Predicting Kenya Law Making.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import pickle\n",
    "import imblearn\n",
    "from utils import *\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import FeatureUnion, Pipeline\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import classification_report, auc, roc_curve, brier_score_loss\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = './data/Kenya_Bill_Text_ID.csv'\n",
    "df = preprocess_kenyan_bills(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['1ST READ', '2ND READ', '3RD READ', 'ASSENT', 'BILL', 'DATECIRC',\n       'DATED', 'GAZETTE NO.', 'MATURITY', 'NA BILL/NO.', 'REMARKS', 'SPONSOR',\n       'SUPP NO.', 'TEXT', 'BILL_ID', 'LABEL', 'YEAR', 'MONTH'],\n      dtype='object')"
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More features\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# confirmed from http://www.parliament.go.ke/index.php/the-national-assembly/mps\n",
    "\n",
    "other_hons = ['joseph limo', \"kimani ichung'wa\", 'maina kamanda', 'mutava musyimi', 'benjamin langat',\\\n",
    "             'samuel chepkonga', \"samuel chepkong'a\", 'rachel nyamai', 'rachael nyamai', 'jeremiah kioni',\\\n",
    "             \"benjamin lang'at\", \"kimani ichungw'ah\", 'joseph lekuton', 'moses lessonet', 'asman kamama',\\\n",
    "             'john olago aluoch', 'amina abdalla', 'ephraim m. maina', 'jared odhiambo opiyo', 'eugene wamalwa',\\\n",
    "             'waititu francis munyua', 'julius melly', 'victor munyaka', 'j.k. limo', 'andrew mwadime',\\\n",
    "             'florence mutua', 'david kangogo bowen', 'ayub savula', 'w.k. ottichilo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_members = []\n",
    "att_gen_minister = []\n",
    "\n",
    "for i in df['SPONSOR']:\n",
    "    if 'member of parliament' in str(i):\n",
    "        house_members.append(i)\n",
    "    elif str(i).startswith('hon.'):\n",
    "        house_members.append(i)\n",
    "    elif 'aden duale' in str(i) or 'aden aduale' in str(i):\n",
    "        house_members.append(i)\n",
    "    elif 'aden dualle' in str(i):\n",
    "        house_members.append(i)\n",
    "    elif any(hon in str(i) for hon in other_hons):\n",
    "        house_members.append(i)\n",
    "\n",
    "\n",
    "for i in df['SPONSOR']:\n",
    "    if 'minister' in str(i) or 'ministry' in str(i):\n",
    "        att_gen_minister.append(i)\n",
    "    elif 'attorney general' in str(i):\n",
    "        att_gen_minister.append(i)\n",
    "    elif 'attorney-general' in str(i):\n",
    "        att_gen_minister.append(i)\n",
    "\n",
    "others = [i for i in df['SPONSOR'] if i not in house_members and i not in att_gen_minister]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Total sponsors =  460\nTotal house_members =  349\nTotal att_gen_minister =  109\nTotal others =  9\n"
    }
   ],
   "source": [
    "print('Total sponsors = ' , len(list(df['SPONSOR'])))\n",
    "print('Total house_members = ' , len(house_members))\n",
    "print('Total att_gen_minister = ' , len(att_gen_minister))\n",
    "print('Total others = ' , len(others))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - sponsor is a house member of parliament\n",
    "# 1 - sponsor is attorney general or minister\n",
    "sponsors2 = []\n",
    "for i, sponsor in enumerate(list(df['SPONSOR'])):\n",
    "    if sponsor in house_members:\n",
    "        sponsors2.append(0)\n",
    "    elif sponsor in att_gen_minister:\n",
    "        sponsors2.append(1)\n",
    "    else:\n",
    "        sponsors2.append(sponsor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 - sponsor is 'aden duale'\n",
    "# 0 - sponsor is not 'aden duale'\n",
    "sponsors = []\n",
    "for i, sponsor in enumerate(list(df['SPONSOR'])):\n",
    "    if 'aden duale' in str(sponsor):\n",
    "        sponsors.append(1)\n",
    "    elif type(sponsor) == float:\n",
    "        sponsors.append(sponsor)\n",
    "    else:\n",
    "        sponsors.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "bill_titles = list(df['BILL'].apply(clean_title))\n",
    "bill_titles_vocab  = []\n",
    "for i in bill_titles:\n",
    "    bill_titles_vocab.extend(i.split())\n",
    "\n",
    "bill_titles_vocab = set(bill_titles_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dimension = 100\n",
    "embeddings_matrix = get_glove(bill_titles_vocab, dimension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_df['bill'] = df['BILL']\n",
    "feature_df['text'] = df['TEXT']\n",
    "feature_df['year_introduced'] = df['YEAR']\n",
    "feature_df['month'] = df['MONTH']\n",
    "feature_df['year_diff'] = 2020 - df['YEAR']\n",
    "feature_df['text_length'] = df['TEXT'].apply(len_text)\n",
    "feature_df['label'] = df['LABEL']\n",
    "feature_df['sponsor'] = sponsors\n",
    "feature_df['sponsor2'] = sponsors2\n",
    "feature_df['len_of_title'] = df['BILL'].apply(title_len)\n",
    "feature_df['title_avg_word_vector'] = df['BILL'].apply(lambda x: extract_embedding_feature(embeddings_matrix, 100, x))\n",
    "feature_df['text_avg_word_vector'] = df['TEXT'].apply(lambda x: extract_embedding_feature(embeddings_matrix, 100, x))\n",
    "feature_df['verdict'] = df['REMARKS'].apply(extract_bill_remark)\n",
    "\n",
    "feature_df[['sponsor', 'sponsor2']] = feature_df[['sponsor', 'sponsor2']].astype('Int64')\n",
    "feature_df[['month', 'year_introduced', 'year_diff', 'text_length', 'len_of_title']] = feature_df[['month', 'year_introduced','year_diff', 'text_length', 'len_of_title']].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_df['sponsor'] = feature_df['sponsor'].fillna(feature_df['sponsor'].mode()[0])\n",
    "feature_df['sponsor2'] = feature_df['sponsor2'].fillna(feature_df['sponsor2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - bill not introduced in an election year\n",
    "# 1 - bill introduced in an election year\n",
    "\n",
