{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPj4ihBOIJi3lHmnZLWn6Ck"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"FGHD2yQmVOHw","executionInfo":{"status":"ok","timestamp":1723025668562,"user_tz":-120,"elapsed":5718,"user":{"displayName":"minji jang","userId":"02515258684939130087"}}},"outputs":[],"source":["from sklearn import datasets\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestClassifier\n","import seaborn as sns"]},{"cell_type":"code","source":["iris=datasets.load_iris()\n","x=iris.data\n","y=iris.target #y should be A categorical variable\n"],"metadata":{"id":"RfaoGxzmVUiX","executionInfo":{"status":"ok","timestamp":1723025960172,"user_tz":-120,"elapsed":275,"user":{"displayName":"minji jang","userId":"02515258684939130087"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["X_train, X_test, Y_train, Y_test = train_test_split(x,y,random_state=42, test_size=0.2)\n","rfc=RandomForestClassifier(n_estimators=150, max_samples=0.5, random_state=42, oob_score=True)\n"],"metadata":{"id":"AOG0GJ6eVVEN","executionInfo":{"status":"ok","timestamp":1723025823559,"user_tz":-120,"elapsed":2,"user":{"displayName":"minji jang","userId":"02515258684939130087"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["rfc.fit(X_train, Y_train)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":92},"id":"f990rwhlVWH6","executionInfo":{"status":"ok","timestamp":1723025826875,"user_tz":-120,"elapsed":628,"user":{"displayName":"minji jang","userId":"02515258684939130087"}},"outputId":"9497339e-1956-4fc3-8fa7-0c708d04eab0"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(max_samples=0.5, n_estimators=150, oob_score=True,\n","                       random_state=42)"],"text/html":["<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_samples=0.5, n_estimators=150, oob_score=True,\n","                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_samples=0.5, n_estimators=150, oob_score=True,\n","                       