notebook/learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to install deps through here\n",
    "#!pip install fastai python-dotenv google-cloud-bigquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.tabular.all import *\n",
    "from fastinference.tabular import *\n",
    "from datetime import datetime,timedelta\n",
    "from dotenv import load_dotenv\n",
    "from google.cloud import bigquery\n",
    "import matplotlib.style\n",
    "import matplotlib as mpl\n",
    "import os\n",
    "sys.path.append('../')\n",
    "from lib.github import fetch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set some defaults\n",
    "mpl.rcParams['font.size'] = 14\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# Load secrets from environment variables to avoid showing them in shared notebook snapshots\n",
    "load_dotenv(dotenv_path='/storage/.env')\n",
    "\n",
    "# Define variables\n",
    "flush = False\n",
    "use_cat = False\n",
    "use_fresh = False\n",
    "var_viewer_login = 'chillu'\n",
    "var_bigquery_table = 'silverstripe.com:api-project-617183295772.github_silverstripe_events.events_chillu'\n",
    "var_date_min=(datetime.now() - timedelta(days=4*365))\n",
    "var_date_max=(datetime.now() - timedelta(days=30))\n",
    "var_results_path='/storage/results.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://towardsdatascience.com/normalization-vs-standardization-cb8fe15082eb\n",
    "def normalise(x):\n",
    "    return (x-min(x))/(max(x)-min(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(df):\n",
    "    # Fill missing values with zeroes\n",
    "    df = df.fillna(value=0)\n",
    "    # Clip outliers in scoring inputs to provide a better range to the regression\n",
    "    df['viewer_events_count'].clip(upper=3, inplace=True)\n",
    "    df['viewer_comments_count'].clip(upper=3, inplace=True)\n",
    "    df['viewer_comments_body_count'].clip(upper=1000, inplace=True)\n",
    "    # Inconsistencies between githubarchive and Github API, cap to avoid noise\n",
    "    df['viewer_repo_issues_opened_count'].clip(upper=5, inplace=True)\n",
    "    df['viewer_repo_pull_requests_opened_count'].clip(upper=5, inplace=True)\n",
    "    df['creator_repo_issues_opened_count'].clip(upper=5, inplace=True)\n",
    "    df['creator_repo_pull_requests_opened_count'].clip(upper=5, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get subset of githubarchive.org data\n",
    "# DANGER DANGER This query can cost up to $30USD\n",
    "query1 = (\"\"\"\n",
    "SELECT \n",
    "id, \n",
    "type, \n",
    "created_at, \n",
    "repo.name AS repo_name, \n",
    "repo.id AS repo_id, \n",
    "actor.login AS actor_login, \n",
    "actor.id AS actor_id, \n",
    "org.id AS org_id, \n",
    "org.login AS org_login, \n",
    "payload\n",
    "FROM `githubarchive.day.2*`\n",
    "WHERE\n",
    "# Date without the leading \"2\" (workaround to filter out \"yesterday\" table naming)\n",
    "_TABLE_SUFFIX BETWEEN '{var_date_min}' AND '{var_date_max}'\n",
    "AND actor.login = '{var_viewer_login}'\n",
    "\"\"\".format(\n",
    "    var_viewer_login=var_viewer_login,\n",
    "    var_date_min=var_date_max.strftime('%Y-%m-%d')[1:],\n",
    "    var_date_max=var_date_max.strftime('%Y-%m-%d')[1:]\n",
    "))\n",
    "# TODO Create view from query1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "query2 = (\"\"\"\n",
    "# Get events with baseline analysis\n",
    "SELECT\n",
    "*,\n",
    "(type IN ('IssueCommentEvent', 'PullRequestReviewCommentEvent', 'PullRequestCommentEvent')) AS is_comment,\n",
    "(JSON_EXTRACT_SCALAR(payload, '$.action') = 'create' AND actor_login = '{var_viewer_login}') AS is_author,\n",
    "(body IS NOT NULL AND body LIKE CONCAT('%@', '{var_viewer_login}', '%')) AS is_at_mentioned,\n",
    "# TODO Includes @param, @return etc.\n",
    "ARRAY_TO_STRING(REGEXP_EXTRACT_ALL(body, r'(?:^|\\s)(@[a-zA-Z0-9-_//]+)'),',') AS at_mentions,\n",
    "ARRAY_LENGTH(REGEXP_EXTRACT_ALL(body, r'(?:^|\\s)(@[a-zA-Z0-9-_//]+)')) AS at_mentions_count,\n",
    "CHAR_LENGTH(title) AS title_chars_count,\n",
    "CHAR_LENGTH(body) AS body_chars_count,\n",
    "ARRAY_LENGTH(REGEXP_EXTRACT_ALL(body, r'[#\\*]')) AS markdown_chars_count\n",
    "FROM (\n",
    "  # Get all relevant events in relevant repos.\n",
    "  # Normalise some data between types.\n",
    "  SELECT\n",
    "    *,\n",
    "    JSON_EXTRACT_SCALAR(payload, '$.action') AS action,\n",
    "    COALESCE(\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.issue.id'),\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.pull_request.id')\n",
    "    ) AS item_id,\n",
    "    COALESCE(\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.issue.url'),\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.pull_request.url')\n",
    "    ) AS item_url,\n",
    "    # TODO Keep empty for comments\n",
    "    COALESCE(\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.issue.title'),\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.pull_request.title')\n",
    "    ) AS title,\n",
    "    COALESCE(\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.comment.body'),\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.issue.body'),\n",
    "      JSON_EXTRACT_SCALAR(payload, '$.pull_request.body')\n",
    "    ) AS body\n",
    "  FROM `{var_bigquery_table}`\n",
    "  WHERE\n",
    "    repo_id IN (\n",
    "      # Get all repos which the user has interacted with\n",
    "      SELECT repo_id\n",
    "      FROM `{var_bigquery_table}`\n",
    "      WHERE\n",
    "        actor_login = '{var_viewer_login}'\n",
    "        AND created_at BETWEEN '{var_date_min}' AND '{var_date_max}'\n",
