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    "# Train a field of study (domain) classification of sentences on the [MAG](https://github.com/allenai/scibert/tree/master/data/text_classification/mag) dataset\n",
    "\n",
    "[SciBERT achieves an F1-score of 0.6571 on this dataset.](https://paperswithcode.com/sota/sentence-classification-on-paper-field) \n",
    "This notebook shows that better results can be achieved without even using transformers."
   ]
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   "execution_count": 1,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "84000it [00:09, 8647.60it/s] \n",
      "22399it [00:03, 6368.63it/s]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from typing import Tuple\n",
    "from great_ai.utilities import clean, parallel_map\n",
    "from tqdm.cli import tqdm\n",
    "\n",
    "\n",
    "def preprocess(line: str) -> Tuple[str, str]:\n",
    "    data_point = json.loads(line)\n",
    "\n",
    "    return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"])\n",
    "\n",
    "\n",
    "with open(\"mag/train.txt\", encoding=\"utf-8\") as f:\n",
    "    training_data = list(tqdm(parallel_map(preprocess, f.readlines())))\n",
    "\n",
    "X_train = [d[0] for d in training_data]\n",
    "y_train = [d[1] for d in training_data]\n",
    "\n",
    "\n",
    "with open(\"mag/test.txt\", encoding=\"utf-8\") as f:\n",
    "    test_data = list(tqdm(parallel_map(preprocess, f.readlines())))\n",
    "\n",
    "X_test = [d[0] for d in test_data]\n",
    "y_test = [d[1] for d in test_data]"
   ]
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   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"])\n",
    "px.bar(df, \"domain\", \"count\", width=1200, height=400).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "\n",
    "def create_pipeline() -> Pipeline:\n",
    "    return Pipeline(\n",
    "        steps=[\n",
    "            (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)),\n",
    "            (\"classifier\", MultinomialNB()),\n",
    "        ]\n",
    "    )"
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   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "text": [
      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.227354</td>\n",
       "      <td>0.011710</td>\n",
       "      <td>0.652844</td>\n",
       "      <td>0.040511</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.677283</td>\n",
       "      <td>0.670398</td>\n",
       "      <td>0.665671</td>\n",
       "      <td>0.671118</td>\n",
       "      <td>0.004768</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.279783</td>\n",
       "      <td>0.037969</td>\n",
       "      <td>0.634761</td>\n",
       "      <td>0.068606</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.676970</td>\n",
       "      <td>0.670244</td>\n",
       "      <td>0.666123</td>\n",
       "      <td>0.671112</td>\n",
       "      <td>0.004470</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.332854</td>\n",
       "      <td>0.171405</td>\n",
       "      <td>0.743611</td>\n",
       "      <td>0.109792</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.675678</td>\n",
       "      <td>0.669321</td>\n",
       "      <td>0.665305</td>\n",
       "      <td>0.670101</td>\n",
       "      <td>0.004271</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.253222</td>\n",
       "      <td>0.072134</td>\n",
       "      <td>0.612344</td>\n",
       "      <td>0.016771</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.675569</td>\n",
       "      <td>0.669482</td>\n",
       "      <td>0.665225</td>\n",
       "      <td>0.670092</td>\n",
       "      <td>0.004245</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.472035</td>\n",
       "      <td>0.080849</td>\n",
       "      <td>0.654935</td>\n",
       "      <td>0.044302</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.675225</td>\n",
       "      <td>0.668912</td>\n",
       "      <td>0.665652</td>\n",
       "      <td>0.669930</td>\n",
       "      <td>0.003974</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.380641</td>\n",
       "      <td>0.054306</td>\n",
       "      <td>0.739966</td>\n",
       "      <td>0.053385</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.675264</td>\n",
       "      <td>0.668781</td>\n",
       "      <td>0.665394</td>\n",
       "      <td>0.669813</td>\n",
       "      <td>0.004095</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.284987</td>\n",
       "      <td>0.113903</td>\n",
       "      <td>0.696876</td>\n",
       "      <td>0.003757</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.674199</td>\n",
       "      <td>0.668005</td>\n",
       "      <td>0.664456</td>\n",
       "      <td>0.668887</td>\n",
       "      <td>0.004026</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.291148</td>\n",
       "      <td>0.101837</td>\n",
       "      <td>0.686561</td>\n",
