb7TDyi1ppTfXzxhOfHeCEEUMn5DF26qCutSeOqndUAqNjnrPckWEMtnHgORP6CkXk5ozlapuUsIIjjjgHgMOjhumXLU/Hqg0dpzsfzhtrarFCK0QovXfgd65vut0o1bll/g6GBH4hhBBCCCFEYkQvo9fNLv0tDZFqc9XWVmp27Qg/rmn08odnj49aV12xeOk4zhm3mXXVO9qda39qfSnhsN92rqeWn8DhQ1bwyZbBoddOQCmby09dwtEdnL+m0RvuHZCbEblRse79HQwckUXOwLQutSleGmtr9/u6Ze49ZWEfpp0hJdE/m4HuXR3u6s1Io+3bpuzOB/5NO8pAZTvH7RX427r0J6I7P0jgF0IIIYQQQiSKjixJ1t1J+5pqIxPIKRUbwqrqUtutq25rxZ49gyFvT5eus6PhmHbLtdnaoGz3wL1uBBj88dWZjLr4vZhQv+yzKp55Z054PPgFs5YyY3Tkhkd9dUuvCfy+hv1Pyme5+/YcBbECLFs7OKbnxjdmlnNlB3um5+eFAz+68wG9smYrMNE5jNgJHaMr/IkggV8IIYQQQgiRGHZU4O9mhd/fEglQg0bnc8q3jws/rtiueOAlHRPUDaU59vhURl18HF1RsV3x0Et2zA0Ew7BRA1S7GwFaG0w+72imneAE+mcXv8AzCy6OuSnwtzenc+EFW6hcuQkAK6i71J54Cra0r+BHV8Azc6wOjuqbqpvb99z485ILGH/xd8nNiL0p1JDmJh1nUkiFh0euuqRT1zCyi2gL/KjWmOOa650lHCXwCyGEEEIIIQ4tbTP0Q7cn7fO3RLpIp2Wkx1TJ0/MCfOOE1Tz97kRsrTCV5u7ZZYwqcXe5mp4zEB57DObNA8sC04T58w1mzbyAx/9m77V8m8WRx3jIGeg89/mbDR32DqhujLTBCnSwJGCSWK3BmMd7V8C/NmszNySpbT1tZ2NKBzdsTLZVpuMt3BjzfKDVC20/MuWhua62U9dISY+scGAbvg6PS83M6kqzu00CvxBCCCGEECIxdA9U+KOW5XOF1olv89H/3ua4w2Hs8GXU7arma6MHMjjLj5ExolvXmjsXZs+GDRtg9GgoLgbw8NhjcNWVNrY2MJTmwunPkp9zOpBNIBCgyGWg1N49DWxGDLNZv8F5bAV6UdXcH7n5UNPgblcBf/bNC/lNedv779sOn6hQr+/Vc0NZjBwRIDtjYMy+abaFHbRBGWi8ZBcO3Pt0HfJZkd4b2vC3O86Tls4JX+9cb4GDJYFfCCGEEEIIkRjRXfq7O0t/VOD3eGMD/+cffwgcQ26Gj8HGOgZn5QJgZnS/+3RxcfugO3cu7PzgV+ysPIOCrBbyUz6iac/xpGVl88QfF5KZW8xF09eyeOnY8E2BS45bxpCiUawPnaM3deknqsBfu7uiwwr4hg2HRuA/q+Ro7pm9gR++VoKlVajnhsncub/pcP9HrnwZ20wB5eXbDyw84Pl1wObh734v/Nh2BZnXiePiRQK/EEIIIYQQIjF0VDf2bk7aF/BFxpt79qrwN1VEZlu3zYbIpdJ7frx0QVEr2aoWANPnpWVPLcHiIDXrV5HhGkPpuEom5y9lR/N4cvNNCj07Md2Rpe+CvahLvwoq2m4/5BR+jmFobDu6d4LF6NHd+3n1Oi6DC4/cyYyRNTR89RjGHG7u80ZGeTmUlWeRl68o7OSs+lZTAMMysUMdCGxPcntydG/gjDjk7Ny5k1tuuYWpU6cyadIkzjrrLFavXp3sZgkhhBBCiENJD0zaF/RFVfhTU2Ne03WRUrXlioz1NzN6fgm0tILs8LZhp9K8p46mpibsbcWsr8ihptFLeuE6xhduJDfDR9CVRSAYaVNvGsOvrMjPIjurhgULFIbh3AIwlOb6Lz11SFT3AZTLicCDs/zMKLX2+b4WLoThw+F3r07jZ0+X8s6Gkk6d324KYFiRurr2JLcnh1T4BXV1dVx00UVMnTqVxx57jNzcXLZs2UJ2dvaBDxZCCCGEEKKzYpbl617tMeB3KvypZgZ11am88aKf0aNsioeA2erGDmVXOyUS/uNR4R9QXEztKmdb6VSaqmt5/HHNj//yQzTOGP7rrtZM1h8DoA2TTRUfAc4wAyvYewK/YZvhz027NXPnQqDybT7/IIOCrBaONv8LfDOpbewpyhXpudC4bDtGavtIXLHL5KqrBoV7OWit+PO7R3Lrs+UMG7T/8werW1C2N/JEWmJm498XCfyCxx57jEGDBnHXXXeFnxs6dGgSWySEEEIIIQ5JPVHh9/tIGTCKDz46k+uvOAyNgaFsfj17A9kD0/CHTmu4Iuc3DmIM/76UHDGFDS9Xhh6lsXlTkB/8OBtNJCQ+smAad56zkvTQTO/V5WXAl4DEBv4dG+v47zPraK73d/i6siOx0AKeuPUd7MYAY4pqATB8vWiCwYPUVuEHaFiyrcN9Vm3JxrYHxzxna8XyFzaTPfzAFXsjKvC7M7u2OkRPk8AfZ+vee4dlzz6Fv7XlwDv3EE9KKid8/RLGHDetU/u/+eabTJs2je9+97usWLGCgQMHcvHFF/O1r30tzi0VQgghhBD9SmhZPjuo0D4NdXVdPkWzDlBfcwFPvj8jHK5tbfDD10q489xBpIeG9Wd5nEq6keVGtzRi9fA/xwdn5KPsrWjDjVZpfPGFGTPuHZzl/HY2pjBqgPO4ZdsuTs50sStoE/QnLkSv/PdWdm9r3OfrHh2JhbaC5jo/hD5btI3bDMS5hYnjHZlN80e79rvPyNwWDKWxY1ZZ0HjyNwPDD3gNpSOBPy0/t7tN7RES+OPswxf/xp7t5Qm/7ooXn+904N+2bRvPPPMMV1xxBVdffTWrV6/ml7/8JW63m3PPPTfOLRVCCCGEEP2Gttn9WQZVqzNBr4BfHdflUwRPOZFdDVntZpK3tKKqYQhtS6APqw4SaF5GYPPb1D35RU+0vh1X6a8IeHKwzXSyqleg1Nkxy72ZSpOXtx0YBYD2adK9ipGmSUVt4gqCLY2RwJ6R6w1n+TaBiqgeEKaLjDwvrb4mXLtrGbzjPdwjD53An3bMQNyD0gnW+va5Tx7w4MBGvnNHBpatQksvriV9cj15U8cf+CL/tyq8OWBwcic/kMAfZ8eefT7v/iXxFf5jzzqv0/trrTniiCP43vec5SMmTJhAWVkZixcvlsAvhBBCCCF6jm1RsyEN9grrXaEwKMhuab/OPRYFOU53azPYTP47i2nd10l6iGE3ATlYZjo5ua18/aQ/8+xbl2BrhaFs7py1mpS83eFl7wx/VCW9qjbOrYsIrwig4LI7S1Eq9vNfeNmy8HZ+UR7f/N4JvLrkcYZfcy8A1aMnJ6qpcaeUwjM0E8/QzP3ud81EOGsu3Df3abILhpOb4aPZbiZt4oADXkOryOoRw0eMPeg2HwwJ/HE25rhpna60J0tBQQGHHXZYzHOjRo3itddeS1KLhBBCCCHEIUlb6KATNpXbIO34E7p8CsN2kZvhC61zPy4UrjUXnPJnMrMLsAEzWE/69BN7uPHtqdZmAGzTQ0OKybQx/+PwolFU1adSmPVPLkzbzgtuIxz4zWBkPLftD3Z0yriwAs7wAZfLaBf2HZEKf3pWduiYyHh/w9U/Y2NxMYwdtBFfqjNTX3NdfaeO08pZPcIMtpCXVxC39nVG//zJiRhHHXUUmzZtinlu8+bNDBkyJEktEkIIIYQQhyTbClfl3TkpDFuwoMunMK++AYDScZUcWbiBitYhFGS1kJf2CbZxBgCGXdetc3fZZb8LbzZ7/aQ2pZKb4SM3w4ftqeadzEwCVjNtKwQqnR45NoHL8gX9zrVMz75WRogsWzig0JmszgpEurwrV3Jnmk8mbURufASb9j0MIJptOIHfsFvweHp+Sciu6N5aGOKQ8s1vfpNPPvmERx99lC1btvDiiy/y7LPPcvHFFye7aUIIIYQQ4lCiLXRb73Kz61FE2zaGFQmfuakrGVNUS26GD2/rmMh+qnOV2IOlVWTYroWFaUUCvWUEqdywDl/1nvBzikg38mBrYtoIkS79LnfHKyNoFQmlxcMOCx0TCbf9tcIPYBuRnhh2S+fmMrDNSOD3er0H2Du+JPALJk2axIMPPshLL73EmWeeycMPP8xtt93G2WefneymCSGEEEKIQ4kdjIy7N7oeRQJ+H4YVCact2VW03UGotI9gfUUONY1etLHvGel7kjYigd8IapSOBHoVcMKh2diEYTnh2VZZkWODHS+RFw/Bti797v1X+JUdoGBwUeiYqC79Zn+u8EcNvfAfeEm+nbu2Y5ttn2fyK/z991aNiDFz5kxmzpyZ7GYIIYQQQohDmLYsZ903YtdD76xAayuGHZkQzchLxbtpO0u2HMszS8ehtUIpzTemb+fKHmv1vlmuSCh22SZaOePf0RaX/fBX5A4diNaax7/1LH6zAMsVCfzKPnB47LF2hrr0u/bRpV8bTig1bD+eVKc6bUXdkDDc/Tc2apcNoR+VCh74Z/bFulVEhki04kpy7wip8AshhBBCCCESw4pUS7vTpT/g86F0JPCn5uSwp6k2HPYBtFY8vfR8yhOwMrZ2R96P4TewTCfwuwP15BQ5k7UppTBsp/u+5Uqnyt7tPJ+gvK+1DnfpNw/Qpd8J/M7EgrGT9iW3Sp1M2h35QalAx59ftB1bNkeOVa37mCQxcSTwCyGEEEIIIRJCR4XI7gT+oK8VpVPDjwcUD6USd8zyfAC2Ntiwofvt7KyopmAGPATdTpd+w6qLfX+6gZpGL+srcljWWOnsE//mAWAFI5MD7qtLv220dUH343I73fftYGS8utmPJ+1TnqilH60D/9QaqqrD21rFe2HIA+u/fTOEEEIIIYQQCRUd+DEPXC3dW8DnQ6vI0nYlE45kwl2Hcd/zNlpHwphpwujRB9XUTjEzPASbnG1XMB8rFKiVjp2Qb+kXo1n0XmloyMFkWmZvoLSkIv4NJDJDP4DL0/FnHg78OjJRnxUV+I1+HPiNFBM7NCWEsg/8OezYZrK9IoeC7BYK3Z2b1T+eJPALIYQQQgghEuMgK/wBX2vMGueFhYMZMCCNxx6DefPAspywP3++s4Z6vKUV5lK/s+3RoPDz2mgIb5eXw6L3zowacmBw62ujeXrgyvg3kL0Df/vPfGdlBdpoi4WRkB/dpd/l7r9d+s00L20DN5TdcXx+5Lpb8DSMYemGcSxafmtkLokTtnNV4praIQn8QgghhBBCiITQUVVjv6HZ0bSjS8fvqqvENp2l7wyrmSbVRLApyBkXwgcnGmz+wsWIw4IUDbHZ0dSjTe9QelE+9audbV9KUfh5y2wKv7f3V3nQOi/mOFsbvN24kxldfP/d0VgfCe4B5W/3mX+69n9ABgBKR15v9Uc+wP48hj81O4O2Or2y238Ob/3nH9jBU6m0U1m0vDR2Lol3z+eu8sTcfNoXCfxCCCGEEEKIhIju0v9OYCf3PveVLh0/fIuX0827ADDsZs588UzYe060XQfbys7LbUnh6/zaeaAi1XO/p4WvhN5bYM9AUK+BjnSnN5SmMLMqvE885TUN5mv8EIDXy1/hx8/9Jeb147eXcCTXt7U83Kbjd9h8OfSs2Y8r/KnZ2dSGtpVu36W/bNkKULOoqkvd51wSyQz8MmmfEEIIIYQQIiG0P6rLeDeSSKE1AG04wVnR3D7sJ1hNaiuG5W/3fGtqZOy2O28nRZffDoYFOGH/wulryU+pS0gbXVHjzoNGoN3rqmoI6ytyqGn0olXURH2RkQCkp2TGtY29WW7hwPC2ov2ND2unMzFfQXYLStkxrxmGnZC5JPZHKvxCCCGEEEKIxIha2x1DccrwU7p0eN7mqH76uqXLx/c0rTWm1UR1SyZVdakUZLeQm+HDynHFtu2yJhpO+xUpT+VSmHkUuRk+lG1y2uAvY+1jIr2ekrIrMpxgaE4xpww/hYaqbGrKB7BzfTGPzT8TrQ2U0lx6XA2nXOG0u2R7ObAKAK83raNT9wuFg4fyGW03ZzxorWOW2jOaU7A8kJvh45LTnufp187HshRK2Vx33WqKi49MTsNDJPALIYQQQgghEkIHI+vWe90e7j3p3i4d/+hbP8BqO5dq6fLxPc2yLL714/dZtPz48ERtF01fy7zzTmH6SWe32/+h974HzYcDYFgu/t+kW/AOKoxrG7d8Ws3zb76AaX5C7mduPn9+Kk+/Mye0qoGmrZuE1oqnlp/OltFnUlwMtbufozIU+LuzosKhYtiIEuBDALTyEggE8HgilX4zmEMg9PCrF+7imp+UM3/+f8jL28Oppx6RhBbHki79QgghhBBCiITQgagu5UbX++MHmiJd5W0j+WucOzPwHxczUdvipeMoGtnx2HyVEgnOynbhr23ocL+eZPlt0n2bMa1z2dN8Pk+/87WoJQw7HnMOsTdnlNl/68SZmVkYlp+aRi9rdwzliy9il9rTKj+0YTNt9jnk5TUzYsRmsrLq8Xq9SWhxrP77kxNhs2bNoqKi/TqgF198MT//+c+T0CIhhBBCCHEoCh7ksnz4dHjTNtqPnU+0sjLQe9VQba0or0hldEn7/c00D8E9zrZhewjUN8a9jcGAhWkVE4AOJ5aL5ow5d96PDlrh55Wr/1b4Ad5bO5Cn3p2M1orfHm7z1a/+i2OO+QQrGKTQfTQAbn8Njz/2BLYdGccf3RMgWSTwC5577jksK/ILXVZWxhVXXMGpp56axFYJIYQQQohDTcDXEnnQjcBvBCLHWO72E9AlWkkJGIbGtiMh2jQ1o0d3HKq92RkEy51tpT34a+rj3sag30arFAAKshpRyo6q8ENbt37T1Myfb0RmlLciFf7+3KW/vJxw2AfQ2uAf/zidkSPXk5legeVy5jcwrd0EArHfyczM5E92KF36BXl5eRQUFIT/LFmyhGHDhvGlL30p2U0TQgghhBCHkED0pH3dqBobwUi90jat/eyZGMXFcNttWzBNp+eBacL8+Wqfy7Bl5eeHt5X24q9JTIVfG6kADEip5bHHjHB+N0245x7FkiWwebNi7tzIcTEV/n7cpb+sjHa9IrQ2sO1RZEUNe9BqD4WFheE/kydPZuzYsYlubjv99yeXIM2rqqj/9xa0L3F/ISmvSdYpw0mbWNDlY/1+P//85z+54oorYmafFEIIIYQQ4mAFAlHjn7tT4bc82KGwqr2949+q55xTzbe/PZRNm0xGj97/muuFQ4ZSGX7kpbU2MRV+23Aq/Kbdyty5MHs2bNjAfturLenSD04vjr17RZgm3HLLObx47zu0deC3PA1ce+2Pk9PI/ZDAH2cNS8sJVrUceMcev25FtwL/G2+8QUNDA+eee24cWiWEEEIIIfqz6DH8Rje6iRt2SuRBSvLHR7cpLobhww+832Elh/MJzqx4Wnnx1TXHuWUQDNhYpvO5KduZ6LC4eP83JgDp0h9SXAyXTf0vi94/CVsrDEOHe3EYDW7sUKK2s4P7P1GSSOCPs8wZxdS/nvgKf+b0Id069m9/+xvTp09n4MCBPdwqIYQQQgjR3wViJu3reohUOhL4vRk5PdGkhBpcNAxlf4423IAXf2P8u/Tv3r0ldD1QdH5lg9hJ+/p3bDyxZBWjD/NQVZ/KjEv3cN6ckykvh7JtR5BT4MzE3+w5mvLyTtxISbD+/ZNLgLSJBd2qtCdDRUUFy5Yt44EHHkh2U4QQQgghxCEoGIxMatadbuJKp4a3s/N7WbLqJNPyETTcaCOFQOueuF+vqnYDMMp5oDvf81hb0cvy9d8Kv8NPboaP3Awf1Z8t4dpvu3j0D9PR+hs4kx4CnMCvH4IFC4iZCyHZZNI+Efb888+Tn5/PSSedlOymCCGEEEKIQ1Bs4O967VGrNGoavayvyMGbenRPNi1hjFC3etvwYvniv9JAU/2O8LZWvv3suZeoMfz040n7ALSK9EzZWXY6j/5hRtSYfhX6A7YN8+Y5M/v3Fv37JyfCbNvm+eef55xzzsHVz7vsCCGEEEKI+LACftpqxV0Zw19e7syW/uqnU3j+oylorXjwZc2CQO+qpnaG0k7otswUgv74D/sNtjSGP3O6EPhju/T37wq/5Y1MrlhVl9pu1v6YfS1nQsTe0rVfKvwCgGXLlrF9+3bOP//8ZDdFCCGEEEIcooJRy5gZLvcB9//L4/fzzdIXGTbUZtYs+Nv/poTDlm2rXldN7QylnQq/NtzU250fU99d2h8J+XZXAn90l/5+XhA84TsX4A68QUrLCoq9y1HK3ue+pumsftBb9O+fnAibNm0a69atS3YzhBBCCCH6rLYqdElJ76nu9TZWVJd+033gKLLlVYNFy89E01ZRja2s9rZqaqfoSOiu8cZ/NS8ViIRT2/DvZ8+9BKVLf5sjJh7LEQuPDT/OW+h03bcsUMr5Y9tO2J8/v3d9H/v3T04IIYQQQogesHAhXHWV849+w+h9E3f1Fl2t8Fc1DN9v9+neVk3tDE0k8Dd3EMB7+saREYh8frbZ+SEEOhhd4e/fXfr3NncuzJ7t3Gxq+/61bfemsA/SpV8IIYQQQoiDUl4eCfvQOyfu6i2sqBBpdiLwF2T6UErv9azz2DR0r6umdkrUBHABIzaAP/qIn2HDnOELw4drFi48+MsZViSs2+7OrxUvs/TvX3ExnHSS89/o7d5GAr8QQgghhBAHoawsEvbbtHU1F7HsqJnfTff+A79tW+RmWlw0fS1GaMy0YWjOOW4D3z3rIz58t7FP9qKIninf1hqtnRsYG8pauPZaV3j2d9tWXHWlfdA3jgwr0qnbdu9982Q/pEv/IUF+ckIIIYQQQhyEkhKnG3906O+LXc0TwbKiK/ye/XZfb66rwzZSKB1XyZGFG/jStWex4+Myqtc5CXjYsL5Zu7SNqKX4tGbnr+5EmQZPfJqN5trYfbXBB7f/Gfeord2+nmF7CEd3FWTnXXd16riWVavC29Klv++SwC+EEEIIIcRBKC52xuy3TeLVGyfu6i3sUNX444kjePmN43n6RhutDQxDs2CBiqnYN9XswTJTAMhLreGkk+C1DX6qQ6+7PH0z8OuowK8sRc1TTwHgnXo2SumYOQsMZZP75mPseXtnt6+npn4jsh3wsedPT3b9HNKlv8/qm78lQgghhBBC9CJz58LmzbBkifPfvtjVPBFsy+bt489jo/cnPP2vS/fbfX3LF+vQhtPtv20pu6A/0o3CdPfNKGO7ooY1WJH3UOAJhIYvON3uDaW5dOoSBrm7H/YBDO0Nb7sCnV+Wr03KhAm4iooOqg0ieaTCL4QQQgghRA9om7xL7JsOWmh1LFV1qe1m37e1EbPE3paN64EjQ686QTXoj4Rlt6dvVp21y4bQyAZlmYxY/AwArt//g9JxlYwv3kNVfSoFWS0MMjcy4qZnDup6y377dni78PDDGXHNLZ0/2DRJGT8epfa9UoLo3STwCyGEEEIIIRLCtm1s00tBdkuH3deLBjcDGQDU7Nkdfq1tojsrak15s4926cejwoHfsFykTp7sbNvvApCb4SM3I1SJb00Nv94dzoSAHwBQ0+jFZX2F6gGT5cZUP9JHf0uEEEIIIYToe8rLnW7//XXJvpZAANv0kpvh49Lj3sE0I93XL5y+jnde/B3+7Y3Yfgt/Y1PUkaEKfyjwK0Nhmn00yngj7TbsyEoFtlkAgBlsxu2vdZ4zUg/qUnZQo41Ulq0dzM+eLuXq7x3D8OH0yHJ/om+QCr8QQgghhBBxtuqTj7j3R1tY9OpX0dpAKZtvX7yEBU99OdlNS6gmT2Z4e3rJGq6f7OHpLR+RnzuJ3AwfrWW17Lr/Y1SqC8tnhcOKDq1d39al39VHx+8DuDJSCNY420p7ANi8pQy/J895PbALrbxADpaZita6213qA36L6uZcnlk6LtybwradCSZnz5YhKP1B3/1NET3Gsix+//vfM2vWLCZNmsTJJ5/MQw89FF4TVAghhBBCdF/A7+fV/7eBRa+eE56kTmuDhU/P4p7v/F+SW5dYza6oirX2MzjLz5jxG8Nd2L0tg5yXWoKoQCTk2mYo8Icq/H11hn6AlOzITQ+lvWitWfric6Da3tPu8CSFtpnCzl3bu30tK2CzozGv3XwJlgUbNnT7tKIP6bu/KaLHPPbYYzzzzDP87Gc/4+WXX+aWW27h8ccfZ9GiRclumhBCCCFEn/fcwvlU+EZ3MEmdYldZQZJalRwBV2TGeK0CpE8dxOyjLsCwfNQ0evmsaiqb65zPSQUjx2nDedAW+PvqDP0AOQMjP3OlPZR9sIz6DZGZ+C1XDUq3hB+vW/NRt6/VWN9Ifm4QpWILeaYJo0d3+7SiD5Eu/YKPP/6YL3/5y5x00kkAFBcX89JLL7Fq1arkNkwIIYQQoo/T2qbhfzv2MUmdZmB615dJ68uCyktk1Lqf1T4NRi7vr2nmyfdL0VrxO2Vzw6nvUDLMpC2mWqbF0sXr8TU7a9j31Rn6AQYPHUFkCgcvL/72LtIzx2OFkpk/pREzEAn8lZs2dvtamzeuJTczyEXT17J46VhsbWCaMH++dOfvLyTwx9mnn37KkiVL8PkS95e51+tl5syZHH744Z3af8qUKTz77LNs2rSJkSNHsnbtWv73v//xwx/+MM4tFUIIIYQ4tK1b9zOMwGhyM3xO6Hp7DLZtYijN2VM3sKuhiPLy/hS+Yiv8q98qp6bRy5PvzwjfDNHa4P5XT+SuCz4g3RnWjm3YrH4rEpNdfTjwjyo5nBV8CoBWKQDU1w6jwpdDQXYLGQVWeFUCgIbd1d2+VvmmdcAISsdVMjn/HY65bi4lY4x+9H0TEvjj7N1332X37t0H3rEHNTQ0sGzZsk4H/quuuorGxkZOO+00TNPEsixuuukmzj777Di3VAghhBDi0LZ86Rp2WKdRVZHKkQNWc9bT36aiYjjLFv+EF94fj9Yl/HY4LFgAc+cmu7UJYEfiR9tEfFV1qeG5DcK7acWu+qMZmefU+C2XxvBHXh81pe8OhRiQPxDD+hjb9BDwDGHVhitZ9N7ZaK1QSnPdnFYmpi0J7++vb+72ter27ARGAJCbUc3MWX13KIToHgn8cXbCCSckpcJfWlra6f1feeUVXnzxRe69915Gjx7N559/zl133UVhYSHnnntuHFsqhBBCCHHosu0Abzz9dZ5eWhoKc5P51TE5TJu2ke/fMr5fzpqudCR+2EaQOT86hu2Vigdf1th27HCHnHwXEOrCn+llzlXHAOBNc5FdkJbQdvc0w27FNj1Ut2Sy6L3ZUb0bFI/8bRb/74LlZLV1hmixun2d1rr68HZ0rwHRf0jgj7PDDz+805X2ZLnnnnu46qqrOOOMMwAYO3Ys27dvZ/78+RL4hRBCCCG6aePGap7+79fRRLqq//Qnp/D007D3Ykhts6Yf6oHfsCJd8bVhUTg8i8JQD4d585zPwVCaC6evJTczEN43NSeDwuFZyWhyXBi2E76d3g17z6Cv2NmUQ1ZO6Ak/3Wa1+MKztNuGBP7+SAK/oLW1td3anqZpyrJ8QgghhBAH4fPPG9AMinnOskApUMqO6cbeX2ZNN3Qk8NumxWPXR8Yx3HlJLl+0DGSouzS8TF+bjLzchLUxEZTdSk2jl4YWNwodvikEznchL3cnMB4AI9D9+QqUzw5v20ZgP3uKQ1WfDfwPPPAADz74YMxzI0eO5NVXX93nMa+88gr33XcfFRUVjBgxgltuuYUZM2bEu6m93syZM3n00UcpKioKd+l/4oknOP/885PdNCGEEEKIPsvjXo1SscvxGYbGst7l0uN389R7X8XWCqVsbrxxLevWVbJuHVRVedmxI5Mvf3kYEyceWkHXsFzYofxqK4v6qshydCY7GZG1hSzz6HbHDSwenqgmJsTbG8ax6L0vhb4bOhz6TUMzf76iaVUNhO55GEH3fs+1PyoQ+e7ZruB+9hSHqj4b+AFKSkp44oknwo9Nc993vz766CNuvvlmvve97zFz5kxefPFFrrvuOp5//nnGjBmTiOb2Wj/5yU+47777uP3226murqawsJCvf/3rXHfddclumhBCCCFEn1W1eSUXTR/P4qXjwsH+jDNeZO3ajzluQiMlIwuoqk8lL/cFdIbJW0uCfL58As+/eSFaGyhl89hjXZ/Mr7wcysqgpKT3DRFQtgtC/2TXpk1adk7M6x4dRNkBtBEbckeO6d1DZLuivJyosA/gTNb35D11TD8vg2GjTB74gREJ/LanS+eO/tkbQTNyg8Xs/lwAou/q04HfNE0KCjo3Q+eTTz7JiSeeyLe//W0AbrzxRpYtW8ZTTz3FHXfcEc9m9noZGRn8+Mc/5sc//nGymyKEEEII0WfpgIVVHxlw3bC5jtJxlYwv3sOe6s9hfD1ZWc4kan63n1zTR26GD8NqZieZpJal8fybF8UsTzdvnmb2bNXp4H7F11/kT389I3zD4Jor3+Gh+dN7/L12l2G7aIudtguuefCpdvss+NZzBDx54cfKDjBs5GEJamH8lZXRbty+rRXFx2QzbJTz2JOWQjA0357SKZ0677e+/i/++NfTwz/7b5zwN04aOZLW0PyGtkuG6/ZHfTrwb9myhWnTpuH1epk8eTI333wzRUVFHe67cuVKLr/88pjnpk2bxhtvvNGta1uWhWVZMY+11uE//V3b57D35yQ6r+1zk89PxJt810SiyHdNJEoyvmvB3S3sfmQVujXqmjUmKMjN8JGd8SlfSjkpPAHbh+53whVcM5jGaf4p/LXW0+EEbuvWWQwefOA2lJcTDvvg3DB49LFp/OA2q9dU+pWOqtwbqsOfkWE1AJHAb1q+Xvv3Rne+a6NGgVJqrzkcNCNH2rSdJiM3l9odzrbS3gOe/+V/LuePfz0j5mbR0+9ewNjhy8gNfdFsl/z925d192fXZwP/pEmTuOuuuxg5ciRVVVU89NBDfOMb3+DFF18kIyOj3f67d+9mwIABMc/l5+eze/fubl3/s88+a/ecy+WipaUF27Y7OKJ/8fl8BAIB1q5dm+ym9HmrV69OdhNEPyHfNZEo8l0TiZLI71ra2gCZrbH/IHf5M/CHllazvAZD7EiILXMNoK6ty7aVQWrARW5+Gkrpvcb82/h8n7Jy5YEnXHtvmRetj4h5ztYGr766jmOOaezmO+tZewf+lStXdrBPbFsNu7XD/XqTrn7X5n41kz/8owRbKwxDc/XFG/j41V18HHq9ZU90VT+VFx9fvt/zvbFkFVqfEPOcrRVV9ankZvgwA414PCm9/nMUPa/PBv7oyfbGjRvHkUceycyZM3nllVeYM2dO3K8/YcIEPJ7IeJrW1la2bNlCamoqKSmd63ZzKDMMA7fbzejRo+Xz6CbLsli9ejUTJ07c7/wUQhws+a6JRJHvmkiUZHzX6is308R2ADyjczDSXRiv5IRfz8gfREpJpPg0cs94VoYyoiKTj1JXkpN1PBdNXxse828ozSXnvMrs2bM71YaqXR+1v2GgbE49dXSvqfB/rN8Mb7s8Ti/ddvuo12MeK7u1w/16g+5+18495VOGpy+jqj6VgqwWctN8VHwced0OFGC4LFAmWqVS8fH+l9QrSK1s97NXyiYv7+8EXdU0UsOEgVN77ecoDszv93dYdD6QPhv495aVlcWIESPYunVrh68PGDCgXTW/urq6XdW/s0zTjPmlNk0TpVT4T3/X9jns/TmJrpPPUCSKfNdEosh3TSRKIr9r0V35c88chWtgGvrV90MvWpR+/UwGjB0f3ifbfxirrnsT2/SgjWyad68HoHRcJUcMrmBHU54TBIvexjRP71Qbane8z0XTM2JuGFx8wmqGDz+y597oQVJECmZed1qHPx/baIo9Rrf2+r8zuvpdG35EPps+2d1u+cE2yszCDLZguTPQRmpoDv99Z4wB3oZ2N4sunL6OXHMUgaqPcAFZ+Vm9/nMU+9bdn90hE/ibmprYtm3bPifxmzx5MsuXL48Zx79s2TK5yyWEEEIIIbrkk3+/zMrXXsIKRpY5m+yeQaE5FIBn7/4xAXc9Qfe5ALj9tSx75G8sJ3aeJ1fwy/jNAQRdWXiaCvClOs8XeD4iK/sY50FT55dSq6uoDE8SGK4cZ/ioq6shOzv5y/vZ2oaowJ+V0XGbLHfrXs/sv7rdFx1+4hAGHZZNc62/w9dt2+b1e/+DRQa2kcrp1xyO273v2fpfvve1yM++dSXfuPZiBg8ayPb1Q3nnGWcfb1pqPN6K6OX6bOD/9a9/zcyZMykqKmLXrl088MADGIbBmWeeCcAPfvADBg4cyM033wzAZZddxqWXXsof/vAHZsyYwcsvv8yaNWv6/Qz9QgghhBCi84KBAG/96XGCgb2C2mDCy83t2rGJYB6kudIBMK1qaivL250rJ6UOGIDlSscMjnSe1Da+jC1gOYHf9Bntjttn22pbAGeSwOjK8aerV1A67ZROnyde/JYfrSKhNTdrYIf72V4LoqYs0OrQC/wA+UUZ5Hc83zgAynZ+npaZSuZgA58/b5/LLRpWKridn33B+O18aZZzM6WpNjJngluG2fZLfTbw79ixg+9973vU1taSl5fH0UcfzbPPPktenjMZSmVlJYYR+QvyqKOO4je/+Q2///3v+e1vf8uIESN46KGHGDNmTLLeghBCCCGE6GP8zU3hsG+YJp4Up2qaEgr3QR3Ak5aKlZoKoZ7pWtWQkt5+UmmCDeHNncHhVFWkMiRlE57hrbhD89YZwc6HNNUEHa0VVf7FeugNgd/2g3JmMTQsPzkD8jrcT2W50NXRzxyagf9AFE7g14bJLecuZ9GyM9EYKGwuPe51Tjjlv1x1+11orVE68v3KGzokvB3wRXpLuL0S+PujPhv4f/e73+339UWLFrV77rTTTuO0006LV5OEEEIIIcQhztfSHN4ee/yJnP6dWwDY/qvl2A0BvDnpXPfrxTz8/e+Fw7flque6hYvbnevxy52epsvWDuaZpePQWqHUZC7+ahPHh4rfhp3W6baZAQ/B0L/u3f494bXs63fs6uK7jI/oCr9h+0jJye9wv7TCHJqiAr9WHXd7P+RpJ6zXNHpZ9N5MdGgMv8bgqfdnM26ESXX1LnI8OSjSw4eNP+rY8HagNXKzxO31JqjhojfpfB8hIYQQQggh+jl/S0t425PqVPe11tjNzlh7I83pQl1Xkcv6ihxqGr0E0zquUNvuVmoaveGw75zL4Jl/XkJNoxPOFB30DNgHIxgZo20GIyHfX9fS0e4JF7AC2Eaowm/7SMvK6nC/YeMnxDy2zf4Z+LVyAn9VXWrM7PvgLLm3oymfd//zIsHqFrRyPkvD8lMyemJ4v+gKv0u69PdLfbbCL4QQQoj+SWvNA29u4INNe5LdFNFLaa1paGgg86MVPb56UnrNFtoGhL6+vpY/Pv4+Hltzc6CWt/f8F1dlHm+XVrJo+Y/R2kApzVdnl/Pe4++3O9cR6TZVOzoIc7bB7loPuRk+tMrkkg6O7chJOlLltY3I6lS++kCnzxFPPnYyy3Aq/Er7uWfpNpo/6eBmhD+b47UNyqlN2kbvaH9H4vldmxGau6Agu6X9kntoGpo9LH99CztXvYRtOjeGXMF6br1rYXi/rN1lZIe2f/vmZho+kXpvX5XlNZg7oes/Pwn8AoDGxkbuu+8+3njjDaqrq5kwYQK33XYbkyZNSnbThBBCiBj/21LDb/+9PtnNEH3BruoD79NFI5p3hwP/progKzbsZiCKt5r+Da6vsrvVy6LlpVEVe8U/XptD0cgluLJiZ59/1zOVyzNfQanJaB31D3llMzi9CkjBMjNZsWF3p0axz4zq1u331IW3XQGDdzbs7uiQhDJdFZxkjnUeaD/LKptprOq4XScGAwTdTtU6aFq9ov37FYfv2omGj5pGL1V1qXz1Sxv4xweHOTeRsNEonvjPESg1gWtO/BcTxjlDP5TdyNC1r3R4vo8rm9lR08s/R7FPqS7F3AkdT3S5PxL4BQA/+clPKCsr45577qGwsJB//vOfXHHFFbz88ssMHNj1L5YQQggRL1UN/XMCL9E7uO1I9/LjfGkcX5tCjqkINmcT8Hbc/Vprg7PLcxhdVLvX2VIg6zjmTF/LX5eOD43h11xw4qfkp+zCxzCC7gy+W+vHx4G7Y2uVCTjdun3eIO7Qin7uoJvv1rY/fr3H4tXUAPtZ3r1HDVY14W2FjxbV8Rh+ANNqoMpXQFVdKjl5mZCTgAb2Mm+XTeHpd0vD34uTp79AbmYdf33pmxA1BOTRt8/k9qHvkZvhQ+mGDs/lUx6qPR1Pkij6Bo/ZvV9UCfyC1tZWXn/9dR5++GGOPdaZ5OM73/kOS5Ys4emnn+amm25KcguFEEKICDtqGvLvzx7L5aUjktYW0TtZlsWq1auYNHESpmn26LnXvAHvPPkGAC7lwYUiQynqLSdsO92v7ZiKvaE0RVmtePeRrKeP28HE4hqq6lMpyGpxgltzKLgpE9tbjtdXcsC2WabTBjPYQDDdDAd+w/Z2eO2Jfhc/uWkquUXp7V6Lh7/9YxMNb4YeaD+rfnnGPve97qS3efL90vBEhg8+rPnmFR2tQZBc8fquVZTDuHuMmJ4i/3n7XO78wSqe/Vdst25bG1TVp4aGgDQy86obYl5XCorGT+SGvAE91j6ReLYVZP3nn3b5OAn8cbZz18ts3Ph7LKspYdc0zXRGjbqJgYWdW5EgGAxiWRbevWbu9Hq9fPTRR/FoohBCCNFtOmrhMa/LIN0r/5wRsSxLkRr6bvR04LdbI2POXe4Ucgenkxu0aGjIAZx10C8542P+/NJR2FphKM3cszYxqsTF/v7pnQuMIhDax0Xzxsi/HbV7O7l5k/fbrtZAEy1VTnA37AZUShqERhAYttPONi31flqbnIXujaBO2O+QrzpS4dcqsM/rlpfDk+/PiJnI8LvXw9lntl9/Ptni9V2r2Ap6r/sbtq3I/tzT7oaSUjYFWc730jZbOOrLX+mxdojew++3u3Wc/D9knG3d8hjNzV8k/rpbH+t04M/IyGDKlCk8/PDDjBo1igEDBvCvf/2LlStXMmzYsDi3VAghhOia6Ap/T0+SJcSBtDZFgnjhiAHMuW0qjcu3s2nBdgBcgQaefHEWd5bDhg0werSiuHgUMKpL11lwxb/C26bRxMU/n7rf/f/z+ousfd4J9Uo3Mu64cWx+ldDj1Jjj339xIx++tBmAoN/qUrsOhr8uugC276E5ZWXEzmkAWJbzefa2wB8vJSVgGBrbjvwdZyrN0UX13HHq5/z81fHY2sBQNhfO+Au5Gc4QXMslQ55ELAn8cTZs+FVs3Pi7hFf4hw27skvH3HPPPdx2221Mnz4d0zSZMGECZ5xxBp9+2vVuI0IIIUQ86aiylyF5XySYr6k5vO0JLXNWXbWLgDsHADPorB5RXHxw4dRyRc1eH1ryr7zcCcMlJe3PXb5+LXA0AFo1UTJhJptfrXJeVGkx+7o9kUp0sJtVw+4INgfC4UOrwD736zDsmjB6dJwb2IsUF8P8R+HqqzWWrTANzW8u286IY9K56mgbT9pfqPCPpSCrhQLvSoLMBsBOTdwNHNE3SOCPs4GFp3W60p5Mw4YN46mnnqK5uZnGxkYKCwu58cYbGTp0aLKbJoQQQsSI7uYqeV8kmr8lEsQ9qU6QXrHhbVCHA6B0bY9cx/YGoW0Mvs/kt1/fzPf/Ojw8TOD/5mzh4uMiM8M3btsROdZowvv3BpQdQBtuNKkx5zbdkep5IIEVflqD4c39Bf7iYliwQDFvnlPZN02YP7//VPfbfPtKxamnRfcUGQIMASD/v6+SPsCp6ruaBxF0O8cYmRLvRCz5RogYaWlppKWlUVdXxzvvvMP3v//9ZDdJCCGEiGFHV/ilxC8SzNcSVeFPc4J01a5tgBP4baO+Zy6UYUCts1lfX8htobAPYGvFD/46nBPzdzI4y1k1QLda4RtglqsVc4+FGWwm6MnGNtMItEaWBFQqiNYBlHIntEu/8kd+d20juJ89Ye5cmD27Lez2v7DfZl89RSxXbdT28PB26sDcBLRK9CUS+AUAb7/9NlprRo4cydatW7nnnnsYNWoU5513XrKbJoQQQsTQMoZfJFEgqsLvDVX47YbIUn2Wu2eGcaYOHEBLLdQ0elm18ahw2A9fRys27HExIKMRADNgYod66tvuAFprDLsZyMYy07n/mxe0u4bhGk7A/4MeaW9nqEDkPegDBH44+GERh7JgSguERmMEPDnh54eWjElOg0SvJYFfANDQ0MBvf/tbduzYQU5ODqeccgo33XQTbrc72U0TQgghYkRX+CXui0TzR1XKvelOhd/0ubFCYdtKP3CQ7YziMRP40z8G88zScaHZ6jXR33ilLD5rup/tW5xu/QF9IpUtORRkt5CW38KOlk0Y2rkRYZte/KaBx4odr28Ht1BbuQUYTiIYVmQogW3KWPODoXMNCI3oqGn0UlWXSkFWE8ccPyu5DRO9jgR+AcDpp5/O6aefnuxmCCGEEAcUvVKVIRV+0UVaa3yff45V372u9/762tCWATt30LS8GjOQEQ78ZpZB0/LlB93OzJrcqLAPTth3Qr+hNOd+ZTF5kwYDg1m+8hj++sp5aG2glGbOKRsoneSH2sjwg4yhRRRYzj