Supervised_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model results with 100,000 samples for MLP and RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "\n",
    "# score_files = glob.glob('scores_10w_*.csv')\n",
    "# temp=[]\n",
    "# for f in score_files:\n",
    "#     temp_df = pd.read_csv(f, index_col=0)\n",
    "#     name = f.split('scores_10w_')[-1].split('.')[0]\n",
    "#     if name == 'glove_simplefeats':\n",
    "#         temp_df['accuracy'] = temp_df['accuracy']*100\n",
    "#         temp_df['f1 score'] = temp_df['f1 score']*100\n",
    "#     temp_df['feat']=name.rstrip('s')\n",
    "#     temp.append(temp_df)\n",
    "    \n",
    "# all_scores0 = pd.concat(temp)\n",
    "# all_scores0.to_csv('all_scores_10w.csv')\n",
    "\n",
    "all_scores0 = pd.read_csv('sample_data/all_scores_10w.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_model0 = all_scores0.sort_values(['model','accuracy'], ascending=False)\n",
    "by_feat0 = all_scores0.sort_values(['feat','accuracy'], ascending=False)\n",
    "by_model01 = by_model0[['model', 'accuracy', 'feat']]\n",
    "by_model01 = by_model01.pivot_table(values='accuracy',index='model',columns='feat')\n",
    "by_model02 = by_model0[['model', 'f1 score', 'feat']]\n",
    "by_model02 = by_model02.pivot_table(values='f1 score',index='model',columns='feat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "ax = by_model01.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('Accuracy Score Sample Size 100k (by model)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = by_model02.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('F1 Score Sample Size 100k (by model)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_feat01 = by_feat0[['model', 'accuracy', 'feat']]\n",
    "by_feat01 = by_feat01.pivot_table(values='accuracy',index='feat',columns='model')\n",
    "by_feat02 = by_feat0[['model', 'f1 score', 'feat']]\n",
    "by_feat02 = by_feat02.pivot_table(values='f1 score',index='feat',columns='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = by_feat01.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('Accuracy Score Sample Size 100k (by feature)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = by_feat02.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('F1 Score Sample Size 100k (by feature)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Smaller 10,000 samples with SVM model results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # load all scores\n",
    "# glove_simplefeat = pd.read_csv('scores_1w_glove_simplefeats.csv', index_col=0)\n",