       "      <td>0.083989</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.673996</td>\n",
       "      <td>0.668048</td>\n",
       "      <td>0.664572</td>\n",
       "      <td>0.668872</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.268873</td>\n",
       "      <td>0.042412</td>\n",
       "      <td>0.645649</td>\n",
       "      <td>0.022738</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.656459</td>\n",
       "      <td>0.647433</td>\n",
       "      <td>0.644864</td>\n",
       "      <td>0.649585</td>\n",
       "      <td>0.004972</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.147120</td>\n",
       "      <td>0.009335</td>\n",
       "      <td>0.599006</td>\n",
       "      <td>0.045637</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.656196</td>\n",
       "      <td>0.646960</td>\n",
       "      <td>0.645436</td>\n",
       "      <td>0.649530</td>\n",
       "      <td>0.004754</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.186988</td>\n",
       "      <td>0.057330</td>\n",
       "      <td>0.642233</td>\n",
       "      <td>0.078248</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.654424</td>\n",
       "      <td>0.644690</td>\n",
       "      <td>0.644991</td>\n",
       "      <td>0.648035</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.413231</td>\n",
       "      <td>0.153288</td>\n",
       "      <td>0.765960</td>\n",
       "      <td>0.099535</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.654622</td>\n",
       "      <td>0.644901</td>\n",
       "      <td>0.644425</td>\n",
       "      <td>0.647983</td>\n",
       "      <td>0.004699</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.193013</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>0.691768</td>\n",
       "      <td>0.027448</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.655125</td>\n",
       "      <td>0.645586</td>\n",
       "      <td>0.643166</td>\n",
       "      <td>0.647959</td>\n",
       "      <td>0.005163</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.043618</td>\n",
       "      <td>0.098733</td>\n",
       "      <td>0.450375</td>\n",
       "      <td>0.061660</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.654254</td>\n",
       "      <td>0.645409</td>\n",
       "      <td>0.643514</td>\n",
       "      <td>0.647726</td>\n",
       "      <td>0.004680</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.301459</td>\n",
       "      <td>0.062143</td>\n",
       "      <td>0.660748</td>\n",
       "      <td>0.056054</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.652727</td>\n",
       "      <td>0.643691</td>\n",
       "      <td>0.642749</td>\n",
       "      <td>0.646389</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.433934</td>\n",
       "      <td>0.155240</td>\n",
       "      <td>0.636608</td>\n",
       "      <td>0.024064</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.652290</td>\n",
       "      <td>0.642975</td>\n",
       "      <td>0.643421</td>\n",
       "      <td>0.646229</td>\n",
       "      <td>0.004290</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.325535</td>\n",
       "      <td>0.073539</td>\n",
       "      <td>0.672542</td>\n",
       "      <td>0.085835</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.05</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.587097</td>\n",
       "      <td>0.577411</td>\n",
       "      <td>0.580170</td>\n",
       "      <td>0.581560</td>\n",
       "      <td>0.004075</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.237677</td>\n",
       "      <td>0.070063</td>\n",
       "      <td>0.651091</td>\n",
       "      <td>0.102538</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.585713</td>\n",
       "      <td>0.577197</td>\n",
       "      <td>0.580763</td>\n",
       "      <td>0.581224</td>\n",
       "      <td>0.003492</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.394873</td>\n",
       "      <td>0.105286</td>\n",
       "      <td>0.637073</td>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.05</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.586346</td>\n",
       "      <td>0.576987</td>\n",
       "      <td>0.579852</td>\n",
       "      <td>0.581062</td>\n",
       "      <td>0.003915</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.270732</td>\n",
       "      <td>0.013414</td>\n",
       "      <td>0.620984</td>\n",
       "      <td>0.025393</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.585634</td>\n",
       "      <td>0.576217</td>\n",
       "      <td>0.580320</td>\n",
       "      <td>0.580723</td>\n",
       "      <td>0.003855</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.177219</td>\n",
       "      <td>0.039633</td>\n",
       "      <td>0.586308</td>\n",
       "      <td>0.044247</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.581096</td>\n",
       "      <td>0.572833</td>\n",