/9a/bU0tzkvA9fU+ImljYsI9ILQSVussBDUd7ooeyphmVrIzeFlNIMnqWYOzfZrRO9iQR+IYQQQvQp0bP0S94XXVX1299S/djj3T7ed/gY51/QykPdoj+ytXIZxpfmhV/PKFvH1suvOOh2rm3+ElrP2OtZxXnHr2fKqCoGeD9hhzsN3zabv75yftSa9YrnXr+E65t+gB5VED7Su3sPR767GoA1Qw9ja17o/TQ1HnRbO0vZLmgL/KYE/oNx4lfO5o9vlMfcFNLamehw9mwZCiEijAPvIoQQQgjRe9hRJX6Zs090VeN/lx7U8bYR+gIqD6YVABRa5TjPaYuxq744qPO3GeHegtqrCq6UzZRRVeRm+HAHDsM0Ktiwek5UL4BQG7XJpt0GWkWGH/g9kZn63VZk2EFr9e4eaW9nGHak1qgl8B+U4uKR1O72tfvZW5Yz0aEQbaTCL4QQQog+JXZZPkn8omu03xnXrrxe8i6/vMvH2+8vAQ0KNzknzyAv/Visz5yZ0d3+OoZ/+6oeaWc+cO/y/3DLX7+MrQ0MZXPHBf+m0F2Dn0Le3DKNZxZdicZg7/H9prKZOGccH5RHJT9vFvnznJ4IaStWguUE/YAv0u0/3pSOmhtKfnUP2oAj/4d6fVboO+AwTWdVA4Dycigrg5ISqfj3ZxL4hRBCCNGn2NKlXxwEHXDWfzcyMym86cYuHWtbFvbFbzgPlAf/Cefy6hfVVPl2kuv2Ydg1XT7n/twEzLm3bWk6g+Li2Tx2xR3UNA7lmbcnoOlgfL9hM3+BwZS5c1l+1c3hcxm2K9y27Dt/DZ+8DUCgtYVEaQv8NY1eduweTXm5BNGD8cN7bqVgLMyb51T2TRPmz3c+04UL4aqrwLbBMGDBAmRsfz8lgV8IIYQQfYpM2icORrjC342ViPyhcGzn57Lys1nc8pVstM5FqcO4aPpaZg37X4+2FdovTeeZkELV0tR2XblB8etf13LxxTnh/W23BaHJ8JXPDFd8A8FB4aOCgVYSobwc1lYexhf1Q/nHB6PR+gQeeUGC6MGaO9cZs+/cFHK+K+XlkbAPzn9lbH//JWP4hRBCCNGnyKR94mC0VfiVpxuBv6WFoLbQVefxzNIL0dr5p7TWisVLx1GdgOz8ze//gOKx/203vt80iQn7AHgjvyDL15zIsGE2s2bBqb+4lPeqzyOog1hBX9zbfP/9DQwbZnPvvy/k7++PDt+saAui5eVxb8IhrbgYTjopEubLyiJhv42M7e+/JPALIYQQok/RMZP2SeIXXXMwFf5Aawtq4EAqWod3MFGeYruZ3iNtPJDv/+YmHnvMwGxbCjCqK3c0M9MLOF3oF713dtQNCoMX3rwCZX8Lnx3fMfzl5XDjjenha+89eF+CaM8rKXG68UeLHtsv+hcJ/EIIIYToU2QMvzgYkQq/p8vH+pqbcRkpFGS3oJSOec1QNtffcfDL8XXW3LmweTMsWeL8t6Nu8SOnTEbZFlV17YcA2FpR4R9NIFu3P7AHlZURFfbbkyDa84qLnaESB7ohJPoHCfxCCCGE6FOil+VTkvhFF2itI4G/m2P43X4PuRk+Lpq+FiPUrd40YcFjBqNLUg9whp61d1fuvc0+50J02ssM8S5HEdvH21CagqwWXMH4xoGq8tfa3Rxpm4lDgmj8dOaGkOgfZNI+IYQQQvQp0WP4Dcn7oitCYR+g0rebf338QJcOt9ftJMWfRtANpeMqGXPY01QfN4UBQ2tpHtjEAx/3dIN7wDdHkYrFhYcvYfEvZ6JtZ4m/C6evIzfDB0EXD3TxczggC9TSIbArFavxFS6aPozFS8dha4WhNGdP3cDXi/7AtPvukrAfR3tP+Cj6Jwn8ghUrVrBw4ULWrFlDVVUVDz30ECeffHL4da01999/P3/961+pr6/nqKOO4he/+AUjRoxIXqOFEEL0W9Fj+JUs5i26wPZHAv+21koWrFrQpeMPK0/nzODZBEOdAwKDtrHE83fYifOnNxsFY34zEN/OoVy2q4hCz5cBMIMeHu3i53AgI/YcwanrrwQgraWA0nGVjC/eQ2Xz/xicdjR5aY2clLmKQRJGhYg76dIvaG5uZuzYsfz85z/v8PXHHnuMRYsW8Ytf/IJnn32W1NRU5s6di88X/1ldhRBCiL1ppMIvukcH/OHtgNn1491Bg5rGYtZX5FDT6GVtQWUPti7+3Hk7yRj/IZ78XeHnzGDPD0NI9+cAzmSBa3eeQE2jl7y0JkYOdjPQU8WoTS/hdUsMESIRpMIvmDFjBjNmzOjwNa01Tz75JNdcc0246n/PPfdQWlrKG2+8wRlnnJHIpgohhBB7jeFPXjtE36OjKvzaZbLgK12rbD/50zpufWE2WiuU0vxwysPc/JVdBz6wl1m5+xXsBmdb6VQe/fKjGHtP634Qdrwd4Jn/VPPcG19FawOlNJdN/S/f+fom0h6937nul8b22PWEEPsmgT/O/rmrlv/bVEmjZR945x6SYRr8YORgzirMOehzlZeXU1VVRWlpafi5zMxMjjzySD7++GMJ/EIIIRIudpZ+Sfyi83TUGH7tNjm+6PhOH1teDguesMOz3WutuOfWw7j264f1uXHS/iOqWFPmbCudwZSsKaRlpfXY+X/1wW957o2bYj6rRe9P5+az1ob3Ua5udLEQQnSZBP44e3jrLsqaE9/1/eGtu3ok8FdVVQGQn58f83x+fj67d+8+6PMLIYQQXRU9ht+QwC+6QPsjXfptV9cq2mVlYO+1vFzbGvJ9LfAfUzqTNc+/D8pAq3Saq2vCgb+83HmvJSXdf1+71ns7WAbQYOOOLMaEHitTuvQLkQgS+OPsumGF3JOECv+1wwoTdj0hhBAikaJn6Ze4L7oipsLfxQpzSQkoZcesKd9X15BPz8zEFWwi6M7ENjJorNrNgJFDWLgQrroKbBsMw1nLvTvLuQ1Oq0cpHRP6TRNG5UQNf3BLDBEiEeQ3Lc7OKszpkUp7shQUFABQXV1NYWHkJkJ1dTXjxo1LVrOEEEL0YzEVfikSii6IqfC7uxb4i4vhohl/YfF/LwwtL2czf77R56r7bUyrkaA7E8uVQUtVNeXlkbAPzn/nzYPZs7te6c9PCXLR9LXhpfhME+bPh8HVe6gK7aMMiSFCJIL8pon9Ki4upqCggPfee4/x48cD0NjYyCeffMJFF12U5NYJIYToj2xZlk9008FU+AGmj/qE8UOGUVWfSv6AvzN37h092byEUroRANv0snHzOtLSZoXDfpvuDlkwdEZ4KT7/wO1cddMciouh6lfByPW7eMNFCNE9EvgFTU1NbN26Nfy4vLyczz//nOzsbIqKirjssst45JFHGD58OMXFxdx3330UFhaGZ+0XQgghEil20r4kNkT0OdEVft2FLuVt49rr6oaQO8BHboaPYJ4VjyYmjA4FfoDtu7dxWonTYyY69Hd3yIJWWQDkZvg46cKh4RsGOhgV+E2JIUIkgvymCdasWcNll10WfnzXXXcBcO6553L33Xdz5ZVX0tLSws9+9jPq6+s5+uijefzxx/F6vclqshBCiH4sqsAvs/SLLrH8kYmUO1PhX/PRe9x901aefntOaHm567lo+lpKx1WSm9PHhzYazeHNlqYmioudMfvz5jmV/bZu+N0ZsqCNTABcgQaGjZoUeSEY6WGBzNIvREJI4BdMnTqVdevW7fN1pRQ33HADN9xwQwJbJYQQQnQsetI+Q/K+6IKgryW83ZkK/7/uWcLTb/8oZnm5xUvHccTgCsYVHh23diaCZUQ+C3xOWX/uXGfM/oYNTmW/O2G/pbmZoMup8JtWPd60yHJ/OirwK5fEECESQX7ThBBCCNGnyLJ8orssf2vkQScCZ/We0R0sL6fYs6cSl2doTzcvoSxX5LMw/JHZL4uLD26ZwU8+XI5tegBQdgPulNTwazoQ1aVfAr8QCSFz2wohhBCiT7FlWT7RTcHWqMDvOXDgLMxsQikd85xSNun5xbg8ffuf0bY7MgeBGXD32HnXf/xheFvTEDPsRluRayp3z11TCLFvfftvKiGEEEL0OzKGX3RXMLrC34nAmZfhLC9nhEK/aWgumr6OgiyNy9O3x6DbKZFt00rZ946dUF4OS5Y4/23YURV+3jabYvaLnrQPmbRPiISQ3zQhhBBC9CkyS7/orqAvOvAf+J/BWqVQOq6Sw4t2UHrVTNy+Pax8oRKgz1f4XWmp6FAeN+y0/e+8HwsXwlVXObP7GwZceNLRlJY4r1lmc+zO0bP0y6R9QiRE3/6bSgghhBD9jozhF91lBSKBvzNdyrVyViTKT63npJOgIDcy6Zyrj68jn1tQFN5WunuBv7w8EvbB+e/iJV+jptH53IIef8z+McvyuaRLvxCJIIFfCCGEEH2KzNIvusv2RZblw+3Z/762hW04Xd0N27lREAxEFqnv6xX+8ROnhbe1yujWOcrKImG/ja0NquqdifosT+yLOhg9hl86GguRCH37byohhBBC9Dt2VIVfCvyiKyx/JPAbnv1XmP3NLVimE/hVW+D3RwJrXx/DP2jEYZhBp8v9npaC8Bj8rigpcbrxRzOUTUFWaMm/lNjPSFtRY/ilwi9EQkjgF0IIIUSfomMCvyR+0XnRFX7l2X+F/4sNn6INpwqtcI4L+iMVa3cfD/xur4lpNbBs7WB++NzZzJoFw4bZfOO0Z6ks39KpcxQXw4IFYIY+CtOES6cuITfD+bzSUzNjD4ip8O//8xdC9AwJ/EIIIYToU2RZPtFdViAypvxAgXPLus8jD7QTYAMxFf6+8c/o6Bn0o7k9JrUNmmeWjkNr5zdJa4PFr81h0Y0vdvr8c+fC5s3ONTZvhmljKgBQdpCB2YNj9o3p0u+SLv1CJIL8pglWrFjBwoULWbNmDVVVVTz00EOcfPLJ4ddff/11Fi9ezKeffkptbS1///vfGT9+fBJbLIQQoj+LHcMvkV90nh3TpX//gb961w5gGACati79fWsM/94z6C9Y4AR0cIYk7GjwhcN+G1srKnzH88gt13BkS+R3LdeTQran4+X7DGBMaNtyHeecP1iP8dmnbL/1h+H9WrfuDG9L4BciMeQ3TdDc3MzYsWM5//zzuf766zt8/aijjuK0007jJz/5SRJaKIQQQkRE9eiXwC+6RPsjs+wbHu9+922qqY08UE7PgL40hr+jGfTnzYPZs52u+C63gTf/c5Sy0Tpy88JQmoLsVtxVQ3llzzp2NxYxIGM7OanbmLa+nKxW/z6uCA0pbqoOP4OqXekM8e6iqOoz6nbu7HDfAw2pEEL0DAn8ghkzZjBjxox9vn7OOecAUN7VmVyEEEKIOIjp0i95X3SBjqrwmwcI/MFmX3jIiO4o8PfyZfk6mkHfsmDDBifwmx6DQlcaFxz9AM/973q0NlHK4uIT1pCb4eOtz77K0++MR2sDpWwumLWIcZmP7jPwv/nlUlZ8NIcnn5mG1grFZG4ctZHD3Iva7Zs1rFnG8AuRIBL4hRBCCNGnaJmlX3STHYhU+NsCf3m5E45LSpwgHN7XZ9MW6W0zFPj70LJ8bTPoR4d+04TRo9u2DTwZJzBt4gaOOeIJMouGUDywnu0rV1HVeDpPvzMhZmz/3968jCOu+YKTb7mi3bU2bttI/SM2T75/cuQYDO7b/EOue/sSigeHbpSs+ivGO3fjTrfA6N03TIQ4VEjgj7OXVlXy23+vo8lnHXjnHpLuNbn5lLGcPnHwgXcWQggh+piYZflk2j7RBToQ26V/f2PcVdQKcrbhHBc7hr93B9a2GfTnzXMq+6YJ8+fH3tRwe71oeyxDBqbxjdudsfdvvuJh7QOqw7H9NZsH4R05st21VvzzL+xoPKX9MbbBNv8wDms7ZFc2pIf+TWxIDBEiEeQ3Lc4WLP2CL6qaEn7d+Us3SuAXQghxiIqatK93F1lFb+OPdEevrc3lmv2McTcCkS+Xdjk7BfvYLP1z5zrvZ8MGp7IfHfbLy6GsMpcsVz0ZuZH3Neu0c/l81ULUq8e1G9s/KL3jf9M2bq+iILsFpXRM6DdNzejRUTcB7KgCmOr9n58QhwIJ/HE2b8Zh3Pt64iv886aPStj1hBBCiESK7qIsFX7RFToYKdvv3Fmw3zHuhmVih4r4HQf+3l3hb1NcHBv0IXr2/okopbns5DK+GfX6dbfOJWVApHeAoTQXTl9LfoqNFQxi7j3DfqMmN8PHRdPXsnjpWGxtoJTFffc2U1ycGdlPR/17WLr0C5EQEvjj7PSJg6XSLoQQQvQgO2ZZviQ2RPQ9UbP0Dz3Mv98x7oblDgd+vE41OhDq0m+4FEYf/fLtPXu/1opFb5Twy/LYGwNtvQNeeWk1O9+uJzfDh2rOon73LnIHFcWc02z1EnRD6bhKSkb8mZ1bLAZkbOfKqx6JvXhMhV8CvxCJIIFf0NTUxNatW8OPy8vL+fzzz8nOzqaoqIja2loqKyvZtWsXAJs2bQJgwIABFBQUJKXNQggh+q/oZfmUzNonuiIYxO/OYMfAqXjKXfzw2irufngAtq0wDM2t1+ym4qMGKj4Cpd3hw2zT4P1/bqSpxpnl391Hqvsd6Wj2flsrVn7QSoY79oUMN5x96hCe/7AcGy+2kU3F2s0olROznxnMIBj6uDJy9pDZshVQuFNSYy8kFX4hEk4Cv2DNmjVcdtll4cd33XUXAOeeey533303b775Jj/60Y/Cr990000AXH/99XznO99JbGOFEEL0e7Isn+g2f5D1JV9jXVopVX9LpSB7PbdftJ6q+lQKslrIDfj48GVnV4/tiTrM5MOXN4cfu9x9d/x5R7P3G0qz8vn/seV1X4fHeIK1+M2BWK4s/vPH5bhSWmNeT9HZkQc+pxeF6Uppf0NOKvxCJJwEfsHUqVNZt27dPl8/77zzOO+88xLYIiGEEGI/okr8hiR+0QUqGOT55gn85e/HR9aX//KbzDisfdA1tDe8rVVazGvDjsiPe1vjZe/Z+9vG5+dmdBz2AQy7HhiI5UrDMne1CxDacAK/sgPYdTWY0L66D7GBX2bpFyIh5DdNCCGEEH1KTIU/ie0Qfc+uhlz+suw0NE6FXmuDv/1nFrfcVsfgQZHvldaaf9+9Ovx49JGHcfQxkwFwp5gMHJ6V0Hb3tLbx+atX+mndXkGmWwOD9rn/5iX/izzIbGbs4bH7bni3GQB3oBaXEUTbkJ7bwWcU06W/7/aSEKIvkcAvhBBCiD7Flgr/AZWXO2O1S0raz9Den5U3DgmH/Ta2NmhWuQwdH3nO39rCnuZ8ymtyKMhu4YzSLzH0sLwEtza+nNn7PcDIA+772H//Et42LIuTr5gQfrxm9QrWvZ8Weq0WbTsrIaSkp7c/kXTpFyLhJPALIYQQok+JnbQvac3old76zz/440+yePL9k9BaoZTm0uNXcPykv3L1I/+X7OYlnR7WjFodu1a8oTSB1pXAlPBzCx4NcuvzF4e6/WsGnBDg6msS397ewnI3h7cNv4HWOjw+/5N3l9L22WlVF97PmxY7DMLZQSbtEyLRJPALIYQQok+RSfv2bfnCNTz5/m3hQKu14qn3jmXs8CZqaqrIze3fq+sER7hDa8WPw9YqPH59+9pX4VQntJaXww03Z8R8htd/x8OZZ/Xf3hJWShBCKxq6/V7+8ZtfYoQC++6K7bhCgd826sPHeNMz2p9IKvxCJJwEfiGEEEL0LdKlf5+q9hTFVK/BWXJtZ2M2Gzas5thjZyWpZb2DCqZSOq6S8cV7qK5xkZ8bJDfDR2P57vA+zrJ1sZ+hZcGGDf038BvZbuzQR2QG0/jiw/fDr3lyR4W3g1E9AVIOFPilwi9EQshsGUIIIYToU6TCv28DM+tQSsc8ZyhNQVYLOzZvTFKreg+XzwmhuRk+jshfFp6ZXjVG9nGWrYv9DE0TRo9OWDN7neyhkUn6DDs2yLsDkdn4Ax4/AN60dMadMKP9ibRU+IVINKnwCyGEEKJPiQ78UuGPlZfm26vLus2F09eRm+GjbmdVspuXdK5gHkGPs+1L2wYc6Tzvi0wwV1wMP/ruJ9x135Hhz3D+fKPfVvcBJkw9gXc/bgJAkcm8R/4Ufm3xTY9guZ3t1MJsLr7jT6RkZOLyeNqfSCr8QiScBH4hhBBC9ClReV+W5duL0p5wl/WMKRr/+++Rnnc0AC01DUluXXJpy0KrQgCUbdGUVk9aq/OaaWVz79fPDO+bkZXB7Rd/h6r6VIrdq5g79/IktLj3mDx5KsuDL2G5UrFVNhl5+eGVIGoai0gLFfmHjZ9ARl7+vk8kk/YJkXDSpV8IIYQQfUr0snxKKvx78QJOl/XTz8giO3tP+BWr0Z+sRvUKjTXVBDzOpIVu/27sXbtB2wBolRuzr5GSQm6GjzFFteSl72l3rv7IDDoT8vlTBnHZtOcZNsxm1iy49YUrWLZ2MMoOMm3m6fs/iUzaJ0TCSYVfCCGEEH1MdJf+JDajF9LKG94eOuwwtCsY+bha7OQ0qpd485W/Y5vOQHzD3kWKy407UEfAk4vlymdwydjwvrsCPgitMKeVLxnN7XUMu46axmFs3JHFU+/ORBNZxWDx0nFMKvyC9Pyc/Z9ER30HpcIvREJI4BesWLGChQsXsmbNGqqqqnjooYc4+eSTAQgEAvz+979n6dKlbNu2jYyMDEpLS7n55psZOHBgklsuhBCiP5IK/36EAr9h+UhNS3cK/qFu6/j7d8fONct3sKviGAqyWxjoqmbIuKnUfVZNwJNL0J3JlK9fzPiJzvCH+f/vJwQl8MdYWjaCRe+XtlsFAkIrQTRsP/Dvo1T4hUi4/v03vwCgubmZsWPH8vOf/7zda62trXz22Wdcc801PP/88zz44INs2rSJa665JgktFUIIIUBrqfDvix0V+AFUWiRUqWD/rfMsXAg/feQn3P+vo/jZ06W8tWkCBcMOQ1MT3ufDJW+Et63m1vC2NgIJbWtvVF4Oi94/pcOwD2Aom3nXnH3gE9nBqIMkhgiRCP33b34RNmPGDGbM6GDpFCAzM5Mnnngi5rmf/vSnzJkzh+3bt1NUVJSIJgohhBBhMRV+mbYvhjZCgV87gd/MTCUYGoJuWB3Mmt4P/P4XD/K9O65Daydgaq145u05XHXzBmzXh+H96st3hbd1a6QSbUvgp6yM8Oe3N9PUPPowjD17UIevx4iZtE9iiBCJIL9possaGxtRSpGVlZXspgghhOiHopflU1IkjGGZTuBXthP40/Ly8G1xXlPau6/DDmnb3stqV5nW2qQuMJSgtzk8x4GqJzLz/J4cckMzz9sui/6upMQpyNvRQ/ANWLwYjj9eUVzcyRtv0qVfiISTwB9vn74AS+4EX2PirunNgJk/hsPP6fFT+3w+fvOb33DGGWeQkZHR4+cXQgghusKQMfxh1dW70EZoQXScwJ8zqCjcaV3ZKUlpV7INzExFKR0T+pWyGH+4i/IPTdo+oA9Wnc31w2y0NlD8lItmrKN0XCXa3b8nOwQoLoYFC2DePLAsME2YPx/mzOniiWTSPiESTgJ/vL17P+xen9hrNgDL7u/xwB8IBLjhhhvQWnP77bf36LmFEEKIzoqp8CexHb3Ntm1l4W0V6tJfNHwEm9qmm6d7gb+t6l1S4gS/vuTzNf8jPT+Xi6avZfHSsdjaQCmLr039J6MO+yp5I4dSXQM1jV6eXB6ZkE5jsHjpOMYX7yF3oHzLAObOhdmzYcMGGD26m98FqfALkXAS+OPthBtgya8SX+Ev/W6PnjIQCHDjjTeyfft2/vSnP0l1XwghRNJE5X2p8EfZVb4NKAw98gMwbNQY3mUFAFp1PfAvXAhXXeV05TYMp8o7d24PNTiOVr72EqvfXUqdrxWXOo/ScZUcOeDfbKquZEDGdgYMmILpNjj1a5fy3LKXqKo7vF23f1sr9tQYTL98ZpLeRe9TXHyQN31ixvBL4BciESTwx9vh58Sla30itYX9LVu28OSTT5Kbm5vsJgkhhOjHYir8kvfD6nZX0Rb4tfr/7N13fFX1/fjx1+ecO7J3gEAgIAnLQcCBooJSK9rhXrRibaPQr7V219Z+a79aqx3+bF2tYLFWUGi1aq2zblTAjYICBpmBELJ37jjn8/vj3NxBJpmEvJ+Phw/PPeNzPvd6TfI+78/n/XEC/vTkJMxgM5YrHq3i0dpCdZBZPTCTX1ISCfbB+ffixU6W91DO9Jdt28prf18KQGLyVKzQX7uJWZ+Tb34OgDLTAEhLy+CMX85i/58eRanCmMJ0hrKZe2WAOWd8dUD7f1iLrtIvBTiEGBDyf5qgsbGRTZs2sWnTJgBKSkrYtGkTe/fuJRAIcN1117Fx40Zuv/12LMuivLyc8vJy/H7/IPdcCCHEcBRTpV8C/rCm6rrIi1DAr5TCsJ0l5rQRRzBY1+Y6Kxjk+iteZtw4m3nzIG+cZtkyJ/i3D5i+blnOkO5DWcXuneFttz8nvN0U1wSA4Z4AZnx4//iJk7jh7l9w//0GZuhZiGnC0vsNzr14zsB0eriwJcMvxECTDL9g48aNXHHFFeHXt912GwDnn38+1157La+88goA5557bsx1Dz30ELNmzRq4jgohhBAAGk40PuV4tRnXGxvBkKgfwF9RGt7Wyg+v/x6Amvp89rSkMTJZEXjrdtwqNea6fz5ZzR9W3Boe0m5rxeJFNmvvXY5hLMS2Y7Pe+199ipLyveSOGMDpigeh7t1IwF/hm8Le6jRGJVVz/mjFy42LwEzCpiT8+bQqyof5K5PYuied/DHVzvt7faB7f5ir3hHZlmX5hBgQ8n+aYNasWWzZsqXD450dE0IIIQbaqMAu7vLc4ryQgCzMbrggsm344dXfsOyDhfzs6R87leeVxuN7ih8ffU/MdRs23dZm/rplGzSu+ydLv/wui5/+E5Z2obDRGi69+TwMZbH0K9+jaObyAXlvB6NubwHWqGP4+MMzeeTNs9FaobBJmP0crqOdGkSaRqfG0gFyQ/9QA3wygJ0ejqRonxADQob0CyGEEGJIGRvcMdhdOCTpoDeybQQpqRvNoqfvDM9L11px/RO3U1I3Oua6rMQASumYfYayyc/YxpWFy7ntgn/xzTM2AAod+tPR1iaLnr6zTVtdKakbzavbTz3o6w7G1oR8AuVX8Mibl8ZU3f/R2t9R3dD6GTX32/1FN4w7CVyewe6FEMPCkM3wL1myhP/+979s27aNuLg4ZsyYwY9//GOOOOKIDq95/PHH+fnPfx6zz+PxsGHDhv7urhBCCCH6iIqq9K1nXIGa+pVB7M0h5M7nI9seTXHhQ9g6Notqa5OtM/5O7qzq8L6032wNLVs3BVsrDKVZeOKb/G/Wj3DVNDIjK5tkXwDNgVXs27bVmWWPjmHRnUdi2wrD0Cy9+ROKLt7T8/fbAevu/7C/PqXtqAVtUl4XT3qSD+0BvvbPPr+36AbTA3knD3YvhBg2hmzA/8477/D1r3+do48+GsuyuOOOOygqKuKZZ54hISGhw+uSkpJ4/vnIL0Ql1X6EEEKIIcXQkUpyauSRMGn+IPbmEGK9HNn2mBTMOx7DiC28Zyib/NNPCI1bd9iqgtlTSpk2uoyqKpv0LC+Z8U0E6l+nobwAvJCd2uwM6Sd2Pv/4U2bABHfn3bIs7lz4ID9edWakToCtWPyro5j/jaP6tOK/tm3MwHNOf5WOCfoNwyY7xcnsa7ct3xshxLAwZIf0L1u2jAsuuICCggKmTJnCb3/7W/bu3csnn3Q+4UopRXZ2dvifrKysAeqxEEIIIfqCIWt5t0sFI5+FijPIzYWlS53AHMBQmq9/5Z9tAuygyyniN8JbRv6I9aQn+bBNDyc0b2WUlQ1AepKPX33xZkwVDLd12ZwtVH72apf9WvPYv9lZP6Ntxr0fKv431dViBj2kJ/lYMGdz+L2bJvzyonWkJ/mcE13BTloRQojDx5DN8B+ovr4egNTU1E7Pa2pq4vTTT8e2baZNm8YPf/hDCgoKDvp+lmVhWVbMa611+J/hrvVzOPBzEt3X+rnJ5yf6m3zXxEDps+9aVMBvY6CH8Xe3pMRZPq+gAAw76s86r8m+ffv48pfhN5f9jdKG+WSnNJM56i327TstfFp9+R4slzMyUtm1JI60CP1Jxfb6GbTETQTA4yvnf0/8I0VH/YOlW76FJ3026Uk+Pn35Y8ZMP6bTPu75dAfZqSPbZNxNU5OaWkEwmN5nIy5ryvZhWHFYLpg9pZSjpj/BCedcT34+7Ht4C+/tygNAu4LyM0/0CfkdKgZKT79jh0XAb9s2t956KzNnzmTSpEkdnjdhwgRuvfVWJk+eTH19PQ888ACXXXYZzzzzDKNGjTqoe3766adt9rlcLpqbm7EPXLR2GPL5fAQCATZv3jzYXRnypMaEGCjyXRMDpbffNcsXKbi2q2QPleb6XvZoaPrXY2n89ncTwhX4F550PCcc7Ryrbq7jvvvuAyArqZ5JKTUA2E06vB8gtX4/Hi5yXuhaPnI3cwRQ3eBla9VCMjNs0pN8mHoTn8RNgjj4in6Ud5uPpbrBy+fbjuDRB14jIb2pw37OLmkMZ9wjdQJsZl72Fr9f9xlT32rhSyfO6pOgf/+WTzDsyNTOzNwAaWnrqaiAivIawAn4/bqZ9evX9/p+QrSS36HiUHVYBPw33XQTxcXFPPLII52eN2PGDGbMmBHz+ktf+hKrVq3i+9///kHdc9q0aXg8keqiLS0t7Ny5k/j4eOLi4g6qrcORYRi43W7y8/Pl8+ghy7LYsGEDRx99NKYpQ1ZF/5HvmhgoffVdW/vyYxAakT1u/ATGTi/smw4OIdu2+fntb73hOfVaK1as/QoFE9aSnuQjaEZmbQZMC09o8KERjJ1v7/FFhrZbRgP/OuVMTvltKo+8eaSznJ3SLJizmaTT07jhpPsB+O9b3+DtFzJY8dZ0J2P/kibl+ztIOLsC7Wo7n3/OO/VoQhn3UbvZ15RFdkozm774AS3JyTwSdxQLc0YzbtTIXn8u7+/ZwV6dGH49eeYMCgsLAXjtsQ/Dxfk9ya7wfiF6Q36HioHi9/vbTTp3ZcgH/DfffDOvvfYaK1asOOgsvdvtZurUqezateug72uaZsz/1KZpopQK/zPctX4OB35O4uDJZygGinzXxEDp7XdNRRXtM0y3M0F7mHli1VtozojZZ2sjUoVemRQEc1DAPlc9BJxzDMvDzHHF4WuqyhNpDG373C00NqTxyJtTI8vZacWq1VMYd0VN+JqX1VxWvHlMpGq/VjT8aTw/KSsh3fsmv/7anJj/Jm6dhh9A24z2riUl7VQAit57inmZ77EzLodNox5jwpjeL9XXUFmBVqGAX9tMP/aU8HctEDVtX3m1/LwTfUp+h4r+1tPv15AN+LXW/PrXv+bFF19k+fLljB079qDbsCyLzz77jLlz5/ZDD4UQQgjRHwyi5jGq4fkHttu3EaW+EFuFXulwFXovScwNTgPgabMEqzXgt70kjl8XvqZi3ZfDJZzr42y+uPtFVuojY+5la8XsmrXk6c8AqG5KianW33pOeX0i6cnTWbxlKS2TnP8u2rKxXF8FwOOvItn7GQ04AX9ZUwFkvkdeSykf7v4AZh7V68+lpnw/2nCmd7qCTSQnR2o7BYKRz0p5e30rIYQYEoZswH/TTTfx9NNP8+c//5nExETKy8sBSE5ODg8h/+lPf8rIkSP50Y9+BMA999xDYWEheXl51NXVsWzZMvbu3cvFF188aO9DCCGEEAdHqvRDQnDPAXPiNZfN2RyuQh8XN4YNmSkAtOxNxt3iXKfs2EhXB9IgtKsu2eCc3Ef5h/FdbDvyuRpGkK+O+RfZ7AOgflI2hrKwddQ5KrLknf/DMfxj97EA5ATKyHPFA2Ba+ylJcWOGCuU3BSaGr6/YU8yzf/m415/L3q37cJkznT5ZDTy/ZGN45OX+lhGRE2W2oRBimBiyAf/KlSsBWLhwYcz+2267jQsuuACA0tJSDCPyBLquro5f/vKXlJeXk5qaypFHHsmqVavIz88fuI4fgt59912WLVvGxo0bKS8v59577+WMMyLDBO+++26eeeYZ9u3bh9vt5sgjj+QHP/gB06dPH8ReCyGEGK6ih/QP14DfbrCZPaWUqblVlNfFk53SHA72DctPU4Wbrbs+B6AhQZHeeqGOY8fLiyMNWZE/BWu8WTR+fC4Lz/ovy5+bj60NDGWz8KwXafz43PDQf9Dc+ZWf8v2nf4elXaBsTpj7AulJzpOD0f5I0bwv+DYC450+qyru9JzLj5v82KaHypZJvLr9VAoyP8efWsvnn/Z+jT6/4UeFHjAYdgM7Pq6MOhqZ228kIIQQw8KQDfi3bNnS5TnLly+PeX3DDTdwww039FeXhqympiYmT57MhRdeyLXXXtvm+Pjx47nxxhsZO3YsLS0tPPjgg3zrW9/ixRdfJCMjYxB6LIQQYjiLyfAfhkP6bb+f/b/9LS2fdFycSSWPBRekJ/kia8uHGLYPXfU8fr+T1g+SBqaTca9uzGTbmi1kJe0lLaGS1Ljzw9c1NNdRs/1Djk5azQ1fXkVFw+jweTUHxOJfLfiUHd9/lq1VR2CmNJCYUM26qv+HNkzijVE87/oJABv9hZSFci8NRhOj/OW4/C5e23kSK1efjtbnYiibr1/8d6bzQC8/OdCjx4QL8xH1iCJaVvl6KlJ6fSshhBgShmzAL/rO3LlzO61j8NWvfjXm9c9//nMee+wxtmzZwkknndTf3RNCCCFiGERn+A+/P2UaXnqJ6kdWdnqOeeKkcHLe27IHX9yY8DHD9mGEgn0As7kREm3WbBkTCrIVCpuLjrubM45OA8AVqGNc5e7wNWkJlaQlRGfHY5U2pTB35HZyU/aG933oK6ElPg+fdxSZVi3Z3gbeD5wZnjJAoIwL9m2kMvAFVq6eEq4/YGuDhx+9grwvv9bpPbslanUCrZo45a2fHXCCjSfQyKvxV/TuPkIIMUQcfr8lRb/y+/384x//IDk5mcmTJw92d4QQQgxDisN7SH9g//4uz1F2ZHg6ehcQCfiV9jGmuj782lIGuy1igmyNwb/ev5ZjJr1FuieIGazh6NLtdHedoYr6RCoT40lLiDxYcLONFvJAGXxYexRfyHqbgBWZN5/YWEFSYz3vtdTEFBsEnHoApWmMztjRzR5EVLZks68pl1EJJQRyIu3aRhOeQH3MuZaCF2cozAxJ8QshhgcJ+PvZCzte4N7199IYaH9YWX9IdCdybeG1nDn+zD5r89VXX+WHP/whzc3NZGdn88ADD8hwfiGEEIPC0FHrqx2GAb9uiQTRuX/+M8nzTm9zzpor7g1vt8SXxwbq2s+C/74ec/5Pznyy3SB72/50kusC5Hq2c9UB1xyslN//hfptznZ53HzMm1+g/OJ/UFKdRnZqM9c98HdS0jM4tQT+lKex7egVBiyuevgP5E32HNQ9ly2D7y0C2wbDgEtdKzl5Uuj9ufxM+mRjeCmr13e/zrWvOFMXv2u4e/VehRBiqJCAv589uPFBttduH/j7fvJgnwb8s2bN4sknn6S6upp//vOffP/73+fRRx8lMzOzz+4hhBBCdIcRXbTvcJzD3xwJ+I2E+HbP0SoJAGVb1KcGSI5KZCvta3P+pxfEo16y0Toy5F2hefClo9AolCokYxkUFfW835PmzGDPtiYAmqrH8K3TXuTB1Zc4UwiUJud0RVER5ObC0qWKRVfbocKAmitOWcWo+K8CHkpKoLgYCgqcdlu3c3Nj71dSAldfHXlPtg3/eO1Spo1eS3qSD8sbjDk/YAfC224J+IUQw4QE/P3sm0d9k3vW3zPgGf4rj7yyT9tMSEggLy+PvLw8CgsLOfPMM3nsscdYvHhx1xcLIYQQfcg4zIf065Zw1TmMuPbXj7PNZABcwXrScnOxNkUfbRvw33jZydSWVbLm5iy0rVBoNEBobIDWBosXw/z5bQPr7igpgb0Nx1Nf8yLJaR7KArk8uHp2ZAqBVjHtFxWB2vdPPnp7EtkpzYxxf8y//ryF/67+Ag+tOzUUxNs4PVUoZbPwpLeZk/8aX7xxNukjsvnrMy1oPTP2c9EG5XXxTiHDBCPmmAT8QojhSAL+fnbm+DP7NNN+qLBtG7/fP9jdEEIIMQzFVOk/DIv2RWf4VXzbDH9TYwNBlxPwG1Ydk6fOYmNUwK9V29/Ps9KSePNXSZQUwbtr9vH8PR+z9I3Yv08sC7ZuPfiAf9kyWLQIbNtEqTNZMGcLWcnNbaYQHNj+UTPTad5dE3pTKZRsO4aH1s2Jus4IPZRwHkisWHsiBRNsnrnt37z6tVFMWteEUjNi7mMoTXaK88DESPESLTrgdx2G3xshhGiP0fUp4nDX2NjIpk2b2LTJ+WuhpKSETZs2sXfvXpqamrjjjjtYv349e/bsYePGjfz85z+nrKyMs846a5B7LoQQYjgyOMyX5esiw7/xw3fQoZENSjdwxJT8A84ItrmmVW4unH/JKH75yJkYho45ZpqQf2BTXSgpaQ32nddaG6x6YwozzvBhqM7bHzu5ILxtqyz2NI9p85Agmq0V5XXxKOZQ/MlUfHsLOfeEreH7GEpz2ZzN4WUKE7Jjpx0GLMnwCyGGH3m8Kdi4cSNXXBFZnua2224D4Pzzz+emm25i27ZtPPHEE1RXV5OWlsbRRx/Nww8/TEFBQUdNCiGEEP3GjJ7Dbxx+uQsdneGPa5vh3/HJBuBo51zVQFZ2NmbwYypa0imvjWeMN73Le7TOo1+82Mm8myYsWXLw2f3i4kiw38q2FeOnn8TS++m0/RF5eSi7GG248XvyyE5tRindYdBvKJvslGbe2DqRj5ZNZj0GSmkumPkBOXmvMzJ+VjjYB8jOHR9zfdCOPAhxmxLwCyGGBwn4BbNmzWLLli0dHr/nnnsGsDdCCCFE5xSH+ZD+qCr9RnzbDH/t3n20Bvy20UR8fDxrN41g+drjnQJ5FJLZjQJ8RUXOnPqtW53Me0/m7hcUOM9cooP+1kz+aad13r5pmrgC9QS8Gdimh/QkHwvmbGbV6snYrXP4lQoV/bM5Z/4TKDvLWV6QSG2AJz6YwS/m/ZP0ukiwb1h+MtJHx9xP5vALIYajw++3pBBCCCEOa4d7lX7d3HZIf3Tl+mBtJLC1zGZKSmD52uMiBfLofgG+3NyeBfrR1y9d2nEmv6v2DbsWiCzzO3tKKZPH/YOpwQsYn+Z8Djtq4hmf1kxOygju/HAbWs+NacPWBtNKz6PetYuyQC7ltfHkJJbj9sYu8ScBvxBiOJKAXwghhBBDinmYV+kPZ/hNE9zuqKJ4TjZ9wSmzOGmqc0rQ7ae4mJjl9qDnBfh6olcjBXR9m11JI2s5JaOF1hUEcse2bnv5ypF5/L8XY5cXNJWmYEwcd61OZvm62eFlABsLdlJYGGlXivYJIYYj+WknhBBCiCHFONyH9Icy/EZcHHv2qJiieLYNK9+4mCljQ2vNe+xOh9UPlJ6OFLCNhjb70o7OJ+eKE9o9Pwe4/8gDRxQoRs6fwfLfT49ZBvDWW/O46iqbvDznWsnwCyGGo8Ov0o0QQgghDmtGdIZfHX5/yrQO6Vfx8e0XxQutNQ+g413hYfVmaLBDTwvwDQbb3RLzWtkBZp9+dqfXFBXBjh3w6qvOv4uKaHeUg20rtm6NvI4p2icBvxBimDj8HosLIYQQ4rBm6OgM/+E7pN+Ii2s3e99arR4gIS0b6JsCfH0lut5AV/3Q8UHwR167/ZVMGvPFLu9x4IiCdj8nQ8eMcohZlk+q9AshhonD77G4EEIIIQ5rMRn+w3FIf2vAHx/Xbvb+/BnvUF4bT3WDlyMmHxe+LjfXqYw/mMH+smWQlwfz5jn/Xras8/Nd6d6Y14ZdidGDpRbbfk6aG27YGfNZyJB+IcRwdPj9lhRCCCHEYS12SP/hleHXWkeG9Mc5w/Zbs/d/+r9l7PjwCB7/8DT0B05huj8c2UTX+fCBUVJCm3oDXa0WkDomi/KyyGvbqO7x/aNHOUyYYFNRUQmMDR+Xon1CiOFIMvxCCCGEGFLMw3hIv/b7QWsgsiQfOAHziJpkJ9iPKkx3/S8TKCkZlK620V69gdbVAjoy6ZgZMa+DnrZV+w9GZ6McJMMvhBiO5PGmEEIIIYYUA5sK02CD1wv73gZP0mB3qc+oukZGhraraWLbrlcB2PSvV9jjOz8c7LeyLMWjb31I4Uk1A9vRdpQneDGMk7DtSB8NU7M/fi2v7vK1e00wMwBaUd2YQHltPBmj4nk19J57w7Ztttdtp2Z3TXiKwJ6GPeHjEvALIYYLCfiFEEIIMaTUmUHm547Bbyh464bB7k6fyqjT/FkZ7Mg7m93mGLb+5SMAJlY0k53ajFI6Nug3LJbs/QnuV8s6aHFgjfrG+ez9+6/ANsGwGHXFTfx68xOwueNrZnzyICvWzERrhVKF5Fj/R8bcJ/qmQ7va3y0BvxBiuJAh/YJ3332Xb3/725xyyilMnjyZl156qcNzb7zxRiZPnsyDDz44cB0UQgghouzw+p1g/zDkDcD+7BnsGP8lrLjpTKg+hgnVx+DxF5Ce5GPBnM0o5YybV8pm9Dduwp1xaAT7ABlzn2Dy7fMZf/03mXz7/C4D90DVSFasmRE1TcFg799/RaBqZKfX9aqPcRlkxmf2W/tCCHEokQy/oKmpicmTJ3PhhRdy7bXXdnjeiy++yEcffcSIESMGsHdCCCFELEtFJoqfNuZUpo+cOYi96VuJ2/dTl9IEQHWDl/LaeJKz3yMnbiIAp01YT8HofVQ3pjDqC3vJPn0c8L1B7HHvbH4nh/+nD8g/2SZfTf0Bk2eWxuyu2pfA/l2pjBhXS8aopk7btW2b0tJScnJyYqr+m8rktLGn4TE9ffYehBDiUCYBv2Du3LnMnTu303PKysr49a9/zbJly1i8ePEA9UwIIYRoy1Y6vH3qmFO5ZOqCQexN32oKfMjKlBd5e2MNK946D42BUoUsmLOF2VNK0XzKT++7EJfHwBN37GB3t9dK0uGPRmyxP9OE75355ZjCe8uWwc9DKwAYhrMEX1FRx+1alsX64HoKjyrENA+vwo5CCHEwZEi/6JJt2/zkJz+hqKiIgoKCwe6OEEKIYU5HZfjdpreTM4ce3dLMfjvIijXno0N/pmltsGr1FKobvARGN5OQ4sETd3jkbHJzneC9NSY3TViyJLbKfkfL/R0qqxMIIcSh7PD4bXEIq3v+ecrvuhu7sXHA7mkkJpJ93XWknDW/T9q7//77cblcXHHFFX3SnhBCCNEbweiA33V4Dc1+9KVPeH/7eW2q8dtaUbt/P9fcdf0g9az/FBXB/PnO8n35+W2X1Otsub/2lt8TQggRIQF/P6tc9gD+bdsG/r4PPNAnAf/GjRt56KGHePzxx1Hq8CyQJIQQYmixiQzp9xiHR8DfUFvLdYve5MFHi9DaADQQtbydYfO9B84nI2PQutivcnM7Dt4LCpxh/AcO+8/PH5i+CSHEUCYBfz/LLCqi/K67BjzDn/mtb/VJW++99x6VlZWcfvrp4X2WZfG73/2Ohx56iFdeeaVP7iOEEEJ0h9Y6Zg7/4bC8WnnZXv72nTd48PFLojL7itag3xnmbgzbbHbrsP/Fi53MfnvD/oUQQrRPAv5+lnLW/D4bWj8Yzj33XGbPnh2zr6ioiHPPPZcLLrhgkHolhBBiuNIaLBXJfrvNoR/wP3rPH9njW9BmGD8o/vhHuOgiCW67GvYvhBCifRLwCxobG9m1a1f4dUlJCZs2bSI1NZXRo0eTnp4ec77b7SYrK4sjjjhioLsqhBBimNPEBvyHwpD+khJnnnlBQc8CUVVmk53ajFI6Jug3Dc1FFykJbkM6G/YvhBCifVKlX7Bx40bOO+88zjvvPABuu+02zjvvPO66667B7ZgQQghxAFvrUMDvGMwMv6+lhW9c9CTjxtnMmwd5eZplyw6+HbdvJOlJPhbM2YwKFSQ0lM2dP94qAa4QQohekQy/YNasWWzZsqXb58u8fSGEEIMlMqTfMZhz+P/0jTtZ/vhPw1l521YsXuwMPe9uoL7r00q0GgvAyZN2MXFsMbW1ueTbH/PNi4/qr64LIYQYJiTDL4QQQoghw9aaYNRU98EM+MtrZraZd9+6XFx3aFvz5AP/wB83EgCPr4SRiUlMGl3DOLai4uL7ustCCCGGGQn4hRBCCDFktMnwD9KQfr/PR0amiYrqC4Bp6m4vF9fSFMAKbA6/1qoEALe/ntGlazHi4/qsv0IIIYYnCfiFEEIIMWRoNNYhkOF//93VpKbAgjmbMUJBv6E0P/z+R90ezt9U56elbCSf7UmjusGLP7Gas+Oe5+S1N5DUuBcjTgJ+IYQQvSNz+IUQQggxZNgaggz+HP5N76wDTmb2lFKm5lZRXhdPdkozeUe8BRR2q40HHoBf/OPnaAyU0nzj4irOSX8Wn3YK96l4GdIvhBCidyTDL4QQQoghQx8wh99jDs6yfM17q8Pb6Uk+Jo2uIT3JR0NZZbeuLymBn/0qER36U0xrxfJ/fYU9lZEgXzL8QggheksCfiGEEEIMGbbmkCjaZ9Sb4W1vc0l4264JdOv64mKnqn80y1LsqEgLv1YS8AshhOglCfiFEEIIMWQcmOEfrIDf5U+IerU7vGX4zLYnt6OgAJSyY/aZJox17wRAeb0oQ/5ME0II0Tvym0QIIYQQQ4Y+VDL8Vmp4258QGcZv+rs3xSA3Fy6b+4+ogn82S5bAyFClfhnOL4QQoi9I0T4hhBBCDBm21gRCAb+hwTS6l1Hva1qlhzZsXBOSCWwP9clK6PiiA8ydsJFpY9ZQXhdP5oh/U1R0E8V/bwGkYJ8QQoi+IQG/EEIIIYYMTSTD79KdntorzRsraPpwP9pu/yaWKwMAd6CWLJVFaWi/0ondvocrmEF6ko/0JB8pU5wHBXaLE/BLhl8IIURfkIBf8O6777Js2TI2btxIeXk59957L2eccUb4+M9+9jOeeOKJmGtOOeUUli1bNtBdFUIIMczZtkUAJ+K3q0by6qvOfPjc3D68R0uQqn9sQQfsdo/v1rsJukcBYFhVTKgYGw74tUrq9n20ygZA2Rann3GBs6+52dknGX4hhBB9QAJ+QVNTE5MnT+bCCy/k2muvbfecU089ldtuuy382uMZnGWQhBBCDHOWRUApKl85l9LlNzFPg2Foli5VFBX10S1qfB0G+wCb2QKMCr2qJsuYjBmowXInoY3kbt2jurKcgGcEAG5/OaMnno4OBtEBp8q/ZPiFEEL0BQn4BXPnzmXu3LmdnuPxeMjOzh6gHgkhhBDts+0grpKp7Ft+M2gjtE+xaJFm/nzVo0x/SYmzTF7rSAGr3h8+ljR7NMmnj405v+XWpyL9MetIKByB+XwJljuJoKt7Af+zTzyMbR4DgGGXo+JM7Mam8HEjXgJ+IYQQvSdV+kW3vPPOO5x00knMnz+fX/3qV1RXVw92l4QQQgxD2rIY9/EctI79E8a2FVu3dr+d+vpalvzipyw87VHGjbOZNw/GjbO5/ifrYwL+fVYCq9/zUFrrwUx2/tG1NtUNXj7bk0Z5wI1nTDKGXe/0w4xj2+efdnn/ys92hLctsxKlFLqlObxPxXe/+J8QQgjREcnw97Ot7+/nnf9sw99iDdg9PXEmJ3z1CPKPHdEn7Z166ql88YtfJDc3l927d3PHHXdw9dVX849//APTHJzqyEIIIYYnrS1yEqpRSqN1ZH0+Q9nk53c/j/HwL35NRdX5PLx6drgdrQ1u/3/H8M0TylC0cP+GVP74+1HYoWkDl837J/Nyqlm3/XJWvFWI1gqlCsmc2oyLhnDbGz98myMmTuv0/q5ag9buB7yhhwWhgn0gQ/qFEEL0DQn4+9mH/91J9b6mrk/sQ43Ahy/u6rOA/8tf/nJ4e/LkyUyePJkzzjgjnPUXQgghBooOBsiKb2TBnM2sWj0FWysMpbn8xOfIzf1y1w2EuOpSKa+Nj3loAGBrg/c2VLNn23rueO7H4eO2rVj18iWMPu+9cLAPzkOC7/wyntsuMEgMJeV3fPAhL9W20JHyqkQ+33EM6dle0pN82KE6f60F+wCUDOkXQgjRByTg72czzswblAz/jC+O67f2x44dS3p6Ojt37pSAXwghxIDStoVheZg9pZSpuVWU18WTndJMjrmLhjff6nY7yo4nO7W5nZECmpKqJyltPqudhwGKz/eltt1vG+xrSGRipvM6WOfno/8+0+5911ReyBMvX47GQCnNgjmbOWdeLg1vvoV/x45IP+KkSr8QQojek4C/n+UfO6LPMu2Hin379lFTUyNF/IQQQgw4y/Jj2M5KMa1r2APYvkx2X3VVt9upOvJnlPvjOfeErTz19hHYmBhKc9mczSRbZQTbfRhgMzb3MZT6WUwNAaUsMjJKgXwAPEEPvnbuWdmYzhOvfAMdKqGktWLV6skscP+L3Y/E9l2K9gkhhOgLEvALGhsb2bVrV/h1SUkJmzZtIjU1ldTUVO655x7mz59PVlYWu3fv5g9/+AN5eXmceuqpg9hrIYQQw5Ev2IzSkWDYsPzYpge/J51Gj0miv+sRdf+quZAbn1iI1k6W/ftZv0cdm0l61lTSk3y4W1JIT/KFpg1MxtYGBhbX5/6ac4qfIDHXz+92/9J5SBDan5peAUGnfVfQy3Gf7W5z34fHnNy22KA2KKtyMfGAc72TJh30ZyOEEEIcSAJ+wcaNG7niiivCr2+77TYAzj//fP7v//6Pzz77jCeffJL6+npGjBjBySefzPe+9z08Hs9gdVkIIcQwFbB8KO0Nv3b79+CLnwDKZOM553Gkp/NAuaw2gV/98YJw4K214s7Kn3BLxm9ISToCANMaSQCYPaWUI0f/kwzvieRm1DMy9QjgR5wPzK59gpKq5PD+tbXVEITqBi/V5dPInTeLkalNMff1v2eg0GgiowZMpTnmrHFkjbwmvM8zYQIpZ5/V+w9LCCHEsCcBv2DWrFls2bKlw+PLli0bwN4IIYQQHQsEfaAiGX7NXmACANX1zbzjy405v7rBS3mtM18/PcnHZ3vSsNvJspc2pJOS4ry2XDnhY4lp1Vi2m52NGawvy4lpCy/sbMxgZ2MGgaTPeHtzDitXT0Hrk/njizaLz36eXxydzB3rDf7035NCDxl0OOg3lea384s59leXYHjkTzIhhBB9T367CCGEEGLICFo+NKGCdtoi4K0OH/P47Jhz14QDcBUukDc1t6rdQn3pmZXh1wFPeuR+bhcuX/ttzZ5SGj6vpmpy+Dg41fuXPHsWlXWP8tibl0Rl9RVg85dzNnHsmHoy0gIYXYxKEEIIIXpKAn4hhBBCDBl+y4c2nIDfDDbTkhTEHaqQ5/IncPI3nOB5336T6+4fFRWAK1a9MYUXVu1jsfsdlv79hNCSfja/+nENI1M0wZK290sfN5Jx46a229Y3v5PGqBFOzYA1701pU71fY/Dom5e2aVNjkDZSkX2EScYXj+iTz0UIIYRojwT8QgghhBgyApYPuzXgt1sgNQ72O8dMK5Wj5jpD+stfBTs24Y9tK1zZOcyb+xhjAkHK6+IZ41nLT3//Hf779BSK2wn4C+fOorY+p8O2jprrvE6bCOonuk3Q3x7T1Jxy41Ryc7s8VQghhOgVo+tThBBCCCEODU0NjVimE/Aru4kRo6OHw0eG4hcUgHHAXzmmCfn5UL+vnPQkH5NG15CevJ8dO3aQOTqvzb2UHeSoo2d12lar3Fy4/36FaepO+2+amiVLlAT7QgghBoQE/H1M685/0Q8X8jkIIYToD9VV+9GGCYDSLXiCNq5AHdUNXjbtn8x//vo0bz/5KHvee5Sf/c+7GIaTmjcMm+u//S573nuU5v214fYsw8eDDz7If556ATPYHHMvV7Aej9dLbi4sXeoE+eD8e8kS2gTtRUWwY4fin/8Ew4j9PWgY8M9/OseLivr4QxFCCCE6IEP6+4jb7QagqamJ+Pj4Qe7N4GtqcpYiav1chBBCiL5Qt6866lUL2z58n082LWL5utlorfjjCzaXz36C2ZPXkeR9ghu+5KaiYTRZSXtJK6/kzZXgSR8fznhYLh/gVP03rQYsV+R3uGHVh7eLimD+fNi61cnsd5Shz82Fiy+GujrF4sVgWZEHBBdf3LefhRBCCNEVCfj7iGmapKWlsX+/M5EwISEBpbqex3e40VrT1NTE/v37SUtLw2xNhwghhBB9wF/bROtvFq1aqKlNYvnak8NV8LU2eHjNBUwaP4pRwc3UJjxBWkJlTBsuy4sdaiToCuKpcKrtK3sskB0+T+n6mOtyczsO9A/U3QcEQgghRH+SgL8PjRo1CiAc9A9naWlp4c9DCCGE6Ct2vT8q4PeRPe1C9L9iZyjaWlFeF096Th6nXvxtMtIyY46/8sDT4YDfjoNLzr8SgNeXvhNznlYNverrwTwgEEIIIfrDQQf8FRUVnHzyyQD89a9/5dRTT+3w3JtvvpmHH36YGTNmsHLlysM+462UIicnhxEjRhAIBAa7O4PG7XZLZl8IIUS/UC2RufG24ePMi2agbrHROhL0G8omO6UZlMGWN97jdPWFmDYMOy6ybbiJezoIgFaNMedps6k/3oIQQggxYA464M/KymLs2LHs3r2bjz76qMOAf/PmzaxatQrDMPjf//3fwz7Yj2aapgS8QgghRD8w/JHA3jYCTDs6g/POeYYnn/oSWhuYSvO12U+QnuRU7K9vroWEAxrRkXn6SWZKVHuxAb7l8fX9GxBCCCEGUI+G9M+cOZPdu3fz8ccfd3jOr3/9ayzL4tJLL+Woo47qcQeFEEIIIVoZwcgDddsVQCnF3NM+Z/wRf6K+OpvvHn0Cb33+Frb1Fef8FhdxMzNi2lB7IwF/wahpxKU5x+1Kf+zN4u1+ehdCCCHEwOhRwD9jxgz+/e9/89FHH7V7/N///jfvvfceqampfP/73+9N/4QQQggxzJWUQHExFBSAEXRhh/56sUwLgKTkJFJSdpOSUse06xbw/h0pNG1zznH5k8m68shwW8FgEP3qu84LbXHi/5yLx+t1Xq+3oCVyXyPN099vTQghhOhXPc7wA9TU1LBz507y8vLCxxoaGvjDH/4AwPe+9z0yMjLabUMIIYQQoit/vreFa7/rQWsDw4Cvn3Qis0IDBy2XE/AnJiaGz7/jjjsI+ppJDb1Wdmb47xIA27bJMCYA4Ao2RYJ9wEh2Y0UF/MmjYov9CSGEEEON0fUpbRUUFJCcnAzQJst/7733Ul5ezuTJk7nssst630MhhBBCDEufflrDtdd6wgX5bBseXnM+1Q1OkK5Do/tTU1PD1zQ1NdHsd2MGm51rzCwaGxvD/zQ3N2OZzgMCw4ot0heXnUZ1g5fP9jj/Hjt5cn+/RSGEEKJf9SjDbxgG06dP58033+Sjjz7inHPOAeDzzz9n+fLlANx4441SuE4IIYQQByWwr5FPlu1g63aD57e/h2ZhzHFbG86Se0k+tMup2D9r1izKysqoqakJn+faVY7lGoffk0likge3y6ncFwi0YLmcOfyGjg34Pyw+jTsfmYXWCqU0d5/UzCmn9d97FUIIIfpbjwJ+cObxtwb8rW655RYCgQBf+cpXOO644/qkg0IIIYQYPm6+fju/WT4tlNWfAmggstJPeMk9wAhNsc/IyODKK6+MaWfZFbcD40AZTByVwQWXfxuAN197mo8+D52kI1X5S0rgrj87wT6A1orvfS+Bc8+F3Ny+f59CCCHEQOjRkH6IzOPfvHkzfr+fF154gTVr1pCQkMBPf/rTPuugEEIIIYaHkhKign0AhQKUcjL5hrJZeOJq0pOc5fLMeN1hW5arJrxdvmlbeLt0x/bwtlbN4e3iYrDt2CWELQu2bu3hmxFCCCEOAT3O8E+fPh3TNAkEArz//vv87ne/A+B//ud/GDlyZJ91UAghhBCHp+jq+7m5znYk2HdoFN/8wgaS4wOMjnuFzPhsfIxF2UHMlI7bthNbIBBqo1KH7/fu2mRSGr3OlAAjEvAXFIBhOHUCWpkm5Of32dsVQgghBlyPM/yJiYlMmjQJgF/84hfs2bOH8ePHtxlSJ4QQQghxoGXLIC9PM28e5OU5ryeOs8LZ/FZKWRwxso5Jo2vIiktAG878e9NqxpOS2F7TAHhGJ4S33U25LDzrn4wbZ/OHpVdy4yOzWbM5B9v0h8/JzYWlS50gH5x/L1kiw/mFEEIMbT0O+CEyrH/Pnj0A3HDDDXg8smatEEIIITpWUgKLFunwEHrbhsWL4bOPPmTBnM0YUUP4r73gBbI95QAE3LlYRpxzzG7BMDr+m+OIE44Nb5fax/Hwfy8Ojx7QWrFq9RSqfLEPDIqKYMcOePVV599FRX31joUQQojB0eMh/eAU7nv44YcBOP3005k7d26fdEoIIYQQQ0PQb1G2ow5tdzyfvpVt29TuCVL8cQO2nRpzzLLg6Sc+YPaUfKbmVlFbvpNLfn42OaNO5IVbHyHomYblTgqfr+xmTNXxnzGzTzmL4uX/wh83ivLa+HAxvnBftKI2Oa/Ndbm5ktUXQghx+OhVwB8X5zxl93g83HDDDX3SISGEEEIMDZZls+rX71Bb3tz1ySHVDV42Va5FMR8dU31fk2GWAPmkJ/nI9n7MO6tGAeA29wHTwteX18aT6y0hx3B3eB+P10vu+TY7n3uOnOQ0lCqMqQ9gKJtrb1jY4fVCCCHE4aDHAb9lWdx9990AFBUVMW7cuD7rlBBCCCEOHb7iYvyh6XvRKqs1YxsUJ6W4UO1c16pYbWFb7Sbe+vREHnnzolDgrVFoNApDaS6cu44Rrhas1nvGNWOGXvgS6jEsWLM5h5Wrp6C1QlHIVRnPcfnX2t6vtRjg0cdeztnnXg5A9hecaQOW1To/35BMvhBCiMNejwP+5cuXs2XLFsaMGcPixYv7sk9CCCGEOETUPvMMe3/043aPVY85lTHHX0apUcmBA/r318VTUp1EQvo2qvels7nmmzzyxlFRWX0FaL51xgYmjKwjIeMhXLV5WKG/TLQRJG/bCwDszNdU13rDwT6AxmDZsrO58cbYIfjLlsGiRU5dAMNwCvEVFTn/zJ/vLLOXny/D9oUQQgwPPQr4n376aW6//XaUUtxyyy3Ex8f3db+EEEIIcQhofGtNh8cCaaP5h+s1XL4KiJoj/+6Gk3j8xfNDmfxTcIL7tmMANIqk+ADpST5USwJKZ4WPTfr0I8bvqQRg9A7FI+POajsP3zbYujUSvDvFACNL67UWA5w/PzI3XwJ9IYQQw0m3A/7XXnuNm2++mdraWhoaGgC45pprmD17dr91rjsefvhhli1bRnl5OVOmTOGXv/wlxxxzTIfnP/fcc9x5553hZQR//OMfS7FBIYQQogN2fV14O/PqqzASIsvdfbLNYkRJOv6408L7qhu8PP7i7KjgvOMFgZSyyE5x5v97fCMIukYA4PLXcvxFX4859+x9u7jjRTt2Hr5hk58feV1cHAn2W1kWMQ8FhBBCiOGk2wH/Bx98wJ49e4iPj2fatGl87Wtf4+KLL+7PvnXp2Wef5bbbbuOmm25i+vTp/P3vf6eoqIjnn3+ezMzMNud/8MEH/OhHP+KHP/whp59+Ov/5z3/4zne+w+OPP86kSZMG4R0IIYQQhzarrj68nXXNNRhRo/r2/OQHuOK+GnN+exXx22MYNj+47G3SE5sAE21MJOhJAcBllZP1P/8Tc/4XgPvHwdWLLLRtgmFxzeI15OaeGj6noMAZxh8d9JumM4RfCCGEGI66HfD/8Ic/5Ic//GF/9uWg/e1vf+OSSy7hwgsvBOCmm27itdde41//+heLFi1qc/5DDz3EqaeeylVXXQXA97//fdasWcOKFSu4+eabD+relmVhWVbXJwrRQ63fL/meif4m37XhrbXAXUFB+1lwqy6U4Xe5sN1utGWFr6nbl0xGaCn7uKbN2GYZY+JT2lTEj6aUZsWKICefbJCTNoOHvvcYvrgxlAXGUF4RT3ZqM6PMina/j1deCY/VX8zmfal4R+7mzLwrsKzISMOcHLjvPsX//I/CshSmqfnLXzQ5ORr5eg8v8nNNDBT5romB0tPvWK+W5RtMfr+fTz75JKZgoGEYzJ49mw8//LDda9avX8+VV14Zs++UU07hpZdeOuj7f/rppwd9jRA9sWHDhsHughgm5Ls2/Dz5ZCa33pqHbSsMQ3PDDTs577zKmHPiKiowAJ2QwEcffRRzjVL/x4I5W5g9pZTGEVs4ecEVAPxi8q6oc5xyflpH7jF5ciUVFVBRAeg9rNl8XKT6vtJ8/eS9zFi/vt0+m6l7SUovxqU1lVW1rD/gvJkz4amn3Oze7WXsWB8jRwbooCkxDMjPNTFQ5LsmDlVDNuCvrq7Gsqw2Q/czMzPZtm1bu9dUVFSQlZXV5vyKioqDvv+0adPweDwHfZ0Q3WVZFhs2bODoo4/GNM3B7o44jMl3bejpKivf3TZ+8xsVHn5v24pbfzOOq64aG9PmVp8PG/Ckp5OYVcittxrYdqhSvjZYtXoKU3OrOOr0oyksLASgsBCuusoOV8QHZx79hAkW1dWVMd+1FwLPxFbf14pH3rqQW7J0u+9Nb3TOc2tNdvaI8D2FiCY/18RAke+aGCh+v79HSechG/APNtM05X9qMSDkuyYGinzXhoY/39vCtd/1oLWBUjbXXPUm9yydc9DtfLYlgNbumH22Nti+HfLynNfatrHrnTn8ZkoK27aZbYri2VpRWe1m/lcXxHx/8vIi7bS+tiyoro79rnmPPh298oDq+wf0I1oAZ0ijW2sw3fKdFZ2Sn2tioMh3TfS3nn6/Oi6de4hLT0/HNE0qK2OHHlZWVrbJ4rfKyspqk83v7HwhhBDiUFJSQjjYByfD/pe/nsL775YcdFstTR+Gh9u3MpQdU+DObmwE7ZxjJieHi+LFXqPJid9JfELiQfcB4NKFp2IYsf3orNBeEOeJg0cDasj+GSOEEEIMiCH7m9Lj8XDkkUeydu3a8D7btlm7di0zZsxo95rCwkLWrVsXs2/NmjUyHFAIIcSQUFxMm2J4tjb496q1HVzRsZrtq1kwZzNGKOg3lObyk96LGUZv10WW5DNSUsjNhaVLnYC89ZrL5mwmM2HHQd+/ldOmCrdpmrBkScdTFYI4/XWjwZBsmhBCCNGZIT2k/5vf/CbXX389Rx11FMcccwx///vfaW5u5oILLgDgpz/9KSNHjuRHP/oRAFdccQULFy7kgQceYO7cuTz77LNs3LjxoCv0CyGEEIOhoAAUNjrqeb2hNPFsPei2mnZXMntKKVNzqyiviyc7pZlsb+woOKs+siSfmZwMQFERzJ8Pv/vuXYxMPJb0JB/BQOxou4PV2mbrnP/O6hIEtQ0qNKTfGNJ/xgghhBD9bkj/pvzSl75EVVUVd911F+Xl5UydOpW//vWv4SH6paWlGFFjD2fOnMntt9/On/70J+644w7Gjx/Pvffey6RJkwbrLQghhBDdlpsLV5z4KsvfnoetVTjDnmTsP+i2VJ0BLkhP8pGe5APAsuMJ+P24Q0VprZgMf3JMP47K2EvQfRQAgcTm3rytcJvdKUAYCA/p12glGX4hhBCiM0M64Ae4/PLLufzyy9s9tnz58jb7zj77bM4+++z+7pYQQgjRL06ZtIP8iWvCWfn0JB+6RXd9YUhLy158vv0YgeTwXwFmsBHLlYg2XJSXlzF6zFiAcMG+fYGRbNk7heNKIkG5GcwgGKr5lzAu88Db9JvwkH5N24ICQgghhIgx5AN+IYQQYjjRKiEmKw9gBNoPfA9cvm9f2X/45JMfABqlrw+f5/aXYLkmA7Dz8+JwwG/V1XOf+hZ3b/8R+k8G6k6bhSf9h1MKPkCb00Mdsph/2cL+ebMH0FoTVK0BvwzpF0IIIboij8aFEEKIIcQ249vsMwKeNvt+9uNPGDfOZt48GDdOs2wZlO9/AUIZcsvlTH9z+evQKjJ0f/+ePYDzsOCf/03l7s0/ilkVYMXacygLnknAkwaAx1fBmNwJffkWOxS0g+FtFxpkSL8QQgjRKQn4hRBCiCGipbkZy0xos9+0vDGvn3v6A35/x7SoQF2xaJFNSYmz3n1NTXI4YHdZ5aAiowXqyytZtgzy8mDxfafHFAgEsLWivC7y0ME2Pu6T99YdATsQ3vZojZIq/UIIIUSnJOAXQgghhojtWz9Fh4axu/yRrLzSsVn/l1Z+hNYqZp9tG2zf7jws2F9cEHWkCtuIBPx7twa4+iob226/D4ayyc57FjKfI27iGhY/cFsv3tHBiQ743ZrwZyGEEEKI9slvSiGEEGKI2LppI+DMrzetCoKkAE7AbzUHMOOdKnojjFKU0jFBv6FsRo3aDIC1P5PWI5a7Hq0igXT5ltw2Wf1WpglLlhgUFf1vH7+z7vFb/vC2W8uQfiGEEKIrkuEXQgghhojKkpKoV1XhLa0S8FVHlubLNIMsmLMZI1TgzlCay098jvSMzwCoLx3DZ3vSqG7wopMtbLcVvjY72UCp2Kr/Sln87/d+w44dUFTU9++ru2RIvxBCCHFwJMMvhBBCdNO+xn28secNLNvq+uR+UFNZTmglPLRqxLB82KYXrRJ4/ZMnqK1zCvEpO53ZU0qZmlsVXr4vx9hKY6Pmkbv/l1UvfAONE9hffE4pp+S9Qeuo/pS0RBbM2cyq1VOwtcIwgvzgB//LqTN282bDKtjc8/7btk1JZQlbtmzB6MGSetUt1eFtZ1k+CfiFEEKIzkjAL4QQQnRDwA5w+bOXU9ZUNmh9uLLx1HDAb5k+TKsR2/RiG4l8uPPfrNy7CYDrjJ8DkJHQEF6+Tzcn8fHDX2PVf69Ehwb0a6149KkLmHTlekaFCv1broTww4Ld9jpOP/dxsrP3sWHnFJa9/Zu+eSOlvW/CjWT4hRBCiK7IkH4hhBCiGyqbKwc82A9UjaRh0/EEqkYC4PZHntNbph/DanK2XfF4tVOpXwUh4MkEwOMvxww2AqCNJKorjmxTzE9rg7L67Db3Tk/yMemoj8nO3gfARldlH7+73ils8aENd9cnCiGEEMOYZPiFEEKIboguGFeYXcglky/pt3vtev1D1v59Mo+8cTFaGyjD5ju3fIw7uBw79KjedgdRLa3BvJuEYAo/CJxLc3ltOBBWdiVm0MRyJWKZSWSnNLYp5qeUTVpWFeg23SA7KzKE/rzjr+Jc98hevS/bttm5cyd5eXk9GtIPUP3CbzmytpiZPh/PKclbCCGEEJ2RgF8IIYTohuiAf0LqBL468av9ch9fSwt3PmfxyBvnhgNzbRv85ZeF3HLhcySnO+dZRhBoDl/XXBek6YP1GMlZeFyt51Rh6ARgJJYrnpQ0HZqfPxlbGyhlcdGx95Aa/BzMuTH9MCwfE7ObaQHQirMLFmKa3l69N8uyWF+3nsIjCjHNng3HL/XfQY7PmaagZFk+IYQQolPym1IIIYToBr8dCfg9pqdXbWmt2bmhkqrSxjbH1n/6JHtaTm0z9N6yoKI2Jxzw24aFVk3h43YLzM4+lw0tmwiG9gXcTcT5GiJthObnT89aT4KVTG5qHSOSEthrHEOZnxiuYD2BeKfyv2pO6VawX1ICxcVQUAC5ud34IHpAaTu8rXv40EAIIYQYLiTgF0IIIbohOsPf24B/54ZKnvnzx+3fx7ON7NTj2gy9N5RmRFLkAUFQaWzlC79WLZA9xo3xWUL4t7s/Cbz+yEOBVpkJpcwfnQukOq/tljYBv7IbsDzOwwKzKa3D99Ia5L//Plx/Pdg2GAYsXdo/S/gpHQxvGzKkXwghhOiU/KYUQgghuiEm4Dd6F/Dv217b4bG4Ji/pST4WzNmMoZyJ9YbSXDTnAzITIuvQK8uLbURemy0mu2b9GsNODe9rGluBbbYN+G3VEPM60YjDDDbH7FM6co7XF9duX5ctg7w8mDcPfvITJ9gH59+LFzsPA/paa4bf0gok4BdCCCE6JRl+IYQQohv6ckh/U12krVMuyic5IxJQv3LnO/hcOEPvR2xhT8s4slOaGXPCOuz3k8Pn5R0xieriEggF2abP6VNl41j2VKWRndJIXH4t1ub0Nve33c34vzoxZp+xbC0W8eHXWkVGEySSyoFKSmDRokiQfyDLgq1b+35ov9IWAEFMDNXFyUIIIcQwJwG/EEII0Q19OaQ/UNXCvGQXyaaCl3YecHSU8y9tk57wIYkZKQA079uHR40AwLD8nPLlL/LCAysI1jinxzeO4qFf3s7yteehtUIpzdXjyjjG+woEDrhFkuaIk0fH7DLub4l5rY1IwB+vctq8h+LijoN9ANOE/PyOj/dUa8BvY2AoifiFEEKIzkjAL4QQQnSD3/aTU6k5vlgzbue7VLxl9bithN0TSXa1LYJXZzfi9zrBtce3n0BcXeRgpYVtJABgWk2YL72CKxAJyvdZp7J87exIZX+tuP93F/B/315D5gH3UaafivuWxO7UsQXwLHczRgN4PlekjSxs09eCAmeufntBv2nCkiX9U7gvPKQfA4n3hRBCiM5JwC+EEEJ0Q6ClmZsetkhrBFjNhsAWdgbyyHPvZJS77KDaMs/8U/g3sK++BG05Kfi1iRvRxlkAGPY+dJyGUOLd5UvCcjsBv7KbqbrrbtxTJ8JI53h5bXybyv5aG5Q3p5N5wLMFz+4dlD/zXMw+dcJ3Yl7bwRZG/dQZyeD+Q0ab95Cb6xTmW7zYGb5vmnDbbXD88U5mv/+q9DsPWizJ8AshhBBdkoBfCCGE6Aa7opL4Rps3Z8/kja3zWPFmEVobKGVz+SnLuKbublJbDhw735ZG4Y5Kpj9f/W9arFA1fHMs7tD+oKscjyuO1okEpp1FILQ0nqGdQnzJ9Q1UhgL+7NTmdir7W4wduQ1qTo7pw9g9pe307ICifaEnDcrtJuG4Y9t9L0VFMH++M1e/P4P8aEZUwI/E+0IIIUSnJOAXQgghuqGpqpr3T7iOMn8hK96MHjpv8PCbV3HURaOY86X9HDnuiE7bafFprJecYNrWNj6rkZqmTCoaRpOnknGnhc6LqyO3Pp0yHUQbLoKusZFGdDO5f/4zrspydrzs7EpP8vG1Uz7l4bemgm2gDM1dPyimcHYc65+NXGoGmznh17e26Zf++5qY14lHjCP38suJO+pI3CNGdPh+cnMHJtBvFRnSb0qGXwghhOiCBPxCCCFEN1Ru2YdK+DLle9oOnbe1Yk/LGN796CVOvLLzxef9exqwXnqFp2oex92SwkclV7F87VdDowU0C+ZsZvaUUlyj4hk3+hiq3qrE7x1J0BOplK9VC8nzTic+EICX36C6wUt5bTzTs9/mhZWg9nr57WnZfKtwCntLv8CGp5qxXE4FfjNYS/K8L7fpl17xWszrUYWFJM87vYefVv+JHdI/yJ0RQgghDnES8AshhBDdEGz046ajofOa7JRmjJau14Vvqm7hrZZXgHMoC3rbFNpbtXoK00aXcfXNPyIrbQQfr/4L4Yn6IVo5IwRcbjdrNuewcvWUUGX+Qn7/RZvF1yqSXc68gcSEfExrSzjgN+zadvulzWDM62nTZ3TnYxlw0UP6lYzpF0IIITrV9V8mQgghhED7nEAzPcnHwhOfwQzNw1fK5rI5m0lP8mEG3J204Gje34ynMQ1ov9CerRU1lXsZMSIHw2NiG1UAVDd4+WxPGtUNXmzDCfhLSggH++BML/jZtS5q90WKBCQmTkTZDZEbqKjK/9Hvzx0pt29YLeSOHdflexkMilDAryXDL4QQQnRFMvxCCCFEd0QlwE+Z9B6/+edX2LoV3nz0FtKtUwAwrLZL7R3IV9GMYacDraMFbLSOPH83lM23770gclt33QFZfM3XTtnDYqC4mDYPDCzLKaLXOq/e5UpG6YbwsP8xCR086/cQWREgWN/l+xgs0cvySYJfCCGE6JwE/EIIIUR3RAX8WgXDxep2fGjSuMXZb9gJXTYTqGrCNrIByIyv5567/Vz3vbjw0nZLlhhMzI8Ln1+pkg/I4itWvnkRvy2BggIwDLAjyXlM06mYH2311nyWr5sdHvafeaZTYT+aEW9SXRF6KOA9uGUGB1LrkH5bluUTQgghuiRD+oUQQohuUHYkuNRGJPofM7Egco7uOuCvKy8j4MkEwB2o4JrvxLFjB7z6KuzY0TYQn3LG4naG/RvhLP7SpYSnFzgPDGKr5peUwPJ1Z8cM+1+82Nkfbc3mE7nxkdnc9fRMfvavS1m2rMu3MihaM/xBqdIvhBBCdEkCfiGEEKIbDCsSXNrKCm8fc3xkjXutktDR6fZ2bGt+D204c/2VXQE4Afppp7W/vN2Js0dhHPDbOjqLX1REpw8MnGH/sQ20DvtvVVICf//3lyIPBWj/ocChwNDOwxYbA4n3hRBCiM5JwC+EEEJ0g2FFCuFpMxLwj8wZgxlsdPYbiTTXt18Ur1VDc3l42zaqu7xvd7L4nT0waB32H+3AYf/deShwqDCIzOGXon1CCCFE5yTgF0IIIbpB2ZFfmdqIzeKbllMF3zKTqK+s6LQdszkSpQa8DZ2cGdFVFr8z3Xlg0J2HAoeKyJB+A6naJ4QQQnROAn4hhBCiGww7OsOvY44p28nwW64Edn3+WcyxkhInUC8pAcuycfsSw8fstNh2Djw/WmdZ/K509cCgOw8FDglahzP8tmT4hRBCiC5JwC+EEEJ0g4oO+N0HHNON4e0tmz8GoLa2misueIJx42zmzYO8PM1991qYVnr43LQJOTHtLFsGeXmEzqdPC+d19cCgN6MIBowdmUphYaBkEr8QQgjRKVmWTwghhOgGpaMCfqVY9+TnUa8jAX/FnjLWPfk5q1e8yIonIxX2bVtx3Q9c3Hb+WBITAG1x+pcuCl9XUgKLFkWW2LNtWLwY5s8fuEx761KDhywdHfCbeCXeF0IIITolAb8QQgjRDYYd+ZUZ1Ir3n98Zfu02msPbujnAuuc3sLfl2LbL6dmKd0uOZHpcDSPdexgzbkL4WHFxJNhv1Vo475AOwgeSHVkO0dKGLMsnhBBCdEGG9AshhBDdoIiM49cqdky/ZbaEt82gwpf4HllpfpQ6cI6+5rG3j+TGR2bz1uasmCNDqXDeoDlgSL8QQgghOie/LYUQQojuiJq4702M59zvF4b/sT2RzLMZdJNkN5Ke5GPBnM0YqjVtr2mtKq+1Yvnbp8UU5hsyhfMGk44N+CXDL4QQQnROhvQLIYQQ3aB05FemNyGZ3CkZkYMJCkIr7LkCXqi3Cbpg9pRSpoxfxYfbpvPEGxfEtGdro81w/aIiZ87+1q1OZl+C/QO0Kdo3iH0RQgghhgAJ+IUQQohuiWT4ExIzYo64UuIJhgJ+w45DBeMIhn7DJqWXMyXlcZQ6D60jA+sMwyY/v+1Au0O+cN5gsmOL9kmGXwghhOicDOkXQgghuiUS8Kemj4o5kjp6RHjbsBNAhV5rG09tM5mJNVx07N0o5QSsSlnccsMOCewPVpsh/YPYFyGEEGIIkAy/EEII0QUrGEArT/j1iBFjY47nHzWdyvda5+onEvA4DwQ8/kqmn/NlMOBUYGHF85SUJnH0zBROPrtwYDp/OImq0m/LkH4hhBCiSxLwCyGEEF3wNzfTmuFXdoC0jOyY40dNn8U79htow0XAnYttegEwrH2ccP73Brq7h6+oIf1BDJRE/EIIIUSnZEi/EEIIcYCSEnj1VcJV9FuaGtHKTXWDl+KSFCprPDHnx8XH4wpN4rfcSeH9llk5YH0eFrQd3rQwkHBfCCGE6Jxk+IUQYqixLaj4LCbb2bv2bOLrPocyd9uF4A9SyV4Xxdu9FEzwkTs62PUFh6Af31TFHUtOQWsDw9As/cNe5p/6EW9sncSKNceiteKuFzRL/7CHoq9Vh68z7EYgDYDqBi/ltfGMSDdg38Z273M4fFYHrbfftZqdkaa0LMsnhBBCdEUCfiGEGEpsG+6fB6Xr+6xJE5gG8Hrv2ln2wUIWPX0ntjYxlMXSr3yPopnL+6CHA+c3JYu444Hfo7UTSNq2YvGPR7Lqa/eyYs3TbfbPLzmD3JS9ACj9WwDWbM5h5eopaK1QqpBRNde1+RwOh8+qJ/rquwYQlCr9QgghRJdkSL8QQgwl1dv7NNjvKyV1o8MBLICtTRY//SdK6kYPcs8OTtXuo8JBfStLu1i368R292+tOiKyQzdS3eANB/sAWhttPofD5bMabLv1CCnaJ4QQQnRBMvxCCDGUWIHIdmY+5J3c6yZtramsrCQzM7PHGdPijwvCAWwrS7vYmvk/5B69tdd9HCgjNzaglI4J7g1lkT+5HvVm7H7TsMmfcyxkTQSgpXYHFZ/Naf/BQNTncLh8Vj3RF9+1tz6v4K2KJB6yvsjXJOAXQgghOiUBvxBCDCVR65CTNxvOuasPmrTYtX49GYWFYJpdnt+egpmgbrTROjJwzDQh/xvXQR+sNV9SAsXFUFBAv65dn/nYn1gwZzOrVk/B1gpDaRac/TgjZ2awoDiy3zQ1S5YY5H7r5vC13zkHXnjmfe56rvPPoWAmGL9yZmd0dM7hqi++a488/AHPlJUCSJV+IYQQogsS8AshxFASXahP9Sxg6g+5ubDgjH+y6qVLw4HyvfcEyc1197rtZctg0SInQDYMWLoUior6oNPtUfHMnlLK1NwqyuviyU5pJj17DbU1kf21+/dz3bLzGDu27eXzv3ws998PixeDZTkx7ZIlsQ8pcnOd99DZOaJjGh3eNiTeF0IIITolAb8QQgwl0Rl+49AJ+AFOnvAuU782Nhwon3yyGzihV20+eP9jXL34gnDG3LadQHn+/P4JkDVxAKQn+UhP8gGgWhQt/pbw/hxjB2PHdhxpFhU5/du6FfLz2+9nd84R7YseGaFkYT4hhBCiUxLwCyHEUBIT7RxaAb/LnxgTKG98732OOrp3Af/HT1TFDI8HJyu+dWvfB8laa7QR32a/ETQJ+FrCVW61CrQ550C5uV33rzvniLZsLRl+IYQQorukSr8QQgwlh3CG37BSY17X7N7b6zZHJTailI7ZZyib/PxeN91G0F+PZcS12W9YHqxg1IMWug74Rf+J/jbIHH4hhBCicxLwCyHEUBIzh/8Q+xGuMmJeWjUtvW4yIz7IgjmbMUJBv6E0C770WL9kxmv378Jytc3wKysOHYyEmVoF25xTUgKvvur8W/QvHZXhl3hfCCGE6JwM6RdCiKHkEM7wW67YgF819z4aU7RTRG/EWuCSXrd9oK1bN4HKBMDjq8DvzQr3gagE/4FD+ge0qKAgKt7v8dJ+QgghxHBxiKWHhBBCdOoQrdK/ceO7BN3JMfvMgKfX7WqVADjF8iaNrnHqA/h63Wy79uzYFt427MrwttLxqJiAP/LfoKQkEuxDpKigZPr7T/Qcfgn3hRBCiM5JwC+EEEOJHTWc/BDK8H/w2ktt9hnBtsPjD5Y2Etu2G+ifwWk1lTWR+1IL2onitUrAsCOftW1E/hsUF8fWUYRIUUHRP2zJ8AshhBDdJgG/EEIMJfrQrNJfv6uizT6lE3rdrmW2bcOw+ifgb6lrCm9r5cMMOq+1kYCyIoGlNiL/DQoKnGH80UyTfikqKBwxRfvkrxghhBCiU/KrUgghhhL70JzDb9S1zbQqndSrNuvra7HMdpbJs3o/VaA9dnPks7UNP6bdDIBlxMdk+LUZOS8315mzb4YOmyYsWSLL7fUnLUP6hRBCiG6Ton1CCDGU6EOvSn9JCWzdcSTpWV5njn2IVr0L+Ddv/iD8Ht3+agKedACUbrt0Xl9Qvkj4aBt+zICT4bdczpD+1pjfNmKXCSwqgvnznWH8+fkS7Pc3KdonhBBCdJ8E/EIIMZTEZPgH/0f4ffcFuOYaE62/iVKaBXM2M3tyCSgTy0zuuoFO7N26FZgIgBmsiAr4vb3tdlhJiTMPv6AAjIAZHi5uGQE8OjTEXxmYwbhwwK/bGViRmyuB/kCxZVk+IYQQotsOjfSQEEKI7jmEluUrKSEU7Du/SrRWrFo9hcaqOgCC7iSaGht63H51aVl4W6vqqCN9k+Fftgzy8mDePOffaz+ZHT4WNCy0ag6/NqyohxeD/5xlWIsO+CXDL4QQQnROAn4hhBhKDoFl+WyfRaC8iSdWvh0O9sPHtKK8NrRPmaz/cE2P7+Orqg9vW2ZdVNX8eILBYEeXdcu2rS1cfbUds5zeI29eRHWDM3rAMiw0kYBfqZTwtnbLr87BpHXX5wghhBDCIXkKIYQYSqKr9Pdxhr+uopkNr+3F1xjo8ByvL8j4XXWU1Xr4aFMxihPQUaXTDGWTnbIXSAVgx6cbmX3KmT3qj9UYCeptlx/TasFyJaCNOBobG0lNTe1RuwDL//AkWl8Ws8/WBuV18U4dAq+JbmmJHFNp4W3D6+7xfUXvyRx+IYQQovsk4BdCiKHE7r+ifWsf38b2j2KX16tu8FJeG092ajPpST6OiTf466ZkbvnvDLSeBWgUGo3CVJobz/iQ9OQy/EwFoK60vMf9US0qMqfeHcTT1IzlSsA2EmhqaupVwJ9YXYpSGq2jH1ZoslOcrL43MR67wR8+FnRH7uWK65+igaJ7NNFD+gexI0IIIcQQIAG/EEIMJf04h796X1PM6zWbc1i5egpaq3BBvrqpT3LLf38WFSgrwOams95kavZn1NpvEjSzw21YtT56ygi4sUO/pew4MBpCy+SZcTQ21gA5XbZRFQiy3x87YiHgayElaRwL5mxm1eop2FphKM1lczaHVxlIzcqkvrIsvOi7bUaCfG9q74oRit6xozL8SjL8QgghRKck4BdCiKGkH+fw+5oCaLuR+CTNtLOP5rvz88KBvVOQbzJX2dNisuIAGgNfxkZ2+Z4BIC45FbQzOqBiTz4lJT2rYG9YnvBvKZXoRlU4Q+y14Wb37mLy86d2ev1LlXVcuWEbQQ1WuRurxIuZ6+PL6/5LavlcpuZW8dvz/8a+pomkZ7pilhTMHTeeT3fuI2oaf1hyWvrBvxnRZ7SWDL8QQgjRXRLwCyHEUNJPGX6tNQ0VbxNoeg1fLSy75wS0vjHmHFsbBN3j2gyFV8rCqFsHCaAME9tjs+6j1tEBJ3PXMza/+53Bccc5y991N/hXdnx425OaCDoSfe/dvb3L6x/bV8UX3n2Jylcm8OwL54cKDNo8xDQ0zqiFi+buZf7YLbQk5EfdN8i48RPZ/P772O0E/Fk5o7r3BkS/kAy/EEII0X0S8AshxFDSTxl+KwBB3yfh1+OTgm0DezRauTn3hK089c5EbG2glMVFx95DWkIlAKkJyexpzgxPBQDQ2uCnP9WAQimbovP/zW+/VNVln5SOBPxxLT4CRIro1Xz+OZXLlnV6fcH6zTRXn8fyF2ZHvQ8jPANca8Vjqy/kuPPvIzEhcp1pNWOuWYOLIP4DGwVGj5/YZd9F/2nN8EusL4QQQnRNAn4hhBhK+qlKf7BF48IiL3kGXiOBPS3emDnuKlQq7W8vH4VSmq/MeoRC/9uMTNhDZlw5VIFpa8Zt2c32gq+2GfYPkeD/gSfO5YTqBZyy9+PO3+qsH4a3E9d9RHXmieHX/tpG9v/h9o6vBVJPuIpdtfHt9CXqPNugtMlDftQ+w26h8Z6/4Dn+KPyJB7wL22Ls2LxO+y36V+sDG6nQL4QQQnRNAn4hhBhK+qlKf6A5yIz0k5iQfBS1di07qhSzp5Ry1KjdFPsbefCJ84gE7Ypn1l3GD464n1GNZW3aOjHehVJ2aAh9O29BG3yUcjxHNTZ22ietnLS7YbWQlnsGVS2VUUfjULOu7fDamjg3tHjJTm1uM1IhmmEESc0uAY4I71N2M3WpE0mqVTQcEPAbth+3KcvyDSa7NcM/yP0QQgghhgIJ+IUQYijppzn8LXVNjPM6Ve+fqv6QfWVfJju1mVGe9xmvvsjfDgivbEyavv0HxkyvbtNW4I0mfj9/Kz97oQBLK5ycbOzyd1lpdXww6odtro3m8tcBYAabSM6eiL1/X/iYaRkk5RzT4bVbkmwCOxtJN30sPOldVqw9zpmCgA1KobXCUDY3X/AG7pHNUBe5VulmPpjxQ4Laj9u2qGpKCC9NmO2pw6XkV+dgaq3ZJxl+IYQQomvyV4sQQgwldjCy3Ydz+H119Sjl5oYPy1jx4i/Q2nCW4pu7m//7VR7GPzW2HQmwTBOmLzielHYK8CWXrOWyQBlzJ1Tz5I5tbKyYzH/eK0RrI7z8XVZcPXZwX9uLQywdRLlGAmDYjbh1JtqMTGcwgp2/9+36M9ymM9d+zsS3uXa6ZkdNPOPTnCp8rds5KS4e94yLCfgJ1QpwKQ/rPk1nxVuF4aUJF570Hosl0BxU4aJ98p9BCCGE6JIE/EIIMZTY/ZPh99fW81TJXla8+NMDluK7hN/lGyxdCosXg2U5wf6SJR1X2/emxWNTT06Kn6NHvkNG8sMcMzKTajWDUSnHk57ko7Y2g937NpCVtDdc8C9aIN6F230dAEo3EQwmEDBaMEPBnhl0sdz6T8fvpyFIFk7Ar40qXkr8FyTC1tYTWrctKFNJFERdq5WPwi+OY3+lyYql42M+jxVrj+U3PVxmUPSN1qJ9siSfEEII0TUJ+IUQYiiJLtrXhxl+XdtMec3MNnPdbdtg61YoKoL582HrVsjP7zzgjc9MpZF6ANyGF4C0hErSRnxKXOAY1mzOYeXq0CgCbC467m5mHfFibH8S4iD0VjVN1LSUElCBcMBvWG58id4O+5C2P5KyD7pr8CWmdnhu4gGRo1Y+Tr4wn1dfjQwfD38e2vk8JOAfPDKkXwghhOg+CfiFEGIo6acMv9HsJzNDtSlwZ5pOgA9OkNudQNebmkhrOb4JRx5LevoEAD7duonqnd7YJfsw+NcH3+XCyzLJzmikvCqRkrJUrMAHVJd7Ka+NZ4zXzbQZGXj3J0CoZIBhO8F+XV0KlZUZZGZWkZISCfLjWhKwPM52wNsEdBzwu0OfaXVD6H5xzrkFBWAYYEc9Y8GwyM/vu89dHDwp2ieEEEJ0nwT8QggxlGiLv6Sl8GxiIvYHt8KGP/W+STRf8R1DSuq00FJ8k7G1AYbFtEX3sPid/8I73W/vqJqJfJeLAVjnLuaZ1LcAKEhPQ3/cdpk82zb4Y+NGmj7PYcN9V4M2gC/jhHQKpQo5PmMpJ4+vZGIo4Ffay5NlSXy85DvOagDKZvriexk370U0cO72ObQ+Gtk9uorNBaWd9jn15fN5+K1jnLn6FLLimjsZe8Z/OXLxmWxYci3YJhgWR3/7HnJzv9f9D0P0OVmWTwghhOg+CfiFEGII2R9o4M/pac6LlorW+nK95vMW4AnC7CmlFGa+xp8L3sE7cjc6o4xd9QfXVrIvsmzd6JoMptjOsAB3MEhSO8vkKWVxRE0Kq+77bijYB4gs6ae1wTvLribz+rdCs/KhuiGLj5Z/K3K+Nvho6TW0TPo3RlYLkBnab/NK1mf4mqLT9LECVSP57K2jY0YdbFhyLf6C/+Ce9VcmF/wHX9lYvCN3c9SkEQf3YYg+15rhlxS/EEII0bUhGfCXlJTw5z//mXXr1lFRUcGIESM455xz+Pa3v43H4+nwuoULF/LOO7FpqksvvZSbb765v7sshBB9otaKRPge5SLBk9Qn7SorMlUgNWUvuYWt5e3SDrotw478ajmu8UiOazwSgAa7iZeTfDGjCEyl+e38zxlX+hVWaqOjJsE2qfKNDL/c3zACtNnmHKv+bDzjKgm6swHw+KuIT0khvpP+1lRNc0YJHNCWu3oqaTk+yPFBzlaSPclcffTV3foMRP+ROfxCCCFE9w3JgH/btm1orbn55pvJy8vjs88+45e//CXNzc1cf/31nV57ySWXcN1114Vfx8d39megEEIcWvx2ILx94ehTueGMu3rdpmVZ/OX1H0Zeu5t447I3etyeDliU/u5d7IZAzP4kIwEzWMfsKaVMz95CmnskM9IMclL8lNZ5MJTG1u0HcaYJK79zK8/+7nVQJtnJPhQ2OmokgMJmf/ACpuz+GMuVAIBhlXf5XkpKIO/W2Ln6pgnPLbpbivMdglqr9Eu8L4QQQnRtSAb8c+bMYc6cOeHXY8eOZfv27axcubLLgD8uLo7s7Oz+7qIQQvQLvx0Mb3sMdydnHhxXwIsV+o1gxwU7P7kLym0y4rsz8BVXhyvttzL+8gaWK57UFMXx58KkUXkApAN3Z9Tx3VuTsbWBQuNUFzAwDJslSwwmHOHBtFqoaMmgonk8587aylPvFGBrhUKjUdT9egLvqjzy52xh9pRSMNou+Xeg3FwOatlBMbhsyfALIYQQ3TYkA/721NfXk5racRXmVv/5z3946qmnyM7O5vTTT+eaa67pUZbfsiysqCGwQvS11u+XfM9EtBYrkjV3Ge4efz9KSqC42KlEn53pwwwmhQN+M7Xn7bZSSS7iZrR9uGrYTUAmlplAY1qQuGMj5yw+Fho+u4M9LfPITmmmOeFZ9vgLOPJIL1deeQmWBWs3jWL52uOc4npKc+6srWTGN/DAq4W0TurW2mDV6ilMza1idHJTt97LlVfCGWfELjso/+v1vb74uaajqvTLz0fREfkdKgaKfNfEQOnpd+ywCPh37tzJihUruszuf+UrX2H06NGMGDGCLVu2cPvtt7N9+3buueeeg77np59+2tPuCnFQNmzYMNhdEIeQitrKcLGy+up61q9ff9BtPPlkJr/5zTi0NlDKZsF5zzMvKTl83Jua2KN2u0PRBIA2TD759APiExNijmfEWyRm1Dgv0usY791BYuII1q9fT1mZm+Vrj40U19OKp97O54r5f0czI6YdWyvK6+IZO9F9UO8lLQ0qKpx/RP/pzc+1Fp8PACsY7LfvqTh8yO9QMVDkuyYOVYdUwH/77bdz//33d3rOs88+y8SJE8Ovy8rKuOqqqzjrrLO45JJLOr320ksvDW9PnjyZ7OxsrrzySnbt2sW4ceMOqq/Tpk3rtECgEL1lWRYbNmzg6KOPxjRl3W/hqNyeQOsi9zkjcigsLDyo60tK4De/UeEidVobrHryfI6+5L8kJwDaZt68L5M7/eDa7a71enV42/I1tOn/er0uvO13eTGArKwsCgsLefVV2hTXs7Xii99YyN9fsGOOGcpmXNoLXPbzX+By993UB9E7ffFzzf3ia9DUgsfjPujvvxg+5HeoGCjyXRMDxe/39yjpfEgF/N/61rc4//zzOz1n7Nix4e2ysjKuuOIKZsyYwa9//euDvt/06dMBZ4TAwQb8pmnK/9RiQMh3TUQLEhnOFe+KO+jvxqZPm9E6dhqTrRWlTdkkp9XgDtQxZsKx/fad06o5vO2rqW5zH62ckQbKDqCJA8Dr9WKaJlOmgGFobDsyd9swNHNOMbn//gPn4BsUFf28X96D6L3e/FwLr8qnkJ+NokvyO1QMFPmuif7W0+/XIRXwZ2RkkJGR0a1zW4P9I488kttuuw3D6GQ5pw5s2rQJQIr4CXGIip5nLgXUHH47EvC7Te9BXVtSAo/e/SiKheioRcwNpclOcQJxw6rBTE7uqIle04Yvst3ka3PccqUA4ArUY5rOryiv13mfTnE9dUBgr8jNhaIimD8/dg6+ODzZoYhfivYJIYQQXTv4KPkQUFZWxsKFC8nJyeH666+nqqqK8vJyysvLY84566yz+PjjjwHYtWsX9957Lxs3bqSkpISXX36Z66+/nuOPP54pU6YM1lsRQnRg2TLIy4N585x/L1s22D06NPh1pIK+19V1wN/47j52/W4NV5z8BOPGaR547go0hKrgO0PfL5uzmfQkJ/hWuhbl6r9nwbbpD2+rZggGg+F/qmsqCbqSnH7ZdeHzoqdPFRXBjh3w6qvOv4uKIm3n5sJpp0mwf7jTUqVfCCGE6LZDKsPfXW+99RY7d+5k586dMcvzAWzZsgWAQCDA9u3baW52slZut5u1a9fy0EMP0dTURE5ODmeeeSbXXHPNgPdfCNG5khJYtCiyLrptO8O158+XYC46w+8xOq8joi2bqic/Y3npK6xY+6twsTun6p/Nb857F7fxMQnZR4SvsY36fuh1VJ/cQULPGjADBrfcckv4mArWkKXOcbZ1pB+tGf5WubnyPRjOWpflE0IIIUTXhmTAf8EFF3DBBRd0ek5ubm44+AfIyclhxYoV/d01IUQfKC6OBPutLMsZrj3cAz1fVIbf00GG39ccxNcUIFBax2M12/how5eign2HxiDXY9OYshmbSMBvuRr6p+Ot4jSEpvG7grG/gtzByJKDWkX60Z0lV8VwEhqdMiTHKAohhBADa0gG/EKIw1tBgfPHfHTQb5rO3Ozhzq+jMvztzOHf+v5+XvzbJ9hBzSefpXDfa1eEqtc7A/lbKWVR3PwgI8cmoBqgusFLeW082Wlx/dp/lWCGA37T8pKXlxc+VrOxKrxtmY3k5RWQk5Mj065EjNYMv0KG9AshhBBdkYBfCHHIcYqzHVh1XbL7cEDA72obnG9eV4od1FQ3eLnvtZlRS9UpWoN+pWwWzNnMqNGXkNLUxPObc1i5egpaK5QqJOe02LnxfcmTlkCw0tk2bC/f/OY3w8fuu+6T8BoE2huIOSZEq0jRvkHuiBBCCDEESMAvhDgkSdX19vl1ZNiD1xXf5riv0RnyX14b32bNelAsuqSUU4+tJTtDAyMorzJZed+U8JB/rY1+rZeQPDKLps+dbUPHPrBQLWaklGxi399bHB6kaJ8QQgjRfRLwCyEOWVKcrS1fVMDf3rJ8/hYn4M/J9qGUHRP0myb88v/lkJubE9736quRAKpVf9ZLGDUuj7I1oRcHBPxGIA5Cb8md2fZhhhAQyfDLiH4hhBCia1LyRggxJJWUOMFqSclg92RgBYgM6fe2M6Tf3+wE/LkZ9Sw88U0M1VrgzG53WoRTLyE24u/PegkF02aGt7VKiDmm7EhaPy13VK/uM1y/H8OCZPiFEEKIbpOAXwgx5CxbBnl5MG+e8+9lywa7RwMnOsPvMdsG/L7mINUNXt7bs5+CCYqbvraGH81/kZ07jXbn5efmwh+v+Qgj9CDB7ODBQF8ZOWI0huVzXhwY8Ouk8PbkwuN7fI/h/P0YDloz/BLuCyGEEF2TIf1CiCGlpAQWLYpU8Ldt+nXO+aHGT1TAf0CG37Y1r68fwcrVk9H6ZJTSLJizmdMmPE9u7hc7bPPrJ33K0c9+n12BcRz3ywUcWXR2v/UfwLSasE0vthE7bF8bKQAYlp9Jk47pUdvD/fsxHNiS4RdCCCG6TTL8Qoghpbg4drk+iMw5Hw4CURPuPQcU7du2NRgK9p0f7VorVq2eQkVq5/Ph7bo6RrnLOCHhXcZNcPd9pw9g2E0AWKaT4W8dfl/Rkg2AK1iHx9u2PkF3DPfvx3CgQ2P6Jd4XQgghuiYZfiHEkOLMOY8N6vpzzvmhxocNWjOiBtSecvxxvvCxja+70Do2jW1rxaSjLsS/Y0eHbQb27A1vGykpfd3lNpRuBsA2PVxx8nusWHtsaEnAM1kwZzOn523vcdvD/fsxHLRm+JVE/EIIIUSXJOAXQgwpubmwdKkzTNuynGCuP+ecH2r8tsXND1tM2QP77rs05pjpno5Sj8RU5lfKIuO2xXx+e1m32jcHIOBH1wNQ3eANB/sQGZFQeP7qHjc93L8fw0J4SP/gdkMIIYQYCmRIvxBiyCkqgh07nGHgO3bQbjG6w1VStc2UPe0fy0xoZOFJ70Yq82Nx84hfMcrdvWBfeb24c3K6PrGXzOlNeJt3UVVph4P9VrZWVI5M6ODK7hnO34/hIFy0TwJ+IYQQokuS4RdCDEm5ucMza6sCUXP4x48nfnqkuF1TIJvZtfsomLCGmgo/lx2xltHJAeCcTtu0taa6poZxl12GmZraX10Pu/JHvwScuft35rUdfn/tDQt7fY/h+v0YDlr/D5CifUIIIUTXJOAXQoghREcF/Elz5zLy5z8Lv6545XOC/9hGutvHKHMXx/35hm61aVkWZevXk1RY2Nfd7ZQMvxc9IcvyCSGEEN0nAb8QQgwhRjAS8Kv42GX5duxYDyrdOaabBrJbPVZU5CyZt3WrU1hPgn3RFS1F+4QQQohuk4BfCCGGkqiA34iLDfjLyj4Hjgu9GhoBP8jwe9F9OmpZSinaJ4QQQnRNivYJIcQQYgQj28obG/C31FeEt23VMlBdEmLA2JF4XzL8QgghRDdIwC+EEEOIEYjaPmBIv93iD29ro3mguiTEgJEMvxBCCHFwJOAXQgwJJSXOMmslJYPdk8HVWYZf+SPl7m2XHyEONzEZfinbJ4QQQnRJAn4hxCHlwMD+3h//kG/MfoZx42zmzYNx42wun/tPGhvqB7ejgyBoB3FHBfwHZvjNoBnetlxBhDjc2FEZfhnRL4QQQnRNAn4hxCHBv7ue6y58LSaw/8bsp6nedyHL130JrZ0fV1obrHzjYv74kz8ObocHgd/y44nJ8HtjjptBd3jb9tgIcTgzJOIXQgghuiQBvxDikPD7/72de56YGxPYr1j3ZbbtS0Xr2D/sba2o3J09GN0cVE7A33GVfsOKPAAw4mURFnH4kQy/EEIIcXAk4BdCHBJqS2e0G9gbdgClYrPVhtKMTKoeyO4dEnyWD29U0T51YMBvR157UhMGqltCDJioeF8y/EIIIUQ3SMAvhDgkZKdaKKVj9hnK5qjT3+L++w1Ms3Wf5rI5m8mIb3+d+cO5uJ/fjh3Sf2CGX+n48Hb6qNED1S0hBoxk+IUQQoiDI2M+hRCDTtua1BSTBXM2s2r1ZGztBPhLlhgUFV0LwPz58KdfLSPNN4X0JB+qxdOmnWXLYNEijW0rDEOzdKmiqGig303/8Vt+PB1k+C3LBiIB/5TphQPXMSEGSPQjQSURvxBCCNElCfiFEIOuYu9egq4kZk8pZXrWR5xw7WXk50NubuSc3FyYfkwFdZt8ACjtjmmjpASuvtoO1wCwbcWiq23mzzdi2hnKDizaF53hDzRbaOUM41d2kIlTpg9094Todzpqdo+E+0IIIUTXZEi/EGLQvb/2NVDOj6PMhDJOO412g/SUrMzICx2b4S8uJhzst7K1wdatfdzZQeSzfB1m+H3NQWwjEQAz2ER8gszhF4ef6CH9hkT8QgghRJck4BdCDLo9nxeHt7Vq6PC87NHjwtuK2CXpxo1tabcGQH5+H3XyEBCwA3g7yPD7m4PYpjOk37Dbr28gxFAX/X+4FO0TQgghuiYBvxBi0LVU1oe3bbPjYHX8xElRr2Iz/JZdzII5mzFCQb9T3G8Lzc0b+7Svg8mZwx8JefaUe8MFCkt378ByhQJ+3ThYXRSiX0nRPiGEEOLgyBx+IcSAaqqrjV1bC6ApMjHXcrfQVFvT7rWpKakoO4g2XGgVm+Hf8fkmZk/JYmpuFeV18WSnNJOe5OPt/75HQcFRff02BoUv6MMThHWzjualneezcrzzURqGZtHlNRzVWrNPS4ZfHJ6if3RI0T4hhBCiaxLwCyEGhNaaf916Izs//rDNscTkqVihn0Z+Vwt/WXR5h+0kJi/GMlxoIzbgr9yzF8giPclHepIvvL9++/4+6f+hwG+1UJuWTZmviJWrz0RrJ+CxbcXS5Sdz09fWkp7kQ6vmQe6pEP1DR2f4B7EfQgghxFAhQ/qFEAOiunRPu8E+gBmMFJgLuILtntPKsJ1gXqvYIf2N1dVR7UWGtKvaw+fHnD/YTFleAaWN2eFgv5WtDcrrnBS/Vi2D0T0h+p0dleGXOfxCCCFE1yTDL4QYEC0NkXn6aSNzyBwbKcBXsTGSrXcne8nLndVuG/7mZmq3OQG/fUCGP1AfCXLd/t1YrikAuPypve/8IcIf9KHsOLJTm1FKxwT9hrLJTnEy+7bh66gJIYY0HVW2zzh8nuUJIYQQ/UYCfiHEgPA1ReaVTznlNE6+5Ovh18uu+Et4e86FlzFjxux222huqOfh7zwKgGV68bW04A1VqrebI+vVWeY+wAn4FRl99h4Gm89qwRXwkp7kY8GczaxaPRlbGxhKc/6M9ymvdTL8GQn+Qe6pEP0jOsOvZFC/EEII0SUJ+IUQA8LXFBlm7z1gjXhtJANgWH6mTW0/2AeIT0pGEcpeK4OdOz5j0pRjnNctkUgg6G3EDDRguZOwzOw+egcDq6QEiouhoAByc519gUALdbUjKW1KY2puFTdfdgv7G77Crv3JPP7OcegPFEppFszdxTWD230h+oWWKv1CCCHEQZEBcUKIAeGPyvB7ExLD2zpgYZkpALiCdeGMfYd0JHu9s3hTeFsFIz/OtNvGFawAIOBOY/fubb3q+0Bbtgzy8jTz5kFenvMa4NVHj+CGR3/CXU/P5MZHZvNp49GM8Wzl3+/kh4f3a61Y+fqllJQM4hsQop9IlX4hhBDi4EiGXwgxIFoaG8LbH75YymfvvQdAwFdJ0J0EgLLr+dfv3+u0HU0k4P903SfUbXLONwJmuNJ/UNl4qQDGgzJ46r4VHJF4VtvGTIOJp41h0qycnr+xPlZSAldfbaO18wDDtmHxYjjmGFj++zPD+7VWrHr6XOJPe7hNAT+tDbZujYwMEOJwYUdF/IbE+0IIIUSXJOAXQgyIhuq68HbNfptdu32U18aTnvM52UZovr2uZ9+2uo6aAMCrIgF/oKmBfZXO+W7LHQ74XabCMmuobvBSXhvPSL/N0SPaL2S3/7FirJkjMd2HxoCn4mLCQX0ry4I33wRtx+63bYOjvnQK6lU75hrThPz8AemuEAMqJsM/eN0QQgghhgwJ+IUQA6Kxpp4Gz3QaygrZsXUk//rgJDQGikIWzN3C7Cml2Kqhy3Z0VMBv6EihPqUjVfsN083r249m1euz0Vqh1Ay2nPgs15xazxg1Jqa9EaaiubqFpBGxdQUGS0EBKGw0sQH8KaeAUgcG9pqLLp1AcpozCsCynHOXLJHsvjg8xWb4JeQXQgghuiIBvxBiQKx6JYeVT18XGn6uac3PaQxWrZ7C1Nwqsr1NXPOX0zttZ2nRy+Ftl0H4/GVXvhPe3+TLYdXrl8bMa1+x7ktMmfAbvn3RsQBUf7AfT8AGwFfnO2QC/txcWDj7HVasnYWtFYayWbLE4Pjj4YIvPcATzxaF99/3F0VuLhQVwfz5sHWrk9mXYF8crqIS/DKHXwghhOgGCfiFEP2upARWPn1R1Fzz2D/Uba34YFs2xx4R1+Uf8dqIZPXx6/D50Rn+Jv8RbYbF21pRVV5A+vkFAOwrrsFT1eI0U3doLWN30tRKCiasobwunlz3hxQVFQFw/NS1FKZNDe1/l6uuXhy+JjdXAn1x+JMq/UIIIcTBkYBfCNHv2puXHkvzxNpJPLnufxl1upOx7ojtssLbyopuM1LdvzA/EcPQ2HYkIjCUJjstksVXHjO87W+IeogwyDZ8+A6WK570JB/pST48viT273BWGXAHPSS17m8pHeSeCjHwoufwS9E+IYQQomsS8PdQRYMPl9se7G6Iw5ht2VS3WJTX+zDMQ6Og3IF0MEjjbbcQWL++0/OMliyUeuiAoL91WH/U8H5tsOgqi/y/f4NRcRXt3/OIyZF2LRf/OedsDA064+LQCTZZm9byjRN38uDab6G1gaFsLpuzhcSMRO799tfJqAuSlDmbnKQjGKFTqK1qZn99S48/h770/luvAceFX/s9mfzt/36EqzmAO2tCeL/lauyzPg+F75o4PPT2u1bREBmNo6RsnxBCCNElCfh76At3vEFzUHd9ohC99Z9XB7sHHZpZtoXfrP13l+eNYCdfP/kxHnnrYmytUMriR5n/D1NZ/K785zHn2pjs2GEwImFnu23p/Imo0LM2wzb5LN7J1KcoJ8NvWi2UmxaTJ7/C92fVUVWVwbTGShIzZwDQ4k1ny6QsoBp4nxF2Kvtea+ShdZ/17EPoY4t9e0mJ3qEMjLQsaC7F7fdihwYmBNw+TvjNy+010XOH8HdNHGb64LtmyLMpIYQQoksS8AsheizVH6mq32K6CRgd/0g5NX8Dk/PGUF4XT2bmY5z13vOUBUZglP8Um8jwegOLjIT91Lvj223HNmzMcMAfuZ824kL7WvDRhNHSREpSDSkpdXjLK8FyAv7EhiTq0yPt7TdqybDqgaSDffv9IqGd1QPdwTjWJ09jdjAuHPBbnuDAdkyIQ8zIlLiuTxJCCCGGOQn4e+j0ydkEtQwnFP1Ha01tbS2pqamHbDXqIwOJ4e03z7ycDTPmtX+i1pzwykvheel1mQZ//clSAE5+fTdvPpiHthXK0Jx85W7+M/eWDu+Zv3EliXXOttJu9hx7KQAFxc4DAsP2safgBErdx2BazbisJmrS68kKzRBwB0bhc0Fqcw0tbmffKHeAM6eN7MUn0Xe8qz1YB/xkjm9KwnPahZgvvUprtQG3iz7r81D4ronDQ19918ZmJHDFSeP7rmNCCCHEYUoC/h7606XT8Xg8g90NcRizLIv169dTWFiIaZpdXzAIqlcWs+8ZZ3vhnEmknX9cu+f5W5pZ/vKa8Ovpx0zl55eGzr0CSm5qXVJOkZs7Hhjf4T2X/f4ZWkIBv2F7uOOnC6mtrWbF9R86O3UL13zzXDyjIxl7v8/H/7vyDfY1ZDA6YTLfufJs/vizh9EjykhJqSPNY7H0ivb73qqkxCk+WFDQv9Xw73/lmTYBf13tOBaMPY73mjaTEKo7OC1e850u+txdQ+G7Jg4P8l0TQgghBpYE/EKIHtP+qAJaHneH5/kaG9EqEoAfe9LcmOMHs6RcQmoakVJ1zkO3zz/bEOmHbsFMjO3L8hVefrFqHhoD0Pz8nxrN11HYXHL6SmbObOz0nj/76QZ+f/uRTgFAA5Yu7Xwlgd4w7OSY12s257By9enox0Gpr7NgzmZOzd/GMUmH1lKCQgghhBDi0CMBvxCix+zogN/dScDf1Ig2nIDfDDQwIndMj++ZlpVFVes9tRPw79nxOZDXejeMqIC/pAQWLSIU7AMoWsttagwefW0BJ+Xf1+H9Skrg9384Mny9bcPixTB/fs8y/Y01Pra+v5+A32r3uFapkXOrGlm5egpat65ioFi1egrTszfjjZeh90IIIYQQonMS8AvRiYEaxj1U6UBk/XrVyRQXX1MTlhkK+K1GVC8W0M4ZdwTbaA69cu5ZXb6f1oBfKx/KFSnfXVzsBOkdsbXBtrqOfxQWF0c/LHBYljMFoSffiRfu30jp57UdHnebaQC4AnWU1daHg/1IfxXldU0YHvnxLYQQQgghOid/MQoR5ZXnnqD4X+swgnG8WTyT5Wu/GlrHXbP0ftVvw7iHqugh/UYnAf+e3duxXE5RPaUbOjyvoHN4hQAAZpZJREFUO8bnT+Yt1jv3V14AWmoibWoVW+a+oMBZvqujoN9QmvTUPR3eLyenDqWSYwJv04T8/IPvu7Y1ZdvrOjzupxHczqJ8ZrCGhFEBlLLROvLAwVA2o5K2djqFQgghhBBCCABZxVaIKNtWfUrQNZ+y4JksX3tOONCytWLxYifjLyK0v3sZ/uIt0XPsO58v35W09AwMy9d6UwCCDc3h47YRO7c9N9eZc99aH0ypyPrdhtJcNmczaYlVdOT91fezYM5mDKVD19gsWdKz7H5zQwDbdtoZMT6FL11zTMw/o48rA+V0VOlarvjul7j1xkZMo/XeFtfn3c3Zn/wDI8578B0QQgghhBDDimT4hYgxAoDy2vg2Q6l7M4z7cKX9kWz6/1t/J2W1qe2eN6YkSArHO9eoRq59+dpe3fdo+wxs04tWXq59+VomNrYO7gfbCLRtfzxcviKN2j3ZpI4pByDjoXFkZBxJepKPYBCufek7ztOAA0z6yGT2lFKm5lZRXhdPTvwLfDh+D9e+fPD99takcgRfAGCL+ojXyx8MH2soT8O9Oos820t6kg9b1XNn7W1wKlz+sNP32S8+yVd27ANAeWWVECGEEEII0TkJ+IWI4fwvkZ3S2GYotWlq8vOlUFq0/XWltA4sf7vqQ3a6nc8nUDUSX9k4vCN34c4o44rmk8PX2EYzr5e83qv7TrdPDbXl5fWS1ynwnx5p3xXsuP0sIPSMYnHuGSjDGZdvBA0+2vQGNSltC+lNb7wQfxykJ/lIT/LhafHxcA/7P656Wjjg/zywmfdC7VS9fj6lD/4fWhsopVkwZzNzJjbEvo8suMAV6V9nUyiEEEIIIYQACfiFiKWc/yUyExq4/36DRVfb2KE5/NcsWktu7uxB7uChpaWpPhzwB0ND5qteP5+9D/4KtAnKYvSVN+GJrw5fY7la2jZ0kJR2hu3bhjOs3W25aH02Y5nBbrURcPvxhOJnM+DitPLpTK2bgVvH/lis07FD55WO63G/E/wp4e1GjzOXP1A1MhzsQ6QS/5TctkP2PVFvTcVJwC+EEEIIITonAb8QUbRywlelgxQVQeXzf2KP7zSyU5rJGvk0IAF/jKgq/QsLv8Uxed+k8Ftp0DodQpuUPXQTDZfcTFoo1rXcQV6/tHcZ/sdfeRwA2/TwxBn/4uXVfyPYOi/f6+pW+6s23YqucbZNy8NVwSsx/ZEfiaV1HjZWacqMZlLjdXi/0gk97v/G5/exfss6rJb3mF/q4eTdF/LmJ8ewRceWU7G1osI1ss19qh6/jAC7nfcpGX4hhBBCCNEFCfiFiKJDGX7DdgLZ5LS9TDJrALAqep+ZPtxEV+lPjE+lYnd6m2r4lqWorB4dDvhtL2TEZfTqvkpHagfU7N6LsiMV6814d7fa96Yl0lLjbBu2F9OO/Dhc9dFIfvpCfswQ+9lTSgHQKqHH/bcb99PS+ALGqDT+u+FEHn3l8lBmXwOR6SJKWZx/3sw296nxh4YkGBrllir9QgghhBCic1KlX4go0Rl+ADsxMoba1dTzodyHragMv8sbF14CL5ppwqikyLJ5ZmJffI6RBw37S0ow7Ei225OU0K0WkkZkhreVjkOjqVNNbB+RFA72ITLEvrrBGWKvVffab09DdTMZnpm0VFzBo68sjKoRoXCCfifYv+pLj3Hy2YVtrm9dFcEwdbiavxBCCCGEEB2RgF+IKK0ZfkIBvzcrMufaDCYPRpcObYHIAxHDG9dmCTzThCVLIDM+kvZPyh7R69tqFZXhL99PdUMmn+1Jo7rBS1JmerfaGJmXF95WOp6X3Rv4q+8Tbnl2R0yxRnCG2JfXxTvbZmKP+125r5SAd3q7q0CA4o7/Z7Nzh2Lp05fi9rZ9MGL7nAcdytRgSMAvhBBCCCE6J0P6hQjRWmMbzv8SrRn+KUcez0clhPZ1L5AcVqICftPjBKhFRTB/vrOEYX6+s4zhstWRrPjEgmN7f18VGVnw7KoxPPLGxeHh9z/MiOPSbjQxeVohn/B5qL14XngzkX+8vqCDIfY2YzxbgSyCrgR8LS144w5+pEJ98xaUOZ3s1GaU0jFBv2FoLr7E6HTZR8nwCyGEEEKIgyEBvxAhvoZmtNE6L9oJrI6fMYeNz7yB5UrANns37/xwpKICfldUEbncXMKBa0kJbNo3kbRsL5nx9eRNnNnr+9qhgL+6wcsjb1wSDpy1Vvzp3pl8/6d0GjgD5OTkYVibsE0PlU3Z/OP186IC8NYh9gqlLC6Zu4rMhFpayAJlsv7pxzh6/JSD67Ot8es9eJlOepKPhSc+x8Nvn41lK0xDc/cNO8nYV0HTvk7a8Dnv28nwywAtIYQQQgjROQn4hQgpLd0d3m7N8BtJHlyBKixXAgF3BrW11aSmSqY/LBTwBw1wuyLLyK17/UU+eOR51n16EiveugCtz0QpzeWz3+frmb2fGhFIqocA7Q6Nt2zF1q1dB/wAptWEbXrY0zK23SH2X52+lOlj32JkUjNwQvjI5n8/Rsq7Ww6qzz5PKur0SBsnT17DlWW/Z1dgHOPcuxj1aBk7H+1eW4ZLMvxCCCGEEKJrkiISIqSibE/UKyeQNZPcKF0JgDZMXnvhiUHo2aGrNcMfcIFLRZ4fbnzgHaoaL2DFWxfGFL97eM1M9lf1/jnjpTf+DLxPkZP4AkrFLgtgms5Ugu4w7GYAslN9KKVjj2FxifkUp1RuIrt8P7ZqDh9rSEw66D77vKm4/ZGHCkFXE6PcZZyQ8C6j3GUH1ZY3JShz+IUQQgghRJckwy9ESHVlOZAdeuUEssprYhs14XNKPy0e8H4dylTQWSYuYIIrVP+guakJy5xGeVXb7LutDXaVKPIn9e6+Wdkj+c6dfwIg+4uweDFYVqRIYHey+wDoJgDSk3wsmLOZVaunYGuFqWz+cOGLfPGEuQDsq6vh7T1RyzImpJFR9K2D6nNLYyqukq1YoZ+6ljdw0G1Q+jHmnldIm9AkGX4hhBBCCNElCfiFCGmoryU64L9v8UIAtNupKl/d4KXsgxxu+/p1pCdVO6cpxcRjT+CMq76DUgcOCT/8GQEn4A+akGA69Q+eXvUAQc+0UGE6O6bivaE0kyb17efUXpHA7lKhgB9g9pRSpuZWUeH7iB//8Rpyc88CzgIgvnw/a37+h8iF2sPIn/zkoPpZvnoP5gP3hwN+94ikg26Dt5fCc08525LhF0IIIYQQXZCAX4iQ5ro6wAnsa/aPJt77MWkJlbhHpPP2hhxWrp6C1iejlM15J9/HyaOfBeDjl56n8Mwvk503YTC7PyiMgIWtDBoTMnFXQY1uomx9MYpppCf5uHz2kzyy7nwsS2EozTfO3kpubkGf9yO6SODB0Kol5nV6ko9RR+1r01ZSZia2O+DU8QNUS9cPLUpKoLgYCgqcvjXW+FB2pH7BiPzxPeiwFdmWDL8QQgghhOiCBPxChASaW1izOTqwv5SFp/2dSRm7Q/taK8Eb/Putb1N44cckGs6afS0N9YPZ9UFjaQ/vnvhL/N40+FM177IOjy+VQKh+38wZrzJp/EjK6+LJTmkmf7K70/YGmo6al99qZP4k6irb7rdcFmZoNUAz4Gr3HF9LCx99vJpnXxjHnXcVYmuFYWjuvCPA5IRGlEoJn3v8qV88+A7bUQG/ZPiFEEIIIUQXJOAXImT/Pm+bwP7h1d/k3qN3tjsXPX3ahfg33wmAFQi0aW84qE0qcIL9KKaVS+unEXSNJT3JR3qSD4C0kakD28Eu2KY/5rWyLT5/KY1dL61tc66OV62rNaIsL8t/EXuO3ygnubGJfdYk/vRIYfg7Y9uK637g4g9fryXF5bx/V6CBsWOPOPgOawn4hRBCCCFE90nAL0RI6f6Utku8WeBNisdQGjvqmKFsxue28Nlm53UwGGS40Vqjo36EpI31kpDmY9/6cQB4fBVMnvWV8PG4RDfHzOvBuPt+ZJuxD2pcgRq0N7Pdc5URGZ1g2PFtjmv3Wnzx51C+p22xQq0NqmohfpQT8JtWbQ87LEP6hRBCCCFE90nAL0RIVtJ+lNIxwZppwkkn2PxufjE/e6EASztz0Rec8imp6XXh86yAv70mD2/BIFpFguCE/Eze+O+bZPj+f3t3Hh9XWeh//POcMzNZm7XpmrZAU1qgpQt7gYIVKVdFb38gyxUQKVJFL15XBLkgoIIKiopCweoVyiIKKCBXryCySFkFWqAtXaBtuqZtmjTbLOc8vz8mmZk0S7NOksn3/Xr11XNmnnPOM5PTvvI9z3YYxflhjL+J0z57zgBW8MBsyGtZkAEA16tm0lGHtVt284YCwvXxbWNzqDhqVKv317/mYg0dTFboM2rye9jGg+LH+z0M/GrhFxEREZFuUOAXaVaUW916abbmJd4mHBLgvJk7OOXgah5at5HCUeMpzg+zZv0qQk42Eb9pWHbpt9Eo9QFDqOkFXnjvw9x716FYOw1jLOfPW81JU7YPdBUPLBuoS32hhgWfm95u0X/e/TZvvt68Y3LblLv7xXwiWfGJ//7jow/zwP+eje/HHxBdcPyLZBesJ9Yc+K3p4ZwPvp/cVgu/iIiIiByAAr9Ii5hNLM22p3oFX13yBcrLIfxBvBV7bEGEivI3iYVGAhCprGPhpC+zq6mSWGQYBv5IhA+mBdlX/WHufWluytwHhgefm8bhM4sGtoJd4ORn4aUEfs/tuOX9oMmHseKVbfhuCGty257LK05s/9vMtXx5yus8tKqawpG5FOc1Et1dR0vfEc+ta3N8l7Rq4Xc6LiciIiIiAug3RpEWzVmqOD9Mxbh1iaXZAiU5iX8psZzkuu1uYwiAkdnlmD0ew40fiRCIBqmqaTtm3beGo//t6wNUs67LLWk9iaCX1Xbm/Rb548pwvXif/t2NpTzzTHzpvRbWiY/9N36M2dE5jM1qZOroV+ITFhqX7H1jk9cJtV4OsMs0hl9EREREukGBX6RFSpayTrLrtFsQovjsQ8k+rIRQaX7i9UC0JFk+PPwCv41EcbxA85h12+o914UjpmcPUM26buSE1pMI+nm2g5KQO7oYx2/gxdVjuer3ZzB/PkyaBEuXxpfjiwbjPT+Ckd0Ul5USGJVLOHdX4vhoaGbyOrk9vF80hl9EREREukGBX6SZ8VP+OaQEfoC8OaMZ+ZkjmP+ZT4NteW904n0baV1+OLDRCI4XpDg/zPnzVmNM/DtomfugfHBNyN+uKTNmUV2XxXtbiqiuyyJ7TFGHZQP5IarrTKulG30fLvucx3e//N/4bhYAjr+bv1T+ij9vvJPG/HjX/eq6LFZvH011XbxMoLTtkIAuUQu/iIiIiHSDxvCLNDOewTZnKN9pv6V38uTDCEXeYkd0AruqD+fQ3EYOKXSHZ+CPRHH8+LCGudO2MW367zn57GupqBgaYR/g709N5dr7A1hrMMZy5dgRHZY1QZcd+2w7wxdcdu0aw5iy5n1nD3u3bwHAszFe3lbCsn/OTFzjk8et46jRJ1FZ2YPvyabcZ2rhFxEREZEDUAu/SDOTsoyadTvu2v3P1aVce/9cfvrkcZy6ZB7fWbEXGx2OgT+CsVmJ/VFjI5x66tAJ+5WVcPkXg60mG/zRT2a0Gpe/v4ppDYmeDC2M8RiXm5zsLxasJXtEAdkjCoi6FSx74chW1/jjSxX89w0fTQwH6Ba18IuIiIhINwzZwD9//nymTp3a6s9dd93V6THhcJjrr7+e4447jtmzZ/Of//mf7Nq1q9NjZPgwNiVABUy7ZSor4Z6XP5wS4Bx+85eP88rmnemo4qBioxFICfz5xSMHsDbdt3Zt61XuADzPsG5dx8csnHIsP1ywLhH6HWO58Pi/MioYS5TxC32++Kv7+eKv7ufEy27HtvlvNjkcYPFiOn3A0IZm6RcRERGRbhjSXfqvuOIKzjnnnMR+Xl5ep+W///3v8+yzz3LbbbcxYsQIbrzxRr70pS/x4IMP9ndVZQgwfkrg76DxdO3aeMhP5VvD2xtj7R+QwWwkgiE5Md+4CQcPYG26b8qUeGZODf2uCxUVHR/jjghx3swdTD14DX/fOJrS4iijQk04XhEtd8CIQ5JzO7R3jVSeB+vWdaNXhJ9yn6mFX0REREQOYEgH/ry8PMrKyrpUdt++fTz88MPccsstnHDCCUD8AcBHP/pR3nzzTWbNmtWta3ueh+cNv5nZM1nrFn6n3Z/vIYeA4zj4frIHgGMsZflVfX4/tJxvsN5nXlMYa5KBf8r0mYO2ru0ZOxbuvNPwhS8YPM/gupY77rCMHWvp6GPkHjeGyKZaZlPEztKXaco9gp11o9lVPZ6RxfEwPmLEOWzc6FFe3vYaYGlp4QdwXcvBB/sdXm9/xosl+gt41tDlAw9gsN9rkjl0r0m66F6TdNG9JunS03tsSAf+u+++mzvuuIOxY8fy8Y9/nIsvvphAoP2P9PbbbxONRpk7d27itcmTJzNu3LgeBf533323N1WXQcik/HPwDbz55pvtlrv66lK+//1J+L7BMZbz5q2mJKumw/K9tXLlyn45b2+5a9ZgTXyZQjfWyJbtO9ixZ/cA16p75syBxx4LsnlzFhMmhBk9OsoBf4z/FgSCRO7ayIurT0uZtb953of7DI5jufrqjfz7v+9udY13383l9tvL4/eOY7nqqo3s2rWbro4smrSripaBE6vfW0vT9r7tWTJY7zXJPLrXJF10r0m66F6TwWrIBv4LL7yQww8/nMLCQt544w1+/OMfU1VVxVVXXdVu+V27dhEMBikoKGj1emlpKVVVVd2+/uGHH04oFOpR3WVwesP+X2I7Oz+7w4dAs2bBpZf6/Pi/7qAsbzbF+WGMF+j2Q6MD8TyPlStXMmPGDFx38HXf3rd9O684YQAcv4mjjjkKY9qf+yAT/fOQ13ngV9NSZu1PfnbfN9x00yQuvXRCm+76X/2qz7p1NK9mMAGY0OVrmo1FsDm+Pe2ww6Fsaq8+Q4vBfq9J5tC9Jumie03SRfeapEskEulRo/OgCvy33HILd999d6dlnnzySSZPnsxnP/vZxGvTpk0jGAxy3XXX8bWvfS0tQdx1Xf2jzjjJn2cwO6fTn++kSXDo5PVQd3j8SC/Yb/fDYL3XTCyG5+YA8cDfUe+aTDV19mfbLNGXyvMM77/vMmlS69cnTaLNa12XnAzADWbFJx3oQ4P1XpPMo3tN0kX3mqSL7jXpbz29vwbVb+iXXHIJCxcu7LTMhAntt4bNnDmTWCxGZWUlhxxySJv3R44cSTQapba2tlUr/+7du7s8D4BkNmOT/xyy83MPWN7ND+HVxbdb1qMfTvbuq8V3x8d3bOPAVmYAHHFECMexreZzSHWgCQB7pNWyfJqlX0REREQ6N6gCf0lJCSUlJT06dtWqVTiOQ2lpabvvT58+nWAwyPLly1mwYAEAGzZsYOvWrX3eFVuGquQ/h7z8wgOWDhWNoHF7fNv4WZ0XzkDrd28D4oHf2KaBrcwAKC+Hu+4yLF4cnzvPmPgf34+H/SVLujH7fle1WpZPrQgiIiIi0rlBFfi76o033uCtt97i+OOPJy8vjzfeeIObbrqJT3ziExQWxoPajh07+MxnPsMPf/hDjjzySEaMGMFZZ53FzTffTGFhIfn5+Xz3u99l9uzZCvzSLPnPobh01AFLF40ZReNqqK7LombnFCor+yHgDWI79+1N2Rt+gR9g0SJYsIDEmHwgZXx+P1ywVQu/Ar+IiIiIdG5IBv5QKMSTTz7J7bffTiQSoby8nIsvvrjVuP5oNMr7779PY2Oyq/HVV1+N4zhcccUVRCIRTjrpJK677rqB+AgyKCX/OYwcObqTcnHjJlbw8OqxiVnafzwJ7rorHgKHg9qG5L8ta8IDWJOBVV7eOtz360MfmxzDrxZ+ERERETmQIRn4jzjiCB566KFOy5SXl7NmzZpWr2VlZXHdddcp5GcoL+rz7j+3Ur2joUfHWxNs3vB5/5UGdq97r9PyG7cWpyzJFu/KvXhxvMV3OLT0R6PJJeF8Z/gG/rRSC7+IiIiIdMOQDPwi7Vnzynaee7DzkN6ZLBP/5+D4MT54ownjVHZa/r0tRW1mafe8eJfu4RD4/YilZdo4a6IDWpdhQ2P4RURERKQbNM2zZIyanT1r2W9hmwO/sVEgeMDyZYWNGGNbvdYvM7MPUiaWDJyeGxnAmgwjmqVfRERERLpBLfySMTwvGb4/dME0Ssvzu3ys9S2P3fg3AIwfY+GVJxMIdr7U3url27jw/ZdYtvx4fGtwXcuSJWZYtO4DODGXlg4OvhvrvLD0DbXwi4iIiEg3KPBLxrApgb9kfB6jDyro8rF+2MM6zV36bZRxFaUY0/766i22b6jh5IrnmHKwT1VtDhddOZKjTpzYs8oPQY7n4jX/D+IF/M4LS9/QGH4RERER6QYFfskYfkrgd93udXe2MT/RpR8bO2DYBwhluxjbSHF+mOL8MHV7NwHDKPD7IVripx+wnZaVPqJZ+kVERESkGzQIVDKG7ydDp3EOHNhT2aiP78TH7Rvbte7pwawA1iTXn9+2eWO3rjnUGT855MFzu/d9Sw+phV9EREREukGBXzKG7yVbP51uBtD1axtZs62M6rosoIuBP9uFlPXn66p3d+uaQ52xWYltP6DwmRZ+yr2pFn4REREROQB16ZeMkdrC73SjhX/pUrjsskJ8fw7GWC46LsqlXTgulOXipwT+SE3vVgkYaozNTm67B17VQPpAYtI+A10YdiIiIiIiw5ta+CVjpI7h72oLf2UlXHYZ+H68vLWGe1+eR2XlgY8NZgfwU5ajs01eJ6UzUTLw+4GcAazHMNLSpV+t+yIiIiLSBQr8kjFsDwL/2rXg7zfBvG8d1q078LGhbBcvZTk6M8yWorcmHviN70Go60sgSi+0TNqn8fsiIiIi0gUK/INYZSU88wxdam0W8FoF/q7d2lOmgLNfUcf4VFQc+NhgtosfSAn8seE1QsY68cDveI2ECvIGuDbDRKKFf3jdayIiIiLSMwr8g9TSpTBpEsyfH/976dKBrtHgZw8whr+9Byjl5XDXXfGQD+AYywXH/ZXy8gNfL5QVwHNTrtmNwJ8JD3N8J96N3/WbcLI1hj8trLr0i4iIiEjXKfAPQslx5fF934fFi4d2OEyHjmbp37x5Axd87CEmTvSbH6DYVg9QFi2Cm77xE644819c/x8vcvKh/+rS9dygg58a+FOWqetMpjzM8dyWLv0K/GnT0sJv9F+3iIiIiByYfmschNobV+55dGlc+XDW0aR9D/7XM9z/v5/C2vjt7vumzQOUguwqDh23l+L8MNZ0ffI9m9K1OnXW+o5kysOcTe+vxzrxkG9sI062upinhVr4RURERKQbFPgHofi4ctvqNdelS+PKh7PUZflMc+Cvr9vHlvBMrG3dxX//Byh+NJb6bpevaUyyZTt1XfqOZMrDnHWrV1Bdl8V7W4rYUx8gEFLgTwtfk/aJiIiISNfpt/RBqLwcLjzrce79w5n41uAYy/e+v5Py8tEDXbVBrVULf/MY/n8+/7+MLBqPMbZV6N//AYof9Wl51zqp4b9zIT91dvoDt/C3TBKYGvqH4sOc3z0wgrvvn4u1BmNmcdH411j87wNdq2FALfwiIiIi0g1q4R+kjp3wD67/jxcT48oPP/jJga7SoNcS+I1jMCYe3ze9/Q7F+WHOn7cax8Tfd4zlG19/p/XEfLFkArdmvyb4TuTYwsRSadYceC36lkkC3ea85rqwZAldmiRwsKishLuXzU88QLHW4d5fHD3khiUMSYkx/Ar8IiIiInJgauEfpMw+l+L8MMX5YQB2btw0wDUa/FoCf+r4/fD2fQDMnbaNw8r3UFWbQ1lBI2OK/wrckChnPZts4e/GGP6srByi+xrxAnlYp3Xgr6yMd+GfMqV1oF+0CBYsiHfjr6gYWmEf4p+pZT6EFr7vsG7d0PssQ06ihV/PakVERETkwBT4B6lAJI9ISg/x8J66gavMENEyhj91ST6n3qGlvX5M4H2Kx40DILx1X6tjnZRGfet0PfA7oSCuFw/8vpOTCPmvvw5XXhnvuu848Vb9RYuSx5WXD91wPGUKGOO3Cv2O41NRoRDa79TCLyIiIiLdoMA/SDl+Qat9u6/r48qHq5Zl+VJb+APhHCLNc+lZNgDxwB+oy2b3/auSB3vJsGr3mzCxM8GsEMZvAuD5tZO5fJLF9w1gobnPQMtM/AsWDN2Qn6q8HC484TWWLT8G3xqM8Vj8rZWUl88a6KplPo3hFxEREZFuUOAfpKwpbrXvhvWjOhDrt+3Sb/wRie1w/m5M83MTN1ZI44pdKeUMLXP6WbcbgT87C2P3UF2Xxf0vHJEyMWD7qwIMtcDfULOXhpq9rV5749XlHDtjNFMOfpGaqr3k5r3KnLOOA2YNRBWHF83SLyIiIiLdoBQ5SHmBklb7TvTAS74Nd4kx/Cld+o0tTGxPzK9g896WvdJWxxrfwTZnqO4F/hDWVFNVk9Nm6b9UjuOzevUbvPGGS3l5I0cdNZpDDjmky9cZCGuWv8Cff/ZD7H7rCDqjJxLibIrzw4wOvEq910gwqACaFmrhFxEREZFuUOAfhDZseJdYcESr1xw/d4BqM3R4nsX6dcQiDVSuehsA3433lHBj9cyZcizb/7mFaKgIzy2Ds5LDJsySlPHn3chSoewQ1eN2Mr5+OYZZ2FYLX8S79TvGctxBK7n88tlY62CMz5lnPsFdd+UxevTgXWpx9T+fxfo+extK2VU3jpH5WwGoXzeLkpIsivPDNOXsxjSOJxjSfyVp4Td3UVELv4iIiIh0gX5LH4Ree/ZvwIxWrxmrwH8g0YaNhGseJFxj+d13IGY9RhT9FwCB2F5efvGfuN4hRCkiFirgnjtvIKsuvgpCfl7K9x3suKV+f6HsbA72J/P29Co+HnmcJ544MxHqT/vQYxyRPYZAlsutfzy61TJ2Tzz+cR6751E+/ZGTE+cy2QGyK4owgcEx+V1l9Tbe2vJZlr24sHmCPh+DwWIwxnL+vNXMnB0g159FSIE/PXzN0i8iIiIiXaff0nvIvLkMTNe7fu/PeoZobajd93a/u579A781+fDq0h5fbziI1G8i3qoe55UUYQkCYPy97PV2Yp1CoAKAffYgNu8MMTJ/KyNykwHKeGHq7v521y66KZtJTROZ4hzMgukRFk38Pyqr8ykvrmNUAby182FW7Ti/TXd/3zq8/fxW9uxe0+r1YP5W8kaviJfBUly1i/p/jcSh6w8h+kr9ewex7MX/l1J3J/HtWmt48LmpHDr+E+SNsATffxp2rU17HYcdq1n6RURERKTrFPh7yPm/b0OsoUfH+jaLHeEleIxs9327719tX3Py4c+Le3S94cL3Pp/YHr+nlm1lpdDcA9qaWkZWrmf7wfHv/MXVY3nguR/FW+PxuHDuExzb8oyloZ7Ntz7TpWs2jJlO07QQR+QcQqmFCflwVL4F8sDPo6DkFGr3Po8xs1uFfsdYykMlNBIhh+SDn3BVDnuWPdLqGlu6/1X0mgWqJv6w03kJfOuwZ18hpflVBF+5A5rC6avgcKcx/CIiIiLSBQr8AyDiT2sV9rfVhni/OoeDixsBWLfxcEpK42OkW3huPpvqC9uca1R2Hdlu19eNz2TWTy5dOHnHXnZWZCf2vcA+pm7dSeWhdeyty+KB56Ylu9jjsmz5J5hy8Ivx79z6bc7dkaAXZWX1i9REqiiypRyyo7LV+1nA/wO8I+7jmrfPx8fFMT7nzVtD7sixPNX4ENNXlLKl+KNMHhNgTF5+r76DvhJzHUYWBzDGdhj6jfEpK2jEmhhB2/PeLtID5ccMdA1EREREZAhQ4O8h/99uxe1hI1v0gyC8BI1+Eze8VM19L5ydGCMdX87tuMQY6ZMr1uMFcvECudy3ZTYhr3W4D4UCLPrCJ8jNzW7vUsOKveXlxHbpiUcQ8PKwzT31Y6EmRn7qFPKiHmu3tJ1R37eGqtocivPDjCrNZuSnTunSNRsb8vErV7O5fjW1OeOYfVT7Y6svPeoFPjr/bd7fM4pVsXoKCk8EYPlrZ/Cl5Z/Atw6OsfxgwVouOns+xnhYa6mpqaGwsBBj0tul/1+RGPk1WZw/bzUPPjcV3zoYfDDxOQhc43H+vDcpzg/TEIgSOOVKyB2X1joOW7mlMHn+QNdCRERERIYABf4esjPOhlD7Y/APpLryNRwaeWjbi9z3wrWtxkgnzm8NDz43jVmlr5BbFp+wzxbnwa7aVueKRGJsCR7GlFkn9KgumcL3LdgXANjbUMq7p13Dvr89Sn7L6ob5LmU33slngZv+43sYM6v5IUucY3zKQ6tx/Df41Pfu7vJ1m1Zthu98Ib5TUEzZjT/osGwZMBMoevox3nnIo7o+l2XLP5n4+fvWcOVfKzj7lls56IhsPM9j85tvcsisWbg9fbrUQxtuvhpqYO60bcwsfYZj//NyKiri39e6dVBR4fLorTuhMQvPiRGY9nEomZbWOoqIiIiISOcU+AeA1xTh2eA77N0x5wBjpA076xwOKovvjygdw+yTzwBg29o1bHr7rXi5mLr0W88S9Rt4ffc5/P7vF2CfcDDm65w/bw1zp20jf2xyCMVV93+bUR+GxYvB88B1YckSh0WL/gP4j25dNysvK7HtxWKdlEw69cOfYP1vf0JVzSntTua39l3LQUd0qxp9orIS1q6FKVMgur0usTphYdFmTj01Wa68vHnDiz8AiDlRAkb/lYiIiIiIDDZa22kA7KzdwwexdygcVYLpZKZ/Y3xGFm1N7Ac8l5POu4iTzruIyUcfl3jdj0X7tb5Dgef5NIXK+f3fL0y03Fvr8OBz06iuy2LmiSe3Kr9oEXzwATzzTPzvRYt6dt2c/JxkHbrxcxh5ZiHjs16Kd5NP4RhLViD9s91/4yuvMXGiz/z5MHGizyv/OinxXiyrqd1jTHPg95woQTeYlnqKiIiIiEjXKfAPgMZwmOCGcqpqcvjksetwTEvo8xMPABzH55prNlNQUJU4zq9LtuS7gWSLquephd/6lprtk9sdm19TVcPMY09sc0x5OZx6akqLdQ9k5yfnTujOg5dPfuoSvvmHy7n7V05iLgjHWM6bt5rNH/yl5xXqgccf+ye3/vSoVg9KHnjhbKrr4r0XAgVtw7y1FuPFK+45MQKOWvhFRERERAYb/ZY+AB55Zgy3/yG+vrkxlss+9w7nnT99vzHSDuXlk/jFF2PQnOedpuQ4bsdN/uh8r2tdyTOZ71lGj9jXZlZ5x/jMu6yg3ya9C4aCxCdatD36OSxaBAsWwI8/dzclpYdTnB+mtnJHn9ezM8/87h2sbf1AxLdOYhLD8WMq2hzjx5I9Uzx16RcRERERGZTUwp9mlZVw+x9OSC4JZw2/Wjqdiop4S3ObVufc5I+odu9Innkmfg4nZRK3ro4dz2RezKc0N8L581bjNPeScF24626HM8866QBH91Jz2PX9nv0cysth8iHvJJZhNPvS+8+yLLCzzdASx1jKChpxvDBzJh7X5phYLDkUIWbUwi8iIiIiMhjpt/Q0W7uWVrPDQ3ziuHXr2u9anlVcQHgfvLh6LA889w3sQ/Gx/Z8+cQ8nTj2chrwaTdoHxCJRIJu507ZxWPkeCo9yWfip43vVXb+rjAlgbRTbw8APECtwCVTHt91Ifh/VrGtKgw3Ny+9Nw7cmMbSgOD9MsLGK/FEz2hzjRZOB33OiCvwiIiIiIoOQfktPsylT4oE9NfS7LlS07TUNwOiDJ/HWu1k88Ny0lF4BDvf/82ymThpHsdvI62/+naM+9sl0VH/QCjfUg4mPOS/OD3P2eVMZPTY91zaOi/XpVeAfMWE8jc2B3/FLOi/cx5xYXuJBSU3VBxSWHQTAe1uKKA+tJ5DfdvnJWDT5kCnmRAk6mrRPRERERGSwUeBPs/JyOH/e73nwuXOaW1N9lixxOmyJnv/xs3n2f5a3Oxldyxjr8I66NNR8cAs3NGJNfAI9x4sweuz4tF3bcQL4gLUeT//Pu/Eh/d09R+NEHC+C74bwnVKe/u27WGvZs6eB6pWr+20OAgDjFwDxByU55f/kXy/NSTxgMmYW1dmVfPj0d1sdE2lKBn7PiSnwi4iIiIgMQgr8A+Ckitc4rLycqtocysr+xKJF13dYNm/ECD72tdH8+G+tewU4xqesoDG+Heu/MDhURBoa8U18iTzHa0zrtZ2WJelsjNUvbe/ROZqyYxRGdxF2xxENlbJy+QaC5AJQRf9O4pdl4oHf+DHq9s1q05vkBw9MINe8mJhjYH/q0i8iIiIiMjhp0r4B4PhBivPDHDpuL8Uj9x2w/IdOn8nddyeXb3NdOO+Uh5KTvPluJ0cPD5GmRny3uYXfb3/d+P6SXJqv53MpOH4A4+8CwDpBIjnv9EHNusYLFAEQiNayq2pih71J2hMzET4YuaJfeyCIiIiIiEjPqFluADh+svtzIK/t+Oj2tCzfFl+yD/74g5cg+gkAjKfnNvv27sVrDvzGJgN/ZWV8osQpU9qfFLEv5BbmUFsF4HH+dcf2KPxu37GNF36yMrFfWFDDWV8/htWrVzFt2mG4bv/8jHdX7+KvP10PgOPXcM7nj+IXf9h/jgnL574zjfHjWs/kf8Xfr+DdxpWYLB8RERERERl8FPgHgLFZie2soq7PyN6ybB+ADYCJNp9PLfzs2LkZzEQgGfiXLoXLLgPfB8eBu+6KPzjpa4Fg8qFNwcgsAsHuj2f3g8VEQsneHnZ3hPpYLu+sK2RCRS6TxvfPz/jVV/8PKATA2BqOPmEMN1y5lut+MAXfOrjGcuedMH1Obptja3N3EYk2kufk9UvdRERERESkdxT4B0Bq4C8aWdazcwQdaB6q7lj9GHfv2QpMbN5rorIyGfYh/vfixfFeEn3d0u+mBPxfXHJejybt8x0Xv3AU2c3D5F96fQGXH2ywdirG+Jx73C85fsrTfVTjpHB+CXmc31yHfSxZfAF5MY9nPvef7NhXxsTRDRx36fHtHhtrXpVA4/dFRERERAYn/aY+IJKBv2zchB6dwQm5tHSwNr5+jA31tYlta8KsXZsM+y08Lz4koq8Df25BYWI7Fml/YrsDsUDjWEN2GKrrsrjn5bmtJs773cuLqRj5MkW5u/uiygnBnGDiAYUXqMPbF6//xELDIcU1UNRxz4KoH+9iEjC6/0REREREBiP9pj4QTDLwH1xxeI9O4WZn07Lqu7FaEs1rDNMSTa0JM2VKvBt/auh33fj8B33tmE+eTUNtDfV7q3t1ng/cAkLhXVTVVLSZOM9al2juHEZOXNura+yvfm8WtvmLi2U1MLL4IBxcAs3L7IWKO+6u39LCH3R1/4mIiIiIDEYK/AMgdb34kaWjenSO+ugoNm4poqywkTGufow2mpwh3zoRysvjY/YXL4637LsuLFnSPxP3lU08iLO/fWOvz/OTm29l2/gXGRl7B2NmtVmG8Uu3frnP63/3JdcSaQ783ogon7n5LmJ7w2y/+RUA3LyOw3yiS79a+EVEREREBiX9pp5m1rf4icDf1Gr8d1ctXQpfv/GLWOtgjOWi4+q5tK8rOsS0TGAI4Dvxnf1XNuivWfr7Sk5WDjVNRXAI/PyqWq64uRDfNzjGcuHxL1BePq/Pr2m8ZAt+oCTeHcJvSH6ZTm4XAr/G8IuIiIiIDEr6TT3Noo0RfCfepd+xTTjdbJ1vmYyupfXXWsM9L53GRx+CuXMHf6jtL24s2QXed2OJ7dSVDQa7nOxsqAEMnH3qLv7tc/nc/oXllJQ6jArt6pdrGluQ2C6eEG/q9+u7GPitAr+IiIiIyGCmBdzTbF/1Xnw3BwDjN3V7zfb2JqOzOJx7LkyaFG/9H46Ml5xczne9TkoOXjm5OYntxn11TJpgOHzUGorzw0RDRezZtrnPr7m7cQzvbSmiphbKy0sB8BuSD0yc3I7DfMukfUFHY/hFRERERAYjNc2l2batm7BOPJwauj+je3uT0bXoz6XnBjvXCxFrfnzlB9r5coaA3LzkWve1H+xh31ObwCYnAnztgYc5bsbCPrnW7vrdXH/XZu79y79jrcEYyzdL6pjZtJHolrpEuY5a+K216tIvIiIiIjLI6Tf1NPvggzXAuPiOber28cnJ6Cye17Z3QH8tPTfYGT8lmAa612tisMjJT46nr99VS90zlXiBmsRrW9/fyL6dm/rkWg+vXc+9f/lMytJ/hh/98uOc5b/K2IJIolxHLfwt3flBgV9EREREZLDSb+o9tPLZStwezE6+df1mWgK/NWHefKr7Ae6oSfDEb+DhX29n6T+OwZIMuP219NxgZ/zkUoc9mQhxMMgtTAb+MPHu8pFQY2LcTTRsoONV8rpsu62lMnJkm6X/fOvwwd6cROA3WS5ZBxW0d4pE6z6oS7+IiIiIyGClwN9Drz72AV70wOX215S3h+zmbUuEf/5hXY/rcMxBe2g4ZQQPPjcV3zr9uvTcYOfYZOAPBbM6KTl45eYmu/QHTyijZMrhNP36T+TWxl8z0TxGXjK919d5afn/UralCGNsq9DvOpbZn5/IyDEWgFB5fodd+lMDv1r4RUREREQGJ/2mnmbGTz4lsE73x/C3OpeNMHfaNqaP3coJiz88JJae6z/JkD8iOGIA69FzOTnJSfuiIyBrShFNY0uTgd8rIvvQ4l5fZ/cfKynOH8P581YnHhY5Toyf315LxbySLp1DgV9EREREZPDTb+o99KGLpuH2IOg8uezxxLZ1oiz4XM9abDe9s5sNT8eHAxSO8Dn11B6dZsiprIyvVDBlyn4PN0x2YrMstyz9FesDqYG/sbERgNDUw2G1D8bBmt6HfYBI1V4cYO60bcwY+zDBY95l/PiNnH3WX7p8jlaBvwdDW0REREREpP/pN/UeOnjmSEKhULePc/8nue07USqOGtWj60caY2x4Ot5bwDouu3fvpLS0Z+caKpYuhcsui69G4DjxyQsXLQLf97DNAyWMH2VU3tD8HlID/3vvvUd9fT319U0UR2NEQ8V4gVIefvjhXl/H1MZomfahqKSSKbNexlqD6+Z3+Rxq4RcRERERGfz0m3qamajBNm/7gZ6vF+8GHYxNzqa+besHGR34v/vZ73Dtb69LjDlPXYKwrCSMdeKB3/WacLO6/yBmMEgN/NXV1VRXV9MQysbxSqiuG0NVTRGhZ54kb2Ssk7Mc2NhIFrHmERBOfnwVAOvnYkzXVzdQ4BcRERERGfz0m3oP1UfqiRA5cMH9GM/FuvFtz/Wpi9R1fkAHYk4ESM4HULllAwdNPbxH5xoK9m49oc2s8p4HK1c1MufIXfjNgd/xm4gFgj3+XgdUECoOrWDde8mJHHMjYZ5fO4F7X5qLtQbDLC46/hnmTXmbaN4q1uQcyu7dJZSW7qGgoPaAl/CMwfXzaYnrodJqAALBwm59Z7WR5LUU+EVEREREBif9pt5Dpz96Ok1+U7eP+5J3Jn5z4I+5UU544IQeXX9C9WGcxTGJ/Udf/x3f2PG9Hp1rKLig6AdtZpU3xuMrb59J0Ts7uMi9Nf6a38iSVQ/w3ANXDlRVeyW6ZzTUTyGnrJKskirCe8p466X/Tnxui8O9L8+nYnI2q1adzgPPTcVaB4xP+en/JG/8TrJL9xIqjIf34txKZoz/P4yJ9yupccdx0PpPJK5XUL4DgA2N27mih/eiluUTERERERmcFPjTzPGT4Sjm+j0+j+dEsSbZwyAvkt1J6aGvcITfPKv8NHxrcIxl4Wn3saZkB2U7irHNrcyGMNFefK8Dac+zC9n6P9eBdcF4jLv4ekKjKuOBPoVvDRt2FPDAc9OSD0CsQ+VfTwIMBp+Ljn+Ok6esIebkUl16MMeUrwTgVUrwnSIAHC9MfmkNYKjvxVc2Kjdzh5KIiIiIiAxlCvw9dMyYY4jZ7o+lNinrxROEE8b2rFU1N6sEa5LXL7GFPT7XUOA7IeZO28Zh5Xuoqs2hrKCRkVnvUDL2BMp22GRB28SEkoNg7JgBq2tP1FUV8eBvvwMt4d66bPvtdXz4e//NB8ZC6nAG4+NG3sXaGfudJbUXwClUTA5SnDuFEa9ug+YVDbYynqJAIQCBWA1uMH5MJDSZE8ZO7Ha9y0eUc+7Uc7t9nIiIiIiI9D8F/h667ZTbejRL/9Jltya2S0aUctfp3+/R9XdV1vHIX3+Q2B9pirjm9Jt7dK7BbtvWTTzySHxce1lWFcXjCgAINozl5ulX8MgbSwg3l7UmzMWzL2HC/FkDU9keeuYZeGC/Vnbru5xd/Bns9Xt4+jsl4BuMY1lwzXqCx0zEPO+3af1v4VtDVW0OxflhQvvKeabsHgA+2LCbqYH45ICOV8ORM+4glDWa+SOO7NakfSIiIiIiMvgp8KddsoU/Py+vx2cJBJ1WLfw23PMZ/we7jZveA+LBNhCtxJqD8AK5eIGJ/OYri7Fjx5GY396EcbOH3iz9U6bElxr0U0K/61omTAjz4dOaeGvqu3hbsnDHh3mjLMob5DDia5XU/ngC+AawJNbaA3B8ygoaATC2nF9V5YIxfOj9fwFHxMuYWsrKTk/XRxQRERERkTRrv3lQ+o9JBv7SESN7fBo36OA7ycDvRzI38O/cvDmxbQkTjGyKvx4ZzXvhk6mtLaG6Lov3thSxuyELZwgG/vJyuOsucJsndHRduOMOy+jRUU4rKSBYFiU0qw63LLkyQ85H9zDygXcp/vE68hdvBad5aINjKfhqJaMDGwGIZJVTtHt3/Dq7diaON259ej6ciIiIiIgMCLXwp5k1zcvHeRFG5Bf2+DyBkIN1UkJ+1HZceIir3bULmBTfMRE8Zw8vrv5Q86R1JwI+8dZtgzGzyJq+l/+aM3D17alFi2DBAli3DioqYOxYy5tvwpyCXF6fezjrG8KdHr/zS/Vsed9h/ME+o8YXs+Yrm4AxWCfAVze8xuGnfYHVT3iJ4Q9kd39ZSRERERERGToU+NPMTwT+Jpysnn/9gaCLdZL9v00sc8dfN9Um14e3JspON9h6hvqUjirWOnz9+mLOviTeaj7UlJcn6+2lPM8ZmxVibFa850JlJaxdGx8G0OozFgPTk7uvOfD+liLKChsZ+8FeTioewdqG5PvBEergIyIiIiKSyRT408x34l36HdtEIJRzgNIdCwQdfMenJfMbL3PDW7S+KbFtTYTAQaemhP22PN+wbt3QDPwHsnQpXHZZfKy/48SHASxa1H65r/3PN/GtgzGWi47fxyLAD2dRHcmiqiaH6aPGp73+IiIiIiKSPgr8abR+XROrd4xjZFGYMW4Tbqigx+cyjsF3bSLwO77bR7UcfPymlMkJnRgXXHwaV1/b8Qz1rmupqMi8Hg+VlcmwD/G/Fy+ODwNIfbiRKNf8/VhruPel0zn3d8/y/Kq53PPyXKw1OH+ejTOp/QcGIiIiIiIy9GVus/AgsXPnNu645pt8esHvmHJoiJ/++WiuvX8uL6w9CDfYu8nlUjO+8TP42U04OXTBd2KUl8PddzuJCe6MAUN8DgPHWH7+Uy8jW/fXrm09iz/Eu/2vW3fgcr51+P2vSrjn5dMSvSN8a1i8OP6AQEREREREMk8Gp8TB4bFv/Jbd3id54G9zE0Er3uI6j89Uv9y7k6c8rsnkwG9iLXEefDc+sH3/Ce7+9JXf8V74UMoKGrnkkuMGrrL9qP2l++Kf/0DlHGPBmjZDIVoeGGTiAxIRERERkeFOLfz9qL5uH9HQkVTV5LQJWr512LJ9RK/Ob5zkj8/YYK/ONZilTkho3eRMduXlcOqp8b9Lcho4dNxeivPDOE7mdeeH9pfuW7KkbVjfv5xjfC487m8cVvgihtZN/+09MBARERERkcyQuc3Cg8Cjy+7Ed4+irLARY1qPOXeMz8EHxzo5+sAsqeP2M/dH6XgBWjowdDRVgZ/yQMVxMzPwQ9ueDR21zLcu51BefjoAJUvj4/49r+MHBiIiIiIikhkyNyUOAjXvbAWOojg/zAUnPsL9y8/C8wyusVw9/xUmlPd8ln4A1yRb9Y3t3XwAg1nqcAUTaD/MW5vcztQW/hapS/d1t1xXHxiIiIiIiMjQp8DfR2zUp+avHxDdllwzPrSvkEh2fPu4Y1/kqx87nLUrYhxU1IgNrMYNHNurawbJTumgncFd+v2UBxuB9keh+C2jU6yPyfDA31tdfWAgIiIiIiJDmwJ/H2l8exd1L2xp9Zpjk+ucTw5NY+Te3YycGN/fVBchEOpdq3yIHJIr1Gdw4E/pvdDeygbW2sSkfg62zfsiIiIiIiLDkSbt6yOxmnCr/Rq/hkhoAgChcBXT7WGJ98JeAxv2vYUb6N3zllybl9i2JnO79EPys2UFs9q+HYthTXxw//6T0omIiIiIiAxXQ7KF/+WXX+aiiy5q973f//73HHnkke2+d+GFF/LKK6+0eu3cc8/lhhtu6HWdbDQZNBs/cgT3PPIkIxoPpjg/jPE3M+6GswH46x0/ZdWLz2CxuL1t4Q8EcbwwvpsFZni08I/IKmrzvh+OJAK/o8AvIiIiIiICDNHAP3v2bF544YVWr/30pz9l+fLlzJgxo9NjzznnHK644orEfk5O7ybOa2FjPpvsJu5akcc9PyzC2gswxnL+vNWcPGUnTigeSKNeEy0d0N1A70K6CQRw/Ci+mzVsWvhLc0ravGsjYXwT76xiNHxfREREREQEGKKBPxQKUVZWltiPRqM8/fTTXHDBBZgDJL7s7OxWx/aVJ1b8js2bjuGev87HNi8RZ63hweemccTsokQ5LxpNbPd2DL/rOhgbiV8rg1v4rWnuxm99SrPbCfzhcEqXfo3hFxERERERgSEa+Pf397//nb1793LWWWcdsOzjjz/OY489RllZGR/60Ie4/PLLe9TK73kenucl9vet3832uuJE2G/hW0PZYWcmysYikeSbxml1ju4yARfjx8/nO6FenWswawn8rhcmlJ3T5nPGGhuxzS38jrEZ8z20fI5M+TwyeOlek3TRvSbpontN0kX3mqRLT++xjAj8f/jDHzjppJMYM2ZMp+U+/vGPM27cOEaNGsWaNWu45ZZbeP/997n99tu7fc1333231b7jOZQVNmKMbRX6jfEZX2558803AajZuzfx3jur3sVxe/4jqA83YGz8YYXvhBLXyDTWifeEcPwwe2ob2nxOs2lzooXfWi/jvoeVK1cOdBVkmNC9Jumie03SRfeapIvuNRmsBlXgv+WWW7j77rs7LfPkk08yefLkxP727dt54YUXuO222w54/nPPPTexPXXqVMrKyrj44ovZtGkTEydO7FZdDz/8cEIpXfJft49QnB/m/HmrefC5qfjWwXUtd9wBCxYckSi36g8hapq3Z8856oBDEDqz/R/vUb+5DgDrBJgwYRylpaN6fL7B6lVnKwDGhhk9djwHz5rV6v0m1+VVUwlAwHWZtd/7Q5XneaxcuZIZM2bguu5AV0cymO41SRfda5IuutckXXSvSbpEIpE2jc5dMagC/yWXXMLChQs7LTNhwoRW+w8//DBFRUXMnz+/29ebOXMmABs3bux24Hddt9U/auPHv8q507Yx4+jHOP7jV1JRYQDDc8/BlClQXg5+LD6GPxAMEejlsnxOyMXY5JwAO3dUEomMZe3a5PUygd/cwm/8CG5WsM1/psbzsE7zLP0OGfef7f73mkh/0b0m6aJ7TdJF95qki+416W89vb8GVeAvKSmhpKTtpGwdsdbyyCOP8O///u8Eg92ftG7VqlUAfTKJn7HJ65eNi3DqqbB0KVx2Gfh+PIjedReY5kn73B7Ud39uVhBIzgnw298G+PFPWl9v0aJeX2ZA7dy5DevEvytjwzihtt9bfNK+5ln6nbRWb8iorCTjHgSJiIiIiEjnhnQ8eumll6isrOTss89u896OHTs444wzWLFiBQCbNm3iF7/4BW+//TaVlZU8/fTTXHnllRxzzDFMmzatD2qTfHaSWzCCyspk2If434sXw669+UDfBP5AVgBL/AFCdV0Wt/54VpvrVVb2+jIDauP7q1L2Is0POVrzw2F809LCr3X59rd0KUyaBPPnx/9eunSgayQiIiIiIukwqFr4u+sPf/gDs2fPbjWmv0U0GuX999+nsbERgGAwyPLly7nnnntoaGhg7NixnH766Vx++eU9unbj2+/gBVK69NvkeP4ca3n7L+vx/db18jzYuqOQCQXgWGhsfhjRYzV7sU4DAFU1OW1WCPA8eOev6yk9pr531xlAW1a8Bcxs3ovg79xO44rWn7PpvXVg4t+1o55UrXT04GnBArX0i4iIiIhkuiEd+G+99dYO3ysvL2fNmjWJ/bFjx7Js2bI+u/aWRYswzQ8TADg2+eDAf+LPZL39JxyexieZQB08ilkLgN25kw/OSU4i2BP144+g5pgJFG1bz7jc8W1WCHCMR+j7i/gguKNX1xlIO0+cAcF44Lcmwp5f3E79znWtyvjGhVN+BqiFf39r1ybDfgvPg3XrFPhFRERERDLdkO7SP7gku5oX7a5hTHAH14++Dof4eokOHtePvo6C/N3xfWt7fUXH9ygMTKLy4C00HvYK14y/AcfE051jLOef9DvGNIf97dHRvNxwLNujo3t93XSKBrMS29ZEMF60TZmWJfkAArk5aanXUDFlSnw+h1SuCxUVA1MfERERERFJnyHdwj+QCs89B9dLNp3a95Jd+ief9nGygkEuBT5a80s+2F3MQaXVjC3I4f6V8fQVKiml+MIP9aoO+XuiuCnPDQoXWb617Rrqtp1NWUEjY9xN/GX+ubz0r9n8/okzsdbBMT4/+uT/8h9Hv9Wra6fNni3QMiLBRCn48HxysmKtikR9F7bEtwPFhemt3yBXXh6fvHHx4njLvuvCkiVq3RcRERERGQ4U+Huo7KtfJRRKhnwuvhcAx4sw6TvXJV4eA8xp3vZiUfh0fNnB7EkTGfPtq3tVh13Pvs6mv/4zsb8PcEb5TLVVWCeIDR/E5tqtibAP4FuHbz7+Mc75+ceGROgzV36dlmcavhNj9OJLyR3feiWHxroIfP0FAJyAOq3sb9Gi+Jj9deviLftD4ecuIiIiIiK9p8DfR6yJh3/Hj3RYxosmu6O7wVCH5boqmBXkoHAhewNR6kwjTm4QyCFr0yaaciYTyRpF7e6aRNhP1GMIjeG