03_notebook_executed.ipynb

{
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  {
   "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": 1,
   "id": "e2bb1e73",
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       "True"
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     "execution_count": 1,
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     "output_type": "execute_result"
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   ],
   "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": 2,
   "id": "f6d138f3",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-12-07T15:27:59.883414Z"
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   "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": 3,
   "id": "f7ddbfc0-53a7-4d76-a64e-975c89fa1e59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:27:59.887703Z",
     "iopub.status.busy": "2022-12-07T15:27:59.887264Z",
     "iopub.status.idle": "2022-12-07T15:28:00.452894Z",
     "shell.execute_reply": "2022-12-07T15:28:00.452096Z"
    }
   },
   "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": 4,
   "id": "0978bbf8-2da1-4e66-bfa8-5cfa1af2f3e2",
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       "      <th>0</th>\n",
       "      <td>update inference output text</td>\n",
       "      <td>Updated `03_model_training.ipynb` to create a ...</td>\n",
       "      <td>XXL</td>\n",
       "      <td>MichaelClifford</td>\n",
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       "      <th>1</th>\n",
       "      <td>Update inference.yaml.</td>\n",
       "      <td>test</td>\n",
       "      <td>S</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1670273067</td>\n",
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       "      <td>[1a92e5f7feba314360bffc318d88d89e6decb4b0, 57a...</td>\n",
       "      <td>[.github/workflows/inference.yaml, action.yml]</td>\n",
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       "      <td>1.670276e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>testing 'on PR' functionality. Do Not Merge</td>\n",
       "      <td>:wave:</td>\n",
       "      <td>XS</td>\n",
       "      <td>MichaelClifford</td>\n",
       "      <td>1670272320</td>\n",
       "      <td>1670276415</td>\n",
       "      <td>MichaelClifford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'github-actions[bot]': 10}</td>\n",
       "      <td>{}</td>\n",
       "      <td>[]</td>\n",
       "      <td>[24c6e24399b482bf9f4e7cd5bd94ed418ca919e3]</td>\n",
       "      <td>[test_file.txt]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Add `with: MODE` functionality</td>\n",
       "      <td>Addresses #32</td>\n",
       "      <td>S</td>\n",
       "      <td>aakankshaduggal</td>\n",
       "      <td>1670259791</td>\n",
       "      <td>1670273808</td>\n",
       "      <td>aakankshaduggal</td>\n",
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       "      <td>[b00830414636895c2790219c0a4b8b6fb812b44f, 705...</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fix how inference was callingPRs</td>\n",
       "      <td>There was a bug in how the inference notebook ...</td>\n",
       "      <td>L</td>\n",
       "      <td>MichaelClifford</td>\n",
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       "      <td>[04_model_inference.ipynb]</td>\n",
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      "text/plain": [
       "                                         title  \\\n",
       "0                 update inference output text   \n",
       "1                      Update inference.yaml.    \n",
       "2  testing 'on PR' functionality. Do Not Merge   \n",
       "3              Add `with: MODE` functionality    \n",
       "4             fix how inference was callingPRs   \n",
       "\n",
       "                                                body size       created_by  \\\n",
       "0  Updated `03_model_training.ipynb` to create a ...  XXL  MichaelClifford   \n",
       "1                                              test     S  aakankshaduggal   \n",
       "2                                            :wave:    XS  MichaelClifford   \n",
       "3                                     Addresses #32     S  aakankshaduggal   \n",
       "4  There was a bug in how the inference notebook ...    L  MichaelClifford   \n",
       "\n",
       "   created_at   closed_at        closed_by     merged_at        merged_by  \\\n",
       "0  1670362761  1670426174  MichaelClifford  1.670426e+09  MichaelClifford   \n",
       "1  1670273067  1670276461  MichaelClifford  1.670276e+09  MichaelClifford   \n",
       "2  1670272320  1670276415  MichaelClifford           NaN             None   \n",
       "3  1670259791  1670273808  aakankshaduggal           NaN             None   \n",
       "4  1669906871  1669907261  aakankshaduggal  1.669907e+09  aakankshaduggal   \n",
       "\n",
       "   commits_number  changed_files_number  \\\n",
       "0               1                     2   \n",
       "1               4                     2   \n",
       "2               1                     1   \n",
       "3               3                     2   \n",
       "4               1                     1   \n",
       "\n",
       "                                        interactions  \\\n",
       "0  {'github-actions[bot]': 20, 'MichaelClifford':...   \n",
       "1                        {'github-actions[bot]': 40}   \n",
       "2                        {'github-actions[bot]': 10}   \n",
       "3                        {'github-actions[bot]': 10}   \n",
       "4                                                 {}   \n",
       "\n",
       "                                             reviews labels  \\\n",
       "0  {'1207369509': {'author': 'oindrillac', 'words...     []   \n",
       "1  {'1205314017': {'author': 'MichaelClifford', '...     []   \n",
       "2                                                 {}     []   \n",
       "3                                                 {}     []   \n",
       "4  {'1201160046': {'author': 'oindrillac', 'words...     []   \n",
       "\n",
       "                                             commits  \\\n",
       "0         [e2bfe03be2fae4095ec22c604c876ae53c4f4c4a]   \n",
       "1  [1a92e5f7feba314360bffc318d88d89e6decb4b0, 57a...   \n",
       "2         [24c6e24399b482bf9f4e7cd5bd94ed418ca919e3]   \n",
       "3  [b00830414636895c2790219c0a4b8b6fb812b44f, 705...   \n",
       "4         [f8d860b1f6f4cf770293e5d377bb2bb37b996102]   \n",
       "\n",
       "                                       changed_files  first_review_at  \\\n",
       "0  [03_model_training.ipynb, 04_model_inference.i...     1.670363e+09   \n",
       "1     [.github/workflows/inference.yaml, action.yml]     1.670276e+09   \n",
       "2                                    [test_file.txt]              NaN   \n",
       "3     [.github/workflows/inference.yaml, action.yml]              NaN   \n",
       "4                         [04_model_inference.ipynb]     1.669907e+09   \n",
       "\n",
       "   first_approve_at  \n",
       "0      1.670367e+09  \n",
       "1      1.670276e+09  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4      1.669907e+09  "
      ]
     },
     "execution_count": 4,
     "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": 5,
   "id": "8bf8212d-82bd-4600-a615-1e150272146f",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      63413.0\n",
       "1       3394.0\n",
       "4        390.0\n",
       "5         93.0\n",
       "6       1355.0\n",
       "8        229.0\n",
       "9        309.0\n",
       "10      5860.0\n",
       "11     24070.0\n",
       "12    240653.0\n",
       "13      5822.0\n",
       "15      9962.0\n",
       "16    324073.0\n",
       "17      2077.0\n",
       "18      7678.0\n",
       "19      3819.0\n",
       "23    101246.0\n",
       "24     65665.0\n",
       "25      2713.0\n",
       "26    937820.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "id": "5ddc1177-ac15-494a-8c49-0d49e3e5d7e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.504926Z",
     "iopub.status.busy": "2022-12-07T15:28:00.504655Z",
     "iopub.status.idle": "2022-12-07T15:28:00.512247Z",
     "shell.execute_reply": "2022-12-07T15:28:00.511495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0        93.0\n",
       "0.1       301.0\n",
       "0.2      1162.0\n",
       "0.3      2522.2\n",
       "0.4      3649.0\n",
       "0.5      5841.0\n",
       "0.6      8591.6\n",
       "0.7     35872.9\n",
       "0.8     72781.2\n",
       "0.9    248995.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "id": "8645c579",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.515482Z",
     "iopub.status.busy": "2022-12-07T15:28:00.515221Z",