    "election_years = [2002, 2007, 2013, 2017]\n",
    "election_year_bill = [1 if year in election_years else 0 for i, year in enumerate(feature_df['year_introduced'])]\n",
    "feature_df['election_year_bill'] = election_year_bill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Bills passed vs bills not passed')"
     },
     "metadata": {},
     "execution_count": 61
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.countplot(feature_df['verdict'])\n",
    "plt.title('Bills passed vs bills not passed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Sponsors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0    338\n1    122\nName: sponsor, dtype: int64\n\n338 sponsors are not aden duale\n122 sponsors are aden duale\n"
    }
   ],
   "source": [
    "print(feature_df['sponsor'].value_counts())\n",
    "print(f'\\n{feature_df.sponsor.value_counts().values[0]} sponsors are not aden duale')\n",
    "print(f'{feature_df.sponsor.value_counts().values[1]} sponsors are aden duale')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Sponsor2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0    358\n1    102\nName: sponsor2, dtype: int64\n\n358 sponsors are members of the house of parliament\n102 sponsors are either ministers or attorney generals\n"
    }
   ],
   "source": [
    "print(feature_df['sponsor2'].value_counts())\n",
    "print(f'\\n{feature_df.sponsor2.value_counts().values[0]} sponsors are members of the house of parliament')\n",
    "print(f'{feature_df.sponsor2.value_counts().values[1]} sponsors are either ministers or attorney generals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "bill                     0\ntext                     0\nyear_introduced          0\nmonth                    0\nyear_diff                0\ntext_length              0\nlabel                    0\nsponsor                  0\nsponsor2                 0\nlen_of_title             0\ntitle_avg_word_vector    0\ntext_avg_word_vector     0\nverdict                  0\nelection_year_bill       0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "source": [
    "feature_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_df['sponsor'] = feature_df['sponsor'].fillna(feature_df['sponsor'].mode()[0])\n",
    "feature_df['sponsor2'] = feature_df['sponsor2'].fillna(feature_df['sponsor2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "bill                     0\ntext                     0\nyear_introduced          0\nmonth                    0\nyear_diff                0\ntext_length              0\nlabel                    0\nsponsor                  0\nsponsor2                 0\nlen_of_title             0\ntitle_avg_word_vector    0\ntext_avg_word_vector     0\nverdict                  0\nelection_year_bill       0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "feature_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Year introduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "2012    88\n2015    61\n2011    57\n2019    56\n2017    42\n2013    40\n2014    38\n2016    36\n2018    21\n2009    16\n2010     5\nName: year_introduced, dtype: int64\n"
    }
   ],
   "source": [
    "print(feature_df['year_introduced'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def str_index(index, months):\n",
    "    index = str(index)\n",
    "    month = 'NA'\n",
    "    if len(index) != 2:\n",
    "        index = '0' + index\n",
    "    if index != '00':\n",
    "        month = list(months.keys())[list(months.values()).index(index)]\n",
    "    return month\n",
    "\n",
    "index = list(pd.DataFrame(feature_df['month'].value_counts()).index)\n",
    "months_count = list(pd.DataFrame(feature_df['month'].value_counts())['month'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Month introduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "        month  count\n0        june     61\n1      august     58\n2       april     53\n3        july     45\n4       march     43\n5    november     40\n6   september     37\n7    december     32\n8    february     27\n9     october     23\n10        may     21\n11    january     14\n12         NA      6",
      "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>month</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>june</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>august</td>\n      <td>58</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>april</td>\n      <td>53</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>july</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>march</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>november</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>september</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>december</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>february</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>october</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>may</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>january</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>NA</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "months_df = pd.DataFrame()\n",
    "months_df['month'] = index\n",
    "months_df['month'] = months_df['month'].apply(lambda x: str_index(x, months))\n",
    "months_df['count'] = months_count\n",
    "months_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most bills were introduced in June, August and April"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "        month  count\n0        june     67\n1      august     58\n2       april     53\n3        july     45\n4       march     43\n5    november     40\n6   september     37\n7    december     32\n8    february     27\n9     october     23\n10        may     21\n11    january     14",