    "# glove_simplefeat['feat'] = 'glove_simplefeat'\n",
    "# glove = pd.read_csv('scores_1w_Glove.csv', index_col=0)\n",
    "# glove['feat'] = 'glove'\n",
    "# simplefeat = pd.read_csv('scores_1w_simplefeats.csv', index_col=0)\n",
    "# simplefeat['feat']='simplefeat'\n",
    "# tfidf = pd.read_csv('scores_1w_tfidf.csv', index_col=0)\n",
    "# tfidf['feat']='tfidf'\n",
    "\n",
    "# # concatenate\n",
    "# all_scores = pd.concat([glove_simplefeat,glove,simplefeat,tfidf])\n",
    "# all_scores = all_scores.reset_index()\n",
    "# all_scores = all_scores.rename(columns={'index':'model'})\n",
    "\n",
    "# all_scores.to_csv('all_scores_1w.csv')\n",
    "all_scores = pd.read_csv('sample_data/all_scores_1w.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "by_model = all_scores.sort_values(['model','accuracy'], ascending=False)\n",
    "by_feat = all_scores.sort_values(['feat','accuracy'], ascending=False)\n",
    "by_model1 = by_model[['model', 'accuracy', 'feat']]\n",
    "by_model1 = by_model1.pivot_table(values='accuracy',index='model',columns='feat')\n",
    "by_model2 = by_model[['model', 'f1 score', 'feat']]\n",
    "by_model2 = by_model2.pivot_table(values='f1 score',index='model',columns='feat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "ax = by_model1.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('Accuracy Score Sample Size 10k (by model)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = by_model2.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('F1 Score Sample Size 10k (by model)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_feat1 = by_feat[['model', 'accuracy', 'feat']]\n",
    "by_feat1 = by_feat1.pivot_table(values='accuracy',index='feat',columns='model')\n",
    "by_feat2 = by_feat[['model', 'f1 score', 'feat']]\n",
    "by_feat2 = by_feat2.pivot_table(values='f1 score',index='feat',columns='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7vSjv+833KbT1O/pAETr+Sg6q56WUNuKjYewx4FfkkH1zSukc8sX3MeQxYD9p7wdR8T5fKOrwH4pW0PfIwfdJ8rifMt8jj0c6kzwrYZk7gF+nYkr4iuWzybPAHQ/0Is9I9wQVv+vFeLOryd3ofkr7nUq+WXFNSumP5PFCJ5DD/GPkz7kt9Wwy0C+ltCv5+2+a1fHU4vdqUFG+5t0VP6KddWkTct28fT7etyQttGxZktrnAOD+iCgbYwJ57Ect+RkyW5JbLi4hDxB/HNg5IuYV44VaW/8V8p3mv5GnAT+AVgZ3R8Sd5AkmvkTuGnUC+WLvp8C66cOpqPcmdyk6iXwxPBTYJiJerjjWa+SLsL9+wjiWpm2fI09X/nfybHc3FefdkPzA17MrNj+afCf9u+QuR58D/l98+HyY/YDfki98byTP0Ld/RJzfShluJAer9Yv9ziYPnt86imfYlOwzi/w8n5+TJz34M3lSiIPJIWCvYrtGPpxy+mryZBH9+PAZWZuwYGaQH4B7NrkV50Fg24iY18L2/0O+8P5mUd6jin0PaukE7fyOmqxJDtwrkp/188+ibE0/IyI/N6xpfM3l5Mk4aovyP96md9+yg8mfd9NU2k+QZ5IrfW5WUX/PAA5KKbXUEtvUza35+vHk7/w88tTc/wK2