     "iopub.status.idle": "2022-12-07T15:28:00.521598Z",
     "shell.execute_reply": "2022-12-07T15:28:00.521034Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0             0:01:00\n",
       "0.1             0:05:00\n",
       "0.2             0:19:00\n",
       "0.3             0:42:00\n",
       "0.4             1:00:00\n",
       "0.5             1:37:00\n",
       "0.6             2:23:00\n",
       "0.7             9:57:00\n",
       "0.8            20:13:00\n",
       "0.9    2 days, 21:09:00\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "id": "28616ba1",
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       "        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>Class_Name</th>\n",
       "      <th>Class_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0:00:00 - 0:01:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0:01:00 - 0:05:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0:05:00 - 0:19:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0:19:00 - 0:42:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0:42:00 - 1:00:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>1:00:00 - 1:37:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>1:37:00 - 2:23:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>2:23:00 - 9:57:00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>9:57:00 - 20:13:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>20:13:00 - 2 days, 21:09:00</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 - 0:01:00            0\n",
       "0.1            0:01:00 - 0:05:00            1\n",
       "0.2            0:05:00 - 0:19:00            2\n",
       "0.3            0:19:00 - 0:42:00            3\n",
       "0.4            0:42:00 - 1:00:00            4\n",
       "0.5            1:00:00 - 1:37:00            5\n",
       "0.6            1:37:00 - 2:23:00            6\n",
       "0.7            2:23:00 - 9:57:00            7\n",
       "0.8           9:57:00 - 20:13:00            8\n",
       "0.9  20:13:00 - 2 days, 21:09:00            9"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "id": "97a47fdd-f513-4428-a4a1-6affe1899eae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.544147Z",
     "iopub.status.busy": "2022-12-07T15:28:00.543558Z",
     "iopub.status.idle": "2022-12-07T15:28:00.551633Z",
     "shell.execute_reply": "2022-12-07T15:28:00.550857Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     7\n",
       "1     3\n",
       "4     1\n",
       "5     0\n",
       "6     2\n",
       "8     0\n",
       "9     1\n",
       "10    5\n",
       "11    6\n",
       "12    8\n",
       "13    4\n",
       "15    6\n",
       "16    9\n",
       "17    2\n",
       "18    5\n",
       "19    4\n",
       "23    8\n",
       "24    7\n",
       "25    3\n",
       "26    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "f769e3cb-e140-4855-8161-0c72bf6403a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.556338Z",
     "iopub.status.busy": "2022-12-07T15:28:00.556056Z",
     "iopub.status.idle": "2022-12-07T15:28:00.559700Z",
     "shell.execute_reply": "2022-12-07T15:28:00.558943Z"
    }
   },
   "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": 11,
   "id": "7d2ab3c3-caed-4971-ba1d-4c484088a057",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.563502Z",
     "iopub.status.busy": "2022-12-07T15:28:00.563237Z",
     "iopub.status.idle": "2022-12-07T15:28:00.566531Z",
     "shell.execute_reply": "2022-12-07T15:28:00.565784Z"
    }
   },
   "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": 12,
   "id": "2ad7979f-afd8-40b2-8c03-929129908953",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.570201Z",
     "iopub.status.busy": "2022-12-07T15:28:00.569943Z",
     "iopub.status.idle": "2022-12-07T15:28:00.573154Z",
     "shell.execute_reply": "2022-12-07T15:28:00.572416Z"
    }
   },
   "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": 13,
   "id": "b74d3d4d-ba0c-43c3-8506-23ed49c6dce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.576275Z",
     "iopub.status.busy": "2022-12-07T15:28:00.576019Z",
     "iopub.status.idle": "2022-12-07T15:28:00.579119Z",
     "shell.execute_reply": "2022-12-07T15:28:00.578491Z"
    }
   },
   "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": 14,
   "id": "71d6e5c3-eaf3-4cfe-9fe6-ef3f9b9edcfe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.582359Z",
     "iopub.status.busy": "2022-12-07T15:28:00.582106Z",
     "iopub.status.idle": "2022-12-07T15:28:00.841040Z",
     "shell.execute_reply": "2022-12-07T15:28:00.840249Z"
    }
   },
   "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": 15,
   "id": "977a1fba-c867-4feb-848d-5456d168e940",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.845125Z",
     "iopub.status.busy": "2022-12-07T15:28:00.844653Z",
     "iopub.status.idle": "2022-12-07T15:28:00.852022Z",
     "shell.execute_reply": "2022-12-07T15:28:00.851371Z"
    }
   },
   "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>.ipynb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.py</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.sh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.yml</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  filetype\n",
       "0   .ipynb\n",
       "1    .yaml\n",
       "2      .py\n",
       "3      .sh\n",
       "4     .yml"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filetype_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c987e49d-d5ab-48ec-b369-6bc5c883d976",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.855649Z",
     "iopub.status.busy": "2022-12-07T15:28:00.855119Z",
     "iopub.status.idle": "2022-12-07T15:28:00.858952Z",
     "shell.execute_reply": "2022-12-07T15:28:00.858358Z"
    }
   },
   "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": 17,
   "id": "952d3564-c7a9-4707-9117-b4ccfaf0065c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:00.862809Z",
     "iopub.status.busy": "2022-12-07T15:28:00.862287Z",
     "iopub.status.idle": "2022-12-07T15:28:01.101224Z",
     "shell.execute_reply": "2022-12-07T15:28:01.100446Z"
    }
   },
   "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": 18,
   "id": "0534487e-f064-49ef-a0a2-15f6022dfcab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.105841Z",
     "iopub.status.busy": "2022-12-07T15:28:01.105259Z",
     "iopub.status.idle": "2022-12-07T15:28:01.112369Z",
     "shell.execute_reply": "2022-12-07T15:28:01.111705Z"
    }
   },
   "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>action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>add</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>api</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>basic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>best</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    title\n",
       "0  action\n",
       "1     add\n",
       "2     api\n",
       "3   basic\n",
       "4    best"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_word_keep_cols_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1be7c50c-dc47-483f-b695-a9c02d6cb51b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.115921Z",
     "iopub.status.busy": "2022-12-07T15:28:01.115366Z",
     "iopub.status.idle": "2022-12-07T15:28:01.120959Z",
     "shell.execute_reply": "2022-12-07T15:28:01.120269Z"
    }
   },
   "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": 20,
   "id": "c4ee7eb8-62d6-452f-b548-1a884e63ce4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.124953Z",
     "iopub.status.busy": "2022-12-07T15:28:01.124423Z",
     "iopub.status.idle": "2022-12-07T15:28:01.131587Z",
     "shell.execute_reply": "2022-12-07T15:28:01.130727Z"
    }
   },
   "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": 21,
   "id": "7fa84a70-dab6-4a48-90fc-844c29d38263",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.135521Z",
     "iopub.status.busy": "2022-12-07T15:28:01.134856Z",
     "iopub.status.idle": "2022-12-07T15:28:01.139736Z",
     "shell.execute_reply": "2022-12-07T15:28:01.138931Z"
    }
   },
   "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": 22,
   "id": "92ddff9e-fb93-477d-ab17-b54ce13185a4",
   "metadata": {
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       "                                         title  \\\n",
       "0                 update inference output text   \n",
       "1                      Update inference.yaml.    \n",
       "2  testing 'on PR' functionality. Do Not Merge   \n",
       "3              Add `with: MODE` functionality    \n",
       "4             fix how inference was callingPRs   \n",
       "\n",
       "                                                body size       created_by  \\\n",
       "0  Updated `03_model_training.ipynb` to create a ...  XXL  MichaelClifford   \n",