    "      GROUP BY repo_id\n",
    "    )\n",
    "    AND created_at BETWEEN '{var_date_min}' AND '{var_date_max}'\n",
    "    AND type IN ('IssuesEvent', 'PullRequestEvent', 'IssueCommentEvent', 'PullRequestReviewCommentEvent', 'PullRequestCommentEvent')\n",
    ")\n",
    "\"\"\".format(\n",
    "    var_viewer_login=var_viewer_login,\n",
    "    var_bigquery_table=var_bigquery_table,\n",
    "    var_date_min=var_date_min.strftime('%Y-%m-%d'),\n",
    "    var_date_max=var_date_max.strftime('%Y-%m-%d')\n",
    "))\n",
    "# TODO Create view from query2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "query3 = (\"\"\"\n",
    "WITH\n",
    "all_events AS (\n",
    "  SELECT *\n",
    "  FROM `{var_bigquery_table}`\n",
    "),\n",
    "opened_events AS (\n",
    "  SELECT *\n",
    "  FROM `{var_bigquery_table}`\n",
    "  WHERE type IN ('IssuesEvent', 'PullRequestEvent') AND action = 'opened' \n",
    "  ORDER BY created_at ASC\n",
    "),\n",
    "viewer_events AS (\n",
    "  SELECT *\n",
    "  FROM `{var_bigquery_table}`\n",
    "  WHERE actor_login = '{var_viewer_login}'\n",
    "  ORDER BY created_at ASC\n",
    "),\n",
    "repo_event_counts_by_actor AS (\n",
    "  SELECT\n",
    "  repo_id,\n",
    "  actor_login,\n",
    "  COUNT(*) AS event_count,\n",
    "  COUNTIF(type = 'IssuesEvent' AND action = 'opened') AS issues_opened_count,\n",
    "  COUNTIF(type = 'PullRequestEvent' AND action = 'opened') AS pull_requests_opened_count\n",
    "  FROM `{var_bigquery_table}`\n",
    "  WHERE created_at BETWEEN '{var_date_min}' AND '{var_date_max}'\n",
    "  GROUP BY 1,2\n",
    ")\n",
    "\n",
    "SELECT DISTINCT\n",
    "# Leading indicators (available on new items)\n",
    "opened_events.item_id,\n",
    "opened_events.item_url,\n",
    "opened_events.org_id,\n",
    "opened_events.repo_id,\n",
    "opened_events.actor_login AS creator_login,\n",
    "opened_events.type,\n",
    "opened_events.created_at,\n",
    "opened_events.is_at_mentioned AS viewer_is_at_mentioned,\n",
    "opened_events.title_chars_count,\n",
    "opened_events.body_chars_count,\n",
    "opened_events.markdown_chars_count,\n",
    "# Scoring indicators\n",
    "repo_event_counts_by_viewer.issues_opened_count AS viewer_repo_issues_opened_count,\n",
    "repo_event_counts_by_viewer.pull_requests_opened_count AS viewer_repo_pull_requests_opened_count,\n",
    "repo_event_counts_by_creator.issues_opened_count AS creator_repo_issues_opened_count,\n",
    "repo_event_counts_by_creator.pull_requests_opened_count AS creator_repo_pull_requests_opened_count,\n",
    "COUNTIF(all_events.actor_login = '{var_viewer_login}') AS viewer_events_count,\n",
    "COUNTIF(all_events.is_comment = true AND all_events.actor_login = '{var_viewer_login}') AS viewer_comments_count,\n",
    "# Trailing indicators (not available on newly created items)\n",
    "STRING_AGG(all_events.at_mentions) AS all_at_mentions,\n",
    "STRING_AGG(all_events.actor_login) AS all_actors,\n",
    "COUNT(all_events.id) AS events_count,\n",
    "COUNTIF(all_events.is_comment = true) AS comments_count,\n",
    "CHAR_LENGTH(STRING_AGG(all_events.body)) AS comments_body_count,\n",
    "SUM(all_events.at_mentions_count) AS at_mentions_count,\n",
    "COUNTIF(all_events.is_at_mentioned = true) AS viewer_at_mentions_count,\n",
    "(SELECT CHAR_LENGTH(STRING_AGG(viewer_events.body)) FROM viewer_events WHERE opened_events.item_id = viewer_events.item_id) AS viewer_comments_body_count\n",
    "# Due to the nature of event streams, the opened event might not be in the source data set (cutoff by date filter)\n",
    "FROM opened_events\n",
    "LEFT JOIN all_events\n",
    "    ON all_events.item_id = opened_events.item_id\n",
    "LEFT JOIN repo_event_counts_by_actor AS repo_event_counts_by_viewer\n",
    "  ON repo_event_counts_by_viewer.actor_login = '{var_viewer_login}' AND opened_events.repo_id = repo_event_counts_by_viewer.repo_id\n",
    "LEFT JOIN repo_event_counts_by_actor AS repo_event_counts_by_creator\n",
    "  ON opened_events.actor_login = repo_event_counts_by_creator.actor_login AND opened_events.repo_id = repo_event_counts_by_creator.repo_id\n",
    "WHERE opened_events.created_at BETWEEN '{var_date_min}' AND '{var_date_max}'\n",
    "GROUP BY 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15\n",
    "ORDER BY opened_events.created_at ASC\n",
    "\"\"\".format(\n",
    "    var_viewer_login=var_viewer_login,\n",
    "    var_bigquery_table=var_bigquery_table,\n",
    "    var_date_min=var_date_min.strftime('%Y-%m-%d'),\n",
    "    var_date_max=var_date_max.strftime('%Y-%m-%d')\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch results if not already present\n",
    "if flush == True or not os.path.isfile(var_results_path):\n",
    "    client = bigquery.Client()\n",
    "    df = client.query(query3).to_dataframe()\n",
    "    df.to_csv(var_results_path)\n",
    "else:\n",
    "    df = pd.read_csv(var_results_path)\n",
    "    \n",
    "df['fresh'] = False\n",
    "df = df.set_index(['item_url', 'fresh'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "    .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></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>item_id</th>\n",
       "      <th>org_id</th>\n",
       "      <th>repo_id</th>\n",
       "      <th>creator_login</th>\n",
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       "      <th>title_chars_count</th>\n",
       "      <th>body_chars_count</th>\n",
       "      <th>markdown_chars_count</th>\n",
       "      <th>viewer_repo_issues_opened_count</th>\n",
       "      <th>viewer_repo_pull_requests_opened_count</th>\n",
