03_model_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c31ddd8-bdcf-473d-95c1-5fed70b7fe51",
   "metadata": {},
   "source": [
    "# Train a model on features engineered from Pull Requests data\n",
    "\n",
    "\n",
    "## What we did previously\n",
    "\n",
    "In the previous [notebook](./02_feature_engineering.ipynb) we explored the various fields in the PR dataset and derived some features from them in order to frame an a Machine Learning where we predict the time taken to merge a PR.\n",
    "\n",
    "## In this step\n",
    "\n",
    "In this notebook, we will first create transformer objects (based on the feature explorations in the previous notebook) to extract features from raw PR data. Then, we will train machine learning models to classify a PR's `time_to_merge` into one of the above 10 bins (or \"classes\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e2bb1e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import gzip\n",
    "import json\n",
    "import boto3\n",
    "import tempfile\n",
    "import datetime\n",
    "\n",
    "import joblib\n",
    "import warnings\n",
    "from io import BytesIO\n",
    "from copy import deepcopy\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import PowerTransformer, OrdinalEncoder\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "\n",
    "from src.features.build_features import (\n",
    "    DateTimeDetailsTransformer,\n",
    "    NumChangedFilesTransformer,\n",
    "    StringLenTransformer,\n",
    "    FileTypeCountTransformer,\n",
    "    TitleWordCountTransformer,\n",
    ")\n",
    "\n",
    "import ceph_comm\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "load_dotenv(find_dotenv(), override=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c4e04bb-a8fc-4bc9-a91d-ffe389212f0e",
   "metadata": {},
   "source": [
    "## Get Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f6d138f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## CEPH Bucket variables\n",
    "## Create a .env file on your local with the correct configs,\n",
    "\n",
    "ORG = os.getenv(\"GITHUB_ORG\")\n",
    "REPO = os.getenv(\"GITHUB_REPO\")\n",
    "\n",
    "## S3 bucket credentials\n",
    "s3_endpoint_url = os.getenv(\"S3_ENDPOINT_URL\")\n",
    "s3_access_key = os.getenv(\"S3_ACCESS_KEY\")\n",
    "s3_secret_key = os.getenv(\"S3_SECRET_KEY\")\n",
    "s3_bucket = os.getenv(\"S3_BUCKET\")\n",
    "\n",
    "REMOTE = os.getenv(\"REMOTE\")\n",
    "\n",
    "s3_input_data_path = os.getenv(\"CEPH_BUCKET_PREFIX\")\n",
    "\n",
    "RAW_DATA_PATH = os.path.join(\n",
    "    s3_input_data_path, ORG, REPO, \"PullRequest.json\"\n",
    ")\n",
    "\n",
    "CHOSEN_MODEL = os.getenv(\"CHOSEN_MODEL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f7ddbfc0-53a7-4d76-a64e-975c89fa1e59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getting dataset from ceph\n"
     ]
    }
   ],
   "source": [
    "# Collect PRs and combine them\n",
    "cc = ceph_comm.CephCommunication(s3_endpoint_url, s3_access_key, s3_secret_key, s3_bucket)\n",
    "\n",
    "print(\"getting dataset from ceph\")\n",
    "pr_df = cc.get_json_from_ceph(RAW_DATA_PATH)\n",
    "# pr_df = pr_df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0978bbf8-2da1-4e66-bfa8-5cfa1af2f3e2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>interactions</th>\n",
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       "      <th>first_approve_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>add ttm train action</td>\n",
       "      <td>## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>MichaelClifford</td>\n",
       "      <td>1669839567</td>\n",
       "      <td>1669841323</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1.669841e+09</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'sesheta': 175, 'aakankshaduggal': 1}</td>\n",
       "      <td>{'1199539984': {'author': 'aakankshaduggal', '...</td>\n",
       "      <td>[approved, size/M, lgtm]</td>\n",
       "      <td>[4e363841a0566de9d278457752c7fe432bae89fe]</td>\n",
       "      <td>[.github/workflows/train-ttm.yaml]</td>\n",
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       "      <td>1.669841e+09</td>\n",
       "    </tr>\n",
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       "      <td>Add slides for workshop at DevConf US 22</td>\n",
       "      <td>## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...</td>\n",
       "      <td>XS</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1660693917</td>\n",
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       "      <td>sesheta</td>\n",
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       "      <td>sesheta</td>\n",
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       "      <td>{'oindrillac': 16, 'aakankshaduggal': 9, 'sesh...</td>\n",
       "      <td>{'1074771058': {'author': 'oindrillac', 'words...</td>\n",
       "      <td>[approved, size/XS, lgtm]</td>\n",
       "      <td>[8d2809ea22f0f6118e29b7b381a014489818d3ac]</td>\n",
       "      <td>[docs/intro_to_workshop_devconf22.pdf, docs/wo...</td>\n",
       "      <td>1.660698e+09</td>\n",
       "      <td>1.660698e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Added new bitwarden link</td>\n",
       "      <td>None</td>\n",
       "      <td>XS</td>\n",
       "      <td>suppathak</td>\n",
       "      <td>1660069228</td>\n",
       "      <td>1660069562</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1.660070e+09</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'aakankshaduggal': 11, 'sesheta': 65}</td>\n",
       "      <td>{'1066819103': {'author': 'aakankshaduggal', '...</td>\n",
       "      <td>[approved, size/XS, lgtm]</td>\n",
       "      <td>[7fd8e441ec8fd8a533dcdfbb6a1affbfd4c515dd]</td>\n",
       "      <td>[docs/workshop/onboarding.md]</td>\n",
       "      <td>1.660069e+09</td>\n",
       "      <td>1.660069e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Added workshop introduction slides</td>\n",
       "      <td>## Related Issues\\r\\n\\r\\nCloses #511</td>\n",
       "      <td>XS</td>\n",
       "      <td>suppathak</td>\n",
       "      <td>1659371667</td>\n",
       "      <td>1659373501</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1.659374e+09</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'sesheta': 65}</td>\n",
       "      <td>{'1057235912': {'author': 'aakankshaduggal', '...</td>\n",
       "      <td>[approved, size/XS, lgtm]</td>\n",
       "      <td>[75d3f000fb51006fb3686c5ea7b45e6aae363a86]</td>\n",
       "      <td>[docs/workshop/Workshop_Introduction.pdf]</td>\n",
       "      <td>1.659372e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Add slides for Openinfra foundation collaboration</td>\n",
       "      <td>## Related Issues and Dependencies\\r\\n\\r\\n#575...</td>\n",
       "      <td>XS</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1659053347</td>\n",
       "      <td>1659114951</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1.659115e+09</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'MichaelClifford': 7, 'sesheta': 65}</td>\n",
       "      <td>{}</td>\n",
       "      <td>[approved, size/XS]</td>\n",
       "      <td>[9626b65ef55b636b2cb2f73cfbec1314beae3373]</td>\n",
       "      <td>[docs/aiops_for_community_health.pdf]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                               title  \\\n",
       "0                               add ttm train action   \n",
       "1           Add slides for workshop at DevConf US 22   \n",
       "2                           Added new bitwarden link   \n",
       "3                 Added workshop introduction slides   \n",
       "4  Add slides for Openinfra foundation collaboration   \n",
       "\n",
       "                                                body size       created_by  \\\n",
       "0  ## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...    M  MichaelClifford   \n",
       "1  ## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...   XS  aakankshaduggal   \n",
       "2                                               None   XS        suppathak   \n",
       "3              ## Related Issues\\r\\n\\r\\nCloses #511    XS        suppathak   \n",
       "4  ## Related Issues and Dependencies\\r\\n\\r\\n#575...   XS  aakankshaduggal   \n",
       "\n",
       "   created_at   closed_at        closed_by     merged_at        merged_by  \\\n",
       "0  1669839567  1669841323  aakankshaduggal  1.669841e+09  aakankshaduggal   \n",
       "1  1660693917  1660699734          sesheta  1.660700e+09          sesheta   \n",
       "2  1660069228  1660069562  aakankshaduggal  1.660070e+09  aakankshaduggal   \n",
       "3  1659371667  1659373501          sesheta  1.659374e+09          sesheta   \n",
       "4  1659053347  1659114951          sesheta  1.659115e+09          sesheta   \n",
       "\n",
       "   commits_number  changed_files_number  \\\n",
       "0               1                     1   \n",
       "1               1                     3   \n",
       "2               1                     1   \n",
       "3               1                     1   \n",
       "4               1                     1   \n",
       "\n",
       "                                        interactions  \\\n",
       "0             {'sesheta': 175, 'aakankshaduggal': 1}   \n",
       "1  {'oindrillac': 16, 'aakankshaduggal': 9, 'sesh...   \n",
       "2             {'aakankshaduggal': 11, 'sesheta': 65}   \n",
       "3                                    {'sesheta': 65}   \n",
       "4              {'MichaelClifford': 7, 'sesheta': 65}   \n",
       "\n",
       "                                             reviews  \\\n",
       "0  {'1199539984': {'author': 'aakankshaduggal', '...   \n",
       "1  {'1074771058': {'author': 'oindrillac', 'words...   \n",
       "2  {'1066819103': {'author': 'aakankshaduggal', '...   \n",
       "3  {'1057235912': {'author': 'aakankshaduggal', '...   \n",
       "4                                                 {}   \n",
       "\n",
       "                      labels                                     commits  \\\n",
       "0   [approved, size/M, lgtm]  [4e363841a0566de9d278457752c7fe432bae89fe]   \n",
       "1  [approved, size/XS, lgtm]  [8d2809ea22f0f6118e29b7b381a014489818d3ac]   \n",
       "2  [approved, size/XS, lgtm]  [7fd8e441ec8fd8a533dcdfbb6a1affbfd4c515dd]   \n",
       "3  [approved, size/XS, lgtm]  [75d3f000fb51006fb3686c5ea7b45e6aae363a86]   \n",
       "4        [approved, size/XS]  [9626b65ef55b636b2cb2f73cfbec1314beae3373]   \n",
       "\n",
       "                                       changed_files  first_review_at  \\\n",
       "0                 [.github/workflows/train-ttm.yaml]     1.669840e+09   \n",
       "1  [docs/intro_to_workshop_devconf22.pdf, docs/wo...     1.660698e+09   \n",
       "2                      [docs/workshop/onboarding.md]     1.660069e+09   \n",
       "3          [docs/workshop/Workshop_Introduction.pdf]     1.659372e+09   \n",
       "4              [docs/aiops_for_community_health.pdf]              NaN   \n",
       "\n",
       "   first_approve_at  \n",
       "0      1.669841e+09  \n",
       "1      1.660698e+09  \n",
       "2      1.660069e+09  \n",
       "3               NaN  \n",
       "4               NaN  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb8188c-d835-4913-a77b-1524fa04eeef",
   "metadata": {},
   "source": [
    "## Extract Labels from Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8bf8212d-82bd-4600-a615-1e150272146f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1756.0\n",
       "1        5817.0\n",
       "2         334.0\n",
       "3        1834.0\n",
       "4       61604.0\n",
       "         ...   \n",
       "241    757448.0\n",
       "242     48415.0\n",
       "243    426198.0\n",
       "244     80593.0\n",
       "245    705281.0\n",
       "Length: 240, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = (pr_df[\"merged_at\"] - pr_df[\"created_at\"]).astype(\"float\")\n",
    "y = y.dropna()\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5ddc1177-ac15-494a-8c49-0d49e3e5d7e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0        120.0\n",
       "0.1       1427.5\n",
       "0.2       3984.2\n",
       "0.3       7060.8\n",
       "0.4      47398.2\n",
       "0.5      72221.0\n",
       "0.6     100187.6\n",
       "0.7     260600.5\n",
       "0.8     504710.2\n",
       "0.9    1158667.7\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_buckets = 10\n",
    "\n",
    "quantiles = y.quantile(q=np.arange(0, 1 + 1e-100, 1 / n_buckets))\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8645c579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0             0:02:00\n",
       "0.1             0:23:00\n",
       "0.2             1:06:00\n",
       "0.3             1:57:00\n",
       "0.4            13:09:00\n",
       "0.5            20:03:00\n",
       "0.6      1 day, 3:49:00\n",
       "0.7     3 days, 0:23:00\n",
       "0.8    5 days, 20:11:00\n",