random_state=42)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["rfc.oob_score_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UKXVXYaNVchY","executionInfo":{"status":"ok","timestamp":1723025828873,"user_tz":-120,"elapsed":268,"user":{"displayName":"minji jang","userId":"02515258684939130087"}},"outputId":"22415773-bc7b-4cec-f0e0-ebf3e00c94e2"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9416666666666667"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["y_pred = rfc.predict(X_test)"],"metadata":{"id":"PrJ1N_iRVeTT","executionInfo":{"status":"ok","timestamp":1723025832509,"user_tz":-120,"elapsed":315,"user":{"displayName":"minji jang","userId":"02515258684939130087"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","y_pred = rfc.predict(X_test)\n","accuracy_score(Y_test, y_pred) #instead of r2 in regression, we use accuracy_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jfTVzauXViop","executionInfo":{"status":"ok","timestamp":1723025833964,"user_tz":-120,"elapsed":2,"user":{"displayName":"minji jang","userId":"02515258684939130087"}},"outputId":"2e89aa17-eeba-4ca9-e8ec-28c692ad5972"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","cm=confusion_matrix(Y_test, y_pred)\n","print(cm)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lVHXeRVoVoRz","executionInfo":{"status":"ok","timestamp":1723025836235,"user_tz":-120,"elapsed":272,"user":{"displayName":"minji jang","userId":"02515258684939130087"}},"outputId":"3e5c071e-fa57-464a-c1c7-8d0099d700ee"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["[[10  0  0]\n"," [ 0  9  0]\n"," [ 0  0 11]]\n"]}]},{"cell_type":"code","source":["sns.heatmap(cm, annot=True, cmap='Blues')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"AZCUY8g8Vweb","executionInfo":{"status":"ok","timestamp":1723025837771,"user_tz":-120,"elapsed":412,"user":{"displayName":"minji jang","userId":"02515258684939130087"}},"outputId":"73ea040a-ceea-44e9-be3f-3b3176910ae4"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAf8AAAGdCAYAAAAczXrvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAe50lEQVR4nO3de3hU9b3v8c8khklgxwiJJEFEOKgEEkCUFIEKolSlikQtlnOoB6IVKgEaOCpmHxHZaqegR3louCg8Cl4iahUKbqVy8AiyCZdw81ZBCla8JJAQkxLCgMmcP3