{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cbb4cecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8125543d",
   "metadata": {},
   "outputs": [],
   "source": [
    "caa_dataset = \"/opt/tmp/src/CyberAgressionAdo-v1\"\n",
    "output_dir = \"../../data/processed/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "151d438f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "filelist = glob.glob(caa_dataset + \"/*/*\")\n",
    "df_dict = {x:pd.read_csv(x, index_col=[0]) for x in filelist}\n",
    "\n",
    "df = df_dict[filelist[0]]\n",
    "verbal_abuse_df = df \\\n",
    "    .loc[df.verbal_abuse.isin([\"none\", \"denigration\"]) == False] \\\n",
    "    [[\"Unnamed: 2\"]]\n",
    "\n",
    "no_abuse_df = df \\\n",
    "    .loc[df.verbal_abuse == \"none\"] \\\n",
    "    [[\"Unnamed: 2\"]]\n",
    "\n",
    "pd.DataFrame([verbal_abuse_df.shape[0], no_abuse_df.shape[0]]).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f3e6783e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N verbal abuses:  945\n",
      "N no verbnal abuses:  3159\n"
     ]
    }
   ],
   "source": [
    "caa_verbal_abuse_df_list = []\n",
    "caa_no_abuse_df_list = []\n",
    "\n",
    "for path in filelist:\n",
    "    df_dict = {x:pd.read_csv(x, index_col=[0]) for x in filelist}\n",
    "    df = df_dict[filelist[0]]\n",
    "    verbal_abuse_df = df \\\n",
    "        .loc[df.verbal_abuse.isin([\"none\", \"denigration\"]) == False] \\\n",
    "        [[\"Unnamed: 2\"]]\n",
    "\n",
    "    no_abuse_df = df \\\n",
    "        .loc[df.verbal_abuse == \"none\"] \\\n",
    "        [[\"Unnamed: 2\"]]\n",
    "    caa_verbal_abuse_df_list.append(verbal_abuse_df)\n",
    "    caa_no_abuse_df_list.append(no_abuse_df)\n",
    "    \n",
    "caa_verbal_abuse_df = pd.concat(caa_verbal_abuse_df_list)\n",
    "caa_no_abuse_df = pd.concat(caa_no_abuse_df_list)\n",
    "print(\"N verbal abuses: \", caa_verbal_abuse_df.shape[0])\n",
    "print(\"N no verbnal abuses: \", caa_no_abuse_df.shape[0])\n",
    "\n",
    "caa_verbal_abuse_df.to_csv(os.path.join(output_dir, \"caa_positive.csv\"))\n",
    "caa_no_abuse_df.to_csv(os.path.join(output_dir, \"caa_negative.csv\"))"
   ]
  }
 ],
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