       "0.9    13 days, 9:51:00\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = quantiles.apply(lambda x: str(datetime.timedelta(minutes=np.floor(x/60))))\n",
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "28616ba1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0.3</th>\n",
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       "      <th>0.4</th>\n",
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       "      <td>4</td>\n",
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       "      <th>0.5</th>\n",
       "      <td>13:09:00 - 20:03:00</td>\n",
       "      <td>5</td>\n",
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       "      <th>0.6</th>\n",
       "      <td>20:03:00 - 1 day, 3:49:00</td>\n",
       "      <td>6</td>\n",
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       "      <th>0.7</th>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>3 days, 0:23:00 - 5 days, 20:11:00</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>5 days, 20:11:00 - 13 days, 9:51:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Class_Name  Class_Label\n",
       "0.0                    0:00:00 - 0:02:00            0\n",
       "0.1                    0:02:00 - 0:23:00            1\n",
       "0.2                    0:23:00 - 1:06:00            2\n",
       "0.3                    1:06:00 - 1:57:00            3\n",
       "0.4                   1:57:00 - 13:09:00            4\n",
       "0.5                  13:09:00 - 20:03:00            5\n",
       "0.6            20:03:00 - 1 day, 3:49:00            6\n",
       "0.7     1 day, 3:49:00 - 3 days, 0:23:00            7\n",
       "0.8   3 days, 0:23:00 - 5 days, 20:11:00            8\n",
       "0.9  5 days, 20:11:00 - 13 days, 9:51:00            9"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Categories = pd.DataFrame(categories,columns=[\"end\"])\n",
    "start = np.insert(Categories[\"end\"].values[:-1], 0, str(datetime.timedelta(minutes=0)))\n",
    "Categories[\"start\"] = start\n",
    "Categories[\"Class_Name\"] = Categories[\"start\"] + \" - \" +  Categories[\"end\"]\n",
    "Categories[\"Class_Label\"] = [x for x in range(10)]\n",
    "Categories = Categories[[\"Class_Name\", \"Class_Label\"]]\n",
    "Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "97a47fdd-f513-4428-a4a1-6affe1899eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      2\n",
       "2      0\n",
       "3      1\n",
       "4      4\n",
       "      ..\n",
       "241    8\n",
       "242    4\n",
       "243    7\n",
       "244    5\n",
       "245    8\n",
       "Length: 240, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_ttm_class(ttm):\n",
    "\n",
    "    for rownum, (indx, val) in enumerate(quantiles.iteritems()):\n",
    "        if ttm < val:\n",
    "            return rownum - 1\n",
    "    return 9\n",
    "\n",
    "\n",
    "y = y.apply(get_ttm_class)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9efc4d-f5ee-4a18-aef6-df840395a55a",
   "metadata": {},
   "source": [
    "## Extract Features from Raw Data\n",
    "\n",
    "In this section, we will create transformer objects to process raw data as per the feature extraction methods that were found to be the most effective in the previous EDA notebook.\n",
    "\n",
    "To ensure that joblib serializes the custom transformer objects correctly, we will write their definitions in [`src/features/build_features.py`](../../src/features/build_features.py) instead of this notebook, and import them from the `src` package. This way, the `src` package can be listed as a dependency wherever these objects need to be deserialized (for example, Seldon server for model deployment)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bdc89e-0a62-41b8-8b70-caf958bb6a2d",
   "metadata": {},
   "source": [
    "#### size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f769e3cb-e140-4855-8161-0c72bf6403a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# map values to 0,1,2,3,4,5\n",
    "pr_size_encoder = OrdinalEncoder(categories=[[\"XS\", \"S\", \"M\", \"L\", \"XL\", \"XXL\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f893270d-0525-4bff-900c-7e630f1197c0",
   "metadata": {},
   "source": [
    "#### created_at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7d2ab3c3-caed-4971-ba1d-4c484088a057",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get day, month, date, etc. from a unix timestamp\n",
    "dt_details_transf = DateTimeDetailsTransformer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b637c29-df8f-43cf-ba10-7be54e0fc018",
   "metadata": {},
   "source": [
    "#### changed_files_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2ad7979f-afd8-40b2-8c03-929129908953",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of files changed in PR\n",
    "n_changed_files_transf = NumChangedFilesTransformer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2fc0cf-ab56-4b53-a190-7b5770bfebbd",
   "metadata": {},
   "source": [
    "#### body_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b74d3d4d-ba0c-43c3-8506-23ed49c6dce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of characters in PR description\n",
    "body_size_transf = StringLenTransformer(\"body\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5840645a-56dc-4658-b91a-782739e8de09",
   "metadata": {},
   "source": [
    "#### filetype_\\<type>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "71d6e5c3-eaf3-4cfe-9fe6-ef3f9b9edcfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many files of the given extension were changed in PR\n",
    "FILETYPE_FEATURES_FILENAME = os.path.join(ORG + REPO + \"FILETYPE.parquet\")\n",
    "FILETYPE_FEATURES_KEY = os.path.join(s3_input_data_path, ORG, REPO, \"features\")\n",
    "filetype_df = cc.read_from_ceph(FILETYPE_FEATURES_KEY, FILETYPE_FEATURES_FILENAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "977a1fba-c867-4feb-848d-5456d168e940",
   "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>filetype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.ipynb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.md</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.po</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  filetype\n",
       "0     .png\n",
       "1   .ipynb\n",
       "2    .html\n",
       "3      .md\n",
       "4      .po"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filetype_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c987e49d-d5ab-48ec-b369-6bc5c883d976",
   "metadata": {},
   "outputs": [],
   "source": [
    "FILE_EXTENSIONS_TO_COUNT = list(filetype_df[\"filetype\"])\n",
    "\n",
    "ftype_count_transf = FileTypeCountTransformer(file_extensions=FILE_EXTENSIONS_TO_COUNT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8797c05d-bc9a-497e-bbca-d9ed6b444f44",
   "metadata": {},
   "source": [
    "#### title_wordcount_\\<word>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "952d3564-c7a9-4707-9117-b4ccfaf0065c",
   "metadata": {},
   "outputs": [],
   "source": [
    "TITLE_FEATURES_FILENAME = os.path.join(ORG + REPO + \"titlewords.parquet\")\n",
    "TITLE_FEATURES_KEY = os.path.join(s3_input_data_path, ORG, REPO, \"features\")\n",
    "title_word_keep_cols_df = cc.read_from_ceph(TITLE_FEATURES_KEY, TITLE_FEATURES_FILENAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0534487e-f064-49ef-a0a2-15f6022dfcab",
   "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>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>access</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>across</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>add</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>added</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    title\n",
       "0  access\n",
       "1  across\n",
       "2  action\n",
       "3     add\n",
       "4   added"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_word_keep_cols_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "1be7c50c-dc47-483f-b695-a9c02d6cb51b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many times these words appeared in PR title\n",
    "WORDS_TO_COUNT = list(title_word_keep_cols_df[\"title\"])\n",
    "\n",
    "title_wc_transf = TitleWordCountTransformer(words=WORDS_TO_COUNT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11932e2c-ffb2-4a98-9bb9-63a8a4984403",
   "metadata": {},
   "source": [
    "## Apply Transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c4ee7eb8-62d6-452f-b548-1a884e63ce4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformer objects compiled into one columntransformer\n",
    "raw_data_processor = ColumnTransformer(\n",
    "    [\n",
    "        (\"pr_size\", pr_size_encoder, [\"size\"]),  # 1 cols generated\n",
    "        (\"created_at_details\", dt_details_transf, [\"created_at\"]),  # 4 cols generated\n",
    "        (\n",
    "            \"n_changed_files\",\n",
    "            n_changed_files_transf,\n",
    "            [\"changed_files_number\"],\n",
    "        ),  # 1 cols generated\n",
    "        (\"body_size\", body_size_transf, [\"body\"]),  # 1 cols generated\n",
    "        (\"n_commits\", \"passthrough\", [\"commits_number\"]),  # 1 cols generated\n",
    "        (\n",
    "            \"filetype_counter\",\n",
    "            ftype_count_transf,\n",
    "            [\"changed_files\"],\n",
    "        ),\n",
    "        (\"title_word_counter\", title_wc_transf, [\"title\"]),\n",
    "    ],\n",
    "    remainder=\"drop\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7fa84a70-dab6-4a48-90fc-844c29d38263",
   "metadata": {},
   "outputs": [],
   "source": [
    "# column names. this is needed because sklearn forcefully converts df to ndarray,\n",
    "# thus losing column information. this is a hack to retain that info. look for alternatives\n",
    "cols = (\n",
    "    [\n",
    "        \"size\",\n",
    "        \"created_at_day\",\n",
    "        \"created_at_month\",\n",
    "        \"created_at_weekday\",\n",
    "        \"created_at_hour\",\n",
    "    ]\n",
    "    + [\n",
    "        \"changed_files_number\",\n",
    "        \"body_size\",\n",
    "        \"commits_number\",\n",
    "    ]\n",
    "    + [f\"filetype_{f}\" for f in FILE_EXTENSIONS_TO_COUNT]\n",
    "    + [f\"title_wordcount_{w}\" for w in WORDS_TO_COUNT]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "92ddff9e-fb93-477d-ab17-b54ce13185a4",
   "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>title</th>\n",
       "      <th>body</th>\n",
       "      <th>size</th>\n",
       "      <th>created_by</th>\n",
       "      <th>created_at</th>\n",
       "      <th>closed_at</th>\n",
       "      <th>closed_by</th>\n",
       "      <th>merged_at</th>\n",
       "      <th>merged_by</th>\n",
       "      <th>commits_number</th>\n",
       "      <th>changed_files_number</th>\n",
       "      <th>interactions</th>\n",
       "      <th>reviews</th>\n",
       "      <th>labels</th>\n",
       "      <th>commits</th>\n",
       "      <th>changed_files</th>\n",
       "      <th>first_review_at</th>\n",
       "      <th>first_approve_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>add ttm train action</td>\n",
       "      <td>## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>MichaelClifford</td>\n",
       "      <td>1669839567</td>\n",
       "      <td>1669841323</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1.669841e+09</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'sesheta': 175, 'aakankshaduggal': 1}</td>\n",
       "      <td>{'1199539984': {'author': 'aakankshaduggal', '...</td>\n",
       "      <td>[approved, size/M, lgtm]</td>\n",
       "      <td>[4e363841a0566de9d278457752c7fe432bae89fe]</td>\n",
       "      <td>[.github/workflows/train-ttm.yaml]</td>\n",
       "      <td>1.669840e+09</td>\n",
       "      <td>1.669841e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Add slides for workshop at DevConf US 22</td>\n",
       "      <td>## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...</td>\n",
       "      <td>XS</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1660693917</td>\n",
       "      <td>1660699734</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1.660700e+09</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>{'oindrillac': 16, 'aakankshaduggal': 9, 'sesh...</td>\n",
       "      <td>{'1074771058': {'author': 'oindrillac', 'words...</td>\n",
       "      <td>[approved, size/XS, lgtm]</td>\n",
       "      <td>[8d2809ea22f0f6118e29b7b381a014489818d3ac]</td>\n",
       "      <td>[docs/intro_to_workshop_devconf22.pdf, docs/wo...</td>\n",
       "      <td>1.660698e+09</td>\n",
       "      <td>1.660698e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Added new bitwarden link</td>\n",
       "      <td>None</td>\n",
       "      <td>XS</td>\n",
       "      <td>suppathak</td>\n",
       "      <td>1660069228</td>\n",
       "      <td>1660069562</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1.660070e+09</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'aakankshaduggal': 11, 'sesheta': 65}</td>\n",
