job_classification_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T10:45:20.276212Z",
     "start_time": "2020-09-14T10:45:20.269812Z"
    }
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "from gensim.parsing.preprocessing import strip_tags\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "params = {'figure.figsize': (15, 10),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large'}\n",
    "\n",
    "pylab.rcParams.update(params)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T10:01:12.847344Z",
     "start_time": "2020-09-14T10:01:12.838213Z"
    }
   },
   "outputs": [],
   "source": [
    "class SpacyTokenizer():\n",
    "    '''\n",
    "    Custom tokenizer\n",
    "    '''\n",
    "    def __init__(self, model='en_core_web_sm'):\n",
    "        self.model = model\n",
    "        self.nlp = spacy.load(model)\n",
    "        self.spacy_tokenizer = self.nlp.Defaults.create_tokenizer(self.nlp)\n",
    "        self.stop_words = self.nlp.Defaults.stop_words\n",
    "        \n",
    "    def tokenizer(self, input_text: str):\n",
    "        '''\n",
    "        Preprocess and split text into tokens.\n",
    "        \n",
    "        Parameters:\n",
    "        \n",
    "        input_text: str\n",
    "            Input text.\n",
    "            \n",
    "        Returns:\n",
    "            np.ndarray: list of tokens\n",
    "        '''\n",
    "        text = strip_tags(input_text.lower())\n",
    "        text = re.sub(r\"[^A-Za-z]\", \" \", text)\n",
    "\n",
    "        spacy_tokens = self.spacy_tokenizer(text)\n",
    "        tokens = [token.lemma_.strip() for token in spacy_tokens \n",
    "                  if (len(token.lemma_.strip()) > 1) and not token.lemma_.strip() in self.stop_words]\n",
    "\n",
    "        return tokens  \n",
    "    \n",
    "def get_top_n_words(corpus: np.ndarray, n: int=5, ngram_range: tuple=(1,3)):\n",
    "    '''\n",
    "    Get top common n-grams in corpus.\n",
    "    \n",
    "    Parameters:\n",
    "    corpus: np.ndarray\n",
    "        Array of texts.\n",
    "    n: int (default: 5)\n",
    "        Number of top n-grams to return.\n",
    "    ngram_range: tuple (default: (1,3))\n",
    "        Range of n-grams.\n",
    "        \n",
    "    Returns:\n",
    "        np.ndarray: list of top common n-grams.\n",
    "        \n",
    "    '''\n",
    "    \n",
    "    tf_idf_vec = TfidfVectorizer(ngram_range=ngram_range, max_features=2000, tokenizer=SpacyTokenizer().tokenizer)\n",
    "    tf_idf_vec.fit(corpus)\n",
    "\n",
    "    bag_of_words = tf_idf_vec.transform(corpus)\n",
    "\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in     \n",
    "                  tf_idf_vec.vocabulary_.items()]\n",
    "    words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    \n",
    "    return words_freq[:n]\n",
    "\n",
    "def tune_pipeline(pipeline: sklearn.pipeline, parameters: np.ndarray, X: pd.Series, y: pd.Series, n_splits: int=2):\n",
    "    '''\n",
    "    Find the best parameters for pipeline.\n",
    "    \n",
    "    Parameters:\n",
    "    pipeline: sklearn.pipeline\n",
    "        Pipeline for tunning.\n",
    "    parameters: np.ndarray\n",
    "        List of parameters are used for tunning.\n",
    "    X: pd.Series\n",
    "        Training vector.\n",
    "    y: pd.Series\n",
    "        Target vector relative to X.\n",
    "    n_splits: int (default: 2)\n",
    "        Number of re-shuffling and splitting iterations.\n",
    "    \n",
    "    Returns:\n",
    "        sklearn.pipeline: tunned pipeline.\n",
    "    '''\n",
    "    grid_search = GridSearchCV(pipeline, parameters, \n",
    "        cv=StratifiedShuffleSplit(n_splits=2, test_size=0.2, random_state=42),\n",
    "        n_jobs=-1, verbose=10)\n",
    "    \n",
    "    grid_search.fit(X, y)\n",
    "    \n",
    "    return grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T13:35:18.710691Z",
     "start_time": "2020-09-10T13:35:18.091322Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size: (72292, 4)\n"
     ]
    }
   ],
   "source": [
    "#If your dataset is too big you may use spark to read csv file. Look down\n",
    "dataset_df = pd.read_csv('./dataset/kaggle_top30.csv')\n",
    "print(f'Dataset size: {dataset_df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pyspark = SparkSession.builder.appName('clf').config('spark.sql.execution.arrow.pyspark.enabled', \"true\")\n",
    "# pyspark = pyspark.getOrCreate()\n",
    "# dataset_df = pyspark.read.csv('./dataset/kaggle_top30.csv', header=True)\n",
    "# dataset_df = dataset_df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_df = dataset_df.drop_duplicates('Description')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T13:35:38.477845Z",
     "start_time": "2020-09-10T13:35:38.465969Z"
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Query</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Administrative Assistant</td>\n",
       "      <td>This Administrative Assistant position is resp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>Administrative Assistant</td>\n",
       "      <td>ADMINISTRATIVE ASSISTANT Part Time The West Or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>Administrative Assistant</td>\n",
       "      <td>Administrative Assistant -  In Bus 26yrs Fashi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Are you ready for something new? Are you tired...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>Customer Service Representative</td>\n",
       "      <td>Superior Staff Resources is currently seeking ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1                            Query  \\\n",
       "0          10            10         Administrative Assistant   \n",
       "1          35            35         Administrative Assistant   \n",
       "2         140           140         Administrative Assistant   \n",
       "3         214           214             Sales Representative   \n",
       "4         350           350  Customer Service Representative   \n",
       "\n",
