Modeling - Word embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.core.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from os.path import join as pjoin\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time, sleep, strftime, localtime\n",
    "from datetime import datetime\n",
    "\n",
    "%reload_ext autoreload\n",
    "%autoreload 1\n",
    "\n",
    "pd.set_option('display.max_rows', 800)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.width', 100)\n",
    "pd.set_option('display.max_colwidth', 500)\n",
    "\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re, string\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from itertools import groupby\n",
    "import seaborn as sns\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction import text \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from scipy import stats\n",
    "from statsmodels.stats import weightstats as stests\n",
    "\n",
    "from wordcloud import WordCloud\n",
    "from textblob import TextBlob\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.metrics import f1_score, classification_report, accuracy_score\n",
    "from sklearn.metrics import precision_recall_curve, auc, confusion_matrix\n",
    "\n",
    "from Utils.UtilsViz import *\n",
    "from Utils.DataUtils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"./train_cleaned.csv\")\n",
    "val_data = pd.read_csv(\"./val_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5329, 10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2284, 10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_data.shape, val_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>hashtags_text</th>\n",
       "      <th>num_hashtags</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1117</td>\n",
       "      <td>blew%20up</td>\n",
       "      <td>H / pez &amp; sophia</td>\n",
       "      <td>freyas video blow</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5772</td>\n",
       "      <td>forest%20fires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>climate consequences forest service say spend half budget fire</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503</td>\n",
       "      <td>body%20bags</td>\n",
       "      <td>NaN</td>\n",
       "      <td>scottwalker send poor middle class children war come body bag  typical gop</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9079</td>\n",
       "      <td>structural%20failure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>photo failure structural integrity affect barn raise faulty concrete</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6867</td>\n",
       "      <td>mass%20murder</td>\n",
       "      <td>Melbourne, Australia</td>\n",
       "      <td>hiroshima  historys worst examples mass murder  hiroshima war atombomb japan</td>\n",
       "      <td>1</td>\n",
       "      <td>['#hiroshima', '#war', '#atombomb', '#japan']</td>\n",
       "      <td>['hiroshima', 'war', 'atombomb', 'japan']</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id               keyword              location  \\\n",
       "0  1117             blew%20up     H / pez & sophia    \n",
       "1  5772        forest%20fires                   NaN   \n",
       "2  1503           body%20bags                   NaN   \n",
       "3  9079  structural%20failure                   NaN   \n",
       "4  6867         mass%20murder  Melbourne, Australia   \n",
       "\n",
       "                                                                           text  target  \\\n",
       "0                                                             freyas video blow       0   \n",
       "1                climate consequences forest service say spend half budget fire       1   \n",
       "2    scottwalker send poor middle class children war come body bag  typical gop       0   \n",
       "3          photo failure structural integrity affect barn raise faulty concrete       0   \n",
       "4  hiroshima  historys worst examples mass murder  hiroshima war atombomb japan       1   \n",
       "\n",
       "                                        hashtags                              hashtags_text  \\\n",
       "0                                             []                                         []   \n",
       "1                                             []                                         []   \n",