       "      <td>{'1066819103': {'author': 'aakankshaduggal', '...</td>\n",
       "      <td>[approved, size/XS, lgtm]</td>\n",
       "      <td>[7fd8e441ec8fd8a533dcdfbb6a1affbfd4c515dd]</td>\n",
       "      <td>[docs/workshop/onboarding.md]</td>\n",
       "      <td>1.660069e+09</td>\n",
       "      <td>1.660069e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Added workshop introduction slides</td>\n",
       "      <td>## Related Issues\\r\\n\\r\\nCloses #511</td>\n",
       "      <td>XS</td>\n",
       "      <td>suppathak</td>\n",
       "      <td>1659371667</td>\n",
       "      <td>1659373501</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1.659374e+09</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'sesheta': 65}</td>\n",
       "      <td>{'1057235912': {'author': 'aakankshaduggal', '...</td>\n",
       "      <td>[approved, size/XS, lgtm]</td>\n",
       "      <td>[75d3f000fb51006fb3686c5ea7b45e6aae363a86]</td>\n",
       "      <td>[docs/workshop/Workshop_Introduction.pdf]</td>\n",
       "      <td>1.659372e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Add slides for Openinfra foundation collaboration</td>\n",
       "      <td>## Related Issues and Dependencies\\r\\n\\r\\n#575...</td>\n",
       "      <td>XS</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1659053347</td>\n",
       "      <td>1659114951</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1.659115e+09</td>\n",
       "      <td>sesheta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'MichaelClifford': 7, 'sesheta': 65}</td>\n",
       "      <td>{}</td>\n",
       "      <td>[approved, size/XS]</td>\n",
       "      <td>[9626b65ef55b636b2cb2f73cfbec1314beae3373]</td>\n",
       "      <td>[docs/aiops_for_community_health.pdf]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0                               add ttm train action   \n",
       "1           Add slides for workshop at DevConf US 22   \n",
       "2                           Added new bitwarden link   \n",
       "3                 Added workshop introduction slides   \n",
       "4  Add slides for Openinfra foundation collaboration   \n",
       "\n",
       "                                                body size       created_by  \\\n",
       "0  ## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...    M  MichaelClifford   \n",
       "1  ## Related Issues and Dependencies\\r\\n\\r\\n…\\r\\...   XS  aakankshaduggal   \n",
       "2                                               None   XS        suppathak   \n",
       "3              ## Related Issues\\r\\n\\r\\nCloses #511    XS        suppathak   \n",
       "4  ## Related Issues and Dependencies\\r\\n\\r\\n#575...   XS  aakankshaduggal   \n",
       "\n",
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       "3  1659371667  1659373501          sesheta  1.659374e+09          sesheta   \n",
       "4  1659053347  1659114951          sesheta  1.659115e+09          sesheta   \n",
       "\n",
       "   commits_number  changed_files_number  \\\n",
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       "1               1                     3   \n",
       "2               1                     1   \n",
       "3               1                     1   \n",
       "4               1                     1   \n",
       "\n",
       "                                        interactions  \\\n",
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       "2             {'aakankshaduggal': 11, 'sesheta': 65}   \n",
       "3                                    {'sesheta': 65}   \n",
       "4              {'MichaelClifford': 7, 'sesheta': 65}   \n",
       "\n",
       "                                             reviews  \\\n",
       "0  {'1199539984': {'author': 'aakankshaduggal', '...   \n",
       "1  {'1074771058': {'author': 'oindrillac', 'words...   \n",
       "2  {'1066819103': {'author': 'aakankshaduggal', '...   \n",
       "3  {'1057235912': {'author': 'aakankshaduggal', '...   \n",
       "4                                                 {}   \n",
       "\n",
       "                      labels                                     commits  \\\n",
       "0   [approved, size/M, lgtm]  [4e363841a0566de9d278457752c7fe432bae89fe]   \n",
       "1  [approved, size/XS, lgtm]  [8d2809ea22f0f6118e29b7b381a014489818d3ac]   \n",
       "2  [approved, size/XS, lgtm]  [7fd8e441ec8fd8a533dcdfbb6a1affbfd4c515dd]   \n",
       "3  [approved, size/XS, lgtm]  [75d3f000fb51006fb3686c5ea7b45e6aae363a86]   \n",
       "4        [approved, size/XS]  [9626b65ef55b636b2cb2f73cfbec1314beae3373]   \n",
       "\n",
       "                                       changed_files  first_review_at  \\\n",
       "0                 [.github/workflows/train-ttm.yaml]     1.669840e+09   \n",
       "1  [docs/intro_to_workshop_devconf22.pdf, docs/wo...     1.660698e+09   \n",
       "2                      [docs/workshop/onboarding.md]     1.660069e+09   \n",
       "3          [docs/workshop/Workshop_Introduction.pdf]     1.659372e+09   \n",
       "4              [docs/aiops_for_community_health.pdf]              NaN   \n",
       "\n",
       "   first_approve_at  \n",
       "0      1.669841e+09  \n",
       "1      1.660698e+09  \n",
       "2      1.660069e+09  \n",
       "3               NaN  \n",
       "4               NaN  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_df.head()"
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  },
  {
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   "execution_count": 94,
   "id": "6960ca90-c8d9-4298-a1b8-2971aede7234",
   "metadata": {
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   "outputs": [],
   "source": [
    "X = raw_data_processor.fit_transform(pr_df)"
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  {
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   "execution_count": 95,
   "id": "8be37f9c-a814-4788-aead-fe6c51c8e557",
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     },
     "execution_count": 95,
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   "execution_count": 96,
   "id": "b3760786-b04a-4216-a690-c60cd19bb454",
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       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246 rows × 315 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     size  created_at_day  created_at_month  created_at_weekday  \\\n",
       "0     2.0            30.0              11.0                 2.0   \n",
       "1     0.0            16.0               8.0                 1.0   \n",
       "2     0.0             9.0               8.0                 1.0   \n",
       "3     0.0             1.0               8.0                 0.0   \n",
       "4     0.0            28.0               7.0                 3.0   \n",
       "..    ...             ...               ...                 ...   \n",
       "241   5.0            15.0               9.0                 1.0   \n",
       "242   5.0            10.0               9.0                 3.0   \n",
       "243   4.0            27.0               8.0                 3.0   \n",
       "244   4.0            26.0               8.0                 2.0   \n",
       "245   4.0            11.0               8.0                 1.0   \n",
       "\n",
       "     created_at_hour  changed_files_number  body_size  commits_number  \\\n",
       "0               15.0                   1.0       57.0             1.0   \n",
       "1               19.0                   3.0       57.0             1.0   \n",
       "2               14.0                   1.0        0.0             1.0   \n",
       "3               12.0                   1.0        5.0             1.0   \n",
       "4               20.0                   1.0       56.0             1.0   \n",
       "..               ...                   ...        ...             ...   \n",
       "241             21.0                   2.0       57.0             3.0   \n",
       "242             19.0                   1.0       46.0             1.0   \n",
       "243             15.0                   2.0       39.0             3.0   \n",
       "244             14.0                   1.0       69.0             1.0   \n",
       "245             15.0                   1.0       28.0             1.0   \n",
       "\n",
       "     filetype_.png  filetype_.ipynb  ...  title_wordcount_variable  \\\n",
       "0              0.0              0.0  ...                       0.0   \n",
       "1              0.0              0.0  ...                       0.0   \n",
       "2              0.0              0.0  ...                       0.0   \n",
       "3              0.0              0.0  ...                       0.0   \n",
       "4              0.0              0.0  ...                       0.0   \n",
       "..             ...              ...  ...                       ...   \n",
       "241            1.0              1.0  ...                       0.0   \n",
       "242            0.0              1.0  ...                       0.0   \n",
       "243            0.0              1.0  ...                       0.0   \n",
       "244            0.0              1.0  ...                       0.0   \n",
       "245            0.0              1.0  ...                       0.0   \n",
       "\n",
       "     title_wordcount_version  title_wordcount_video  \\\n",
       "0                        0.0                    0.0   \n",
       "1                        0.0                    0.0   \n",
       "2                        0.0                    0.0   \n",
       "3                        0.0                    0.0   \n",
       "4                        0.0                    0.0   \n",
       "..                       ...                    ...   \n",
       "241                      0.0                    0.0   \n",
       "242                      0.0                    0.0   \n",
       "243                      0.0                    0.0   \n",
       "244                      0.0                    0.0   \n",
       "245                      0.0                    0.0   \n",
       "\n",
       "     title_wordcount_visualization  title_wordcount_walkthrough  \\\n",
       "0                              0.0                          0.0   \n",
       "1                              0.0                          0.0   \n",
       "2                              0.0                          0.0   \n",
       "3                              0.0                          0.0   \n",
       "4                              0.0                          0.0   \n",
       "..                             ...                          ...   \n",
       "241                            0.0                          0.0   \n",
       "242                            0.0                          0.0   \n",
       "243                            0.0                          0.0   \n",
       "244                            0.0                          0.0   \n",
       "245                            0.0                          0.0   \n",
       "\n",
       "     title_wordcount_wip  title_wordcount_workflows  title_wordcount_workshop  \\\n",
       "0                    0.0                        0.0                       0.0   \n",
       "1                    0.0                        0.0                       1.0   \n",
       "2                    0.0                        0.0                       0.0   \n",
       "3                    0.0                        0.0                       1.0   \n",
       "4                    0.0                        0.0                       0.0   \n",
       "..                   ...                        ...                       ...   \n",
       "241                  0.0                        0.0                       0.0   \n",
       "242                  1.0                        0.0                       0.0   \n",
       "243                  0.0                        0.0                       0.0   \n",
       "244                  0.0                        0.0                       0.0   \n",
       "245                  0.0                        0.0                       0.0   \n",
       "\n",
       "     title_wordcount_yaml  title_wordcount_⬆️  \n",
       "0                     0.0                 0.0  \n",
       "1                     0.0                 0.0  \n",
       "2                     0.0                 0.0  \n",
       "3                     0.0                 0.0  \n",
       "4                     0.0                 0.0  \n",
       "..                    ...                 ...  \n",
       "241                   0.0                 0.0  \n",
       "242                   0.0                 0.0  \n",
       "243                   0.0                 0.0  \n",
       "244                   0.0                 0.0  \n",
       "245                   0.0                 0.0  \n",
       "\n",
       "[246 rows x 315 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756725bc-85c4-45b0-8f24-08cba0f57113",
