diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb index 6b9bd7d..20ba785 100644 --- a/lab_2/lab2.ipynb +++ b/lab_2/lab2.ipynb @@ -370,6 +370,319 @@ " plt.show() # Отображение графика" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Функция для создания выборок\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "def split_stratified_into_train_val_test(\n", + " data_input,\n", + " stratify_colname=\"y\",\n", + " frac_train=0.6,\n", + " frac_val=0.15,\n", + " frac_test=0.25,\n", + " random_state=None,\n", + "):\n", + " \"\"\"\n", + " Splits a Pandas dataframe into three subsets (train, val, and test)\n", + " following fractional ratios provided by the user, where each subset is\n", + " stratified by the values in a specific column (that is, each subset has\n", + " the same relative frequency of the values in the column). It performs this\n", + " splitting by running train_test_split() twice.\n", + "\n", + " Parameters\n", + " ----------\n", + " data_input : Pandas dataframe\n", + " Input dataframe to be split.\n", + " stratify_colname : str\n", + " The name of the column that will be used for stratification. Usually\n", + " this column would be for the label.\n", + " frac_train : float\n", + " frac_val : float\n", + " frac_test : float\n", + " The ratios with which the dataframe will be split into train, val, and\n", + " test data. The values should be expressed as float fractions and should\n", + " sum to 1.0.\n", + " random_state : int, None, or RandomStateInstance\n", + " Value to be passed to train_test_split().\n", + "\n", + " Returns\n", + " -------\n", + " data_train, data_val, data_test :\n", + " Dataframes containing the three splits.\n", + " \"\"\"\n", + "\n", + " if frac_train + frac_val + frac_test != 1.0:\n", + " raise ValueError(\n", + " \"fractions %f, %f, %f do not add up to 1.0\"\n", + " % (frac_train, frac_val, frac_test)\n", + " )\n", + "\n", + " if stratify_colname not in data_input.columns:\n", + " raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n", + "\n", + " X = data_input # Contains all columns.\n", + " y = data_input[\n", + " [stratify_colname]\n", + " ] # Dataframe of just the column on which to stratify.\n", + "\n", + " # Split original dataframe into train and temp dataframes.\n", + " data_train, data_temp, y_train, y_temp = train_test_split(\n", + " X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n", + " )\n", + "\n", + " # Split the temp dataframe into val and test dataframes.\n", + " relative_frac_test = frac_test / (frac_val + frac_test)\n", + " data_val, data_test, y_val, y_test = train_test_split(\n", + " data_temp,\n", + " y_temp,\n", + " stratify=y_temp,\n", + " test_size=relative_frac_test,\n", + " random_state=random_state,\n", + " )\n", + "\n", + " assert len(data_input) == len(data_train) + len(data_val) + len(data_test)\n", + "\n", + " return data_train, data_val, data_test" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hazardous\n", + "False 81996\n", + "True 8840\n", + "Name: count, dtype: int64\n", + "\n", + "Обучающая выборка: (54501, 6)\n", + "hazardous\n", + "False 49197\n", + "True 5304\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Контрольная выборка: (18167, 6)\n", + "hazardous\n", + "False 16399\n", + "True 1768\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Тестовая выборка: (18168, 6)\n", + "hazardous\n", + "False 