       "2                                             []                                         []   \n",
       "3                                             []                                         []   \n",
       "4  ['#hiroshima', '#war', '#atombomb', '#japan']  ['hiroshima', 'war', 'atombomb', 'japan']   \n",
       "\n",
       "   num_hashtags  polarity  subjectivity  \n",
       "0             0      0.00           0.0  \n",
       "1             0     -0.17           0.0  \n",
       "2             0     -0.19           0.0  \n",
       "3             0     -0.08           0.0  \n",
       "4             4     -1.00           1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>hashtags_text</th>\n",
       "      <th>num_hashtags</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2701</td>\n",
       "      <td>crush</td>\n",
       "      <td>NaN</td>\n",
       "      <td>love love aint playa just crush lot</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6245</td>\n",
       "      <td>hijacking</td>\n",
       "      <td>brisbane, australia</td>\n",
       "      <td>hot  funtenna hijack computers send data sound wave black hat  prebreak best</td>\n",
       "      <td>0</td>\n",
       "      <td>['#hot', '#prebreak', '#best']</td>\n",
       "      <td>['hot', 'prebreak', 'best']</td>\n",
       "      <td>3</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8261</td>\n",
       "      <td>rioting</td>\n",
       "      <td>A little house in the outback.</td>\n",
       "      <td>bbcnews ass british insurers say riot cost insurers  police number reduce blind fat contr</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5510</td>\n",
       "      <td>flames</td>\n",
       "      <td>NaN</td>\n",
       "      <td>advantage apropos flame favorable regard mississauga ontario pwhvgwax</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7046</td>\n",
       "      <td>meltdown</td>\n",
       "      <td>The Universe</td>\n",
       "      <td>ltmeltdown proportion commence manage calm long turn water hot wait steam cloud vision</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id    keyword                        location  \\\n",
       "0  2701      crush                             NaN   \n",
       "1  6245  hijacking             brisbane, australia   \n",
       "2  8261    rioting  A little house in the outback.   \n",
       "3  5510     flames                             NaN   \n",
       "4  7046   meltdown                    The Universe   \n",
       "\n",
       "                                                                                        text  \\\n",
       "0                                                        love love aint playa just crush lot   \n",
       "1               hot  funtenna hijack computers send data sound wave black hat  prebreak best   \n",
       "2  bbcnews ass british insurers say riot cost insurers  police number reduce blind fat contr   \n",
       "3                      advantage apropos flame favorable regard mississauga ontario pwhvgwax   \n",
       "4     ltmeltdown proportion commence manage calm long turn water hot wait steam cloud vision   \n",
       "\n",
       "   target                        hashtags                hashtags_text  num_hashtags  polarity  \\\n",
       "0       0                              []                           []             0      0.50   \n",
       "1       0  ['#hot', '#prebreak', '#best']  ['hot', 'prebreak', 'best']             3      0.37   \n",
       "2       1                              []                           []             0     -0.25   \n",
       "3       0                              []                           []             0      0.00   \n",
       "4       0                              []                           []             0      0.17   \n",
       "\n",
       "   subjectivity  \n",
       "0           1.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    train_data.head(),\n",
    "    val_data.head()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop empty tweets\n",
    "train_data.dropna(axis=0, subset=[\"text\"], inplace=True)\n",
    "val_data.dropna(axis=0, subset=[\"text\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use TFIDF onto the dataset to convert the text data into a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ycol = [\"target\"]\n",
    "xcol = [\"text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quick helper functions\n",