       "                                         Description  \n",
       "0  This Administrative Assistant position is resp...  \n",
       "1  ADMINISTRATIVE ASSISTANT Part Time The West Or...  \n",
       "2  Administrative Assistant -  In Bus 26yrs Fashi...  \n",
       "3  Are you ready for something new? Are you tired...  \n",
       "4  Superior Staff Resources is currently seeking ...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T07:52:33.373795Z",
     "start_time": "2020-09-14T07:52:33.336332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      31992\n",
       "Unnamed: 0.1    31992\n",
       "Query           31992\n",
       "Description     31992\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T08:24:24.596525Z",
     "start_time": "2020-09-14T08:24:24.378795Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_df['Query'].value_counts().plot(kind='bar')\n",
    "plt.title('Job descriptions per job category')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in dataset_df['Query'].unique():\n",
    "    if len(dataset_df[dataset_df['Query'] == x]) < 50:\n",
    "        dataset_df = dataset_df[dataset_df['Query'] != x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_df['Query'].value_counts().plot(kind='bar')\n",
    "plt.title('Job descriptions per job category')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T13:35:40.664868Z",
     "start_time": "2020-09-10T13:35:40.654720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Administrative Assistant            4017\n",
       "Customer Service Representative     3294\n",
       "Staff Accountant                    2743\n",
       "Project Manager                     2621\n",
       "Business Analyst                    2302\n",
       "Senior Accountant                   2124\n",
       "Financial Analyst                   1661\n",
       "Maintenance Technician              1593\n",
       "Sales Representative                1546\n",
       "Executive Assistant                 1511\n",
       "Receptionist                        1427\n",
       "Java Developer                      1307\n",
       "Account Executive                   1082\n",
       "Restaurant Manager                   993\n",
       "Store Manager                        963\n",
       "Inside Sales Representative          897\n",
       "Physical Therapist                   792\n",
       "Retail Sales Associate               548\n",
       "Account Representative               195\n",
       "Cashier                              185\n",
       "Retail Wireless Sales Consultant      76\n",
       "Sales Management Trainee              67\n",
       "Name: Query, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df['Query'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T16:23:42.111523Z",
     "start_time": "2020-09-10T16:18:57.548179Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22/22 [40:42<00:00, 111.03s/it] \n"
     ]
    }
   ],
   "source": [
    "word_freq_dict = {}\n",
    "for query in tqdm.tqdm(dataset_df['Query'].unique()):\n",
    "    word_freq_dict[query] = get_top_n_words(dataset_df[dataset_df['Query']==query]['Description'], n=10, ngram_range=(1,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T16:24:39.971936Z",
     "start_time": "2020-09-10T16:24:39.964692Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===\n",
      "Administrative Assistant\n",
      "\n",
      "nbsp                                242.91\n",
      "administrative                      231.05\n",
      "assistant                           178.76\n",
      "administrative assistant            164.01\n",
      "office                              162.17\n",
      "work                                136.95\n",
      "support                             132.19\n",
      "skill                               129.17\n",
      "experience                          122.66\n",
      "include                             121.85\n",
      "    \n",
      "\n",
      "===\n",
      "Sales Representative\n",
      "\n",
      "sale                                155.23\n",
      "nbsp                                155.00\n",
      "customer                            86.33\n",
      "service                             70.91\n",
      "representative                      63.40\n",
      "business                            62.32\n",
      "company                             61.12\n",
      "product                             56.98\n",
      "sale representative                 55.18\n",
      "nbsp nbsp                           52.75\n",
      "    \n",
      "\n",
      "===\n",
      "Customer Service Representative\n",
      "\n",
      "customer                            381.85\n",
      "service                             276.77\n",
      "customer service                    199.06\n",
      "nbsp                                196.27\n",
      "representative                      130.80\n",
      "order                               128.82\n",
      "work                                127.77\n",
      "service representative              123.42\n",
      "company                             117.05\n",
      "customer service representative     113.73\n",
      "    \n",
      "\n",
      "===\n",
      "Java Developer\n",
      "\n",
      "experience                          80.06\n",
      "java                                72.80\n",
      "development                         68.91\n",
      "application                         62.84\n",
      "work                                55.91\n",
      "technology                          55.88\n",
      "service                             55.34\n",
      "nbsp                                54.27\n",
      "developer                           53.97\n",
      "technical                           53.88\n",
      "    \n",
      "\n",
      "===\n",
      "Financial Analyst\n",
      "\n",
      "financial                           144.94\n",
      "report                              89.45\n",
      "nbsp                                86.84\n",
      "analysis                            84.07\n",