       "      <th>creator_repo_issues_opened_count</th>\n",
       "      <th>creator_repo_pull_requests_opened_count</th>\n",
       "      <th>viewer_events_count</th>\n",
       "      <th>viewer_comments_count</th>\n",
       "      <th>all_at_mentions</th>\n",
       "      <th>all_actors</th>\n",
       "      <th>events_count</th>\n",
       "      <th>comments_count</th>\n",
       "      <th>comments_body_count</th>\n",
       "      <th>at_mentions_count</th>\n",
       "      <th>viewer_at_mentions_count</th>\n",
       "      <th>viewer_comments_body_count</th>\n",
       "    </tr>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49</th>\n",
       "      <th>False</th>\n",
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       "    <tr>\n",
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       "      <th>False</th>\n",
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       "      <th>https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102</th>\n",
       "      <th>False</th>\n",
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       "    <tr>\n",
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       "      <th>False</th>\n",
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       "    <tr>\n",
       "      <th>https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104</th>\n",
       "      <th>False</th>\n",
       "      <td>4</td>\n",
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       "item_url                                                                               fresh                                    \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                             14.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                             14.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                              1.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                              1.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                              1.0   \n",
       "...                                                                                                                       ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                             24.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                             39.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                            119.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                            119.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                             14.0   \n",
       "\n",
       "                                                                                              viewer_repo_pull_requests_opened_count  \\\n",
       "item_url                                                                               fresh                                           \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                                     7.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                                     7.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                                     3.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                                     3.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                                     3.0   \n",
       "...                                                                                                                              ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                                    19.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                                    16.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                                   123.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                                   123.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                                     7.0   \n",
       "\n",
       "                                                                                              creator_repo_issues_opened_count  \\\n",
       "item_url                                                                               fresh                                     \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                                 2   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                                 2   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                                 1   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                                 1   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                                 1   \n",
       "...                                                                                                                        ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                                19   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                                 0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                                21   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                                21   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                                 2   \n",
       "\n",
       "                                                                                              creator_repo_pull_requests_opened_count  \\\n",