Yb9/wSxcGVrMlvvV8+64+sCWt9p89qPvl+1y+zfKFQKCQAAOAZMW4XAAAAWhbhDwCAxxD+AAB4DOEPAIDHEP4AAHgM4Q8AgMcQ/gAAeAzhDwCAxxD+AAB4zFluF/BPCSOecLsERJHKNdPcLgFAFItv5vRK6DfZsWPV7ip07FhOiZrwBwAgavjsHozb/e4AAEAjdP4AAJh8PrcraFaEPwAAJsvH/oQ/AAAmyzt/u3+1AQAAjdD5AwBgYuwPAIDHMPYHAAA2ofMHAMDE2B8AAI9h7A8AAGxC5w8AgImxPwAAHsPYHwAA2ITOHwAAE2N/AAA8xvKxP+EPAIDJ8s7f7ncHAAAaofMHAMBkeedP+AMAYIqx+56/3b/aAACARuj8AQAwMfYHAMBjLP9TP7t/tQEAAI3Q+QMAYGLsDwCAxzD2BwAANiH8AQAw+WKc2yKwceNGjRw5Up06dZLP59OqVavCXg+FQnrggQeUnp6uhIQEDR8+XJ988knEb4/wBwDA5PM5t0WgpqZGffv21YIFC5p8fe7cuZo/f74WL16srVu3ql27drr22mt14sSJiM7DPX8AAEwuLfgbMWKERowY0eRroVBI8+bN0/33369Ro0ZJkp599lmlpqZq1apVGjNmzA8+D50/AADNKBgMqrq6OmwLBoMRH+fgwYMqLS3V8OHDG/YlJSVpwIABKi4ujuhYhD8AACYHx/6BQEBJSUlhWyAQiLik0tJSSVJqamrY/tTU1IbXfijG/gAAmBwc+xcUFGj69Olh+/x+v2PHPxOEPwAAzcjv9zsS9mlpaZKksrIypaenN+wvKyvTJZdcEtGxGPsDAGByabX/9+nWrZvS0tK0fv36hn3V1dXaunWrBg4cGNGx6PwBADC5tNr/2LFj2r9/f8PXBw8e1O7du9WhQwd16dJF+fn5evjhh3XRRRepW7dumjlzpjp16qScnJyIzkP4AwAQJUpKSjRs2LCGr/+5VmDcuHFatmyZ7r33XtXU1GjChAn6+uuv9dOf/lRr165VfHx8ROfxhUKhkKOVn6GEEU+4XQKiSOWaaW6XACCKxTdz65owcqFjx6pdM8mxYzmFzh8AABMP9gEAADah8wcAwOTSgr+WQvgDAGCyfOxP+AMAYLK887f73QEAgEbo/AEAMDH2BwDAW3yWhz9jfwAAPIbOHwAAg+2dP+EPAIDJ7uxn7A8AgNfQ+QMAYGDsDwCAx9ge/oz9AQDwGDp/AAAMdP5w3OCs8/THB0fpwPN3qvbNaRo5sHuj75l520AdeGGCjq6aon//3S3q3umcli8UrlpR9IJG/OwqZffrrbFjRuv9995zuyS4iOuhZfl8Pse2aET4u6BdfJzeP3BE+QvfbvL1/zW6vybdeImm/uH/akj+i6o5cUprHr5Z/rjYFq4Ubln75ht6bG5AEyflacUrK9WjR4bumniHKioq3C4NLuB6cIHPwS0KEf4ueKvkU81+drNWb/5rk6/n5VyqOSu26fUtB/TBp+X69WNrlZ7cTjcOajwhgJ2eW/6Mbv7Frcq56RZ1v/BC3T9rtuLj47XqtVfdLg0u4HqA0yK+519eXq6nn35axcXFKi0tlSSlpaVp0KBBGj9+vM4991zHi/SSrmlJSu/QTm/v+qxhX/Xxk9q+t1QDMjrplQ37XKwOLeHUyZP6y0cf6o47Jzbsi4mJ0eWXD9J7e3a5WBncwPXgjmgd1zslos5/+/btuvjiizV//nwlJSVpyJAhGjJkiJKSkjR//nxlZGSopKTktMcJBoOqrq4O20L135zxm7BJWvu2kqTDlcfD9h+uPK7Uf7wGu1V+Xam6ujolJyeH7U9OTlZ5eblLVcEtXA/usP2ef0Sd/5QpUzR69GgtXry40RsKhUL6zW9+oylTpqi4uPh7jxMIBDR79uywfbHdr1HcRddFUg4AADgDEXX+e/bs0bRp05r8Tcbn82natGnavXv3aY9TUFCgqqqqsO2s7sMjKcVapf/o+DsaXX7H9m1VZkwDYKf257RXbGxso8VcFRUVSklJcakquIXrwR22d/4RhX9aWpq2bdv2na9v27ZNqamppz2O3+/X2WefHbb5YvjIAUn6