       "1                                              test     S  aakankshaduggal   \n",
       "2                                            :wave:    XS  MichaelClifford   \n",
       "3                                     Addresses #32     S  aakankshaduggal   \n",
       "4  There was a bug in how the inference notebook ...    L  MichaelClifford   \n",
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       "\n",
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       "4               1                     1   \n",
       "\n",
       "                                        interactions  \\\n",
       "0  {'github-actions[bot]': 20, 'MichaelClifford':...   \n",
       "1                        {'github-actions[bot]': 40}   \n",
       "2                        {'github-actions[bot]': 10}   \n",
       "3                        {'github-actions[bot]': 10}   \n",
       "4                                                 {}   \n",
       "\n",
       "                                             reviews labels  \\\n",
       "0  {'1207369509': {'author': 'oindrillac', 'words...     []   \n",
       "1  {'1205314017': {'author': 'MichaelClifford', '...     []   \n",
       "2                                                 {}     []   \n",
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       "4  {'1201160046': {'author': 'oindrillac', 'words...     []   \n",
       "\n",
       "                                             commits  \\\n",
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       "\n",
       "                                       changed_files  first_review_at  \\\n",
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       "1     [.github/workflows/inference.yaml, action.yml]     1.670276e+09   \n",
       "2                                    [test_file.txt]              NaN   \n",
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       "\n",
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       "2               NaN  \n",
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       "4      1.669907e+09  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_df.head()"
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  {
   "cell_type": "code",
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   "metadata": {
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    "X = raw_data_processor.fit_transform(pr_df)"
   ]
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  {
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       "   size  created_at_day  created_at_month  created_at_weekday  \\\n",
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       "\n",
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       "\n",
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       "3                    0.0                       0.0                      0.0   \n",
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       "\n",
       "[5 rows x 70 columns]"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(X, index=pr_df.index, columns=cols)\n",
    "X.head()"
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     "shell.execute_reply": "2022-12-07T15:28:01.320345Z"
    }
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>size</th>\n",
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       "      <th>4</th>\n",
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       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>6</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</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>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",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</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>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",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</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",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>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",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</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>1.0</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",
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       "    <tr>\n",
       "      <th>14</th>\n",
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       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</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>16</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</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>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</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>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>1.0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</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",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</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",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</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>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>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</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>1.0</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>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</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>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.0</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>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</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>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    size  created_at_day  created_at_month  created_at_weekday  \\\n",
       "0    5.0             6.0              12.0                 1.0   \n",
       "1    1.0             5.0              12.0                 0.0   \n",
       "2    0.0             5.0              12.0                 0.0   \n",
       "3    1.0             5.0              12.0                 0.0   \n",
       "4    3.0             1.0              12.0                 3.0   \n",
       "5    0.0            30.0              11.0                 2.0   \n",
       "6    0.0            30.0              11.0                 2.0   \n",
       "7    2.0            30.0              11.0                 2.0   \n",
       "8    0.0            30.0              11.0                 2.0   \n",
       "9    0.0            30.0              11.0                 2.0   \n",
       "10   1.0            22.0              11.0                 1.0   \n",
       "11   5.0            22.0              11.0                 1.0   \n",
       "12   5.0            18.0              11.0                 4.0   \n",
       "13   2.0            17.0              11.0                 3.0   \n",
       "14   2.0            17.0              11.0                 3.0   \n",
       "15   5.0            17.0              11.0                 3.0   \n",
       "16   5.0            11.0              11.0                 4.0   \n",
       "17   1.0            11.0              11.0                 4.0   \n",
       "18   2.0            11.0              11.0                 4.0   \n",
       "19   2.0            11.0              11.0                 4.0   \n",
       "20   0.0            11.0              11.0                 4.0   \n",
       "21   0.0            10.0              11.0                 3.0   \n",
       "22   0.0            10.0              11.0                 3.0   \n",
       "23   4.0             9.0              11.0                 2.0   \n",
       "24   5.0             8.0              11.0                 1.0   \n",
       "25   5.0             2.0              11.0                 2.0   \n",
       "26   5.0            21.0              10.0                 4.0   \n",
       "\n",
       "    created_at_hour  changed_files_number  body_size  commits_number  \\\n",
       "0              21.0                   2.0       41.0             1.0   \n",
       "1              20.0                   2.0        1.0             4.0   \n",
       "2              20.0                   1.0        1.0             1.0   \n",
       "3              17.0                   2.0        2.0             3.0   \n",
       "4              15.0                   1.0       53.0             1.0   \n",
       "5              20.0                   1.0        2.0             1.0   \n",
       "6              17.0                   1.0        3.0             2.0   \n",
       "7              16.0                   1.0       12.0             1.0   \n",
       "8              15.0                   1.0        6.0             1.0   \n",
       "9              14.0                   2.0       10.0             2.0   \n",
