github_issues_scores_senticr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": 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",
    "import datetime as dt\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",
      "Reading data from oracle..\n",
      "Training classifier model..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/keanu/anaconda3/envs/augur/lib/python3.6/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function _ratio_float is deprecated; Use a float for 'ratio' is deprecated from version 0.2. The support will be removed in 0.4. Use a dict, str, or a callable instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "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', 'mail_lists_sentiment_scores_senticr', 'mailing_list_jobs']\n",
      "   augurmsgID backend_name                      repo_link   owner repo  \\\n",
      "0           1       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "1           2       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "2           3       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "3           4       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "4           5       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "\n",
      "                 subject  status category  issue_number            timestamp  \\\n",
      "0  Purpose of nodejs/CTC  closed    issue             1  2016-06-09 12:43:54   \n",
      "1  Purpose of nodejs/CTC  closed    issue             1  2016-06-09 12:53:53   \n",
      "2  Purpose of nodejs/CTC  closed    issue             1  2016-06-09 13:34:16   \n",
      "3  Purpose of nodejs/CTC  closed    issue             1  2016-06-09 13:57:58   \n",
      "4  Purpose of nodejs/CTC  closed    issue             1  2016-06-09 13:59:05   \n",
      "\n",
      "    issue_id         user                                               body  \n",
      "0  159398443        rvagg  @nodejs/collaborators I'm not sure if this is ...  \n",
      "1  159398443  vkurchatkin             > Core Technical Team (CTC)\\n\\nTypo?\\n  \n",
      "2  159398443        rvagg                       blerh, yes, fixing, thanks\\n  \n",
      "3  159398443   benjamingr  If I want to being an issue to the CTC's atten...  \n",
      "4  159398443   benjamingr  > Issues that Node.js needs to begin consideri...  \n",
      "      augurmsgID backend_name                      repo_link   owner repo  \\\n",
      "6707        6708       GitHub  https://github.com/nodejs/TSC  nodejs  TSC   \n",
      "6708        6709       GitHub  https://github.com/nodejs/TSC  nodejs  TSC   \n",
      "6709        6710       GitHub  https://github.com/nodejs/TSC  nodejs  TSC   \n",
      "6710        6711       GitHub  https://github.com/nodejs/TSC  nodejs  TSC   \n",
      "6711        6712       GitHub  https://github.com/nodejs/TSC  nodejs  TSC   \n",
      "\n",
      "                                                subject status category  \\\n",
      "6707  Proposal: add all new core modules under a sco...   open    issue   \n",
      "6708  Proposal: add all new core modules under a sco...   open    issue   \n",
      "6709  Proposal: add all new core modules under a sco...   open    issue   \n",
      "6710  Proposal: add all new core modules under a sco...   open    issue   \n",
      "6711  Proposal: add all new core modules under a sco...   open    issue   \n",
      "\n",
      "      issue_number            timestamp   issue_id        user  \\\n",
      "6707           389  2018-08-01 14:41:02  266702892    mhdawson   \n",
      "6708           389  2018-08-01 15:10:37  266702892      ljharb   \n",
      "6709           389  2018-08-02 14:44:17  266702892  benjamingr   \n",
      "6710           389  2018-08-02 17:20:02  266702892      ljharb   \n",
      "6711           389  2018-08-02 22:25:45  266702892    mhdawson   \n",
      "\n",
      "                                                   body  \n",
      "6707  In the last TSC meeting we discussed again, th...  \n",
      "6708  @mhdawson i think the list would either be “em...  \n",
      "6709  I'm +1 on moving existing modules with new API...  \n",
      "6710  I think creating new APIs while moving existin...  \n",
      "6711  @ljharb I think the key point was that we shou...  \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",
    "connect,list1,path = augurApp.github_issues()\n",
    "DB_STR = 'mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8'.format(\n",
    "            list1[0], list1[1], list1[2],\\\n",
    "            list1[3], list1[4]\n",
    "        )\n",
    "db = s.create_engine(DB_STR)\n",
    "\n",
    "table_names = s.inspect(db).get_table_names()\n",
    "print(table_names)\n",
    "\n",
    "if(\"github_issues_2\" in table_names):\n",
    "    lists_createdSQL = s.sql.text(\"\"\"SELECT repo FROM github_issues_2\"\"\")\n",
    "    df1 = pd.read_sql(lists_createdSQL, db)\n",
    "    #print(df1)\n",
    "    val = True\n",
    "\n",
    "    \n",
    "col = 'score','sentiment'\n",
    "\n",
    "SQL = \"\"\"SELECT * FROM github_issues_2\"\"\"\n",
    "df_users =  pd.read_sql(SQL, db)\n",
    "print(df_users.head())\n",
    "print(df_users.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481\n"
     ]
    }
   ],
   "source": [
    "grouped = df_users.groupby('subject').groups\n",
    "num = 0\n",
    "col = 'score','sentiment'\n",
