Twitter_Sentiment_Analisys.ipynb

{
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   "metadata": {},
   "source": [
    "# DSL Project 2020: classification on tweet sentiment\n",
    "\n",
    "| Student Name | Student Surname | ID |\n",
    "| --- | --- | --- |\n",
    "|Pouya  |Rivandi  | **299984** |\n",
    "|Matteo  |Merlo  | **287576** |\n",
    "\n",
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "22df9c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numeric\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Tools\n",
    "import os, sys\n",
    "import re\n",
    "import string\n",
    "import csv\n",
    "import string\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "# Classifiers\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction import text \n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import matplotlib.ticker as mticker\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "%matplotlib inline\n",
    "\n",
    "# Flags\n",
    "flag_tuning = True\n",
    "flag_pipeline_model_selection = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1b05f5",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n",
    "The dataset chosen for this text classification project contains tweets with a relative sentiment: positive or negative.\n",
    "\n",
    "The dataset is composed of 224994 labelled tweets and for each tweet there are also some informations such as user that published it, the tweet identifier and the date.\n",
    "\n",
    "The classes are:\n",
    "\n",
    "* ”1” if the tweet shows a positive sentiment.\n",
    "* ”0” if the tweet shows a negative sentiment.\n",
    "\n",
    "The distribution of the tweets between the two classes is quite balanced: there are 170506 tweets classified as 1 and 124488 tweets classified as 0.\n",
    "\n",
    "Analysing the dataset, it pops also out that there are a few tweets that are duplicates, around 270 tweets.\n",
    "\n",
    "The quality of the tweets is also quite low: there are many slang or wrong written, emoticon and web-links, most of them are trimmed and they are also hardly classifiable even for a human:\n",
    "\n",
    "`@kristianinicole I left it @ home  http://myloc.me/21cN`<br/>\n",
    "`LOL You just have to love the irony @lastfm  http://is.gd/LsmE (via @igorajdisek)`<br/>\n",
    "`@Ms_lonelystar Thank you honey  Hope you're having a great week so far  xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx`<br/>\n",
    "\n",
    "Due to the previously explained problems about the tweets, in the following steps (especially in the Preprocessing one) we will see that a non-invasive text cleaning approach pays more for this text classification task, since in some tweets there are so few features that even the user mentioned (’@’) could be useful to classify a tweet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb6bfca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/.local/lib/python3.8/site-packages/dateutil/parser/_parser.py:1207: UnknownTimezoneWarning: tzname PDT identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.\n",
      "  warnings.warn(\"tzname {tzname} identified but not understood.  \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "224994\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>ids</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1833972543</td>\n",
       "      <td>2009-05-18 01:08:27</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Killandra</td>\n",
       "      <td>@MissBianca76 Yes, talking helps a lot.. going...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1980318193</td>\n",
       "      <td>2009-05-31 06:23:17</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>IMlisacowan</td>\n",
       "      <td>SUNSHINE. livingg itttt. imma lie on the grass...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1994409198</td>\n",
       "      <td>2009-06-01 11:52:54</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>yaseminx3</td>\n",
       "      <td>@PleaseBeMine Something for your iphone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1824749377</td>\n",
       "      <td>2009-05-17 02:45:34</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>no_surprises</td>\n",
       "      <td>@GabrielSaporta couldn't get in to the after p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2001199113</td>\n",
       "      <td>2009-06-02 00:08:07</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Rhi_ShortStack</td>\n",
       "      <td>@bradiewebbstack awww is andy being mean again...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment         ids                date      flag            user  \\\n",
       "0          1  1833972543 2009-05-18 01:08:27  NO_QUERY       Killandra   \n",
       "1          1  1980318193 2009-05-31 06:23:17  NO_QUERY     IMlisacowan   \n",
       "2          1  1994409198 2009-06-01 11:52:54  NO_QUERY       yaseminx3   \n",
       "3          0  1824749377 2009-05-17 02:45:34  NO_QUERY    no_surprises   \n",
       "4          0  2001199113 2009-06-02 00:08:07  NO_QUERY  Rhi_ShortStack   \n",
       "\n",
       "                                                text  \n",
       "0  @MissBianca76 Yes, talking helps a lot.. going...  \n",
       "1  SUNSHINE. livingg itttt. imma lie on the grass...  \n",
       "2           @PleaseBeMine Something for your iphone   \n",
       "3  @GabrielSaporta couldn't get in to the after p...  \n",
       "4  @bradiewebbstack awww is andy being mean again...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_path = \"DSL2122_january_dataset/\"\n",
    "dev_dataset = pd.read_csv(dir_path + \"development.csv\",  infer_datetime_format=True, parse_dates=[\"date\"])\n",
    "\n",
    "\n",
    "print(dev_dataset.iloc[:,0].size)\n",
    "dev_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0e7d0297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 224994 entries, 0 to 224993\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count   Dtype         \n",
      "---  ------     --------------   -----         \n",
      " 0   sentiment  224994 non-null  int64         \n",
      " 1   ids        224994 non-null  int64         \n",
      " 2   date       224994 non-null  datetime64[ns]\n",
      " 3   flag       224994 non-null  object        \n",
      " 4   user       224994 non-null  object        \n",
      " 5   text       224994 non-null  object        \n",
      "dtypes: datetime64[ns](1), int64(2), object(3)\n",
      "memory usage: 10.3+ MB\n"
     ]
    }
   ],
   "source": [
    "dev_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9e5378d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Development dataset size: 224994\n"
     ]
    }
   ],
   "source": [
