{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Domain classifier from the [semantic scholar dataset](https://api.semanticscholar.org/corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "from sklearn import metrics, set_config\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import ComplementNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from good_ai.utilities.clean import clean\n",
    "from good_ai.utilities.parallel_map import parallel_map\n",
    "from good_ai.utilities.language import is_english, predict_language\n",
    "from good_ai import save_model, set_default_config, LargeFile\n",
    "\n",
    "from preprocess import preprocess\n",
    "from config import model_key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PREFIX = \"domain-\"\n",
    "DATASET_KEY = \"data\"\n",
    "MAX_FILE_COUNT = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;39m2022-04-10 09:40:33,153 |     INFO | Running in production mode ✅\u001b[0m\n",
      "\u001b[38;5;226m2022-04-10 09:40:33,156 |  WARNING | The selected persistence driver (TinyDbDriver) is not threadsafe\u001b[0m\n",
      "\u001b[38;5;39m2022-04-10 09:40:33,157 |     INFO | Defaults: configured ✅\u001b[0m\n",
      "\u001b[38;5;39m2022-04-10 09:40:33,348 |     INFO | Fetching online versions of data\u001b[0m\n",
      "\u001b[38;5;39m2022-04-10 09:40:33,743 |     INFO | Found versions: [0, 1]\u001b[0m\n",
      "\u001b[38;5;39m2022-04-10 09:40:33,744 |     INFO | Lastest version of data-1 is 1\u001b[0m\n",
      "\u001b[38;5;39m2022-04-10 09:40:33,745 |     INFO | File data-1 found in cache\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "set_default_config()\n",
    "corpus_path = LargeFile(DATASET_KEY).get()\n",
    "\n",
    "set_config(display=\"diagram\")\n",
    "plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
    "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
    "plt.rcParams[\"font.size\"] = 12\n",
    "plt.rcParams[\"axes.xmargin\"] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,7093)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,811)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,196)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,561)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,1370)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,215)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,330)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,603)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected closing environment: 'document' @(26,0)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected closing math mode: '\\]' @(1,533)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,713)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,6244)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,6549)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,997)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,549)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected closing math mode: '\\)' @(1,674)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,748)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,70)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,128)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,112)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,175)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,321)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected closing math mode: '\\]' @(1,700)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,1180)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,754)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,487)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,390)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,942)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,1471)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,2235)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,27)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,1176)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,2502)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,2545)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,2856)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,84)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,257)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,391)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,648)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,346)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,716)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,782)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,958)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,964)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,1017)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,1287)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,104)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,119)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,1139)\n",
      "INFO:good_ai.utilities.external.pylatexenc.latexwalker:Ignoring parse error (tolerant parsing mode): Unexpected mismatching closing brace: '}' @(1,366)\n",
      "100%|██████████| 5/5 [34:00<00:00, 408.07s/it]   \n"
     ]
    }
   ],
   "source": [
    "def clean_file(p: Path) -> None:\n",
    "    try:\n",
    "        processed_path = p.with_name(f\"{PREFIX}{p.stem}{p.suffix}\")\n",
    "\n",
    "        if processed_path.exists():\n",
    "            return\n",
    "\n",
    "        with open(p) as f:\n",
    "            content = json.load(f)\n",