j49PQX5z8d/L2/VO+KB76xfIv9fXFud5DdgqIl5rRz27inyj4c/AAcXn8RU+vLFxErmOPN+GYrW1Lm0PPBgVDzSWpMVBTWNjS5MNSVqcFd21XgF2iAjvJneQlB8yemxEtDomTdWRUroEqIuI/edj3/OBtSJiQboYLlSKbrqvAd+M4uHRkrS4sBuepI9IKa0M7E+eqOJZPj4+RFrYnQo8klIaWbSMtSqldAiwLnnij31a2XxRcyD5YcA3dHVBJKmz2Q1PUnM15K5yfcnd32x+1iIlIl4gT0BxWiubVlqf3C3wNxHxsTGLi6piNsIfAgf5t0DS4shueJIkSZJUoqu64fUmP8PhDT4+BaokSZJULT3ID1l+mNZnlZU+oqvC0gbAvV10bkmSJC1+tuDD5/lJbdJVYekNgPfem05Dg90Aq2HYsAFMmvSJsztLXcK6qe7M+qnuyrpZPbW1NQwZ0h+K60+pPboqLNUDNDQ0GpaqyM9S3ZV1U92Z9VPdlXWz6hz6oXZzNjxJkiRJKtFqy1JK6UDg+xWLBgPLFz8/Ar5YHGd0RPyuIwopSZIkSZ2t1bAUEZcClwKklHoC/wB+DuwBrAqsAQwEHkwpPRoRD3VccSVJkiSpc7S3G94PgLcj4vfA7sCYiJgXEe8BVwH7V7uAkiRJktQV2jzBQ0ppSeAY4HPFohWAVyo2eRVYqz0nHzZsQHs2VyuGDx/Y1UWQSlk31Z1ZP9VdWTelrtee2fC+BdwQES8Vr8tapdo1y8ikSdOc6aVKhg8fyMSJU7u6GNLHWDfVnVk/1V1ZN6untrbGG/Sab+3phrc3MKbi9QTy05CbLEduXZIkSZKkhV6bWpZSSkOAVYAHKhbfABySUhoLDAD2AQ6tegklSZIkqQu0tRveKsAbETG3YtkFwMrAE0Av4PcRcU+VyydJkiR1mnHjxvWqre3xB2BzoEdXl0cdqh64r6Gh/pvrrbfenLIN2hSWIuJhcmCqXDYPOHqBiyhJkiR1EzU1NYf16dNvs6FDl55cW1vr4PpFWENDQ8277761+cyZ0w4Dzinbpj0TPEhajA0Z3Iu6Xr3na9/5mdFp3pzZvDel9CaPJEkdpra2x8FLLLHkdIPSoq+2trZx8OBh02bPnnkQhiVJC6KuV29ePH2PTjvfSj++FjAsSZI6V2Nj4+AePeomdXU51Dnq6nrObWxsHNrS+vY+lFaSJElalNXU1NR0dRnUSYrvusVMZMuSJEmS1IKBg/qu3ad3XdWvmWfNnjdv6vszn6j2cVVdhiVJkiSpBX1619XtfMwNVT/u2LN2rWvLY4cnTBjfa99991xz222/+M7JJ5/+ctPyp556su9hhx3ymaOOOnb8OeeMHnXffY+Ma77vbrvtsGavXr0b6urqGhsboUeP2sbDDjvi1c0229InHreRYUmSJEnqxgYMGDDvscfGDaqvr6dHjzyb+W233TR04MBB81rbd/Toc54bOXLUHIC7775j0GmnnbzSjTfe+mTPnj2dwKINDEuSpIWeszVKWpT16dOnYdSolWb885/3D2xqFXrssXGD1lpr7ffbc5yNNtp06tSp79dNmTK5x5JLDm81aMmwJElaBDhbo6RF3VZbbfPenXfePmSzzbac+thj4/qNGvWpmY2N7Wscuv76vwxbeullZhuU2s6wJEmSJHVz22yz/eT//d+Ll2toaOC2224Zus022797++23tjjldZNjjz1q1bq6usZ58+bVLLnk8Dknn3zGC51R3kWFYUmSJEnq5gYOHNiw4oqjZvzrXw8OePLJxwcec8wPXm1LWKocs6T28zlLkiRJ0kJg6623fe/CC89bfpVVVp1RV/3ZzFXCT1mSVHUDB/WlT2//FyNp4Tdr9rx5Y8/atUOes9TefbbZZvvJ55wzesVDDvn2ax9ft9m6Tf9ecsnhc66++vp/L2gZZViSJHWAPr3r6IjnkrRk7Fm7dtq5JC1epr4/84mufCjRyJGj5lx//S1PAQwYMKDhzjsfeKxp3emnnzkeYK+99plUtm/Tfpp/hiVJktppTv3c+ZpyfH7MmjuHqZNnd8q5JEkfZVjqZnxWiCR1f7169OQrVx/WKee6Zu8LmIphSZK6gmGpm/FZIZIkSVL34Gx4kiRJklTCsCRJkiRJJQxLkiRJklTCMUuSJElSC4YO7rV2j17Vf3Bc/ZzZ896dMueJ1rabMGF8rwMO2HuN5ZZbfhZAY2MjM2fO6PGFL2w36cgjj3l9Qcrw5z9fNezxx8cNbJqCfEGOc9FFF6wwbNiSHwyEHzx4ibkXXHDxcwty3JY8+ugj/e688/+GHHvsDz/2vKlqMyxJkiRJLejRq3ddR0y+tdKPr61r6yRbQ4YMmXvFFdc+0/T6jTde77nffnutscMOX3p3tdVWn1X1ws2H9dffcPKChq62euGF5/u+9967PTvjXIYlSZIkaSHy9ttv9YRG+vcfUH/iiT9cccKE8X2nTJnSc9lll5s1evS5z0+c+FbPH/7w2FVGjlxx5osvvtBviSWWmPvzn//qhSFDhtZfd901Q6+44rJl+/btVz98+FKz+/bt0wAwbtxD/c8991crzJkzt3bQoIHzfvCDE15eaaWVZ3/zmwemlVZaZcYTTzw2cM6cObXf/e7RE6699pqlX3llQp/ddtvjrUMO+dbbn1TWTzrugAED573yyoS+J510+gvvvPN2zzFj/rBcfX19zVJLLT37hBN+On7o0GH1v/jF6cs//vijg2praxs33njTyQcccPDbl102ZtnZs2f1OP/8c5f5zneOfLMjP2vHLEmSJEnd2Hvvvddz3333+Mxee+3y2R122GrtCy88f7kTTzz1hQkTXu7ds2fPxksuufI/1177t6fmzJlde9ddtw8GmDDh5b777nvgm1dfff2/+/cfUH/jjX8d9sYbr/e8+OILlz///Iv+M2bMn56dOXNGD4A5c+bUnHrqiSsdffRxE6688tpndt5594knnfSjlSrLcNVVf31m6623nfTb35498swzz3nhvPMujKuuunzZpvWPPPLQEvvuu8dnmn7uv/8fA1s77qc+tfLMv/xl7NMjRoyY+4c/XLD8Oef87r9/+tNfntlgg42mnH32mcu/8srLvR599OHBV1557TMXX3zZf1577dU+ffr0aTjggINfX3/9DSd3dFACW5YkSZKkbq2pG15DQwO/+MVpK4wf/2LfzTbbcmrPnj0bhwwZMu+yy8YMf/nl8X3eeOON3jNm5AA0cOCgeWuuufZMgFGjPjXz/fff7/Hoo48MSGn16UsttfQ8gO22++KkceMeHvTCC8/17t9/QP266643A2CnnXZ979xzz1pxypQpPQA23XTzKQDLLDNizmqrrT69X79+Df36rTin6VxQ3g3v2Wf/3eeTjrvGGmtOA3j88Uf7T5r0Tq/DD/9GAmhoaGDAgIH1I0YsN6dXr14NBx+83+obbbTJ5O9854jX+vTp09ihH3YzhiVJ3dKc+rkMHz6w0843a+4cpk6e3WnnkySpvWpraznuuB+9st9+e3724ot/v/RKK60885JLLlpu9933fGuXXXaf9P77U+oaG3OW6NWrZ8OHe9bQ2NhYU1NT09jQ8OHiHj1yFGhoaKxpfq7GRmhoqAegZ8+ejR/uU9vmsNLacZuCT319fU1Kn5527rm/ex5g1qxZNdOmTe1RV1fHJZdc+eyDD94/8P777x182GHfWP2ccy6Itp6/GgxLkrqlXj168pWrD+u0812z9wVMxbAkSere6urqOPTQ775y+uknr7zZZlu+u8UWW727115fnfTGG6/3fOaZfw9cb70Npra07/rrbzjtvPPOGfn666/1XGaZEXPvuuv2If37969fZZVVZ02dOrXHY4+N67fuuuvNGDv2+iFLLjl8zpAhQ+sXpKxtPe4663xu2tlnn7ni888/13uVVVadfcEF5y77zjsTe+6zz/5v/frXZ4688MJLYvPNt5z64ovP9XvppRf61NX1aKyvb/hYEOsIhiVJkiSpBfVzZs/LM9dV/7jzu+/WW2/7/tVXXzHtzTdf7x3xbP/77rtnaF1dz4bVVkvTX3/99V4t7bfUUkvP+853jnzlqKMOW61Pnz4Nyy8/ciZA7969G0888dQXzz77zJGzZs3qMWDAwHk//ekZL85v+Zq09bhLL73MvO9//wfjTzzx+JUbGhoYNmzJuaee+vMXhw4dVr/66p+e/tWvfvmzvXr1blhppZVnbL31tlPGj3+p92WXXbLs6NE/X+7YY4/v0OnDa5qa6jrZKOClSZOm0dDQJefvtoYPH0hHTE/ZkpV+fC0TJ7Z4A0L6QFfUzc5uWfJ3oXqGDx/Izsfc0GnnG3vWrots/bRuLp6GDx/o914ltbU1DBs2AOBTwPjWtn/sscfHL7/8yu90dLnUfbz66gtLrrvuOqPK1jkbniRJkiSVaFOTYkppTeA3wGCgHvh2RIxLKf0IOLA4zuXAKRFhU5EkSZKkhV6rYSml1A+4Dfh6RNyUUtoV+FNK6fvAXsB65AB1K/AMcE0HlldSYeCgvvTp7bBDSerOhgzuRV2v3vO17/zMCDpvzmzemzJnvs4n6ePacqW1PfBCRNxUvL4ReAk4ArgiIqYDpJTGAPtjWJI6RZ/edZ0+JkSS1D51vXp3+ng6MCxJ1dKWsLQa8GZK6WJgbWAy8D/ACsAdFdu9CizfnpMXg+26tTlz6+nVs0frGy7EOvNZNlJ35u+CuivrptrD+iJVT1vCUk9gR2DriPhX0Q3vJuDZkm3bNRf7wjAbXlfM6NTZnG1n4eT/DKvP34XqsX5Wl3Vz4dUVvwvWl4+qmA1Pare2hKXXgf9ExL8AIuKGlNJFQAMwomK75citS5IkSdIiYeASvdfu07NX1QcJz5o7Z97UybOfqPZxVV1t+eJvBs5KKa1XzIC3JdAInA2clFK6EJgHHARc0lEFlSRJkjpbn5696jriuWrX7H1B3VRmt7rd3/9+45Arr7xsmfr6hprGxga22Wb7SYMGDaq/++47h1xwwcXPVW77k5/8z6hVVlltRv/+A+rPOWf0qOOO+9FLu+765Xeb1o8Z84elLr749ytcccVfnho5cpSD29qg1ecsRcSbwG7A+Smlp4FfA1+OiLHAdcBDwNPAOODSDiyrJEmStNh4/fXXel544fnLn3vu7/575ZXXPnPRRZf955577hy6xBJD5j3//H/