senticr_piper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os,json\n",
    "import sys\n",
    "import augur\n",
    "import sqlalchemy as s\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plot\n",
    "import matplotlib.dates as mdates\n",
    "#!{sys.executable} -m pip install sklearn\n",
    "#!{sys.executable} -m pip install xlrd\n",
    "#!{sys.executable} -m pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/keanu/temp/augur_push/augur5/augur/SentiCR\n",
      "/home/keanu/temp/augur_push/augur5/augur/SentiCR/SentiCR\n"
     ]
    }
   ],
   "source": [
    "if(\"notebooks\" in os.getcwd()):\n",
    "    os.chdir(\"../SentiCR\")\n",
    "    from SentiCR.SentiCR import SentiCR\n",
    "\n",
    "sentiment_analyzer = SentiCR()\n",
    "\n",
    "if(\"notebooks\" not in os.getcwd()):\n",
    "    os.chdir(\"../../notebooks\")\n",
    "\n",
    "augurApp = augur.Application('../augur.config.json')\n",
    "\n",
    "connect = augurApp.ghtorrentplus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['github_issues', 'github_issues_2', 'github_issues_repo_jobs', 'github_issues_sentiment_scores', 'github_pull_request_repo_jobs', 'github_pull_requests', 'github_pull_requests_2', 'github_pull_requests_sentiment_scores', 'github_pull_requests_sentiment_scores_2', 'issue_response_time', 'mail_lists', 'mail_lists_sentiment_scores', 'mailing_list_jobs']\n",
      "          project\n",
      "0      aalldp-dev\n",
      "1      aalldp-dev\n",
      "2  archetypes-dev\n",
      "3  archetypes-dev\n"
     ]
    }
   ],
   "source": [
    "table_names = s.inspect(connect.db).get_table_names()\n",
    "print(table_names)\n",
    "\n",
    "if(\"mailing_list_jobs\" in table_names):\n",
    "    lists_createdSQL = s.sql.text(\"\"\"SELECT project FROM mailing_list_jobs\"\"\")\n",
    "    df1 = pd.read_sql(lists_createdSQL, connect.db)\n",
    "    print(df1)\n",
    "    val = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aalldp-dev Mailing List\n",
      "Here!!!!\n",
      "\n",
      "\n",
      "\n",
      "aalldp-dev Mailing List\n",
      "Here!!!!\n",
      "\n",
      "\n",
      "\n",
      "archetypes-dev Mailing List\n",
      "Here!!!!\n",
      "\n",
      "\n",
      "\n",
      "archetypes-dev Mailing List\n",
      "Here!!!!\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "col = 'score','sentiment'\n",
    "columns1 = 'augurmsgID', 'backend_name','project','mailing_list','category',\\\n",
    "           'message_part','message_parts_tot', 'subject','date',\\\n",
    "           'message_from','message_id','message_text'\n",
    "df3 = pd.DataFrame(columns = col)\n",
    "df_list = pd.DataFrame(columns = columns1)\n",
    "for i in df1['project'].values:\n",
    "    print(i,\"Mailing List\")\n",
    "    SQL = s.sql.text(\"\"\"SELECT * \n",
    "    FROM mail_lists WHERE mailing_list = \"\"\" +  \"'\" + i + \"'\")\n",
    "    df2 = pd.read_sql(SQL, connect.db)\n",
    "    df_list = df_list.append(df2)\n",
    "    #print(df2)\n",
    "    grouped = df2.groupby('message_id').groups\n",
    "    print(\"Here!!!!\")\n",
    "    #print(\"\\n\\n\",\"-\"*70,\"\\n\\n\")\n",
    "    for group in grouped:\n",
    "        parts = 0\n",
    "        numb = len(df2.loc[df2['message_id'] == group]['message_parts_tot'].tolist())\n",
    "        message = (df2.loc[df2['message_id'] == group]['message_text']).tolist()\n",
    "        message_text = ''.join(message)\n",
    "        #print(message_text)\n",
    "        score=sentiment_analyzer.get_sentiment_polarity(message_text)\n",
    "        score = int(score[0])\n",
    "        compound = parts = 0\n",
    "        sentiment = \"Positive\"\n",
    "        if(score == -1):\n",
    "            sentiment = \"Negative\"\n",
    "        #print(message)\n",
    "        for i in range(numb):\n",
    "            li = [ [score, sentiment] ]\n",
    "            df_temp = pd.DataFrame(li,columns = col)\n",
    "            #print(df_temp['score'])\n",
    "            df3 = df3.append(df_temp)\n",
    "            #print(df3)\n",
    "    print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  augurmsgID backend_name                                            project  \\\n",
      "0          1    Pipermail  https://lists.opendaylight.org/pipermail/aalld...   \n",
      "1          2    Pipermail  https://lists.opendaylight.org/pipermail/aalld...   \n",
      "2          3    Pipermail  https://lists.opendaylight.org/pipermail/aalld...   \n",