    "print(f\"Development dataset size: {dev_dataset['text'].size}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9c9ca947",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>ids</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1833972543</td>\n",
       "      <td>2009-05-18 01:08:27</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Killandra</td>\n",
       "      <td>@MissBianca76 Yes, talking helps a lot.. going...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1980318193</td>\n",
       "      <td>2009-05-31 06:23:17</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>IMlisacowan</td>\n",
       "      <td>SUNSHINE. livingg itttt. imma lie on the grass...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1994409198</td>\n",
       "      <td>2009-06-01 11:52:54</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>yaseminx3</td>\n",
       "      <td>@PleaseBeMine Something for your iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1824749377</td>\n",
       "      <td>2009-05-17 02:45:34</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>no_surprises</td>\n",
       "      <td>@GabrielSaporta couldn't get in to the after p...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2001199113</td>\n",
       "      <td>2009-06-02 00:08:07</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Rhi_ShortStack</td>\n",
       "      <td>@bradiewebbstack awww is andy being mean again...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment         ids                date      flag            user  \\\n",
       "0          1  1833972543 2009-05-18 01:08:27  NO_QUERY       Killandra   \n",
       "1          1  1980318193 2009-05-31 06:23:17  NO_QUERY     IMlisacowan   \n",
       "2          1  1994409198 2009-06-01 11:52:54  NO_QUERY       yaseminx3   \n",
       "3          0  1824749377 2009-05-17 02:45:34  NO_QUERY    no_surprises   \n",
       "4          0  2001199113 2009-06-02 00:08:07  NO_QUERY  Rhi_ShortStack   \n",
       "\n",
       "                                                text  hour  weekday  month  \\\n",
       "0  @MissBianca76 Yes, talking helps a lot.. going...     1        0      5   \n",
       "1  SUNSHINE. livingg itttt. imma lie on the grass...     6        6      5   \n",
       "2           @PleaseBeMine Something for your iphone     11        0      6   \n",
       "3  @GabrielSaporta couldn't get in to the after p...     2        6      5   \n",
       "4  @bradiewebbstack awww is andy being mean again...     0        1      6   \n",
       "\n",
       "   day  \n",
       "0   18  \n",
       "1   31  \n",
       "2    1  \n",
       "3   17  \n",
       "4    2  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creation column of hour, weekday, month from date\n",
    "dev_dataset[\"hour\"] = dev_dataset[\"date\"].apply(lambda x: x.hour)\n",
    "dev_dataset[\"weekday\"] = dev_dataset[\"date\"].apply(lambda x: x.weekday())\n",
    "dev_dataset[\"month\"] = dev_dataset[\"date\"].apply(lambda x: x.month)\n",
    "dev_dataset[\"day\"] = dev_dataset[\"date\"].apply(lambda x: x.day)\n",
    "\n",
    "\n",
    "dev_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31009754",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_322534/2996525026.py:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axs[1].set_xticklabels([\"void\", \"Mon\", \"Tue\", \"Wen\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(6,12))\n",
    "fig, axs = plt.subplots(3, figsize=(8,10))\n",
    "\n",
    "fig.tight_layout(pad=2)\n",
    "axs[0].hist((dev_dataset[dev_dataset['sentiment'] == 1][\"hour\"], dev_dataset[dev_dataset['sentiment'] == 0][\"hour\"]),  bins=np.arange(25)-0.5, label=['positive', 'negative'], color=['seagreen', 'indianred'])\n",
    "axs[1].hist((dev_dataset[dev_dataset['sentiment'] == 1][\"weekday\"], dev_dataset[dev_dataset['sentiment'] == 0][\"weekday\"]),  bins=np.arange(8)-0.5, label=['positive', 'negative'], color=['seagreen', 'indianred'])\n",
    "axs[2].hist((dev_dataset[dev_dataset['sentiment'] == 1][\"month\"], dev_dataset[dev_dataset['sentiment'] == 0][\"month\"]),  bins=3, label=['positive', 'negative'], color=['seagreen', 'indianred'])\n",
    "# np.arange(4,8)-0.5\n",
    "axs[0].set_title(\"Hour of day\")\n",
    "axs[1].set_title(\"Weekday\")\n",
    "axs[2].set_title(\"Month\")\n",
    "\n",
    "axs[0].set_xticks(np.arange(0, 23, 4))\n",
    "\n",
    "axs[1].set_xticklabels([\"void\", \"Mon\", \"Tue\", \"Wen\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"])\n",
    "\n",
    "axs[2].set_xticks([4.33,5,5.66])\n",
    "axs[2].set_xticklabels([\"April\", \"May\", \"June\"])\n",
    "axs[2].legend(loc='upper left')    \n",
    "\n",
    "plt.savefig(\"dateDistributionHour.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e2409c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute distribution percentages\n",
    "pos_perc = 100 * dev_dataset[dev_dataset['sentiment'] == 1].size / dev_dataset.size\n",
    "neg_perc = 100 - pos_perc\n",
    "\n",
    "fig, ax = plt.subplots(dpi=300, figsize=(6,3))\n",
    "ax = sns.countplot(x = 'sentiment', data = dev_dataset, #color=['red', 'green'])\n",
    "              palette=sns.color_palette(['indianred', 'seagreen'], 2))\n",
    "ax.set_ylabel('Num of tweet')\n",
    "ax.set_xlabel('Sentiment label')\n",
    "# ax.set_title('Distribution of sentiment')\n",
    "ax.legend(handles=ax.patches, labels=(f'negative: {neg_perc:.1f}%', f'positive: {pos_perc:.1f}%'))\n",
    "plt.savefig('DistributionOfSentiment.svg', bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61c9c5a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "224716\n"
     ]
    }
   ],
   "source": [
    "ds = dev_dataset.copy()\n",
    "# how many tweet ids are duplicates? around 270\n",
    "ds.drop_duplicates(['ids'],inplace=True)\n",
    "print(ds.iloc[:,0].size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "429be9f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10647\n"
     ]
    }
   ],
   "source": [
    "ds = dev_dataset.copy()\n",
    "# how many user are there? around 10000\n",
    "ds.drop_duplicates(['user'],inplace=True)\n",
    "print(ds.iloc[:,0].size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bac8bffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1888\n"
     ]
    }
   ],
   "source": [
    "ds = dev_dataset.copy()\n",
    "# how many copies of tweet text are there? around 1800\n",
    "ds.drop_duplicates(['text'],inplace=True)\n",
    "print( dev_dataset.iloc[:,0].size - ds.iloc[:,0].size )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c387329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Killandra</td>\n",
       "      <td>@MissBianca76 Yes, talking helps a lot.. going...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>IMlisacowan</td>\n",
