results-hyperparameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we provide an initial exploratory analysis of the impact of the hyperparameters of the three best performing models (Random Forests, K Nearest Neighbours and Elastic Net) on the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean = pd.read_csv('data/runs/clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Odds Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we develop a number of methods to study the overrepresentation of certain characteristics on a top quantile of the configurations used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These methods should be used when a pair of characteristics is being analyzed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OR(indices, rest, top, group):\n",
    "    li = []\n",
    "    for i in indices:\n",
    "        model_t = top[(top[group[0]]==i[0])&(top[group[1]]==i[1])].shape[0]\n",
    "        nmodel_t = top[(top[group[0]]!=i[0])|(top[group[1]]!=i[1])].shape[0]\n",
    "        model_r = rest[(rest[group[0]]==i[0])&(rest[group[1]]==i[1])].shape[0]\n",
    "        nmodel_r = rest[(rest[group[0]]!=i[0])|(rest[group[1]]!=i[1])].shape[0]\n",
    "        if model_r > 0 and nmodel_r > 0:\n",
    "            li.append((model_t/model_r)/(nmodel_t/nmodel_r))\n",
    "        else:\n",
    "            li.append(0)\n",
    "    return li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_OR(data, group, q=0.95, score='r2_score'):\n",
    "    top = data[data[score] >= data[score].quantile(q)]\n",
    "    counts = data[group +[score]].groupby(group).count()\n",
    "    topcounts = top[group +[score]].groupby(group).count()\n",
    "    topcounts['OR'] = OR(topcounts.index, data[data[score]<data[score].quantile(q)], top, group)\n",
    "    return topcounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These methods should be used when a single characteristic is being analyzed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ORone(indices, rest, top, group):\n",
    "    li = []\n",
    "    for i in indices:\n",
    "        model_t = top[(top[group[0]]==i)].shape[0]\n",
    "        nmodel_t = top[(top[group[0]]!=i)].shape[0]\n",
    "        model_r = rest[(rest[group[0]]==i)].shape[0]\n",
    "        nmodel_r = rest[(rest[group[0]]!=i)].shape[0]\n",
    "        if model_r > 0 and nmodel_r > 0:\n",
    "            li.append((model_t/model_r)/(nmodel_t/nmodel_r))\n",
    "        else:\n",
    "            li.append(0)\n",
    "    return li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_OR(data, group, q=0.95, score='r2_score'):\n",
    "    top = data[data[score] >= data[score].quantile(q)]\n",
    "    counts = data[group +[score]].groupby(group).count()\n",
    "    topcounts = top[group +[score]].groupby(group).count()\n",
    "    topcounts['OR'] = ORone(topcounts.index, data[data[score]<data[score].quantile(q)], top, group)\n",
    "    return topcounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here some initial exploration of the hyper parameters of the models is performed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = clean[clean['model']=='KNeighborsRegressor'][['Drug', 'fs', 'da','ajive', 'catn', 'n_neighbors', 'weights', 'algorithm', 'p','r2_score', 'r2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>fs</th>\n",
       "      <th>da</th>\n",
       "      <th>ajive</th>\n",
       "      <th>catn</th>\n",
       "      <th>n_neighbors</th>\n",