      "analyst                             81.74\n",
      "financial analyst                   71.90\n",
      "account                             65.40\n",
      "experience                          60.06\n",
      "business                            58.75\n",
      "budget                              56.80\n",
      "    \n",
      "\n",
      "===\n",
      "Project Manager\n",
      "\n",
      "project                             434.03\n",
      "nbsp                                142.58\n",
      "management                          129.63\n",
      "manager                             120.88\n",
      "experience                          115.31\n",
      "team                                110.36\n",
      "work                                107.23\n",
      "plan                                103.34\n",
      "project manager                     102.99\n",
      "manage                              99.75\n",
      "    \n",
      "\n",
      "===\n",
      "Executive Assistant\n",
      "\n",
      "nbsp                                104.26\n",
      "executive                           102.03\n",
      "assistant                           69.72\n",
      "support                             63.84\n",
      "executive assistant                 61.29\n",
      "administrative                      56.05\n",
      "experience                          53.46\n",
      "work                                51.34\n",
      "include                             50.31\n",
      "office                              50.21\n",
      "    \n",
      "\n",
      "===\n",
      "Maintenance Technician\n",
      "\n",
      "maintenance                         120.95\n",
      "nbsp                                109.38\n",
      "equipment                           88.65\n",
      "repair                              75.34\n",
      "work                                73.85\n",
      "technician                          61.55\n",
      "experience                          60.45\n",
      "maintenance technician              54.82\n",
      "perform                             54.68\n",
      "electrical                          52.08\n",
      "    \n",
      "\n",
      "===\n",
      "Physical Therapist\n",
      "\n",
      "nbsp                                67.09\n",
      "physical                            60.69\n",
      "patient                             58.84\n",
      "therapist                           47.42\n",
      "therapy                             43.76\n",
      "care                                41.66\n",
      "physical therapist                  37.14\n",
      "time                                32.47\n",
      "physical therapy                    31.67\n",
      "service                             31.24\n",
      "    \n",
      "\n",
      "===\n",
      "Store Manager\n",
      "\n",
      "store                               113.28\n",
      "customer                            72.91\n",
      "nbsp                                70.10\n",
      "manager                             54.00\n",
      "company                             47.43\n",
      "sale                                46.10\n",
      "service                             45.37\n",
      "finance                             44.31\n",
      "work                                43.22\n",
      "include                             40.21\n",
      "    \n",
      "\n",
      "===\n",
      "Staff Accountant\n",
      "\n",
      "account                             212.85\n",
      "nbsp                                169.61\n",
      "accountant                          160.69\n",
      "staff                               151.66\n",
      "staff accountant                    110.90\n",
      "experience                          106.43\n",
      "financial                           101.04\n",
      "report                              98.01\n",
      "reconciliation                      87.59\n",
      "company                             85.98\n",
      "    \n",
      "\n",
      "===\n",
      "Inside Sales Representative\n",
      "\n",
      "sale                                88.55\n",
      "customer                            72.65\n",
      "nbsp                                70.35\n",
      "service                             42.26\n",
      "product                             39.58\n",
      "inside                              35.93\n",
      "business                            35.42\n",
      "inside sale                         34.77\n",
      "representative                      32.03\n",
      "company                             31.46\n",
      "    \n",
      "\n",
      "===\n",
      "Account Executive\n",
      "\n",
      "nbsp                                102.64\n",
      "sale                                95.43\n",
      "account                             62.22\n",
      "client                              61.26\n",
      "business                            59.95\n",
      "service                             51.09\n",
      "customer                            48.51\n",
      "product                             43.13\n",
      "executive                           43.09\n",
      "market                              42.91\n",
      "    \n",
      "\n",
      "===\n",
      "Receptionist\n",
      "\n",
      "nbsp                                83.65\n",
      "receptionist                        82.74\n",
      "office                              57.73\n",
      "phone                               45.71\n",
      "duty                                44.06\n",
      "position                            42.94\n",
      "work                                42.87\n",
      "service                             42.29\n",
      "company                             42.01\n",
      "customer                            41.11\n",
      "    \n",
      "\n",
      "===\n",
      "Senior Accountant\n",
      "\n",
      "account                             170.37\n",
      "nbsp                                125.36\n",
      "accountant                          115.62\n",