       "      <td>0.576280</td>\n",
       "      <td>0.576736</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.165583</td>\n",
       "      <td>0.046999</td>\n",
       "      <td>0.603675</td>\n",
       "      <td>0.031774</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.580841</td>\n",
       "      <td>0.572391</td>\n",
       "      <td>0.575731</td>\n",
       "      <td>0.576321</td>\n",
       "      <td>0.003475</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.907388</td>\n",
       "      <td>0.121543</td>\n",
       "      <td>0.354655</td>\n",
       "      <td>0.023775</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
       "      <td>0.579769</td>\n",
       "      <td>0.572373</td>\n",
       "      <td>0.576699</td>\n",
       "      <td>0.576280</td>\n",
       "      <td>0.003034</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.210845</td>\n",
       "      <td>0.041339</td>\n",
       "      <td>0.653606</td>\n",
       "      <td>0.028117</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100</td>\n",
       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
       "      <td>0.579743</td>\n",
       "      <td>0.571607</td>\n",
       "      <td>0.576352</td>\n",
       "      <td>0.575901</td>\n",
       "      <td>0.003337</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "12       1.227354      0.011710         0.652844        0.040511   \n",
       "15       1.279783      0.037969         0.634761        0.068606   \n",
       "18       1.332854      0.171405         0.743611        0.109792   \n",
       "21       1.253222      0.072134         0.612344        0.016771   \n",
       "3        1.472035      0.080849         0.654935        0.044302   \n",
       "0        1.380641      0.054306         0.739966        0.053385   \n",
       "9        1.284987      0.113903         0.696876        0.003757   \n",
       "6        1.291148      0.101837         0.686561        0.083989   \n",
       "13       1.268873      0.042412         0.645649        0.022738   \n",
       "16       1.147120      0.009335         0.599006        0.045637   \n",
       "4        1.186988      0.057330         0.642233        0.078248   \n",
       "1        1.413231      0.153288         0.765960        0.099535   \n",
       "19       1.193013      0.079000         0.691768        0.027448   \n",
       "22       1.043618      0.098733         0.450375        0.061660   \n",
       "7        1.301459      0.062143         0.660748        0.056054   \n",
       "10       1.433934      0.155240         0.636608        0.024064   \n",
       "14       1.325535      0.073539         0.672542        0.085835   \n",
       "17       1.237677      0.070063         0.651091        0.102538   \n",
       "2        1.394873      0.105286         0.637073        0.041708   \n",
       "5        1.270732      0.013414         0.620984        0.025393   \n",
       "20       1.177219      0.039633         0.586308        0.044247   \n",
       "8        1.165583      0.046999         0.603675        0.031774   \n",
       "23       0.907388      0.121543         0.354655        0.023775   \n",
       "11       1.210845      0.041339         0.653606        0.028117   \n",
       "\n",
       "   param_classifier__alpha param_classifier__fit_prior  \\\n",
       "12                       1                        True   \n",
       "15                       1                        True   \n",
       "18                       1                       False   \n",
       "21                       1                       False   \n",
       "3                      0.5                        True   \n",
       "0                      0.5                        True   \n",
       "9                      0.5                       False   \n",
       "6                      0.5                       False   \n",
       "13                       1                        True   \n",
       "16                       1                        True   \n",
       "4                      0.5                        True   \n",
       "1                      0.5                        True   \n",
       "19                       1                       False   \n",
       "22                       1                       False   \n",
       "7                      0.5                       False   \n",
       "10                     0.5                       False   \n",
       "14                       1                        True   \n",
       "17                       1                        True   \n",
       "2                      0.5                        True   \n",
       "5                      0.5                        True   \n",
       "20                       1                       False   \n",
       "8                      0.5                       False   \n",
       "23                       1                       False   \n",