      "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>month</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>june</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>august</td>\n      <td>58</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>april</td>\n      <td>53</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>july</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>march</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>november</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>september</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>december</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>february</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>october</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>may</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>january</td>\n      <td>14</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "#replace month 0 with the most common month i.e June(6)\n",
    "feature_df['month'] = feature_df['month'].astype('int')\n",
    "feature_df['month'] = feature_df['month'].replace(to_replace=0,value=6)\n",
    "\n",
    "index = list(pd.DataFrame(feature_df['month'].value_counts()).index)\n",
    "months_count = list(pd.DataFrame(feature_df['month'].value_counts())['month'])\n",
    "\n",
    "months_df = pd.DataFrame()\n",
    "months_df['month'] = index\n",
    "months_df['month'] = months_df['month'].apply(lambda x: str_index(x, months))\n",
    "months_df['count'] = months_count\n",
    "months_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Election year or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0    378\n1     82\nName: election_year_bill, dtype: int64\n\n378 bills were not introduced in an election year\n82 bills were introduced in an election year\n"
    }
   ],
   "source": [
    "print(feature_df['election_year_bill'].value_counts())\n",
    "print(f'\\n{feature_df.election_year_bill.value_counts().values[0]} bills were not introduced in an election year')\n",
    "print(f'{feature_df.election_year_bill.value_counts().values[1]} bills were introduced in an election year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "government operation and international affairs    150\nlaws, civil rights, safety and security           100\ntrade, commerce and macro economics                59\nlabour, sports and social welfare                  44\nhealth and agriculture                             38\npublic land, housing and transportation            24\neducation, research and technology                 23\nenergy, environment and natural resources          22\nName: label, dtype: int64"
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "source": [
    "feature_df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Average title length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The average title length is 32\n"
    }
   ],
   "source": [
    "avg_title_len = round(sum(list(feature_df['len_of_title']))/len(feature_df['len_of_title']))\n",
    "print(f'The average title length is {avg_title_len}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "460"
     },
     "metadata": {},
     "execution_count": 74
    }
   ],
   "source": [
    "len(feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "not passed    395\npassed         65\nName: verdict, dtype: int64"
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "source": [
    "feature_df['verdict'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a class imbalance in the dataset with only 65 bills passed and 386 bills not passed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = feature_df.drop(['verdict', 'bill', 'text'], axis=1), feature_df['verdict']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "y_train = encoder.fit_transform(y_train)\n",
    "y_test = encoder.transform(y_test)\n",
    "\n",
    "label_col_encoder = LabelEncoder()\n",
    "X_train['label'] = label_col_encoder.fit_transform(X_train.label)\n",
    "X_test['label'] = label_col_encoder.transform(X_test.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "not passed    395\npassed         65\nName: verdict, dtype: int64"
     },
     "metadata": {},
     "execution_count": 78
    }
   ],
   "source": [
    "feature_df['verdict'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                            label  verdict\n0              education, research and technology       23\n1       energy, environment and natural resources       22\n2  government operation and international affairs      150\n3                          health and agriculture       38\n4               labour, sports and social welfare       44\n5         laws, civil rights, safety and security      100\n6         public land, housing and transportation       24\n7             trade, commerce and macro economics       59",
      "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>label</th>\n      <th>verdict</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>education, research and technology</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>energy, environment and natural resources</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>government operation and international affairs</td>\n      <td>150</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>health and agriculture</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>labour, sports and social welfare</td>\n      <td>44</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>laws, civil rights, safety and security</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>public land, housing and transportation</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>trade, commerce and macro economics</td>\n      <td>59</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "source": [
    "labels_df = feature_df.groupby('label').agg({'verdict': 'count'}).reset_index()\n",
    "labels_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "enacted_df = feature_df[feature_df.verdict == 'passed']\n",