   "metadata": {},
   "source": [
    "## Drop NA + Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "da703bc5-696f-4f63-b471-018c18557ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop entries for which labels are unknown\n",
    "# also make sure labels and features are consistent\n",
    "X = X.reindex(y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "86235437",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "82ac1a13-b294-4414-906b-271e72ac091f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>created_at_day</th>\n",
       "      <th>created_at_month</th>\n",
       "      <th>created_at_weekday</th>\n",
       "      <th>created_at_hour</th>\n",
       "      <th>changed_files_number</th>\n",
       "      <th>body_size</th>\n",
       "      <th>commits_number</th>\n",
       "      <th>filetype_.png</th>\n",
       "      <th>filetype_.ipynb</th>\n",
       "      <th>...</th>\n",
       "      <th>title_wordcount_variable</th>\n",
       "      <th>title_wordcount_version</th>\n",
       "      <th>title_wordcount_video</th>\n",
       "      <th>title_wordcount_visualization</th>\n",
       "      <th>title_wordcount_walkthrough</th>\n",
       "      <th>title_wordcount_wip</th>\n",
       "      <th>title_wordcount_workflows</th>\n",
       "      <th>title_wordcount_workshop</th>\n",
       "      <th>title_wordcount_yaml</th>\n",
       "      <th>title_wordcount_⬆️</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 315 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     size  created_at_day  created_at_month  created_at_weekday  \\\n",
       "24    3.0            20.0               7.0                 2.0   \n",
       "6     5.0            27.0               7.0                 2.0   \n",
       "95    4.0            21.0               9.0                 1.0   \n",
       "112   5.0            23.0               8.0                 0.0   \n",
       "107   2.0            26.0               8.0                 3.0   \n",
       "175   3.0            23.0               3.0                 1.0   \n",
       "239   2.0            21.0              10.0                 2.0   \n",
       "88    5.0             1.0              10.0                 4.0   \n",
       "9     2.0            26.0               7.0                 1.0   \n",
       "146   4.0            30.0               4.0                 4.0   \n",
       "45    3.0            17.0               6.0                 4.0   \n",
       "211   0.0            12.0               1.0                 1.0   \n",
       "117   0.0            28.0               7.0                 2.0   \n",
       "171   5.0            25.0               3.0                 3.0   \n",
       "116   5.0            28.0               7.0                 2.0   \n",
       "236   5.0            29.0              10.0                 3.0   \n",
       "188   3.0             3.0               3.0                 2.0   \n",
       "15    0.0            22.0               7.0                 4.0   \n",
       "155   5.0            14.0               4.0                 2.0   \n",
       "128   5.0            15.0               6.0                 1.0   \n",
       "130   4.0             9.0               6.0                 2.0   \n",
       "19    1.0            22.0               7.0                 4.0   \n",
       "241   5.0            15.0               9.0                 1.0   \n",
       "30    2.0            18.0               7.0                 0.0   \n",
       "179   5.0            16.0               3.0                 1.0   \n",
       "153   3.0            16.0               4.0                 4.0   \n",
       "10    1.0            25.0               7.0                 0.0   \n",
       "206   0.0            20.0               1.0                 2.0   \n",
       "243   4.0            27.0               8.0                 3.0   \n",
       "156   2.0             7.0               4.0                 2.0   \n",
       "98    3.0            15.0               9.0                 2.0   \n",
       "234   4.0             6.0              11.0                 4.0   \n",
       "25    4.0            20.0               7.0                 2.0   \n",
       "185   3.0            10.0               3.0                 2.0   \n",
       "18    0.0            22.0               7.0                 4.0   \n",
       "70    4.0            16.0              12.0                 3.0   \n",
       "196   4.0            10.0               2.0                 2.0   \n",
       "55    0.0            25.0               3.0                 4.0   \n",
       "81    0.0            20.0              10.0                 2.0   \n",
       "178   5.0            16.0               3.0                 1.0   \n",
       "233   5.0             9.0              11.0                 0.0   \n",
       "115   5.0            11.0               8.0                 2.0   \n",
       "226   0.0            19.0              11.0                 3.0   \n",
       "16    2.0            22.0               7.0                 4.0   \n",
       "159   4.0             6.0               4.0                 1.0   \n",
       "67    1.0            11.0               1.0                 1.0   \n",
       "168   4.0             1.0               4.0                 3.0   \n",
       "38    2.0             7.0               7.0                 3.0   \n",
       "\n",
       "     created_at_hour  changed_files_number  body_size  commits_number  \\\n",
       "24              20.0                   4.0        9.0             1.0   \n",
       "6               19.0                   3.0       68.0             2.0   \n",
       "95              15.0                   3.0       64.0             1.0   \n",
       "112             21.0                   3.0       87.0             1.0   \n",
       "107             22.0                   3.0       31.0             1.0   \n",
       "175             19.0                   4.0       93.0             1.0   \n",
       "239             20.0                   1.0       39.0             1.0   \n",
       "88              18.0                   1.0       42.0             1.0   \n",
       "9               18.0                  13.0       66.0             1.0   \n",
       "146             20.0                   2.0       98.0             1.0   \n",
       "45               0.0                   1.0      347.0             1.0   \n",
       "211             21.0                   1.0       76.0             1.0   \n",
       "117             18.0                   1.0       33.0             1.0   \n",
       "171             21.0                   4.0       83.0             1.0   \n",
       "116             19.0                   2.0       60.0             1.0   \n",
       "236             22.0                   1.0       96.0             1.0   \n",
       "188             21.0                   1.0       94.0             1.0   \n",
       "15              17.0                   1.0        0.0             1.0   \n",
       "155             20.0                   9.0       46.0             1.0   \n",
       "128             21.0                   1.0       72.0             1.0   \n",
       "130             16.0                   1.0       62.0             1.0   \n",
       "19              12.0                   4.0        2.0             1.0   \n",
       "241             21.0                   2.0       57.0             3.0   \n",
       "30              17.0                   7.0       57.0             1.0   \n",
       "179             19.0                   1.0       80.0             1.0   \n",
       "153             19.0                   1.0       58.0             1.0   \n",
       "10              21.0                   3.0       32.0             1.0   \n",
       "206             17.0                   2.0       61.0             1.0   \n",
       "243             15.0                   2.0       39.0             3.0   \n",
       "156             22.0                   7.0       50.0             1.0   \n",
       "98              21.0                   8.0       61.0             1.0   \n",
       "234             22.0                   4.0       99.0             1.0   \n",
       "25              19.0                   3.0       16.0             1.0   \n",
       "185             21.0                   1.0       54.0             1.0   \n",
       "18              14.0                   9.0        6.0             2.0   \n",
       "70              20.0                   4.0       88.0             1.0   \n",
       "196             22.0                   1.0      158.0             1.0   \n",
       "55              21.0                   1.0        6.0             1.0   \n",
       "81              14.0                   3.0       28.0             1.0   \n",
       "178             21.0                   3.0       95.0             1.0   \n",
       "233             14.0                   1.0      107.0             2.0   \n",
       "115             16.0                   1.0       68.0             1.0   \n",
       "226             21.0                   1.0       48.0             1.0   \n",
       "16              17.0                   2.0      237.0             1.0   \n",
       "159             23.0                   1.0       39.0             1.0   \n",
       "67              13.0                   1.0       59.0             1.0   \n",
       "168             15.0                   1.0       58.0             1.0   \n",
       "38              18.0                   1.0       20.0             1.0   \n",
       "\n",
       "     filetype_.png  filetype_.ipynb  ...  title_wordcount_variable  \\\n",
       "24             0.0              0.0  ...                       0.0   \n",
       "6              0.0              1.0  ...                       0.0   \n",
       "95             0.0              1.0  ...                       0.0   \n",
       "112            0.0              2.0  ...                       0.0   \n",
       "107            0.0              0.0  ...                       0.0   \n",
       "175            0.0              2.0  ...                       0.0   \n",
       "239            0.0              0.0  ...                       0.0   \n",
       "88             0.0              0.0  ...                       0.0   \n",
       "9              2.0              4.0  ...                       0.0   \n",
       "146            0.0              1.0  ...                       0.0   \n",
       "45             0.0              0.0  ...                       0.0   \n",
       "211            0.0              0.0  ...                       0.0   \n",
       "117            0.0              0.0  ...                       0.0   \n",
       "171            0.0              2.0  ...                       0.0   \n",
       "116            0.0              2.0  ...                       0.0   \n",
       "236            0.0              1.0  ...                       0.0   \n",
       "188            0.0              1.0  ...                       0.0   \n",
       "15             0.0              0.0  ...                       0.0   \n",
       "155            0.0              9.0  ...                       0.0   \n",
       "128            0.0              1.0  ...                       0.0   \n",
       "130            0.0              1.0  ...                       0.0   \n",
       "19             3.0              0.0  ...                       0.0   \n",
       "241            1.0              1.0  ...                       0.0   \n",
       "30             6.0              0.0  ...                       0.0   \n",
       "179            0.0              1.0  ...                       0.0   \n",
       "153            0.0              0.0  ...                       0.0   \n",
       "10             1.0              0.0  ...                       0.0   \n",
       "206            0.0              2.0  ...                       0.0   \n",
       "243            0.0              1.0  ...                       0.0   \n",
       "156            6.0              0.0  ...                       0.0   \n",
       "98             0.0              0.0  ...                       0.0   \n",
       "234            0.0              2.0  ...                       0.0   \n",
       "25             0.0              0.0  ...                       0.0   \n",
       "185            0.0              1.0  ...                       0.0   \n",
       "18             8.0              0.0  ...                       0.0   \n",
       "70             0.0              1.0  ...                       0.0   \n",
       "196            0.0              1.0  ...                       0.0   \n",
       "55             0.0              0.0  ...                       0.0   \n",
       "81             0.0              0.0  ...                       0.0   \n",
       "178            0.0              1.0  ...                       0.0   \n",
       "233            0.0              1.0  ...                       0.0   \n",
       "115            0.0              1.0  ...                       0.0   \n",
