model/Monitoring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pull in results from dynamo/hot\n",
    "- Pull in predictions from sql, filter for stuff older than 5 hours\n",
    "- apply \"true\" labels based on what is in the dynamo data. One for anything that is in the dynamo/hot table, one for anything that has at least a score of 10k.\n",
    "- plot performance metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Set-up\" data-toc-modified-id=\"Set-up-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Set up</a></span></li><li><span><a href=\"#Pull-in-Dynamo-Hot-Data\" data-toc-modified-id=\"Pull-in-Dynamo-Hot-Data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Pull in Dynamo Hot Data</a></span></li><li><span><a href=\"#Pull-in-SQL-data\" data-toc-modified-id=\"Pull-in-SQL-data-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Pull in SQL data</a></span></li><li><span><a href=\"#Join-data-and-create-true-value-labels\" data-toc-modified-id=\"Join-data-and-create-true-value-labels-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Join data and create true value labels</a></span></li><li><span><a href=\"#Plot-performance-metrics\" data-toc-modified-id=\"Plot-performance-metrics-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Plot performance metrics</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T14:49:15.207248Z",
     "start_time": "2023-05-12T14:49:14.936437Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "import modelUtils as mu\n",
    "from datetime import datetime, timedelta\n",
    "import sys\n",
    "import os\n",
    "#THIS_DIR = os.path.dirname(os.path.abspath(__file__))\n",
    "#sys.path.append(os.path.join(THIS_DIR, '../'))\n",
    "sys.path.append('../')\n",
    "import configUtils as cu\n",
    "import boto3\n",
    "from pyspark.sql import SparkSession\n",
    "import pandas as pd\n",
    "import sqlUtils as su"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T14:41:03.401997Z",
     "start_time": "2023-05-12T14:40:54.742300Z"
    }
   },
   "outputs": [],
   "source": [
    "os.environ['TZ'] = 'UTC'\n",
    "\n",
    "\n",
    "cfg_file = cu.findConfig()\n",
    "cfg = cu.parseConfig(cfg_file)\n",
    "\n",
    "\n",
    "spark = (\n",
    "  SparkSession\n",
    "  .builder\n",
    "  .appName('redditData')\n",
    "  .config('spark.driver.extraJavaOptions', '-Duser.timezone=GMT')\n",
    "  .config('spark.executor.extraJavaOptions', '-Duser.timezone=GMT')\n",
    "  .config('spark.sql.session.timeZone', 'UTC')\n",
    "  .config(\"fs.s3a.access.key\", cfg['S3_access']['ACCESSKEY'])\n",
    "  .config(\"fs.s3a.secret.key\", cfg['S3_access']['SECRETKEY'])\n",
    "  .getOrCreate()\n",
    ")\n",
    "\n",
    "dynamodb_resource = boto3.resource('dynamodb', region_name='us-east-2')\n",
    "\n",
    "engine = su.makeEngine(cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pull in Dynamo Hot Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T14:44:35.668058Z",
     "start_time": "2023-05-12T14:44:26.285513Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of posts found: 120\n"
     ]
    }
   ],
   "source": [
    "now = datetime.now()\n",
    "twoDaysAgo = now - timedelta(days=2)\n",
    "twoDaysAgoDate = twoDaysAgo.date()\n",
    "datesToQuery = mu.daysUntilNow(twoDaysAgoDate)\n",
    "\n",
    "hotTable = dynamodb_resource.Table('hot')\n",
    "\n",
    "postIdQueryResult = mu.queryByRangeOfDates(hotTable, datesToQuery)\n",
    "hotPosts = mu.queryByRangeOfDates(hotTable, datesToQuery)\n",
    "uniqueHotPostIds = set([p['postId'] for p in hotPosts])\n",
    "print(\"Number of posts found:\", len(uniqueHotPostIds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T15:20:20.806569Z",
     "start_time": "2023-05-12T15:19:20.546936Z"
    }
   },
   "outputs": [],
   "source": [
    "# we mainly need this for making a target around score\n",
    "hotPostsData = mu.getPostIdPdDataFrame(\n",
    "  table=hotTable\n",
    "  , postIds=uniqueHotPostIds\n",
    "  , chunkSize=100\n",
    "  , flatten=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T17:52:13.385796Z",
     "start_time": "2023-05-12T17:52:13.311135Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>postId</th>\n",
       "      <th>hotPostSubreddit</th>\n",
       "      <th>hotPostMaxScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12k6m37</td>\n",
       "      <td>funny</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12ondqr</td>\n",
       "      <td>aww</td>\n",
       "      <td>1678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132h290</td>\n",
       "      <td>movies</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>134io09</td>\n",
       "      <td>funny</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13878rl</td>\n",
       "      <td>movies</td>\n",
       "      <td>3667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    postId hotPostSubreddit  hotPostMaxScore\n",
       "0  12k6m37            funny              350\n",
       "1  12ondqr              aww             1678\n",
       "2  132h290           movies              232\n",
       "3  134io09            funny               81\n",
       "4  13878rl           movies             3667"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotPostMaxScore = hotPostsData[['postId', 'subreddit', 'score']].groupby(['postId', 'subreddit']).max().reset_index(drop=False).rename(columns={'score':'hotPostMaxScore', 'subreddit':'hotPostSubreddit'})\n",