       "item_url                                                                               fresh                                            \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                                        7   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                                        7   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                                        2   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                                        2   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                                        2   \n",
       "...                                                                                                                               ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                                       18   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                                       12   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                                       83   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                                       83   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                                        5   \n",
       "\n",
       "                                                                                              viewer_events_count  \\\n",
       "item_url                                                                               fresh                        \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                    0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                    0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                    0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                    0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                    0   \n",
       "...                                                                                                           ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                    0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                    0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                    0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                    0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                    0   \n",
       "\n",
       "                                                                                              viewer_comments_count  \\\n",
       "item_url                                                                               fresh                          \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                      0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                      0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                      0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                      0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                      0   \n",
       "...                                                                                                             ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                      0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                      0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                      0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                      0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                      0   \n",
       "\n",
       "                                                                                             all_at_mentions  \\\n",
       "item_url                                                                               fresh                   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False               ,   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False               ,   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False           ,,,,,   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False  @Firesphere,,,   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False               ,   \n",
       "...                                                                                                      ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False               ,   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False             NaN   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False              ,,   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False               ,   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False             NaN   \n",
       "\n",
       "                                                                                                                                                  all_actors  \\\n",
       "item_url                                                                               fresh                                                                   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                                              wilr,SilbinaryWolf   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                                              SilbinaryWolf,wilr   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False  Firesphere,nyeholt,robbieaverill,Firesphere,nyeholt,Firesphere   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False            robbieaverill,robbieaverill,Firesphere,robbieaverill   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                                              Firesphere,nyeholt   \n",
