analyze_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> For conversion of .out files from batch transform to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m \r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mcsv\u001b[39;49;00m\r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mre\u001b[39;49;00m \r\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mnumpy\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnp\u001b[39;49;00m\r\n",
      "\r\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mgive_prediction_label\u001b[39;49;00m(result_string):\r\n",
      "    \u001b[37m#x = re.match(\"[(.*)]\", result_string)\u001b[39;49;00m\r\n",
      "    x = result_string[result_string.find(\u001b[33m'\u001b[39;49;00m\u001b[33m[\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)+\u001b[34m1\u001b[39;49;00m : result_string.find(\u001b[33m'\u001b[39;49;00m\u001b[33m]\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)].split(\u001b[33m'\u001b[39;49;00m\u001b[33m,\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "    x_float = [ \u001b[36mfloat\u001b[39;49;00m(xi) \u001b[34mfor\u001b[39;49;00m xi \u001b[35min\u001b[39;49;00m x ]\r\n",
      "    ind = x_float.index(\u001b[36mmax\u001b[39;49;00m(x_float))        \r\n",
      "    \r\n",
      "    \u001b[34mreturn\u001b[39;49;00m ind \r\n",
      "\r\n",
      "\r\n",
      "\r\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mmake_results_csv\u001b[39;49;00m( results_directory , csv_path , csv_file_name  , eval_image_path ):\r\n",
      "    \u001b[34mwith\u001b[39;49;00m \u001b[36mopen\u001b[39;49;00m(os.path.join(csv_path , csv_file_name), mode=\u001b[33m'\u001b[39;49;00m\u001b[33mw\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m) \u001b[34mas\u001b[39;49;00m \u001b[36mfile\u001b[39;49;00m:\r\n",
      "        writer = csv.writer(\u001b[36mfile\u001b[39;49;00m, delimiter=\u001b[33m'\u001b[39;49;00m\u001b[33m,\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, quotechar=\u001b[33m'\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, quoting=csv.QUOTE_MINIMAL)    \r\n",
      "        writer.writerow([\u001b[33m'\u001b[39;49;00m\u001b[33mImage_path\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mground_truth_label\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPredicted\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\r\n",
      "        i = \u001b[34m0\u001b[39;49;00m\r\n",
      "        sorted_list = \u001b[36msorted\u001b[39;49;00m(os.listdir(results_directory))\r\n",
      "        \u001b[34mfor\u001b[39;49;00m single_class \u001b[35min\u001b[39;49;00m sorted_list:\r\n",
      "            list_of_files = os.listdir(os.path.join( results_directory , single_class ))\r\n",
      "            \u001b[34mfor\u001b[39;49;00m img_result \u001b[35min\u001b[39;49;00m list_of_files:\r\n",
      "                result_file = \u001b[36mopen\u001b[39;49;00m(  os.path.join( results_directory , single_class , img_result )  , \u001b[33m'\u001b[39;49;00m\u001b[33mr\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\r\n",
      "                result_string = result_file.read()\r\n",
      "                prediction_val = give_prediction_label(result_string)\r\n",
      "                \u001b[37m#print(result_string.split(','))\u001b[39;49;00m\r\n",
      "                label = \u001b[36mstr\u001b[39;49;00m(i)\r\n",
      "                img_path = os.path.join( eval_image_path , single_class ,img_result[:-\u001b[34m4\u001b[39;49;00m] )\r\n",
      "                writer.writerow([img_path, label , prediction_val  ])            \r\n",
      "                result_file.close()\r\n",
      "            \u001b[37m#print(i)\u001b[39;49;00m\r\n",
      "            i += \u001b[34m1\u001b[39;49;00m\r\n",
      "\u001b[37m#make_results_csv('analyze/analyze' , 'dump' , 'results.csv' , 'nsfw_dataset_test' )\u001b[39;49;00m\r\n"
     ]
    }
   ],
   "source": [
    "#make_results_csv( 'batch_transform_results' , '' , 'results.csv'  , 'nsfw_dataset_test' )\n",
    "!pygmentize useful_scripts/useful_scripts/csv_maker.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>This notebook is used for first convert the results of batch transform to a csv file and then analyzing it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('results.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Image_path</th>\n",
       "      <th>ground_truth_label</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=SW...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=-z...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=4Q...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nsfw_dataset_test/animated/ouwo5EE.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=Sx...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Image_path  ground_truth_label  \\\n",
       "0  nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=SW...                   0   \n",
       "1  nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=-z...                   0   \n",
       "2  nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=4Q...                   0   \n",
       "3             nsfw_dataset_test/animated/ouwo5EE.jpg                   0   \n",
       "4  nsfw_dataset_test/animated/(m=e-yaaGqaa)(mh=Sx...                   0   \n",
       "\n",
       "   Predicted  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison = df.iloc[:, [1,2]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1901    2   28    7   45]\n",
      " [  13  494  755   11  186]\n",
      " [ 137   38 3492    7  473]\n",
      " [ 155   23   53  806   55]\n",
      " [  10    5   27    0 2413]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "samples = comparison.shape[0]\n",
    "conf_matrix = np.zeros( (5, 5) , dtype=np.int32 )\n",
    "\n",
    "for i in range(samples):\n",
    "    conf_matrix[ int(comparison[i][0]) ][ int(comparison[i][1])  ] += 1\n",
    "\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b78123940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "df_cm = pd.DataFrame(conf_matrix, index = ['animated_ori' , 'nude_ori' , 'porn_ori' , 'safe for work_ori' , 'semi_nude_ori'],\n",
    "                  columns = ['animated_pred' , 'nude_pred' , 'porn_pred' , 'safe for work_pred' , 'semi_nude_pred'])\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Cannot show wrong examples . You know it why"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong_examples = df[df['ground_truth_label'] != df['Predicted']].sample(n=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# from IPython.display import Image\n",
    "# from IPython.display import display\n",
    "\n",
    "\n",
    "\n",
    "# for i in range(9):\n",
    "#     x= Image(wrong_examples.iloc[i,0])\n",
    "#     display(x)\n",
    "\n",
    "# ;\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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 "nbformat": 4,
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