       "11                     0.5                       False   \n",
       "\n",
       "   param_vectorizer__max_df param_vectorizer__min_df  \\\n",
       "12                     0.05                        5   \n",
       "15                      0.1                        5   \n",
       "18                     0.05                        5   \n",
       "21                      0.1                        5   \n",
       "3                       0.1                        5   \n",
       "0                      0.05                        5   \n",
       "9                       0.1                        5   \n",
       "6                      0.05                        5   \n",
       "13                     0.05                       20   \n",
       "16                      0.1                       20   \n",
       "4                       0.1                       20   \n",
       "1                      0.05                       20   \n",
       "19                     0.05                       20   \n",
       "22                      0.1                       20   \n",
       "7                      0.05                       20   \n",
       "10                      0.1                       20   \n",
       "14                     0.05                      100   \n",
       "17                      0.1                      100   \n",
       "2                      0.05                      100   \n",
       "5                       0.1                      100   \n",
       "20                     0.05                      100   \n",
       "8                      0.05                      100   \n",
       "23                      0.1                      100   \n",
       "11                      0.1                      100   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "12  {'classifier__alpha': 1, 'classifier__fit_prio...           0.677283   \n",
       "15  {'classifier__alpha': 1, 'classifier__fit_prio...           0.676970   \n",
       "18  {'classifier__alpha': 1, 'classifier__fit_prio...           0.675678   \n",
       "21  {'classifier__alpha': 1, 'classifier__fit_prio...           0.675569   \n",
       "3   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.675225   \n",
       "0   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.675264   \n",
       "9   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.674199   \n",
       "6   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.673996   \n",
       "13  {'classifier__alpha': 1, 'classifier__fit_prio...           0.656459   \n",
       "16  {'classifier__alpha': 1, 'classifier__fit_prio...           0.656196   \n",
       "4   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.654424   \n",
       "1   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.654622   \n",
       "19  {'classifier__alpha': 1, 'classifier__fit_prio...           0.655125   \n",
       "22  {'classifier__alpha': 1, 'classifier__fit_prio...           0.654254   \n",
       "7   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.652727   \n",
       "10  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.652290   \n",
       "14  {'classifier__alpha': 1, 'classifier__fit_prio...           0.587097   \n",
       "17  {'classifier__alpha': 1, 'classifier__fit_prio...           0.585713   \n",
       "2   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.586346   \n",
       "5   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.585634   \n",
       "20  {'classifier__alpha': 1, 'classifier__fit_prio...           0.581096   \n",
       "8   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.580841   \n",
       "23  {'classifier__alpha': 1, 'classifier__fit_prio...           0.579769   \n",
       "11  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.579743   \n",
       "\n",
       "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "12           0.670398           0.665671         0.671118        0.004768   \n",
       "15           0.670244           0.666123         0.671112        0.004470   \n",
       "18           0.669321           0.665305         0.670101        0.004271   \n",
       "21           0.669482           0.665225         0.670092        0.004245   \n",
       "3            0.668912           0.665652         0.669930        0.003974   \n",
       "0            0.668781           0.665394         0.669813        0.004095   \n",
       "9            0.668005           0.664456         0.668887        0.004026   \n",
       "6            0.668048           0.664572         0.668872        0.003891   \n",
       "13           0.647433           0.644864         0.649585        0.004972   \n",
       "16           0.646960           0.645436         0.649530        0.004754   \n",
       "4            0.644690           0.644991         0.648035        0.004519   \n",
       "1            0.644901           0.644425         0.647983        0.004699   \n",