      "financial                           102.69\n",
      "senior                              101.77\n",
      "report                              91.12\n",
      "experience                          86.74\n",
      "senior accountant                   79.02\n",
      "company                             71.82\n",
      "year                                68.41\n",
      "    \n",
      "\n",
      "===\n",
      "Business Analyst\n",
      "\n",
      "business                            229.57\n",
      "requirement                         130.26\n",
      "project                             118.08\n",
      "experience                          111.72\n",
      "work                                105.45\n",
      "system                              102.02\n",
      "process                             100.77\n",
      "analyst                             93.10\n",
      "datum                               86.24\n",
      "nbsp                                84.80\n",
      "    \n",
      "\n",
      "===\n",
      "Restaurant Manager\n",
      "\n",
      "nbsp                                80.02\n",
      "restaurant                          77.33\n",
      "manager                             52.24\n",
      "plan                                37.41\n",
      "team                                37.09\n",
      "experience                          36.71\n",
      "service                             30.80\n",
      "nbsp nbsp                           30.33\n",
      "year                                29.79\n",
      "food                                29.19\n",
      "    \n",
      "\n",
      "===\n",
      "Account Representative\n",
      "\n",
      "nbsp                                20.85\n",
      "sale                                15.36\n",
      "customer                            11.86\n",
      "account                             11.18\n",
      "company                             9.01\n",
      "service                             8.85\n",
      "business                            8.33\n",
      "aflac                               8.20\n",
      "nbsp nbsp                           7.34\n",
      "representative                      7.10\n",
      "    \n",
      "\n",
      "===\n",
      "Retail Sales Associate\n",
      "\n",
      "sale                                61.78\n",
      "nbsp                                60.01\n",
      "customer                            45.53\n",
      "costco                              42.95\n",
      "product                             39.79\n",
      "costco member                       32.21\n",
      "member                              31.18\n",
      "duty                                28.87\n",
      "service                             23.78\n",
      "associate                           23.71\n",
      "    \n",
      "\n",
      "===\n",
      "Retail Wireless Sales Consultant\n",
      "\n",
      "sale                                13.88\n",
      "wireless                            13.88\n",
      "gowireless                          12.43\n",
      "product                             9.24\n",
      "service                             9.24\n",
      "experience                          9.07\n",
      "opportunity                         7.78\n",
      "time                                7.77\n",
      "management                          7.68\n",
      "retail                              7.61\n",
      "    \n",
      "\n",
      "===\n",
      "Sales Management Trainee\n",
      "\n",
      "aerotek                             11.87\n",
      "recruit                             11.52\n",
      "sale                                9.94\n",
      "candidate                           9.14\n",
      "service                             8.65\n",
      "work                                6.99\n",
      "customer                            6.43\n",
      "customer service                    6.23\n",
      "recruiter                           6.12\n",
      "client                              4.28\n",
      "    \n",
      "\n",
      "===\n",
      "Cashier\n",
      "\n",
      "customer                            14.52\n",
      "cashier                             13.61\n",
      "store                               9.47\n",
      "cash                                8.57\n",
      "work                                7.62\n",
      "service                             7.58\n",
      "aldi                                7.41\n",
      "look                                6.74\n",
      "nbsp                                5.97\n",
      "experience                          5.86\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "for query in word_freq_dict:\n",
    "    stat_string = \"\\n\".join([f\"{word_freq[0]:35} {word_freq[1]:.2f}\" for word_freq in word_freq_dict[query]])\n",
    "    print(f'''\n",
    "===\n",
    "{query}\n",
    "\n",
    "{stat_string}\n",
    "    ''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T09:18:26.748246Z",
     "start_time": "2020-09-14T09:18:26.742630Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This Administrative Assistant position is responsible for performing a variety of clerical and administrative support functions in the areas of communications, data entry and retrieval, typing, filing, copying, and coordination of related administrative activities. \\\\r\\\\n\\\\r\\\\nJOB REQUIREMENTS:\\\\r\\\\nAttention to detail\\\\r\\\\nAbility to work in a fast paced environment\\\\r\\\\nInvoicing\\\\r\\\\nProficient in Word, Excel and Outlook\\\\r\\\\nOther Administrative responsibilities as needed.\\\\r\\\\n\\\\r\\\\nQUALIFICATIONS:\\\\r\\\\nHigh School Diploma\\\\r\\\\nAdministrative Assistant Experience\\\\r\\\\n1-2 Years work experience\\\\r\\\\nWorking knowledge of all basic office machines (fax, copier, 10key, etc.)\\\\r\\\\nStrong verbal and written communication skills\\\\r\\\\nStrong work ethic.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df['Description'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove tags and stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T09:18:29.503231Z",