       "226            0.0              0.0  ...                       0.0   \n",
       "16             0.0              0.0  ...                       0.0   \n",
       "159            0.0              1.0  ...                       0.0   \n",
       "67             0.0              0.0  ...                       0.0   \n",
       "168            0.0              1.0  ...                       0.0   \n",
       "38             0.0              0.0  ...                       0.0   \n",
       "\n",
       "     title_wordcount_version  title_wordcount_video  \\\n",
       "24                       0.0                    0.0   \n",
       "6                        0.0                    0.0   \n",
       "95                       0.0                    0.0   \n",
       "112                      0.0                    0.0   \n",
       "107                      0.0                    0.0   \n",
       "175                      0.0                    0.0   \n",
       "239                      0.0                    0.0   \n",
       "88                       0.0                    0.0   \n",
       "9                        0.0                    0.0   \n",
       "146                      0.0                    0.0   \n",
       "45                       0.0                    0.0   \n",
       "211                      0.0                    1.0   \n",
       "117                      0.0                    0.0   \n",
       "171                      0.0                    0.0   \n",
       "116                      0.0                    0.0   \n",
       "236                      0.0                    0.0   \n",
       "188                      0.0                    0.0   \n",
       "15                       0.0                    0.0   \n",
       "155                      0.0                    0.0   \n",
       "128                      0.0                    0.0   \n",
       "130                      0.0                    0.0   \n",
       "19                       0.0                    0.0   \n",
       "241                      0.0                    0.0   \n",
       "30                       0.0                    0.0   \n",
       "179                      0.0                    0.0   \n",
       "153                      0.0                    0.0   \n",
       "10                       0.0                    0.0   \n",
       "206                      0.0                    0.0   \n",
       "243                      0.0                    0.0   \n",
       "156                      0.0                    0.0   \n",
       "98                       0.0                    0.0   \n",
       "234                      0.0                    0.0   \n",
       "25                       0.0                    0.0   \n",
       "185                      0.0                    0.0   \n",
       "18                       0.0                    0.0   \n",
       "70                       0.0                    0.0   \n",
       "196                      0.0                    0.0   \n",
       "55                       0.0                    0.0   \n",
       "81                       0.0                    0.0   \n",
       "178                      0.0                    0.0   \n",
       "233                      0.0                    0.0   \n",
       "115                      0.0                    0.0   \n",
       "226                      0.0                    0.0   \n",
       "16                       1.0                    0.0   \n",
       "159                      0.0                    0.0   \n",
       "67                       0.0                    0.0   \n",
       "168                      0.0                    0.0   \n",
       "38                       0.0                    0.0   \n",
       "\n",
       "     title_wordcount_visualization  title_wordcount_walkthrough  \\\n",
       "24                             0.0                          0.0   \n",
       "6                              0.0                          0.0   \n",
       "95                             0.0                          0.0   \n",
       "112                            0.0                          0.0   \n",
       "107                            0.0                          0.0   \n",
       "175                            0.0                          0.0   \n",
       "239                            0.0                          0.0   \n",
       "88                             0.0                          0.0   \n",
       "9                              0.0                          0.0   \n",
       "146                            0.0                          0.0   \n",
       "45                             0.0                          0.0   \n",
       "211                            0.0                          0.0   \n",
       "117                            0.0                          0.0   \n",
       "171                            0.0                          0.0   \n",
       "116                            0.0                          0.0   \n",
       "236                            0.0                          0.0   \n",
       "188                            0.0                          0.0   \n",
       "15                             0.0                          0.0   \n",
       "155                            0.0                          0.0   \n",
       "128                            1.0                          0.0   \n",
       "130                            0.0                          0.0   \n",
       "19                             0.0                          0.0   \n",
       "241                            0.0                          0.0   \n",
       "30                             0.0                          0.0   \n",
       "179                            0.0                          0.0   \n",
       "153                            0.0                          0.0   \n",
       "10                             0.0                          0.0   \n",
       "206                            0.0                          0.0   \n",
       "243                            0.0                          0.0   \n",
       "156                            0.0                          0.0   \n",
       "98                             0.0                          0.0   \n",
       "234                            0.0                          0.0   \n",
       "25                             0.0                          0.0   \n",
       "185                            0.0                          0.0   \n",
       "18                             0.0                          0.0   \n",
       "70                             0.0                          0.0   \n",
       "196                            0.0                          0.0   \n",
       "55                             0.0                          0.0   \n",
       "81                             0.0                          0.0   \n",
       "178                            0.0                          0.0   \n",
       "233                            0.0                          0.0   \n",
       "115                            0.0                          0.0   \n",
       "226                            0.0                          0.0   \n",
       "16                             0.0                          0.0   \n",
       "159                            0.0                          0.0   \n",
       "67                             0.0                          0.0   \n",
       "168                            0.0                          0.0   \n",
       "38                             0.0                          0.0   \n",
       "\n",
       "     title_wordcount_wip  title_wordcount_workflows  title_wordcount_workshop  \\\n",
       "24                   0.0                        0.0                       0.0   \n",
       "6                    0.0                        0.0                       0.0   \n",
       "95                   0.0                        0.0                       0.0   \n",
       "112                  0.0                        0.0                       0.0   \n",
       "107                  0.0                        0.0                       0.0   \n",
       "175                  0.0                        0.0                       0.0   \n",
       "239                  0.0                        0.0                       0.0   \n",
       "88                   0.0                        0.0                       0.0   \n",
       "9                    0.0                        0.0                       0.0   \n",
       "146                  0.0                        0.0                       0.0   \n",
       "45                   0.0                        0.0                       0.0   \n",
       "211                  0.0                        0.0                       0.0   \n",
       "117                  0.0                        0.0                       0.0   \n",
       "171                  0.0                        0.0                       0.0   \n",
       "116                  0.0                        0.0                       0.0   \n",
       "236                  0.0                        0.0                       0.0   \n",
       "188                  0.0                        0.0                       0.0   \n",
       "15                   0.0                        0.0                       0.0   \n",
       "155                  0.0                        0.0                       0.0   \n",
       "128                  0.0                        0.0                       0.0   \n",
       "130                  0.0                        0.0                       0.0   \n",
       "19                   0.0                        0.0                       1.0   \n",
       "241                  0.0                        0.0                       0.0   \n",
       "30                   0.0                        0.0                       1.0   \n",
       "179                  0.0                        0.0                       0.0   \n",
       "153                  0.0                        0.0                       0.0   \n",
       "10                   0.0                        0.0                       0.0   \n",
       "206                  0.0                        0.0                       0.0   \n",
       "243                  0.0                        0.0                       0.0   \n",
       "156                  0.0                        0.0                       0.0   \n",
       "98                   0.0                        0.0                       0.0   \n",
       "234                  0.0                        0.0                       0.0   \n",
       "25                   0.0                        0.0                       0.0   \n",
       "185                  0.0                        0.0                       0.0   \n",
       "18                   0.0                        0.0                       1.0   \n",
       "70                   0.0                        0.0                       0.0   \n",
       "196                  0.0                        0.0                       0.0   \n",
       "55                   0.0                        0.0                       0.0   \n",
       "81                   0.0                        0.0                       0.0   \n",
       "178                  0.0                        0.0                       0.0   \n",
       "233                  0.0                        0.0                       0.0   \n",
       "115                  0.0                        0.0                       0.0   \n",
       "226                  0.0                        0.0                       0.0   \n",
       "16                   0.0                        0.0                       0.0   \n",
       "159                  0.0                        0.0                       0.0   \n",
       "67                   0.0                        0.0                       0.0   \n",
       "168                  0.0                        0.0                       0.0   \n",
       "38                   0.0                        0.0                       1.0   \n",
       "\n",
       "     title_wordcount_yaml  title_wordcount_⬆️  \n",
       "24                    0.0                 0.0  \n",
       "6                     0.0                 0.0  \n",
       "95                    0.0                 0.0  \n",
       "112                   0.0                 0.0  \n",
       "107                   0.0                 0.0  \n",
       "175                   0.0                 0.0  \n",
       "239                   0.0                 0.0  \n",
       "88                    0.0                 0.0  \n",
       "9                     0.0                 0.0  \n",
       "146                   0.0                 0.0  \n",
       "45                    0.0                 0.0  \n",
       "211                   0.0                 0.0  \n",
       "117                   0.0                 0.0  \n",
       "171                   0.0                 0.0  \n",