       "...                                                                                                                                                      ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                                  maxime-rainville,robbieaverill   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                                                         bergice   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                 maxime-rainville,robbieaverill,maxime-rainville   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                                  maxime-rainville,robbieaverill   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                                                      emteknetnz   \n",
       "\n",
       "                                                                                              events_count  \\\n",
       "item_url                                                                               fresh                 \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False             2   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False             2   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False             6   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False             4   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False             2   \n",
       "...                                                                                                    ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False             2   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False             1   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False             3   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False             2   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False             1   \n",
       "\n",
       "                                                                                              comments_count  \\\n",
       "item_url                                                                               fresh                   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False               0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False               0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False               4   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False               2   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False               0   \n",
       "...                                                                                                      ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False               0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False               0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False               2   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False               0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False               0   \n",
       "\n",
       "                                                                                              comments_body_count  \\\n",
       "item_url                                                                               fresh                        \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                455.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                189.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                525.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                750.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                  1.0   \n",
       "...                                                                                                           ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                247.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                 77.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                315.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                389.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                110.0   \n",
       "\n",
       "                                                                                              at_mentions_count  \\\n",
       "item_url                                                                               fresh                      \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                0.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                0.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                0.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                1.0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                0.0   \n",
       "...                                                                                                         ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                0.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                0.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                0.0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                0.0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                0.0   \n",
       "\n",
       "                                                                                              viewer_at_mentions_count  \\\n",
       "item_url                                                                               fresh                             \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                         0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                         0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                         0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                         0   \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                         0   \n",