     "start_time": "2020-09-14T09:18:29.079463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['administrative',\n",
       " 'assistant',\n",
       " 'position',\n",
       " 'responsible',\n",
       " 'perform',\n",
       " 'variety',\n",
       " 'clerical',\n",
       " 'administrative',\n",
       " 'support',\n",
       " 'function',\n",
       " 'area',\n",
       " 'communication',\n",
       " 'datum',\n",
       " 'entry',\n",
       " 'retrieval',\n",
       " 'type',\n",
       " 'file',\n",
       " 'copy',\n",
       " 'coordination',\n",
       " 'relate',\n",
       " 'administrative',\n",
       " 'activity',\n",
       " 'njob',\n",
       " 'requirement',\n",
       " 'nattention',\n",
       " 'detail',\n",
       " 'nability',\n",
       " 'work',\n",
       " 'fast',\n",
       " 'pace',\n",
       " 'environment',\n",
       " 'ninvoicing',\n",
       " 'nproficient',\n",
       " 'word',\n",
       " 'excel',\n",
       " 'outlook',\n",
       " 'nother',\n",
       " 'administrative',\n",
       " 'responsibility',\n",
       " 'need',\n",
       " 'nqualifications',\n",
       " 'nhigh',\n",
       " 'school',\n",
       " 'diploma',\n",
       " 'nadministrative',\n",
       " 'assistant',\n",
       " 'experience',\n",
       " 'year',\n",
       " 'work',\n",
       " 'experience',\n",
       " 'nworking',\n",
       " 'knowledge',\n",
       " 'basic',\n",
       " 'office',\n",
       " 'machine',\n",
       " 'fax',\n",
       " 'copier',\n",
       " 'key',\n",
       " 'etc',\n",
       " 'nstrong',\n",
       " 'verbal',\n",
       " 'write',\n",
       " 'communication',\n",
       " 'skill',\n",
       " 'nstrong',\n",
       " 'work',\n",
       " 'ethic']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SpacyTokenizer().tokenizer(dataset_df['Description'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test(dataset_df, y, test_size=0.2):\n",
    "    # remove types occur only ones\n",
    "    temp_df = dataset_df[y.isin(y.value_counts()[y.value_counts()>1].index)]\n",
    "    return train_test_split(temp_df, stratify=y, test_size=test_size, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T08:25:08.347653Z",
     "start_time": "2020-09-14T08:25:08.007088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train, test = split_train_test(dataset_df, dataset_df['Query'], test_size=0.2)\n",
    "split_df = pd.concat([train['Query'].value_counts(), test['Query'].value_counts()], axis=1)\n",
    "split_df.columns = ['train', 'test']\n",
    "\n",
    "split_df.plot(kind='bar', stacked=True)\n",
    "plt.title('Job descriptions per job category')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-10T16:57:46.216708Z",
     "start_time": "2020-09-10T16:57:46.207465Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train/test: 25555/6389\n"
     ]
    },
    {
     "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>train</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Administrative Assistant</th>\n",
       "      <td>3213</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Service Representative</th>\n",
       "      <td>2635</td>\n",
       "      <td>659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff Accountant</th>\n",
       "      <td>2194</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project Manager</th>\n",
       "      <td>2097</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Analyst</th>\n",
       "      <td>1841</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Accountant</th>\n",
       "      <td>1699</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Analyst</th>\n",
       "      <td>1329</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maintenance Technician</th>\n",
       "      <td>1274</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Representative</th>\n",
       "      <td>1237</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executive Assistant</th>\n",
       "      <td>1209</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Receptionist</th>\n",
       "      <td>1142</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java Developer</th>\n",
       "      <td>1046</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account Executive</th>\n",
       "      <td>866</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restaurant Manager</th>\n",
       "      <td>794</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store Manager</th>\n",
       "      <td>770</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inside Sales Representative</th>\n",
       "      <td>718</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physical Therapist</th>\n",
       "      <td>634</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail Sales Associate</th>\n",
       "      <td>438</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account Representative</th>\n",
       "      <td>156</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cashier</th>\n",
       "      <td>148</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail Wireless Sales Consultant</th>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Management Trainee</th>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  train  test\n",
       "Administrative Assistant           3213   804\n",
       "Customer Service Representative    2635   659\n",
       "Staff Accountant                   2194   549\n",
       "Project Manager                    2097   524\n",
       "Business Analyst                   1841   461\n",
       "Senior Accountant                  1699   425\n",
       "Financial Analyst                  1329   332\n",
       "Maintenance Technician             1274   319\n",
       "Sales Representative               1237   309\n",
       "Executive Assistant                1209   302\n",
       "Receptionist                       1142   285\n",
       "Java Developer                     1046   261\n",
       "Account Executive                   866   216\n",
       "Restaurant Manager                  794   199\n",