       "116                   0.0                 0.0  \n",
       "236                   0.0                 0.0  \n",
       "188                   0.0                 0.0  \n",
       "15                    0.0                 0.0  \n",
       "155                   0.0                 0.0  \n",
       "128                   0.0                 0.0  \n",
       "130                   0.0                 0.0  \n",
       "19                    0.0                 0.0  \n",
       "241                   0.0                 0.0  \n",
       "30                    0.0                 0.0  \n",
       "179                   0.0                 0.0  \n",
       "153                   0.0                 0.0  \n",
       "10                    0.0                 0.0  \n",
       "206                   0.0                 0.0  \n",
       "243                   0.0                 0.0  \n",
       "156                   0.0                 0.0  \n",
       "98                    0.0                 0.0  \n",
       "234                   0.0                 0.0  \n",
       "25                    0.0                 0.0  \n",
       "185                   0.0                 0.0  \n",
       "18                    0.0                 0.0  \n",
       "70                    0.0                 0.0  \n",
       "196                   0.0                 0.0  \n",
       "55                    0.0                 0.0  \n",
       "81                    0.0                 0.0  \n",
       "178                   0.0                 0.0  \n",
       "233                   0.0                 0.0  \n",
       "115                   0.0                 0.0  \n",
       "226                   1.0                 0.0  \n",
       "16                    0.0                 0.0  \n",
       "159                   0.0                 0.0  \n",
       "67                    0.0                 0.0  \n",
       "168                   0.0                 0.0  \n",
       "38                    0.0                 0.0  \n",
       "\n",
       "[48 rows x 315 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fbbbd016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "TEST_DATA_PATH = os.path.join(s3_input_data_path, ORG, REPO, \"test-data\")\n",
    "\n",
    "# upload X_test and y_test to S3 bucket for testing / running sanity check on the model inference service\n",
    "\n",
    "ret = cc.upload_to_ceph(X_test, TEST_DATA_PATH, \"X_test.parquet\")\n",
    "print(ret[\"ResponseMetadata\"][\"HTTPStatusCode\"])\n",
    "\n",
    "ret = cc.upload_to_ceph(y_test.to_frame(\"ttm_class\"), TEST_DATA_PATH, \"y_test.parquet\")\n",
    "print(ret[\"ResponseMetadata\"][\"HTTPStatusCode\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4ee74938",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from pandas series to lists to avoid warnings during training\n",
    "y_train = y_train.to_list()\n",
    "y_test = y_test.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98107599",
   "metadata": {},
   "source": [
    "## Scale data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "47bb3149",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets apply a yeo johnson transform to try to make the data more gaussian\n",
    "scaler = PowerTransformer()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0dd02f",
   "metadata": {},
   "source": [
    "## Define Training and Evaluation Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ab12e4",
   "metadata": {},
   "source": [
    "Here, we will define a function to train a given classifier on the training set and then evaluate it on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2cacd2c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_accuracy = {}\n",
    "def train_evaluate_classifier(clf, xtrain, ytrain, xtest, ytest):\n",
    "    # Train our classifier\n",
    "    clf.fit(xtrain, ytrain)\n",
    "\n",
    "    # Make predictions\n",
    "    preds = clf.predict(xtest)\n",
    "\n",
    "    # View classification report\n",
    "    report = (classification_report(ytest, preds, output_dict=True))\n",
    "#     exec(\"Accuracy\" + str(clf)+ \"= 'report[accuracy]'\")\n",
    "    accuracy = report['accuracy']\n",
    "    print(\"Accuracy :\", accuracy)\n",
    "    dict_accuracy[str(type(clf)).split(\".\")[-1][:-2]] = accuracy\n",
    "\n",
    "    # Plot confusion matrix heatmap\n",
    "    plt.figure(figsize=(16, 12))\n",
    "    cf_matrix = confusion_matrix(ytest, preds)\n",
    "    group_counts = [\"{0:0.0f}\\n\".format(value) for value in cf_matrix.flatten()]\n",
    "    group_percentages = [\n",
    "        \"{0:.2%}\".format(value) for value in cf_matrix.flatten() / np.sum(cf_matrix)\n",
    "    ]\n",
    "    box_labels = [\n",
    "        f\"{v1}{v2}\".strip() for v1, v2 in zip(group_counts, group_percentages)\n",
    "    ]\n",
    "    box_labels = np.asarray(box_labels).reshape(cf_matrix.shape[0], cf_matrix.shape[1])\n",
    "\n",
    "    sns.heatmap(cf_matrix, cmap=\"OrRd\", annot=box_labels, fmt=\"\")\n",
    "    plt.xlabel(\"Predicted TTM Label\")\n",
    "    plt.ylabel(\"True TTM Label\")\n",
    "    plt.title(\"Confusion Matrix Heatmap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8add6423",
   "metadata": {},
   "source": [
    "## Define Models and Parameters\n",
    "\n",
    "Next, we will define and initialize the classifiers that we will be exploring for the time-to-merge prediction task."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c72681",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e6d48ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize classifier\n",
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804ae1e0",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6c4bfaa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize classifier\n",
    "svc = SVC(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3b3017",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1e623025",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize classifier\n",
    "rf = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    max_features=0.75,\n",
    "    random_state=42,\n",
    "    n_jobs=-1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7687a4e0",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d8eb71ce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initialize classifier\n",
    "xgbc = XGBClassifier(\n",
    "    n_estimators=125,\n",
    "    learning_rate=0.1,\n",
    "    random_state=42,\n",
    "    verbosity=1,\n",
    "    n_jobs=-1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57aa765",
   "metadata": {},
   "source": [
    "## Compare Model Results\n",
    "\n",
    "Finally, we will run the train all of the classifiers defined above and evaluate their performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216dd1b2",
   "metadata": {},
   "source": [
    "### Train using all features\n",
    "\n",
    "First, lets train the classifiers using all the engineered features as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e72772aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.16666666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    train_evaluate_classifier(gnb, X_train_scaled, y_train, X_test_scaled, y_test)\n",
    "except:\n",
    "    print(\"Naive Bayes Model cannot be trained correctly with current dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c3f8cc61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.125\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:    \n",
    "    train_evaluate_classifier(svc, X_train_scaled, y_train, X_test_scaled, y_test)\n",
    "except:\n",
    "    print(\"Support Vector Machine Model cannot be trained correctly with current dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8b961727",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.125\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    train_evaluate_classifier(rf, X_train_scaled, y_train, X_test_scaled, y_test)\n",
    "except:\n",
    "    print(\"RandomForest Model cannot be trained correctly with current dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "14249146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.14583333333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    train_evaluate_classifier(xgbc, X_train_scaled, y_train, X_test_scaled, y_test)\n",
    "except:\n",
    "    print(\"XGboost Model cannot be trained correctly with current dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8985b82e-7d89-4b69-b063-db05943458c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GaussianNB': 0.16666666666666666,\n",
       " 'SVC': 0.125,\n",
       " 'RandomForestClassifier': 0.125,\n",
       " 'XGBClassifier': 0.14583333333333334}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "79eebe0e-4254-4b02-8c33-bee3ddc75e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_accuracy = max(dict_accuracy.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "afaacc68-b887-48cc-9673-04c482f37169",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = max(dict_accuracy, key = dict_accuracy.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "65b42b90-5014-429a-a8f2-7477c7d91335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GaussianNB'"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40309a3",
   "metadata": {},
   "source": [
    "Note that for model deployment (which is the eventual goal), we will also need to include any scaler or preprocessor objects. This is because the input to the inference service will be raw unscaled data. We plan to address this issue by using `sklearn.Pipeline` object to package the preprocessor(s) and model as one \"combined\" model. \n",
    "\n",
    "In the step below, we create a copy of the best performing model that is the Random Forest Classifier, so that we can save it to S3 later on and use it for model deployment.\n",
    "\n",
    "From your explorations, if you wish to save another better performing model to the pipeline set `best_model` as one of rf, xgbc, svc or gnb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "987a23d4-2e8d-4d4d-8e5c-f3f4230f4b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\"RandomForestClassifier\": rf, \"XGBClassifier\": xgbc, \"SVC\": svc, \"GaussianNB\": gnb}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "647eb700",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a clone (create a copy of the object with the learned weights)\n",
    "selected_model = deepcopy(models[best_model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2d739cb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.60      0.55         5\n",
      "           1       0.00      0.00      0.00         3\n",
      "           2       0.00      0.00      0.00         2\n",
      "           3       0.18      0.50      0.27         4\n",
      "           4       0.00      0.00      0.00         4\n",
      "           5       0.00      0.00      0.00         2\n",
      "           6       0.20      0.20      0.20        10\n",
      "           7       0.50      0.17      0.25         6\n",
      "           8       0.00      0.00      0.00         8\n",
      "           9       0.00      0.00      0.00         4\n",
      "\n",
      "    accuracy                           0.17        48\n",
      "   macro avg       0.14      0.15      0.13        48\n",
      "weighted avg       0.17      0.17      0.15        48\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# sanity check\n",
    "print(classification_report(y_test, selected_model.predict(X_test_scaled)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7741b21e",
   "metadata": {},
   "source": [
    "## Create sklearn Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20053832",
   "metadata": {},
   "source": [
    "Here, we will create an sklearn pipeline consisting of 2 steps, scaling of the input features and the classifier itself. We will then save this Pipeline as a `model.joblib` file on S3 for serving the model pipeline using the Seldon Sklearn Server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1c581342",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(\n",
    "    steps=[\n",
    "        (\"extract_features\", raw_data_processor),\n",
    "        (\"scale\", scaler),\n",
    "        (\"rf\", selected_model),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e1bf4b",
   "metadata": {},
   "source": [
    "## Write Model to S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8fb0ba41",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MODEL_KEY = os.path.join(s3_input_data_path, ORG, REPO, \"ttm-model\")\n",
    "MODEL_FILENAME = \"model.joblib\"\n",
    "\n",
    "s3_resource = boto3.resource(\n",
    "    \"s3\",\n",
    "    endpoint_url=s3_endpoint_url,\n",
    "    aws_access_key_id=s3_access_key,\n",
    "    aws_secret_access_key=s3_secret_key,\n",
    ")\n",
    "\n",
    "with tempfile.TemporaryFile() as fp:\n",
    "    joblib.dump(pipe, fp)\n",
    "    fp.seek(0)\n",
    "    s3_obj = s3_resource.Object(s3_bucket, f\"{MODEL_KEY}/{MODEL_FILENAME}\")\n",