       "10             19.0                   2.0        2.0             3.0   \n",
       "11             14.0                   7.0       39.0            10.0   \n",
       "12             21.0                   5.0      124.0             1.0   \n",
       "13             20.0                   2.0        9.0             2.0   \n",
       "14             20.0                   1.0       15.0             1.0   \n",
       "15             17.0                   4.0       24.0             1.0   \n",
       "16             22.0                  16.0      153.0             5.0   \n",
       "17             21.0                   1.0        9.0             1.0   \n",
       "18             18.0                   4.0       26.0             8.0   \n",
       "19             16.0                   4.0       31.0             1.0   \n",
       "20             14.0                   1.0       19.0             1.0   \n",
       "21             22.0                   1.0        0.0             1.0   \n",
       "22             20.0                   1.0        7.0             2.0   \n",
       "23             16.0                   2.0       13.0             1.0   \n",
       "24             20.0                   2.0       47.0             1.0   \n",
       "25             15.0                   2.0       22.0             1.0   \n",
       "26             18.0                   4.0        2.0            11.0   \n",
       "\n",
       "    filetype_.ipynb  filetype_.yaml  ...  title_wordcount_text  \\\n",
       "0               2.0             0.0  ...                   1.0   \n",
       "1               0.0             1.0  ...                   0.0   \n",
       "2               0.0             0.0  ...                   0.0   \n",
       "3               0.0             1.0  ...                   0.0   \n",
       "4               1.0             0.0  ...                   0.0   \n",
       "5               0.0             0.0  ...                   0.0   \n",
       "6               0.0             0.0  ...                   0.0   \n",
       "7               0.0             0.0  ...                   0.0   \n",
       "8               0.0             0.0  ...                   0.0   \n",
       "9               0.0             1.0  ...                   0.0   \n",
       "10              0.0             1.0  ...                   0.0   \n",
       "11              5.0             0.0  ...                   0.0   \n",
       "12              1.0             1.0  ...                   0.0   \n",
       "13              0.0             1.0  ...                   0.0   \n",
       "14              0.0             1.0  ...                   0.0   \n",
       "15              1.0             0.0  ...                   0.0   \n",
       "16              8.0             1.0  ...                   0.0   \n",
       "17              0.0             0.0  ...                   0.0   \n",
       "18              0.0             1.0  ...                   0.0   \n",
       "19              1.0             0.0  ...                   0.0   \n",
       "20              0.0             1.0  ...                   0.0   \n",
       "21              0.0             0.0  ...                   0.0   \n",
       "22              0.0             1.0  ...                   0.0   \n",
       "23              1.0             1.0  ...                   0.0   \n",
       "24              1.0             0.0  ...                   0.0   \n",
       "25              2.0             0.0  ...                   0.0   \n",
       "26              1.0             2.0  ...                   0.0   \n",
       "\n",
       "    title_wordcount_train  title_wordcount_training  title_wordcount_trigger  \\\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",
       "5                     0.0                       0.0                      0.0   \n",
       "6                     0.0                       0.0                      0.0   \n",
       "7                     0.0                       0.0                      0.0   \n",
       "8                     0.0                       0.0                      0.0   \n",
       "9                     1.0                       0.0                      0.0   \n",
       "10                    0.0                       1.0                      0.0   \n",
       "11                    0.0                       0.0                      0.0   \n",
       "12                    0.0                       0.0                      0.0   \n",
       "13                    0.0                       0.0                      0.0   \n",
       "14                    0.0                       0.0                      0.0   \n",
       "15                    0.0                       0.0                      0.0   \n",
       "16                    0.0                       0.0                      0.0   \n",
       "17                    0.0                       0.0                      0.0   \n",
       "18                    0.0                       0.0                      0.0   \n",
       "19                    0.0                       0.0                      0.0   \n",
       "20                    0.0                       0.0                      1.0   \n",
       "21                    0.0                       0.0                      0.0   \n",
       "22                    0.0                       0.0                      0.0   \n",
       "23                    0.0                       0.0                      0.0   \n",
       "24                    0.0                       0.0                      0.0   \n",
       "25                    0.0                       0.0                      0.0   \n",
       "26                    0.0                       0.0                      0.0   \n",
       "\n",
       "    title_wordcount_update  title_wordcount_upstream  title_wordcount_use  \\\n",
       "0                      1.0                       0.0                  0.0   \n",
       "1                      1.0                       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",
       "5                      0.0                       0.0                  0.0   \n",
       "6                      1.0                       0.0                  0.0   \n",
       "7                      1.0                       0.0                  0.0   \n",
       "8                      1.0                       0.0                  0.0   \n",
       "9                      0.0                       0.0                  0.0   \n",
       "10                     0.0                       0.0                  0.0   \n",
       "11                     0.0                       0.0                  0.0   \n",
       "12                     0.0                       0.0                  0.0   \n",
       "13                     1.0                       0.0                  0.0   \n",
       "14                     1.0                       0.0                  1.0   \n",
       "15                     0.0                       0.0                  0.0   \n",
       "16                     0.0                       0.0                  0.0   \n",
       "17                     1.0                       0.0                  0.0   \n",
       "18                     0.0                       0.0                  0.0   \n",
       "19                     0.0                       0.0                  0.0   \n",
       "20                     0.0                       1.0                  0.0   \n",
       "21                     0.0                       0.0                  0.0   \n",
       "22                     1.0                       0.0                  0.0   \n",
       "23                     0.0                       0.0                  0.0   \n",
       "24                     0.0                       0.0                  0.0   \n",
       "25                     0.0                       0.0                  0.0   \n",
       "26                     0.0                       0.0                  0.0   \n",
       "\n",
       "    title_wordcount_workflow  title_wordcount_yaml  title_wordcount_yml  \n",
       "0                        0.0                   0.0                  0.0  \n",
       "1                        0.0                   1.0                  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",
       "5                        0.0                   0.0                  0.0  \n",
       "6                        0.0                   0.0                  0.0  \n",
       "7                        0.0                   0.0                  0.0  \n",
       "8                        0.0                   0.0                  0.0  \n",
       "9                        0.0                   0.0                  0.0  \n",