    "not_enacted_df = feature_df[feature_df.verdict != 'passed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                            label  verdict\n0              education, research and technology        1\n1       energy, environment and natural resources        8\n2  government operation and international affairs       21\n3                          health and agriculture        6\n4               labour, sports and social welfare        4\n5         laws, civil rights, safety and security       13\n6         public land, housing and transportation        4\n7             trade, commerce and macro economics        8",
      "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>label</th>\n      <th>verdict</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>education, research and technology</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>energy, environment and natural resources</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>government operation and international affairs</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>health and agriculture</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>labour, sports and social welfare</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>laws, civil rights, safety and security</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>public land, housing and transportation</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>trade, commerce and macro economics</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 81
    }
   ],
   "source": [
    "grouped_enacted_df = enacted_df.groupby('label').agg({'verdict': 'count'}).reset_index()\n",
    "grouped_enacted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                            label  verdict\n0              education, research and technology       22\n1       energy, environment and natural resources       14\n2  government operation and international affairs      129\n3                          health and agriculture       32\n4               labour, sports and social welfare       40\n5         laws, civil rights, safety and security       87\n6         public land, housing and transportation       20\n7             trade, commerce and macro economics       51",
      "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>label</th>\n      <th>verdict</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>education, research and technology</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>energy, environment and natural resources</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>government operation and international affairs</td>\n      <td>129</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>health and agriculture</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>labour, sports and social welfare</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>laws, civil rights, safety and security</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>public land, housing and transportation</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>trade, commerce and macro economics</td>\n      <td>51</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 82
    }
   ],
   "source": [
    "grouped_not_enacted_df = not_enacted_df.groupby('label').agg({'verdict': 'count'}).reset_index()\n",
    "grouped_not_enacted_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "text_col shape: (322, 100)\ntitle_col shape: (322, 100)\n"
    }
   ],
   "source": [
    "text_col = np.array([X_train['text_avg_word_vector']])\n",
    "title_col = np.array([X_train['title_avg_word_vector']])\n",
    "shape = title_col.shape\n",
    "\n",
    "text_col = text_col.reshape(shape[1], shape[2])\n",
    "title_col = title_col.reshape(shape[1], shape[2])\n",
    "\n",
    "print(f'text_col shape: {text_col.shape}\\ntitle_col shape: {title_col.shape}')\n",
    "\n",
    "X_train = X_train.drop(['title_avg_word_vector', 'text_avg_word_vector'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    276\n1     46\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 84
    }
   ],
   "source": [
    "pd.value_counts(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state=1, k_neighbors=6)\n",
    "\n",
    "text_col_smote, y_smote = smote.fit_resample(text_col, y_train)\n",
    "title_col_smote, y_smote = smote.fit_resample(title_col, y_train)\n",
    "X_train_smote = smote.fit_sample(X_train, y_train)\n",
    "\n",
    "X_train_smt_df = pd.DataFrame(X_train_smote[0], columns=X_train.columns)\n",
    "y_train_smt_df = pd.DataFrame(X_train_smote[1], columns=['verdict'])\n",
    "\n",
    "X_train_smt_df['title_avg_word_vector'] = title_col_smote.tolist()\n",
    "X_train_smt_df['text_avg_word_vector'] = text_col_smote.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1    276\n0    276\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 86
    }
   ],
   "source": [
    "pd.value_counts(y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NumberSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, column):\n",
    "        self.column = column\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[[self.column]]\n",
    "\n",
    "class WordVectorSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, column):\n",
    "        self.column = column\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        vec = np.array([X[self.column]])\n",
    "        shape = vec.shape\n",
    "        vec = np.array([vec]).reshape(shape[1], shape[2])\n",
    "        return vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = [('lr', LogisticRegression(C=4, solver='liblinear')),('svc', SVC(probability=True))]\n",
    "stacked_clf = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression(), verbose=3, \n",
    "                cv=StratifiedKFold(n_splits=5, random_state=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Pipeline([\n",
    "    ('union', FeatureUnion(\n",
    "        transformer_list = [\n",
    "            ('year_diff', Pipeline([\n",
    "                ('selector', NumberSelector(column='year_diff')),\n",
    "                ('standard', StandardScaler())\n",
    "            ])),\n",