2kWQvCt+J4ma1/d58L9nNQWP421dePjR+3iIiIiIi0ncU+Hvo0Zuuw/fi4/OxBmuOA8DYCA9c+812j/G9ZFf0QB8syxfMCpIXC7AwciwA4//7REzA4e5LrgUmU12XBbW5OI7F95NBeCiN4TYREi381vUOHPhdBX4RERERERFQ4O+xbevew4uEAQiYEFn5JwNg/Chb17x7wONDObm9rkMwO4RnvcS+jfmYgINX1sSLL4/lgeemYe2JgI8BLAZjfJYscYZMa6/xnGTgdyxOsG2XfeurhV9ERERERGR/Cvx9wDUBfCfeYm9sx136W+QWFnHkhxf0+rqhrCC+TfYasLF49/cxx/4bD/xoWsoSfQ5gueS0lUwe+SfOP/+rQO8fOKSDE3Pxm+9S61pMOy34rVv4NYZfREREREQEFPh77PN3LUtM2rd74zYe+uF7ABgifOX+P3V6rDEGs/9aaT0Qb+FPBv6GN6tw8oKEds9KCftxFkN+TpTRwSy2vrSescXtL8/n5ATIrijCDJLJ74yfcou6TvzPflIDf3sPBERERERERIYjBf4eclwXx42v/751+6bkGzaaeL2/hfYL/DVPbACgrDaEY47FTwn9jrGUFTRizSgqn1pNtr+3w/PmHTeG4oVT+q3e3WFscq4DE3Aw7XTZ9/3kxH4awy8iIiIiIhI3OJpxh7id2zan7EU7LNfX3GCAPeHtbV4fWxDhBwvW4pp4y7drLBec8ArF+WGiodHstDWdnjf8fm2/1LcnjB+iui6L97YUUVdf1m6Z1BZ+V2P4RUREREREALXw94m91VVAPIxak77A77gB1u97g/pYDQW5h3DqhZ9KvPf5T8KZn9/BhsoAh5THeOHh5TRxJL4bYnveRoo+/FEAtux0WL85wOQJMfLf2IBt8rDhWEeXTJvHf/8bdj6+j2c2LWyefNBgzCxGHh1fVz6VuvSLiIiIiIi0pcDfBxrq6lL20tjCHwhgsWxrXE+VjfLxE8a1en9q8x+A5/60O/G6v6eG/BPG8V9fWs7Pfnkc1joYfC46/i1OOvQ1sozHhRyXts/Rnm3/u56d/r8lwj6AtQ6LF8OCBa3XlNekfSIiIiIiIm0pHfWBaH19YjutLfyB5FwB1vc6KQlefmNiO2tfiMpK+Nkv4mEfwOJw78uns907lciWYqy1HZ0qLZzYCKpqctpMPuh5sG5d67K+luUTERERERFpQ4G/D8SakkvxWZO+7vCO0/XAnz2xMLEdjBTz2J/+hd3vx+9bQ1VtDr5Tio36+58irQy5lBU2YkzrBw+uCxUVrcv6nibtExERERER2Z8Cfx/ww7HExHJ7GtK8vr2Jh35rvU5b5Y/6yEeSh9gydr38xzZh2jF+fCZ/Jwsb7vwBQn+zJpfi/DDnz1uNY/zm+lmWLGndnR/279KvwC8iIiIiIgIaw98n/vnGMTzw9NzExHJlp7edWK6/GONirQfWx/csbqD9wDtj5vEsjzxCNFREVWQS+/aM5JPHruOxVyrwrcEYPzGTvx/LJtoQxR0RSs+HaIc18QcnJ1es5/LDd7N1bwFjyvZw8qJT25RV4BcREREREWlLgb+XKivhgafPOeDEcv3FOC7WB/CIRX3cQMedNtzYDp7dcFjKrPeWhUetpGTmCspG1XLIlhzCTMJzs9ixu45Jo/P6/wN0wHfj13a9eiYVFDKpoIbGrHC7ZW3KGH6jMfwiIiIiIiKAAn+vrV1LYuK7Fi0Ty6Ul8Ld06cfHi/qQ03HZ3Y1N+816b/jj69PZ+OiRlJfD0ovubD6pw8atm5h0+Oj+rn67GhsaiAXiLfzGb0i+4bZfPrWF39Us/SIiIiIiIoACf69NmQLG+K1Cf3sTy/UX0zJxn/V4ZtlqglkdpGJgSySrzaz3vnV44KfvM3NaA5BsQV/x8kqaNqZ5PoJmO/etprp+DFU1OZSHNiVeb2yI8n9L32lTft/u5AoEauEXERERERGJU+DvpfJy+PSJD3P/P8/GtwbH+CxZ4qSldR9SZ+r3+WDFrk7LFhUc1ObhhGMs4W1bWbsvTHZK4K+t3snazTv6o8oH9Mz6LB5pmROBWZgz1nHezB2EmzzWvtp5nTqaw0BERERERGS4UeDvAycd+i+mThpHVW0OZeVPsGjRtWm7dlZuFpFGgAMvozcqL5vz563hweemNT+csJw3bzXF+fGgb53k8oLYhg7O0r+q67J45OlTk8MOcPjWX6dwysHV+KGOVyEAyCkIMWn6yHRUU0REREREZNBT4O8Dxg9SnB+mOD+MU1qf1mtn5YbYtxsCQbjwuyccsPyFwDVbm9jwvsMhB/uMH3cQcBAAD33jH4lyoWC0S+fra8+94GDv229OBGv4YG8OM2flc+ElHdcpryir00kLRUREREREhhMF/j5gbPJrDOSkdyk7x41f2/diFIzsZMa+FAUj4bAj275unWhi20S8Lp+vL806up05EYzloKJG3Cx3QOokIiIiIiIyFKk5tA+kBv5QXnonunMDLYHfw9rOu7wfiHVbB/6BUF4O/zHvDzgm/lmM8bl5wVrGFkQwar0XERERERHpMrXw9wUbTGzmFhWm9dJOIDkrv+95iQcAPWECscRUAE6stzXruXmT32Ra+XiqanMoLnuE88oWxOsU7HgFAhEREREREWlNgb8PGJKBv7C0NK3XbunSD+DHYr0K/E7QT6zM53qtZ7uvrIS1a+PLEPb3CgTGDyXmRAgX1iRfVwu/iIiIiIhIlylB9Ylk4C8bNyGtV04N+J7Xu2Z5Nzs5JMD14+et3bmVz574f0ycaJk/HyZO9Ln7rv7t7m/85Dh9k5X8bp2gnk+JiIiIiIh0lQJ/H7AmOVHf+PKD03ptx23dpb83QrnJcznNgf+h7z/Ib5d/JLlMnnX4/BcMlZW9ulSnjE0G/uxQSvhXl34REREREZEuU+DvE/FWaON7jBo1Nq1XTu3S78WinZQ8sOz8lNb05okIP9hYlAj7LXzfYd26Xl2qU9bkJbbznRGJbVct/CIiIiIiIl2mwN8HWlr4HT+S9mundun3Y71r4c8rSK4w4NgQ1lrKcmowpvXs/47xqajo1aU6ZU28Hm6sgRxS6hRS4BcREREREekqJag+0BL4zQAEficl8P/pRzfihkKdlO6cz3bglPiODfE/3/4GIwIlnD9vNQ8+Nw3fGhxj+czch3j2jld6WfNO6uHG6+B69dTv3AXZ8dedLN2uIiIiIiIiXaUE1QesE+8K79jedanviVB2cox71aYPenWunOJkDwFDFps+2Ex5YAJzp23jsPI9VNXmUFbQyDjnDbave69X1+qIZ33yij8ar4PfAJFYIvAHsrL65ZoiIiIiIiKZSIG/D/hOcwu/TX8L/4z5p/P+m69Tu2tnr88V8yzJOQBDRAL50DyevmWZPAAacsGYds/R6zrk5oGJjzQxtoGATY46CeVl98s1RUREREREMpECfy811TfgN7fwMwCBf/QhFXzu9qV9cq516/Zw+3+tZ2RRhDFuFmd8/bus/vE/2pRznDy+9uDjfXLN/T3xh3vY+FR829LAyQvPh7/vAcC4mnJCRERERESkqxT4e2nb1s0pLdLp79LfV5YuhcsuK8b3j8EYy4UnBCioacJz8wFwo3V4wfi2n7JsXl/bs2UrUA6AdRoJmAB+y5uB/ulVICIiIiIikonUZNpL2yo3puwNzcBfWQmXXQa+Hw/U1hqWLT+GNav34QXiXfqDsV2J8pb+CfyVlfDW6yOprouP1fedJoIkxhiohV9ERERERKQb1MLfS7t3bgUmNu+lv0t/X1i7Fny/9Wu+ddj89i5Kmvcd6nFjDXiBXHzTd4E/Gonw6yuuY/lbp3HPSx/CcinGWM6ft5oTp4QJWJfmmQMwAQV+ERERERGRrlLg76V91XtoCfzWxAa2Mj00ZQo4TuvQ7xhLifsecCgAxmnA8Rrjgd/N7db5I5FdbHj/54TD21q9vmNHEX/5yRTc7P/HPS8fjSXZw+DB56YxdVIeTuqDCFdd+kVERERERLpKgb+XmurqU/aGZpf+8nK46y5YvBg8Lx72z5u3muzG9bQE/qhtwLENQCmem4sXi+EGunb7/O0vt7H1UQfjTU289vza6dz70kewOIAFWod53xp25hdgYzbxmlHgFxERERER6TIF/l6orIS33xlLSTSL4vww1hmagR9g0SJYsAB+uugPFJaNpTg/TDRC4g6JOGGyYw0AWMeldtsmiicc0qVzb/mTJZp1amK/ui6Le1+em2jRj4f91qHfcTxGjc7FeskmfnXpFxERERER6ToF/h764hmvsfQfx2Ntcsz5KQcPzS79LcrLYdqY9TTlxEfu58SyibUEfjdGVqwpUXbP5ve7HPidWCEEk/tVNTlYu39rvcFxLL5vMMbnYx97grIyA16yhR9N2iciIiIiItJlSlA99OCrx2Jt/OtrGXO+u6n/lqtLF+OEE9tBW5jYHj06D0tjYn/vti1dP6dNpv2iGW8z95xqHGNblXFdeOklw1NPefzXf93GnDlvEAqFsLHUFn516RcREREREekqtfD30P4t1L41+FNmD1Bt+pBJrjTgJ+boh7zSEeyr3JfY37ezukun87wGUpv3Z5x0EkfOnENdJDlngOvCkiVwzDHQ2Bjh+edrAeKB30sdw6/nUyIiIiIiIl2lwN9DjtO2hfozl310gGrTd4ybDPxeIBn4d+4xeCkPAz5YvZ0H7nyV42p8cvZb0i9VOHsbmFBi3/3TbrY9+TIfBV7+eogPdmdxUGmYcTsjbLsJ6myyF4F9v57GyO5k3dTCLyIiIiIi0mVqMu2hn/zE4rrx7ZYW6vLyga1TXzCB5MSDsWBBYvu+qeOIOMk5CnYay1emBrl8iotXE+nwTzi6A9vSwm89cuuS5UdTx3GluxlNXeK1pn3JwO9GDLR06XfAhNz+/fAiIiIiIiIZRC38PXThhZYFC2DdOqioyIywDxAIRNuuLmh9NoycSDj4eqJzfnZzY/+bxQEoCNFRFPeLarFmHACuFyG7aGSn1/d9B5qfK2SFQri5IXANeceNxcnW7SoiIiIiItJVSlC9UF6eOUG/hZvltQn8bqyBbx9xJHv+NwDNc/plRVMm4vvm0eQH2o/84Q9exy6Pd+k3fpSxVx/X6fUbN2yAe5bHzzt3AmNP67y8iIiIiIiItE9d+ntoS9cnqR9SAtltX3O9ehZPGEV2QV7Ka8nA3+B1PIi/KbwN68QDv2MjHZZrEYkky2RlZXWlyiIiIiIiItIOtfD30MyZDj/5CSxaNNA16VvBPBd2tX7NsfW8/vrrRIIhWqbfM14yjL+yYgVjO3h0VFe/Gt+ZGz/GRnj99dc7vf6WlCcpoVCok5IiIiIiIiLSGQX+HvJ9w+LFsGBBZnXrD+Ulg3x1XRZVNTlMCBkef/xx9tR7TG150ya7AvzlH88ysr623fPNmLEO32mZtC/C448/3vW6KPCLiIiIiIj0mAJ/L3hefNK+TAr8+SXxmflfXD2WB56bhrUGY2bx8ejjjD/inZSSOYmtmNvxbeR5Dph4879pMxtg58oz6YsVERERERFJMwX+XnDd+Az9maRo/Ciq67ISYR/AWoc///kT3PHpiXhP7cB3Q1gnN3HM0SeexJxg23NZG2HVu0+lvBDhE5/4RJfqMX78eMrKynr1WURERERERIYzBf4ecl3LkiWZ1boPUD5rFvt2v4O1J7Z63fcN4wqns9V7H98N4acE/nGHHMKcsqI252po2Mibr+alvBJlzpw5/VRzERERERERSaVZ+nvozTf9jJuwDyDg5jH74t/gOF6r110XZh4RwPEbqK7L4t0d4/Gq4s36Hc3SHw5vJ9KQMu2/6V6XfhEREREREek5tfD30PjxA12D/uG4OZSVbecrX/k2P/nJ9/B9F8fxuOrqpeys+gcvvPd57nlpbnxs///6jP7CalZfcDevb32rzbmi0T34TQWJfavALyIiIiIikjYK/NJKMBAP6P/20d9z9DHPs2XLJMaP30hZ2XbWrh3DPctPwpIc27/zjmlsmvc79pa93O75/PAJiW4k1sTS8RFEREREREQEBX7ZT1bWaMaMWcj27X+krGw7ZWXbE+9tqTwIu98oEN867NwwGjqYX8+Gk7eY3W+YgIiIiIiIiPQfBX5p44jDb+Gwad/D2tavV0yGb37T4vsm8ZpjLBXjpnHqKe+2e64NT32XSKKwAr+IiIiIiEi6aNI+aZfjZOG6rf9MmpTFXXcZXLe5jLGcN281hf7GNmVb/viRlJAfaH9yPxEREREREel7auGXblm0CBYsgJ9eei+FIw8BYMMruVR+pIMlCiPJkG/dNFVSRERERERE1MIv3VdeDtPGrWNVZQnX3j+X39x5LpMmwdKl7RROnacvaNopICIiIiIiIv1BgV96ZE9TLg88Nw1r4yHe92HxYqisbF3OxJK3mMlSE7+IiIiIiEi6KPBLj1Q1libCfgvPg3Xr9ivoJ28xJ0sjSERERERERNJFgV96ZNyo3RjTehp/14WKitbljJ9s1XdzstJRNREREREREUGBX3po9Jgmzp+3Gqc59LsuLFnSduI+4ydb9UN5uemsooiIiIiIyLCmPtbSI1mFucydto3DyvdQvWstX/nVxe3P0m+Tt1h2QX76KigiIiIiIjLMKfBLj+SXFgNQnB9mjLu1/bAPGIKJ7dyiojTUTERERERERECBX3qoaOyY5I7tZGy+DVJdl0VVTQ7lfk7/V0xEREREREQABX7poVETDwJ2AmDIIepbgo5pU+75tUdyz8tzsdZw+5OWm2+Go4+GKVPajvcXERERERGRvqNJ+6RHxh1UAdYHwJpsGjyvTZnKSrjn5VMSy/f5vuGb34T582HSJFi6NK1VFhERERERGVYU+KVHQtnZuF4TANbkUO/5bcqsXQvWtn+L+T4sXhx/KCAiIiIiIiJ9T4FfesxpDvy+k0OD3zbwT5kCpnnZvvZ4Hqxb12/VExERERERGdYU+KXHHNsIgBfIoaGdFv7ycrhg7r9wOgj9rgsVFf1aRRERERERkWFLk/ZJjxk/HvitE2Tnzu0w4pA2ZeZO28qhBzWxd1cTY077MN/6Vrxl33VhyRJN3CciIiIiItJfFPilF5oSW3veXw+T2wZ+3wlRnB9mjLuFS78O550X78ZfUaGwLyIiIiIi0p8U+KXnTDLw12/b0ubturo6rBNs3osA8ZCvoC8iIiIiItL/BuUY/jvuuIPzzjuPmTNncvTRR7dbZuvWrVx22WXMnDmTE044gR/84AfEYrFOz7t3716+9rWvMWfOHI4++miuvvpq6uvr++MjDAvWRBLb4ardbd7fsS05Bb+x0bTUSUREREREROIGZeCPRqOcccYZnH/++e2+73keixcvJhqN8uCDD3LzzTfz6KOP8rOf/azT8379619n3bp1/OY3v+HOO+/ktdde49prr+2PjzA8uMnA79fWtXl7587tKXuRNu+LiIiIiIhI/xmUgf+KK67g4osv5tBDD233/RdeeIF169bxox/9iMMOO4xTTjmFL3/5y9x3331EIu0Hy/Xr1/P888/z3e9+N9Fz4JprruHPf/4zO3bs6M+Pk7Gsm+xRYRvCbd7fuyul1d+ohV9ERERERCSdhuQY/jfffJNDDz2UkSNHJl476aST+M53vsO6des4/PDD2xzzxhtvUFBQwIwZMxKvzZ07F8dxWLFiBR/5yEe6dG1r40vMdfRgYThx8lxaMr/xnDbfScPevbjB4ub3fX1n3eR5HhC/11zXHeDaSCbTvSbpontN0kX3mqSL7jVJl5Ys1ZJHu2pIBv5du3a1CvtAYr+qqqrDY0pKSlq9FggEKCws7PCY9vh+fL35NWvWdKfKGWnWBf+esreQlStXtnr/oOmzOGh6y96H27wvXfPuu+8OdBVkmNC9Jumie03SRfeapIvuNUmXljzaVWkL/Lfccgt33313p2WefPJJJk+enKYa9UwgEGDGjBk4joMxZqCrIyIiIiIiIhnOWovv+wQC3YvwaQv8l1xyCQsXLuy0zIQJE7p0rpEjR7JixYpWr+3atQuAsrKyDo/Zs2dPq9disRg1NTUdHtMex3EIhUJdLi8iIiIiIiIyENIW+EtKStp0qe+pWbNmceedd7J7925KS0sBePHFF8nPz6eioqLdY2bPnk1tbS1vv/0206fH+5m/9NJL+L7PkUce2Sf1EhERERERERksBuUs/Vu3bmXVqlVs3boVz/NYtWoVq1ator6+HohP0FdRUcE3v/lNVq9ezfPPP89tt93Gpz/96UTr+4oVKzjjjDMSM/BPnjyZk08+mf/+7/9mxYoVvP7669x444187GMfY/To0QP2WUVERERERET6g7HdneYvDb71rW/x6KOPtnn9nnvu4bjjjgNgy5YtfOc73+GVV14hJyeHhQsX8rWvfS0xpuHll1/moosu4umnn6a8vByAvXv3cuONN/L3v/8dx3E4/fTTueaaa8jLy0vfhxMRERERERFJg0EZ+EVERERERESkdwZll34RERERERER6R0FfhEREREREZEMpMAvIiIiIiIikoEU+EVEREREREQykAJ/L3z+85/n1FNPZcaMGZx00kl84xvfSCwDKNJXKisrufrqq5k/fz5HHnkkp512Gj/72c+IRCIDXTXJQHfccQfnnXceM2fO5Oijjx7o6kgGue+++5g/fz4zZszgU5/6FCtWrBjoKkkGevXVV/n85z/PSSedxNSpU3nqqacGukqSgZYsWcJZZ53F7NmzOeGEE7j88svZsGHDQFdLMtD999/PmWeeyZw5c5gzZw7nnnsuzz77bLfOocDfC8cffzy33XYbf/nLX/jZz37G5s2b+fKXvzzQ1ZIMs2HDBqy13HDDDfz5z3/mqquu4sEHH+QnP/nJQFdNMlA0GuWMM87g/PPPH+iqSAZ58sknuemmm/jiF7/Io48+yrRp01i0aBG7d+8e6KpJhmloaGDq1Klcd911A10VyWCvvPIKn/70p3nooYf4zW9+QywWY9GiRTQ0NAx01STDjBkzhq9//es88sgjPPzwwxx//PF88YtfZO3atV0+h5bl60NPP/00X/ziF1m5ciXBYHCgqyMZ7Fe/+hUPPPAATz/99EBXRTLUI488wve//31ee+21ga6KZIBPfepTzJgxg2uvvRYA3/c55ZRTuPDCC7nssssGuHaSqaZOncovfvELTjvttIGuimS4PXv2cMIJJ7Bs2TKOOeaYga6OZLhjjz2Wb3zjG3zqU5/qUnm18PeRvXv38vjjjzN79myFfel3+/bto7CwcKCrISJyQJFIhHfeeYe5c+cmXnMch7lz5/LGG28MYM1ERPrGvn37APS7mfQrz/P485//TENDA7Nnz+7ycYF+rNOw8KMf/Yj77ruPxsZGZs2axZ133jnQVZIMt3HjRpYtW8aVV1450FURETmg6upqPM+jtLS01eulpaUa8yoiQ57v+3z/+99nzpw5HHrooQNdHclAa9as4bzzziMcDpObm8svfvELKioquny8Av9+brnlFu6+++5Oyzz55JNMnjwZgEWLFnH22WezdetWbr/9dq688kqWLFmCMSYd1ZUhrLv3GsCOHTu49NJLOeOMMzjnnHP6u4qSIXpyr4mIiMiBXX/99axdu5b7779/oKsiGerggw/mj3/8I/v27eOvf/0rV155JcuWLety6Ffg388ll1zCwoULOy0zYcKExHZJSQklJSUcfPDBTJ48mVNOOYU333yzW90sZHjq7r22Y8cOLrroImbPns2NN97Y39WTDNLde02kLxUXF+O6bpsJ+nbv3s3IkSMHqFYiIr13ww038I9//INly5YxZsyYga6OZKhQKMSkSZMAmD59OitXruSee+7hhhtu6NLxCvz7aQnwPeH7PoCWS5Mu6c691hL2jzjiCG666SYcR9NvSNf15v81kd4KhUIcccQRLF++PDF5mu/7LF++nAsuuGCAayci0n3WWm688Ub+9re/ce+99+qhuaSV7/vdypsK/D301ltvsXLlSo466igKCgrYtGkTP/3pT5k4caJa96VP7dixgwsvvJBx48Zx5ZVXsmfPnsR7ZWVlA1gzyURbt26lpqaGrVu34nkeq1atAmDixInk5eUNcO1kqPrsZz/LlVdeyfTp0znyyCP57W9/S2NjI//v//2/ga6aZJj6+no2bdqU2K+srGTVqlUUFhYybty4AayZZJLrr7+eJ554gl/+8pfk5eVRVVUFwIgRI8jOzh7g2kkmufXWW5k3bx5jx46lvr6eJ554gldeeYWlS5d2+Rxalq+H1qxZw/e+9z3WrFlDQ0MDZWVlnHzyyVx++eWMHj16oKsnGeSRRx7hqquuave9NWvWpLk2kum+9a1v8eijj7Z5/Z577uG4444bgBpJpli2bBlLly6lqqqKww47jGuuuYaZM2cOdLUkw7z88stcdNFFbV5fuHAhN9988wDUSDLR1KlT2339pptu0oNM6VNXX301L730Ejt37mTEiBFMnTqVz33uc5x44oldPocCv4iIiIiIiEgG0kBgERERERERkQykwC8iIiIiIiKSgRT4RURERERERDKQAr+IiIiIiIhIBlLgFxEREREREclACvwiIiIiIiIiGUiBX0RERERERCQDKfCLiIiIiIiIZCAFfhEREREREZEMpMAvIiIiIiIikoEU+EVEREREREQykAK/iIiI9Mrrr7/O1KlTmTp1Kk8++WS7Zd566y1mz57N1KlT+cEPfpDmGoqIiAxPCvwiIiLSK0cddRTz588H4Oc//zme57V6f8OGDVx22WU0NDSwcOFCvvnNbw5ENUVERIYdBX4RERHpta997Wu4rsuGDRt47LHHEq/v2LGDSy+9lL179/KhD32I7373uxhjBrCmIiIiw4cCv4iIiPRaRUUFCxcuBOD2228nGo1SW1vLpZdeypYtWzjqqKO47bbbCAQCA1xTERGR4cNYa+1AV0JERESGvh07dnD66afT1NTEVVddxVNPPcWrr77KoYceyn333UdBQcFAV1FERGRYUeAXERGRPnPLLbdw9913J/bHjx/Pgw8+yKhRo9qUra+v59e//jVvv/02b7/9Nrt27WLhwoXcfPPN6ayyiIhIxlKXfhEREekzF154IY4T//WiqKiIX//61+2GfYDq6mpuv/123nnnHaZPn57OaoqIiAwLGkgnIiIifSIWi3Hdddfh+z4AjY2NZGdnd1h+1KhRPPfcc4wePZpwOMyRRx6ZrqqKiIgMC2rhFxERkV6z1nLNNdfwzDPPUFJSQnl5OeFwmJ/97GcdHhMKhRg9enQaaykiIjK8KPCLiIhIr/3whz/k0UcfJTc3lyVLlvCVr3wFgD/+8Y+sW7dugGsnIiIyPCnwi4iISK8sXbqUX//61wSDQW6//XaOPPJIPvaxjzF16lQ8z+PWW28d6CqKiIgMSwr8IiIi0mN//OMf+dGPfoQxhptuuokTTzwRAGMMX/7ylwH4+9//zuuvvz6Q1RQRERmWFPhFRESkR5599lm+/e1vY63lW9/6FmeeeWar9z/84Q8zc+ZMIL5cn4iIiKSXAr+IiIh02xtvvMGXv/xlYrEYn/vc57j44ovbLdcylv9f//oXTz31VBprKCIiIlqWT0RERLpt9uzZvPnmmwcsd8IJJ7BmzZr+r5CIiIi0oRZ+ERERERERkQykFn4REREZMMuWLaO2thbP8wBYs2YNv/zlLwE45phjOOaYYwayeiIiIkOasdbaga6EiIiIDE/z589ny5Yt7b73pS99if/8z/9Mc41EREQyhwK/iIiIiIiISAbSGH4RERERERGRDKTALyIiIiIiIpKBFPhFREREREREMpACv4iIiIiIiEgGUuAXERERERERyUAK/CIiIiIiIiIZSIFfREREREREJAMp8IuIiIiIiIhkIAV+ERERERERkQykwC8iIiIiIiKSgRT4RURERERERDLQ/wfnR4iWGaN9bwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## ENTER YOUR CODE HERE (/¯◡ ‿ ◡)/¯☆*##\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n", + "depth = []\n", + "for i in range (1, 16):\n", + " clf = DecisionTreeRegressor(max_depth=i, random_state=SEED)\n", + " clf.fit(X_train, y_train)\n", + " plot_regression_predictions(clf.fit(X_train, y_train), X_test, y_test, axes=[-3, 3, -10, 10], ylabel=\"$y$\")\n", + " depth.append(i)\n", + " \n", + "plt.legend(depth)\n", + "plt.plot(X.reshape(-1), y.reshape(-1), \"b.\") " + ] + }, + { + "cell_type": "markdown", + "id": "aa0d38ae", + "metadata": {}, + "source": [ + "Видим, что после n = 7, все остальные линии почти совпадают, построим отдельно" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8d36d8d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n", + "depth = []\n", + "fig, axs = plt.subplots(5, 3, figsize=(15, 15)) \n", + "\n", + "for i in range(1, 16):\n", + "\n", + " clf = DecisionTreeRegressor(max_depth=i, random_state=SEED)\n", + " clf.fit(X_train, y_train)\n", + " \n", + " plot_regression_predictions(clf, X_test, y_test, axes=[-3, 3, -10, 10], ylabel=\"$y$\")\n", + " plt.subplot(5, 3, i)\n", + " \n", + " plt.plot(X.reshape(-1), y.reshape(-1), \"b.\", color = 'pink')\n", + " plt.title(f'Depth = {i%15+1}') \n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# я так и не поняла почему сабплоты расположились так, я удивилась, поразилась, негодовала, но потом нашла как все правильно собрать и подписать.\n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "ff1a7668-6c5b-4817-98fd-78337f3e9685", + "metadata": {}, + "source": [ + "При глубине больше 8 модели стремятс предсказать каждую точку и становятся переобученными, мне кажется все модели при глубине более 4 неплохо отражают тренд, оптимальной глубиной кажется 6, ну или 7" + ] + }, + { + "cell_type": "markdown", + "id": "b185effa-2fd7-496a-a5f6-f802d6a32eea", + "metadata": {}, + "source": [ + "### Задание 2. Random forest\n", + "\n", + "Теперь давайте немного подготовимся к тому, чтобы реализовать свой собственный случайный лес, а потом реализуем его." + ] + }, + { + "cell_type": "markdown", + "id": "573cfb4a-1fc3-4c0f-b165-a4ab6a341843", + "metadata": {}, + "source": [ + "#### Задание 2. 1. Простое ансамблирование\n", + "\n", + "**1 балла**\n", + "\n", + "Представим, что у нас есть 101 классификатор. Каждый может с вероятностью `p` (равной для всех моделей) правильно предсказать класс объекта. Будем делать предсказания по большинству голосов (majority vote). Постройте зависимость вероятности правильно классифицировать объект от значения `p`. Вам может быть полезная следующая формула:" + ] + }, + { + "cell_type": "markdown", + "id": "9a47cc99-0337-4831-bc83-5dc09dc55e52", + "metadata": {}, + "source": [ + "$$ \\large \\mu = \\sum_ {i = 51} ^ {101} C_{101} ^ ip ^ i (1-p) ^ {101-i} $$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "33d1ecd5-b310-4106-915a-2f7afb26f608", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "p_values = np.linspace(0, 1, 100)\n", + "p_correct = []\n", + "for p in p_values:\n", + " prob_correct = sum([(math.factorial(101)/(math.factorial(k)*math.factorial(101-k))) * p**k * (1-p)**(101-k) for k in range(51, 102)])\n", + " p_correct.append(prob_correct)\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(p_values, p_correct, label='Probability of Correct Classification')\n", + "plt.xlabel('p')\n", + "plt.ylabel('Probability')\n", + "plt.title('Probability of Correct Classification vs. p')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "b3d10f32-08d6-4a78-a167-82818f8acd93", + "metadata": {}, + "source": [ + "А теперь давайте посмотрим на другую ситуацию. У нас есть фиксированная вероятность того, что модель правильно классифицирует объект `p = 0.65`. Постройте зависимость вероятности правильно классифицировать объект от числа моделей в ансамбле." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3f6736e1-bfaf-4a90-bc8f-a2eae5e09dee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "p = 0.65\n", + "\n", + "n_values =(np.linspace(1, 1000, 100))\n", + "n_values = n_values.astype(int)\n", + "p_correct = []\n", + "\n", + "for n in n_values:\n", + " prob_correct = 0\n", + " for k in range(n//2+1 + n%2, n+1):\n", + " prob_correct += math.comb(n, k) * (p**k) * ((1-p)**(n-k))\n", + " p_correct.append(prob_correct)\n", + "#print(p_correct)\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot(n_values, p_correct, label='Probability of Correct Classification')\n", + "plt.xlabel('n')\n", + "plt.ylabel('Probability')\n", + "plt.title('Probability of Correct Classification vs. n')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67a5606e-4e84-46cc-a092-c63d299108e2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a3d929c1-3c47-4c09-ac74-d5a5f206b66e", + "metadata": {}, + "source": [ + "Опишите ваши наблюдения:\n", + "\n", + "* При вероятности 0.5 даже при 101 классификаторе итоговая вероятность будет 0.5, но .в целом логично, при этом дальше зависимость быстро сходится к 1, то есть даже если возникает небольшой перевес в сторону правильного определния, то при большом количестве классификаторов, модель все быстрее пиходит к правильной работе\n", + "* При увеличение числа классификаторов ероятность правильного определения нарастает экспоненциально и сходится к 1" + ] + }, + { + "cell_type": "markdown", + "id": "a8914245-d761-4ce3-9836-7d832aea7005", + "metadata": {}, + "source": [ + "#### Задание 2. 2. Реализация простого RF\n", + "\n", + "**4 балла**\n", + "\n", + "Реализуйте свой собственный класс `RandomForestClassifierCustom`, используя в качестве базовой модели `DecisionTreeClassifier` из `sklearn`.\n", + "\n", + "Небольшое описание:\n", + "- Используйте приведенный ниже код\n", + "- В методе `fit` в цикле (`i` от 0 до `n_estimators-1`):\n", + " * Зафиксируйте генератор случайных чисел следующим образом np.random.seed(`random_state + i`). Идея в том, что на каждой итерации у нас будет новое значение для генератора случайных чисел, что добавит побольше \"случайности\", но в то же время мы сможем иметь воспроизводимые результаты\n", + " * После чего выберите `max_features` признаков **без возвращения/without replacement**, сохраните список выбранных признаков (их индексов) в `self.feat_ids_by_tree`\n", + " * Также создайте псевдовыборку при помощи бутстрэпа (выбор **с возвращением/with replacement**) из тренировочных данных. Может помочь функция `np.random.choice` и ее аргумент `replace`\n", + " * Обучите дерево решений с параметрами, заданными в конструкторе класса `max_depth`, `max_features` и `random_state` на полученной псевдовыборке.