       "Store Manager                       770   193\n",
       "Inside Sales Representative         718   179\n",
       "Physical Therapist                  634   158\n",
       "Retail Sales Associate              438   110\n",
       "Account Representative              156    39\n",
       "Cashier                             148    37\n",
       "Retail Wireless Sales Consultant     61    15\n",
       "Sales Management Trainee             54    13"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"train/test: {split_df['train'].sum()}/{split_df['test'].sum()}\")\n",
    "split_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T06:18:35.941571Z",
     "start_time": "2020-09-14T06:14:31.187869Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  5.2min\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   4 | elapsed:  5.3min remaining:  5.3min\n",
      "[Parallel(n_jobs=2)]: Done   4 out of   4 | elapsed: 10.0min remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   4 out of   4 | elapsed: 10.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('tfidf', TfidfVectorizer(max_features=2000, ngram_range=(1, 2),\n",
      "                tokenizer=<bound method SpacyTokenizer.tokenizer of <__main__.SpacyTokenizer object at 0x7fc36792db90>>)), ('clf', MultinomialNB(alpha=0.001))]\n",
      "                                  precision    recall  f1-score   support\n",
      "\n",
      "               Account Executive       0.78      0.79      0.79       216\n",
      "          Account Representative       0.83      0.26      0.39        39\n",
      "        Administrative Assistant       0.79      0.87      0.83       804\n",
      "                Business Analyst       0.91      0.74      0.82       461\n",
      "                         Cashier       0.89      0.65      0.75        37\n",
      " Customer Service Representative       0.82      0.77      0.80       659\n",
      "             Executive Assistant       0.84      0.69      0.76       302\n",
      "               Financial Analyst       0.77      0.88      0.82       332\n",
      "     Inside Sales Representative       0.76      0.47      0.58       179\n",
      "                  Java Developer       0.76      0.89      0.82       261\n",
      "          Maintenance Technician       0.92      0.96      0.94       319\n",
      "              Physical Therapist       0.94      1.00      0.97       158\n",
      "                 Project Manager       0.92      0.85      0.88       524\n",
      "                    Receptionist       0.94      0.77      0.85       285\n",
      "              Restaurant Manager       0.96      0.97      0.97       199\n",
      "          Retail Sales Associate       0.99      0.85      0.92       110\n",
      "Retail Wireless Sales Consultant       1.00      1.00      1.00        15\n",
      "        Sales Management Trainee       1.00      0.77      0.87        13\n",
      "            Sales Representative       0.56      0.90      0.69       309\n",
      "               Senior Accountant       0.79      0.83      0.81       425\n",
      "                Staff Accountant       0.87      0.86      0.87       549\n",
      "                   Store Manager       0.71      0.73      0.72       193\n",
      "\n",
      "                        accuracy                           0.82      6389\n",
      "                       macro avg       0.85      0.80      0.81      6389\n",
      "                    weighted avg       0.83      0.82      0.82      6389\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(tokenizer=SpacyTokenizer().tokenizer, max_features=2000)),\n",
    "    ('clf', MultinomialNB(fit_prior=True, class_prior=None)),\n",
    "])\n",
    "\n",
    "parameters = {\n",
    "    'tfidf__ngram_range': [(1, 2)],\n",
    "    'clf__alpha': (1e-2, 1e-3)\n",
    "}\n",
    "\n",
    "nb_pipeline = tune_pipeline(pipeline, parameters, train['Description'], train['Query'], n_splits=5)\n",
    "print(nb_pipeline.steps)\n",
    "joblib.dump(nb_pipeline, './models/nb_pipeline.joblib')\n",
    "\n",
    "pred = nb_pipeline.predict(test['Description'])\n",
    "print(classification_report(test['Query'], pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T06:32:08.215426Z",
     "start_time": "2020-09-14T06:22:12.704871Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 6 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  5.3min\n",
      "[Parallel(n_jobs=2)]: Done   4 tasks      | elapsed: 10.8min\n",
      "[Parallel(n_jobs=2)]: Done   9 tasks      | elapsed: 26.1min\n",
      "[Parallel(n_jobs=2)]: Done  12 out of  12 | elapsed: 31.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('tfidf', TfidfVectorizer(max_features=2000, ngram_range=(1, 2),\n",
      "                tokenizer=<bound method SpacyTokenizer.tokenizer of <__main__.SpacyTokenizer object at 0x7fc2d4734ed0>>)), ('clf', LogisticRegression(C=1, solver='sag'))]\n",
      "                                  precision    recall  f1-score   support\n",
      "\n",
      "               Account Executive       0.86      0.89      0.88       216\n",
      "          Account Representative       0.91      0.26      0.40        39\n",
      "        Administrative Assistant       0.87      0.92      0.90       804\n",
      "                Business Analyst       0.94      0.93      0.93       461\n",
      "                         Cashier       1.00      0.68      0.81        37\n",
      " Customer Service Representative       0.89      0.96      0.92       659\n",
      "             Executive Assistant       0.89      0.82      0.85       302\n",
      "               Financial Analyst       0.94      0.93      0.94       332\n",
      "     Inside Sales Representative       0.83      0.73      0.78       179\n",
      "                  Java Developer       0.99      0.96      0.97       261\n",
      "          Maintenance Technician       0.98      0.98      0.98       319\n",
      "              Physical Therapist       0.97      0.97      0.97       158\n",