       "      <td>SUNSHINE. livingg itttt. imma lie on the grass...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>yaseminx3</td>\n",
       "      <td>@PleaseBeMine Something for your iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>no_surprises</td>\n",
       "      <td>@GabrielSaporta couldn't get in to the after p...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Rhi_ShortStack</td>\n",
       "      <td>@bradiewebbstack awww is andy being mean again...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment            user  \\\n",
       "0          1       Killandra   \n",
       "1          1     IMlisacowan   \n",
       "2          1       yaseminx3   \n",
       "3          0    no_surprises   \n",
       "4          0  Rhi_ShortStack   \n",
       "\n",
       "                                                text  hour  weekday  month  \\\n",
       "0  @MissBianca76 Yes, talking helps a lot.. going...     1        0      5   \n",
       "1  SUNSHINE. livingg itttt. imma lie on the grass...     6        6      5   \n",
       "2           @PleaseBeMine Something for your iphone     11        0      6   \n",
       "3  @GabrielSaporta couldn't get in to the after p...     2        6      5   \n",
       "4  @bradiewebbstack awww is andy being mean again...     0        1      6   \n",
       "\n",
       "   day  \n",
       "0   18  \n",
       "1   31  \n",
       "2    1  \n",
       "3   17  \n",
       "4    2  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop useless column and duplicates\n",
    "dev_dataset.drop_duplicates(['text'],keep='first',inplace=True)\n",
    "\n",
    "df_done = dev_dataset.drop(columns=[\"flag\", \"ids\", \"date\"])\n",
    "\n",
    "df_done.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9796d2bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 223106 entries, 0 to 224993\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   sentiment  223106 non-null  int64 \n",
      " 1   user       223106 non-null  object\n",
      " 2   text       223106 non-null  object\n",
      " 3   hour       223106 non-null  int64 \n",
      " 4   weekday    223106 non-null  int64 \n",
      " 5   month      223106 non-null  int64 \n",
      " 6   day        223106 non-null  int64 \n",
      "dtypes: int64(5), object(2)\n",
      "memory usage: 13.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_done.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe85887f",
   "metadata": {},
   "source": [
    "##  Text preprocessing\n",
    "\n",
    "The full preprocessing pipeline scheduled for this project is the following: \n",
    "\n",
    "* Cut mentions (’@’) to user\n",
    "* Cut websites links\n",
    "* Translate short form of verbs or slang version of the same\n",
    "* Replace special character and special short word or slang\n",
    "* Replace 3 or more same consecutive letter with only 2 letter\n",
    "\n",
    "Although, some preprocess operations don't improve overall accuracy and so we decided to remove them. In particular we don't use:\n",
    "\n",
    "* Stemming and Lemmatization of the text\n",
    "* Remove stopwords since many relevant word are considered stopword\n",
    "* Remove unicode error from translation of HTML such as $&quot;$ , $&lt;$ and $&gt;$ \n",
    "\n",
    "We have removed the repeating characters from the words along with removing the URLs as they do not have any significant importance.\n",
    "To avoid the increase of dispersion of feature importance, many slang word are translated to the base form.\n",
    "\n",
    "Future develpment: remove and translate emoticon to word: instead of being discarded as punctation, emoticon could become a feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "08940405",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text_tweet_for_tfidfVect( tweet ):\n",
    "    \n",
    "    tweet = tweet.lower()\n",
    "    tweet = tweet.replace(\"@\", \"\") # remove @ before user name\n",
    "    tweet = tweet.replace(\"#\", \"\").replace('&', \" \") # remove # of hashtag and &    \n",
    "    \n",
    "    tweet = re.sub(r'https?://[A-Za-z0-9./]+',\" \",tweet) # remove links\n",
    "#     tweet = re.sub(r'(https?:\\/\\/)?([\\da-z\\.-]+)\\.([a-z\\.]{2,6})([\\/\\w \\.-]*)',\" \",tweet) # remove links other version\n",
    "    tweet = re.sub(r\"[^\\w\\d'\\s]+\",\" \",tweet) # remove punctuation except for ' \n",
    "    \n",
    "    # translate short form of verbs and slang\n",
    "    tweet = tweet.replace(\"'ve\", \" have \").replace(\" ive \", \" i have \").replace(\" hav \", \" have \").replace(\" havent \", \" have not \")\n",
    "    tweet = tweet.replace(\" cant \", \"can not \").replace(\"can't\", \"can not \").replace(\"'d\", \" would \").replace(\"couldnt\", \" could not \")\n",
    "    tweet = tweet.replace(\"'ll\", \" will \").replace(\"won't\", \" will not \")\n",
    "    tweet = tweet.replace(\"it's\", \"it is \").replace(\"i'm\", \" i am \").replace(\"im \",\" i am \").replace(\"'re\", \" are\")\n",
    "    tweet = tweet.replace(\"he's\", \"he is \").replace(\"she's\", \"she is \").replace(\"there's \", \"there is \")\n",
    "    tweet = tweet.replace(\"n't\", \" not \").replace(\"n t \", \" not \").replace(\"dont \", \" not \").replace(\"didnt \", \" not \")\n",
    "    tweet = tweet.replace(\"what's \", \"what is \").replace(\"how's \", \" how is \").replace(\"where's \", \"where is \").replace(\"here's \", \"here is \")\n",
    "    tweet = tweet.replace(\"'s \", \" own \")\n",
    "\n",
    "\n",
    "#     replace special character and special short word\n",
    "    tweet = tweet.replace(\"%\", \" percent \").replace(\"$\", \" dollar \").replace(\"_\", \" \").replace(\"-\", \" \")\n",
    "    tweet = tweet.replace(\" ye \", \" you \").replace(\" yeh \", \" yeah \").replace(\" yt \", \" youtube \").replace(\" ty \", \" thank \")\n",
    "    tweet = tweet.replace(\"in'\", \"ing \").replace(\" u \", \" you \").replace(\" r \", \" your \").replace(\" ur \", \" your \").replace(\" 2 \", \" to \").replace(\" 4 \", \" for \")\n",
    "    tweet = tweet.replace(\"every1 \", \" everyone \").replace(\" n \", \" and \").replace(\"yrs\", \" years \").replace(\"2day\", \" today \")\n",
    "    tweet = tweet.replace(\" wth \", \" with \").replace(\"jst\", \" just \").replace(\" 4got \", \" forget \")\n",
    "    \n",
    "#     tweet = tweet.replace('btw', ' by the way ').replace(\" thats \", \" that is \").replace(\" idk \", \" i do not know \")\n",
    "    \n",
    "    # remove meaningless word from unicode error (probably from HTML translation error)\n",
    "#     tweet = tweet.replace(' amp ',\" \").replace(' gt ',\" \").replace(' quot ', \" \").replace(' lt ',\" \") #in some way important do not remove\n",
    "    \n",
    "    tweet = re.sub(r'[^A-Za-z0-9]+',\" \",tweet) # remove all non alphanumeric char\n",