      "3          4    Pipermail  https://lists.opendaylight.org/pipermail/aalld...   \n",
      "4          5    Pipermail  https://lists.opendaylight.org/pipermail/aalld...   \n",
      "\n",
      "  mailing_list category message_part message_parts_tot  \\\n",
      "0   aalldp-dev  message            1                 2   \n",
      "1   aalldp-dev  message            2                 2   \n",
      "2   aalldp-dev  message            1                 2   \n",
      "3   aalldp-dev  message            2                 2   \n",
      "4   aalldp-dev  message            1                 2   \n",
      "\n",
      "                                           subject                date  \\\n",
      "0            [aalldp-dev] AALLDP Active Committers 2016-03-24 20:37:11   \n",
      "1            [aalldp-dev] AALLDP Active Committers 2016-03-24 20:37:11   \n",
      "2  [aalldp-dev] Plans for Archiving AALLDP Project 2016-03-24 20:37:08   \n",
      "3  [aalldp-dev] Plans for Archiving AALLDP Project 2016-03-24 20:37:08   \n",
      "4           [aalldp-dev] change LLDP Multicast Mac 2018-07-05 14:41:28   \n",
      "\n",
      "                                     message_from  \\\n",
      "0                     An.Ho at huawei.com (An Ho)   \n",
      "1                     An.Ho at huawei.com (An Ho)   \n",
      "2                     An.Ho at huawei.com (An Ho)   \n",
      "3                     An.Ho at huawei.com (An Ho)   \n",
      "4  corajososprojetosdn at gmail.com (Projeto sdn)   \n",
      "\n",
      "                                          message_id  \\\n",
      "0  <EEEA408CE50B48498B9F866085AEF2912FAEEAAD@SJCE...   \n",
      "1  <EEEA408CE50B48498B9F866085AEF2912FAEEAAD@SJCE...   \n",
      "2  <EEEA408CE50B48498B9F866085AEF2912FAEEAA0@SJCE...   \n",
      "3  <EEEA408CE50B48498B9F866085AEF2912FAEEAA0@SJCE...   \n",
      "4  <CAAEbTdOB4-jAKLfvMpwKmsVVESyaSk0igOgmUY=vxw0n...   \n",
      "\n",
      "                                        message_text score sentiment  \n",
      "0  Hi Brian Kaczynski <kaczynskib at avaya.com>, ...     0  Positive  \n",
      "1  this email to indicate that you are still an a...     0  Positive  \n",
      "2  Hi AALLDP Team,\\n\\n\\n\\n1. Does your project ha...     0  Positive  \n",
      "3   for any future active development, project co...     0  Positive  \n",
      "4  Hi guys,\\n\\nI'm new to the opendaylight univer...    -1  Negative  \n",
      "(28, 14)\n"
     ]
    }
   ],
   "source": [
    "df3 = df3.reset_index(drop=True)\n",
    "df_list = df_list.reset_index(drop=True)\n",
    "combine = (df_list.join(df3))\n",
    "\n",
    "print(combine.head())\n",
    "print(combine.shape)\n",
    "combine.to_sql(name='mail_lists_sentiment_scores_senticr',con=connect.db,\\\n",
    "               if_exists='replace',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20,)\n",
      "(20,)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8,)\n",
      "(8,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = combine.groupby('mailing_list').groups\n",
    "dates = []\n",
    "mail_list = None\n",
    "j = 0\n",
    "for group in grouped:\n",
    "    dates = (combine.loc[combine['mailing_list'] == group]['date']).tolist()\n",
    "    values = []\n",
    "    for val in dates:\n",
    "        #print(val)\n",
    "        values.append((combine.loc[combine['date'] == val]['score']).tolist()[0])\n",
    "    #values = (combine.loc[combine['mailing_list'] == group]['score']).unique().tolist()\n",
    "    x = np.array(dates)\n",
    "    #dates = np.unique(x)\n",
    "    #print(dates)\n",
    "    dates1 = [r.to_pydatetime() for r in x]\n",
    "    fin_dates = mdates.date2num(dates1)\n",
    "    fin_values = np.asarray(values)\n",
    "    print(fin_dates.shape)\n",
    "    print(fin_values.shape)\n",
    "    plot.plot_date(fin_dates, fin_values)\n",
    "    #plot.plot(fin_dates,fin_values)\n",
    "    plot.gcf().autofmt_xdate()\n",
    "    plot.title(\"Mailing lst: \"+ group)\n",
    "    plot.ylabel('Score')\n",
    "    plot.xlabel('Date')\n",
    "    #print(fin_dates)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(fin_dates,fin_values)\n",
    "    line = slope*fin_dates+intercept\n",
    "    plot.plot(fin_dates,fin_values,'o', fin_dates, line)\n",
    "    #plot.savefig('foo' + str(x) + '.png')\n",
    "    #x+=1\n",
    "    plot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (augur)",
   "language": "python",
   "name": "augur"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}