      "                 Project Manager       0.95      0.95      0.95       524\n",
      "                    Receptionist       0.94      0.92      0.93       285\n",
      "              Restaurant Manager       0.98      0.98      0.98       199\n",
      "          Retail Sales Associate       0.99      0.91      0.95       110\n",
      "Retail Wireless Sales Consultant       1.00      1.00      1.00        15\n",
      "        Sales Management Trainee       1.00      0.77      0.87        13\n",
      "            Sales Representative       0.79      0.86      0.83       309\n",
      "               Senior Accountant       0.92      0.89      0.91       425\n",
      "                Staff Accountant       0.92      0.94      0.93       549\n",
      "                   Store Manager       0.97      0.93      0.95       193\n",
      "\n",
      "                        accuracy                           0.92      6389\n",
      "                       macro avg       0.93      0.87      0.89      6389\n",
      "                    weighted avg       0.92      0.92      0.92      6389\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(tokenizer=SpacyTokenizer().tokenizer, max_features=2000)),\n",
    "    ('clf', LogisticRegression(solver='sag')),\n",
    "])\n",
    "parameters = {\n",
    "    'tfidf__ngram_range': [(1, 2)],\n",
    "    \"clf__C\": [0.01, 0.1, 1],\n",
    "    \"clf__class_weight\": ['balanced', None],\n",
    "}\n",
    "\n",
    "logistic_regression_pipeline = tune_pipeline(pipeline, parameters, train['Description'], train['Query'], n_splits=5)\n",
    "print(logistic_regression_pipeline.steps)\n",
    "joblib.dump(logistic_regression_pipeline, './models/logistic_regression_pipeline.joblib')\n",
    "\n",
    "pred = logistic_regression_pipeline.predict(test['Description'])\n",
    "print(classification_report(test['Query'], pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T06:41:34.585656Z",
     "start_time": "2020-09-14T06:33:45.267895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 4 candidates, totalling 8 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   8 | elapsed: 12.0min remaining: 36.1min\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   8 | elapsed: 12.4min remaining: 20.6min\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   8 | elapsed: 12.4min remaining: 12.4min\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   8 | elapsed: 12.4min remaining:  7.5min\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   8 | elapsed: 12.7min remaining:  4.2min\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed: 12.8min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed: 12.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('tfidf', TfidfVectorizer(max_features=2000, ngram_range=(1, 2),\n",
      "                tokenizer=<bound method SpacyTokenizer.tokenizer of <__main__.SpacyTokenizer object at 0x7fc2f4f28610>>)), ('clf', DecisionTreeClassifier())]\n",
      "                                  precision    recall  f1-score   support\n",
      "\n",
      "               Account Executive       0.80      0.81      0.81       216\n",
      "          Account Representative       0.72      0.54      0.62        39\n",
      "        Administrative Assistant       0.84      0.83      0.83       804\n",
      "                Business Analyst       0.87      0.84      0.86       461\n",
      "                         Cashier       0.57      0.65      0.61        37\n",
      " Customer Service Representative       0.88      0.91      0.89       659\n",
      "             Executive Assistant       0.78      0.77      0.77       302\n",
      "               Financial Analyst       0.89      0.85      0.87       332\n",
      "     Inside Sales Representative       0.70      0.73      0.72       179\n",
      "                  Java Developer       0.91      0.92      0.91       261\n",
      "          Maintenance Technician       0.97      0.95      0.96       319\n",
      "              Physical Therapist       1.00      0.97      0.99       158\n",
      "                 Project Manager       0.91      0.90      0.90       524\n",
      "                    Receptionist       0.84      0.86      0.85       285\n",
      "              Restaurant Manager       0.92      0.95      0.94       199\n",
      "          Retail Sales Associate       0.94      0.93      0.93       110\n",
      "Retail Wireless Sales Consultant       1.00      1.00      1.00        15\n",
      "        Sales Management Trainee       1.00      0.85      0.92        13\n",
      "            Sales Representative       0.76      0.81      0.78       309\n",
      "               Senior Accountant       0.84      0.87      0.85       425\n",
      "                Staff Accountant       0.89      0.87      0.88       549\n",
      "                   Store Manager       0.93      0.92      0.92       193\n",
      "\n",
      "                        accuracy                           0.86      6389\n",
      "                       macro avg       0.86      0.85      0.86      6389\n",
      "                    weighted avg       0.87      0.86      0.86      6389\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(tokenizer=SpacyTokenizer().tokenizer, max_features=2000)),\n",
    "    ('clf', DecisionTreeClassifier()),\n",
    "])\n",
    "parameters = {\n",
    "    'tfidf__ngram_range': [(1, 2), (1, 3)],\n",
    "    \"clf__class_weight\": ['balanced', None],\n",
    "}\n",
    "\n",
    "decision_tree_pipeline = tune_pipeline(pipeline, parameters, train['Description'], train['Query'], n_splits=5)\n",
    "print(decision_tree_pipeline.steps)\n",
    "joblib.dump(decision_tree_pipeline, './models/decision_tree_pipeline.joblib')\n",
    "\n",
    "pred = decision_tree_pipeline.predict(test['Description'])\n",
    "print(classification_report(test['Query'], pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LinearSVC classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T06:50:55.381379Z",