16400\n", + "True 1768\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAADECAYAAABp29OTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5wElEQVR4nO3dd1xT1/sH8E8SQgJhhi0OkKEoThTrQNCqiFjFqrRq6/riqLWtVmtr+1VxtPxad922VVGsrava2jqrVrFW6wBFUZHhqrIUhEAISc7vD5p8CWELXEye9+uVl+Zy7rnPvbk5zx3n3PAYYwyEEEIIeanxuQ6AEEIIIS+OEjohhBBiACihE0IIIQaAEjohhBBiACihE0IIIQaAEjohhBBiACihE0IIIQaAEjohhBBiACihE0IIMRp5eXlIS0uDTCbjOpQ6RwmdkEZg/PjxsLCw4DqMOhMZGQkej8d1GKSB5OfnY9WqVdr3OTk5WLduHXcBlcIYw+bNm/HKK6/A3NwcVlZWcHd3R0xMDNeh1bkaJfRt27aBx+NpX2KxGN7e3pg+fTrS09PrK0ZCCCGNmJmZGf773/9i586dePDgASIjI/HLL79wHRYAYPTo0Zg6dSp8fHywY8cOHD9+HCdOnMDrr7/OdWh1zqQ2My1atAju7u6Qy+WIjY3Fhg0b8NtvvyEhIQHm5uZ1HSMhhJBGTCAQYOHChRg7dizUajWsrKzw66+/ch0Wtm/fjh9//BExMTEYPXo01+HUu1ol9JCQEHTp0gUAEBERATs7O6xYsQIHDx7EqFGj6jRAQkjjo1QqoVarYWpqynUopJGYNWsW3njjDTx48AA+Pj6wsbHhOiQsXboUo0aNMopkDtTRPfS+ffsCAFJTUwEAT58+xezZs9GuXTtYWFjAysoKISEhiI+P15tXLpcjMjIS3t7eEIvFcHFxweuvv47k5GQAQFpams5l/rKvoKAgbV2nT58Gj8fDjz/+iE8//RTOzs6QSCQYMmQIHjx4oLfsCxcuYODAgbC2toa5uTkCAwNx7ty5ctcxKCio3OVHRkbqlY2JiYGfnx/MzMwglUrx5ptvlrv8ytatNLVajVWrVqFt27YQi8VwcnLClClT8OzZM51ybm5uGDx4sN5ypk+frldnebEvXbpUb5sCQFFRERYsWABPT0+IRCI0a9YMc+bMQVFRUbnbqrSgoCC9+j7//HPw+Xx8//33tdoey5YtQ48ePWBnZwczMzP4+flh79695S4/JiYG/v7+MDc3h62tLXr37o1jx47plDl8+DACAwNhaWkJKysrdO3aVS+2PXv2aD9Te3t7vPXWW3j06JFOmfHjx+vEbGtri6CgIJw9e7bK7aTx6NEjhIWFwcLCAg4ODpg9ezZUKlWN179sLOXtswqFAvPnz4efnx+sra0hkUgQEBCAU6dO6dSl+VyWLVuGVatWwcPDAyKRCDdv3gQAxMbGomvXrhCLxfDw8MCmTZvKXTelUonFixdr53dzc8Onn36qtx9V9L1yc3PD+PHjte+Li4uxcOFCeHl5QSwWw87ODr169cLx48cr3cZlbx2am5ujXbt2+Pbbb2s0X3mvbdu2acvfunULI0aMgFQqhVgsRpcuXfDzzz/r1ZuTk4OZM2fCzc0NIpEITZs2xdixY5GVlaVt0yp7ld5WV69eRUhICKysrGBhYYFXX30Vf/31V63X/+TJkwgICIBEIoGNjQ2GDh2KxMREnTKl+0s0bdoU3bt3h4mJCZydncHj8XD69OlKt6tmfs3L0tIS/v7+OHDggE65oKAg+Pr6VliPZj/VfAYymQwJCQlo1qwZQkNDYWVlBYlEUuF3MiUlBSNHjoRUKoW5uTleeeUVvasMNckxNWn7apKLKlOrM/SyNMnXzs4OQMmGOXDgAEaOHAl3d3ekp6dj06ZNCAwMxM2bN9GkSRMAgEqlwuDBg/H777/jzTffxAcffIC8vDwcP34cCQkJ8PDw0C5j1KhRGDRokM5y586dW248n3/+OXg8Hj7++GNkZGRg1apV6NevH+Li4mBmZgagZEcNCQmBn58fFixYAD6fj61bt6Jv3744e/Ys/P399ept2rQpoqKiAJR0AnnnnXfKXfa8efMQHh6OiIgIZGZmYs2aNejduzeuXr1a7lHr5MmTERAQAADYv38/fvrpJ52/T5kyBdu2bcOECRPw/vvvIzU1FWvXrsXVq1dx7tw5CIXCcrdDTeTk5GjXrTS1Wo0hQ4YgNjYWkydPho+PD65fv46VK1fizp07el+6qmzduhX//e9/sXz58gqPmqvaHqtXr8aQIUMwZswYKBQK/PDDDxg5ciQOHTqE0NBQbbmFCxciMjISPXr0wKJFi2BqaooLFy7g5MmTGDBgAICSxm3ixIlo27Yt5s6dCxsbG1y9ehVHjhzRxqfZ9l27dkVUVBTS09OxevVqnDt3Tu8ztbe3x8qVKwEADx8+xOrVqzFo0CA8ePCgyjMWlUqF4OBgdOvWDcuWLcOJEyewfPlyeHh46Oxr1Vn/KVOmoF+/fjr1HzlyBDt37oSjoyMA4Pnz5/j2228xatQoTJo0CXl5efjuu+8QHByMixcvomPHjnqfnVwux+TJkyESiSCVSnH9+nUMGDAADg4OiIyMhFKpxIIFC+Dk5KS3fhEREYiOjsaIESMwa9YsXLhwAVFRUUhMTNT7jKsjMjISUVFRiIiIgL+/P54/f45Lly7hypUr6N+/f5Xzr1y5Evb29nj+/Dm2bNmCSZMmwc3NTW+7afTu3Rs7duzQvv/8888BAJ999pl2Wo8ePQAAN27cQM+ePeHq6opPPvkEEokEu3fvRlhYGPbt24dhw4YBKGlHAgICkJiYiIkTJ6Jz587IysrCzz//jIcPH2rv+2ps3rwZiYmJ2n0MANq3b69dZkBAAKysrDBnzhwIhUJs2rQJQUFB+OOPP9CtW7carf+JEycQEhKCli1bIjIyEoWFhVizZg169uyJK1euwM3NrcJtu3z58hr3q9KsZ1ZWFtavX4+RI0ciISEBrVq1qlE9GtnZ2QCAL7/8Es7Ozvjoo48gFovxzTffoF+/fjh+/Dh69+4NAEhPT0ePHj1QUFCA999/H3Z2doiOjsaQIUOwd+9e7eelUZ0cU1ZFbV9tclGFWA1s3bqVAWAnTpxgmZmZ7MGDB+yHH35gdnZ2zMzMjD18+JAxxphcLmcqlUpn3tTUVCYSidiiRYu007Zs2cIAsBUrVugtS61Wa+cDwJYuXapXpm3btiwwMFD7/tSpUwwAc3V1Zc+fP9dO3717NwPAVq9era3by8uLBQcHa5fDGGMFBQXM3d2d9e/fX29ZPXr0YL6+vtr3mZmZDABbsGCBdlpaWhoTCATs888/15n3+vXrzMTERG96UlISA8Cio6O10xYsWMBKfyxnz55lANjOnTt15j1y5Ije9BYtWrDQ0FC92N99911W9qMuG/ucOXOYo6Mj8/Pz09mmO3bsYHw+n509e1Zn/o0bNzIA7Ny5c3rLKy0wMFBb36+//spMTEzYrFmzyi1bne3BWMnnVJpCoWC+vr6sb9++OnXx+Xw2bNgwvX1R85nn5OQwS0tL1q1bN1ZYWFhuGYVCwRwdHZmvr69OmUOHDjEAbP78+dpp48aNYy1atNCpZ/PmzQwAu3jxYrnrXHpeADrfD8YY69SpE/Pz86vx+peVlJTErK2tWf/+/ZlSqWSMMaZUKllRUZFOuWfPnjEnJyc2ceJE7TTNd9DKyoplZGTolA8LC2NisZjdu3dPO+3mzZtMIBDofG5xcXEMAIuIiNCZf/bs2QwAO3nypHZa2X1To0WLFmzcuHHa9x06dCh3f6+Kph1LTU3VTrtz5w4DwL766qtq11N63y7r1VdfZe3atWNyuVw7Ta1Wsx49ejAvLy/ttPnz5zMAbP/+/Xp1lG6bNMrbxzTCwsKYqakpS05O1k77559/mKWlJevdu7d2WnXXv2PHjszR0ZFlZ2drp8XHxzM+n8/Gjh2rnVb2O5qRkcEsLS1ZSEgIA8BOnTpVbrwVzc8YY8eOHWMA2O7du7XTAgMDWdu2bSusR7Ofbt26Vee9qakpu3PnjrZcZmYms7Oz0/lezZgxgwHQaefy8vKYu7s7c3Nz07Yh1c0xmniravtqk4sqU6tL7v369YODgwOaNWuGN998ExYWFvjpp5/g6uoKABCJRODzS6pWqVTIzs6GhYUFWrVqhStXrmjr2bdvH+zt7fHee+/pLeNFhryMHTsWlpaW2vcjRoyAi4sLfvvtNwBAXFwckpKSMHr0aGRnZyMrKwtZWVmQyWR49dVXcebMGajVap065XI5xGJxpcvdv38/1Go1wsPDtXVmZWXB2dkZXl5eepcyFQoFgJLtVZE9e/bA2toa/fv316nTz88PFhYWenUWFxfrlMvKyoJcLq807kePHmHNmjWYN2+e3tCpPXv2wMfHB61bt9apU3ObpezyK3Lx4kWEh4dj+PDhWLp0abllqrM9AOgcAT979gy5ubkICAjQ2bcOHDgAtVqN+fPna/dFDc2+dfz4ceTl5eGTTz7R+2w1ZS5duoSMjAxMmzZNp0xoaChat26td0lOrVZrt1FcXBy2b98OFxcX+Pj4VLpOGlOnTtV5HxAQgJSUlBqvf2kymQzDhg2Dra0tdu3aBYFAAKCkI5PmHrharcbTp0+hVCrRpUuXcusaPnw4HBwctO9VKhWOHj2KsLAwNG/eXDvdx8cHwcHBOvNqvnsffvihzvRZs2YBQK06UNnY2ODGjRtISkqq8bxAybbLyspCSkoKVq5cCYFAgMDAwFrVVdrTp09x8uRJhIeHIy8vT7s/ZGdnIzg4GElJSdrbNfv27UOHDh30zgCBmrWBKpUKx44dQ1hYGFq2bKmd7uLigtGjRyM2NhbPnz/Xmaey9X/8+DHi4uIwfvx4SKVS7Tzt27dH//79tZ9neRYvXgxra2u8//771Y4fgHY7JSYmYuPGjZBIJHjllVf01lNTTtNeVGXo0KHw8vLSvre3t8f48eNx+fJl7VWE3377Df7+/ujVq5e2nIWFBSZPnoy0tDTt7SWNqnJMaZW1fbXJRZWp1SX3devWwdvbGyYmJnByckKrVq10Gk21Wo3Vq1dj/fr1SE1N1bkHqLksD5Rcqm/VqhVMTOrkyr9W6Q8PKPlieHp6Ii0tDQC0DcC4ceMqrCM3Nxe2trba91lZWXr1lpWUlATGWIXlyl4az8nJAYBKxx8nJSUhNzdXe5m0rIyMDJ33x44d02l0q2PBggVo0qQJpkyZoncvNikpCYmJiRXWWXb55Xn06BFCQ0Mhk8mQnZ1dYUNVne0BAIcOHcKSJUsQFxenc/+1dL3Jycng8/lo06ZNhfVobhVVdl/u3r17AFDuZb/WrVsjNjZWZ9qDBw90tpWLiwv27dtXrTHmYrFYbzvb2trq9ZWozvqXNmnSJCQnJ+PPP//U+f4BQHR0NJYvX45bt26huLhYO93d3V2vnrLTMjMzUVhYWO7+3qpVK53G7d69e+Dz+fD09NQp5+zsDBsbG+12rolFixZh6NCh8Pb2hq+vLwYOHIi3335bewm6Kp07d9b+XyQSYe3atTW7vFmBu3fvgjGGefPmYd68eeWWycjIgKurK5KTkzF8+PAXXmZmZiYKCgrK3U99fHygVqvx4MEDtG3bVju9svWvbL/38fHB0aNHIZPJIJFIdP6WmpqKTZs2YcOGDVWeAJVVet+3srLCzp070axZM50yt27d0pbT7E8LFiwo9/ad5vvQunXrctcBKLnv7uTkhHv37undkihd7t69ezrtRFU5RqOqtq82uagytcqk/v7+2l7u5fniiy8wb948TJw4EYsXL4ZUKgWfz8eMGTNqdLRRXzQxLF26VO8+oUbpBlihUODx48dV3pdTq9Xg8Xg4fPiw9iyoojoB4MmTJwBKGrXK6nR0dMTOnTvL/XvZBNCtWzcsWbJEZ9ratWtx8ODBcudPTEzEtm3bEBMTU+69eLVajXbt2mHFihXlzl/2C1eeu3fvonPnzli5ciXefvttREdHl7sDV2d7nD17FkOGDEHv3r2xfv16uLi4QCgUYuvWrXodTbjg5OSkfWBFbm4utmzZgoEDByI2Nhbt2rWrdN7y9pmyarr+q1evxq5duxATE6O3r8fExGD8+PEICwvDRx99BEdHRwgEAkRFRWkPdkqr6N5gTbzIlbeynQN79+6N5ORkHDx4EMeOHcO3336LlStXYuPGjYiIiKiyvpiYGDg5OUEul+PkyZN49913IRaLdTre1YamfZk9e7belQqNsgc2XKiP9f/ss8/g5eWFcePG1agzKABtZ0aZTIZ9+/YhPDwchw4d0ml33dzc8M033wAouUf+9ddf4+2330bLli312o262F9fVFVtX01zUVXq9tT4X3v37kWfPn3w3Xff6UzPycmBvb299r2HhwcuXLiA4uLiOunYpVH2EhxjDHfv3tUeuWs621lZWVXYAaa0+Ph4FBcXV3oQo6mXMQZ3d3d4e3tXWe/NmzfB4/Eq7fTh4eGBEydOoGfPntXaQe3t7fXWqbKOa3PnzkXHjh3xxhtvVLj8+Ph4vPrqq7VujDWXopycnHDw4EHMmjULgwYN0jsYqc722LdvH8RiMY4ePapzaX7r1q16cavVaty8ebPCL4pmP0hISKiwgW3RogUA4Pbt29rbDBq3b9/W/l1DLBbrbP8hQ4ZAKpVi7dq1Ffb+ronqrj9Qkvxnz56NGTNmYMyYMXp/37t3L1q2bIn9+/frfLYLFiyoViwODg4wMzMr95L37du3dd63aNECarUaSUlJOrcf0tPTkZOTo7MdbW1ttVdrNDQH1WVJpVJMmDABEyZMQH5+Pnr37o3IyMhqJfSePXtqO3YNHjwYN27cQFRU1AsndM0lb6FQWGX74uHhgYSEhBdaHlDyWZibm+ttd6DkrJbP5+sdfFe2/qX3+/Lqs7e31zs7v3r1Kn744QccOHCgWgenZZXeVkOHDsWFCxewbNkynYQukUh0ygUEBMDV1RXHjh3D2LFjdeqzt7eHhYVFhesAQLv+LVq0qLRc2e95VTlGo6q2r6a5qCr18uhXgUAAxpjOtD179ugN8xk+fDiysrKwdu1avTrKzl8T27dvR15envb93r178fjxY4SEhAAA/Pz84OHhgWXLliE/P19v/szMTL3YBQJBuUPCSnv99de1D1goGz9jTNvrEigZwrNv3z74+/tXegQWHh4OlUqFxYsX6/1NqVTqNXw1cf78eRw8eBD/93//V2GyDg8Px6NHj7RHxaUVFhZW63nI3t7e2l7Pa9asgVqtxgcffKBTprrbQyAQgMfj6ZytpaWl6R20hIWFgc/nY9GiRXpXhTSfzYABA2BpaYmoqCi9fgaaMl26dIGjoyM2btyoc3n78OHDSExM1OlVXx6FQgGlUlmtIX7VUd31f/z4McLDw9GrV68K+yxoGt3S++qFCxdw/vz5ascSHByMAwcO4P79+9rpiYmJOHr0qE5ZzQiV0o8HBaC98lN6O3p4eODMmTM65TZv3qx3hl76+wSUnMl4enrWelsXFhbWyefk6OiIoKAgbNq0qdyDkNLty/DhwxEfH19uL/+atIECgQADBgzAwYMHdS77pqen4/vvv0evXr1gZWVVaR2l19/FxQUdO3ZEdHS0ThuTkJCAY8eO6Y04AoBPPvkEPXv2xJAhQ6odd0VUKhUUCkWVn4fmu13eAQSfz8fAgQNx8OBB7ZBqoKSPQ3R0NLp06aJtlwYNGoSLFy/q7PsymQybN2+Gm5ub3q27qnKMRlVtX01zUVXq5Qx98ODBWLRoESZMmIAePXrg+vXr2Llzp05nDaCkY8H27dvx4Ycf4uLFiwgICIBMJsOJEycwbdo0DB06tFbLl0ql6NWrFyZMmID09HSsWrUKnp6emDRpEoCSD/rbb79FSEgI2rZtiwkTJsDV1RWPHj3CqVOnYGVlhV9++QUymQzr1q3D119/DW9vb53xlJqNf+3aNZw/fx7du3eHh4cHlixZgrlz5yItLQ1hYWGwtLREamoqfvrpJ0yePBmzZ8/GiRMnMG/ePFy7dq3KxyMGBgZiypQpiIqKQlxcHAYMGAChUIikpCTs2bMHq1evxogRI2q1nY4dO4b+/ftXemT49ttvY/fu3Zg6dSpOnTqFnj17QqVS4datW9i9ezeOHj1a5ZWL0pydnbF06VJERETgrbfewqBBg2q0PUJDQ7FixQoMHDgQo0ePRkZGBtatWwdPT09cu3ZNW87T0xOfffYZFi9ejICAALz++usQiUT4+++/0aRJE0RFRcHKygorV65EREQEunbtitGjR8PW1hbx8fEoKChAdHQ0hEIhvvzyS0yYMAGBgYEYNWqUdtiam5sbZs6cqROfTCbTueS+Y8cOyOXycjs91UZ11//9999HZmYm5syZgx9++EGnjvbt26N9+/YYPHgw9u/fj2HDhiE0NBSpqanYuHEj2rRpU27jUp6FCxfiyJEjCAgIwLRp06BUKrFmzRq0bdtWJ54OHTpg3Lhx2Lx5M3JychAYGIiLFy8iOjoaYWFh6NOnj7ZsREQEpk6diuHDh6N///6Ij4/H0aNHda7uAUCbNm0QFBQEPz8/SKVSXLp0CXv37sX06dOrFfuBAwdgb2+vveR89uxZzJgxo1rzVmXdunXo1asX2rVrh0mTJqFly5ZIT0/H+fPn8fDhQ+0zOT766CPs3bsXI0eOxMSJE+Hn54enT5/i559/xsaNG9GhQ4dqL3PJkiU4fvw4evXqhWnTpsHExASbNm1CUVERvvrqqxqv/9KlSxESEoLu3bvjP//5j3bYmrW1dbnPCTh27Fitxk5raL43MpkMBw4cQFpamt7nkZ+fjyNHjgAoScxff/01hEJhhQfWixYtwpEjR7TbRCQS4ZtvvkFubi6WL1+uLffJJ59g165dCAkJwfvvvw+pVIro6GikpqZi3759eh1rq8ox5Smv7atuLqq2mnSJ1wx3+PvvvystJ5fL2axZs5iLiwszMzNjPXv2ZOfPny93mEdBQQH77LPPmLu7OxMKhczZ2ZmNGDFCO/SiNsPWdu3axebOncscHR2ZmZkZCw0N1RlWo3H16lX2+uuvMzs7OyYSiViLFi1YeHg4+/3333WWXdWr9FAaxhjbt28f69WrF5NIJEwikbDWrVuzd999l92+fZsxxth7773HevfuzY4cOaIXU3lDOBgrGf7k5+fHzMzMmKWlJWvXrh2bM2cO++eff7RlajpsjcfjscuXL+tML+8zUigU7Msvv2Rt27ZlIpGI2draMj8/P7Zw4UKWm5urt7yq6mOMsb59+7LmzZuzvLy8Gm+P7777jnl5eTGRSMRat27Ntm7dWuF227JlC+vUqZM27sDAQHb8+HGdMj///DPr0aMHMzMzY1ZWVszf35/t2rVLp8yPP/6orUcqlbIxY8Zoh2lqaIaeaV4WFhasc+fObMeOHZVuI828EomkztY/MDCwwv1VMyRMrVazL774grVo0YKJRCLWqVMndujQIb2hUZV9Bxlj7I8//mB+fn7M1NSUtWzZkm3cuLHcuIuLi9nChQu13/VmzZqxuXPn6gztYowxlUrFPv74Y2Zvb8/Mzc1ZcHAwu3v3rt6wtSVLljB/f39mY2PDzMzMWOvWrdnnn3/OFApFpdta045pXqampszT05PNnz9fL5bKVDZsjTHGkpOT2dixY5mzszMTCoXM1dWVDR48mO3du1enXHZ2Nps+fTpzdXVlpqamrGnTpmzcuHEsKytLr87Khq0xxtiVK1dYcHAws7CwYObm5qxPnz7szz//rPX6nzhxgvXs2VP73XjttdfYzZs3dcpoPuuhQ4fqTNe0x9UdtqZ5mZmZsTZt2rCVK1fqDOUqu0/b2Niwnj17ssOHDzPG9Ietld0mEomEmZubs6CgIL1huIyVfF4jRoxgNjY2TCwWM39/f3bo0KFy16k6OaY6bZ9GVbmouniMvcC17Ubm9OnT6NOnD/bs2VPrs9bS0tLS4O7ujtTU1AofohAZGYm0tDSdJ0QRQggxPHWdY+oa/XwqIYQQYgDq5R66obCwsMCYMWMq7aTVvn177aNsCSGEEK5QQq+Evb29tqNGRQzxN3UJIYS8fAzqHjohhBBirOgeOiGEEGIAKKETQgghBoASOiGEEGIAKKETQgghBoASOiGEEGIAKKETQgghBoASOiGEEGIAKKETQgghBoASOiGEEGIAKKETQgghBoASOiGkxng8XqWvyMhIrkMkxOjQj7MQQmrs8ePH2v//+OOPmD9/Pm7fvq2dVvoXChljUKlUMDGh5oaQ+kRn6ISQGnN2dta+rK2twePxtO9v3boFS0tLHD58GH5+fhCJRIiNjcX48eMRFhamU8+MGTMQFBSkfa9WqxEVFQV3d3eYmZmhQ4cO2Lt3b8OuHCEvKTpkJoTUi08++QTLli1Dy5YtYWtrW615oqKiEBMTg40bN8LLywtnzpzBW2+9BQcHBwQGBtZzxIS83CihE0LqxaJFi9C/f/9qly8qKsIXX3yBEydOoHv37gCAli1bIjY2Fps2baKETkgVKKETQupFly5dalT+7t27KCgo0DsIUCgU6NSpU12GRohBooROCKkXEolE5z2fzwdjTGdacXGx9v/5+fkAgF9//RWurq465UQiUT1FSYjhoIROCGkQDg4OSEhI0JkWFxcHoVAIAGjTpg1EIhHu379Pl9cJqQVK6IS85IqUKqTnFiEzvwhZmleeArmFxVCq1VCqGb4QbgEEIkAgBExEJS/r5oC9J2DvDYgs6z3Ovn37YunSpdi+fTu6d++OmJgYJCQkaC+nW1paYvbs2Zg5cybUajV69eqF3NxcnDt3DlZWVhg3bly9x0jIy4wSOiEvkce5hbjx6DluPXmOxCd5uPX4OdKyC6BSs0rn+0K8pfKKLZwBe69/X96A3b//t2kO8Hh1EntwcDDmzZuHOXPmQC6XY+LEiRg7diyuX7+uLbN48WI4ODggKioKKSkpsLGxQefOnfHpp5/WSQyEGDIeK3tTixDSaOQXKfHn3SycTcrC2aRMpGUX1KqeNPHo2gVg4Qx49gO8+gEefQGxde3qIYTUO0rohDQyGXlyHIp/jMMJj3H1fg6UVZx9V0etE3ppfBOgeXegzVCg7TBAYv/idRJC6gwldEIaAYVSjWM3n2D3pYc4dzerykvoNVUnCb00ngBw7w34Dgd8XwdMJVXPQwipV5TQCeHQU5kCW8+lIuave3hWUFz1DLVU5wm9NHM7oPt0wH8yILKoujwhpF5QQieEA49zC7HpjxT8+PcDFBar6n159ZrQNcykwCvTgG5TALFV/S+PEKKDEjohDehxbiFWHU/C/qsPUaxquK9egyR0DbEN8Mo7QLepgJlNwy2XECNHCZ2QBlCkVOGbMylYdyq5Qc7Iy2rQhK4hsi45W+8+DTCr3o+zEEJqjxI6IfXs+M10LD50E/ef1m7IWV3gJKFrmNsBg5aWdKAjhNQbSuiE1JOMPDk+2XcdJ29lcB0Ktwldw2cIELoCsHDgOhJCDBIldELqwYmb6fh43zVkyxRchwKgkSR0ADCT4vaQg2jl057rSAgxOHyuAyDEkMiLVZh3IAER2y81mmTemGTadkBw9APM2RuPAoWS63AIMSh0hk5IHbmbkY93Yi4jKSOf61D0NIYzdLW5PYKLvkSSzAwA4OlogS3juqK5nTnHkRFiGOgMnZA68MedTAxbf65RJvPGYp3lB9pkDpQcAIWtP4eLqU85jIoQw0EJnZAXtOOve5i47W/kyekSckXuNhuO5fc89KY/lSnw1rcXsPfyQw6iIsSwUEInpJYYY/jyyC3MO5BQ589eNyTF1i3xxr0hFf5doVJj9p54fHXkVgNGRYjhod9DJ6SW5h1MQMxf97kOo1FjfBN8ypuObIWwyrLrTydDXqzG/NfaNEBkhBgeOkMnpBYW/nKDknk1/OU6EXueOFe7/JZzqVh86GY9RkSI4aKETkgNRf2WiK3n0rgOo9HLd+iE8cm9azzfd7GpWEJJnZAao4ROSA0sP3Ybm86kcB1Go8eEEkzJn4wide2amG9jU/El3VMnpEboHjoh1RTz1z2sOXmX6zCqpC4qQM7ZGBQknYe6IBemji3x9yAVuroKyi0//kAhouP1f4u9jQMfN6aV/L75zmvF+OR3OfIVDBM6mmJFsFhbLi1HjQE7CnBpsgRWIh4AYL/jNJxLtn6h9dhwOhnudhKEd232QvUQYizowTKEVMOFlGy89d2FBv3J09rKPPglijPvQRo8DQILKWQ3ToFd2oWb0yzgaqV/xpwrZyhU/m+9lGqgw0YZ3vMXIjJIjKwCNZqtzMe2oWZoactH6PcF2DJUjMHeJR3dBu0sQERnIV73KXmf0eRV+Kf8p07WxVTAR0xEN/i7S+ukPkIMGV1yJ6QKD58VYNrOKy9FMlcXF6Hg9jnY9JkAcTNfCG2bwKbXGHhK+dhwqfxH0VqLeXC24Gtfl/5R4VlhyZk4AKQ8Y7AW8fCGrxBdXQXo4y5AYqYaALDrejGEAmiTudrcAaOe1N1T6RQqNd6JuYwHHP5SHSEvC0rohFSiQKHEpO2XX57nsqtVAFODJ9AdJmZmwkPs/er9Dvt3V4vRr6UALWxKmgcvKR8FxQxXH6vwtJDh70cqtHcS4Fkhw7xTcqwN+d/l99UWHyC5wKyiqmslW6ZARPQlyIrowT2EVIYSOiGVmHfgBhIfP+c6jGrji8whatIauX/+AGVeNphahfwbp3D+oQqP86u+wvBPnhqHk5SI6GyqnWZrxkN0mBnGHiiE/zf5GNtBiGBPE8w+Jsd0f1Ok5qjRaVM+vL/h44tj/9TLet1Oz0PkzzfqpW5CDAV1iiOkAsduPMG+Ky/fI0ntBs9C9uHVeLR+HMDjw9TZA6N8hbj8uOoz9Oi4YtiIeQhrrds0DPMRYpjP/876/0hT4lqGCmsGieH5dT62j/PAe+qZyP7uI4ib+UIgsanr1cKeyw8R1MoRoe1d6rxuQgwBJXRCyvFMpsCnPyVwHUatCG1d4Dz6/6BWyKFWFMDEQoriQ2FoaVv5BTnGGLbEFePt9kKYCngVlitSMkz7TY4dw8xw96kaSgb85vEpCtMdIZS6oujxbZh7dqvr1QIAfHbgOrq628LRUlx1YUKMDF1yJ6Qc/z2QgKz8Iq7DeCF8UzFMLKRQyfNx9K4SQ1tVfvz+xz0V7j5V4z+dK39M65IzRRjoYYLOLgKo1ICcibA/3REAwNRKQK2us3UoK6egGJ/ufzkPtAipb3SGTkgZh679g1+vP+Y6jForTLkMADCRukL57DGend6CzvYCTOhYkqjnnpDjUR7D9mG6nde+u1qMbq4C+DqWP14dAG5mqvDjDSWuTpEAAJr6+EHGrkIYfwwCC1sUZz+EqYtXPa1ZiROJ6fj12mO69E5IGZTQCSmlQKF86Z8lri4qQM6ZaCjzsiAQW8K8VQ8c7Z8F4b+X0R/nM9zP1T2LzpUz7LtZjNUDK76UzRjD5F/kWBEsgsSUB2ZqgenyqbAbdAtPj28AUxVD2n8qTCzt63X9ACDqcCL6tXGEyKTigw9CjA09WIaQUpYfu/1SPA2uptLEdTc2XGN3k48xJ6VDnddbXXMGtsK0IE/Olk9IY0P30An515NcOb45S89pr44nTfpzmswBYP2pZGTmvdz9HAipS5TQCfnXyuN3IC+uvw5dhkIlccKbj0dxHQbyi5RYfuw212EQ0mhQQicEQGqWDHtfwjHnXFhp/gHSChvHsLE9lx/SY2EJ+RcldEIAfHs2BSo1dSepyq1mb2DtAzeuw9BSqRm+pdskhACghE4IcgoU2H/lEddhNHoKWy+8kRbKdRh69lx+iJyCl+RZ+4TUI0roxOjtvHAfhcXV++ESY8X4Qnykfhe5xY1vpGuBQoUd5+9xHQYhnKOEToyaQqlG9J9pXIfR6J1xjcDBf58G1xhFn0+DnA7KiJGjhE6M2uGEx8igoU+VynPsgol3e3IdRqWy8hU4fjOd6zAI4RQldGLUDlyle+eVYSJLTMidBBVr/E3Fwbj6+elWQl4Wjf9bSkg9ySlQIPZuFtdhNGq77KbjUq4l12FUy5k7mcgtKOY6DEI4QwmdGK3DCU9QrKKhahV57BqMT1PacR1GtSlUavyW8PL+qA4hL4oSOjFav8TTJdqKqCTOeOOfN7gOo8boFgoxZpTQiVHKzi/ChdSnXIfRKDHwsMz8A9xvJE+Dq4m/057imYzGpBPjRAmdGKXzKdn0ZLgK3Gz2JjY8aMF1GLWiZqB+EcRoUUInRulCCp2dl6fIthXeTA3hOowXEptECZ0YJ0roxChdSM3mOoRGhwlMMUv5LvKUje9pcDXxF322xEhRQidG56lMgaSMfK7DaHRONZmEQ5n2XIfxwu5lFyDjuZzrMAhpcJTQidG5mJoNRrfPdTx36oZJd7tzHUaduXzvGdchENLgKKETo3PtYS7XITQqTGSF8Tn/eSmeBlddt9PzuA6BkAZnON9gQqrpDjX2OmKk03El14LrMOrUXbqlQowQJXRidOj++f88cg3BvFRfrsOoc5TQiTGihE6MSrFKjYfPCrkOo1FQWTTBG4/CuQ6jXqRkyeg5A8ToUEInRuXhs0Jq6FHyNLgo8Qd4KBdxHUq9UCjVuP+0gOswCGlQlNCJUXlEZ+cAgIRmo/Htw2Zch1Gv0mnoGjEylNCJUckppOd8y6Wt8WbqQK7DqHc59FOqxMhQQidG5XmhkusQOMUEIswsngaZUsB1KPUup4AO3ohxoYROjEpuoXGftf3eZDIOG8DT4KrjGZ2hEyNDCZ0Yledy423kc5y7Y9LdV7gOo8HQGToxNpTQiVF5bqRn6ExkjbFPJ4AxHtehNJgChYrrEAhpUJTQiVEx1gFr0dL3ce25YT0NrioCvvEcvBACUEInRsZUYJy7fGSqD9chNDg+jxI6MS7G2boRo2VqQru8sTARUEInxoVaN2JUhNTIGw265E6MDSV0YlSERnrJ3RgJKaETI0OtGzEqFiITrkMgDcTG3JTrEAhpUJTQiVFxsTbjOgTSQOwtDfOHZwipCCV0YlRcbMRch0AaiIMFJXRiXCihE6PShM7QjYarDX3WxLhQQidGxdFSBBPqLGXw+Dy6GkOMDyV0YlT4fB6cramhN3RNbc1pRAMxOrTHE6PTysmS6xBIPfN1teI6BEIaHCV0YnTaNKHG3tC1bWLNdQiENDhK6MTo+LpSY2/o6DMmxogSOjE6nZrZcB0CqWe+dBWGGCFK6MToOFqJaUiTAXO1MYMdjUEnRogSOjFKPTzsuA6B1JPe3g5ch0AIJyihE6P0qo8j1yGQetKnFSV0YpwooROjFODlAFMap2xwTAV89PKy5zoMQjhBLRoxShKRCbq1lHIdBqlj/u5SmJvSL+oR40QJnRitfj5OXIdA6lif1nQrhRgvSujEaAW3dYaAnutuMEz4PAzp0ITrMAjhDCV0YrScrcUIpB7RBqNPa0c40G+gEyNGCZ0YtVH+zbkOgdSRN7s24zoEQjhFCZ0Ytb6tHeFsRb++9rJzshIhqBXdPyfGjRI6MWoCPg/hXZpyHQZ5QSP8mlJ/CGL0KKETozeqW3Mak/4SMzXhY2x3N67DIIRz1IoRo+dibYY36P7rS2t456ZwotsmhFBCJwQA3u3jCVMT+jq8bEz4PLwT6MF1GIQ0CtSCEYKSIWyjqcf7S2dkl6ZobmfOdRiENAqU0An517QgD4joLP2lYWrCx/S+XlyHQUijQQ89JuRfjlZiTOzljg2nk+ulfnVRAXLOxqAg6TzUBbkwdWwJ236TIXLxBgAwxpAbuxP58UehLpJB5OoD6YBpEEpdK6wz7+pvyLv6G5S56QAAoX1z2PQYBTOPLtoyT3//BrKE38ETimETOA4Wbfto/ya7FQtZwu9wHLGgXta5Pr0T6EG/a09IKXQ6Qkgp7/X1RBPr+ulglX1kDeRpcbAfPAsuE9dC7N4J6T/8F8q8LADA8wv78PzyL5AGvwvnt5eDJxQjY/d8MKWiwjoFlnawDRwHl3Gr4DJuFcQtOiBj/xIoMu8BAAruXoAs8Q84hi+GbdAEPD2yBqqCXACAukiGnDPbIR3wTr2sb31yszPHO0F075yQ0iihE1KKuakJ5g1uU+f1qouLUHD7HGz6TIC4mS+Etk1g02sMhLYuyLt6GIwx5F06COvub8Dc6xWYOrrDfvCHUOY/RcGd8xXH69kNZh5dIZS6Qih1hW3vseCbilH0z20AQHH2A4ibtYPIxQuSNoHgmZprz+afndoKy06DYGL18j2QJXJIW4iFAq7DIKRRoYROSBkh7VzQv00d/xKbWgUwNXgCoc5knokIRQ9vQJmbDpXsGczcOmr/xhdJIGrSCkX/3KrWIphaBdnNP6AulkPk2hoAYOrgDsWTu1DJ81H05C6Ysggmtk0gf3gDivRkWPq9Vmer2FBCfJ3pqXCElIPuoRNSjiVhvvgrJRt5cmWd1McXmUPUpDVy//wBQrtmEEhsIEs8g6J/bsHE1gWq/Gcl5SQ2OvMJzG2gkuVUWrciMw1PdswGUyrAMzWD47DPYGpf0mPfrKUfJG2D8CR6JngmprAPnQm+UISnR9fDLnRmyT34K4cgMLOCNHg6TB1a1Mn61hcbcyEWvNaW6zAIaZToDJ2QcjhZifHl8PZ1Wqfd4FkAgEfrx+H+smHIu/wzJD69AbzYI0uFUle4TPgazmNXwLJTCLJ+XQlF1n3t3216jYHrlG/Q5D/rYO7dA7nn90Ds1hE8vgC553+E85ivYNF+ALJ/XfFCcTSEr4a3h3M99XEg5GVHCZ2QCgxq54Jx3evujFVo6wLn0f+HZjP3wnXaNriMXQmmVkFo4wyBhS0AQF3mbFxVkANBmbP2sngCIYS2TSBy9oRt4HiYOroj79LP5ZYtzn4A2c1TsAl4C/L71yFu6guBuTXMWwdAkZ4MdVFBXaxqvRjfww0D2jpzHQYhjRYldEIq8VloG7Rzta7TOvmmYphYSKGS56Mw9QrMvF6BibUTBBJbyO/FacupiwpQ9M9tiJq0rlH9jDEwVXG507OProNt3wjwTc0ApgZT/3tLQfMvU9d2teqVr6sVPh3kw3UYhDRqlNAJqYSpCR/rRneGpfjFu5sUplxGYcplFOc8QWHqVaTvmguhtCks2vUDj8eDZZehyP3zRxQkXYAiMw1Zv66AiYUU5t7dtXWk//Apnl/+Rfv+2R/bIH+QAGVuOhSZaXj2xzYU3b8OSZsgveXnxx+FwMwK5p7dAAAiVx/I711D0aNbeP73QQjtmoMvtnjh9axrliITrB3VmR7NS0gVqFMcIVVobmeONaM6ISL6EpRqVut61EUFyDkTDWVeFgRiS5i36gGb3mPBE5R8Da26DQcrliP76Bqo5TKIm7aBY/gi8ExMtXUUP3sCUeFz7XuVLBdZh1ZAJXsKvkgCUwc3OIYvgpl7J51lq2TPkHt+N5zfWqqdJmrSClb+w5CxdyH45tawD51Z63WrL0IBDxve8oObvYTrUAhp9HiMsdq3UIQYkb2XH2L2nniuwzAqK9/ogGGd6PfqCakOuoZFSDWN8GuKOQNbcR2G0fgouBUlc0JqgBI6ITUwLcgT43u4cR2GwXvrleZ4t48n12EQ8lKhhE5IDS14rQ3e7NqM6zAM1ij/Zlg81JfrMAh56dA9dEJqKeq3RGw6k8J1GAZlbPcWWDikLXi8F3vYDiHGiBI6IS9g/em7+OrIba7DMAjv9/XEhwOojwIhtUUJnZAXtPPCPcw7kIAXGNFm1AR8HuYPboNx1DeBkBdCCZ2QOnDqdgY+2HUVz+vox1yMhZ3EFGtGd0IPD3uuQyHkpUcJnZA6kpYlw5Qdl3E7PY/rUF4KHZpaY8NbfmhiY8Z1KIQYBErohNShQoUK8w8mYM/lh1yH0qiFd2mKxWG+EJkIuA6FEINBCZ2QenAw7hEW/nITT2UKrkNpVOwtTLFwiC9C27twHQohBocSOiH1JDu/CIsO3cTBuH+4DqVRGNqxCSJfawtbiWnVhQkhNUYJnZB6dupWBj776Tr+yZVzHQonnKxE+DysHfq1ceI6FEIMGiV0QhpAfpESG08nY8u5VBQoVFyH0yAsRCaYFNASEQHukIjohx0JqW+U0AlpQFn5RVh78i6+v3gfCqWa63DqhamAjzGvNMf0Pp6wsxBxHQ4hRoMSOiEceJRTiK9PJOGnq4+gUBlGYjcV8BHWqQne6+uFZlJzrsMhxOhQQieEQ5l5Rfjx7/v4/sL9l/Yeu72FCKP8m+Ht7i3gaCnmOhxCjBYldEIaAZWa4URiOnacv4c/k7Ma/WNkBXweXmkpxZtdm2OgrzOEAvrhRkK4RgmdkDqwbds2zJgxAzk5OS9cV8ZzOQ4nPMFv1x/j8r1nUDaS7C4U8NDDwx6D2jmjfxtnSGn4GSGNCnU9JaSU8ePHIzo6Wm96UlISPD09GyQGRysxxvVww7gebsgtKMbpOxmITcrClfvPkJIlQ0MdgvN4gJejBfxaSNHNXYo+rR1hbSZsmIUTQmqMEjohZQwcOBBbt27Vmebg4MBJLNbmQgzt6IqhHV0BALkFxbj64Bmu3s/B9Ue5uJctw8NnhSh6wR7zYiEfzaXmaC6VoJWzBbq0kKJzc1tYm1MCJ+RlQQmdkDJEIhGcnZ11pq1YsQJbt25FSkoKpFIpXnvtNXz11VewsLAot474+HjMmDEDly5dAo/Hg5eXFzZt2oQuXboAAGJjYzF37lxcunQJ9vb2GDZsGKKioiCRSCqNzdpciKBWjghq5aidxhhDZl4RHjwrwIOnhcgpUKCgWAW5QoXC4pJXsZJBLORDJBRAYmoCKzMTWJsJ4WpjhhZ2EjhZicDj8V5wyxFCuEQJnZBq4PP5+Prrr+Hu7o6UlBRMmzYNc+bMwfr168stP2bMGHTq1AkbNmyAQCBAXFwchMKSs93k5GQMHDgQS5YswZYtW5CZmYnp06dj+vTpelcGqoPH48HRSgxHKzH8WrzQahJCXmLUKY6QUsaPH4+YmBiIxf8bfhUSEoI9e/bolNu7dy+mTp2KrKwsAPqd4qysrLBmzRqMGzdObxkREREQCATYtGmTdlpsbCwCAwMhk8l0lk0IIdVFZ+iElNGnTx9s2LBB+14ikeDEiROIiorCrVu38Pz5cyiVSsjlchQUFMDcXP8hKh9++CEiIiKwY8cO9OvXDyNHjoSHhweAksvx165dw86dO7XlGWNQq9VITU2Fj49P/a8kIcTg0OBRQsqQSCTw9PTUvoqKijB48GC0b98e+/btw+XLl7Fu3ToAgEJR/s+jRkZG4saNGwgNDcXJkyfRpk0b/PTTTwCA/Px8TJkyBXFxcdpXfHw8kpKStEmfEEJqis7QCanC5cuXoVarsXz5cvD5JcfAu3fvrnI+b29veHt7Y+bMmRg1ahS2bt2KYcOGoXPnzrh582aDDYMjhBgHOkMnpAqenp4oLi7GmjVrkJKSgh07dmDjxo0Vli8sLMT06dNx+vRp3Lt3D+fOncPff/+tvZT+8ccf488//8T06dMRFxeHpKQkHDx4ENOnT2+oVSKEGCBK6IRUoUOHDlixYgW+/PJL+Pr6YufOnYiKiqqwvEAgQHZ2NsaOHQtvb2+Eh4cjJCQECxcuBAC0b98ef/zxB+7cuYOAgAB06tQJ8+fPR5MmTRpqlQghBoh6uRNCCCEGgM7QCSGEEANACZ0QQggxAJTQCSGEEANACZ0QQggxAJTQCSGEEANACZ0QQggxAJTQCSGEEANACZ0QQggxAJTQCSGEEANACZ0QQggxAJTQCSGEEANACZ0QQggxAJTQCSGEEANACZ0QQggxAJTQCSGEEANACZ0QQggxAJTQCSGEEAPw/72QACeITxV8AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Вывод распределения количества наблюдений по меткам (классам)\n", + "print(data.hazardous.value_counts())\n", + "print()\n", + "\n", + "\n", + "data = data[['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude', 'hazardous']].copy()\n", + "\n", + "data_train, data_val, data_test = split_stratified_into_train_val_test(\n", + " data, stratify_colname=\"hazardous\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n", + ")\n", + "\n", + "print(\"Обучающая выборка: \", data_train.shape)\n", + "print(data_train.hazardous.value_counts())\n", + "hazardous_counts = data_train['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))# Установка размера графика\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n", + "plt.title('Распределение классов hazardous в обучающей выборке')# Добавление заголовка\n", + "plt.show()# Отображение графика\n", + "\n", + "print(\"Контрольная выборка: \", data_val.shape)\n", + "print(data_val.hazardous.value_counts())\n", + "hazardous_counts = data_val['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в контрольной выборке')\n", + "plt.show()\n", + "\n", + "print(\"Тестовая выборка: \", data_test.shape)\n", + "print(data_test.hazardous.value_counts())\n", + "hazardous_counts = data_test['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в тестовой выборке')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после oversampling: (100249, 6)\n", + "hazardous\n", + "True 51052\n", + "False 49197\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from imblearn.over_sampling import ADASYN\n", + "\n", + "# Создание экземпляра ADASYN\n", + "ada = ADASYN()\n", + "\n", + "# Применение ADASYN\n", + "X_resampled, y_resampled = ada.fit_resample(data_train.drop(columns=['hazardous']), data_train['hazardous'])\n", + "\n", + "# Создание нового DataFrame\n", + "data_train_adasyn = pd.DataFrame(X_resampled)\n", + "data_train_adasyn['hazardous'] = y_resampled # Добавление целевой переменной\n", + "\n", + "# Вывод информации о новой выборке\n", + "print(\"Обучающая выборка после oversampling: \", data_train_adasyn.shape)\n", + "print(data_train_adasyn['hazardous'].value_counts())\n", + "hazardous_counts = data_train_adasyn['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в тренировачной выборке после ADASYN')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

проведём также балансировку данных методом андерсемплинга. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести его в соответствие с классом меньшинства.

" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после undersampling: (10608, 6)\n", + "hazardous\n", + "False 5304\n", + "True 5304\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from imblearn.under_sampling import RandomUnderSampler\n", + "\n", + "rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n", + "\n", + "# Применение RandomUnderSampler\n", + "X_resampled, y_resampled = rus.fit_resample(data_train.drop(columns=['hazardous']), data_train['hazardous'])\n", + "\n", + "# Создание нового DataFrame\n", + "data_train_undersampled = pd.DataFrame(X_resampled)\n", + "data_train_undersampled['hazardous'] = y_resampled # Добавление целевой переменной\n", + "\n", + "# Вывод информации о новой выборке\n", + "print(\"Обучающая выборка после undersampling: \", data_train_undersampled.shape)\n", + "print(data_train_undersampled['hazardous'].value_counts())\n", + "\n", + "# Визуализация распределения классов\n", + "hazardous_counts = data_train_undersampled['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в тренировочной выборке после Undersampling')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null,