       "19           0.645586           0.643166         0.647959        0.005163   \n",
       "22           0.645409           0.643514         0.647726        0.004680   \n",
       "7            0.643691           0.642749         0.646389        0.004498   \n",
       "10           0.642975           0.643421         0.646229        0.004290   \n",
       "14           0.577411           0.580170         0.581560        0.004075   \n",
       "17           0.577197           0.580763         0.581224        0.003492   \n",
       "2            0.576987           0.579852         0.581062        0.003915   \n",
       "5            0.576217           0.580320         0.580723        0.003855   \n",
       "20           0.572833           0.576280         0.576736        0.003389   \n",
       "8            0.572391           0.575731         0.576321        0.003475   \n",
       "23           0.572373           0.576699         0.576280        0.003034   \n",
       "11           0.571607           0.576352         0.575901        0.003337   \n",
       "\n",
       "    rank_test_score  \n",
       "12                1  \n",
       "15                2  \n",
       "18                3  \n",
       "21                4  \n",
       "3                 5  \n",
       "0                 6  \n",
       "9                 7  \n",
       "6                 8  \n",
       "13                9  \n",
       "16               10  \n",
       "4                11  \n",
       "1                12  \n",
       "19               13  \n",
       "22               14  \n",
       "7                15  \n",
       "10               16  \n",
       "14               17  \n",
       "17               18  \n",
       "2                19  \n",
       "5                20  \n",
       "20               21  \n",
       "8                22  \n",
       "23               23  \n",
       "11               24  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "optimisation_pipeline = GridSearchCV(\n",
    "    create_pipeline(),\n",
    "    {\n",
    "        \"vectorizer__min_df\": [5, 20, 100],\n",
    "        \"vectorizer__max_df\": [0.05, 0.1],\n",
    "        \"classifier__alpha\": [0.5, 1],\n",
    "        \"classifier__fit_prior\": [True, False],\n",
    "    },\n",
    "    scoring=\"f1_macro\",\n",
    "    cv=3,\n",
    "    n_jobs=-1,\n",
    "    verbose=1,\n",
    ")\n",
    "optimisation_pipeline.fit(X_train, y_train)\n",
    "\n",
    "results = pd.DataFrame(optimisation_pipeline.cv_results_)\n",
    "results.sort_values(\"rank_test_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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;vectorizer&#x27;,\n",
       "                 TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)),\n",
       "                (&#x27;classifier&#x27;, MultinomialNB(alpha=1))])</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;vectorizer&#x27;,\n",
       "                 TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)),\n",
       "                (&#x27;classifier&#x27;, MultinomialNB(alpha=1))])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=1)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vectorizer',\n",
       "                 TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)),\n",
       "                ('classifier', MultinomialNB(alpha=1))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import set_config\n",
    "\n",
    "set_config(display=\"diagram\")\n",
    "\n",
    "classifier = create_pipeline()\n",
    "classifier.set_params(**optimisation_pipeline.best_params_)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    business     0.6412    0.7258    0.6808      3198\n",
      "   economics     0.6635    0.7146    0.6881      3189\n",
      "   geography     0.7461    0.6791    0.7111      3207\n",
      "    medicine     0.8806    0.9021    0.8912      3187\n",
      "    politics     0.5563    0.5895    0.5724      3169\n",
      "  psychology     0.7654    0.6811    0.7208      3252\n",
      "   sociology     0.5165    0.4698    0.4921      3197\n",
      "\n",
      "    accuracy                         0.6803     22399\n",
      "   macro avg     0.6814    0.6803    0.6795     22399\n",
      "weighted avg     0.6817    0.6803    0.6796     22399\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y_predicted = classifier.predict(X_test)\n",
    "\n",
    "%matplotlib inline\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\n",
    "\n",
    "print(metrics.classification_report(y_test, y_predicted, digits=4))\n",
    "metrics.ConfusionMatrixDisplay.from_predictions(\n",
    "    y_true=y_test,\n",
    "    y_pred=y_predicted,\n",
    "    xticks_rotation=\"vertical\",\n",
    "    normalize=\"pred\",\n",
    "    values_format=\".2f\",\n",
    ")\n",
    "None"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('.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": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}