     "start_time": "2020-09-14T06:41:47.251800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 6 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed: 10.6min\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  12 | elapsed: 11.2min remaining: 33.7min\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  12 | elapsed: 11.6min remaining: 16.2min\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of  12 | elapsed: 11.8min remaining:  8.4min\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  12 | elapsed: 17.7min remaining:  5.9min\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed: 18.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('tfidf', TfidfVectorizer(max_features=2000, ngram_range=(1, 2),\n",
      "                tokenizer=<bound method SpacyTokenizer.tokenizer of <__main__.SpacyTokenizer object at 0x7fc2d50bfbd0>>)), ('clf', LinearSVC(C=1, class_weight='balanced'))]\n",
      "                                  precision    recall  f1-score   support\n",
      "\n",
      "               Account Executive       0.88      0.93      0.90       216\n",
      "          Account Representative       0.62      0.64      0.63        39\n",
      "        Administrative Assistant       0.92      0.87      0.90       804\n",
      "                Business Analyst       0.94      0.93      0.94       461\n",
      "                         Cashier       0.72      0.78      0.75        37\n",
      " Customer Service Representative       0.93      0.93      0.93       659\n",
      "             Executive Assistant       0.83      0.87      0.85       302\n",
      "               Financial Analyst       0.92      0.95      0.93       332\n",
      "     Inside Sales Representative       0.77      0.83      0.80       179\n",
      "                  Java Developer       0.97      0.99      0.98       261\n",
      "          Maintenance Technician       0.98      0.99      0.99       319\n",
      "              Physical Therapist       0.97      0.98      0.97       158\n",
      "                 Project Manager       0.96      0.96      0.96       524\n",
      "                    Receptionist       0.91      0.94      0.93       285\n",
      "              Restaurant Manager       0.98      0.98      0.98       199\n",
      "          Retail Sales Associate       0.96      0.95      0.96       110\n",
      "Retail Wireless Sales Consultant       1.00      1.00      1.00        15\n",
      "        Sales Management Trainee       1.00      0.92      0.96        13\n",
      "            Sales Representative       0.83      0.84      0.84       309\n",
      "               Senior Accountant       0.91      0.90      0.90       425\n",
      "                Staff Accountant       0.94      0.92      0.93       549\n",
      "                   Store Manager       0.98      0.97      0.98       193\n",
      "\n",
      "                        accuracy                           0.92      6389\n",
      "                       macro avg       0.91      0.91      0.91      6389\n",
      "                    weighted avg       0.92      0.92      0.92      6389\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(tokenizer=SpacyTokenizer().tokenizer, max_features=2000)),\n",
    "    ('clf', LinearSVC(multi_class='ovr'))\n",
    "])\n",
    "\n",
    "parameters = {\n",
    "    'tfidf__ngram_range': [(1, 2)],\n",
    "    \"clf__C\": [0.01, 0.1, 1],\n",
    "    \"clf__class_weight\": ['balanced', None],\n",
    "}\n",
    "\n",
    "linear_svc_pipeline = tune_pipeline(pipeline, parameters, train['Description'], train['Query'], n_splits=5)\n",
    "print(linear_svc_pipeline.steps)\n",
    "joblib.dump(linear_svc_pipeline, './models/linear_svc_pipeline.joblib')\n",
    "\n",
    "pred = linear_svc_pipeline.predict(test['Description'])\n",
    "print(classification_report(test['Query'], pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T09:08:11.830604Z",
     "start_time": "2020-09-14T09:08:09.559704Z"
    }
   },
   "outputs": [],
   "source": [
    "classifier_pipeline = joblib.load('./models/linear_svc_pipeline.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T09:16:19.826527Z",
     "start_time": "2020-09-14T09:16:19.815122Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category: Project Manager\n",
      "Description: <strong>Immediate opening for a Project Engineer!!</strong>\\r\\nAdecco Engineering and Technical, a division of the world leader in the recruitment of engineering and information technology professionals, has an immediate opening for an Project Manager on a direct hire opportunity with a leading company in Irvine, Ca. \\r\\n\\r\\n<strong> </strong>\\r\\n<strong>Qualifications:</strong>\\r\\n\\r\\n<li>B.S. Engineering required.\\r\\n</li>\\r\\n<li>Minimum 0-3 years engineering experience, preferably in a Medical Device or Bio-Pharm manufacturing environment\\r\\n</li>\\r\\n<li>Successful track record in project management and use of project management tools (i.e. MS Project) and team management techniques.\\r\\n</li>\\r\\n<li>Good understanding of quality systems and FDA regulations related to manufacturing.\\r\\n</li>\\r\\n<li>Experience with small batch and lot segregation processing preferred.\\r\\n</li>\\r\\n<li>Experience with human tissue based manufacturing preferred.\\r\\n</li>\\r\\n<li>Solidworks, Minitab, and Autocad experience strongly preferred.\\r\\n</li>\\r\\n<li>Experience with MS Word, MS Excel, MS Outlook, MS Project, and MS Vizio Required.\\r\\n</li>\\r\\n\\r\\nTo apply please send all word resumes to jason.concepcion@adeccona.com today!!\n"
     ]
    }
   ],
   "source": [
    "test_record = test.sample().iloc[0]\n",
    "print(f\"Category: {test_record['Query']}\")\n",
    "print(f\"Description: {test_record['Description']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T09:16:37.333006Z",
     "start_time": "2020-09-14T09:16:37.322728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Project Manager'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_pipeline.predict([test_record['Description']])"
   ]
  }
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