    "    tweet = re.sub(r' +',\" \",tweet)# remove multiple space\n",
    "    tweet = re.sub(r'[^\\x00-\\x7F]+',' ',tweet) # Removing non ASCII character\n",
    "    tweet = re.sub(r\"(.)\\1+\", r\"\\1\\1\", tweet) # remove multiple repeated character\n",
    "    \n",
    "    return tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "79cbba99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text Preprocessing complete.\n",
      "Time Taken: 6 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Killandra</td>\n",
       "      <td>missbianca76 yes talking helps a lot going thr...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>IMlisacowan</td>\n",
       "      <td>sunshine livingg itt imma lie on the grass lis...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>yaseminx3</td>\n",
       "      <td>pleasebemine something for your iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>no_surprises</td>\n",
       "      <td>gabrielsaporta could not get in to the after p...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Rhi_ShortStack</td>\n",
       "      <td>bradiewebbstack aww is andy being mean again n...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment            user  \\\n",
       "0          1       Killandra   \n",
       "1          1     IMlisacowan   \n",
       "2          1       yaseminx3   \n",
       "3          0    no_surprises   \n",
       "4          0  Rhi_ShortStack   \n",
       "\n",
       "                                                text  hour  weekday  month  \\\n",
       "0  missbianca76 yes talking helps a lot going thr...     1        0      5   \n",
       "1  sunshine livingg itt imma lie on the grass lis...     6        6      5   \n",
       "2            pleasebemine something for your iphone     11        0      6   \n",
       "3  gabrielsaporta could not get in to the after p...     2        6      5   \n",
       "4  bradiewebbstack aww is andy being mean again n...     0        1      6   \n",
       "\n",
       "   day  \n",
       "0   18  \n",
       "1   31  \n",
       "2    1  \n",
       "3   17  \n",
       "4    2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "t = time.time()\n",
    "\n",
    "# Preprocessing\n",
    "df_done[\"text\"] = df_done[\"text\"].apply(lambda tweet: preprocess_text_tweet_for_tfidfVect(tweet))\n",
    "\n",
    "print(f'Text Preprocessing complete.')\n",
    "print(f'Time Taken: {round(time.time()-t)} seconds') \n",
    "\n",
    "df_done[\"text\"].to_csv(\"developmentElaborated.csv\")\n",
    "df_done.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3598ba28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,12)) \n",
    "wc = WordCloud(max_words = 150 , width = 1600 , height = 800, background_color=\"white\").generate(\" \".join(df_done[df_done.sentiment == 1].text))\n",
    "plt.imshow(wc , interpolation = 'bilinear')\n",
    "plt.title('Distribution of positive sentiment')\n",
    "plt.savefig('WordCloudPositive.svg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad476a5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,12)) \n",
    "wc = WordCloud(max_words = 150 , width = 1600 , height = 800, background_color=\"white\").generate(\" \".join(df_done[df_done.sentiment == 0].text))\n",
    "plt.imshow(wc , interpolation = 'bilinear')\n",
    "plt.title('Distribution of negative sentiment')\n",
    "plt.savefig('WordCloudNegative.svg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec15cf3d",
   "metadata": {},
   "source": [
    "## Algorithm Choice\n",
    "\n",
    "Initially, we make an inspection using 4 different classifiers:\n",
    "\n",
    "* MultinomialNB\n",
    "* Random Forest Classifier\n",
    "* Logistic Regressor\n",
    "* LinearSVC \n",
    "\n",
    "The test set was made up of the 20% of the development set.\n",
    "\n",
    "![title](ModelsMeanAccuracy.svg)\n",
    "\n",
    "| Model | Mean Accuracy | Standard Deviation |\n",
    "| --- | --- | --- |\n",
    "| LinearSVC | 0.847 | 0.001212 |\n",
    "| LogisticRegression | 0.837 | 0.001729 |\n",
    "| MultinomialNB | 0.740 | 0.001889 |\n",
    "| RandomForest | 0.786 | 0.003056 |\n",
    "\n",
    "Before doing hyperparameters tuning, the MultinomialNB reached 74,05% of accuracy. After this first attempt, we have compared it to the other classifiers and we have decided to not tune it because it wasn’t promising and the resulted training was quite long to do.\n",
    "\n",
    "After the MultinomialNB, I tried the RandomForestClassifier, which not performed very well on evaluation set and led to an accuracy of just 78.6%. So, I decided to do discard this method.\n",
    "\n",
    "In addition, I tried to use the LogisticRegressor; I thought that this regressor could be a good one for this project because of its capability on modelling binary classification problems. Using the LogisticRegressor the accuracy reach a good initial 83.71%.\n",
    "By the way, the improvements achieved by means of the hyperparameters tuning on LogisticRegressor were inexistent on evaluation set, and the final accuracy remained almost the same, with 84.7%.\n",
    "\n",
    "Finally, I decided to try the LinearSVC classifiers, since it is based on Support Vector machine. It is modelling specifically for binary classification problems. The LinearSVC achieved initially a promising accuracy of 84.71%. Also in this case the parameter tuning do not improve significantly the accuracy. The final accuracy achieved is 85.2%\n",
    "\n",
    "Even if Support Vector Machine algorithm (with linear kernel) perform similarly to the Logistic Regression, I think that the first one performs better on this project because of the sensitivity to marginal values. The sigmoid function of the LogisticRegressor tends to not properly identify simil-neutral values, while the Support Vector Machine algorithm tries to construct the best widest possible separating line to split this two clusters.\n",
    "\n",
    "All the evaluation for the accuracy were done with accuracy score, as suggested in the assignment, and all the classifiers were provided by the Scikit Learn package.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6489d82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((223106, 6), (223106,))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_done.drop(columns=[\"sentiment\"])\n",
    "y = df_done[\"sentiment\"]\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "10f68161",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2acbeb3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# time to complete the search of best tecnique: around 180 min\n",
    "if flag_pipeline_model_selection:   \n",
    "    models = [  MultinomialNB(), #0.74\n",
    "                RandomForestClassifier(n_jobs=-1), #0.78\n",