    "\n",
    "        result = {\n",
    "            preprocess(\n",
    "                clean(f'{c[\"title\"]} {c[\"abstract\"]}', convert_to_ascii=True)\n",
    "            ): c[\"domain\"]\n",
    "            for c in content\n",
    "            if (\n",
    "                c[\"domain\"]\n",
    "                and c[\"abstract\"]\n",
    "                and is_english(predict_language(c[\"abstract\"]))\n",
    "            )\n",
    "        }\n",
    "\n",
    "        with open(processed_path, \"w\") as f:\n",
    "            json.dump(result, f)\n",
    "    except Exception as e:\n",
    "        print(f\"Error ({e}) processing {p}\")\n",
    "\n",
    "\n",
    "parallel_map(\n",
    "    clean_file,\n",
    "    list(corpus_path.glob(\"s2-corpus-*.json\"))[:MAX_FILE_COUNT],\n",
    "    chunk_size=1,\n",
    ")\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5 files\n",
      "Found 76862 documents\n"
     ]
    }
   ],
   "source": [
    "corpora = list(corpus_path.glob(f\"{PREFIX}*.json\"))[:MAX_FILE_COUNT]\n",
    "print(f\"Found {len(corpora)} files\")\n",
    "\n",
    "data = []\n",
    "for p in corpora:\n",
    "    with open(p) as f:\n",
    "        data.extend(json.load(f).items())\n",
    "\n",
    "print(f\"Found {len(data)} documents\")\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    [d[0] for d in data], [d[1] for d in data], test_size=0.1, random_state=1\n",
    ")\n",
    "\n",
    "X_train = [x for x, y in zip(X_train, y_train) for domain in y]\n",
    "y_train = [domain for x, y in zip(X_train, y_train) for domain in y]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 72 candidates, totalling 216 fits\n"
     ]
    },
    {
     "data": {
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       "      <td>0.054138</td>\n",
       "      <td>0.1</td>\n",
       "      <td>True</td>\n",
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       "      <td>7.424036</td>\n",
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       "      <td>0.103824</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
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       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
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       "      <td>0.469357</td>\n",
       "      <td>0.002484</td>\n",
       "      <td>67</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.288773</td>\n",
       "      <td>0.153564</td>\n",
       "      <td>3.557420</td>\n",
       "      <td>0.152864</td>\n",
       "      <td>0.1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>{'classifier__alpha': 0.1, 'classifier__fit_pr...</td>\n",
       "      <td>0.470711</td>\n",
       "      <td>0.469985</td>\n",
       "      <td>0.467028</td>\n",
       "      <td>0.469241</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>627.484071</td>\n",
       "      <td>438.167044</td>\n",
       "      <td>3.580379</td>\n",
       "      <td>0.105862</td>\n",
       "      <td>0.1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>{'classifier__alpha': 0.1, 'classifier__fit_pr...</td>\n",
       "      <td>0.470711</td>\n",
       "      <td>0.469985</td>\n",
       "      <td>0.467028</td>\n",
       "      <td>0.469241</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>341.006728</td>\n",
       "      <td>471.608243</td>\n",
       "      <td>3.780825</td>\n",
       "      <td>0.007465</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.470638</td>\n",
       "      <td>0.469516</td>\n",
       "      <td>0.464513</td>\n",
       "      <td>0.468223</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>677.439482</td>\n",
       "      <td>473.755547</td>\n",
       "      <td>4.152508</td>\n",
       "      <td>0.296995</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.470638</td>\n",
       "      <td>0.469516</td>\n",
       "      <td>0.464513</td>\n",
       "      <td>0.468223</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "37     353.990156    490.126502         3.731860        0.097140   \n",
       "25     671.987925    469.392562       335.414978      468.916068   \n",
       "1      334.102561    460.200041         4.549770        0.351197   \n",
       "13       6.975998      0.301279         3.720828        0.145939   \n",
       "7        8.587186      0.258101         3.718949        0.054138   \n",
       "..            ...           ...              ...             ...   \n",
       "34       7.424036      0.104495         3.518187        0.103824   \n",
       "22       7.288773      0.153564         3.557420        0.152864   \n",
       "10     627.484071    438.167044         3.580379        0.105862   \n",
       "58     341.006728    471.608243         3.780825        0.007465   \n",
       "70     677.439482    473.755547         4.152508        0.296995   \n",
       "\n",
       "   param_classifier__alpha param_classifier__fit_prior  \\\n",
       "37                     0.5                       False   \n",
       "25                     0.5                        True   \n",
       "1                      0.1                        True   \n",
       "13                     0.1                       False   \n",
       "7                      0.1                        True   \n",
       "..                     ...                         ...   \n",
       "34                     0.5                        True   \n",
       "22                     0.1                       False   \n",