\n", + "- Метод `fit` должен возвращать текущий экземпляр класса `RandomForestClassifierCustom`, то есть `self` (все по-взрослому, как в `sklearn`)\n", + "- В методе `predict_proba` мы должны пройти циклом по всем деревьям. Для каждого предсказания, нам нужно будет брать только те признаки, на которых училось изначальное дерево, поэтому мы и сохраняли эту информацию в артрибуте `self.feat_ids_by_tree`. Этот метод должен возвращать предсказанные вероятности (можно делать двумя способами: для каждого дерева предсказывать значение при помощи метода `predict_proba` и потом усреднять эти вероятности, или к примеру пользоваться методом `predict` и также считать среднее." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c19fa91b-815f-4e65-bac7-5f218ccd4dfe", + "metadata": {}, + "outputs": [], + "source": [ + "class RandomForestClassifierCustom(BaseEstimator):\n", + "\n", + " def __init__(\n", + " self, n_estimators=10, max_depth=None, max_features=None, random_state=SEED\n", + " ):\n", + " self.n_estimators = n_estimators\n", + " self.max_depth = max_depth\n", + " self.max_features = max_features\n", + " self.random_state = random_state\n", + " self.random_state_initial = random_state\n", + "\n", + " self.trees = []\n", + " self.feat_ids_by_tree = []\n", + "\n", + " def fit(self, X, y):\n", + " self.classes_ = sorted(np.unique(y))\n", + " for i in range (0, self.n_estimators - 1):\n", + " self.random_state = np.random.seed(self.random_state_initial + i)\n", + " indices_features = np.random.choice(range(X.shape[1]), size=self.max_features, replace=False)\n", + " self.feat_ids_by_tree.append(indices_features)\n", + " pseudo_X_indices = np.random.choice(range(X.shape[0]), size=X.shape[0], replace=True)\n", + " pseudo_X = X[pseudo_X_indices]\n", + " pseudo_y = y[pseudo_X_indices]\n", + " tree = DecisionTreeClassifier(max_depth=self.max_depth, random_state=self.random_state)\n", + " tree.fit(pseudo_X[:, indices_features], pseudo_y)\n", + " self.trees.append(tree)\n", + " return self\n", + "\n", + "\n", + " def predict_proba(self, X):\n", + " predictions = np.zeros((X.shape[0], len(self.classes_))) # Создаем массив для предсказаний вероятностей для каждого класса\n", + "\n", + " for i in range(0, self.n_estimators-1):\n", + " tree = self.trees[i]\n", + " feat_ids = self.feat_ids_by_tree[i]\n", + " prediction = tree.predict(X[:, feat_ids])\n", + "\n", + " for j in range(X.shape[0]):\n", + " pred_class = prediction[j]\n", + " predictions[j, pred_class] += 1 # Увеличиваем счетчик для предсказанного класса\n", + "\n", + " return predictions / self.n_estimators\n", + " \n", + " def predict(self, X):\n", + " probas = self.predict_proba(X)\n", + " predictions = np.argmax(probas, axis=1)\n", + " \n", + " return predictions" + ] + }, + { + "cell_type": "markdown", + "id": "2d011ba4-85f2-453a-b284-78e711ab1733", + "metadata": {}, + "source": [ + "Протестируем нашу реализацию на искусственных данных. Визуализируйте разделяющую границу, которую рисует ваша модель при помощи функции `plot_decision_boundary` (см. примеры в лекции)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4e67ff2b-62a9-4f94-8bb2-7f5c1636999d", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n", + " x1s = np.linspace(axes[0], axes[1], 100)\n", + " x2s = np.linspace(axes[2], axes[3], 100)\n", + " x1, x2 = np.meshgrid(x1s, x2s)\n", + " X_new = np.c_[x1.ravel(), x2.ravel()]\n", + " y_pred = clf.predict(X_new).reshape(x1.shape)\n", + " custom_cmap = ListedColormap([\"#ffdab9\",\"#9898ff\", \"#4B0082\"])\n", + " plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n", + " if contour:\n", + " custom_cmap2 = ListedColormap([\"#ffdab9\", \"#4c4c7f\", \"#4B0082\"])\n", + " plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n", + " plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n", + " plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n", + " plt.axis(axes)\n", + " plt.xlabel(r\"$x_1$\", fontsize=18)\n", + " plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a76e9b76-94c2-47b5-9ef9-150bc798a307", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y = datasets.make_moons(n_samples=500, noise=0.30, random_state=SEED)\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n", + "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cb4de83e-dfe0-48f4-8bd8-f0f9fdc36e80", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n", + "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n", + "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n", + "clf = RandomForestClassifierCustom(n_estimators=10, max_depth=20, max_features=2, random_state=SEED)\n", + "clf.fit(X_train, y_train)\n", + "plot_decision_boundary(clf, X, y, alpha=0.02, contour=True)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c59e2333-fb8d-4fa4-9405-a0c186596b19", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "da484a87-bb76-43a7-8953-1af94efbd0aa", + "metadata": {}, + "source": [ + "Подберите наилучшие гиперпараметры, при которых разделяющая граница будет, на ваш взгляд, оптимальной с точки зрения bias-variance. Можно также подключить какие-то метрики для выбора лучшей модели." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fccbd1cc-740c-4411-9b27-680a9cf73c9f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "k = 1\n", + "plt.figure(figsize=(24, 16))\n", + "for i in range(2, 22, 4):\n", + " for j in range(2, 20, 4):\n", + " plt.subplot(5, 5, k)\n", + " #plt.figure(figsize=(10, 10))\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n", + " plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n", + " plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n", + " clf = RandomForestClassifierCustom(n_estimators=(i), max_depth=(j), max_features=2, random_state=SEED)\n", + " clf.fit(X_train, y_train)\n", + " plot_decision_boundary(clf, X, y, alpha=0.02, contour=True)\n", + " \n", + " plt.title(f'Depth = {j}, n_estim = {i}')\n", + " k +=1 \n", + " \n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e93cf1e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n", + "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n", + "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n", + "clf = RandomForestClassifierCustom(n_estimators=10, max_depth=5, max_features=2, random_state=SEED)\n", + "clf.fit(X_train, y_train)\n", + "plot_decision_boundary(clf, X, y, alpha=0.02, contour=True)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "293aac4e-a7c9-400a-a939-e3b1122d5517", + "metadata": {}, + "source": [ + "Я сделала, это вручную, при глубине дерева больше 6 начинают появляться отдельные кластеры для индивидуальных точек, которые не попадают в основной пул, модель становится переобученной, это не кажется разумным. При увеличении количества деревьев лесу > 10 не приводит к особому изменению модели.\n", + "Я бы остановилась на глубине 5 и количестве деревьев - 10" + ] + }, + { + "cell_type": "markdown", + "id": "5fad02ca-af42-4142-8201-93602cfea49f", + "metadata": {}, + "source": [ + "#### Задание 2. 3. Корреляция базовых моделей\n", + "\n", + "**3 балла**\n", + "\n", + "Как мы выянили на лекции, для того, чтобы bagging работал хорошо, предсказания наших моделей не должны сильно коррелировать. Для этого в случайном лесе применяются различные подходы, в том числе и RSM. Давайте посмотрим, как влияет параметр `max_features` на корреляцию базовых моделей в случайном лесу из `sklearn`. В качестве примера будем использовать датасет `breast_cancer`. Для расчета корреляций используйте приведенную ниже функцию `base_model_pair_correlation`. Для каждой модели у вас будет получаться набор значений (попарные корреляции всех деревьев), дальше можно изобразить их в виде боксплотов, как мы на лекции рисовали распределение метрик." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c755182a-f385-4f0b-a153-60ef6e572f3b", + "metadata": {}, + "outputs": [], + "source": [ + "# Функция для расчета попарных корреляций базовых моделей в случайном лесу\n", + "def base_model_pair_correlation(ensemble, X):\n", + " corrs = []\n", + " for (i, est1), (j, est2) in combinations(enumerate(ensemble.estimators_), 2):\n", + " Xi_test = X\n", + " Xj_test = X\n", + "\n", + " ypred_t1 = est1.predict_proba(Xi_test)[:, 1]\n", + " ypred_t2 = est2.predict_proba(Xj_test)[:, 1]\n", + "\n", + " corrs.append(pearsonr(ypred_t1, ypred_t2)[0])\n", + " return np.array(corrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "65ab9ab7-311a-4bb1-8adc-f66c063e827e", + "metadata": {}, + "outputs": [], + "source": [ + "# Загрузим данные\n", + "breast_cancer = datasets.load_breast_cancer()\n", + "X = breast_cancer.data\n", + "y = breast_cancer.target\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d3d30460", + "metadata": {}, + "source": [ + "прочитала, что в этом датасете 30 признаков и 569 образцов, бинарная классификация" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d34e1758-d2dc-4820-afe5-2abdd850b590", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "correlations = []\n", + "max_features = [1, 2, 3, 5, 10, 15, 20, 25, 30]\n", + "for mf in max_features:\n", + " rf_clf = RandomForestClassifier(n_estimators = 15, max_features=mf, random_state= SEED)\n", + " rf_clf.fit(X_train, y_train)\n", + " correlations.append(base_model_pair_correlation(rf_clf, X_test))\n", + "correlations \n", + "plt.figure(figsize=(10, 6))\n", + "plt.boxplot(correlations, labels = max_features)\n", + "plt.xlabel('max_features')\n", + "plt.ylabel('Correlation')\n", + "plt.title('Boxplot of max_features vs Correlations')\n", + "plt.grid(True)\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "511f49f6-b433-449d-b27c-522098ef7a32", + "metadata": {}, + "source": [ + "Теперь давайте посмотрим, как на это влияет параметр `max_depth`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "26d98e98-b0ba-480b-944c-a27486b8b7c5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "correlations = []\n", + "max_d = [1, 2, 3, 4, 5, 6, 7, 8, 10, 20, 100]\n", + "for md in max_d:\n", + " rf_clf = RandomForestClassifier(n_estimators = 30, max_depth=md, random_state= SEED)\n", + " rf_clf.fit(X_train, y_train)\n", + " correlations.append(base_model_pair_correlation(rf_clf, X_test))\n", + "plt.figure(figsize=(10, 6))\n", + "plt.boxplot(correlations, labels = max_d)\n", + "#plt.xlabel('max_features')\n", + "plt.ylabel('Correlation')\n", + "plt.title('Boxplot of max_features vs Correlations')\n", + "plt.grid(True)\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "id": "efafb748-a14d-4fd0-9995-4eee6e092445", + "metadata": {}, + "source": [ + "Опишите ваши наблюдения:\n", + "\n", + "* При увеличении количества features, корреляция растет, а потом немного падает\n", + "* max_depth, видим, что с какого-то момента показатели не мзменяются, то есть построение дерева останавливается до достижения максимальной глубины, при ее увеличении показатель корреляции растет, так как больше возможностей задать вопрос - стремимся к чему-то похожему, а потом чуть падает до стабильного уровня" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "aef2bd46-a98e-4f8f-ba17-0bdfe51ba395", + "metadata": {}, + "source": [ + "### Задание 3. Строим большой ансамбль\n", + "\n", + "**4 балла + 3 дополнительных за скор выше 0.87**\n", + "\n", + "В данной задаче вам нужно диагностировать сердечное заболевание у людей по медицинским показателям." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "475ce2d1-1a24-4731-a424-df36cef9b270", + "metadata": {}, + "outputs": [], + "source": [ + "#!gdown --id 1VFbDK-Ad-hpf0_GGCBzn4thdn9mkQ-Y- -O heart.csv -q\n", + "heart_dataset = pd.read_csv(\"../data/heart.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8bde25c5-2da7-4fb9-93e1-bd347cecef8e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", + "178 43 1 0 120 177 0 0 120 1 2.5 \n", + "298 57 0 0 140 241 0 1 123 1 0.2 \n", + "201 60 1 0 125 258 0 0 141 1 2.8 \n", + "246 56 0 0 134 409 0 0 150 1 1.9 \n", + "153 66 0 2 146 278 0 0 152 0 0.0 \n", + "\n", + " slope ca thal \n", + "178 1 0 3 \n", + "298 1 0 3 \n", + "201 1 1 3 \n", + "246 1 2 3 \n", + "153 1 1 2 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = heart_dataset.drop(\"target\", axis=1)\n", + "y = heart_dataset[\"target\"]\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=SEED)\n", + "X_train.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b144a808-145d-4370-9b6c-4859ee231d91", + "metadata": {}, + "source": [ + "Обучите разнообразные классификаторы, приведенные ниже, а также ансамбль `VotingClassifier` из `sklearn.ensemble`, объединяющий эти классификаторы с помощью жесткого или мякого голосования (параметр `voting =` `'hard'` или `'soft'` соответственно). Оцените качество моделей с помощью кросс-валидации на тренировочном наборе, используя функцию `cross_val_score` и метрику `f1`. Часть моделей отсюда мы не проходили, о них можно почитать дополнительно, но в принципе для задания не очень важно знать принципы их работы (но, если есть время, то почитайте, там интересно)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "099f6e1a-641c-4acc-8334-e4b304aa8810", + "metadata": {}, + "outputs": [], + "source": [ + "dt = DecisionTreeClassifier(random_state=SEED, max_depth=10, min_samples_leaf=10)\n", + "rf = RandomForestClassifier(n_estimators=50, random_state=SEED)\n", + "etc = ExtraTreesClassifier(random_state=SEED)\n", + "knn = KNeighborsClassifier(n_neighbors=5, weights=\"distance\")\n", + "svc_lin = SVC(kernel='linear', probability=True, random_state=SEED)\n", + "svc_rbf = SVC(kernel='rbf', probability=True, random_state=SEED)\n", + "cat = catboost.CatBoostClassifier(verbose=0, random_seed=SEED)\n", + "lgbm = lightgbm.LGBMClassifier(random_state=SEED)\n", + "lgbm_rf = lightgbm.LGBMClassifier(boosting_type=\"rf\", bagging_freq=1, bagging_fraction=0.7, random_state=SEED)\n", + "xgb = xgboost.XGBClassifier(random_state=SEED)\n", + "xgb_rf = xgboost.XGBRFClassifier(random_state=SEED)\n", + "lr = LogisticRegression(solver='liblinear', max_iter=10000)\n", + "nb = GaussianNB()\n", + "\n", + "base_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"ETC\", etc), (\"KNN\", knn), \n", + " (\"SVC_LIN\", svc_lin), (\"SVC_RBF\", svc_rbf), \n", + " (\"CAT\", cat), (\"LGBM\", lgbm), \n", + " (\"LGBM_RF\", lgbm_rf), (\"XGB\", xgb), \n", + " (\"XGB_RF\", xgb_rf), (\"LR\", lr), (\"NB\", nb)]" + ] + }, + { + "cell_type": "markdown", + "id": "afb6e5b6-da19-48e8-9882-63e0c5cafdde", + "metadata": {}, + "source": [ + "Здесь могут возникать различные предупреждения при обучении бустингов, не волнуйтесь, все нормально, просто они обычно очень разговорчивые)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "03d7be2e-568b-47ce-b29c-7221ba2d5bee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006255 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: 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gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000077 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000076 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000092 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000067 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.8455110375195781\n", + "DecisionTreeClassifier: 0.797997226792219\n", + "RandomForestClassifier: 0.8328751280279528\n", + "CatBoostClassifier: 0.8342715174922052\n", + "ExtraTreesClassifier: 0.8281746031746032\n", + "KNeighborsClassifier: 0.6493313763861709\n", + "SVC: 0.8403098469098905\n", + "SVC: 0.6973119072190279\n", + "XGBClassifier: 0.8134522115571786\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000145 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000069 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000093 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "LGBMClassifier: 0.817010631644778\n", + "XGBRFClassifier: 0.8499478840942256\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000080 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000071 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000067 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "LGBMClassifier: 0.8132478632478634\n", + "LogisticRegression: 0.8500073681108163\n", + "GaussianNB: 0.8140676625250128\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000141 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000077 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000070 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000164 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000065 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.8494440278941667\n" + ] + } + ], + "source": [ + "\n", + "voting_hard = VotingClassifier(estimators=base_models, voting='hard')\n", + "voting_soft = VotingClassifier(estimators=base_models, voting = 'soft')\n", + "\n", + "\n", + "## END YOUR CODE HERE ##\n", + "results = []\n", + "\n", + "for model in [voting_soft, dt, rf, cat, etc, knn, svc_lin, svc_rbf, xgb, lgbm, xgb_rf, lgbm_rf, lr, nb, voting_hard]: \n", + " scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\"f1\")\n", + " results.append(f\"{model.__class__.__name__}: {scores.mean()}\")\n", + " print(f\"{model.__class__.__name__}: {scores.mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a8175050-ebb9-4482-b5a4-26711016f03b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VotingClassifier: 0.8455110375195781\n", + "DecisionTreeClassifier: 0.797997226792219\n", + "RandomForestClassifier: 0.8328751280279528\n", + "CatBoostClassifier: 0.8342715174922052\n", + "ExtraTreesClassifier: 0.8281746031746032\n", + "KNeighborsClassifier: 0.6493313763861709\n", + "SVC: 0.8403098469098905\n", + "SVC: 0.6973119072190279\n", + "XGBClassifier: 0.8134522115571786\n", + "LGBMClassifier: 0.817010631644778\n", + "XGBRFClassifier: 0.8499478840942256\n", + "LGBMClassifier: 0.8132478632478634\n", + "LogisticRegression: 0.8500073681108163\n", + "GaussianNB: 0.8140676625250128\n", + "VotingClassifier: 0.8494440278941667\n" + ] + } + ], + "source": [ + "#посмотрим без warning'оф\n", + "for r in results:\n", + " print(r)" + ] + }, + { + "cell_type": "markdown", + "id": "1f542f4c", + "metadata": {}, + "source": [ + "Максимальное значение 0.8494\n", + "Надо пробовать убирать коррелирующие модели" + ] + }, + { + "cell_type": "markdown", + "id": "a95d780c-3960-47e0-84b1-496a128d5a5b", + "metadata": {}, + "source": [ + "Вы можете заметить, что ансамбль показывает хорошее, но не лучшее качество предсказания, попробуем его улучшить. Как вы знаете, ансамбли работают лучше, когда модели, входящие в них не скоррелированы друг с другом. Определите корреляцию предсказаний базовых моделей в ансамбле на тестовом наборе данных, и удалите из ансамбля те модели, чьи предсказания будут сильнее коррелировать с остальными. Воспользуйтесь функцией `base_model_pair_correlation_for_voting_clf`. **Спойлер**: далеко не факт, что если вы удалите две модели с корреляцией 0.95, то все станет сильно лучше, здесь все будет немного сложнее. Чтобы добиться максимального качества может понадобиться долгий перебор различных комбинаций моделей. Наилучший скор, который мне удалось достичь, это 0.915, но он получен весьма странной комбинацией алгоритмов, а еще и простым перебором всех вариантов)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6b132bb4-e551-4791-88e7-95eaa7df909c", + "metadata": {}, + "outputs": [], + "source": [ + "def base_model_pair_correlation_for_voting_clf(ensemble, X):\n", + " corrs = []\n", + " base_model_names = [f\"{est.