    "                LogisticRegression(n_jobs=-1), # 0.837\n",
    "                LinearSVC(), #0.847\n",
    "             ]\n",
    "\n",
    "    entries = []\n",
    "\n",
    "    for i in range(len(models)):\n",
    "        model_name = models[i].__class__.__name__\n",
    "        pipeModel = make_pipeline(\n",
    "                        make_column_transformer(\n",
    "                            (TfidfVectorizer(ngram_range= (1, 3)), \"text\"),\n",
    "                            (OneHotEncoder(handle_unknown=\"ignore\"), [\"user\", \"hour\", \"weekday\", \"month\", \"day\"]),\n",
    "                            remainder=\"passthrough\",\n",
    "                            n_jobs=-1\n",
    "                        ),\n",
    "                        models[i]\n",
    "                    )\n",
    "\n",
    "        accuracies = cross_val_score(pipeModel, X_train, y_train, scoring='f1_macro', cv=5, n_jobs=-1)\n",
    "        for fold_idx, accuracy in enumerate(accuracies):\n",
    "            entries.append((model_name, fold_idx, accuracy))\n",
    "\n",
    "    cross_val_result_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])\n",
    "    mean_accuracy = cross_val_result_df.groupby('model_name').accuracy.mean()\n",
    "    std_accuracy = cross_val_result_df.groupby('model_name').accuracy.std()\n",
    "    acc = pd.concat([mean_accuracy, std_accuracy], axis= 1, \n",
    "              ignore_index=True)\n",
    "    acc.columns = ['Mean Accuracy', 'Standard deviation']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95304e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if flag_pipeline_model_selection:\n",
    "    plt.figure(figsize=(8,5))\n",
    "    sns.boxplot(x='model_name', y='accuracy', \n",
    "                data=cross_val_result_df, \n",
    "                color='lightblue', \n",
    "                showmeans=True)\n",
    "    plt.title(\"Models accuracy f1_score (cv = 5)\\n\", size=14);\n",
    "    plt.savefig('ModelsMeanAccuracy.svg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1290c4d2",
   "metadata": {},
   "source": [
    "## Tuning hyperparameter and validation\n",
    "\n",
    "First we try to find the best parameter for TfidfVectorizer. We decided that ngram is the most relevat parameter to tuning.\n",
    "\n",
    "Then in order to tune properly the classifiers I tried different values of C and tol for Logistic Regressor and LinearSVC, since we thought that they are the most relevant parameter for the accuracy.\n",
    "\n",
    "After some attempts, for the LinearSVC classifier I ended up that C = 0.4 and tol = 0.01 were the best in terms of accuracy. So, this classifier works better with a large margin around the hyperplane. The accuracy reached on evaluation set is 84.52%.\n",
    "\n",
    "The best C value found for the Logistic Regressor is 6.5 and a tol of 0.001. The accuracy on evaluation set is 84.50%.\n",
    "\n",
    "So, comparing the Logistic Regressor with the LinearSVC and their best C values, we can see that the LinearSVC performs slightly better.\n",
    "\n",
    "Parameter tested:\n",
    "* LinearSVC C = { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2 }\n",
    "* Logistic Regressor C = { 1.0, 3.0, 5.0, 6.0, 7.0, 8.0, 10.0, 12.0 }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49f8eaff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessor pipeline with tfIdfVectorizer and Onehotencoding\n",
    "\n",
    "text_features = 'text' # column passed to tfidfvectorizer\n",
    "text_transformer = Pipeline( steps=[\n",
    "                    ('vectorizer', TfidfVectorizer())\n",
    "                         ])\n",
    "\n",
    "encoding_features = [\"user\", \"hour\", \"weekday\", \"month\", \"day\"] # column for encoding\n",
    "encoding_transformer = Pipeline(steps=[\n",
    "                    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "                    transformers=[\n",
    "                        ('vect', text_transformer, text_features),\n",
    "                        ('cat', encoding_transformer, encoding_features)\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c47531a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
      "Text Preprocessing complete.\n",
      "Time Taken: 798 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([13.08416734, 13.44404626, 13.23426547, 11.28296676, 24.48629737,\n",
       "        25.29809875, 26.73207922, 24.48092518, 43.33205156, 42.83236289,\n",
       "        45.07498603, 41.06265469, 60.13214316, 60.466013  , 60.43283415,\n",
       "        60.32642546, 13.42854071, 15.63076239, 12.37645183, 12.20146565,\n",
       "        26.95557013, 24.92124639, 27.46573172, 23.39147696, 43.02643156,\n",
       "        43.99366655, 44.99502344, 42.41890578, 60.45585928, 58.96514602,\n",
       "        60.47974143, 50.16825986]),\n",
       " 'std_fit_time': array([0.65085021, 0.71776375, 0.30218519, 0.99478867, 0.98308836,\n",
       "        1.17713106, 1.01136675, 0.82319482, 1.61754372, 2.62619673,\n",
       "        1.07100442, 2.13087276, 1.00094746, 1.33526816, 1.91685912,\n",
       "        0.75416525, 1.82351339, 0.71121958, 0.64677521, 1.38830345,\n",
       "        0.58764502, 0.46122376, 1.0383576 , 1.53838609, 0.76875349,\n",
       "        1.23974701, 1.40382133, 1.0042605 , 1.96349187, 1.50139635,\n",
       "        1.04080423, 6.93201239]),\n",
       " 'mean_score_time': array([1.02530069, 0.92832794, 0.84110308, 0.87867565, 2.29552073,\n",
       "        1.97499208, 2.05452991, 1.65442581, 3.41506572, 2.52846398,\n",
       "        2.99190168, 2.47170973, 3.62190709, 3.33039689, 3.54161863,\n",
       "        2.88575482, 1.08835096, 0.95575156, 1.09931874, 0.78267279,\n",
       "        2.10740094, 1.97522845, 1.86723666, 1.7318707 , 3.00880561,\n",
       "        2.63121586, 2.9310092 , 2.28728209, 4.10670385, 3.31306605,\n",
       "        3.5831522 , 1.93651595]),\n",
       " 'std_score_time': array([0.18517373, 0.25260807, 0.1493286 , 0.11541336, 0.2604021 ,\n",
       "        0.3267357 , 0.31425799, 0.20450833, 0.35428461, 0.5026226 ,\n",
       "        0.41231897, 0.3825809 , 0.1480803 , 0.75636941, 0.26028315,\n",
       "        0.36440499, 0.2800125 , 0.21237228, 0.16700292, 0.06685921,\n",
       "        0.12418427, 0.54233943, 0.28760346, 0.26597435, 0.07027378,\n",
       "        0.21724414, 0.35707438, 0.30678062, 0.39218442, 0.83212798,\n",
       "        0.44024178, 0.39136036]),\n",
       " 'param_preprocessor__vect__vectorizer__binary': masked_array(data=[True, True, True, True, True, True, True, True, True,\n",
       "                    True, True, True, True, True, True, True, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_preprocessor__vect__vectorizer__ngram_range': masked_array(data=[(1, 1), (1, 1), (1, 1), (1, 1), (1, 2), (1, 2), (1, 2),\n",