       "10                       0.0                   0.0                  0.0  \n",
       "11                       0.0                   0.0                  0.0  \n",
       "12                       0.0                   0.0                  0.0  \n",
       "13                       0.0                   1.0                  0.0  \n",
       "14                       0.0                   1.0                  0.0  \n",
       "15                       0.0                   0.0                  0.0  \n",
       "16                       0.0                   0.0                  0.0  \n",
       "17                       0.0                   0.0                  1.0  \n",
       "18                       0.0                   0.0                  0.0  \n",
       "19                       0.0                   0.0                  0.0  \n",
       "20                       0.0                   0.0                  0.0  \n",
       "21                       1.0                   0.0                  0.0  \n",
       "22                       0.0                   1.0                  0.0  \n",
       "23                       0.0                   0.0                  0.0  \n",
       "24                       0.0                   0.0                  0.0  \n",
       "25                       0.0                   0.0                  0.0  \n",
       "26                       1.0                   0.0                  0.0  \n",
       "\n",
       "[27 rows x 70 columns]"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "id": "da703bc5-696f-4f63-b471-018c18557ac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.325204Z",
     "iopub.status.busy": "2022-12-07T15:28:01.324580Z",
     "iopub.status.idle": "2022-12-07T15:28:01.329410Z",
     "shell.execute_reply": "2022-12-07T15:28:01.328673Z"
    }
   },
   "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": 27,
   "id": "86235437",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.332730Z",
     "iopub.status.busy": "2022-12-07T15:28:01.332457Z",
     "iopub.status.idle": "2022-12-07T15:28:01.337323Z",
     "shell.execute_reply": "2022-12-07T15:28:01.336563Z"
    }
   },
   "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": 28,
   "id": "82ac1a13-b294-4414-906b-271e72ac091f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.340869Z",
     "iopub.status.busy": "2022-12-07T15:28:01.340454Z",
     "iopub.status.idle": "2022-12-07T15:28:01.366782Z",
     "shell.execute_reply": "2022-12-07T15:28:01.365875Z"
    }
   },
   "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>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_.ipynb</th>\n",
       "      <th>filetype_.yaml</th>\n",
       "      <th>...</th>\n",
       "      <th>title_wordcount_text</th>\n",
       "      <th>title_wordcount_train</th>\n",
       "      <th>title_wordcount_training</th>\n",
       "      <th>title_wordcount_trigger</th>\n",
       "      <th>title_wordcount_update</th>\n",
       "      <th>title_wordcount_upstream</th>\n",
       "      <th>title_wordcount_use</th>\n",
       "      <th>title_wordcount_workflow</th>\n",
       "      <th>title_wordcount_yaml</th>\n",
       "      <th>title_wordcount_yml</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.0</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>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</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>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</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>1.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    size  created_at_day  created_at_month  created_at_weekday  \\\n",
       "0    5.0             6.0              12.0                 1.0   \n",
       "24   5.0             8.0              11.0                 1.0   \n",
       "19   2.0            11.0              11.0                 4.0   \n",
       "1    1.0             5.0              12.0                 0.0   \n",
       "\n",
       "    created_at_hour  changed_files_number  body_size  commits_number  \\\n",
       "0              21.0                   2.0       41.0             1.0   \n",
       "24             20.0                   2.0       47.0             1.0   \n",
       "19             16.0                   4.0       31.0             1.0   \n",
       "1              20.0                   2.0        1.0             4.0   \n",
       "\n",
       "    filetype_.ipynb  filetype_.yaml  ...  title_wordcount_text  \\\n",
       "0               2.0             0.0  ...                   1.0   \n",
       "24              1.0             0.0  ...                   0.0   \n",
       "19              1.0             0.0  ...                   0.0   \n",
       "1               0.0             1.0  ...                   0.0   \n",
       "\n",
       "    title_wordcount_train  title_wordcount_training  title_wordcount_trigger  \\\n",
       "0                     0.0                       0.0                      0.0   \n",
       "24                    0.0                       0.0                      0.0   \n",
       "19                    0.0                       0.0                      0.0   \n",
       "1                     0.0                       0.0                      0.0   \n",
       "\n",
       "    title_wordcount_update  title_wordcount_upstream  title_wordcount_use  \\\n",
       "0                      1.0                       0.0                  0.0   \n",
       "24                     0.0                       0.0                  0.0   \n",
       "19                     0.0                       0.0                  0.0   \n",
       "1                      1.0                       0.0                  0.0   \n",
       "\n",
       "    title_wordcount_workflow  title_wordcount_yaml  title_wordcount_yml  \n",
       "0                        0.0                   0.0                  0.0  \n",
       "24                       0.0                   0.0                  0.0  \n",
       "19                       0.0                   0.0                  0.0  \n",
       "1                        0.0                   1.0                  0.0  \n",
       "\n",
       "[4 rows x 70 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fbbbd016",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:01.370721Z",
     "iopub.status.busy": "2022-12-07T15:28:01.370023Z",
     "iopub.status.idle": "2022-12-07T15:28:02.338960Z",
     "shell.execute_reply": "2022-12-07T15:28:02.338120Z"
    }
   },
   "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": 30,
   "id": "4ee74938",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.342873Z",
     "iopub.status.busy": "2022-12-07T15:28:02.342308Z",
     "iopub.status.idle": "2022-12-07T15:28:02.349115Z",
     "shell.execute_reply": "2022-12-07T15:28:02.347052Z"
    }
   },
   "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": 31,
   "id": "47bb3149",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.353627Z",
     "iopub.status.busy": "2022-12-07T15:28:02.353014Z",
     "iopub.status.idle": "2022-12-07T15:28:02.501595Z",
     "shell.execute_reply": "2022-12-07T15:28:02.500722Z"
    }
   },
   "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": 32,
   "id": "2cacd2c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.506513Z",
     "iopub.status.busy": "2022-12-07T15:28:02.505785Z",
     "iopub.status.idle": "2022-12-07T15:28:02.516149Z",
     "shell.execute_reply": "2022-12-07T15:28:02.515369Z"
    }
   },
   "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": 33,
   "id": "e6d48ea1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.520389Z",
     "iopub.status.busy": "2022-12-07T15:28:02.519689Z",
     "iopub.status.idle": "2022-12-07T15:28:02.524783Z",
     "shell.execute_reply": "2022-12-07T15:28:02.524011Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize classifier\n",
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804ae1e0",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6c4bfaa2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.529123Z",
     "iopub.status.busy": "2022-12-07T15:28:02.528489Z",
     "iopub.status.idle": "2022-12-07T15:28:02.533408Z",
     "shell.execute_reply": "2022-12-07T15:28:02.532724Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize classifier\n",
    "svc = SVC(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3b3017",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1e623025",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.537171Z",
     "iopub.status.busy": "2022-12-07T15:28:02.536766Z",
     "iopub.status.idle": "2022-12-07T15:28:02.541398Z",
     "shell.execute_reply": "2022-12-07T15:28:02.540682Z"
    }
   },
   "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": 36,