tLRKXx2t0bBLzm/Yl9i2jbJ7pGnrx1+6WBlaSlybNurZK1Nbt3w7Qauvr9fWrcXq07efi5XBDVwP7rA9/CNK3LvvvlsTJkzQjh07dPXVVzcEfVlZmdavX68lS5bosccea5ZCbdIuPi7s7/a7pp6tPv/tXFX+/YQOHfm7FqzaqRljBmj/F1/r07IqzbptkL6qqPnOvw6AfW4bl6uZ/zpDmZlZyurdR88/t1y1tbXKuelmt0uDC7ge4LSIwj8vL08pKSl64okntHDhQtXV1UmSYmNjddlll2nZsmW69dZbm6VQm1x6Uaremju64eu5E6+UJD237kNNePwt/Z9XStQ2Pk6FU4frnH/xa/OHX+rGma8peKrOpYrR0q4b8XNVHj2qhYXzVV5+RD0yemrhk0uVzJjXk7geXBCdDbtjfKFQKHQm//DUqVMNK01TUlIUFxf3owpJGPHEj/r3sEvlmmlulwAgisU3853ilPErHDtW+bIxjh3LKWf8P19cXJzS09OdrAUAALQAVtkBAGCI1oV6TiH8AQAwEP4AAHiN3dnPg30AAPAaOn8AAAyM/QEA8Bjbw5+xPwAAHkPnDwCAwfbOn/AHAMBge/gz9gcAwGPo/AEAMNnd+BP+AACYGPsDAACr0PkDAGCwvfMn/AEAMBD+AAB4jd3Zzz1/AAC8hs4fAAADY38AADzG9vBn7A8AgMfQ+QMAYLC98yf8AQAw2B7+jP0BAPAYOn8AAEx2N/6EPwAAJsb+AADAKnT+AAAYbO/8CX8AAAyWZz9jfwAATD6fz7EtEnV1dZo5c6a6deumhIQEde/eXQ899JBCoZCj74/OHwCAKDFnzhwtWrRIy5cvV2ZmpkpKSpSbm6ukpCRNnTrVsfMQ/gAAGNwa+2/evFmjRo3S9ddfL0nq2rWrXnzxRW3bts3R8zD2BwDA4OTYPxgMqrq6OmwLBoNNnnfQoEFav3699u3bJ0nas2ePNm3apBEjRjj6/gh/AACaUSAQUFJSUtgWCASa/N777rtPY8aMUUZGhuLi4tSvXz/l5+dr7NixjtbE2B8AAIOTY/+CggJNnz49bJ/f72/ye19++WW98MILKioqUmZmpnbv3q38/Hx16tRJ48aNc6wmwh8AAENMjHPp7/f7vzPsTffcc09D9y9JvXv31t/+9jcFAgFHw5+xPwAAUeL48eOKiQmP5tjYWNXX1zt6Hjp/AAAMbq32HzlypB555BF16dJFmZmZ2rVrlx5//HHdfvvtjp6H8AcAwODWx/v+4Q9/0MyZMzVp0iQdPnxYnTp10sSJE/XAAw84eh7CHwCAKJGYmKh58+Zp3rx5zXoewh8AAIPtn+1P+AMAYOCpfgAAeIzt4c+f+gEA4DF0/gAAGCxv/Al/AABMjP0BAIBV6PwBADBY3vgT/gAAmBj7AwAAq9D5AwBgsLzxJ/wBADAx9gcAAFah8wcAwGB540/4AwBgsn3sT/gDAGCwPPujJ/wr10xzuwREkc6/XuF2CYginy8d43YJgFWiJvwBAIgWjP0BAPAYy7OfP/UDAMBr6PwBADAw9gcAwGMsz37G/gAAeA2dPwAABsb+AAB4jO3hz9gfAACPofMHAMBgeeNP+AMAYLJ97E/4AwBgsDz7uecPAIDX0PkDAGBg7A8AgMdYnv2M/QEA8Bo6fwAADDGWt/6EPwAABsuzn7E/AABeQ+cPAICB1f4AAHhMjN3ZT/gDAGCyvfPnnj8AAB5D5w8AgMHyxp/wBwDA5JPd6c/YHwAAj6HzBwDAwGp/AAA8htX+AADAKnT+AAAYLG/8CX8AAEy2P9WPsT8AAB5D5w8AgMHyxp/wBwDAZPtqf8IfAACD5dnPPX8AALyGzh8AAIPtq/0JfwAADHZHP2N/AAA8h84fAACD7av96fwBADDE+JzbIvXFF1/oV7/6lZKTk5WQkKDevXurpKTE0fdH5w8AQJSorKzU4MGDNWzYML355ps699xz9cknn6h9+/aOnofwBwDA4NbYf86cOTr//PP1zDPPNOzr1q2b4+dh7A8AgMHnc24LBoOqrq4O24LBYJPnXb16tfr376/Ro0erY8eO6tevn5YsWeL4+yP8AQBoRoFAQElJSWFbIBBo8nsPHDigRYsW6aKLLtKf//xn3XXXXZo6daqWL1/uaE2+UCgUcvSIZ+jEN25XgGjS+dcr3C4BUeTzpWPcLgFRJr6Zb1r/z6L3HDvWklt6NOr0/X6//H5/o+9t06aN+vfvr82bNzfsmzp1qrZv367i4mLHauKePwAAhjNZpf9dvivom5Kenq5evXqF7evZs6deffVV5woS4Q8AQCNuLfgbPHiw9u7dG7Zv3759uuCCCxw9D/f8AQCIEtOmTdOWLVv0u9/9Tvv371dRUZGeeuop5eXlOXoewh8AAIPPwS0S2dnZWrlypV588UVlZWXpoYce0rx58zR27FgH3tW3GPsDAGBw86l+N9xwg2644YZmPQedPwAAHkPnDwCAwfLn+hD+AACYeKofAACwCuEfJVYUvaARP7tK2f16a+yY0Xr/Pec+XQqtz7/En6WH/0c/7XpspA499Qu98b+Hq1+3Dm6XBRfxM6JlOfnZ/tGI8I8Ca998Q4/NDWjipDyteGWlevTI0F0T71BFRYXbpcEl83J/oisz0zTpqS0acv9avfNhqV6950qlnZPgdmlwAT8jWl6Mz+fYFo0I/yjw3PJndPMvblXOTbeo+4UX6v5ZsxUfH69Vrzn7cY5oHeLjYnVD/86a/fJuFe87ooOHj2nuqg908PAx5V51odvlwQX8jIDTCH+XnTp5Un/56ENdPnBQw76YmBhdfvkgvbdnl4uVwS1nxfp0VmyMTpysD9tfe7JOl198rktVwS38jHAHY/8IHTp0SLfffvv3fk8kzza2XeXXlaqrq1NycnLY/uTkZJWXl7tUFdx07MQ32vZJue4elam0c+IV4/Np9MALlH1hslKT4t0uDy2MnxHu8Pl8jm3RyPHwP3r06GmfO9zUs40fndP0s40BL5r01Bb5JH0wL0dfLh2tO392sV7b8pnqo+MJ3ID1YhzcolHEf+e/evXq7339wIEDpz1GQUGBpk+fHrYvFPvDHndom/bntFdsbGyjhTsVFRVKSUlxqSq47dMjx3Tj799W2zaxSkyIU1nVCS29a5D+dqTG7dLQwvgZgeYQcfjn5OTI5/Mp9D0dyOnGHE092/jEN5FWYoe4Nm3Us1emtm4p1lVXD5ck1dfXa+vWYo35779yuTq47fjJOh0/WaektnEa1jtNs1/a43ZJaGH8jHBHtI7rnRLxRCI9PV2vvfaa6uvrm9x27tzZHHVa7bZxuXrtjy9r9aqVOvDXv+rhf3tQtbW1yrnpZrdLg0uGZaXpqt5p6pLSTkMzU7Xqvqv0yVfVKtp0+ska7MPPiJYX43Nui0YRd/6XXXaZduzYoVGjRjX5+ummAmjsuhE/V+XRo1pYOF/l5UfUI6OnFj65VMmM9Dzr7IQ43T+6rzq1T9DXNSe1puSQHnn1fX1Tx/+3vIifEXCaLxRhUr/77ruqqanRdddd1+TrNTU1Kikp0dChQyMqxKtjfzSt869XuF0CosjnS8e4XQKiTHwzP5lm+uqPHTvW4zdmOHYsp0T8P98VV1zxva+3a9cu4uAHACCacM8fAABYhUf6AgBgiNaFek4h/AEAMFg+9WfsDwCA19D5AwBgiNZH8TqF8AcAwGD7WJzwBwDAYHnjb/0vNwAAwEDnDwCAgXv+AAB4jOXZz9gfAACvofMHAMDAJ/wBAOAxtt/zZ+wPAIDH0PkDAGCwvPEn/AEAMNl+z5+xPwAAHkPnDwCAwSe7W3/CHwAAg+1jf8IfAACD7eHPPX8AADyGzh8AAIPP8r/1I/wBADAw9gcAAFah8wcAwGD51J/wBwDAxIN9AACAVej8AQAw2L7gj/AHAMBg+dSfsT8AAF5D5w8AgCGGB/sAAOAtto/9CX8AAAy2L/jjnj8AAB5D5w8AgMH2D/kh/AEAMFie/Yz9AQDwGjp/AAAMjP0BAPAYy7OfsT8AAF5D+AMAYIhxcDtTv//97+Xz+ZSfn/8jjtI0xv4AABh8Ls/9t2/frieffFJ9+vRpluPT+QMAEEWOHTumsWPHasmSJWrfvn2znIPwBwDA4HNwCwaDqq6uDtuCweB3njsvL0/XX3+9hg8f3lxvj/AHAMAU4/M5tgUCASUlJYVtgUCgyfOuWLFCO3fu/M7XncI9fwAADE7e8S8oKND06dPD9vn9/kbfd+jQIf32t7/VunXrFB8f72AFjRH+AAA0I7/f32TYm3bs2KHDhw/r0ksvbdhXV1enjRs3qrCwUMFgULGxsY7URPgDAGBwY7H/1Vdfrffffz9sX25urjIyMjRjxgzHgl8i/AEAaMSNP/VLTExUVlZW2L527dopOTm50f4fiwV/AAB4DJ0/AACGaOmM33nnnWY5LuEPAIDB7U/4a27R8ssNAABoIXT+AAAY7O77CX8AABqxfexP+CMqfb50jNslIIq0z57sdgmIMrW7Ct0uoVUj/AEAMNi+II7wBwDAwNgfAACPsTv67Z9sAAAAA50/AAAGy6f+hD8AAKYYywf/jP0BAPAYOn8AAAyM/QEA8BgfY38AAGATOn8AAAyM/QEA8BhW+wMAAKvQ+QMAYGDsDwCAxxD+AAB4DH/qBwAArELnDwCAIcbuxp/wBwDAxNgfAABYhc4fAAADq/0BAPAYxv4AAMAqdP4AABhY7Q8AgMcw9gcAAFah8wcAwMBqfwAAPMby7Cf8AQAwxVje+nPPHwAAj6HzBwDAYHffT/gDANCY5enP2B8AAI+h8wcAwGD7h/wQ/gAAGCxf7M/YHwAAr6HzBwDAYHnjT/gDANCI5enP2B8AAI+h8wcAwMBqfwAAPMb21f6EPwAABsuzn3v+AAB4DZ0/AAAmy1t/wh8AAIPtC/4Y+wMA4DF0/gAAGFjtDwCAx1ie/Yz9AQDwGjp/AABMlrf+hD8AAAZW+wMAgBYRCASUnZ2txMREdezYUTk5Odq7d6/j5yH8AQAw+HzObZHYsGGD8vLytGXLFq1bt06nTp3SNddco5qaGkffH2N/AAAMbg39165dG/b1smXL1LFjR+3YsUNDhgxx7DyEPwAAJgfTPxgMKhgMhu3z+/3y+/2n/bdVVVWSpA4dOjhXkBj7R40VRS9oxM+uUna/3ho7ZrTef+89t0uCy7gmvGnwpd31x3kTdeCtR1S7q1Ajr+wT9vqoq/pqzcI8ff7/5qh2V6H6XHyeS5XihwoEAkpKSgrbAoHAaf9dfX298vPzNXjwYGVlZTlaE+EfBda++YYemxvQxEl5WvHKSvXokaG7Jt6hiooKt0uDS7gmvKtdgl/v7/tC+YGXmny9bUIbbd79V90/f1XLFuYxPgf/KygoUFVVVdhWUFBw2hry8vL0wQcfaMWKFY6/P8b+UeC55c/o5l/cqpybbpEk3T9rtjZufEerXntVd9w5weXq4AauCe966z8+0lv/8dF3vv7iv2+XJHVJd3YMjHBOfrzvDx3x/1eTJ0/W66+/ro0bN6pz587OFfMPdP4uO3XypP7y0Ye6fOCghn0xMTG6/PJBem/PLhcrg1u4JgDvCoVCmjx5slauXKm3335b3bp1a5bzRBz+tbW12rRpkz76qPFvpidOnNCzzz7rSGFeUfl1perq6pScnBy2Pzk5WeXl5S5VBTdxTQDu8zm4RSIvL0/PP/+8ioqKlJiYqNLSUpWWlqq2ttaBd/WtiMJ/37596tmzp4YMGaLevXtr6NCh+uqrrxper6qqUm5u7mmPEwwGVV1dHbaZKyEBAHCNS+m/aNEiVVVV6corr1R6enrD9tJLTa8BOVMRhf+MGTOUlZWlw4cPa+/evUpMTNTgwYP12WefRXTSplY+Pjrn9CsfbdT+nPaKjY1ttJCroqJCKSkpLlUFN3FNAN4VCoWa3MaPH+/oeSIK/82bNysQCCglJUUXXnih1qxZo2uvvVZXXHGFDhw48IOP09TKx3tmnH7lo43i2rRRz16Z2rqluGFffX29tm4tVp++/VysDG7hmgDc5+Rq/2gU0Wr/2tpanXXWt//E5/Np0aJFmjx5soYOHaqioqIfdJymVj6e+CaSSuxy27hczfzXGcrMzFJW7z56/rnlqq2tVc5NN7tdGlzCNeFd7RLaqPv55zZ83fW8ZPW5+DxVVh/XodJKtT+7rc5Pa6/0jkmSpIu7pkqSyiqqVVbxd1dqtpGTq/2jUUThn5GRoZKSEvXs2TNsf2FhoSTpxhtvdK4yD