       "                    (1, 2), (1, 3), (1, 3), (1, 3), (1, 3), (1, 4), (1, 4),\n",
       "                    (1, 4), (1, 4), (1, 1), (1, 1), (1, 1), (1, 1), (1, 2),\n",
       "                    (1, 2), (1, 2), (1, 2), (1, 3), (1, 3), (1, 3), (1, 3),\n",
       "                    (1, 4), (1, 4), (1, 4), (1, 4)],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_preprocessor__vect__vectorizer__sublinear_tf': masked_array(data=[True, True, False, False, True, True, False, False,\n",
       "                    True, True, False, False, True, True, False, False,\n",
       "                    True, True, False, False, True, True, False, False,\n",
       "                    True, True, False, False, True, True, False, False],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_preprocessor__vect__vectorizer__use_idf': masked_array(data=[True, False, True, False, True, False, True, False,\n",
       "                    True, False, True, False, True, False, True, False,\n",
       "                    True, False, True, False, True, False, True, False,\n",
       "                    True, False, True, False, True, False, True, False],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': True,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 1),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 2),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': True},\n",
       "  {'preprocessor__vect__vectorizer__binary': False,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 4),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': False,\n",
       "   'preprocessor__vect__vectorizer__use_idf': False}],\n",
       " 'split0_test_score': array([0.82887217, 0.83296397, 0.82887217, 0.83296397, 0.84415737,\n",
       "        0.84469903, 0.84415737, 0.84469903, 0.8467004 , 0.84611733,\n",
       "        0.8467004 , 0.84611733, 0.84443673, 0.84587164, 0.84443673,\n",
       "        0.84587164, 0.83020679, 0.83367523, 0.8297573 , 0.83446634,\n",
       "        0.84488054, 0.84522816, 0.84448963, 0.84467891, 0.84747942,\n",
       "        0.84679218, 0.84730935, 0.84727191, 0.84447523, 0.84700573,\n",
       "        0.84464908, 0.84727904]),\n",
       " 'split1_test_score': array([0.82829281, 0.8343059 , 0.82829281, 0.8343059 , 0.8457802 ,\n",
       "        0.8462939 , 0.8457802 , 0.8462939 , 0.84919483, 0.84856141,\n",
       "        0.84919483, 0.84856141, 0.84747554, 0.84903075, 0.84747554,\n",
       "        0.84903075, 0.82892587, 0.83519335, 0.82811415, 0.83416418,\n",
       "        0.84549465, 0.84604436, 0.84573394, 0.84605996, 0.84945449,\n",
       "        0.84815664, 0.84865472, 0.84824199, 0.8480982 , 0.84861458,\n",
       "        0.84784235, 0.84888166]),\n",
       " 'split2_test_score': array([0.82904887, 0.83335258, 0.82904887, 0.83335258, 0.84427435,\n",
       "        0.84495441, 0.84427435, 0.84495441, 0.84640696, 0.84638226,\n",
       "        0.84640696, 0.84638226, 0.84475093, 0.84616984, 0.84475093,\n",
       "        0.84616984, 0.82905298, 0.83384359, 0.82885784, 0.83369694,\n",
       "        0.84485421, 0.84530085, 0.84465874, 0.84539776, 0.84709973,\n",
       "        0.84675334, 0.84667036, 0.84765344, 0.84539961, 0.84685767,\n",
       "        0.84523904, 0.84699533]),\n",
       " 'split3_test_score': array([0.82767606, 0.83445876, 0.82767606, 0.83445876, 0.84679244,\n",
       "        0.84647265, 0.84679244, 0.84647265, 0.84979988, 0.85003277,\n",
       "        0.84979988, 0.85003277, 0.84924527, 0.84990049, 0.84924527,\n",
       "        0.84990049, 0.82838962, 0.83538769, 0.82781768, 0.83470438,\n",
       "        0.84700448, 0.84741477, 0.84600553, 0.8466605 , 0.85057008,\n",
       "        0.84977785, 0.8505676 , 0.85039094, 0.84960073, 0.85081879,\n",
       "        0.84992422, 0.85064285]),\n",
       " 'split4_test_score': array([0.82828068, 0.83337095, 0.82828068, 0.83337095, 0.84620668,\n",
       "        0.84623812, 0.84620668, 0.84623812, 0.84689762, 0.84803844,\n",
       "        0.84689762, 0.84803844, 0.84535441, 0.84720054, 0.84535441,\n",
       "        0.84720054, 0.82768894, 0.83270488, 0.82715847, 0.83288628,\n",
       "        0.84520337, 0.84579438, 0.84493695, 0.84518794, 0.84771864,\n",
       "        0.84793882, 0.84757797, 0.84743213, 0.84438706, 0.84694307,\n",
       "        0.84410832, 0.8466662 ]),\n",
       " 'mean_test_score': array([0.82843412, 0.83369043, 0.82843412, 0.83369043, 0.84544221,\n",
       "        0.84573162, 0.84544221, 0.84573162, 0.84779994, 0.84782644,\n",
       "        0.84779994, 0.84782644, 0.84625258, 0.84763465, 0.84625258,\n",
       "        0.84763465, 0.82885284, 0.83416095, 0.82834109, 0.83398362,\n",
       "        0.84548745, 0.84595651, 0.84516496, 0.84559701, 0.84846447,\n",
       "        0.84788377, 0.848156  , 0.84819808, 0.84639216, 0.84804797,\n",
       "        0.8463526 , 0.84809302]),\n",
       " 'std_test_score': array([0.00048744, 0.00058533, 0.00048744, 0.00058533, 0.00105228,\n",
       "        0.00074728, 0.00105228, 0.00074728, 0.00140777, 0.00144636,\n",
       "        0.00140777, 0.00144636, 0.00183362, 0.00158275, 0.00183362,\n",
       "        0.00158275, 0.0008304 , 0.00100275, 0.00089406, 0.00064365,\n",
       "        0.00079391, 0.00079005, 0.0005991 , 0.00069214, 0.00132686,\n",
       "        0.00110765, 0.00136547, 0.0011447 , 0.00209216, 0.00153117,\n",
       "        0.00219794, 0.00148505]),\n",
       " 'rank_test_score': array([30, 27, 30, 27, 22, 18, 22, 18,  9,  7,  9,  7, 15, 11, 15, 11, 29,\n",
       "        25, 32, 26, 21, 17, 24, 20,  1,  6,  3,  2, 13,  5, 14,  4],\n",
       "       dtype=int32)}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = time.time()\n",
    "\n",
    "#Hyperparameter tuning for TfidfVectorizer\n",
    "parameters = {\n",
    "    'preprocessor__vect__vectorizer__ngram_range': ((1, 1), (1, 2), (1, 3), (1, 4)),  # unigrams or bigrams\n",
    "    'preprocessor__vect__vectorizer__use_idf': (True, False),\n",
    "    'preprocessor__vect__vectorizer__sublinear_tf': (True, False),\n",
    "    'preprocessor__vect__vectorizer__binary': (True, False)\n",
    "}\n",
    "\n",
    "tfidfModel = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', LinearSVC(random_state=42, max_iter=1000) )\n",
    "                      ])\n",
    "\n",
    "\n",
    "grid_search = GridSearchCV(tfidfModel, parameters, cv=5,  scoring='f1_macro', n_jobs=-1, verbose=2)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(f'Text Preprocessing complete.') # 912 seconds\n",
    "print(f'Time Taken: {round(time.time()-t)} seconds') \n",