    "hotPostMaxScore.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learned from this that stickied posts are ending up in the hot view, made a patch for that, going forward should get a better selection of posts for subreddits that have stickied posts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pull in SQL data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T18:01:43.933652Z",
     "start_time": "2023-05-12T18:01:43.099928Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3300\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>postId</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>createdTSUTC</th>\n",
       "      <th>maxScore20m</th>\n",
       "      <th>maxScore21_40m</th>\n",
       "      <th>maxScore41_60m</th>\n",
       "      <th>maxNumComments20m</th>\n",
       "      <th>maxNumComments21_40m</th>\n",
       "      <th>maxNumComments41_60m</th>\n",
       "      <th>...</th>\n",
       "      <th>maxUpvoteRatio21_40m</th>\n",
       "      <th>maxUpvoteRatio41_60m</th>\n",
       "      <th>maxNumGildings20m</th>\n",
       "      <th>maxNumGildings21_40m</th>\n",
       "      <th>maxNumGildings41_60m</th>\n",
       "      <th>maxScoreGrowth21_40m41_60m</th>\n",
       "      <th>maxNumCommentsGrowth21_40m41_60m</th>\n",
       "      <th>predict_proba_1</th>\n",
       "      <th>stepUp</th>\n",
       "      <th>modelName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13f2gey</td>\n",
       "      <td>memes</td>\n",
       "      <td>Hashtag badass</td>\n",
       "      <td>2023-05-11 22:35:34</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.01647</td>\n",
       "      <td>0</td>\n",
       "      <td>models/Reddit_model_20230503-235329_GBM.sav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13eybbo</td>\n",
       "      <td>pics</td>\n",
       "      <td>Once I noticed it. I had to do it lol</td>\n",
       "      <td>2023-05-11 20:03:53</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.77</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>models/Reddit_model_20230503-235329_GBM.sav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13ey6tm</td>\n",
       "      <td>aww</td>\n",
       "      <td>Another day, another collection of random stuf...</td>\n",
       "      <td>2023-05-11 19:59:35</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01647</td>\n",
       "      <td>0</td>\n",
       "      <td>models/Reddit_model_20230503-235329_GBM.sav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13erc7c</td>\n",
       "      <td>funny</td>\n",
       "      <td>Corn is better than lobster FACT</td>\n",
       "      <td>2023-05-11 15:54:28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01647</td>\n",
       "      <td>0</td>\n",
       "      <td>models/Reddit_model_20230503-235329_GBM.sav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13e2qal</td>\n",
       "      <td>funny</td>\n",
       "      <td>Funny song and music video!</td>\n",
       "      <td>2023-05-10 20:47:07</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01647</td>\n",
       "      <td>0</td>\n",
       "      <td>models/Reddit_model_20230503-235329_GBM.sav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    postId subreddit                                              title  \\\n",
       "0  13f2gey     memes                                     Hashtag badass   \n",
       "1  13eybbo      pics              Once I noticed it. I had to do it lol   \n",
       "2  13ey6tm       aww  Another day, another collection of random stuf...   \n",
       "3  13erc7c     funny                   Corn is better than lobster FACT   \n",
       "4  13e2qal     funny                        Funny song and music video!   \n",
       "\n",
       "         createdTSUTC  maxScore20m  maxScore21_40m  maxScore41_60m  \\\n",
       "0 2023-05-11 22:35:34           11              11              11   \n",
       "1 2023-05-11 20:03:53            7               7               7   \n",
       "2 2023-05-11 19:59:35            4               9              12   \n",
       "3 2023-05-11 15:54:28            2               0               0   \n",
       "4 2023-05-10 20:47:07            3               3               1   \n",
       "\n",
       "   maxNumComments20m  maxNumComments21_40m  maxNumComments41_60m  ...  \\\n",
       "0                  1                     1                     1  ...   \n",
       "1                  0                     0                     0  ...   \n",
       "2                  0                     0                     0  ...   \n",
       "3                  1                     0                     0  ...   \n",
       "4                  2                     2                     2  ...   \n",
       "\n",
       "   maxUpvoteRatio21_40m  maxUpvoteRatio41_60m  maxNumGildings20m  \\\n",
       "0                  0.87                  0.87                  0   \n",
       "1                  0.89                  0.77                  0   \n",
       "2                  0.91                  0.93                  0   \n",
       "3                  0.00                  0.00                  0   \n",
       "4                  0.80                  0.57                  0   \n",