7T5z49gcNHzNmzKgdN+7hwbvssvskgKFDh8695547h1Qe7777/jGkX79+7Ro2s7hrU5NiRPwD2Khk+RnAGdUulCRJkrS4e/fdd+vq6+fVzJgxo3bo0GH1AwYMaPjxj09+qXfvPo0bbrjx5JtuGjv0a1/7+tsAt9128xJrrLHW1KFDh9UDfOYza0599tl/D5g+fXpt//79G1555eVeffv2rTcstU+rLUuSJEmSOt8aa6w5c4MNNp683357rnnggYdjAKgAABcYSURBVHt/evTony1XX99Qs9JKK8/eaadd37nrrtuHNm172203D/vSl3Z5p+l1jx61jeus87n377rr9sEAt9xy05Ctttrmva54Hwszw5IkSZLUTZ100mkTrrzyuqd23nm3t996663eRxzxrU/ffPPflth4482mTZ06te7ll8f3euutN+veeOP1PltuufX7lftus8327zZ1xXvggfuGbLfdDpO75l0svAxLkiRJUjd0++23DR479q9Dll12ubl77fXVSWeeefaL3/rW4RP+/vcbl6ypqeELX9hu0t//fuPQv/3thmFbbfWFSbW1H72032yzLaY+99x/+z/77L/7DBo0aO7gwYPtgtdOVZ8GUZIkSV1jTv3cTn2206y5c5g6ufUZ3TR/+vbt03DeeWePXHvtdaePHDlqTmNjI+PHv9h35ZVXnQGw665ffuf447+/Sl1dXeMpp/zsxeb79+jRg3XW+dz7v/zl6aN23XWPtzv/HSz8DEuSJEmLiF49etIR01y35Jq9L6At018vzGbNnTPvmr0v6JDnLLW2zWabbTn1jTfeeP24445etb6+vgZg3XXXm3L44Ue9AbD88ivMHTRo8LyGhgZWXLF8KvBtttn+3ZNO+uEq2277RbvgzQfDkiRJWmwMHNSXPr29/FHbTZ08+4muDIR77rn3pD333HtSS+vPP/+i55ov22uvfSbttdc+kwC22OLzU++884HHmtZdf/0tT3VMSRdN/rWQJEmLjT6969j5mBs67Xxjz9q1084lqfqc4EGSJEmSShiWJEmSpA81NDY21nR1IdQ5iu+6oaX1dsNbzDlrjiRJ0kc8PXXq5M8MHLjElJqamsauLow6TmNjY83UqZMHA0+3tI1haTHnrDmSJEkfamio/8b777970fvvv7sG9sJa1DUATzc01H+jpQ0MS5IkSVJhvfXWexvYpavLoe7BtCxJkiRJJQxLkiRJklTCsCRJkiRJJQxLkiRJklTCsCRJkiRJJQxLkiRJklTCsCRJkiRJJQxLkiRJklTCsCRJkiRJJQxLkiRJklTCsCRJkiRJJQxLkiRJklTCsCRJkiRJJQxLkiRJklTCsCRJkiRJJQxLkiRJklTCsCRJkiRJJeraslFK6SxgL+DdYlFExN4ppR8BBxbHuRw4JSIaO6SkkiRJktSJ2hSWgE2BfSLigaYFKaUdyQFqPaAeuBV4Brim2oWUJEmSpM7WalhKKfUG1gWOTSmtDDwPfA/YHbgiIqYX240B9sewJEmSJGkR0JaWpWWBO4EfAv8FjgVuAN4G7qjY7lVg+facfNiwAe3ZXIuI4cMHdnURpFLWTXVX1k11Z9ZPLcpaDUsR8RKwY9PrlNJo4ATgnZLN69tz8kmTptHQ0L2HOPkHoPomTpza1UVYJFg3q8+6WT3Wz+qyblaPdbP6unv9rK2t8Qa95lurs+GllNZKKR3QbHEN8DIwomLZcuTWJUmSJEla6LVl6vAG4NyU0qeK14cBT5K74u2XUupfjGs6CLi+Q0opSZIkSZ2s1bAUEU8DRwBjU0rPkid2+GpEjAWuAx4CngbGAZd2YFklSZIkqdO0aerwiLic/Byl5svPAM6odqEkSZIkqau1pRueJEmSJC12DEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEklDEuSJEmSVMKwJEmSJEkl6tq6YUppN+DSiBhUvP4RcGBxjMuBUyKisUNKKUmSJEmdrE0tSymlVYHRTdunlHYE9gLWA9YAti5eS5IkSdIiodWwlFLqR245+n7F4t2BKyJiekTMAsYA+3dMESVJkiSp87WlG97vi58nK5atANxR8fpVYPn2nnzYsAHt3UWLgOHDB3Z1EaRS1k11V9ZNdWfWTy3KPjEspZS+A8yLiD+mlEZVrCprkapv78knTZpGQ0P3HubkH4DqmzhxalcXYZFg3aw+62b1WD+ry7pZPdbN6uvu9bO2tsYb9JpvrbUsHQT0Syk9DvQC+hb/fhQYUbHdcuTWJUmSJElaJHxiWIqIDZv+XbQsPR0R66SUdgZOSildCMwjh6pLOq6YkiRJktS55us5SxExFrgOeAh4GhgHXFrFckmSJElSl2rzc5YiYjwwoOL1GcAZHVAmSZIkSepy89WyJEmSJEmLOsOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSibq2bJRS+i5wGNAIvAB8E5gE/Ar4YnGc0RHxuw4qpyRJkiR1qlZbllJK6wHHAptGxBrAc8CpwLeBVYE1gA2Ao1NKG3ZgWSVJkiSp07QaliJiHLBqRExJKfUBliO3Ku0OjImIeRHxHnAVsH+HllaSJEmSOkmbuuFFxNyU0m7ARcBs4ETgy8ArFZu9CqzVnpMPGzagPZtrETF8+MCuLoJUyrqp7sq6qe7M+qlFWZvCEkBEXA9cn1L6JnArMK9ks/r2nHzSpGk0NDS2Z5dO5x+A6ps4cWpXF2GRYN2sPutm9Vg/q8u6WT3Wzerr7vWztrbGG/Sab20Zs7RKSmnzikV/BFYEXgNGVCxfjty6JEmSJEkLvbZMHT4CuCqltGTxej/gaeA64JCUUl1KaQlgH+D6jimmJEmSJHWuVrvhRcS9KaXTgbtTSvOA14HdyOOVVgaeAHoBv4+IezqysJIkSZLUWdo6wcMFwAUlq46ubnEkSZIkqXtoSzc8SZIkSVrsGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqYRhSZIkSZJKGJYkSZIkqURdWzZKKe0PHAc0AjOAIyPikZTSj4ADi+NcDpwSEY0dVVhJkiRJ6iyttiyllBJwJrBDRKwDnAZcl1LaEdgLWA9YA9i6eC1JkiRJC722dMObDXwjIt4oXj8CLEMORldExPSImAWMAfbvmGJKkiRJUudqtRteRIwHxgOklGqAXwE3AiOAWys2fRVYvuollCRJkqQu0KYxSwAppf7AJcAKwA7ANSWb1bfn5MOGDWjP5lpEDB8+sKuLIJWybqq7sm6qO7N+alHW1gkeRgJjgWeBrSNiZkppArl1qcly5NalNps0aRoNDd17Pgj/AFTfxIlTu7oIiwTrZvVZN6vH+lld1s3qsW5WX3evn7W1Nd6g13xrNSyllIYC9wCXRMQpFatuAE5KKV0IzAMOIrc8SZIkSdJCry0tS4cBI4HdU0q7VyzfBrgOeAjoRQ5Pl1a9hJIkSZLUBdoywcPpwOktrD6j+JEkSZKkRUpbpg6XJEmSpMWOYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKmEYUmSJEmSShiWJEmSJKlEXVs3TCnVAGOApyNidEqpB/Ar4IvFcUZHxO86ppiSJEmS1Lna1LKUUvo0cAfwlYrF3wZWBdYANgCOTiltWPUSSpIkSVIXaGs3vMPJrUrXVCzbHRgTEfMi4j3gKmD/KpdPkiRJkrpEm7rhRcR3AVJK21QsXgF4peL1q8Ba7Tn5sGED2rO5FhHDhw/s6iJIpayb6q6sm+rOrJ9alLV5zFKJslap+vYcYNKkaTQ0NC5AETqefwCqb+LEqV1dhEWCdbP6rJvVY/2sLutm9Vg3q6+718/a2hpv0Gu+LchseBOAERWvlyO3LkmSJEnSQm9BWpZuAA5JKY0FBgD7AIdWpVSSJEmS1MUWJCxdAKwMPAH0An4fEfdUpVSSJEmS1MXaFZYi4qCKf88Djq52gSRJkiSpO1iQMUuSJEmStMgyLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJUwLEmSJElSCcOSJEmSJJWoW5CdU0pfAn4G9AaeBL4eEe9Xo2CSJEmS1JXmu2UppTQcGAPsEREJeBH4ebUKJkmSJEldaUFalrYHHo6I54rXFwBPpJQOj4jGVvbtAVBbW7MAp+88Sw3p26nnqxs8vFPPN7zf0E4938LyvS8MrJvVZd2sLutn9Vg3q8u6WV3dvX5WlK9HV5ZDC6eaxsbWck25lNLxwKiIOLR4XQfMBQa3oSve5sC983ViSZIkqf22AO7r6kJo4bIgLUstdeGrb8O+D5Mr7Btt3F6SJEmaHz2AEeTrT6ldFiQsTQA2qni9HPBeRExvw76zMdlLkiSpc7zQ1QXQwmlBpg6/Ddg4pbRq8fpQ4IYFL5IkSZIkdb35HrMEkFLakTx1eC9yYj8wIt6tUtkkSZIkqcssUFiSJEmSpEXVgnTDkyRJkqRFlmFJkiRJkkoYliRJkiSphGFJkiRJkkoYliRJkiSpxII8lFadKKV0CfB0RIzu6rKoe+pOdSSl9DiwVURMrsKx7gZ+GxF/+YRtegDXAZ8Gzo2I387HeTYAvh4Rh85vWdVxUkrrA8dHxJ5VOl4jMDwi3vmEbVYAbgbqgUMj4sH5OM83gF4Rcf58F1aLpJTSbcC+wOeAPwBvA38EBkfEz0u2nwasERHjU0oXADsAV0TEjzux2NJix7AkqeoiYp1OPuVywBeB/hFRP5/H+CywfPWKpGqKiEeAqgSldtgaeDMitl2AY2wOPF2l8mjRsl3x332AP0TEae3Y99vAyIh4tfrFklTJsNQNpZSOB74OTAX+AewG3F2xfgvgTKAfMAf4SUTcklJ6APhV0x34lNLPgZqI+EFK6evAd8hdLycB342I/3Teu1I1dZc6klI6Bdi9OMck4KCIeKPprj2wE7AH0BcYBUwAzgO+C6xWlOWslNJBwFeLcy8HvAZ8LSJeb3a+TYFfAP2BBuBk4B7gFqAnMC6ltAf5QdnnAMOAHuTWpj+mlGqBXwMbAwOBGuAbRbl+CgxOKY2JiIM/6X2rY6WUBgBjgFXJ3/M44Ery97hG0Yo6E9gAWAa4BpgI7Fy8/kZE3Fls10hucRwO3AYcGRFzm53vY3UfGAGcRq4Td0XE1imlnYGfkOvXDODYiHgwpbQ08Htg6eL8LwNfATYDdgG2SynNjIjzqv1ZaeGUUhpT/PMVoA/wekppMDAdWDIivlv8Hf8NuQ4/TDF0IqV0L/lv180ppe9ExL2d/gakxYhjlrqZlNIXgYPIFwHrkS/oKtcPA/4CHBURawFfAy5PKX2K3Ix/ULFdD2B/4KKU0ueL7baIiHWBX5K7LGkh1F3qSNFF6Whgg4hYn3whulHJplsAB5PD0dLku6jbADsCpxUBBvKF5eER8RnyxfG5zc43hHwBfUBEfI58EXoBMKQ41syiRevl4v0fHxHrAZ8Hjk0pbVyUb1lgk+I8/1ts9wpwInCvQalb2B0YWHyfGxTLVmq2zbrAJsD6wPeAaRGxKTkkH1+x3drAtsBnip9vVx6kpbofEXfxYZ3YOqW0KnAGsGOx3beA61JK/cl1+sGI2KQo5wxyPf0rcCPwa4OSKlX8nVmB/Hfo1xFxXNP6lFIv4M/AMUV9u4t804mI2KLYbGuDktTxDEvdz47AnyNickQ0ku/CV9oIeD4i/gUQEf8G7ge2It9d3SSltAy5S9LzEfEc8CVgFeCBYizJL4GhKaWhnfGGVHXdpY68BjwBPJpSGg08HhHXl2z3cES8EhENwEvAbcW/XyDfUe1XbHdbRPy3+PcfivJV2oR8t//6oow3ke+4rtVsu9WAlYE/FtvdQ77IWLcYc/IT4NtFmfcEBnzCe1TXuA/4bDFe7XjgbOD5ZtuMjYi5EfEm+W78LcXyF4DKentJREyLiNnApXy8XrW17m9Hrn93FNv9idzqtUpEnFPs/33gfGANrFdaMGsCcyPiDoCIuJLck0BSJ7MbXvczj9y83qT5+IuygFsL9IyI6SmlP5MHjG5CvuCE3A3psoj4AUBxJ39Z4L1qFlydplvUkYhoKO7Kr0++c//rorvSUc02nd3s9VzKzWtW3ubvqwfwbER80HqVUlqW3P1quWbbTa4cN1V0k5qSUvoSueXhLOAG4D/k1jV1IxHxUkppFXLA/wJwO3BEs82qWa/aUvd7AHdExN5NC4rW1ddTSr8ANiQPzr+L3CW0Bmn+NfLxOjSvbENJHcuWpe7n78AeRd9lyONSGivW/xNIKaUNyf/4LLAlH45XaepmtSlwbbHsNuCrKaURxetDgTs6qPzqeN2ijqSU1iYPXH82In5GHgu09ny/K9gmpdQUeg4FxjZb/09g1ZTSlsX51wGeI1/YVgpgVkpp/2K7FYpyrkduHRgbEReQxwDsRr4Ihnwh0nMByq8qSSkdRu5yeVsRYm4ljyOaH3unlHqnlPqQu9s1r1dtrft3AtunlFYvyrgj8CS5dfSLwNkRcRl5RrPtsF6pdfW0XDeeAmqKekZKaRdyl2NJncyw1M1ExJ3ki9kHU0qPAIPJ/d+b1r8D7AX8JqX0FHAFcHBT96WIGEf+n/O1ETGrWHYreVD8/6WUniS3Kny56MKlhUx3qSMR8QS5W98jRTkOIY8dmV+vApellJ4lTwZxdLPzTSRPFnFmSukJ4DLyuJCXm203B9gV+EbxXm4DToiI+4HfAZ8vlj9I7rL1qaI14UFg9ZTSXxfgPag6LiWHjWeKujWI3CI4P2YA95IvPu8lh7APtLXuF91ZvwVcVdS/U4FdImI6eXKQ0SmlceSxfveRu/ZBnnr8yJTSD+ez/Fp0NdWVj3XZLCYh2Q04tej2+WVyEJfUyWoaG71e7k5SfpbIphFxbvH6+8BGlV0/tHhbFOtIMRvenhGxU1eXRYuO1I2ePSZJWjg5Zqn7+S/wg5TSt8hdqyaQ72ZKTTqtjqSUjgP2a2H1mRHxp444ryRJUndgy5IkSZIklXDMkiRJkiSVMCxJkiRJUgnDkiRJkiSVMCxJkiRJUgnDkiRJkiSV+P97nz3mzcBhOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = by_feat1.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('Accuracy Score Sample Size 10k (by feature)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = by_feat2.plot(kind='bar', figsize=(12,5), fontsize=12);\n",
    "ax.legend(bbox_to_anchor=(1, 0, 0.22, 1), loc=2, mode=\"expand\", borderaxespad=1);\n",
    "ax.set_title('F1 Score Sample Size 10k (by feature)', fontsize=16);\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC curve compile for RandomForest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['roc_tfidf_100k.csv',\n",
       " 'roc_glove_100k.csv',\n",
       " 'roc_glove_simple_100k.csv',\n",