7luxM9VefSoFhbOV3n5EfXI6KmFTy5VMiNez+Ka8K5Le12gt5b+tuHruXf/5597Prd6iybMel7XD+2tJf92W8Prz825XZL08OI39MiTb7RssWi1fKFQKPRDvzkQCOjdd9/VG280fYFNmjRJixcvVn19fcSFeLnzB/D92mdPdrsERJnaXYXNevx9pccdO9bFaW0dO5ZTIgr/5kT4A/guhD9MzR7+ZQ6Gf2r0hT+f8AcAgCFaF+o5hU/4AwDAY+j8AQAwsNofAACPsTz7GfsDAOA1dP4AAJgsb/0JfwAADKz2BwAAVqHzBwDAwGp/AAA8xvLsZ+wPAIDX0PkDAGCyvPUn/AEAMNi+2p/wBwDAYPuCP+75AwDgMXT+AAAYLG/8CX8AAEyM/QEAgFXo/AEAaMTu1p/wBwDAwNgfAABYhc4fAACD5Y0/4Q8AgImxPwAAsAqdPwAABj7bHwAAr7E7+wl/AABMlmc/9/wBAPAaOn8AAAy2r/Yn/AEAMNi+4I+xPwAAHkPnDwCAye7Gn/AHAMBkefYz9gcAwGvo/AEAMLDaHwAAj2G1PwAAsAqdPwAABtvH/nT+AAB4DJ0/AAAGOn8AAGAVOn8AAAy2r/Yn/AEAMDD2BwAAVqHzBwDAYHnjT/gDANCI5enP2B8AAI+h8wcAwMBqfwAAPIbV/gAAwCp0/gAAGCxv/On8AQBoxOfgFqEFCxaoa9euio+P14ABA7Rt27Yf+24aIfwBADD4HPwvEi+99JKmT5+uWbNmaefOnerbt6+uvfZaHT582NH3R/gDABAlHn/8cd15553Kzc1Vr169tHjxYrVt21ZPP/20o+fhnj8AAAYnV/sHg0EFg8GwfX6/X36/P2zfyZMntWPHDhUUFDTsi4mJ0fDhw1VcXOxcQYqi8I+PmkrcEwwGFQgEVFBQ0OiigPdwPXyrdleh2yW4juuhZTmZSQ8+HNDs2bPD9s2aNUsPPvhg2L7y8nLV1dUpNTU1bH9qaqo+/vhj5wqS5AuFQiFHj4gzVl1draSkJFVVVenss892uxy4jOsB/xXXQ+v1Qzv/L7/8Uuedd542b96sgQMHNuy/9957tWHDBm3dutWxmui3AQBoRk0FfVNSUlIUGxursrKysP1lZWVKS0tztCYW/AEAEAXatGmjyy67TOvXr2/YV19fr/Xr14dNApxA5w8AQJSYPn26xo0bp/79++snP/mJ5s2bp5qaGuXm5jp6HsI/ivj9fs2aNYvFPJDE9YBwXA/e8Mtf/lJHjhzRAw88oNLSUl1yySVau3Zto0WAPxYL/gAA8Bju+QMA4DGEPwAAHkP4AwDgMYQ/AAAeQ/hHiZZ4hCNah40bN2rkyJHq1KmTfD6fVq1a5XZJcFEgEFB2drYSExPVsWNH5eTkaO/evW6XhVaO8I8CLfUIR7QONTU16tu3rxYsWOB2KYgCGzZsUF5enrZs2aJ169bp1KlTuuaaa1RTU+N2aWjF+FO/KDBgwABlZ2ersPA/H15SX1+v888/X1OmTNF9993ncnVwk8/n08qVK5WTk+N2KYgSR44cUceOHbVhwwYNGTLE7XLQStH5u+yfj3AcPnx4w77meoQjgNavqqpKktShQweXK0FrRvi77Pse4VhaWupSVQCiUX19vfLz8zV48GBlZWW5XQ5aMT7eFwBaiby8PH3wwQfatGmT26WglSP8XdaSj3AE0HpNnjxZr7/+ujZu3KjOnTu7XQ5aOcb+LmvJRzgCaH1CoZAmT56slStX6u2331a3bt3cLgkWoPOPAi31CEe0DseOHdP+/fsbvj548KB2796tDh06qEuXLi5WBjfk5eWpqKhIf/rTn5SYmNiwFigpKUkJCQkuV4fWij/1ixKFhYV69NFHGx7hOH/+fA0YMMDtsuCCd955R8OGDWu0f9y4cVq2bFnLFwRX+Xy+Jvc/88wzGj9+fMsWA2sQ/gAAeAz3/AEA8BjCHwAAjyH8AQDwGMIfAACPIfwBAPAYwh8AAI8h/AEA8BjCHwAAjyH8AQDwGMIfAACPIfwBAPAYwh8AAI/5/4R+IL8buYz1AAAAAElFTkSuQmCC\n"},"metadata":{}}]}]}