       "\n",
       "   maxNumGildings21_40m  maxNumGildings41_60m  maxScoreGrowth21_40m41_60m  \\\n",
       "0                     0                     0                    0.000000   \n",
       "1                     0                     0                    0.000000   \n",
       "2                     0                     0                    0.333333   \n",
       "3                     0                     0                    0.000000   \n",
       "4                     0                     0                   -0.666667   \n",
       "\n",
       "   maxNumCommentsGrowth21_40m41_60m  predict_proba_1  stepUp  \\\n",
       "0                               0.0          0.01647       0   \n",
       "1                               0.0          0.01647       0   \n",
       "2                               0.0          0.01647       0   \n",
       "3                               0.0          0.01647       0   \n",
       "4                               0.0          0.01647       0   \n",
       "\n",
       "                                     modelName  \n",
       "0  models/Reddit_model_20230503-235329_GBM.sav  \n",
       "1  models/Reddit_model_20230503-235329_GBM.sav  \n",
       "2  models/Reddit_model_20230503-235329_GBM.sav  \n",
       "3  models/Reddit_model_20230503-235329_GBM.sav  \n",
       "4  models/Reddit_model_20230503-235329_GBM.sav  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twoDaysAgoStr = twoDaysAgo.strftime('%Y-%m-%d %H:%M:%S')\n",
    "# this accounts for lags in posts hitting the front page\n",
    "sixHoursAgo = now-timedelta(hours=6)\n",
    "sixHoursAgoStr = sixHoursAgo.strftime('%Y-%m-%d %H:%M:%S')\n",
    "query = f\"\"\"SELECT\n",
    "  *\n",
    "FROM\n",
    "  public.\"scoredData\"\n",
    "WHERE\n",
    "  \"createdTSUTC\" >= '{twoDaysAgoStr}'\n",
    "  AND \"createdTSUTC\" <= '{sixHoursAgoStr}' \n",
    "\"\"\"\n",
    "recentlyScoredData = pd.read_sql(query, engine)\n",
    "print(len(recentlyScoredData))\n",
    "recentlyScoredData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join data and create true value labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T18:04:03.569927Z",
     "start_time": "2023-05-12T18:04:03.512377Z"
    }
   },
   "outputs": [],
   "source": [
    "mergedDf = pd.merge(recentlyScoredData, hotPostMaxScore, on=['postId'], how='outer')\n",
    "mergedDf['coalesceSubreddit'] = mergedDf[['subreddit', 'hotPostSubreddit']].bfill(axis=1).iloc[:, 0]\n",
    "mergedDf['hotPost'] = mergedDf['hotPostMaxScore'].apply(lambda x: 0 if pd.isna(x) else 1)\n",
    "mergedDf['hotPost10k'] = mergedDf['hotPostMaxScore'].apply(lambda x: 0 if pd.isna(x) or x<10000 else 1)\n",
    "mergedDf['stepUp'] = mergedDf['stepUp'].fillna(0).apply(int)\n",
    "mergedDf['stepUp&HotPost'] = mergedDf['stepUp']&mergedDf['hotPost']\n",
    "mergedDf['stepUp&HotPost10k'] = mergedDf['stepUp']&mergedDf['hotPost10k']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T18:06:33.764722Z",
     "start_time": "2023-05-12T18:06:33.738384Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coalesceSubreddit</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aww</th>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funny</th>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaming</th>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memes</th>\n",
       "      <td>904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movies</th>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>news</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pics</th>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>todayilearned</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worldnews</th>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count\n",
       "coalesceSubreddit       \n",
       "aww                  608\n",
       "funny                218\n",
       "gaming               232\n",
       "memes                904\n",
       "movies               223\n",
       "news                 112\n",
       "pics                 583\n",
       "todayilearned        129\n",
       "worldnews            339"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedDf[['coalesceSubreddit', 'stepUp']].groupby('coalesceSubreddit').count().rename(columns={'stepUp':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T18:12:58.785948Z",
     "start_time": "2023-05-12T18:12:58.732997Z"
    }
   },
   "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>stepUp</th>\n",
       "      <th>hotPost</th>\n",
       "      <th>hotPost10k</th>\n",
       "      <th>stepUp&amp;HotPost</th>\n",
       "      <th>stepUp&amp;HotPost10k</th>\n",
       "      <th>recall_hotPost</th>\n",
       "      <th>precision_hotPost</th>\n",
       "      <th>recall_hotPost10k</th>\n",
       "      <th>precision_hotPost10k</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coalesceSubreddit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>worldnews</th>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aww</th>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pics</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>todayilearned</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>news</th>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funny</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memes</th>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movies</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaming</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   stepUp  hotPost  hotPost10k  stepUp&HotPost  \\\n",