    "            ('year_introduced', Pipeline([\n",
    "                ('selector', NumberSelector(column='year_introduced')),\n",
    "            ])),\n",
    "            ('len_of_title', Pipeline([\n",
    "                ('selector', NumberSelector(column='len_of_title')),\n",
    "                ('standard', StandardScaler())\n",
    "            ])),\n",
    "            ('text_length', Pipeline([\n",
    "                ('selector', NumberSelector(column='text_length')),\n",
    "                ('standard', StandardScaler())\n",
    "            ])),\n",
    "            ('sponsor', Pipeline([\n",
    "                ('selector', NumberSelector(column='sponsor')),\n",
    "            ])),\n",
    "            ('sponsor2', Pipeline([\n",
    "                ('selector', NumberSelector(column='sponsor2')),\n",
    "            ])),\n",
    "            ('month', Pipeline([\n",
    "                ('selector', NumberSelector(column='month')),\n",
    "            ])),\n",
    "            ('label', Pipeline([\n",
    "                ('selector', NumberSelector(column='label')),\n",
    "            ])),\n",
    "            ('title_avg_word_vector', Pipeline([\n",
    "                ('selector', WordVectorSelector('title_avg_word_vector'))\n",
    "            ])),\n",
    "            ('text_avg_word_vector', Pipeline([\n",
    "                ('selector', WordVectorSelector('text_avg_word_vector'))\n",
    "            ]))\n",
    "        ]\n",
    "    )),\n",
    "    ('clf', stacked_clf)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.5s finished\n[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s remaining:    0.0s\n[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.3s remaining:    0.0s\n[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    2.9s finished\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Pipeline(memory=None,\n         steps=[('union',\n                 FeatureUnion(n_jobs=None,\n                              transformer_list=[('year_diff',\n                                                 Pipeline(memory=None,\n                                                          steps=[('selector',\n                                                                  NumberSelector(column='year_diff')),\n                                                                 ('standard',\n                                                                  StandardScaler(copy=True,\n                                                                                 with_mean=True,\n                                                                                 with_std=True))],\n                                                          verbose=False)),\n                                                ('year_introduced',\n                                                 Pipeline(memory=None,\n                                                          steps=[('selector',\n                                                                  NumberSelector(column='year_introduce...\n                                                     shrinking=True, tol=0.001,\n                                                     verbose=False))],\n                                    final_estimator=LogisticRegression(C=1.0,\n                                                                       class_weight=None,\n                                                                       dual=False,\n                                                                       fit_intercept=True,\n                                                                       intercept_scaling=1,\n                                                                       l1_ratio=None,\n                                                                       max_iter=100,\n                                                                       multi_class='auto',\n                                                                       n_jobs=None,\n                                                                       penalty='l2',\n                                                                       random_state=None,\n                                                                       solver='lbfgs',\n                                                                       tol=0.0001,\n                                                                       verbose=0,\n                                                                       warm_start=False),\n                                    n_jobs=None, passthrough=False,\n                                    stack_method='auto', verbose=3))],\n         verbose=False)"
     },
     "metadata": {},
     "execution_count": 90
    }
   ],
   "source": [
    "model.fit(X_train_smt_df, y_train_smt_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(X_test)\n",
    "pred_prob = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n              precision    recall  f1-score   support\n\n  not passed       0.93      0.85      0.89       119\n      passed       0.38      0.58      0.46        19\n\n    accuracy                           0.81       138\n   macro avg       0.65      0.71      0.67       138\nweighted avg       0.85      0.81      0.83       138\n\n\nAUC: 0.73\nBrier score: 0.15 \n\n"
    }
   ],
   "source": [
    "target_names = ['not passed', 'passed']\n",
    "print(f'\\n{classification_report(y_true=y_test, y_pred=preds, target_names=target_names)}')\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_true=y_test, y_score=pred_prob[:, 0], pos_label=0)\n",
    "auc_value = auc(fpr, tpr)\n",
    "\n",
    "brier_score = brier_score_loss(y_true=y_test, y_prob=pred_prob[:, 0], pos_label=0)\n",
    "\n",
    "print(f'\\nAUC: {round(auc_value, 2)}')\n",
    "print(f'Brier score: {round(brier_score, 2)} \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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