    "def get_confusion_mat(y_true, y_pred, colnames=[\"Pred_0\", \"Pred_1\"], index=[\"True_0\", \"True_1\"]):\n",
    "    return pd.DataFrame(confusion_matrix(y_true, y_pred), columns=colnames, index=index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1667334085260895\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e62374630>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rx7Sidazduifa1RGROKKgCDTcHhvN5yj258bTBpOSmMC9b66IdlVEJI4oKAJfXlG036DIyUzl2pMGMH3BBpZu3Bnt6ohInFBQBNp711OD6ycMonNqEne/rqsKETk8FBSByupazCAtuX3/k3TtlMx3ThnEm0s388kX5dGujojEgfb9V/EwqqiuIz05kdAUU+3bN04cQI/OKfxOd0CJyGGgoAjsaYMpxttKRmoSN542mFmrtvHByq3Rro6IxDgFRaCylRctamtXjOtH365p/HbGMi10JCJtSkERaO31sttaalIiN585lAUlO3j9s83Rro6IxDAFRaCypnXXyz4cLj42l4E9Mrj79eXU1euqQkTahoIisKeqlowOMkbRICkxgVvOHsqKzbv56ycl0a6OiMQoBUWgorrjXVEAnDeiD8f268Z//n0J83W7rIi0AQVFoLKmY41RNEhIMP749UJ6dknl2ifmsqp0d7SrJCIxRkER6GiD2eFyMlP587VjSUwwrn50Dpt37o12lUQkhigoAhVVtR3mOYqm9M/O4PFvjKW8oppvPD6XnXtrol0lEYkRCgrA3anooF1P4UbmdeWhq8awcvMubvjzPKpq66JdJRGJAQoKoKq2Hvf2uWjRgTp5aA6/vfRoPlq9jVumLaBet82KyCHquH0trWjfzLHJHT8oAC46Jo/SXVX81yvLALh2/ACO7de9Q8xjJSLtj4KC0DMUAJ1SY+ef49sTBrK7qo6H3l3Fyws3ktstnQtG9WXSqL4c2SdToSEiEYudv4yHoLKm/S9adKDMjFvOGsq3JxTw+pLNTF+wgUfeX81D765icM/OXDImj+tOKiA5Ub2PItIyBQUdY3W7g5WZlsxXx+Tx1TF5bNtdxSuLNzH90/Xc+eoyZizZxP9ccSy53dKjXU0Racci+jhpZhPNbLmZFZvZrU28n2pmzwbvzzazAWHv3RbsX25m5zQ6LtHM5pvZS2H7CoJzrAzOmXLwzYtMw3rZ6cmxnZvZnVP5+vH9ee47J3L/5cewYtMuvnL/+7y1VJMKikjz9hsUZpYIPACcCwwHLjez4Y2KXQeUu/tg4F7gruDY4cAU4ChgIvBgcL4GNwFLG53rLuBedx8ClAfnblMVVaEriozU2LuiaM6kUX156fsT6NM1neueLOI3ryylpq4+2tUSkXYokiuKsUCxu69292pgKjC5UZnJwJPB6+eBMyw0WjoZmOruVe6+BigOzoeZ5QFfAf7UcJLgmNODcxCc88KDadiBqIjBMYpIFPTI4MXvnsiV4/rxx/dWM+Xhj9mwvTLa1RKRdiaSoMgF1oVtlwT7mizj7rXADiB7P8feB/wYCP8Ymw1sD87R3M8CwMyuN7MiMysqLS2NoBnNq2zoeurAT2YfrLTkRO64aCT3X34Myzbu5Cv3v8+6sopoV0tE2pFIgqKp+ygbP8XVXJkm95vZ+cAWd593ED8rtNP9YXcvdPfCnJycpopErGEwOyPOrijCTRrVl7/dOJ6de2t5Zs4X0a6OiLQjkQRFCZAftp0HbGiujJklAV2BshaOHQ9MMrO1hLqyTjezvwBbgW7BOZr7Wa2uIShi4cnsQzGkVyanDs3hhXkl1Gq8QkQCkQTFXGBIcDdSCqHB6emNykwHrgleXwLM9NBCztOBKcFdUQXAEGCOu9/m7nnuPiA430x3vyo45u3gHATn/PshtC8iFdW1JCYYKXqmgEsL89myq4r3Vh5ad56IxI79/mUMxgu+B8wgdIfSNHdfYma3m9mkoNijQLaZFQO3ALcGxy4BpgGfAa8BN7r7/maq+wlwS3Cu7ODcbaqiuo5OyYl6Whk4/YieZGekMG2uVswTkZCIRm/d/RXglUb7/jPs9V7g0maOvQO4o4VzvwO8E7a9muDOqMOlsrqOTnF0a2xLUpISuOiYXJ6YtZZtu6vI7pwa7SqJSJSprwXYU13XodeiaG2XFuZTW++8OH99tKsiIu2AgoLQ7bHpMTJzbGsY1juTUfndmFa0jtCwkYjEMwUFHXsZ1LZyWWEeKzbvZmHJjmhXRUSiTEFBEBQxNMV4a7hgVF9SkxKYVrRu/4VFJKYpKAjdHhsrixa1li5pyZw3sg/TP91AZbWWVBWJZwoK1PXUnEsL89hVVcuMJZuiXRURiSIFBaHbY+P9qeymHF+QTX5WurqfROKcgoLQFUWGxij+RUKCcemYfGat2qaJAkXiWNwHRX29U1lTp9tjm/HVMXmYwXPz9KS2SLyK+6CIxfWyW1Nut3ROGtyD54vWUVevZypE4lHc97fE8nrZreWywnz+/X/nc/2fi0hJSqCmrp7qOqe6to6aOmdY70y+fnx/juzTJdpVFZE2EPdBUbkvKOL+n6JZZw3vxdgBWawq3U1KUgLJiaGvlMQEkhONF+aV8MzsLxhbkMU3ThzAWcN7kayZeEViRtz/ddwTrG6nK4rmpSUnMu07JzT7fvmeaqYVreOpjz/nu09/Qu8uaVw5rh9TxvYjJ1OTCop0dHH/sU+LFh267hkp3HDKIN790Wn86epChvTqzN1vrGD8XTP5yfMLWbl5V7SrKCKHIO6vKNT11HoSE4wzh/fizOG9WFW6m8c/XMNzRSU8W7SO04bl8O0JAzlhULbW/RDpYHRFoa6nNjEopzO/vnAkH912BrecNZRF63dwxZ9mc/7vP+DlhRujXT0ROQAKCt311KayMlL4/hlD+OAnp3PnxSOpqq3nxmc+4Xk9lyHSYSgo1PV0WKQlJzJlbD9eu2kCJwzM5mcvLmLJBk1hLtIRKCiCricNZh8eSYkJ/P6KY+jeKYV/+8sn7KioiXaVRGQ/4j4oKtX1dNj16JzKA1cey8YdlfzHtE+p1xPfIu1a3AfFnuq64MGxuP+nOKzG9O/Oz88fzsxlW3jg7eJoV0dEWhD3fx0rq2vV7RQlXz++PxeO7ss9b67gvRWl0a6OiDQj7oNCixZFj5nxXxePZFivTG6aOp+Sck1lLtIeKShqFBTR1CkliT9cNYbaOue7T39C+Z7qaFdJRBqJKCjMbKKZLTezYjO7tYn3U83s2eD92WY2IOy924L9y83snGBfmpnNMbMFZrbEzH4ZVv4JM1tjZp8GX6MPvZnNq6iq1a2xUVbQI4O7LxvF4vU7OP43b/GT5xfy2Yad0a6WiAT2+xfSzBKBB4CzgBJgrplNd/fPwopdB5S7+2AzmwLcBXzNzIYDU4CjgL7Am2Y2FKgCTnf33WaWDHxgZq+6+8fB+X7k7s+3ViNbUqFlUNuFs4/qzas3ncwTs9by4vzQtB9jB2TxjfEDOHt4L5J0s4FI1ETyf99YoNjdV7t7NTAVmNyozGTgyeD188AZFprQZzIw1d2r3H0NUAyM9ZDdQfnk4Csq90hW1tSRoaBoF4b1zuQ3F4/k49vO4KfnHcGGHZV89+lPmPDfb/OXjz/XwkkiURJJUOQC68K2S4J9TZZx91pgB5Dd0rFmlmhmnwJbgDfcfXZYuTvMbKGZ3WtmTc5TbWbXm1mRmRWVlh78HTOhwWx1PbUn3TqlcP3JodloH7m6kLzu6fyfvy3mwgc+ZP4X5dGunkjciSQomprqs/FHu+bKNHusu9e5+2ggDxhrZiOC928DjgCOA7KAnzRVKXd/2N0L3b0wJydn/61oRkWVbo9trxITjLOG92LaDSdw/+XHsHnnXi56cBY/eX4h23ZXRbt6InEjkqAoAfLDtvOADc2VMbMkoCtQFsmx7r4deAeYGGxvDLqmqoDHCXV9tRnd9dT+mRmTRvVl5g9P5fqTB/LCJyWcfve7PKXuKJHDIpKgmAsMMbMCM0shNDg9vVGZ6cA1wetLgJnu7sH+KcFdUQXAEGCOmeWYWTcAM0sHzgSWBdt9gu8GXAgsPpQG7o+6njqOzqlJ/PS8I3n1pgkM79OFn/9tMWfd8y5/en+15owSaUP7/Qvp7rVm9j1gBpAIPObuS8zsdqDI3acDjwJPmVkxoSuJKcGxS8xsGvAZUAvc6O51QRg8GdxRlQBMc/eXgh/5tJnlEOq2+hT4Tms2OFxtXT3VtfW6ouhghvTK5Jlvj+PVxZt45P3V/Prlpfx2xnIuGNWXK8f1Y3R+Ny2OJNKKLPTBv2MrLCz0oqKiAz5u594ajv7F6/yfrxzJtyYMbIOayeGwZMMOnp79BX+bv56K6jqO6tuFb44v4OJjcklIUGCINMfM5rl74f7KxfXN6ZVaLzsmHNW3K/910Uhm//QMfnXhCOrqnR8+t4DLH/mYtVv3RLt6Ih1eXAdFw6JFGRqjiAmZacl8/fj+vHrTBO68eCSfbdjJOfe9x8PvraK2rj7a1RPpsOI8KLRoUSwyM6aM7ccbt5zChCE5/Ncry/jqH2axbJOmBRE5GHEeFFq0KJb17prGI1eP4feXH0NJeSUX/P4D7nx1GW8v28LKzbv2fVAQkZbFdZ+LgiL2mRkXjOrL+ME9+OU/lvDQu6t46N1V+97Pykghr3s6+VmduHb8AMb0z4pibUXap7gOisrgE6Weo4h9WRkp/L8px/CzrxzJurIKSsorw74qmL16G68s2sjVx/fnRxOPoHOqfidEGsT1/w26oog/PTPT6JmZxpj+/7x/d1Utv5uxnCc/WsubS7fw64tGcNqwnlGpo0h7E9djFHt0e6wEOqcm8YtJR/H8d04gPSWRbz4+l/949lPKtJCSSHwHhbqepLEx/bN4+fsn8f3TB/OPBRs46553eX5eCfWaU0riWFwHRUPXU3qyrijkS6lJidxy9jBe+v5J5Gd14ofPLeCiBz9k3udl0a6aSFTEdVBUVteRlpxAoqZ5kCYc0bsLf/23E7nnslFs2rmXr/7hI77/v/NZv70y2lUTOaziOij2VGu9bGlZQoJx8bF5zPzBqXz/9MHMWLKJM+5+h3veWKHnMCRuxPWkgKtLd1O6q4pxA7PboFYSi0rKK7jz1WW8tHAjOZmp3HjqIC4f14/UJHVfSscT6aSAcR0UIgeraG0Z/z1jOXPWlNG3axr/fsYQLhmTR3JiXF+kSwejoBBpY+7Oh8XbuPuN5cz/Yjv9sjpx0xlDuPCYXI17SYegoBA5TNydd5aX8rvXl7Nkw076Z3fiwtG5TBrdl0E5naNdPZFmKShEDjN3Z8aSzTw5ay0fr9mGO4zI7cKkUX25YFRf+nRNj3YVRf6JgkIkijbv3Ms/FmzgHws2sKBkB2YwdkAWl4zJ47yRfcjQXFLSDigoRNqJtVv3MH3BBv42fz2rt+4hIyWR84/uy2XH5XNsP63vLdGjoBBpZ9ydeZ+XM61oHS8t3EhFdR2DcjK4rDCfywrz6Z6REu0qSpxRUIi0Y7uranll4UaeLVrHvM/LSU9O5LLCPL41YSD5WZ2iXT2JEwoKkQ5i+aZdPPL+av7+6Xrq6p1zR/bhhpMHcnRet2hXTWKcgkKkg9m0Yy9PzFrL07M/Z9feWsYVZPGDs4cxtkCr7knbUFCIdFC79tbw7Nx1PPrBGjbt3Ms3TyzgR+cM07op0uoiDQrNNyDSzmSmJfOtCQN56wencPXx/XnswzV85f73+eSL8mhXTeJUREFhZhPNbLmZFZvZrU28n2pmzwbvzzazAWHv3RbsX25m5wT70sxsjpktMLMlZvbLsPIFwTlWBufUrSASlzqlJPHLySN4+lvjqKqt55I/zOLOV5dRVVsX7apJnNlvUJhZIvAAcC4wHLjczIY3KnYdUO7ug4F7gbuCY4cDU4CjgInAg8H5qoDT3X0UMBqYaGbHB+e6C7jX3YcA5cG5ReLW+ME9eO3mCVxWmM9D767igt9/wLzPy4iFbmPpGCK5ohgLFLv7anevBqYCkxuVmQw8Gbx+HjjDQk8RTQamunuVu68BioGxHrI7KJ8cfHlwzOnBOQjOeeFBtk0kZmSmJXPnV4/m8W8ex47KGr76h4+YeN/7PPLearbs2hvt6kmMiyQocoF1Ydslwb4my7h7LbADyG7pWDNLNLNPgS3AG+4+Ozhme3CO5n4WwfHXm1mRmRWVlpZG0AyRju+0YT1585ZTuOOiEXRKTeSOV5Zywm9mcu0Tc3ll0UZ1S0mbiGTCmabmF2h8zdtcmWaPdfc6YLSZdQNeNLMRwOYIfhbB8Q8DD0Porqemqy4SezIN8jtkAAANn0lEQVTTkrlyXH+uHNefVaW7eWFeCX/9ZD3fXfYJ3TuF3rv6xP70zEyLdlUlRkRyRVEC5Idt5wEbmitjZklAV6AskmPdfTvwDqExjK1At+Aczf0sEQkMyunMjycewYe3ns6frx3LcQOyeOCdYk66821++NwClm3aGe0qSgyIJCjmAkOCu5FSCA1OT29UZjpwTfD6EmCmh0bapgNTgruiCoAhwBwzywmuJDCzdOBMYFlwzNvBOQjO+feDb55IfEhMME4emsPDVxfy9g9OZcrYfF5euJGJ973P1x+dzdvLt1BdWx/takoHFdEDd2Z2HnAfkAg85u53mNntQJG7TzezNOAp4BhCVxJT3H11cOzPgGuBWuBmd3/VzI4mNFCdSCisprn77UH5gYQGzLOA+cBV7l7VUv30wJ3Iv9peUc3Ts7/gyVlr2bKrivTkRAoHdOfEQT04YVA2I/p2IUlLt8Y1PZktIgBU1dbx9rItzFq1jY9WbWPlltANh5mpSRxXkMWw3pnkd+9EflY6ed07kdstnZQkBUg8iDQotHqKSIxLTUpk4og+TBzRB4DSXVV8vHobH63exuzV23hvRSm19V9+YDSD3l3S6NM1jZ6ZafTskkqvLmnkZKbSMzOVI3p3oXdXDZTHE11RiMS5unpn0869rCurCH2VV1JSVsGmnXvZsquKzTv3smtv7b7yKYkJ/Pvpg7nhlEG68ujgdEUhIhFJTDByu6WT2y2d4wdmN1lmb00dW3ZWsXlXaIbbu99YwcuLNnLnV49mdL6mQ491+jggIvuVlpxIv+xOodtvrziWR64uZHtFDRc/+CG/eukzKqpr938S6bAUFCJywM4a3ovXbzmZK8b149EP1nD2ve8xc9lmaut0C24s0hiFiBySOWvKuPWFhazeuoeMlESO7d+dcQVZHDcgi1H53UhL1joa7ZVujxWRw2ZvTR1vLt3MnDVlzFlTxrJNu4DQwPfo/G6cfmRPJh7VmwE9MqJcUwmnoBCRqNleUU3R2nLmrC3jw+KtLNkQmkrkiN6ZnHNUb84d2ZthvTIJTRgt0aKgEJF2o6S8ghlLNjNjySbmri3DHQZkd+KcEb05b0Qfjs7rqtCIAgWFiLRLpbuqeHPpZl5dvIlZxVuprXdyu6UzcURvzhvZm2Pyu5OQoNA4HBQUItLu7aio4Y2lm3l10UbeX7mV6rp6enVJ5ZShOYzK78aovG4M651JsuakahMKChHpUHburWHm0i28smgjc9aWsb2iBoCUpASG9+nCqLyunDAom7OG9yZRVxytQkEhIh2Wu7OurJKF67ezsGQHC9ZtZ/H6HeyprmNIz87cfOZQzh3RW11Uh0hBISIxpa7eeW3xJu59cwXFW3ZzRO9M/uOsoZw9vJcGwg+SgkJEYlJdvfOPBRu4780VrN1Wwcjcrtx0xhAmDO1BapIe7jsQCgoRiWm1dfX8df567n9rJSXllaQlJ3DcgCxOGJTNiYN6aGGmCCgoRCQuVNfW887yLxdmWr459FR4ZmoSYwuyGJHblSN6ZzK0dyYDsjM0EB5G04yLSFxISUrg7KN6c/ZRvYEvF2aatWobs9dsY+byLTR8Hk5NSmBwz84M651Jbrd0unVKoXunZLp3SqFb8L131zTNT9WIgkJEYkpOZioXjOrLBaP6AlBZXUfxlt0s27STFZt3sWzTLj4s3krprirqm+hQyUhJ5JwRvblwdC4nDspW9xUKChGJcekpiYzM68rIvK7/tL++3tm5t4byihrKK6rZXlFN+Z4a5qwp45XFG/nrJ+vp0TmVC0b14cLRuXE9zYjGKEREGtlbU8c7y7fwt/kbmLlsC9V19Qzt1ZlfTR7BuGZWAeyINJgtItIKdlTW8NrijfzP28WsK6vkquP78ZOJR5CZlhztqh2ySINCnW8iIi3omp7M147rx4ybT+a6kwp4ZvYX+1b0ixcKChGRCHRKSeLn5w/nhX87kcy0JK59ooibps5n2+6qaFetzUUUFGY20cyWm1mxmd3axPupZvZs8P5sMxsQ9t5twf7lZnZOsC/fzN42s6VmtsTMbgor/wszW29mnwZf5x16M0VEWscx/brz0r9P4OYzh/DKoo2c9rt3+Objc/jNq0t5YV4Ji9fvYG9NXbSr2ar2e9eTmSUCDwBnASXAXDOb7u6fhRW7Dih398FmNgW4C/iamQ0HpgBHAX2BN81sKFAL/MDdPzGzTGCemb0Rds573f13rdVIEZHWlJKUwM1nDuW8kX146N1VLN24iw+Lt1FdVw+AGQzIzuDYft0ZPzib8YN70KtLWpRrffAiuT12LFDs7qsBzGwqMBkID4rJwC+C188D/2Oh+8gmA1PdvQpYY2bFwFh3/wjYCODuu8xsKZDb6JwiIu3a0F6Z3HPZaCA0pcjnZRWs2LSL5Zt3sWzjLmYu28wLn5QAMCgng/GDe3DioB6cMCibrukdZzA8kqDIBdaFbZcA45or4+61ZrYDyA72f9zo2NzwA4NuqmOA2WG7v2dmVwNFhK48yhtXysyuB64H6NevXwTNEBFpO0mJCQzK6cygnM6cO7IPEHpW47ONO5m1aisfFm/juaIS/vzR5yQYjMzrxknB1caY/t3b9YSGkQRFU0+YNL6ntrkyLR5rZp2BF4Cb3X1nsPsPwK+Ccr8C7gau/ZeTuD8MPAyh22NbboKIyOGXkGCMyO3KiNyuXH/yIKpr65n/RTkfrtrGh8Vbeejd1Tzw9qp9ExqePbwXl4zJJz2lfYVGJEFRAuSHbecBG5opU2JmSUBXoKylY80smVBIPO3uf20o4O777jkzs0eAlyJtjIhIe5aSlMC4gdmMG5jNLWcNZdfeGmavLuPDVVv5YOVWfv73Jdz75kq+eeIArj5hAF07tY/uqUiCYi4wxMwKgPWEBqevaFRmOnAN8BFwCTDT3d3MpgPPmNk9hAazhwBzgvGLR4Gl7n5P+InMrI+7bww2LwIWH1zTRETat8y0ZM4c3oszh/cCoGhtGQ++s4q731jBQ++u4qrj+3PdSQX0jPJAeERPZge3qN4HJAKPufsdZnY7UOTu080sDXiK0FhDGTAlbPD7Z4S6jmoJdTG9amYnAe8Di4D64Mf81N1fMbOngNGEup7WAjeEBUeT9GS2iMSSpRt38od3VvHSwg0kJSZw0ehczhnRi+MHZtMppfWm6NMUHiIiHdzarXv443ureXF+CXtr6kNdVwVZnDI0h1OH5TAop/MhTVSooBARiRF7a+qYu7aMd5aX8u6KUoq37AYgt1s6v730aE4c1OOgzquFi0REYkRaciIThuQwYUgOPwfWlVXw3spS3lleSp+u6W3+8xUUIiIdTH5WJ64c158rx/U/LD9PkwKKiEiLFBQiItIiBYWIiLRIQSEiIi1SUIiISIsUFCIi0iIFhYiItEhBISIiLYqJKTzMrBT4/CAP7wFsbcXqdBRqd/yJ17ar3c3r7+45+ztRTATFoTCzokjmOok1anf8ide2q92HTl1PIiLSIgWFiIi0SEERrLsdh9Tu+BOvbVe7D1Hcj1GIiEjLdEUhIiItUlCIiEiL4joozGyimS03s2IzuzXa9WkrZvaYmW0xs8Vh+7LM7A0zWxl87x7NOrYFM8s3s7fNbKmZLTGzm4L9Md12M0szszlmtiBo9y+D/QVmNjto97NmlhLturYFM0s0s/lm9lKwHfPtNrO1ZrbIzD41s6JgX6v9nsdtUJhZIvAAcC4wHLjczIZHt1Zt5glgYqN9twJvufsQ4K1gO9bUAj9w9yOB44Ebg//Gsd72KuB0dx8FjAYmmtnxwF3AvUG7y4HroljHtnQTsDRsO17afZq7jw57dqLVfs/jNiiAsUCxu69292pgKjA5ynVqE+7+HlDWaPdk4Mng9ZPAhYe1UoeBu29090+C17sI/fHIJcbb7iG7g83k4MuB04Hng/0x124AM8sDvgL8Kdg24qDdzWi13/N4DopcYF3YdkmwL170cveNEPqDCvSMcn3alJkNAI4BZhMHbQ+6Xz4FtgBvAKuA7e5eGxSJ1d/3+4AfA/XBdjbx0W4HXjezeWZ2fbCv1X7Pk1qhgh2VNbFP9wrHIDPrDLwA3OzuO0MfMmObu9cBo82sG/AicGRTxQ5vrdqWmZ0PbHH3eWZ2asPuJorGVLsD4919g5n1BN4ws2WtefJ4vqIoAfLDtvOADVGqSzRsNrM+AMH3LVGuT5sws2RCIfG0u/812B0XbQdw9+3AO4TGaLqZWcOHw1j8fR8PTDKztYS6kk8ndIUR6+3G3TcE37cQ+mAwllb8PY/noJgLDAnuiEgBpgDTo1ynw2k6cE3w+hrg71GsS5sI+qcfBZa6+z1hb8V0280sJ7iSwMzSgTMJjc+8DVwSFIu5drv7be6e5+4DCP3/PNPdryTG221mGWaW2fAaOBtYTCv+nsf1k9lmdh6hTxyJwGPufkeUq9QmzOx/gVMJTTu8Gfi/wN+AaUA/4AvgUndvPODdoZnZScD7wCK+7LP+KaFxiphtu5kdTWjwMpHQh8Fp7n67mQ0k9Ek7C5gPXOXuVdGradsJup5+6O7nx3q7g/a9GGwmAc+4+x1mlk0r/Z7HdVCIiMj+xXPXk4iIREBBISIiLVJQiIhIixQUIiLSIgWFiIi0SEEhIiItUlCIiEiL/j/EFzE4fiM30gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run truncated SVD on the dataset\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "num_svd_comp = 50\n",
    "svd = TruncatedSVD(n_components=num_svd_comp, n_iter=7, random_state=42)\n",
    "X_train_svd = svd.fit_transform(X_train)\n",
    "X_val_svd = svd.transform(X_val)\n",
    "print(np.sum(svd.explained_variance_ratio_))\n",
    "sns.lineplot(x=list(range(num_svd_comp)), y=svd.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "# model=TSNE(learning_rate=100)\n",
    "# Tsne_transformed_train = model.fit_transform(X_train_svd)\n",
    "# Tsne_transformed_val = model.fit_transform(X_val_svd)\n",
    "# xs_train = Tsne_transformed_train[:,0]\n",
    "# ys_train = Tsne_transformed_train[:,1]\n",
    "# xs_val = Tsne_transformed_val[:,0]\n",
    "# ys_val = Tsne_transformed_val[:,1]\n",
    "# save_to_disk(xs_train, \"tsne_x_train.pkl\")\n",
    "# save_to_disk(ys_train, \"tsne_y_train.pkl\")\n",
    "# save_to_disk(xs_val, \"tsne_x_val.pkl\")\n",
    "# save_to_disk(ys_val, \"tsne_y_val.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs_train = load_from_disk(\"./tsne_x_train.pkl\")\n",
    "ys_train = load_from_disk(\"./tsne_y_train.pkl\")\n",
    "xs_val = load_from_disk(\"./tsne_x_val.pkl\")\n",
    "ys_val = load_from_disk(\"./tsne_y_val.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Ground truth: Val')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,2,figsize=(17,8))\n",
    "g = sns.scatterplot(xs_train,ys_train, hue=train_data[ycol].values.ravel(), ax=ax[0])\n",
    "g.set(title=\"Ground truth: Train\")\n",
    "# g = sns.scatterplot(xs_train,ys_train, hue=train_ylabels, ax=ax[0][1], palette=rev_palette)\n",
    "# g.set(title=\"Predicted: Train\")\n",
    "g = sns.scatterplot(xs_val,ys_val, hue=val_data[ycol].values.ravel(), ax=ax[1])\n",
    "g.set(title=\"Ground truth: Val\")\n",
    "# g = sns.scatterplot(xs_val,ys_val, hue=val_ylabels, ax=ax[1][1], palette=rev_palette)\n",
    "# g.set(title=\"Predicted: Val\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quick helper variable\n",
    "classification_df = {\n",
    "    \"classifier\":[],\n",
    "    \"training_acc\":[],\n",