    "    s3_obj.put(Body=fp.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fcfd6c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = cc.upload_to_ceph(Categories, MODEL_KEY, \"label-map\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9392bb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;extract_features&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;pr_size&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;XS&#x27;,\n",
       "                                                                              &#x27;S&#x27;,\n",
       "                                                                              &#x27;M&#x27;,\n",
       "                                                                              &#x27;L&#x27;,\n",
       "                                                                              &#x27;XL&#x27;,\n",
       "                                                                              &#x27;XXL&#x27;]]),\n",
       "                                                  [&#x27;size&#x27;]),\n",
       "                                                 (&#x27;created_at_details&#x27;,\n",
       "                                                  DateTimeDetailsTransformer(),\n",
       "                                                  [&#x27;created_at&#x27;]),\n",
       "                                                 (&#x27;n_changed_files&#x27;,\n",
       "                                                  NumChangedFilesTransformer(),\n",
       "                                                  [&#x27;changed_files_number&#x27;]),\n",
       "                                                 (&#x27;body_size&#x27;,\n",
       "                                                  StringLenTransformer(),\n",
       "                                                  [&#x27;body&#x27;]),\n",
       "                                                 (&#x27;n_commits&#x27;, &#x27;p...\n",
       "                                                  TitleWordCountTransformer(words=[&#x27;access&#x27;,\n",
       "                                                                                   &#x27;across&#x27;,\n",
       "                                                                                   &#x27;action&#x27;,\n",
       "                                                                                   &#x27;add&#x27;,\n",
       "                                                                                   &#x27;added&#x27;,\n",
       "                                                                                   &#x27;adds&#x27;,\n",
       "                                                                                   &#x27;adjustments&#x27;,\n",
       "                                                                                   &#x27;aicoe&#x27;,\n",
       "                                                                                   &#x27;analysis&#x27;,\n",
       "                                                                                   &#x27;append&#x27;,\n",
       "                                                                                   &#x27;architecture&#x27;,\n",
       "                                                                                   &#x27;argo&#x27;,\n",
       "                                                                                   &#x27;attempt&#x27;,\n",
       "                                                                                   &#x27;automatic&#x27;,\n",
       "                                                                                   &#x27;base&#x27;,\n",
       "                                                                                   &#x27;beyond&#x27;,\n",
       "                                                                                   &#x27;bits&#x27;,\n",
       "                                                                                   &#x27;bitwarden&#x27;,\n",
       "                                                                                   &#x27;black&#x27;,\n",
       "                                                                                   &#x27;black_nb&#x27;,\n",
       "                                                                                   &#x27;bleach&#x27;,\n",
       "                                                                                   &#x27;broken&#x27;,\n",
       "                                                                                   &#x27;bs4&#x27;,\n",
       "                                                                                   &#x27;bugfix&#x27;,\n",
       "                                                                                   &#x27;bugzilla&#x27;,\n",
       "                                                                                   &#x27;build_pass_failure&#x27;,\n",
       "                                                                                   &#x27;bump&#x27;,\n",
       "                                                                                   &#x27;calculate&#x27;,\n",
       "                                                                                   &#x27;catch&#x27;,\n",
       "                                                                                   &#x27;cell&#x27;, ...]),\n",
       "                                                  [&#x27;title&#x27;])])),\n",
       "                (&#x27;scale&#x27;, PowerTransformer()), (&#x27;rf&#x27;, GaussianNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;extract_features&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;pr_size&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;XS&#x27;,\n",
       "                                                                              &#x27;S&#x27;,\n",
       "                                                                              &#x27;M&#x27;,\n",
       "                                                                              &#x27;L&#x27;,\n",
       "                                                                              &#x27;XL&#x27;,\n",
       "                                                                              &#x27;XXL&#x27;]]),\n",
       "                                                  [&#x27;size&#x27;]),\n",
       "                                                 (&#x27;created_at_details&#x27;,\n",
       "                                                  DateTimeDetailsTransformer(),\n",
       "                                                  [&#x27;created_at&#x27;]),\n",
       "                                                 (&#x27;n_changed_files&#x27;,\n",
       "                                                  NumChangedFilesTransformer(),\n",
       "                                                  [&#x27;changed_files_number&#x27;]),\n",
       "                                                 (&#x27;body_size&#x27;,\n",
       "                                                  StringLenTransformer(),\n",
       "                                                  [&#x27;body&#x27;]),\n",
       "                                                 (&#x27;n_commits&#x27;, &#x27;p...\n",
       "                                                  TitleWordCountTransformer(words=[&#x27;access&#x27;,\n",
       "                                                                                   &#x27;across&#x27;,\n",
       "                                                                                   &#x27;action&#x27;,\n",
       "                                                                                   &#x27;add&#x27;,\n",
       "                                                                                   &#x27;added&#x27;,\n",
       "                                                                                   &#x27;adds&#x27;,\n",
       "                                                                                   &#x27;adjustments&#x27;,\n",
       "                                                                                   &#x27;aicoe&#x27;,\n",
       "                                                                                   &#x27;analysis&#x27;,\n",
       "                                                                                   &#x27;append&#x27;,\n",
       "                                                                                   &#x27;architecture&#x27;,\n",
       "                                                                                   &#x27;argo&#x27;,\n",
       "                                                                                   &#x27;attempt&#x27;,\n",
       "                                                                                   &#x27;automatic&#x27;,\n",
       "                                                                                   &#x27;base&#x27;,\n",
       "                                                                                   &#x27;beyond&#x27;,\n",
       "                                                                                   &#x27;bits&#x27;,\n",
       "                                                                                   &#x27;bitwarden&#x27;,\n",
       "                                                                                   &#x27;black&#x27;,\n",
       "                                                                                   &#x27;black_nb&#x27;,\n",
       "                                                                                   &#x27;bleach&#x27;,\n",
       "                                                                                   &#x27;broken&#x27;,\n",
       "                                                                                   &#x27;bs4&#x27;,\n",
       "                                                                                   &#x27;bugfix&#x27;,\n",
       "                                                                                   &#x27;bugzilla&#x27;,\n",
       "                                                                                   &#x27;build_pass_failure&#x27;,\n",
       "                                                                                   &#x27;bump&#x27;,\n",
       "                                                                                   &#x27;calculate&#x27;,\n",
       "                                                                                   &#x27;catch&#x27;,\n",
       "                                                                                   &#x27;cell&#x27;, ...]),\n",
       "                                                  [&#x27;title&#x27;])])),\n",
       "                (&#x27;scale&#x27;, PowerTransformer()), (&#x27;rf&#x27;, GaussianNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">extract_features: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;pr_size&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;XS&#x27;, &#x27;S&#x27;, &#x27;M&#x27;,\n",
       "                                                             &#x27;L&#x27;, &#x27;XL&#x27;,\n",
       "                                                             &#x27;XXL&#x27;]]),\n",
       "                                 [&#x27;size&#x27;]),\n",
       "                                (&#x27;created_at_details&#x27;,\n",
       "                                 DateTimeDetailsTransformer(), [&#x27;created_at&#x27;]),\n",
       "                                (&#x27;n_changed_files&#x27;,\n",
       "                                 NumChangedFilesTransformer(),\n",
       "                                 [&#x27;changed_files_number&#x27;]),\n",
       "                                (&#x27;body_size&#x27;, StringLenTransformer(), [&#x27;body&#x27;]),\n",
       "                                (&#x27;n_commits&#x27;, &#x27;passthrough&#x27;,\n",
       "                                 [&#x27;commits_number&#x27;]),\n",
       "                                (&#x27;fi...\n",
       "                                (&#x27;title_word_counter&#x27;,\n",
       "                                 TitleWordCountTransformer(words=[&#x27;access&#x27;,\n",
       "                                                                  &#x27;across&#x27;,\n",
       "                                                                  &#x27;action&#x27;,\n",
       "                                                                  &#x27;add&#x27;,\n",
       "                                                                  &#x27;added&#x27;,\n",
       "                                                                  &#x27;adds&#x27;,\n",
       "                                                                  &#x27;adjustments&#x27;,\n",
       "                                                                  &#x27;aicoe&#x27;,\n",