    "grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3505abdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8501214881649999\n",
      "{'memory': None, 'steps': [('preprocessor', ColumnTransformer(transformers=[('vect',\n",
      "                                 Pipeline(steps=[('vectorizer',\n",
      "                                                  TfidfVectorizer(ngram_range=(1,\n",
      "                                                                               3),\n",
      "                                                                  sublinear_tf=True))]),\n",
      "                                 'text'),\n",
      "                                ('cat',\n",
      "                                 Pipeline(steps=[('onehot',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                 ['user', 'hour', 'weekday', 'month', 'day'])])), ('classifier', LinearSVC(random_state=42))], 'verbose': False, 'preprocessor': ColumnTransformer(transformers=[('vect',\n",
      "                                 Pipeline(steps=[('vectorizer',\n",
      "                                                  TfidfVectorizer(ngram_range=(1,\n",
      "                                                                               3),\n",
      "                                                                  sublinear_tf=True))]),\n",
      "                                 'text'),\n",
      "                                ('cat',\n",
      "                                 Pipeline(steps=[('onehot',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                 ['user', 'hour', 'weekday', 'month', 'day'])]), 'classifier': LinearSVC(random_state=42), 'preprocessor__n_jobs': None, 'preprocessor__remainder': 'drop', 'preprocessor__sparse_threshold': 0.3, 'preprocessor__transformer_weights': None, 'preprocessor__transformers': [('vect', Pipeline(steps=[('vectorizer',\n",
      "                 TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))]), 'text'), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['user', 'hour', 'weekday', 'month', 'day'])], 'preprocessor__verbose': False, 'preprocessor__verbose_feature_names_out': True, 'preprocessor__vect': Pipeline(steps=[('vectorizer',\n",
      "                 TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))]), 'preprocessor__cat': Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), 'preprocessor__vect__memory': None, 'preprocessor__vect__steps': [('vectorizer', TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))], 'preprocessor__vect__verbose': False, 'preprocessor__vect__vectorizer': TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True), 'preprocessor__vect__vectorizer__analyzer': 'word', 'preprocessor__vect__vectorizer__binary': False, 'preprocessor__vect__vectorizer__decode_error': 'strict', 'preprocessor__vect__vectorizer__dtype': <class 'numpy.float64'>, 'preprocessor__vect__vectorizer__encoding': 'utf-8', 'preprocessor__vect__vectorizer__input': 'content', 'preprocessor__vect__vectorizer__lowercase': True, 'preprocessor__vect__vectorizer__max_df': 1.0, 'preprocessor__vect__vectorizer__max_features': None, 'preprocessor__vect__vectorizer__min_df': 1, 'preprocessor__vect__vectorizer__ngram_range': (1, 3), 'preprocessor__vect__vectorizer__norm': 'l2', 'preprocessor__vect__vectorizer__preprocessor': None, 'preprocessor__vect__vectorizer__smooth_idf': True, 'preprocessor__vect__vectorizer__stop_words': None, 'preprocessor__vect__vectorizer__strip_accents': None, 'preprocessor__vect__vectorizer__sublinear_tf': True, 'preprocessor__vect__vectorizer__token_pattern': '(?u)\\\\b\\\\w\\\\w+\\\\b', 'preprocessor__vect__vectorizer__tokenizer': None, 'preprocessor__vect__vectorizer__use_idf': True, 'preprocessor__vect__vectorizer__vocabulary': None, 'preprocessor__cat__memory': None, 'preprocessor__cat__steps': [('onehot', OneHotEncoder(handle_unknown='ignore'))], 'preprocessor__cat__verbose': False, 'preprocessor__cat__onehot': OneHotEncoder(handle_unknown='ignore'), 'preprocessor__cat__onehot__categories': 'auto', 'preprocessor__cat__onehot__drop': None, 'preprocessor__cat__onehot__dtype': <class 'numpy.float64'>, 'preprocessor__cat__onehot__handle_unknown': 'ignore', 'preprocessor__cat__onehot__sparse': True, 'classifier__C': 1.0, 'classifier__class_weight': None, 'classifier__dual': True, 'classifier__fit_intercept': True, 'classifier__intercept_scaling': 1, 'classifier__loss': 'squared_hinge', 'classifier__max_iter': 1000, 'classifier__multi_class': 'ovr', 'classifier__penalty': 'l2', 'classifier__random_state': 42, 'classifier__tol': 0.0001, 'classifier__verbose': 0}\n"
     ]
    }
   ],
   "source": [
    "score = f1_score(y_test, grid_search.predict(X_test), average='macro')\n",
    "print(score) # 0.849\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "\n",
    "print(best_parameters) # ngram = (1,3), use_idf = True(default), sublinear_tf = True, binary = False(default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0bef6983",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8316303947239241\n"
     ]
    }
   ],
   "source": [
    "#Baseline LinearSVC\n",
    "lsvc = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', LinearSVC(random_state=42) )\n",
    "                      ])\n",
    "lsvc.fit(X_train,y_train)\n",
    "linearSVCscore = f1_score(y_test, lsvc.predict(X_test), average='macro')\n",
    "print(linearSVCscore) # 0.831"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cae2d5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n",
      "Text Preprocessing complete.\n",
      "Time Taken: 644 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([23.41535778, 23.75037723, 25.18800402, 24.67123022, 25.41494646,\n",
       "        26.98250222, 27.66068583, 31.59394951, 27.52448597, 30.31889424,\n",
       "        33.21140289, 35.47865229, 32.22279482, 33.06172843, 37.03793845,\n",
       "        41.85951033, 34.28751316, 39.04807944, 44.42355156, 50.19157476,\n",
       "        47.56446877, 55.74907813, 66.74098048, 65.56548958]),\n",
       " 'std_fit_time': array([0.95312825, 0.90281801, 0.43575394, 1.33101091, 1.31232923,\n",
       "        1.43213714, 1.38837157, 0.2850561 , 1.13125836, 1.087914  ,\n",
       "        1.07925872, 0.86140728, 1.34577654, 1.25214329, 1.01670529,\n",
       "        0.81758331, 1.58290896, 0.47047378, 1.39790757, 1.3108094 ,\n",
       "        0.98668902, 1.81282527, 1.40759771, 4.33473358]),\n",
       " 'mean_score_time': array([2.67696195, 2.74683886, 2.38972383, 2.83313375, 2.41234818,\n",
       "        2.83522553, 2.88428288, 2.33125529, 2.93868484, 2.44334273,\n",
       "        2.96398611, 2.77401099, 2.54623809, 3.02891455, 2.89905553,\n",
       "        3.04513731, 2.75951314, 2.81195354, 3.35068088, 3.09567432,\n",
       "        3.02861977, 3.47170491, 3.23610063, 1.95885701]),\n",