    "    \"val_acc\":[]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression - Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Training data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.93      0.88      3014\n",
      "           1       0.90      0.75      0.82      2310\n",
      "\n",
      "    accuracy                           0.85      5324\n",
      "   macro avg       0.86      0.84      0.85      5324\n",
      "weighted avg       0.86      0.85      0.85      5324\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Pred_0</th>\n",
       "      <th>Pred_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>True_1</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "        Pred_0  Pred_1\n",
       "True_0    2811     203\n",
       "True_1     576    1734"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Test data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.87      0.83      1324\n",
      "           1       0.79      0.69      0.74       957\n",
      "\n",
      "    accuracy                           0.79      2281\n",
      "   macro avg       0.79      0.78      0.78      2281\n",
      "weighted avg       0.79      0.79      0.79      2281\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
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   ],
   "source": [
    "logit = LogisticRegression()\n",
    "logit.fit(X_train, train_data[ycol].values)\n",
    "\n",
    "train_ylabels = logit.predict(X_train)\n",
    "\n",
    "print(20*\"=\", \"Training data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(train_data[ycol].values.ravel(), train_ylabels))\n",
    "\n",
    "val_ylabels = logit.predict(X_val)\n",
    "\n",
    "print(20*\"=\", \"Test data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=val_data['target'].values, y_pred=val_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(val_data['target'].values.ravel(), val_ylabels))\n",
    "\n",
    "\n",
    "# Append info to the classification dictionary\n",
    "classification_df[\"classifier\"].append(\"LogReg_baseline\")\n",
    "classification_df[\"training_acc\"].append(accuracy_score(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "classification_df[\"val_acc\"].append(accuracy_score(y_true=val_data[ycol].values, y_pred=val_ylabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression - Truncated SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Training data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.89      0.79      3014\n",
      "           1       0.79      0.53      0.64      2310\n",
      "\n",
      "    accuracy                           0.74      5324\n",
      "   macro avg       0.75      0.71      0.71      5324\n",
      "weighted avg       0.75      0.74      0.72      5324\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
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       "      <th>True_0</th>\n",
       "      <td>2684</td>\n",
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       "        Pred_0  Pred_1\n",
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       "True_1    1079    1231"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Test data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.89      0.79      1324\n",
      "           1       0.77      0.52      0.62       957\n",
      "\n",
      "    accuracy                           0.73      2281\n",
      "   macro avg       0.74      0.70      0.71      2281\n",
      "weighted avg       0.74      0.73      0.72      2281\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
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   ],
   "source": [
    "logit = LogisticRegression()\n",
    "logit.fit(X_train_svd, train_data[ycol].values)\n",
    "\n",
    "train_ylabels = logit.predict(X_train_svd)\n",
    "\n",
    "print(20*\"=\", \"Training data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(train_data[ycol].values.ravel(), train_ylabels))\n",
    "\n",
    "val_ylabels = logit.predict(X_val_svd)\n",
    "\n",
    "print(20*\"=\", \"Test data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=val_data['target'].values, y_pred=val_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(val_data['target'].values.ravel(), val_ylabels))\n",
    "\n",
    "# Append info to the classification dictionary\n",
    "classification_df[\"classifier\"].append(\"LogReg_SVD\")\n",
    "classification_df[\"training_acc\"].append(accuracy_score(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "classification_df[\"val_acc\"].append(accuracy_score(y_true=val_data[ycol].values, y_pred=val_ylabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression - L1 regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Training data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.92      0.86      3014\n",
      "           1       0.88      0.71      0.78      2310\n",
      "\n",
      "    accuracy                           0.83      5324\n",
      "   macro avg       0.84      0.81      0.82      5324\n",
      "weighted avg       0.83      0.83      0.83      5324\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Test data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.86      0.81      1324\n",
      "           1       0.77      0.64      0.70       957\n",
      "\n",
      "    accuracy                           0.77      2281\n",
      "   macro avg       0.77      0.75      0.76      2281\n",
      "weighted avg       0.77      0.77      0.77      2281\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
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   ],
   "source": [
    "lasso = LogisticRegression(penalty='l1', tol=1e-5)\n",
    "lasso.fit(X_train, train_data[ycol].values)\n",
    "\n",
    "train_ylabels = lasso.predict(X_train)\n",
    "\n",
    "print(20*\"=\", \"Training data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(train_data[ycol].values.ravel(), train_ylabels))\n",
    "\n",
    "val_ylabels = lasso.predict(X_val)\n",
    "\n",
    "print(20*\"=\", \"Test data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=val_data['target'].values, y_pred=val_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(val_data['target'].values.ravel(), val_ylabels))\n",
    "\n",
    "# Append info to the classification dictionary\n",
    "classification_df[\"classifier\"].append(\"Lasso\")\n",
    "classification_df[\"training_acc\"].append(accuracy_score(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "classification_df[\"val_acc\"].append(accuracy_score(y_true=val_data[ycol].values, y_pred=val_ylabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM - non linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Training data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      1.00      0.72      3014\n",
      "           1       0.00      0.00      0.00      2310\n",
      "\n",
      "    accuracy                           0.57      5324\n",
      "   macro avg       0.28      0.50      0.36      5324\n",
      "weighted avg       0.32      0.57      0.41      5324\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Test data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      1.00      0.73      1324\n",
      "           1       0.00      0.00      0.00       957\n",
      "\n",
      "    accuracy                           0.58      2281\n",
      "   macro avg       0.29      0.50      0.37      2281\n",
      "weighted avg       0.34      0.58      0.43      2281\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "d:\\softwares\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
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       "True_1     957       0"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = SVC(probability=True, kernel='rbf', C=1)\n",
    "svc = svc.fit(X=X_train, y=train_data[ycol].values.ravel())\n",
    "\n",
    "train_ylabels = svc.predict(X_train)\n",
    "\n",
    "print(20*\"=\", \"Training data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(train_data[ycol].values.ravel(), train_ylabels))\n",
    "\n",
    "val_ylabels = svc.predict(X_val)\n",
    "\n",
    "print(20*\"=\", \"Test data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=val_data['target'].values, y_pred=val_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(val_data['target'].values.ravel(), val_ylabels))\n",
    "\n",
    "# Append info to the classification dictionary\n",
    "classification_df[\"classifier\"].append(\"SVM\")\n",
    "classification_df[\"training_acc\"].append(accuracy_score(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "classification_df[\"val_acc\"].append(accuracy_score(y_true=val_data[ycol].values, y_pred=val_ylabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\softwares\\python36\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Training data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      3014\n",