       "                                                                  &#x27;analysis&#x27;,\n",
       "                                                                  &#x27;append&#x27;,\n",
       "                                                                  &#x27;architecture&#x27;,\n",
       "                                                                  &#x27;argo&#x27;,\n",
       "                                                                  &#x27;attempt&#x27;,\n",
       "                                                                  &#x27;automatic&#x27;,\n",
       "                                                                  &#x27;base&#x27;,\n",
       "                                                                  &#x27;beyond&#x27;,\n",
       "                                                                  &#x27;bits&#x27;,\n",
       "                                                                  &#x27;bitwarden&#x27;,\n",
       "                                                                  &#x27;black&#x27;,\n",
       "                                                                  &#x27;black_nb&#x27;,\n",
       "                                                                  &#x27;bleach&#x27;,\n",
       "                                                                  &#x27;broken&#x27;,\n",
       "                                                                  &#x27;bs4&#x27;,\n",
       "                                                                  &#x27;bugfix&#x27;,\n",
       "                                                                  &#x27;bugzilla&#x27;,\n",
       "                                                                  &#x27;build_pass_failure&#x27;,\n",
       "                                                                  &#x27;bump&#x27;,\n",
       "                                                                  &#x27;calculate&#x27;,\n",
       "                                                                  &#x27;catch&#x27;,\n",
       "                                                                  &#x27;cell&#x27;, ...]),\n",
       "                                 [&#x27;title&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pr_size</label><div class=\"sk-toggleable__content\"><pre>[&#x27;size&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;XS&#x27;, &#x27;S&#x27;, &#x27;M&#x27;, &#x27;L&#x27;, &#x27;XL&#x27;, &#x27;XXL&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">created_at_details</label><div class=\"sk-toggleable__content\"><pre>[&#x27;created_at&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DateTimeDetailsTransformer</label><div class=\"sk-toggleable__content\"><pre>DateTimeDetailsTransformer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">n_changed_files</label><div class=\"sk-toggleable__content\"><pre>[&#x27;changed_files_number&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NumChangedFilesTransformer</label><div class=\"sk-toggleable__content\"><pre>NumChangedFilesTransformer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">body_size</label><div class=\"sk-toggleable__content\"><pre>[&#x27;body&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StringLenTransformer</label><div class=\"sk-toggleable__content\"><pre>StringLenTransformer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">n_commits</label><div class=\"sk-toggleable__content\"><pre>[&#x27;commits_number&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">filetype_counter</label><div class=\"sk-toggleable__content\"><pre>[&#x27;changed_files&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FileTypeCountTransformer</label><div class=\"sk-toggleable__content\"><pre>FileTypeCountTransformer(file_extensions=[&#x27;.png&#x27;, &#x27;.ipynb&#x27;, &#x27;.html&#x27;, &#x27;.md&#x27;,\n",
       "                                          &#x27;.po&#x27;, &#x27;.py&#x27;, &#x27;.doctree&#x27;, &#x27;.yaml&#x27;,\n",
       "                                          &#x27;.js&#x27;, &#x27;.lock&#x27;, &#x27;.css&#x27;, &#x27;Pipfile&#x27;,\n",
       "                                          &#x27;.svg&#x27;, &#x27;.pipeline&#x27;, &#x27;.yml&#x27;, &#x27;.mdx&#x27;,\n",
       "                                          &#x27;.csv&#x27;, &#x27;environment&#x27;, &#x27;OWNERS&#x27;,\n",
       "                                          &#x27;env_example_workshop&#x27;, &#x27;None&#x27;, &#x27;.gz&#x27;,\n",
       "                                          &#x27;.pdf&#x27;, &#x27;.eot&#x27;, &#x27;.ttf&#x27;, &#x27;.woff&#x27;,\n",
       "                                          &#x27;.woff2&#x27;, &#x27;.rst&#x27;, &#x27;.parquet&#x27;, &#x27;.map&#x27;, ...])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">title_word_counter</label><div class=\"sk-toggleable__content\"><pre>[&#x27;title&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TitleWordCountTransformer</label><div class=\"sk-toggleable__content\"><pre>TitleWordCountTransformer(words=[&#x27;access&#x27;, &#x27;across&#x27;, &#x27;action&#x27;, &#x27;add&#x27;, &#x27;added&#x27;,\n",
       "                                 &#x27;adds&#x27;, &#x27;adjustments&#x27;, &#x27;aicoe&#x27;, &#x27;analysis&#x27;,\n",
       "                                 &#x27;append&#x27;, &#x27;architecture&#x27;, &#x27;argo&#x27;, &#x27;attempt&#x27;,\n",
       "                                 &#x27;automatic&#x27;, &#x27;base&#x27;, &#x27;beyond&#x27;, &#x27;bits&#x27;,\n",
       "                                 &#x27;bitwarden&#x27;, &#x27;black&#x27;, &#x27;black_nb&#x27;, &#x27;bleach&#x27;,\n",
       "                                 &#x27;broken&#x27;, &#x27;bs4&#x27;, &#x27;bugfix&#x27;, &#x27;bugzilla&#x27;,\n",
       "                                 &#x27;build_pass_failure&#x27;, &#x27;bump&#x27;, &#x27;calculate&#x27;,\n",
       "                                 &#x27;catch&#x27;, &#x27;cell&#x27;, ...])</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('extract_features',\n",
       "                 ColumnTransformer(transformers=[('pr_size',\n",
       "                                                  OrdinalEncoder(categories=[['XS',\n",
       "                                                                              'S',\n",
       "                                                                              'M',\n",
       "                                                                              'L',\n",
       "                                                                              'XL',\n",
       "                                                                              'XXL']]),\n",
       "                                                  ['size']),\n",
       "                                                 ('created_at_details',\n",
       "                                                  DateTimeDetailsTransformer(),\n",
       "                                                  ['created_at']),\n",
       "                                                 ('n_changed_files',\n",
       "                                                  NumChangedFilesTransformer(),\n",
       "                                                  ['changed_files_number']),\n",
       "                                                 ('body_size',\n",
       "                                                  StringLenTransformer(),\n",
       "                                                  ['body']),\n",
       "                                                 ('n_commits', 'p...\n",
       "                                                  TitleWordCountTransformer(words=['access',\n",
       "                                                                                   'across',\n",
       "                                                                                   'action',\n",
       "                                                                                   'add',\n",
       "                                                                                   'added',\n",
       "                                                                                   'adds',\n",
       "                                                                                   'adjustments',\n",
       "                                                                                   'aicoe',\n",
       "                                                                                   'analysis',\n",
       "                                                                                   'append',\n",
       "                                                                                   'architecture',\n",
       "                                                                                   'argo',\n",
       "                                                                                   'attempt',\n",
       "                                                                                   'automatic',\n",
       "                                                                                   'base',\n",
       "                                                                                   'beyond',\n",
       "                                                                                   'bits',\n",
       "                                                                                   'bitwarden',\n",
       "                                                                                   'black',\n",
       "                                                                                   'black_nb',\n",
       "                                                                                   'bleach',\n",
       "                                                                                   'broken',\n",
       "                                                                                   'bs4',\n",
       "                                                                                   'bugfix',\n",
       "                                                                                   'bugzilla',\n",
       "                                                                                   'build_pass_failure',\n",
       "                                                                                   'bump',\n",
       "                                                                                   'calculate',\n",
       "                                                                                   'catch',\n",
       "                                                                                   'cell', ...]),\n",
       "                                                  ['title'])])),\n",
       "                ('scale', PowerTransformer()), ('rf', GaussianNB())])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sanity Check\n",
    "buffer = BytesIO()\n",
    "s3_object = s3_resource.Object(s3_bucket, f\"{MODEL_KEY}/{MODEL_FILENAME}\")\n",
    "s3_object.download_fileobj(buffer)\n",
    "model = joblib.load(buffer)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "36484009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.60      0.55         5\n",
      "           1       0.00      0.00      0.00         3\n",
      "           2       0.00      0.00      0.00         2\n",
      "           3       0.18      0.50      0.27         4\n",
      "           4       0.00      0.00      0.00         4\n",
      "           5       0.00      0.00      0.00         2\n",
      "           6       0.20      0.20      0.20        10\n",
      "           7       0.50      0.17      0.25         6\n",
      "           8       0.00      0.00      0.00         8\n",
      "           9       0.00      0.00      0.00         4\n",
      "\n",
      "    accuracy                           0.17        48\n",
      "   macro avg       0.14      0.15      0.13        48\n",
      "weighted avg       0.17      0.17      0.15        48\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# take raw pr data and predict ttm classes\n",
    "preds = model.predict(pr_df.reindex(X_test.index))\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42cf7c8e-07d8-44df-b4a0-ddcc8982ea37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check all raw data files in s3\n",
    "bucket = cc.s3_resource.Bucket(s3_bucket)\n",
    "objects = [i for i in bucket.objects.all() if s3_input_data_path + \"/\" in i.key]\n",
    "sorted(objects, key=lambda x: x.key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "933cd0ed-99c2-4453-a94b-02eace47bd20",
   "metadata": {},
   "source": [
    "## Next Step\n",
    "\n",
    "We can run these notebooks in automation using Elyra and Kubeflow pipelines. As the immediate next step, we will to deploy the best model from this notebook as an inference service using Seldon."
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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