   "id": "d8eb71ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.545229Z",
     "iopub.status.busy": "2022-12-07T15:28:02.544801Z",
     "iopub.status.idle": "2022-12-07T15:28:02.549979Z",
     "shell.execute_reply": "2022-12-07T15:28:02.549293Z"
    },
    "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": 37,
   "id": "e72772aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.554124Z",
     "iopub.status.busy": "2022-12-07T15:28:02.553519Z",
     "iopub.status.idle": "2022-12-07T15:28:02.897520Z",
     "shell.execute_reply": "2022-12-07T15:28:02.896666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 38,
   "id": "c3f8cc61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:02.901992Z",
     "iopub.status.busy": "2022-12-07T15:28:02.901458Z",
     "iopub.status.idle": "2022-12-07T15:28:03.176990Z",
     "shell.execute_reply": "2022-12-07T15:28:03.175992Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 39,
   "id": "8b961727",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.181351Z",
     "iopub.status.busy": "2022-12-07T15:28:03.180652Z",
     "iopub.status.idle": "2022-12-07T15:28:03.857388Z",
     "shell.execute_reply": "2022-12-07T15:28:03.856722Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 40,
   "id": "14249146",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.860944Z",
     "iopub.status.busy": "2022-12-07T15:28:03.860388Z",
     "iopub.status.idle": "2022-12-07T15:28:03.865855Z",
     "shell.execute_reply": "2022-12-07T15:28:03.865251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGboost Model cannot be trained correctly with current dataset\n"
     ]
    }
   ],
   "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": 41,
   "id": "8985b82e-7d89-4b69-b063-db05943458c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.869471Z",
     "iopub.status.busy": "2022-12-07T15:28:03.868682Z",
     "iopub.status.idle": "2022-12-07T15:28:03.873625Z",
     "shell.execute_reply": "2022-12-07T15:28:03.873032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GaussianNB': 0.0, 'SVC': 0.0, 'RandomForestClassifier': 0.0}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "79eebe0e-4254-4b02-8c33-bee3ddc75e15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.877188Z",
     "iopub.status.busy": "2022-12-07T15:28:03.876417Z",
     "iopub.status.idle": "2022-12-07T15:28:03.880079Z",
     "shell.execute_reply": "2022-12-07T15:28:03.879450Z"
    }
   },
   "outputs": [],
   "source": [
    "best_accuracy = max(dict_accuracy.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "afaacc68-b887-48cc-9673-04c482f37169",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.883678Z",
     "iopub.status.busy": "2022-12-07T15:28:03.882919Z",
     "iopub.status.idle": "2022-12-07T15:28:03.886526Z",
     "shell.execute_reply": "2022-12-07T15:28:03.885948Z"
    }
   },
   "outputs": [],
   "source": [
    "best_model = max(dict_accuracy, key = dict_accuracy.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "65b42b90-5014-429a-a8f2-7477c7d91335",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.890123Z",
     "iopub.status.busy": "2022-12-07T15:28:03.889374Z",
     "iopub.status.idle": "2022-12-07T15:28:03.893985Z",
     "shell.execute_reply": "2022-12-07T15:28:03.893408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GaussianNB'"
      ]
     },
     "execution_count": 44,
     "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": 45,
   "id": "987a23d4-2e8d-4d4d-8e5c-f3f4230f4b12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.897559Z",
     "iopub.status.busy": "2022-12-07T15:28:03.896812Z",
     "iopub.status.idle": "2022-12-07T15:28:03.900528Z",
     "shell.execute_reply": "2022-12-07T15:28:03.899943Z"
    }
   },
   "outputs": [],
   "source": [
    "models = {\"RandomForestClassifier\": rf, \"XGBClassifier\": xgbc, \"SVC\": svc, \"GaussianNB\": gnb}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "647eb700",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.903899Z",
     "iopub.status.busy": "2022-12-07T15:28:03.903336Z",
     "iopub.status.idle": "2022-12-07T15:28:03.907193Z",
     "shell.execute_reply": "2022-12-07T15:28:03.906342Z"
    }
   },
   "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": 47,
   "id": "2d739cb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.910964Z",
     "iopub.status.busy": "2022-12-07T15:28:03.910307Z",
     "iopub.status.idle": "2022-12-07T15:28:03.919143Z",
     "shell.execute_reply": "2022-12-07T15:28:03.918367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.00      0.00      0.00       0.0\n",
      "           3       0.00      0.00      0.00       1.0\n",
      "           4       0.00      0.00      0.00       1.0\n",
      "           5       0.00      0.00      0.00       0.0\n",
      "           7       0.00      0.00      0.00       2.0\n",
      "           8       0.00      0.00      0.00       0.0\n",
      "\n",
      "    accuracy                           0.00       4.0\n",
      "   macro avg       0.00      0.00      0.00       4.0\n",
      "weighted avg       0.00      0.00      0.00       4.0\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": 48,
   "id": "1c581342",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.922877Z",
     "iopub.status.busy": "2022-12-07T15:28:03.922378Z",
     "iopub.status.idle": "2022-12-07T15:28:03.926402Z",
     "shell.execute_reply": "2022-12-07T15:28:03.925612Z"
    }
   },
   "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": 49,
   "id": "8fb0ba41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:03.929887Z",
     "iopub.status.busy": "2022-12-07T15:28:03.929623Z",
     "iopub.status.idle": "2022-12-07T15:28:04.596496Z",
     "shell.execute_reply": "2022-12-07T15:28:04.595574Z"
    },
    "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": 50,
   "id": "fcfd6c08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:04.600826Z",
     "iopub.status.busy": "2022-12-07T15:28:04.600471Z",
     "iopub.status.idle": "2022-12-07T15:28:04.901562Z",
     "shell.execute_reply": "2022-12-07T15:28:04.900674Z"
    }
   },
   "outputs": [],
   "source": [
    "f = cc.upload_to_ceph(Categories, MODEL_KEY, \"label-map\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9392bb4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:04.906563Z",
     "iopub.status.busy": "2022-12-07T15:28:04.905950Z",
     "iopub.status.idle": "2022-12-07T15:28:05.269185Z",
     "shell.execute_reply": "2022-12-07T15:28:05.268344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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;action&#x27;,\n",
       "                                                                                   &#x27;add&#x27;,\n",
       "                                                                                   &#x27;api&#x27;,\n",
       "                                                                                   &#x27;basic&#x27;,\n",
       "                                                                                   &#x27;best&#x27;,\n",
       "                                                                                   &#x27;callingprs&#x27;,\n",
       "                                                                                   &#x27;changed&#x27;,\n",
       "                                                                                   &#x27;choose&#x27;,\n",
       "                                                                                   &#x27;clean&#x27;,\n",
       "                                                                                   &#x27;cleanup&#x27;,\n",
       "                                                                                   &#x27;comment&#x27;,\n",
       "                                                                                   &#x27;condition&#x27;,\n",
       "                                                                                   &#x27;data&#x27;,\n",
       "                                                                                   &#x27;download&#x27;,\n",
       "                                                                                   &#x27;engineering&#x27;,\n",
       "                                                                                   &#x27;entrypoint&#x27;,\n",
       "                                                                                   &#x27;feature&#x27;,\n",
       "                                                                                   &#x27;fix&#x27;,\n",
       "                                                                                   &#x27;fixed&#x27;,\n",
       "                                                                                   &#x27;functionality&#x27;,\n",