       "...                                                                                                                ...   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                         0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                         0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                         0   \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                         0   \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                         0   \n",
       "\n",
       "                                                                                              viewer_comments_body_count  \n",
       "item_url                                                                               fresh                              \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/49                 False                         NaN  \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/50                 False                         NaN  \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/102  False                         NaN  \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/issues/103 False                         NaN  \n",
       "https://api.github.com/repos/silverstripe-australia/silverstripe-queuedjobs/pulls/104  False                         NaN  \n",
       "...                                                                                                                  ...  \n",
       "https://api.github.com/repos/silverstripe/silverstripe-cms/pulls/2574                  False                         NaN  \n",
       "https://api.github.com/repos/silverstripe/silverstripe-asset-admin/pulls/1129          False                         NaN  \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9634            False                         NaN  \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/9635            False                         NaN  \n",
       "https://api.github.com/repos/dnadesign/silverstripe-elemental/pulls/822                False                         NaN  \n",
       "\n",
       "[15015 rows x 25 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview raw data\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = preprocess(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preview some values used for scoring\n",
    "scoring = {\n",
    "    'viewer_events_count': 1, \n",
    "    'viewer_comments_count': 2, \n",
    "    'viewer_comments_body_count': 1\n",
    "}\n",
    "scoring_fields = list(scoring.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore data distribution to get intuition on scoring\n",
    "# Even after removing outliers in preprocessing,\n",
    "# the two ranges have very different dimensionality without normalisation.\n",
    "df[scoring_fields].where(df[scoring_fields] > 0).plot.box(vert=False,figsize=(15,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Naive min-max normalisation to avoid one feature overly influencing the scoring\n",
    "df[scoring_fields] = df[scoring_fields].apply(normalise)\n",
    "df[scoring_fields].where(df[scoring_fields] > 0).plot.box(vert=False,figsize=(15,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate score with different weights\n",
    "# TODO Use apply() and add()\n",
    "scoring_weights = list(scoring.values())\n",
    "df['score'] = normalise(\n",
    "    df[scoring_fields[0]] * scoring_weights[0]\n",
    "    + df[scoring_fields[1]] * scoring_weights[1]\n",
    "    + df[scoring_fields[2]] * scoring_weights[2]\n",
    ")\n",
    "df[scoring_fields + ['score']].sort_values(by='score', ascending=False).head()\n",
    "\n",
    "# Optionally use score as a categorical value, lower fidelity but also lower noise\n",
    "if use_cat:\n",
    "    df['score'] = df['score'].apply(lambda x: 1 if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scoring distribution (excl. zeroes)\n",
    "# It's skewed to lower scores, since low interactions are more common than high interactions\n",
    "# This plot is only useful with use_cat=False\n",
    "df['score'].where(df['score'] > 0).plot.box(vert=False,figsize=(15,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scoring histogram (incl. zeroes)\n",
    "# There are way more items where the viewer has never interacted (score=0)\n",
    "# This plot is only useful with use_cat=False\n",
    "df['score'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8837162837162837"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of rows where score=0\n",
    "len(df[df['score'] == 0]) / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>item_id</th>\n",
       "      <th>org_id</th>\n",
       "      <th>repo_id</th>\n",
       "      <th>creator_login</th>\n",
       "      <th>type</th>\n",
       "      <th>created_at</th>\n",
       "      <th>viewer_is_at_mentioned</th>\n",
       "      <th>title_chars_count</th>\n",
       "      <th>body_chars_count</th>\n",
       "      <th>markdown_chars_count</th>\n",
       "      <th>viewer_repo_issues_opened_count</th>\n",
       "      <th>viewer_repo_pull_requests_opened_count</th>\n",
       "      <th>creator_repo_issues_opened_count</th>\n",
       "      <th>creator_repo_pull_requests_opened_count</th>\n",
       "      <th>viewer_events_count</th>\n",
       "      <th>viewer_comments_count</th>\n",
       "      <th>all_at_mentions</th>\n",
       "      <th>all_actors</th>\n",
       "      <th>events_count</th>\n",
       "      <th>comments_count</th>\n",
       "      <th>comments_body_count</th>\n",
       "      <th>at_mentions_count</th>\n",