       " 'roc_simple_100k.csv']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_files = glob.glob('sample_data/roc_*_100k.csv')\n",
    "roc_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "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>rf_fpr</th>\n",
       "      <th>rf_tpr</th>\n",
       "      <th>feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004933</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007499</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008091</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008189</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8527</th>\n",
       "      <td>0.988343</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8528</th>\n",
       "      <td>0.992499</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8529</th>\n",
       "      <td>0.994222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8530</th>\n",
       "      <td>0.996756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8531</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>tfidf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8532 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        rf_fpr    rf_tpr   feat\n",
       "0     0.000000  0.000000  tfidf\n",
       "1     0.000000  0.004933  tfidf\n",
       "2     0.000000  0.007499  tfidf\n",
       "3     0.000000  0.008091  tfidf\n",
       "4     0.000000  0.008189  tfidf\n",
       "...        ...       ...    ...\n",
       "8527  0.988343  1.000000  tfidf\n",
       "8528  0.992499  1.000000  tfidf\n",
       "8529  0.994222  1.000000  tfidf\n",
       "8530  0.996756  1.000000  tfidf\n",
       "8531  1.000000  1.000000  tfidf\n",
       "\n",
       "[8532 rows x 3 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(roc_files[0], index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import auc\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "lw = 2\n",
    "\n",
    "for f in roc_files:\n",
    "    temp = pd.read_csv(f, index_col=0)\n",
    "    fpr = temp['rf_fpr'].tolist()\n",
    "    tpr = temp['rf_tpr'].tolist()\n",
    "    feat = temp['feat'].unique()[0]\n",
    "    \n",
    "    area = auc(fpr, tpr)\n",
    "    area = round(area,3)\n",
    "    plt.plot(fpr, tpr,\n",
    "             lw=lw, label=f'{feat} (area = {area})')\n",
    "    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    \n",
    "plt.title('RandomForest ROC Curve with 100k Samples');\n",
    "plt.savefig('RandomForest ROC Curve with 100k Samples.jpg');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}