       "                                                                                   &#x27;github&#x27;,\n",
       "                                                                                   &#x27;inference&#x27;,\n",
       "                                                                                   &#x27;inputs&#x27;,\n",
       "                                                                                   &#x27;merge&#x27;,\n",
       "                                                                                   &#x27;mode&#x27;,\n",
       "                                                                                   &#x27;model&#x27;,\n",
       "                                                                                   &#x27;modes&#x27;,\n",
       "                                                                                   &#x27;new&#x27;,\n",
       "                                                                                   &#x27;notebook&#x27;,\n",
       "                                                                                   &#x27;notebook3&#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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" 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;action&#x27;,\n",
       "                                                                                   &#x27;add&#x27;,\n",
       "                                                                                   &#x27;api&#x27;,\n",
       "                                                                                   &#x27;basic&#x27;,\n",
       "                                                                                   &#x27;best&#x27;,\n",
       "                                                                                   &#x27;callingprs&#x27;,\n",
       "                                                                                   &#x27;changed&#x27;,\n",
       "                                                                                   &#x27;choose&#x27;,\n",
       "                                                                                   &#x27;clean&#x27;,\n",
       "                                                                                   &#x27;cleanup&#x27;,\n",
       "                                                                                   &#x27;comment&#x27;,\n",
       "                                                                                   &#x27;condition&#x27;,\n",
       "                                                                                   &#x27;data&#x27;,\n",
       "                                                                                   &#x27;download&#x27;,\n",
       "                                                                                   &#x27;engineering&#x27;,\n",
       "                                                                                   &#x27;entrypoint&#x27;,\n",
       "                                                                                   &#x27;feature&#x27;,\n",
       "                                                                                   &#x27;fix&#x27;,\n",
       "                                                                                   &#x27;fixed&#x27;,\n",
       "                                                                                   &#x27;functionality&#x27;,\n",
       "                                                                                   &#x27;github&#x27;,\n",
       "                                                                                   &#x27;inference&#x27;,\n",
       "                                                                                   &#x27;inputs&#x27;,\n",
       "                                                                                   &#x27;merge&#x27;,\n",
       "                                                                                   &#x27;mode&#x27;,\n",
       "                                                                                   &#x27;model&#x27;,\n",
       "                                                                                   &#x27;modes&#x27;,\n",
       "                                                                                   &#x27;new&#x27;,\n",
       "                                                                                   &#x27;notebook&#x27;,\n",
       "                                                                                   &#x27;notebook3&#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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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;action&#x27;,\n",
       "                                                                  &#x27;add&#x27;, &#x27;api&#x27;,\n",
       "                                                                  &#x27;basic&#x27;,\n",
       "                                                                  &#x27;best&#x27;,\n",
       "                                                                  &#x27;callingprs&#x27;,\n",
       "                                                                  &#x27;changed&#x27;,\n",
       "                                                                  &#x27;choose&#x27;,\n",
       "                                                                  &#x27;clean&#x27;,\n",
       "                                                                  &#x27;cleanup&#x27;,\n",
       "                                                                  &#x27;comment&#x27;,\n",
       "                                                                  &#x27;condition&#x27;,\n",
       "                                                                  &#x27;data&#x27;,\n",
       "                                                                  &#x27;download&#x27;,\n",
       "                                                                  &#x27;engineering&#x27;,\n",
       "                                                                  &#x27;entrypoint&#x27;,\n",
       "                                                                  &#x27;feature&#x27;,\n",
       "                                                                  &#x27;fix&#x27;,\n",
       "                                                                  &#x27;fixed&#x27;,\n",
       "                                                                  &#x27;functionality&#x27;,\n",
       "                                                                  &#x27;github&#x27;,\n",
       "                                                                  &#x27;inference&#x27;,\n",
       "                                                                  &#x27;inputs&#x27;,\n",
       "                                                                  &#x27;merge&#x27;,\n",
       "                                                                  &#x27;mode&#x27;,\n",
       "                                                                  &#x27;model&#x27;,\n",
       "                                                                  &#x27;modes&#x27;,\n",
       "                                                                  &#x27;new&#x27;,\n",
       "                                                                  &#x27;notebook&#x27;,\n",
       "                                                                  &#x27;notebook3&#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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" 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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" 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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" 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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" 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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" 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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" 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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" 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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FileTypeCountTransformer</label><div class=\"sk-toggleable__content\"><pre>FileTypeCountTransformer(file_extensions=[&#x27;.ipynb&#x27;, &#x27;.yaml&#x27;, &#x27;.py&#x27;, &#x27;.sh&#x27;,\n",
       "                                          &#x27;.yml&#x27;, &#x27;.txt&#x27;, &#x27;.md&#x27;, &#x27;LICENSE&#x27;,\n",