__class__.__name__}\" for est in ensemble.estimators_]\n", + " for (i, est1), (j, est2) in combinations(enumerate(ensemble.estimators_), 2):\n", + " Xi_test = X\n", + " Xj_test = X\n", + "\n", + " if not isinstance(est1, SVC):\n", + " ypred_t1 = est1.predict_proba(Xi_test)[:, 1]\n", + " else:\n", + " ypred_t1 = est1.decision_function(Xi_test)\n", + "\n", + "\n", + " if not isinstance(est2, SVC):\n", + " ypred_t2 = est2.predict_proba(Xi_test)[:, 1]\n", + " else:\n", + " ypred_t2 = est2.decision_function(Xi_test)\n", + " corrs.append((est1, est2, pearsonr(ypred_t1, ypred_t2)[0]))\n", + "\n", + "\n", + " return corrs" + ] + }, + { + "cell_type": "markdown", + "id": "9650657e", + "metadata": {}, + "source": [ + "Построим таблицу с корреляциями для двух моделей между собой и отсортируем значения по возрастанию" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "21fb93e4-8065-473c-a11a-cd423baf71d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 118, number of negative: 109\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000094 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 236\n", + "[LightGBM] [Info] Number of data points in the train set: 227, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519824 -> initscore=0.079337\n", + "[LightGBM] [Info] Start training from score 0.079337\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further 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Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 118, number of negative: 109\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000116 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 236\n", + "[LightGBM] [Info] Number of data points in the train set: 227, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519824 -> initscore=0.079337\n", + "[LightGBM] [Info] Start training from score 0.079337\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_fraction=0.7\n" + ] + } + ], + "source": [ + "voting_soft_cls = VotingClassifier(estimators=base_models, voting = 'soft')\n", + "voting_soft_cls.fit(X_train, y_train)\n", + "solf_voting_corr = base_model_pair_correlation_for_voting_clf(voting_soft_cls, X)\n", + "solf_voting_corr = pd.DataFrame(solf_voting_corr, columns=['Model A', 'Model B', 'Corr'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8d69062c-68d8-487b-8b02-2e31221977b9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Model AModel BCorr
0DecisionTreeClassifier(max_depth=10, min_sampl...(DecisionTreeClassifier(max_features='sqrt', r...0.859324
1DecisionTreeClassifier(max_depth=10, min_sampl...(ExtraTreeClassifier(random_state=481767252), ...0.793948
2DecisionTreeClassifier(max_depth=10, min_sampl...KNeighborsClassifier(weights='distance')0.735671
3DecisionTreeClassifier(max_depth=10, min_sampl...SVC(kernel='linear', probability=True, random_...0.812515
4DecisionTreeClassifier(max_depth=10, min_sampl...SVC(probability=True, random_state=111)0.504944
............
73XGBClassifier(base_score=None, booster=None, c...LogisticRegression(max_iter=10000, solver='lib...0.792146
74XGBClassifier(base_score=None, booster=None, c...GaussianNB()0.733600
75XGBRFClassifier(base_score=None, booster=None,...LogisticRegression(max_iter=10000, solver='lib...0.882941
76XGBRFClassifier(base_score=None, booster=None,...GaussianNB()0.814237
77LogisticRegression(max_iter=10000, solver='lib...GaussianNB()0.924957
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" + ], + "text/plain": [ + " Model A \\\n", + "0 DecisionTreeClassifier(max_depth=10, min_sampl... \n", + "1 DecisionTreeClassifier(max_depth=10, min_sampl... \n", + "2 DecisionTreeClassifier(max_depth=10, min_sampl... \n", + "3 DecisionTreeClassifier(max_depth=10, min_sampl... \n", + "4 DecisionTreeClassifier(max_depth=10, min_sampl... \n", + ".. ... \n", + "73 XGBClassifier(base_score=None, booster=None, c... \n", + "74 XGBClassifier(base_score=None, booster=None, c... \n", + "75 XGBRFClassifier(base_score=None, booster=None,... \n", + "76 XGBRFClassifier(base_score=None, booster=None,... \n", + "77 LogisticRegression(max_iter=10000, solver='lib... \n", + "\n", + " Model B Corr \n", + "0 (DecisionTreeClassifier(max_features='sqrt', r... 0.859324 \n", + "1 (ExtraTreeClassifier(random_state=481767252), ... 0.793948 \n", + "2 KNeighborsClassifier(weights='distance') 0.735671 \n", + "3 SVC(kernel='linear', probability=True, random_... 0.812515 \n", + "4 SVC(probability=True, random_state=111) 0.504944 \n", + ".. ... ... \n", + "73 LogisticRegression(max_iter=10000, solver='lib... 0.792146 \n", + "74 GaussianNB() 0.733600 \n", + "75 LogisticRegression(max_iter=10000, solver='lib... 0.882941 \n", + "76 GaussianNB() 0.814237 \n", + "77 GaussianNB() 0.924957 \n", + "\n", + "[78 rows x 3 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Model AModel BCorr
64LGBMClassifier(random_state=111)XGBClassifier(base_score=None, booster=None, c...0.990632
57<catboost.core.CatBoostClassifier object at 0x...LGBMClassifier(random_state=111)0.987400
16(DecisionTreeClassifier(max_features='sqrt', r...<catboost.core.CatBoostClassifier object at 0x...0.983311
59<catboost.core.CatBoostClassifier object at 0x...XGBClassifier(base_score=None, booster=None, c...0.978323
17(DecisionTreeClassifier(max_features='sqrt', r...LGBMClassifier(random_state=111)0.974589
29(ExtraTreeClassifier(random_state=481767252), ...XGBClassifier(base_score=None, booster=None, c...0.970791
12(DecisionTreeClassifier(max_features='sqrt', r...(ExtraTreeClassifier(random_state=481767252), ...0.968670
26(ExtraTreeClassifier(random_state=481767252), ...<catboost.core.CatBoostClassifier object at 0x...0.968586
19(DecisionTreeClassifier(max_features='sqrt', r...XGBClassifier(base_score=None, booster=None, c...0.968047
27(ExtraTreeClassifier(random_state=481767252), ...LGBMClassifier(random_state=111)0.963169
48SVC(kernel='linear', probability=True, random_...LogisticRegression(max_iter=10000, solver='lib...0.962236
60<catboost.core.CatBoostClassifier object at 0x...XGBRFClassifier(base_score=None, booster=None,...0.958406
65LGBMClassifier(random_state=111)XGBRFClassifier(base_score=None, booster=None,...0.948960
20(DecisionTreeClassifier(max_features='sqrt', r...XGBRFClassifier(base_score=None, booster=None,...0.947727
69LGBMClassifier(bagging_fraction=0.7, bagging_f...XGBRFClassifier(base_score=None, booster=None,...0.940418
\n", + "
" + ], + "text/plain": [ + " Model A \\\n", + "64 LGBMClassifier(random_state=111) \n", + "57 initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] 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further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000083 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.8532349609599787\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002121 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000089 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000113 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.8555555555555555\n", + "VotingClassifier: 0.8532349609599787\n", + "VotingClassifier: 0.8555555555555555\n" + ] + } + ], + "source": [ + "\n", + "\n", + "good_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"ETC\", etc), (\"KNN\", knn), \n", + " (\"SVC_LIN\", svc_lin), (\"SVC_RBF\", svc_rbf), \n", + " (\"CAT\", cat), \n", + " (\"LGBM_RF\", lgbm_rf), \n", + " (\"LR\", lr), (\"NB\", nb)]\n", + "\n", + "results = []\n", + "voting_hard = VotingClassifier(estimators=good_models, voting='hard')\n", + "voting_soft = VotingClassifier(estimators=good_models, voting = 'soft')\n", + "\n", + "for model in [voting_soft, voting_hard]: \n", + " scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\"f1\")\n", + " results.append(f\"{model.__class__.__name__}: {scores.mean()}\")\n", + " print(f\"{model.__class__.__name__}: {scores.mean()}\")\n", + "for r in results:\n", + " print(r)" + ] + }, + { + "cell_type": "markdown", + "id": "58903b85", + "metadata": {}, + "source": [ + "Из лучшего:\n", + "```good_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"ETC\", etc), (\"KNN\", knn), \n", + " (\"SVC_LIN\", svc_lin), (\"SVC_RBF\", svc_rbf), \n", + " (\"CAT\", cat), \n", + " (\"LGBM_RF\", lgbm_rf), \n", + " (\"LR\", lr), (\"NB\", nb)]\n", + "С этим набором было 0.8555 \n", + "\n", + "good_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"ETC\", etc), (\"KNN\", knn), \n", + " (\"SVC_LIN\", svc_lin), \n", + " (\"CAT\", cat), \n", + " (\"LGBM_RF\", lgbm_rf), \n", + " (\"LR\", lr), (\"NB\", nb)]\n", + "с этим 0.857 \n", + "\n", + "good_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"SVC_LIN\", svc_lin), \n", + " (\"LGBM_RF\", lgbm_rf)]\n", + " c этим 0.859```\n", + "\n", + "Еще я делала переборы по 2 и 3 классификатора и не добивалась успеха, билась вокруг 0.858, 0.859 " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2ffe276d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000101 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000083 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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-inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000071 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000094 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000095 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000127 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.86\n", + "VotingClassifier: 0.84\n" + ] + } + ], + "source": [ + "good_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"SVC_LIN\", svc_lin), \n", + " (\"LGBM_RF\", lgbm_rf)]\n", + "\n", + "results = []\n", + "voting_hard = VotingClassifier(estimators=good_models, voting='hard')\n", + "voting_soft = VotingClassifier(estimators=good_models, voting = 'soft')\n", + "\n", + "for model in [voting_soft, voting_hard]: \n", + " scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\"f1\")\n", + " results.append(f\"{model.__class__.__name__}: {round(scores.mean(), 2)}\")\n", + " #print(f\"{model.__class__.__name__}: {scores.mean()}\")\n", + "for r in results:\n", + " print(r)" + ] + }, + { + "cell_type": "markdown", + "id": "7818c2e1", + "metadata": {}, + "source": [ + "Чтобы просто увидеть 0.86 я сделаю вот так, это стоило мне слишком много времени, и сил на решение дз дальше что-то не хватило" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "fdf83683", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000157 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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-inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000089 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000113 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000254 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000085 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.86\n", + "VotingClassifier: 0.86\n" + ] + } + ], + "source": [ + "good_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"ETC\", etc), (\"KNN\", knn), \n", + " (\"SVC_LIN\", svc_lin), \n", + " (\"CAT\", cat), \n", + " (\"LGBM_RF\", lgbm_rf), \n", + " (\"LR\", lr), (\"NB\", nb)]\n", + "results = []\n", + "voting_hard = VotingClassifier(estimators=good_models, voting='hard')\n", + "voting_soft = VotingClassifier(estimators=good_models, voting = 'soft')\n", + "\n", + "for model in [voting_soft, voting_hard]: \n", + " scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\"f1\")\n", + " results.append(f\"{model.__class__.__name__}: {round(scores.mean(), 2)}\")\n", + " #print(f\"{model.__class__.__name__}: {scores.mean()}\")\n", + "for r in results:\n", + " print(r) " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "cbd8cb57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000084 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000078 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000069 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits 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gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.8532349609599787\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 72\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000101 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.523179 -> initscore=0.092782\n", + "[LightGBM] [Info] Start training from score 0.092782\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 78, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000100 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 193\n", + "[LightGBM] [Info] Number of data points in the train set: 151, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.516556 -> initscore=0.066249\n", + "[LightGBM] [Info] Start training from score 0.066249\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "[LightGBM] [Info] Number of positive: 79, number of negative: 73\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000071 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Info] Number of data points in the train set: 152, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.519737 -> initscore=0.078988\n", + "[LightGBM] [Info] Start training from score 0.078988\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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Current value: bagging_freq=1\n", + "[LightGBM] [Warning] bagging_fraction is set=0.7, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7\n", + "VotingClassifier: 0.8555555555555555\n", + "VotingClassifier: 0.86\n", + "VotingClassifier: 0.86\n", + "VotingClassifier: 0.8532349609599787\n", + "VotingClassifier: 0.8555555555555555\n" + ] + } + ], + "source": [ + "good_models = [(\"DT\", dt), (\"RF\", rf), \n", + " (\"ETC\", etc), (\"KNN\", knn), \n", + " (\"SVC_LIN\", svc_lin), (\"SVC_RBF\", svc_rbf), \n", + " (\"CAT\", cat), \n", + " (\"LGBM_RF\", lgbm_rf), \n", + " (\"LR\", lr), (\"NB\", nb)]\n", + "\n", + "\n", + "voting_hard = VotingClassifier(estimators=good_models, voting='hard')\n", + "voting_soft = VotingClassifier(estimators=good_models, voting = 'soft')\n", + "\n", + "for model in [voting_soft, voting_hard]: \n", + " scores = cross_val_score(model, X_train, y_train, cv=3, scoring=\"f1\")\n", + " results.append(f\"{model.__class__.__name__}: {scores.mean()}\")\n", + " print(f\"{model.__class__.__name__}: {scores.mean()}\")\n", + "for r in results:\n", + " print(r)" + ] + }, + { + "cell_type": "markdown", + "id": "054b2673-1725-48a9-ae6a-4e348d50169d", + "metadata": {}, + "source": [ + "### Задание 4. Определение оттока клиентов из телекома\n", + "\n", + "**6 баллов + 7 дополнительных за высокое качество модели и различные эксперименты**\n", + "\n", + "Будем предсказывать, уйдет ли от нас клиент (переменная `Churn?`). Данные можно скачать [здесь](https://www.kaggle.com/venky12347/churn-telecom). Это будет уже совсем взрослое задание, так как правильного ответа на него нет. Вам нужно будет разобраться с данными, правильно подготовить их для моделей, а также выбрать лучшую модель. \n", + "\n", + "Задача минимум:\n", + "\n", + "Выберите 2 модели — один случайный лес и один бустинг из приведенных ниже:\n", + "\n", + "1. `xgboost.XGBClassifier`\n", + "2. `xgboost.XGBRFClassifier` — случайный лес от xgboost\n", + "3. `lightgbm.LGBMClassifier`\n", + "4. `lightgbm.LGBMClassifier(boosting_type=\"rf\")` — случайный лес от lightgbm\n", + "5. `catboost.CatBoostClassifier`\n", + "\n", + "И попробуйте разобраться с тем, как для этих моделей правильно настраивать гиперпараметры. Советую гуглить примерно следующее `how to choose best hyperparameters for lightgbm`. Там вы найдете кучу сложного и непонятного кода, но если с ним разобраться и научиться обучать нестандартные бустинги, то в плане табличных данных равных вам не будет)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "47cd58e0-3c7b-4dc6-8eba-617eec699586", + "metadata": {}, + "outputs": [], + "source": [ + "#data = pd.read_csv(\"churn.csv\")\n", + "#data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "691d25ae-0bf3-4ca6-bc71-3ec9964f9906", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "aed4b39e-db2b-4891-9c83-26cf78bcba8c", + "metadata": {}, + "source": [ + "### Задание 5. Рисуем\n", + "\n", + "**дополнительно 0.5 балла**\n", + "\n", + "Наверняка, в процессе выполнения этого задания вас переполняли какие-то эмоции. Нарисуйте что-то, что бы могло бы передать их (я сам не умею, так что, если это будет просто квадрат, тоже подойдет). Прикрепите сюда свой рисунок:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "17b2bb34-4dd9-44ab-b441-e5132ea5039b", + "metadata": {}, + "outputs": [], + "source": [ + "## PASTE YOUR MASTERPIECE HERE (/¯◡ ‿ ◡)/¯☆*##" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e1434723-a8b2-4311-a786-6a76dd1b1526", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5, 5))\n", + "\n", + "plt.text(0.5, 0.5, 'help me', size=50, ha='center')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e6c52141-d648-49a7-8c2a-06724d3e88af", + "metadata": {}, + "source": [ + "### Therapy time\n", + "\n", + "Напишите здесь ваши впечатления о задании (можно и не о задании): было ли интересно, было ли слишком легко или наоборот сложно и тд. Также сюда можно написать свои идеи по улучшению заданий, а также предложить данные, на основе которых вы бы хотели построить следующие дз.\n", + "\n", + "**Ваши мысли:**" + ] + }, + { + "cell_type": "markdown", + "id": "e3db0b95-89ca-48a2-9600-53a28090b241", + "metadata": {}, + "source": [ + "Очень, интересно, но подбор комбинаций для классификаторов ранил, ранил и убил" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ML", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/HW5/data/heart.csv b/HW5/data/heart.csv new file mode 100644 index 0000000..08b5462 --- /dev/null +++ b/HW5/data/heart.csv @@ -0,0 +1,304 @@ 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