       "coalesceSubreddit                                                \n",
       "worldnews              21       14           8               7   \n",
       "aww                    14       13           8               5   \n",
       "pics                    6       16          11               5   \n",
       "todayilearned          13       14          11               5   \n",
       "news                   18       13           9               5   \n",
       "funny                  11       10           7               3   \n",
       "memes                  16       18           8               4   \n",
       "movies                  5        9           3               1   \n",
       "gaming                  5       13           7               2   \n",
       "\n",
       "                   stepUp&HotPost10k  recall_hotPost  precision_hotPost  \\\n",
       "coalesceSubreddit                                                         \n",
       "worldnews                          5        0.500000           0.333333   \n",
       "aww                                4        0.384615           0.357143   \n",
       "pics                               5        0.312500           0.833333   \n",
       "todayilearned                      5        0.357143           0.384615   \n",
       "news                               4        0.384615           0.277778   \n",
       "funny                              3        0.300000           0.272727   \n",
       "memes                              3        0.222222           0.250000   \n",
       "movies                             1        0.111111           0.200000   \n",
       "gaming                             2        0.153846           0.400000   \n",
       "\n",
       "                   recall_hotPost10k  precision_hotPost10k  \n",
       "coalesceSubreddit                                           \n",
       "worldnews                   0.625000              0.238095  \n",
       "aww                         0.500000              0.285714  \n",
       "pics                        0.454545              0.833333  \n",
       "todayilearned               0.454545              0.384615  \n",
       "news                        0.444444              0.222222  \n",
       "funny                       0.428571              0.272727  \n",
       "memes                       0.375000              0.187500  \n",
       "movies                      0.333333              0.200000  \n",
       "gaming                      0.285714              0.400000  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metricsDf = mergedDf[['coalesceSubreddit', 'stepUp', 'hotPost', 'hotPost10k', 'stepUp&HotPost', 'stepUp&HotPost10k']].groupby('coalesceSubreddit').agg('sum')\n",
    "metricsDf['recall_hotPost'] = metricsDf['stepUp&HotPost']/metricsDf['hotPost']\n",
    "metricsDf['precision_hotPost'] = metricsDf['stepUp&HotPost']/metricsDf['stepUp']\n",
    "metricsDf['recall_hotPost10k'] = metricsDf['stepUp&HotPost10k']/metricsDf['hotPost10k']\n",
    "metricsDf['precision_hotPost10k'] = metricsDf['stepUp&HotPost10k']/metricsDf['stepUp']\n",
    "metricsDf = metricsDf.sort_values('recall_hotPost10k', ascending=False)\n",
    "metricsDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot performance metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T19:38:41.828140Z",
     "start_time": "2023-05-12T19:38:41.255856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "meltedDf = metricsDf.reset_index(drop=False).melt(id_vars='coalesceSubreddit', value_vars=['recall_hotPost', 'precision_hotPost'])\n",
    "\n",
    "sns.set(style='white')\n",
    "sns.barplot(data=meltedDf, x='coalesceSubreddit', y='value', hue='variable')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T04:49:59.562857Z",
     "start_time": "2023-05-13T04:49:59.096747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "meltedDf = metricsDf.reset_index(drop=False).melt(id_vars='coalesceSubreddit', value_vars=['recall_hotPost10k', 'precision_hotPost10k'])\n",
    "\n",
    "sns.set(style='white')\n",
    "sns.barplot(data=meltedDf, x='coalesceSubreddit', y='value', hue='variable')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('subreddit')\n",
    "plt.title('Precision and recall of a Hot Posts with >= 10k upvotes\\n2023-05-10 to 2023-05-12')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hotPost10k is basically \"did a post reach the top 3 on the subreddit and did it have a max score of at least 10k.\" This is more strict than how the current model (2023-05-12) trains which does not have the 10k limit. It can make the recall look better than \"hotPost\" because there are fewer targets to predict correctly.  Precisions can be lower in some cases because there are fewer true targets to match out of everything predicted to step up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "py37",
   "language": "python",
   "name": "py37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "428.667px",
    "left": "60px",
    "top": "196px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}