      "           1       0.99      0.97      0.98      2310\n",
      "\n",
      "    accuracy                           0.98      5324\n",
      "   macro avg       0.98      0.98      0.98      5324\n",
      "weighted avg       0.98      0.98      0.98      5324\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
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       "      <th>Pred_0</th>\n",
       "      <th>Pred_1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2986</td>\n",
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       "      <th>True_1</th>\n",
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       "        Pred_0  Pred_1\n",
       "True_0    2986      28\n",
       "True_1      80    2230"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Test data ====================\n",
      "Classification Reports ---\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.82      0.80      1324\n",
      "           1       0.73      0.68      0.71       957\n",
      "\n",
      "    accuracy                           0.76      2281\n",
      "   macro avg       0.76      0.75      0.75      2281\n",
      "weighted avg       0.76      0.76      0.76      2281\n",
      "\n",
      "-------------------------------------------------------\n",
      "confusion matrix ---\n"
     ]
    },
    {
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   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "rf.fit(X_train, train_data[ycol])\n",
    "\n",
    "train_ylabels = rf.predict(X_train)\n",
    "\n",
    "print(20*\"=\", \"Training data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(train_data[ycol].values.ravel(), train_ylabels))\n",
    "\n",
    "val_ylabels = rf.predict(X_val)\n",
    "\n",
    "print(20*\"=\", \"Test data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=val_data['target'].values, y_pred=val_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(val_data['target'].values.ravel(), val_ylabels))\n",
    "\n",
    "# Append info to the classification dictionary\n",
    "classification_df[\"classifier\"].append(\"RF\")\n",
    "classification_df[\"training_acc\"].append(accuracy_score(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "classification_df[\"val_acc\"].append(accuracy_score(y_true=val_data[ycol].values, y_pred=val_ylabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random forest - hyperparameters tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsc = GridSearchCV(\n",
    "    n_jobs=1,\n",
    "    estimator=RandomForestClassifier(n_estimators=100, n_jobs=-1),\n",
    "    param_grid={\n",
    "        'min_samples_split': [2,3,5],\n",
    "        'max_features': ['sqrt', 'log2', None, int(len(xcol)/2)],\n",
    "    },\n",
    "    cv=10\n",
    ")\n",
    "grid_result = gsc.fit(X_train, train_data[ycol].values.ravel())\n",
    "\n",
    "print(\"Best parameters : %s\" % grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, **grid_result.best_params_)\n",
    "rf.fit(X_train, train_data[ycol])\n",
    "\n",
    "train_ylabels = rf.predict(X_train)\n",
    "\n",
    "print(20*\"=\", \"Training data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(train_data[ycol].values.ravel(), train_ylabels))\n",
    "\n",
    "val_ylabels = rf.predict(X_val)\n",
    "\n",
    "print(20*\"=\", \"Test data\", 20*\"=\")\n",
    "print(\"Classification Reports ---\\n\\n\")\n",
    "print(classification_report(y_true=val_data['target'].values, y_pred=val_ylabels))\n",
    "print(55*\"-\")\n",
    "print(\"confusion matrix ---\")   \n",
    "display(get_confusion_mat(val_data['target'].values.ravel(), val_ylabels))\n",
    "\n",
    "# Append info to the classification dictionary\n",
    "classification_df[\"classifier\"].append(\"RF_tuned\")\n",
    "classification_df[\"training_acc\"].append(accuracy_score(y_true=train_data[ycol].values, y_pred=train_ylabels))\n",
    "classification_df[\"val_acc\"].append(accuracy_score(y_true=val_data[ycol].values, y_pred=val_ylabels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classification_df = pd.DataFrame(classification_df)\n",
    "\n",
    "f, ax = plt.subplots(2, 1, figsize=(10,10))\n",
    "g = sns.barplot(y=\"classifier\", x=\"training_acc\", data=classification_df, ax=ax[0])\n",
    "g = sns.barplot(y=\"classifier\", x=\"val_acc\", data=classification_df, ax=ax[1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}