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       "\n",
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      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['score'] > 0)][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Duplicate any items without \"temporal\" data.\n",
    "# The model should be able to perform well on newly created issues\n",
    "# without penalising them for not having any events (yet).\n",
    "# It's important to do this *after* calculating the scores\n",
    "if use_fresh:\n",
    "    temporal_overrides = {\n",
    "      'viewer_events_count': 0,\n",
    "      'viewer_comments_count': 0,\n",
    "      'viewer_comments_body_count': 0,\n",
    "    }\n",
    "    df_fresh = df.copy().assign(**temporal_overrides)\n",
    "    # Avoid index clashes and make altered rows identifiable during validation\n",
    "    df_fresh['fresh'] = True\n",
    "    df = df.append(df_fresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Don't include scoring_fields in here since that would be cheating\n",
    "cat_names = [\n",
    "  'org_id',\n",
    "  'repo_id',\n",
    "  'creator_login',\n",
    "  'type',\n",
    "  'viewer_is_at_mentioned',\n",
    "]\n",
    "cont_names = [\n",
    "  'title_chars_count',\n",
    "  'body_chars_count',\n",
    "  'markdown_chars_count',\n",
    "  'viewer_repo_issues_opened_count',\n",
    "  'viewer_repo_pull_requests_opened_count',\n",
    "  'creator_repo_issues_opened_count',\n",
    "  'creator_repo_pull_requests_opened_count',\n",
    "  'events_count',\n",
    "  'comments_count',\n",
    "  'comments_body_count',\n",
    "  'viewer_at_mentions_count',\n",
    "  'at_mentions_count',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create fast.ai (PyTorch) data set\n",
    "tp = TabularPandas(\n",
    "    df[cat_names + cont_names + ['score']],\n",
    "    cat_names=cat_names,\n",
    "    cont_names=cont_names,\n",
    "    procs=[\n",
    "      FillMissing,\n",
    "      Categorify,\n",
    "      Normalize\n",
    "    ],\n",
    "    y_names='score',\n",
    "    # Optionally use score as a categorical value, lower fidelity but also lower noise\n",
    "    y_block=CategoryBlock if use_cat else None,\n",
    "    splits=RandomSplitter(valid_pct=0.2)(range_of(df))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = tabular_learner(tp.dataloaders(bs=64), metrics=accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(lr_min=0.002754228748381138, lr_steep=0.0006918309954926372)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Intuit the learning rate\n",
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.019352</td>\n",
       "      <td>0.017965</td>\n",
       "      <td>0.881785</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(1, lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>org_id</th>\n",
       "      <th>repo_id</th>\n",
       "      <th>creator_login</th>\n",
       "      <th>type</th>\n",
       "      <th>viewer_is_at_mentioned</th>\n",
       "      <th>title_chars_count</th>\n",
       "      <th>body_chars_count</th>\n",
       "      <th>markdown_chars_count</th>\n",
       "      <th>viewer_repo_issues_opened_count</th>\n",
       "      <th>viewer_repo_pull_requests_opened_count</th>\n",
       "      <th>creator_repo_issues_opened_count</th>\n",
       "      <th>creator_repo_pull_requests_opened_count</th>\n",
       "      <th>events_count</th>\n",
       "      <th>comments_count</th>\n",
       "      <th>comments_body_count</th>\n",
       "      <th>viewer_at_mentions_count</th>\n",
       "      <th>at_mentions_count</th>\n",
       "      <th>score</th>\n",
       "      <th>score_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.020639</td>\n",
       "      <td>-0.374517</td>\n",
       "      <td>-0.245031</td>\n",
       "      <td>0.758111</td>\n",
       "      <td>0.878591</td>\n",
       "      <td>0.872615</td>\n",
       "      <td>0.755815</td>\n",
       "      <td>-0.421998</td>\n",
       "      <td>-0.251955</td>\n",
       "      <td>-0.410965</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>-0.322746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.885197</td>\n",
       "      <td>-0.374517</td>\n",
       "      <td>-0.245031</td>\n",
       "      <td>-1.594302</td>\n",
       "      <td>-0.945088</td>\n",
       "      <td>-1.576415</td>\n",
       "      <td>-0.759304</td>\n",
       "      <td>-0.421998</td>\n",
       "      <td>-0.449907</td>\n",
       "      <td>-0.427575</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>-0.322746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.110307</td>\n",
       "      <td>3.778246</td>\n",
       "      <td>3.556430</td>\n",
       "      <td>0.758111</td>\n",
       "      <td>0.878591</td>\n",
       "      <td>0.872615</td>\n",
       "      <td>0.755815</td>\n",
       "      <td>2.692557</td>\n",
       "      <td>2.717324</td>\n",
       "      <td>4.118535</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>0.236560</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.626001</td>\n",
       "      <td>-0.374517</td>\n",
       "      <td>-0.245031</td>\n",
       "      <td>0.758111</td>\n",
       "      <td>0.878591</td>\n",
       "      <td>0.872615</td>\n",
       "      <td>0.755815</td>\n",
       "      <td>-0.421998</td>\n",
       "      <td>-0.449907</td>\n",
       "      <td>-0.427575</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>-0.322746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.294180</td>\n",
       "      <td>-0.374517</td>\n",
       "      <td>-0.245031</td>\n",
       "      <td>-1.594302</td>\n",
       "      <td>-0.489169</td>\n",