    "columns1 = 'augurmsgID',\"backend_name\",'repo_link',\"owner\",\"repo\",\"subject\",\\\n",
    "          \"status\",\"category\",\"issue_number\",\"timestamp\",\\\n",
    "          \"issue_id\",\"user\",\"body\"\n",
    "df3 = pd.DataFrame(columns=col)\n",
    "df_list = pd.DataFrame(columns = columns1)\n",
    "df_list = df_list.append(df_users)\n",
    "for group in grouped:\n",
    "    #print(group)\n",
    "    issue_num = (df_users.loc[df_users['subject'] == group]['issue_number']).values\n",
    "    messages = (df_users.loc[df_users['subject'] == group]['body']).tolist()\n",
    "    id1 = (df_users.loc[df_users['subject'] == group]['body']).tolist()\n",
    "    #print(\"Issue number: \",issue_num[0])\n",
    "    #break\n",
    "    for i in messages:\n",
    "        #print(i)\n",
    "        score=sentiment_analyzer.get_sentiment_polarity(i)\n",
    "        score = int(score[0])\n",
    "        compound = parts = 0\n",
    "        sentiment = \"Positive\"\n",
    "        if(score == -1):\n",
    "            sentiment = \"Negative\"\n",
    "        li = [ [score,sentiment]]\n",
    "        df_temp = pd.DataFrame(li,columns=col)\n",
    "        df3 = df3.append(df_temp)\n",
    "        #if(avg < -0.5):\n",
    "            #print(i,avg)\n",
    "            #print(\"\\n\\n\\n\\n\\n\\n\")\n",
    "    #if(num == 40):\n",
    "    #    break\n",
    "    num+=1\n",
    "    #print(\"\\n\\n\\n\")\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  augurmsgID backend_name                      repo_link   owner repo  \\\n",
      "0          1       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "1          2       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "2          3       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "3          4       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "4          5       GitHub  https://github.com/nodejs/CTC  nodejs  CTC   \n",
      "\n",
      "                 subject  status category issue_number            timestamp  \\\n",
      "0  Purpose of nodejs/CTC  closed    issue            1  2016-06-09 12:43:54   \n",
      "1  Purpose of nodejs/CTC  closed    issue            1  2016-06-09 12:53:53   \n",
      "2  Purpose of nodejs/CTC  closed    issue            1  2016-06-09 13:34:16   \n",
      "3  Purpose of nodejs/CTC  closed    issue            1  2016-06-09 13:57:58   \n",
      "4  Purpose of nodejs/CTC  closed    issue            1  2016-06-09 13:59:05   \n",
      "\n",
      "    issue_id         user                                               body  \\\n",
      "0  159398443        rvagg  @nodejs/collaborators I'm not sure if this is ...   \n",
      "1  159398443  vkurchatkin             > Core Technical Team (CTC)\\n\\nTypo?\\n   \n",
      "2  159398443        rvagg                       blerh, yes, fixing, thanks\\n   \n",
      "3  159398443   benjamingr  If I want to being an issue to the CTC's atten...   \n",
      "4  159398443   benjamingr  > Issues that Node.js needs to begin consideri...   \n",
      "\n",
      "  score sentiment  \n",
      "0    -1  Negative  \n",
      "1     0  Positive  \n",
      "2    -1  Negative  \n",
      "3    -1  Negative  \n",
      "4    -1  Negative  \n"
     ]
    }
   ],
   "source": [
    "#print(df3)\n",
    "#print(df_list)\n",
    "df3 = df3.reset_index(drop=True)\n",
    "#print(df3.head())\n",
    "df_list = df_list.reset_index(drop=True)\n",
    "combine = (df_list.join(df3))\n",
    "\n",
    "print(combine.head())\n",
    "\n",
    "combine.to_sql(name='github_issues_sentiment_scores_senticr',\\\n",
    "               con=db,if_exists='replace',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(126,)\n",
      "(126,)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(357,)\n",
      "(357,)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repos = combine.groupby('repo_link')\n",
    "x = 0\n",
    "for repo in repos:\n",
    "    fin_dates = []\n",
    "    fin_values = []\n",
    "    dates = []\n",
    "    values = []\n",
    "    mail_list = None\n",
    "    j = 0\n",
    "    rep = (combine.loc[combine['repo_link'] == repo[0]])\n",
    "    grouped = rep.groupby('subject').groups\n",
    "    for group in grouped:\n",
    "        #print(group)\n",
    "        dates = (combine.loc[combine['subject'] == group]['timestamp']).tolist()\n",
    "        values = []\n",
    "        for val in dates:\n",
    "            #print(val)\n",
    "            values.append((combine.loc[combine['timestamp'] == val]['score']).tolist()[0])\n",
    "        #print(\"Issue number: \",issue_num[0])\n",
    "        x = np.array(dates)\n",
    "        dates = np.unique(x)\n",
    "        #print(type(x[0]))\n",
    "        #print(dates)\n",
    "        dates = [dt.datetime.strptime(d[:10],'%Y-%m-%d').date() for d in x]\n",
    "        #print(dates)\n",
    "        dates1 = [x for x in dates]\n",
    "        values = sum(values[0:len(values)])/len(values)\n",
    "        if(values >= -0.5):\n",
    "            values = 0\n",
    "        else:\n",
    "            values = -1\n",
    "        fin_dates.append(dates1[0])\n",
    "        fin_values.append(values)\n",
    "        num+=1\n",
    "        j+=1\n",
    "    fin_dates = mdates.date2num(fin_dates)\n",
    "    fin_values = np.asarray(fin_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(\"Issues: \"+ str(repo[0]))\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
}