       "10                     0.1                        True   \n",
       "58                       1                        True   \n",
       "70                       1                       False   \n",
       "\n",
       "   param_vectorizer__max_df param_vectorizer__min_df  \\\n",
       "37                      0.1                        5   \n",
       "25                      0.1                        5   \n",
       "1                       0.1                        5   \n",
       "13                      0.1                        5   \n",
       "7                       0.3                        5   \n",
       "..                      ...                      ...   \n",
       "34                      0.3                       30   \n",
       "22                      0.3                       30   \n",
       "10                      0.3                       30   \n",
       "58                      0.3                       30   \n",
       "70                      0.3                       30   \n",
       "\n",
       "   param_vectorizer__sublinear_tf  \\\n",
       "37                          False   \n",
       "25                          False   \n",
       "1                           False   \n",
       "13                          False   \n",
       "7                           False   \n",
       "..                            ...   \n",
       "34                           True   \n",
       "22                           True   \n",
       "10                           True   \n",
       "58                           True   \n",
       "70                           True   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "37  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.495838   \n",
       "25  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.495838   \n",
       "1   {'classifier__alpha': 0.1, 'classifier__fit_pr...           0.495637   \n",
       "13  {'classifier__alpha': 0.1, 'classifier__fit_pr...           0.495637   \n",
       "7   {'classifier__alpha': 0.1, 'classifier__fit_pr...           0.495246   \n",
       "..                                                ...                ...   \n",
       "34  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.471803   \n",
       "22  {'classifier__alpha': 0.1, 'classifier__fit_pr...           0.470711   \n",
       "10  {'classifier__alpha': 0.1, 'classifier__fit_pr...           0.470711   \n",
       "58  {'classifier__alpha': 1, 'classifier__fit_prio...           0.470638   \n",
       "70  {'classifier__alpha': 1, 'classifier__fit_prio...           0.470638   \n",
       "\n",
       "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "37           0.499754           0.501146         0.498912        0.002247   \n",
       "25           0.499754           0.501146         0.498912        0.002247   \n",
       "1            0.499200           0.501269         0.498702        0.002326   \n",
       "13           0.499200           0.501269         0.498702        0.002326   \n",
       "7            0.498718           0.500819         0.498261        0.002298   \n",
       "..                ...                ...              ...             ...   \n",
       "34           0.470317           0.465951         0.469357        0.002484   \n",
       "22           0.469985           0.467028         0.469241        0.001593   \n",
       "10           0.469985           0.467028         0.469241        0.001593   \n",
       "58           0.469516           0.464513         0.468223        0.002663   \n",
       "70           0.469516           0.464513         0.468223        0.002663   \n",
       "\n",
       "    rank_test_score  \n",
       "37                1  \n",
       "25                1  \n",
       "1                 3  \n",
       "13                3  \n",
       "7                 5  \n",
       "..              ...  \n",
       "34               67  \n",
       "22               69  \n",
       "10               69  \n",
       "58               71  \n",
       "70               71  \n",
       "\n",
       "[72 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = GridSearchCV(\n",
    "    Pipeline(steps=[(\"vectorizer\", TfidfVectorizer()), (\"classifier\", ComplementNB())]),\n",
    "    {\n",
    "        \"vectorizer__max_df\": [0.1, 0.3],\n",
    "        \"vectorizer__min_df\": [5, 10, 30],\n",
    "        \"vectorizer__sublinear_tf\": [True, False],\n",
    "        \"classifier__alpha\": [0.1, 0.5, 1],\n",
    "        \"classifier__fit_prior\": [True, False],\n",
    "    },\n",
    "    scoring=\"f1_macro\",\n",
    "    cv=3,\n",
    "    n_jobs=4,\n",
    "    verbose=1,\n",
    ")\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "results = pd.DataFrame(classifier.cv_results_)\n",
    "results.sort_values(\"rank_test_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 {color: black;background-color: white;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 pre{padding: 0;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-toggleable {background-color: white;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 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-1fa5b40f-2f8e-4546-b20e-71b11555a110 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-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-estimator:hover {background-color: #d4ebff;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-item {z-index: 1;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-parallel-item:only-child::after {width: 0;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 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;position: relative;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-1fa5b40f-2f8e-4546-b20e-71b11555a110 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-1fa5b40f-2f8e-4546-b20e-71b11555a110 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-1fa5b40f-2f8e-4546-b20e-71b11555a110\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
       "                 TfidfVectorizer(max_df=0.1, min_df=10, token_pattern=&#x27;[^ ]+&#x27;)),\n",
       "                (&#x27;classifier&#x27;, ComplementNB(alpha=0.5, fit_prior=False))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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=\"bac440fb-cd3b-4b65-a890-81be3b816d3e\" type=\"checkbox\" ><label for=\"bac440fb-cd3b-4b65-a890-81be3b816d3e\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
       "                 TfidfVectorizer(max_df=0.1, min_df=10, token_pattern=&#x27;[^ ]+&#x27;)),\n",
       "                (&#x27;classifier&#x27;, ComplementNB(alpha=0.5, fit_prior=False))])</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=\"946d8659-da99-4c2e-9327-1c019371e267\" type=\"checkbox\" ><label for=\"946d8659-da99-4c2e-9327-1c019371e267\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_df=0.1, min_df=10, token_pattern=&#x27;[^ ]+&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8d7e4863-30f6-4d30-bd87-8b569f71b293\" type=\"checkbox\" ><label for=\"8d7e4863-30f6-4d30-bd87-8b569f71b293\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ComplementNB</label><div class=\"sk-toggleable__content\"><pre>ComplementNB(alpha=0.5, fit_prior=False)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vectorizer',\n",
       "                 TfidfVectorizer(max_df=0.1, min_df=10, token_pattern='[^ ]+')),\n",
       "                ('classifier', ComplementNB(alpha=0.5, fit_prior=False))])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = Pipeline(\n",
    "    steps=[\n",
    "        (\"vectorizer\", TfidfVectorizer(min_df=10, max_df=0.1, token_pattern=r\"[^ ]+\")),\n",
    "        (\"classifier\", ComplementNB(alpha=0.5, fit_prior=False)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       precision    recall  f1-score   support\n",
      "\n",
      "                  Art       0.44      0.44      0.44        80\n",
      "              Biology       0.75      0.72      0.73       541\n",
      "             Business       0.45      0.52      0.48       174\n",
      "            Chemistry       0.77      0.71      0.74       728\n",
      "     Computer Science       0.65      0.87      0.75       851\n",
      "            Economics       0.65      0.56      0.60       171\n",
      "          Engineering       0.60      0.36      0.45       473\n",
      "Environmental Science       0.69      0.43      0.53       177\n",
      "            Geography       0.51      0.23      0.32       165\n",
      "              Geology       0.72      0.63      0.67       123\n",
      "              History       0.41      0.19      0.26        73\n",
      "    Materials Science       0.68      0.86      0.76       656\n",
      "          Mathematics       0.77      0.59      0.67       307\n",
      "             Medicine       0.88      0.92      0.90      2033\n",
      "           Philosophy       0.64      0.12      0.21        56\n",
      "              Physics       0.79      0.66      0.72       389\n",
      "    Political Science       0.46      0.54      0.50       197\n",
      "           Psychology       0.50      0.63      0.56       295\n",
      "            Sociology       0.37      0.38      0.38       198\n",
      "\n",
      "             accuracy                           0.71      7687\n",
      "            macro avg       0.62      0.55      0.56      7687\n",
      "         weighted avg       0.71      0.71      0.70      7687\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = classifier.predict(X_test)\n",
    "\n",
    "y_test_aligned = [p if p in y else y[0] for p, y in zip(predicted, y_test)]\n",
    "\n",
    "print(metrics.classification_report(y_test_aligned, predicted))\n",
    "metrics.ConfusionMatrixDisplay.from_predictions(\n",
    "    y_true=y_test_aligned,\n",
    "    y_pred=predicted,\n",
    "    xticks_rotation=\"vertical\",\n",
    "    normalize=\"pred\",\n",
    "    values_format=\".2f\",\n",
    ")\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:open_s3:Fetching online versions of small-domain-prediction-v2\n",
      "INFO:open_s3:No versions found\n",
      "INFO:open_s3:Copying file for small-domain-prediction-v2-0\n",
      "INFO:open_s3:Compressing small-domain-prediction-v2-0\n",
      "INFO:open_s3:Uploading small-domain-prediction-v2-0 to S3 from /var/folders/5g/yd_5_wg548q0yb4lnvg0y3q40000gn/T/large-file-pab4oxsr\n",
      "INFO:open_s3:Uploading small-domain-prediction-v2-0.tar.gz 0.26/7.64 MB (3.4%)\n",
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      "INFO:open_s3:Uploading small-domain-prediction-v2-0.tar.gz 7.64/7.64 MB (100.0%)\n",
      "INFO:models:Model small-domain-prediction-v2 uploaded with version 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_model(classifier, key=model_key, keep_last_n=1)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b1d2807ee4cacf26d55377aa5a9287d91820f3fc1d1a8b3c12cb9914c0c55aa2"
  },
  "kernelspec": {
   "display_name": "Python 3.9.2 ('.env': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}