       "      <td>-1.576415</td>\n",
       "      <td>-1.264344</td>\n",
       "      <td>-0.421998</td>\n",
       "      <td>-0.449907</td>\n",
       "      <td>-0.427575</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>-0.322746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>2.0</td>\n",
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  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
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       "                                                                                    org_id  \\\n",
       "item_url                                                                     fresh           \n",
       "https://api.github.com/repos/silverstripe/silverstripe-framework/pulls/6829  False  379348   \n",
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       "...                                                                                    ...   \n",
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       "...                                                                                       ...   \n",
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       "\n",
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       "...                                                                                          ...   \n",
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       "\n",
       "[6006 rows x 19 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp = Interpretation.from_learner(learn)\n",
    "top_losses = interp.top_losses()\n",
    "to_top_losses = learn.dls.valid.dataset.iloc[top_losses.indices]\n",
    "df_top_losses = to_top_losses.decode().items\n",
    "df_top_losses['loss'] = top_losses.values\n",
    "df_top_losses[cat_names + cont_names + ['score', 'loss']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dl_train = learn.dls[1]\n",
    "# _,_,preds = learn.get_preds(dl=dl_train, with_decoded=True)\n",
    "# df_train = dl_train.all_cols\n",
    "# df_train['score_pred'] = preds\n",
    "# learn.dls.show_results(b, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validate with a new (previously unseen) test data set\n",
    "test_data = fetch(\n",
    "    url='https://github.com/silverstripe/silverstripe-framework/issues/9688',\n",
    "    viewer_login=var_viewer_login,\n",
    "    github_api_token=os.getenv('GITHUB_API_TOKEN')\n",
    ")\n",
    "# Create DataFrame\n",
    "test_df = pd.DataFrame.from_dict([test_data])\n",
    "# Reapply preprocessing\n",
    "test_df = preprocess(test_df)\n",
    "# Select fields we've actually used\n",
    "# This also ensures data is presented in the same order, which is important for the Random Forest classifier\n",
    "test_df = test_df[cat_names + cont_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <th>type</th>\n",
       "      <th>viewer_is_at_mentioned</th>\n",
       "      <th>title_chars_count</th>\n",
       "      <th>body_chars_count</th>\n",
       "      <th>markdown_chars_count</th>\n",
       "      <th>viewer_repo_issues_opened_count</th>\n",
       "      <th>viewer_repo_pull_requests_opened_count</th>\n",
       "      <th>creator_repo_issues_opened_count</th>\n",
       "      <th>creator_repo_pull_requests_opened_count</th>\n",
       "      <th>events_count</th>\n",
       "      <th>comments_count</th>\n",
       "      <th>comments_body_count</th>\n",
       "      <th>viewer_at_mentions_count</th>\n",
       "      <th>at_mentions_count</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>379348</td>\n",
       "      <td>1318892</td>\n",
       "      <td>sminnee</td>\n",
       "      <td>PullRequestEvent</td>\n",
       "      <td>False</td>\n",
       "      <td>117.999999</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4988.999952</td>\n",
       "      <td>1.236510e-09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([0.2009])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run neural net prediction\n",
    "row, pred, probs = learn.predict(test_df.iloc[0])\n",
    "row.show()\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export('/storage/model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5173345188733971"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative approach: Random Forest\n",
    "from sklearn import ensemble\n",
    "xs,y = tp.train.xs,tp.train.y\n",
    "valid_xs,valid_y = tp.valid.xs,tp.valid.y\n",
    "cls = ensemble.RandomForestClassifier if use_cat else ensemble.RandomForestRegressor\n",
    "tree_model = cls(\n",
    "    n_jobs=-1,\n",
    "    n_estimators=100,\n",
    "    min_samples_leaf=20,\n",
    "    oob_score=True\n",
    ")\n",
    "tree_model.fit(xs, y)\n",
    "tree_model.score(valid_xs, valid_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18737284])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run Random Forest prediction\n",
    "# Apply processing to data by creating it as a one-element test dataloader.\n",
    "# Unlike tabular_learner(), scikit doesn't handle this automatically.\n",
    "# CAUTION: Assumes parameters are in the same order\n",
    "test_dl = learn.dls.test_dl(test_df)\n",
    "test_dl.dataset.conts = test_dl.dataset.conts.astype(np.float32)\n",
    "test_dl.dataset.loc[0]\n",
    "tree_model.predict([test_dl.dataset.loc[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='cols'>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi = pd.DataFrame({\n",
    "    'cols':xs.columns, \n",
    "    'imp':tree_model.feature_importances_\n",
    "}).sort_values('imp', ascending=True)\n",
    "fi.plot('cols', 'imp', 'barh', figsize=(8,7), legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}