       "                                          &#x27;Dockerfile&#x27;, &#x27;Containerfile&#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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" 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-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TitleWordCountTransformer</label><div class=\"sk-toggleable__content\"><pre>TitleWordCountTransformer(words=[&#x27;action&#x27;, &#x27;add&#x27;, &#x27;api&#x27;, &#x27;basic&#x27;, &#x27;best&#x27;,\n",
       "                                 &#x27;callingprs&#x27;, &#x27;changed&#x27;, &#x27;choose&#x27;, &#x27;clean&#x27;,\n",
       "                                 &#x27;cleanup&#x27;, &#x27;comment&#x27;, &#x27;condition&#x27;, &#x27;data&#x27;,\n",
       "                                 &#x27;download&#x27;, &#x27;engineering&#x27;, &#x27;entrypoint&#x27;,\n",
       "                                 &#x27;feature&#x27;, &#x27;fix&#x27;, &#x27;fixed&#x27;, &#x27;functionality&#x27;,\n",
       "                                 &#x27;github&#x27;, &#x27;inference&#x27;, &#x27;inputs&#x27;, &#x27;merge&#x27;,\n",
       "                                 &#x27;mode&#x27;, &#x27;model&#x27;, &#x27;modes&#x27;, &#x27;new&#x27;, &#x27;notebook&#x27;,\n",
       "                                 &#x27;notebook3&#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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" 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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" 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=['action',\n",
       "                                                                                   'add',\n",
       "                                                                                   'api',\n",
       "                                                                                   'basic',\n",
       "                                                                                   'best',\n",
       "                                                                                   'callingprs',\n",
       "                                                                                   'changed',\n",
       "                                                                                   'choose',\n",
       "                                                                                   'clean',\n",
       "                                                                                   'cleanup',\n",
       "                                                                                   'comment',\n",
       "                                                                                   'condition',\n",
       "                                                                                   'data',\n",
       "                                                                                   'download',\n",
       "                                                                                   'engineering',\n",
       "                                                                                   'entrypoint',\n",
       "                                                                                   'feature',\n",
       "                                                                                   'fix',\n",
       "                                                                                   'fixed',\n",
       "                                                                                   'functionality',\n",
       "                                                                                   'github',\n",
       "                                                                                   'inference',\n",
       "                                                                                   'inputs',\n",
       "                                                                                   'merge',\n",
       "                                                                                   'mode',\n",
       "                                                                                   'model',\n",
       "                                                                                   'modes',\n",
       "                                                                                   'new',\n",
       "                                                                                   'notebook',\n",
       "                                                                                   'notebook3', ...]),\n",
       "                                                  ['title'])])),\n",
       "                ('scale', PowerTransformer()), ('rf', GaussianNB())])"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "id": "36484009",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:05.273204Z",
     "iopub.status.busy": "2022-12-07T15:28:05.272918Z",
     "iopub.status.idle": "2022-12-07T15:28:05.329362Z",
     "shell.execute_reply": "2022-12-07T15:28:05.328522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.00      0.00      0.00       0.0\n",
      "           3       0.00      0.00      0.00       1.0\n",
      "           4       0.00      0.00      0.00       1.0\n",
      "           5       0.00      0.00      0.00       0.0\n",
      "           7       0.00      0.00      0.00       2.0\n",
      "           8       0.00      0.00      0.00       0.0\n",
      "\n",
      "    accuracy                           0.00       4.0\n",
      "   macro avg       0.00      0.00      0.00       4.0\n",
      "weighted avg       0.00      0.00      0.00       4.0\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": 53,
   "id": "42cf7c8e-07d8-44df-b4a0-ddcc8982ea37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T15:28:05.333858Z",
     "iopub.status.busy": "2022-12-07T15:28:05.333173Z",
     "iopub.status.idle": "2022-12-07T15:29:02.520394Z",
     "shell.execute_reply": "2022-12-07T15:29:02.519183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/12.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/13.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/14.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/15.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/16.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/18.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/19.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/2.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/25.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/26.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/27.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/28.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/29.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/3.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/30.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/33.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/34.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/35.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/36.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/37.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/38.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/44.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/46.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/47.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/49.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/6.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PRs/8.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/PullRequest.json'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/closed_prs/redhat-ettime-to-merge-toolCLOSED_PR_IDS.parquet'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/features/redhat-ettime-to-merge-toolFILETYPE.parquet'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/features/redhat-ettime-to-merge-tooltitlewords.parquet'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/test-data/X_test.parquet'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/test-data/y_test.parquet'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/ttm-model/label-map'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/ttm-model/model.joblib'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/redhat-et/time-to-merge-tool/ttm_feature_engineered_dataset.parquet'),\n",
       " s3.ObjectSummary(bucket_name='opf-datacatalog', key='oindrilla/srcopsmetrics/bot_knowledge/redhat-et/time-to-merge-tool/PullRequest.json')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 ('ttm2')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "675feb49d8b9f57a6c7838bbe31323f9ec5521e61f1f5118c23cd9b0b9101d34"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}