       "      <th>weights</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>p</th>\n",
       "      <th>r2_score</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>AZD6482</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>29.0</td>\n",
       "      <td>uniform</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.416520</td>\n",
       "      <td>0.236597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>AZD7762</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>13.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.377099</td>\n",
       "      <td>0.109259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>BMS-754807</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>20.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.112876</td>\n",
       "      <td>0.033824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Dasatinib</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>14.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.427305</td>\n",
       "      <td>0.157184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>24.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>kd_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.107432</td>\n",
       "      <td>-0.010444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Drug     fs    da  ajive catn  n_neighbors   weights  algorithm  \\\n",
       "207     AZD6482  Lasso  none    NaN  low         29.0   uniform  ball_tree   \n",
       "208     AZD7762  Lasso  none    NaN  low         13.0  distance  ball_tree   \n",
       "209  BMS-754807  Lasso  none    NaN  low         20.0  distance  ball_tree   \n",
       "210   Dasatinib  Lasso  none    NaN  low         14.0  distance  ball_tree   \n",
       "211   Docetaxel  Lasso  none    NaN  low         24.0  distance    kd_tree   \n",
       "\n",
       "       p  r2_score        r2  \n",
       "207  1.0  0.416520  0.236597  \n",
       "208  1.0  0.377099  0.109259  \n",
       "209  2.0  0.112876  0.033824  \n",
       "210  1.0  0.427305  0.157184  \n",
       "211  1.0  0.107432 -0.010444  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='weights', y='r2', hue='algorithm', data=knn[knn['n_neighbors']>5], kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn['ajive'] = knn['ajive'].fillna(0)\n",
    "sns.set(font_scale=1.1)\n",
    "order= ['very low', 'low', 'mid', 'high', 'very high']\n",
    "\n",
    "g = sns.catplot(x='catn', y='r2',order=order, hue='ajive', data=knn, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "order= ['very low', 'low', 'mid', 'high', 'very high']\n",
    "g = sns.catplot(x='catn', y='r2',hue='da', data=knn,order=order, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11eb34668>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "order= ['very low', 'low', 'mid', 'high', 'very high']\n",
    "g = sns.catplot(x='catn', y='r2', hue='p', data=knn,order=order, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "g.set(xlabel='features')\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAFZCAYAAADaRJQBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deViVdf7/8RdwADFlEEQQcwkibdAWGyVTJ3eLvghuaTg1meWouWUp1lgmEaiQimYaqWXNGH2tlBQdpzI10tRSc2lKIzULQRaNULbDOb8//HW+MYpgcp/D8nxcl9flvZ3P+z6fA+fF596crFarVQAAAICBnB1dAAAAAOo/QicAAAAMR+gEAACA4QidAAAAMByhEwAAAIYzOboAe8jLK5TFwkX6AAAARvP1bXrZ+Yx0AgAAwHB2CZ3r16/XoEGDFBERoZEjR+rQoUOXrHPw4EENHTpU9957rx588EFlZWXZlr3//vsKCwvTgAED9Oyzz6q0tNQeZQMAAKCGGB46MzIylJCQoBUrVig1NVXjx4/X448/XmGd0tJSTZo0SdHR0dq8ebMiIiI0Y8YMSdLRo0e1cOFCvfnmm9qyZYvKysqUnJxsdNkAAACoQYaHTjc3N7344otq0aKFJKljx47Kzc1VcXGxbZ1Dhw6pUaNG6tq1qyRp8ODBOnTokLKzs/Xxxx+rd+/eat68uZycnDRy5Eh98MEHRpcNAACAGmT4hUStW7dW69atJUlWq1Xx8fHq3bu3GjVqZFsnKytLLVu2tE27uLioefPmyszM1OnTp+Xv729b5u/vr8zMzKuqwcenyTXuBQAAAK6F3a5ev3DhgmbOnKmsrCytWLGiwjKLxXLZbZydnXW5R8M7O1/dAC1XrwOorqKi8yosPKfycrOjS6kVXFxMatLESx4e1zm6FAB1RGVXr9sldP70008aP368goKC9Oabb1YY5ZSkgIAAZWdn26YtFotyc3Pl7++vgICAChcVZWdnVxj5BICaUlR0Xr/8clZeXr5ydXWTk5OTo0tyKKvVqrKyUp07lyNJBE8A18Twczrz8/P1l7/8RQMGDNDChQsvCZySdMstt+j8+fPavXu3JCk1NVVBQUHy8/NTnz59tG3bNp05c0ZWq1UpKSnq16+f0WUDaIAKC8/Jy8tXbm7uDT5wSpKTk5Pc3Nzl5eWrwsJzji4HQB1n+EjnmjVrlJWVpQ8//FAffvihbf6yZcs0fvx4JScny8/PT0uXLlVMTIwuXLigpk2bKjExUZLUvn17PfHEExo9erTMZrM6duyoSZMmGV02gAaovNwsV1c3R5dR67i6unG6AYBr5mS93EmT9QzndAKojqysk/L3b+voMmol3hsA1eXQczoBoK5q5u0hk4txvyrN5WadzS8y7PUBoLYgdALAFZhcTNr7007DXr9Lq7sMe20AqE149joA1GHr17+r1atXSpKeemqyMjK+c3BFAHB5jHTiEk083eTh7u7QGopKSlRYUOrQGoC6IDJymO3/iYmLHVgJAFwZoROX8HB3123PhTm0hgMxm1QoQifwK4vFoqVLk3Tw4AEVFxeptLRUU6dO19dfH1Z+fp6mT39Gw4aF6/nn4/Tee+8oMPBGPfjgw5Kkf/0rTf/+92YtWPCyDh8+qGXLlqio6IIkadSoh9W3b38H7hmAhoLQCQB1wNdfH9Hp05lavnyVXFxctGHDeq1c+aq6det+ybqDBg1WYmK8LXRu3JiqYcNGqKCgQLGxs/XSS0vUqtX1ys/P09ixDyswMEg33BBo5z0C0NAQOgGgDujYsZMmTJisDRvWKzPzRx069JVttPK/3X77HbJarTp8+JCaNWumU6dOqkePu7V37+fKy8vT3/8+o8L6x459S+gEYDhCJwDUAbt2pWvBggSNHBmlO+/srpCQTnr11aWVrh8ePlj/+tdG/eEPXho48D6ZTCaVl1vUunUbrVr1D9t6ubm5+sMf/mCPXQDQwHH1OgDUAXv27Fa3bndp6NARuuWW27Rt21ZZLJZK17/33v/Rzp3p+uijLRo0aLAkqWPHW3T6dKa+/HKvJOnkyROKihqqU6d+sMs+AGjYGOkEgCswl5sNvZemuZqPl4yMHKo5c2bpoYdGyNnZRV26hCovL0/FxcWXXd/Ly0udOt2qs2fzdf31rW3z4uMTtWzZEhUXF6u8vFxPPfW0AgODamx/AKAyPAYTl/D1bVorrl7PyfnFoTWg4amLj3pcsmSBJCdNmvSEoe3UxfcGgGNU9hhMDq8DQB01Z84sffLJx+rTh1seAaj9OLwOAHXU7Nmxji4BAKqNkU4AAAAYjtAJAAAAwxE6AQAAYDhCJwAAAAzHhUQAcAWeXu5yd3Uz7PVLykpVcK7EsNcHgNqC0AkAV+Du6qbnPnzesNeP6f+8pOqFzsTEeO3evUu9e/fVhAlTqrVNjx5/Umrqv+Tj0/z3FwkANYDQCQB1RGrq+/rf/01Vy5YBji4FAK4aoRMA6oAJEx6V1WrVjBlTFRX1kD79dLuysk7LbC5Tjx5367HHxsvJyUnp6du1fPlSubm5qlOnW23bWywWLV2apIMHD6i4uEilpaWaOnW6unXr7sC9AtCQcCERANQBr7yyQpK0aNEr2rx5owYODNOqVf/QihVv6dixb/XBB+t09uxZxcbO1pw5cVq16p+68cabbNt//fURnT6dqeXLV+mtt/5Xf/nLw1q58lVH7Q6ABoiRTgCoQywWi/bv/1IFBQV6/fXXJEnFxcX67rtj8vLyUtu2Nygo6EZJ0n33DdKCBfMkSR07dtKECZO1YcN6ZWb+qEOHvlJR0QWH7QeAhofQCQB1SHm5RVarVa+88poaN75OkvTzz+fk6uqqvXt3y2q12tZ1dnaWi4uLJGnXrnQtWJCgkSOjdOed3RUS0kmvvrrUIfsAoGHi8DoA1CGuriZ16nSr3n77H5KkCxcuaMqUCdqyZbNuu62zTp36Qd988x9J0scf/1vFxcWSpD17dqtbt7s0dOgI3XLLbdq2bassFovD9gNAw8NIJwBcQUlZ6f+/rZFxr3+1Zs9+UYsWzddDD41QaWmZ7r67tyIjh8rJyUkxMXGKj58jZ2dndegQoiZNmkqSIiOHas6cWXrooRFydnZRly6hysvLU1FRkTw8PGp6twDgEk7W3x6Lqafy8gplsdT73awxvr5NddtzYQ6t4UDMJuXk/OLQGtDwZGWdlL9/W0eXUSvx3gCoLl/fppedb9eRzqSkJOXl5SkmJqbC/G+//VYzZsywTVssFh09elRLlizRgAED9Nhjj+nkyZO2v8a7dOmiWbNm2bN0AAAAXAO7hM7MzEzNnTtXO3bs0KBBgy5Z3r59e6Wmptqm582bp+DgYA0YMEBWq1VfffWVNm/eLB8fH3uUCwAAgBpml9C5du1ahYaGKigoSHl5eVdcd9++fdq8ebM2bNggSTp27JisVqueeeYZZWZmqmPHjoqOjpaXl5c9SgcAAEANsMvV61OmTNGoUaPk7Fx1c/Pnz9fkyZPVtOnF8wHOnj2rbt26KS4uTuvXr1fjxo01c+ZMo0sGAABADapVV6/v379f2dnZioiIsM0LDQ1VaGiobXrixInq3r27SkpK5O7uXq3X9fFpUuO1wniVnYgMGOXMGWeZTNxJ7nKcnZ35mQRwTWpV6ExLS1NERITtZsaStGvXLpWUlKhXr16SJKvVKicnp2qNmv6Kq9evTm35YuHqddibxWKR2cy9Ky/HYrHwMwmgWmrF1etV2bt3r5555pkK8woKCpSQkKDOnTvL09NTK1asUP/+/eXq6uqgKgE0JE083eRRzaMqv0dRSYkKC67+Xp0AUNc4NHRGREQoNjZWnTp1kiSdPHlSrVq1qrDOwIEDdfz4cY0YMUIWi0Xt27e/5JZLAGAUD3d3Q+9beyBmkwpF6ARQ/9k1dE6aNKnC9G9vkyRJBw4cuOx248aN07hx4wyrCwAAAMbijHkAAAAYrlad0wkAqNy+fV9o+fKX1aZNW33//XcqLi7W1KnT1bnzn/Tyywv1xRd75OzsrPbtb9aUKU+pSZMmmjhxrEJCOunIkUPKyjqtkJCOevbZF2QymXTixHElJSXq559/Vnm5WeHhkRo2bKSjdxNAPcVIJwDUId9887WGDRupVav+qaFDR+i1117R6tUrlZ2dpTfeeFtvvPG23N3dtXDhfNs2P/74g5KSlumtt/5XR44c1t69n8tsNmvWrBn6298e16pV/9CyZSu1adMG7dnzuQP3DkB9xkgnANQhvr4t1KHDzZKk9u07KCXlH/r888/0yCN/k8l08Vf6sGEjNWHCo7Zt7rqrp1xcXOTh4aE2bdrq3LlzOnXqB/3004+aOzfWtt6FC+f17bffqGvXO+27UwAaBEInANQhFR+K4SSr1XrJfYgv3m/UXMU2FjVu3FhvvLHGtuTs2Xx5eDQ2qHIADR2H1wGgjrvzzrv0wQfvy2w2y2q16v33/1ddunS94jZt27ZT48ZNtGnTBklSXl6uHnnkL9q//0t7lAygAWKkEwCuoKikRAdiNhn6+tfqoYce0SuvJOnhh6NUXm7WTTd10PTpf7/iNiaTSXPnvqSkpES9884alZWVauTIUerWrfs11wMAl+NktVrr/fMheQzm1fH1bWrozbCr40DMJh65B7vLyjopf/+2ji6jVuK9AVBdlT0Gk8PrAAAAMByH1wHUS828PWRyubpfcWfOOMtkqpm/xa1Wq8rLOcICAL8idAKol0wuJu39aefVbVPuocKSX+Tk5HTN7V/n1kRS/QidDeAsLAB2wOF1APj/rM5WmcvKHF1GrVNWViqXqxw1BoD/RugEgP+vvHGJfv45X2WlpYzu6eIIZ2lpic6dy1GTJl6OLgdAHcefrgDwK3erynRBZ38ul5Pl2g6x/+ziLovFUkOFOY6Li0lNmzaTh8d1ji4FQB1H6ASA33K3qty9+Jpf5vZWt3PbLwD4DQ6vAwAAwHCETgAAABiO0AkAAADDEToBAABgOEInAAAADEfoBAAAgOEInQAAADAcoRMAAACG4+bwl9HM20MmBz5n2Fxu1tn8Ioe1DwAAUNMInZdhcjFp7087HdZ+l1Z3OaxtAAAAI3B4HQAAAIYjdAIAAMBwhE4AAAAYzq7ndCYlJSkvL08xMTGXLNu0aZPmzJkjf39/27y33npLnp6e2r59uxITE1VaWqrg4GDFxcXJ09PTnqUDAADgGthlpDMzM1OTJ0/W66+/Xuk6+/bt09ixY5Wammr75+npqfz8fEVHRyspKUlbtmxRcHCw4uPj7VE2AAAAaohdQufatWsVGhqq0aNHV7rOvn379Nlnn2nIkCGKiorS3r17JUnp6ekKCQlRYGCgJCkqKkppaWkym832KB0AAAA1wC6H16dMmSJJWrJkyWWXWywWNW/eXKNGjdLdd9+tL774QhMmTND69euVlZVV4ZC7r6+vzGaz8vPz1aJFi2q17+PT5Np3ws58fZs6ugSH4z1AXcdnGAD+T624T6ezs7OSk5Nt03/605/UuXNnpaeny2KxVLpNdeXlFcpisVZ7/drwRZGT84vD2q4N+y859j1A3VcbPsd8hgE0RJX9/q0VV6/n5eVVCJ2SZLVaZTKZFBAQoOzsbNv83Nxcubq6ysvLy95lAgAA4HeqFSOdTZo00apVqxQUFKS+ffvq66+/1oEDBxQXFycnJyfFxcUpIyNDQUFBSklJUa9evWQy1YrSAQCAgXg0df3h0OQWERGh2NhYderUScuWLVNcXJwWLlwoFxcXLVq0SD4+PpKkhIQETZs2TaWlpQoICFBCQoIjywYAAHbCo6nrD7uGzkmTJlWYTk1Ntf3/9ttv19q1ay+7Xc+ePdWzZ09DawMAAIBxasU5nQAAAKjfCJ0AAAAwHKETAAAAhiN0AgAAwHCETgAAABiO0AkAAADDEToBAABgOEInAAAADEfoBAAAgOEInQAAADAcoRMAAACGI3QCAADAcIROAAAAGI7QCQAAAMMROgEAAGA4QicAAAAMR+gEAACA4QidAAAAMByhEwAAAIYjdAIAAMBwhE4AAAAYjtAJAAAAwxE6AQAAYDhCJwAAAAxncnQBAAD8Hs28PWRycezXmLncrLP5RQ6tAagrCJ0AgDrJ5GLS3p92OrSGLq3ucmj7QF1C6ASAeqqJp5s83N0d1n5RSYkKC0od1j6A2sWuoTMpKUl5eXmKiYm5ZNnBgwcVFxenoqIiWSwWPfroo4qIiJAkPf/889q+fbs8PT0lSW3bttXixYvtWToA1Dke7u667bkwh7V/IGaTCkXoBHCRXUJnZmam5s6dqx07dmjQoEGXLLdYLHr88cc1b9483XXXXcrKylJkZKQ6duyooKAg7du3T4sXL1anTp3sUS4AAABqmF1C59q1axUaGqqgoCDl5eVdsrykpESTJ0/WXXddPDfG399f3t7eysrKkp+fn77//nslJyfrxIkTateunZ5++mkFBATYo3QAAADUALuEzilTpkiSlixZctnlHh4eGj58uG36nXfeUWFhoW6//XadPn1a3bt31/Tp09W6dWutXLlS48eP17p16+TsXL07Pvn4NLn2nbAzX9+mji7B4XgPUNfxGW4Y70FD2MeGjj6uGbXuQqLk5GS9+eabWrFihRo3bqygoCC9+uqrtuVjxozRK6+8oh9++EHt2rWr1mvm5RXKYrFWu4ba8OHKyfnFYW3Xhv2XHPseoO6rDZ9jR3+G6/t7UBv2T3J8P9d3taGf6eOrU1mf1ZrQWVJSopkzZyojI0MpKSm6/vrrJUmHDx/W8ePHFR4eblvXarXKZKo1pQMAAKAKV/VEoqIiY26Aa7VaNWnSJJ0/f75C4JQuXmQUGxurzMxMSdKaNWt04403VlgHAAAAtVuVw4WlpaVatGiR3nvvPRUUFKhx48aKjIxUdHS03NzcrqnxiIgIxcbGqqioSNu3b1e7du30wAMP2JZPmzZNd999t6ZPn67HHntMFotF/v7+Wrhw4TW1CwAAAPuqMnQuWbJEn3/+uRITE9WqVSudOnVKSUlJWrhwoaKjo6+qsUmTJlWYTk1Ntf3/22+/rXS7YcOGadiwYVfVFgAAAGqPKkNnWlqa1qxZI39/f0lSYGCgbrrpJg0fPvyqQycAAAAapirP6Tx//ry8vb0rzGvWrJnKysoMKwoAAAD1S5Wh809/+pPmz59vC5llZWVKSEhQ586dDS8OAAAA9UOVh9dnzpypRx55RO+9955atGihM2fO6Prrr9eyZcvsUR8AAzTxdJOHu7tDaygqKVFhAc/lBoCGosrQ2bp1a23atElffvml8vLy1LJlS91yyy3cJxOowzzc3XXbc2EOreFAzCYVitAJAA1Fpcnxm2++UYcOHXTkyBFJUtOmTdW06cU7zP96pXlISIgdSgQAAEBdV2nojIqK0r59+zR06NDLLndyctJ//vMfwwoDAABA/VFp6Ny3b5+kiyOeAAAAwLWo8ur13z7z/Ld69epV07UAAACgnrrsSOepU6cUFxcnq9Wq48ePa9y4cRWWnz9/Xlar1S4FAgAAoO67bOhs3bq1evToobNnzyo9PV0dO3assNzNzY2RTgAAAFRbped0jho1StLFx16GhTn21ioAAACo26q82ebAgQO1efNmZWdn2w6pl5WV6fvvv9fcuXMNLxAAAAB1X5Wh8+9//7u2bdsmLy8vlZWV6brrrtPRo0c1aNAge9QHAACAeqDK0Ll161a9++67ys3N1erVq5WUlKS33npLe/bssUd9AAAAqAeqvGWSk5OT2rRpo6CgINvN4EeOHGm7jycAAABQlSpDZ+vWrXXgwAH94Q9/0IULF5STk6Pz58+ruLjYHvUBAACgHqjy8Pqjjz6q0aNHa+PGjRo2bJhGjBghk8mkP//5z/aoDwAAAPVAlaHznnvuUadOneTn56epU6cqKChI58+f15AhQ+xRHwAAAOqBKg+vS1KrVq1kMl3Mp+Hh4RoxYoTWrFljaGEAAACoPyoNnSdOnNDIkSPVuXNnTZw4UYWFhZKkkydPKioqSklJSXYrEgAAAHVbpaEzJiZGvr6+WrhwocrKyrR06VLt2rVLw4YNk5OTk9atW2fPOgEAAFCHVXpO58GDB7V161Z5enoqJCREw4cP17p16/S3v/1NY8aMkZOTkz3rBAAAQB1Waei0WCzy9PSUJDVv3lw5OTl68cUXFRERYbfiAAAAUD9U60IiSXJzcyNwAgAA4Hepduj89ep1AAAA4GpVmiRLSko0btw42/SFCxcqTEvS8uXLjasMAAAA9UaloXP8+PEVpjt27Gh4MQAAAKifKg2dEydOrPHGkpKSlJeXp5iYmEuWnTx5Us8884zOnj2rRo0aad68eQoODpYkbd++XYmJiSotLVVwcLDi4uJsFzkBAACg9qv2OZ3XIjMzU5MnT9brr79e6TpPPvmkhg8frk2bNmn69OmaOHGiLBaL8vPzFR0draSkJG3ZskXBwcGKj4+3R9kAAACoIXYJnWvXrlVoaKhGjx592eXZ2dn67rvvFB4eLknq1q2bnJyctH//fqWnpyskJESBgYGSpKioKKWlpclsNtujdAAAANQAu1ySPmXKFEnSkiVLLrv89OnT8vX1lYuLi22ev7+/Tp8+raysLPn7+9vm+/r6ymw2Kz8/Xy1atKhW+z4+Ta6hesfw9W3q6BIcjveg/qvvfVzf9686GsJ70BD2saGjj2vGVYfOvXv3ysvLy3a+ZU2wWCyXne/s7HzFZdWVl1coi8Va7fVrw4crJ+cXh7VdG/Zfcux7UN81hD6uDfvo6M9wfX8PasP+SY7v5/quNvQzfXx1KuuzKpPbjh071KtXL0nSq6++qjFjxmjYsGF69913a6y4Vq1aKTc3t0LAzM7Olp+fnwICApSdnW2bn5ubK1dXV3l5edVY+wAAADBWlaFz8eLFtot6Vq9eraVLlyolJUWvvvpqjRXh5+enwMBApaamSpJ2796tCxcuqFOnTurRo4cOHz6sjIwMSVJKSop69erFzeoBAADqkCqT26lTpzRs2DAdPHhQpaWl6t69u5ydnZWXl3fNjUdERCg2NladOnXSggULNGvWLK1cuVKurq5avHix3Nzc5O3trYSEBE2bNk2lpaUKCAhQQkLCNbcNAAAA+6kydHp6eur48eNKS0tTaGionJ2dtXv3bvn6+l51Y5MmTaow/evIpiS1bdtWb7311mW369mzp3r27HnV7QEAAKB2qDJ0jh07VuHh4TKZTFq9erW+/PJLjR07VrGxsfaoDwAAAPVAlaFz+PDh6tu3r5ydneXl5aWCggKlpqaqXbt2digPAAAA9UGloXPv3r1X3DAnJ0ddunSp8YIAAABQ/1QaOseOHStJcnJyUlFRkdzc3OTj46OzZ8+quLhYLVu21NatW+1WKAAAAOquSkPn/v37JUlJSUk6f/68nnrqKbm5uamsrEyLFi1ScXGx3YoEAABA3VblfTr/+c9/avr06XJzc5Mkubq6aurUqRWuPAcAAACupMrQ2bhxYx09erTCvIMHD8rT09OwogAAAFC/VHn1+mOPPaa//vWvCg8Pl7+/v3766Sdt3LhRzz33nD3qAwAAQD1QZegcNWqU2rRpo7S0NO3Zs0ctWrTQ8uXL1bVrV3vUBwAAgHqgWg8w54lAAAAAuBaVhs4HH3xQTk5OV9z4zTffrPGCAAAAUP9UeiFR79691atXL7Vu3Vrff/+9unbtqsGDB+uuu+7SiRMnFBwcbM86AQAAUIdVOtL5yCOPSJIGDx6sFStWqEOHDrZl/fr10+TJk42vDgAAAPVClbdMOnHihIKCgirMa9WqlbKysgwrCgAAAPVLlaGzc+fOmjNnjvLz8yVdfOb6rFmzdOeddxpeHAAAAOqHKq9ef/HFFzVlyhTdddddMplMKi8vV9euXbVw4UJ71AcAAIB6oMrQ6e/vr3feeUc//vijcnJy5Ofnp4CAAHvUBgAAgHqiytD5zTff6KOPPlJubq6aN2+ufv36EToBAABwVa54Tue8efM0bNgw7d69WwUFBUpPT9ewYcMUHx9vr/oAAABQD1Q60rl+/Xp9+OGH2rhxo9q1a2eb/+233+rxxx/XzTffrMjISHvUCAAAgDqu0tD59ttv64UXXqgQOCWpffv2mj17tl5++WVCJ+qsZt4eMrlU6ymwhjCXm3U2v8hh7QMAYG+Vfut+//33ld4WKTQ0VE888YRhRQFGM7mYtPennQ5rv0uruxzWNgAAjlDpOZ1Wq1Xnz5+/7LKSkhK5uLgYVhQAAADql0pD56233qpNmzZddtmmTZt02223GVYUAAAA6pdKQ+e4ceP00ksvacuWLSovL5ckmc1mvffee1qwYIH+9re/2a1IAAAA1G2VntPZpUsXzZ49W7Nnz9aMGTPk5eWlvLw8eXp66oUXXlDnzp3tWScAAADqsCtevhsWFqa+fftq//79ys3NlY+Pj+644w65ubnZqz4AAADUA1XeM8bd3b3Sq9ira/v27UpMTFRpaamCg4MVFxcnT09P2/JPP/1UiYmJtuni4mKdOHFC7733njp27KiwsDA5OTnJZLpY7n333aexY8deU00AAACwH8NvVJifn6/o6GitWbNGgYGBSkpKUnx8fIWnGvXs2VM9e/a0TU+ePFn9+/dXx44dde7cOeXk5Ojzzz/ninkAAIA66oqPwawJ6enpCgkJUWBgoCQpKipKaWlpMpvNl10/LS1NP/zwg6ZOnSpJ2r9/v5o0aaJHH31U4eHhiouLU3FxsdFlAwAAoAYZPtKZlZUlf39/27Svr6/MZrPy8/PVokWLCuuWlZXppZdeUnx8vO1QelFRke68807NmjVLLi4ueuqpp5SQkKBnn3222jX4+DSpmZ2xI1/fpo4uweHq+3tQ3/evOur7e1Df9686GsJ70BD2saGjj2uG4aHTYrFcdr6z86WDrFu2bJGfn59CQ0Nt88LCwhQWFmabHjdunMaPH39VoTMvr1AWi7Xa69eGD1dOzi8Oa7s27L9k7HtQG/aRPqaPjVbf34PasH+S4/u5vqsN/UwfX53K+szww+sBAQHKzs62Tefm5srV1VVeXl6XrJuWlqYhQ4ZUmLdly4l73zgAABZzSURBVBZ9+eWXtmmr1SpXV1fjCgYAAECNMzx09ujRQ4cPH1ZGRoYkKSUlRb169bIdPv+V1WrVF198oW7dulWY/9NPPykhIUElJSUqLy/X66+/rnvvvdfosgEAAFCDDD+87u3trYSEBE2bNk2lpaUKCAhQQkKCsrOzNXbsWCUnJ8vPz09nz57VL7/8ooCAgArb//Wvf9Xp06cVGRmp8vJyde3aVZMnTza6bAAAANQgw0OndOktkX6Vmppq+7+3t7e++eabS9ZxcXHR3//+d0PrAwAAgLHsEjoBAADqorLyModezFRSVqqCcyUOa78mEToBAAAq4eriquc+fN5h7f+91zMOv4K/qKREhQWl1/w6hE4AAIBayt3VTbc9F1b1igY6ELNJhbr20Gn41esAAAAAoRMAAACGI3QCAADAcIROAAAAGI7QCQAAAMMROgEAAGA4QicAAAAMR+gEAACA4QidAAAAMByhEwAAAIYjdAIAAMBwhE4AAAAYjtAJAAAAw5kcXQAAAHVVWXmZfH2bOqz94rISNXJ1d1j7RSUlKiwodVj7qFsInQAA/E6uLq567sPnHdZ+TP/nddtzYQ5r/0DMJhWK0Inq4fA6AAAADEfoBAAAgOEInQAAADAcoRMAAACGI3QCAADAcIROAAAAGI7QCQAAAMMROgEAAGA4QicAAAAMZ5cnEm3fvl2JiYkqLS1VcHCw4uLi5OnpWWGd1157TatXr5aPj48kycPDQykpKZKk999/XytWrJDZbFZoaKieffZZubm52aN0AAAA1ADDRzrz8/MVHR2tpKQkbdmyRcHBwYqPj79kvX379mnWrFlKTU1VamqqLXAePXpUCxcu1JtvvqktW7aorKxMycnJRpcNAACAGmT4SGd6erpCQkIUGBgoSYqKilLfvn31wgsvyGT6v+b3798vq9WqZcuWqXnz5oqOjtZNN92kjz/+WL1791bz5s0lSSNHjtSMGTM0ceJEo0sHgN+trLxMvr5NHV0GANQahofOrKws+fv726Z9fX1lNpuVn5+vFi1aSJLOnj2rkJAQTZgwQbfccos2bdqkMWPGaPPmzTp9+nSF7f39/ZWZmWl02QBwTVxdXPXch887tIaY/o5tHwB+y/DQabFYLjvf2fn/juw3a9ZMK1eutE2HhYVp2bJl+uqrr2S1Wq+4bXX4+DS5qvVrA0ZI6v97UN/3rzp4D+o/+rj+o48bhproZ8NDZ0BAgL744gvbdG5urlxdXeXl5WWbd/LkSX322WeKioqyzbNarTKZTAoICFBWVpZtfnZ2doWRz+rIyyuUxXJpeK1MbfgBysn5xWFt14b9l4x9D2rDPtLH9b+PQR83BEb/LqOfa4er6efK+szwC4l69Oihw4cPKyMjQ5KUkpKiXr16VTif093dXYmJiTpy5Igkadu2bTp//rxuv/129enTR9u2bdOZM2dktVqVkpKifv36GV02AAAAapDhI53e3t5KSEjQtGnTVFpaqoCAACUkJCg7O1tjx45VcnKy/P399dJLL+mZZ56R2WxWkyZN9Morr8jNzU3t27fXE088odGjR8tsNqtjx46aNGmS0WUDAACgBtnlPp09e/ZUz549L5mfmppq+3/v3r3Vu3fvy24fGRmpyMhIw+oDAACAsXgiEQAAAAxH6AQAAIDhCJ0AAAAwHKETAAAAhiN0AgAAwHCETgAAABiO0AkAAADDEToBAABgOEInAAAADEfoBAAAgOEInQAAADAcoRMAAACGI3QCAADAcIROAAAAGI7QCQAAAMMROgEAAGA4QicAAAAMR+gEAACA4QidAAAAMByhEwAAAIYjdAIAAMBwhE4AAAAYzuToAoCGqKy8TL6+TR1dBgAAdkPorIUIJPWfq4urnvvweYe1H9PfcW0DABomQmctRCABAAD1Ded0AgAAwHCETgAAABiO0AkAAADD2eWczu3btysxMVGlpaUKDg5WXFycPD09K6yzY8cOLVq0SOXl5XJ2dta0adPUs2dPSdJjjz2mkydPysPDQ5LUpUsXzZo1yx6lAwAAoAYYHjrz8/MVHR2tNWvWKDAwUElJSYqPj1d8fLxtnZ9//llPPfWU/vnPfyo4OFjffPONRo0apU8++URNmzbVV199pc2bN8vHx8focgEAAGAAww+vp6enKyQkRIGBgZKkqKgopaWlyWw229YpLy/X7NmzFRwcLEm68cYbJV0MrMeOHZPVatUzzzyj8PBwPf300zp37pzRZQMAAKAGGT7SmZWVJX9/f9u0r6+vzGaz8vPz1aJFC0mSt7e37rvvPts6ixcvVps2bdS2bVvt2bNH3bp10+zZs+Xl5aW4uDjNnDlTy5cvr3YNPj5Nam6HYDfcq7T+o4/rP/q4/qOPG4aa6GfDQ6fFYrnsfGfnSwdZzWaz5s6dqx07duiNN96Qk5OTQkNDFRoaaltn4sSJ6t69u0pKSuTu7l6tGvLyCmWxWKtdMz9AtUNOzi+GvTZ9XDvQx/UffVz/GdnHEv1cW1xNP1fWZ4YfXg8ICFB2drZtOjc3V66urvLy8qqw3rlz5zR69GgdPXpU77zzjgICAiRJu3bt0rZt22zrWa1WOTk5XTa0AgAAoHYyPLn16NFDhw8fVkZGhiQpJSVFvXr1ksn0f4OsJSUlGj16tNq0aaNVq1apWbNmtmUFBQWKjY1VQUGBJGnFihXq37+/XF1djS4dAAAANcTww+ve3t5KSEjQtGnTVFpaqoCAACUkJCg7O1tjx45VcnKydu7cqa+//lpms1lDhw61bTt37lwNHDhQx48f14gRI2SxWNS+fXvFxMQYXTYAAABqkF3u09mzZ0/bPTd/KzU1VZI0ePBgDR48uNLtx40bp3HjxhlWHwAAAIzFiZEAAAAwHKETAAAAhiN0AgAAwHCETgAAABiO0AkAAADDEToBAABgOEInAAAADEfoBAAAgOEInQAAADAcoRMAAACGI3QCAADAcIROAAAAGI7QCQAAAMMROgEAAGA4QicAAAAMR+gEAACA4QidAAAAMByhEwAAAIYjdAIAAMBwhE4AAAAYjtAJAAAAwxE6AQAAYDhCJwAAAAxH6AQAAIDhCJ0AAAAwHKETAAAAhiN0AgAAwHB2CZ3bt29XeHi4Bg4cqIkTJ6qgoOCSdQ4ePKihQ4fq3nvv1YMPPqisrCzbsvfff19hYWEaMGCAnn32WZWWltqjbAAAANQQw0Nnfn6+oqOjlZSUpC1btig4OFjx8fEV1iktLdWkSZMUHR2tzZs3KyIiQjNmzJAkHT16VAsXLtSbb76pLVu2qKysTMnJyUaXDQAAgBpkMrqB9PR0hYSEKDAwUJIUFRWlvn376oUXXpDJdLH5Q4cOqVGjRurataskafDgwXrxxReVnZ2tjz/+WL1791bz5s0lSSNHjtSMGTM0ceLEatfg7Ox01XW7ubhf9TY1yauRl0PbD/Bq4dD2pd/Xb1eDPqaPjeboPpYc38/1vY8lx/dzfe9jyfH93ND7WKqZfnayWq3WGqilUsnJyTp58qRefPFF27w//vGP2rZtm1q0uPgmpqWlae3atXrjjTds6/Tv31/z58/XunXr5O/vrwkTJkiSsrKy1K9fPx0+fNjIsgEAAFCDDD+8brFYLt+ws3O11rlcJv7ttgAAAKj9DE9vAQEBys7Otk3n5ubK1dVVXl5ela5jsViUm5srf3//S5ZlZ2fL39/f6LIBAABQgwwPnT169NDhw4eVkZEhSUpJSVGvXr1s53NK0i233KLz589r9+7dkqTU1FQFBQXJz89Pffr00bZt23TmzBlZrValpKSoX79+RpcNAACAGmT4OZ2S9OmnnyoxMVGlpaUKCAhQQkKCysrKNHbsWCUnJ8vPz0+HDh1STEyMLly4oKZNm2ru3Llq166dJGn9+vV67bXXZDab1bFjR8XGxsrDw8PosgEAAFBD7BI6AQAA0LBxRQ4AAAAMR+gEAACA4QidAAAAMByhEwAAAIYjdAIAAMBwhM4G4sEHH1RaWpqjy0A17d69W/fcc4+jy4CDZWdna/jw4ZddtmTJEj333HN2rgjVUdnP79tvv61ly5Zdcdsff/xRnTp1Mqo0ONCVvocjIiJ09uzZK24/c+ZMJScnG1Ga3ZiqXgUA4Ah+fn5au3ato8tADXnggQccXQJqqdTUVEeXYBeMdDrQk08+WeGvlvXr12vMmDGSpP3792vUqFEaPHiwBg8erE2bNkm6+Bd0eHi4Ro4cqfvuu09PP/205s+fb3uNjz76SA8++OAV2/3iiy80YsQIhYeHa8iQIdq6daskKTIyUtu3b5ckbd26VR06dLD95ZWYmKiXX3655nYe1VJWVqbY2FiFhYUpPDxcM2fO1C+//KKPPvqowgjYkCFDFBsbK0kqLCxU165dVVxc7KiyUQ27d+/W8OHDNWXKFIWFhSksLEzbtm3T2LFj1b9/fz3++OM6efKkbdTr/PnzevLJJzVw4EA98MADOnLkiIP3AFdSVFSkJ598UpGRkRowYIA+++yzCqPTBw8e1JAhQxQeHq7HH39cgwcPtj2Vz2q1as6cORo8eLD69OnTYAKJI9j7e/iTTz7R8OHD1adPHz355JMqKyuTJLVv3145OTkqLy9XfHy8+vXrpyFDhuj555+v8FoHDhzQyJEj1b9/fz366KMqKCio8ffESIROBxoxYoTWrVtnm37vvfd0//336+eff1Z0dLTmzp2rdevW6bXXXlNiYqK+++47SdKxY8c0f/58paWlafTo0Vq/fr3tg/v2228rKiqq0jbPnj2rSZMmKTo6Whs2bFBiYqKefvppZWRkqH///tqxY4ckaceOHWrevLl27dol6eIPEYd77W/ZsmXKzMxUamqqUlNT5e7urhdeeEE9e/ZURkaG8vPzlZubq5ycHO3cuVOStH37dnXr1k2NGjVycPWoyqFDhzRmzBht2rRJISEhiouL04IFC5SWlqZvv/3WFkIk2f7o27x5s5KTk3Xq1ClHlY1qOHPmjEaNGqX169fr4Ycf1qJFi2zLzGazJk2apIkTJ2rDhg168MEH9Z///Me2vKysTF26dNG6des0e/ZszZs3zxG70CDY+3u4sLBQKSkp2rx5s/bv32/7vf2rtWvX6uDBg9q4caNSUlIu+TnPysrSqlWrtGXLFhUWFmrjxo01+XYYjsPrDtS1a1dZrVYdOHBA3t7eOn78uPr06aOdO3cqJydHEydOrLD+119/LT8/P7Vo0UJt2rSRJN10000KDAzUhx9+qE6dOunYsWPq379/pW0ePHhQ119/vTp37ixJCgwMVLdu3bRz504NGDBAEyZMkCTt2rVLjz32mHbu3Kn27dvLyclJN954o0HvBCqzY8cOTZo0Sa6urpKkhx56SFFRUXJ3d1ePHj20Y8cOWa1WDRo0SJs3b1Z2drY++ugjDRw40MGVozoCAgJ0yy23SJLatGkjV1dXNWnSRJLUsmVLnTt3zrZuenq6ZsyYIWdnZzVt2lT/8z//o9OnTzukblStVatWtt+zN998s1asWGFbdvToUZWXl6tPnz6SpDvvvFPBwcG25S4uLrr33ntt2+bl5dmx8obF3t/D99xzj1xcXOTi4qIbbrhB+fn5FZZv375dERERtkGDESNG6K233rIt79u3rxo3bmxr97+3r+0InQ52//33a926dWrWrJkiIiLk6uqq8vJy3XDDDXr//fdt6505c0ZeXl7av3+/7QP3q6ioKL333ns6evSohg0bJpOp8m61WCyXnWc2mxUcHCxnZ2d99NFH8vHx0T333KPVq1erTZs2GjBgQM3tNKrtv/vLYrHY/poeMGCAtm7dKqvVqvvvv18FBQX65JNPtGfPHtuhdtRubm5uFaav9LMrXTzsWt114Vi/7R8nJ6cKy1xcXPTfT6B2cXGp8P9ft/nvbVHz7Pk9/OsAwq/++3NgMpkqzHN2rnhAuqrtazsOrztYZGSktm3bprS0NN1///2SpNtuu00//vij7dB2RkaG7rnnHp08efKyr9G/f39lZGTogw8+0IgRI67Y3q233qpTp05p//79kqTjx49r586d6tatm6SLQSYhIUHdu3eXn5+frrvuOq1Zs4ZD6w7y5z//We+8847KyspktVr1j3/8Q927d5ck9erVS3v27NFXX32lO+64Qz169NDy5ct166236rrrrnNw5ahpvXr10rvvvqvy8nJduHBB//rXvxxdEn6noKAgeXh4aNu2bZIuHoE6evQoAdNB7P09fCV33323NmzYoJKSEpnN5gqH/usD/lR2MG9vb91xxx3Kzc1V27ZtbfOWLl2qxMRExcfHq7y8XHPmzFFwcHCFc7x+5erqqrCwMJ06dUp+fn5Vtrd48WLFx8erqKhIkjRnzhx16NBB0sUfnOTkZPXo0UOS1KNHD3344Ye6+eaba3K3UU3jx4/X/PnzFRkZKbPZrJCQEM2ZM0eS1KRJE/3xj3+UxWKRm5ubunXrpjNnzmjq1KkOrhpGePzxxxUTE6N7771XzZo1U7t27RxdEn4nk8mkJUuWaPbs2Vq0aJHatWsnHx8fzsN2EHt/D1/JkCFDdPz4cUVGRuq6667T9ddfLw8Pj9/9erWNk7Wujc3iEsXFxfrLX/6i6dOnKzQ01NHlAACqMG/ePI0ZM0bNmzdXZmamIiIi9PHHH8vT09PRpeF3qKnv4fT0dOXl5SkiIkKSFBMTIw8PD02fPr2mSnUoRjrruPT0dE2bNk2DBg0icAJAHdGqVSs9/PDDtnP4XnzxRQJnHVWT38PBwcFauXKlVq5cqfLycnXo0EFPPPFEDVXqeIx0AgAAwHBcSAQAAADDEToBAABgOEInANRyxcXFysnJcXQZAHBNCJ0AUMtFRUXpyy+/dHQZAHBNCJ0AUMv99nGYAFBXccskALCjL7/8UvPmzdOxY8fUokULTZkyRX379tX8+fP16aef6syZM/L29tbUqVM1aNAgjRs3TpmZmZoxY4ZOnDihFi1aaMOGDWrZsqX+/e9/q2nTpnrooYc0evRoR+8aAFwRI50AYCd5eXkaO3asIiIitHfvXsXGxurpp5/Wa6+9piNHjmjt2rXat2+fHn74Yc2ZM0dlZWVavny5AgICNH/+fI0bN06StHPnTnXq1Emff/65nnrqKSUkJCgrK8vBewcAV0boBAA72bZtm3x9fTVq1CiZTCZ16dJFa9as0ZAhQ/TKK6+oadOmOn36tDw8PFRYWKjz589f9nW8vb31wAMPyGQy6b777pOLi4tOnTpl570BgKvD4XUAsJO8vDy1bNmywryQkBD9+OOPmjlzpg4cOKDrr79eN9xwgyTJYrFc9nV8fHwqTJtMpkrXBYDagtAJAHbi5+d3yWHw1atX6+OPP9YNN9ygpUuXytXVVUeOHNHGjRsdVCUAGIPD6wBgJ3fffbfy8/OVkpKi8vJy7dmzR4sXL9a3336rRo0aycXFRWfOnNGCBQskSWazWZLk6uqqwsJCR5YOANeMkU4AsBMvLy+99tpriouLU0JCgvz8/JSQkCAfHx/NmjVLd9xxh5o1a6bhw4fr66+/VkZGhlq0aKEhQ4YoNjZWJ0+etB16B4C6xslqtVodXQQAAADqNw6vAwAAwHCETgAAABiO0AkAAADDEToBAABgOEInAAAADEfoBAAAgOEInQAAADAcoRMAAACG+38fNS7LEmIqtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#hue = ['DecisionTreeRegressor', 'mutual_info_regression', 'ElasticNet', 'f_regression', 'Lasso', 'RandomForestRegressor', ]\n",
    "g = sns.catplot(x = 'catn', y='OR', hue='da',order=order,data=top_OR(knn, ['da', 'catn'], score='r2').reset_index(), kind='bar', palette='Greens', height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "g.set_ylabels('Odds Ratio')\n",
    "plt.legend(loc='upper center')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('graphs/adjusted-top-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = clean[clean['model']=='RandomForestRegressor'][['Drug', 'fs', 'da', 'catn', 'n_estimators', 'max_depth', 'max_features','r2_score', 'r2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#hue = ['DecisionTreeRegressor', 'mutual_info_regression', 'ElasticNet', 'f_regression', 'Lasso', 'RandomForestRegressor', ]\n",
    "g = sns.catplot(x = 'max_depth', y='OR', hue='max_features',data=top_OR(rf, ['max_depth', 'max_features'], score='r2').reset_index(), kind='bar', height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "g.set_ylabels('Odds Ratio')\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('graphs/adjusted-top-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "\n",
    "g = sns.catplot(x='n_estimators', y='r2', hue='max_depth', data=rf, kind='bar',palette='Greens',height=5, aspect=1.9, legend=False)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "plt.legend(title='max_depth',ncol=2)\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-depth.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='max_features', y='r2', data=rf, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-depth.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='max_depth', y='r2', hue='fs', data=rf, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='n_estimators', y='r2', hue='max_depth', data=rf, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "en = clean[clean['model']=='ElasticNet'][['Drug', 'fs', 'da', 'catn', 'alpha', 'l1_ratio','r2_score', 'r2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#hue = ['DecisionTreeRegressor', 'mutual_info_regression', 'ElasticNet', 'f_regression', 'Lasso', 'RandomForestRegressor', ]\n",
    "g = sns.catplot(x = 'alpha', y='OR', hue='l1_ratio',data=top_OR(en, ['l1_ratio', 'alpha'], score='r2').reset_index(), kind='bar', palette='Greens', height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "g.set_ylabels('Odds Ratio')\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('graphs/adjusted-top-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='alpha', y='r2', data=en, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "#plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}