       " 'std_score_time': array([0.34038924, 0.63919695, 0.26248032, 0.54067116, 0.41970253,\n",
       "        0.53206004, 0.61324825, 0.10016159, 0.14648609, 0.27712475,\n",
       "        0.3853141 , 0.48527642, 0.1603692 , 0.35729682, 0.35252181,\n",
       "        0.25119284, 0.4190115 , 0.34222274, 0.38632521, 0.24467369,\n",
       "        0.34065071, 0.2060132 , 0.49839346, 0.42703031]),\n",
       " 'param_classifier__C': masked_array(data=[0.1, 0.1, 0.1, 0.1, 0.3, 0.3, 0.3, 0.3, 0.5, 0.5, 0.5,\n",
       "                    0.5, 0.7, 0.7, 0.7, 0.7, 1, 1, 1, 1, 2, 2, 2, 2],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_classifier__tol': masked_array(data=[0.01, 0.001, 0.0001, 1e-05, 0.01, 0.001, 0.0001, 1e-05,\n",
       "                    0.01, 0.001, 0.0001, 1e-05, 0.01, 0.001, 0.0001, 1e-05,\n",
       "                    0.01, 0.001, 0.0001, 1e-05, 0.01, 0.001, 0.0001, 1e-05],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_preprocessor__vect__vectorizer__ngram_range': masked_array(data=[(1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3),\n",
       "                    (1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3),\n",
       "                    (1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3),\n",
       "                    (1, 3), (1, 3), (1, 3)],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_preprocessor__vect__vectorizer__sublinear_tf': masked_array(data=[True, True, True, True, True, True, True, True, True,\n",
       "                    True, True, True, True, True, True, True, True, True,\n",
       "                    True, True, True, True, True, True],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'classifier__C': 0.1,\n",
       "   'classifier__tol': 0.01,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.1,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.1,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.1,\n",
       "   'classifier__tol': 1e-05,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.3,\n",
       "   'classifier__tol': 0.01,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.3,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.3,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.3,\n",
       "   'classifier__tol': 1e-05,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.5,\n",
       "   'classifier__tol': 0.01,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.5,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.5,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.5,\n",
       "   'classifier__tol': 1e-05,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.7,\n",
       "   'classifier__tol': 0.01,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.7,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.7,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 0.7,\n",
       "   'classifier__tol': 1e-05,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 1,\n",
       "   'classifier__tol': 0.01,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 1,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 1,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 1,\n",
       "   'classifier__tol': 1e-05,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 2,\n",
       "   'classifier__tol': 0.01,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 2,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 2,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 2,\n",
       "   'classifier__tol': 1e-05,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True}],\n",
       " 'split0_test_score': array([0.84434961, 0.84440379, 0.84440379, 0.84440379, 0.84766106,\n",
       "        0.84768818, 0.84768818, 0.84768818, 0.84808338, 0.84802913,\n",
       "        0.84802913, 0.84802913, 0.84793317, 0.84790357, 0.84790357,\n",
       "        0.84790357, 0.84747942, 0.84747942, 0.84747942, 0.84747942,\n",
       "        0.84567996, 0.84570707, 0.84570707, 0.84570707]),\n",
       " 'split1_test_score': array([0.84619762, 0.84617343, 0.84611922, 0.84611922, 0.84982366,\n",
       "        0.8498508 , 0.8498508 , 0.8498508 , 0.84936041, 0.84938754,\n",
       "        0.84938754, 0.84938754, 0.84966898, 0.84970131, 0.84970131,\n",
       "        0.84970131, 0.84945449, 0.84945449, 0.84945449, 0.84945449,\n",
       "        0.84842208, 0.84844921, 0.84844921, 0.84844921]),\n",
       " 'split2_test_score': array([0.84352307, 0.84358294, 0.84361287, 0.84361287, 0.84755416,\n",
       "        0.84755416, 0.84755416, 0.84755416, 0.84774259, 0.84776972,\n",
       "        0.84776972, 0.84776972, 0.84723762, 0.84723762, 0.84723762,\n",
       "        0.84723762, 0.84712932, 0.84709973, 0.84709973, 0.84709973,\n",
       "        0.84562076, 0.84562076, 0.84562076, 0.84562076]),\n",
       " 'split3_test_score': array([0.84776932, 0.84773934, 0.84773934, 0.84773934, 0.85084643,\n",
       "        0.85087617, 0.85087617, 0.85087617, 0.85133162, 0.85133162,\n",
       "        0.85133162, 0.85133162, 0.85138204, 0.85135489, 0.85135489,\n",
       "        0.85135489, 0.85057008, 0.85057008, 0.85057008, 0.85057008,\n",
       "        0.84871002, 0.84868289, 0.84868289, 0.84868289]),\n",
       " 'split4_test_score': array([0.84292735, 0.84302283, 0.84302283, 0.84302283, 0.84798297,\n",
       "        0.84798297, 0.84798297, 0.84798297, 0.84877127, 0.84877127,\n",
       "        0.84877127, 0.84877127, 0.84880868, 0.84880868, 0.84880868,\n",
       "        0.84880868, 0.84771864, 0.84771864, 0.84771864, 0.84771864,\n",
       "        0.84714155, 0.84717112, 0.84717112, 0.84717112]),\n",
       " 'mean_test_score': array([0.84495339, 0.84498446, 0.84497961, 0.84497961, 0.84877365,\n",
       "        0.84879045, 0.84879045, 0.84879045, 0.84905785, 0.84905786,\n",
       "        0.84905786, 0.84905786, 0.8490061 , 0.84900121, 0.84900121,\n",
       "        0.84900121, 0.84847039, 0.84846447, 0.84846447, 0.84846447,\n",
       "        0.84711487, 0.84712621, 0.84712621, 0.84712621]),\n",
       " 'std_test_score': array([0.00178845, 0.00174083, 0.00172878, 0.00172878, 0.00132281,\n",
       "        0.00133192, 0.00133192, 0.00133192, 0.00126674, 0.00127109,\n",
       "        0.00127109, 0.00127109, 0.00144237, 0.00144098, 0.00144098,\n",
       "        0.00144098, 0.00132082, 0.00132686, 0.00132686, 0.00132686,\n",
       "        0.00130732, 0.00130046, 0.00130046, 0.00130046]),\n",
       " 'rank_test_score': array([24, 21, 22, 22, 12,  9,  9,  9,  4,  1,  1,  1,  5,  6,  6,  6, 13,\n",
       "        14, 14, 14, 20, 17, 17, 17], dtype=int32)}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = time.time()\n",
    "\n",
    "#Hyperparameter tuning for LinearSVC\n",
    "parameters = {\n",
    "    'classifier__C': (0.1, 0.3, 0.5, 0.7, 1, 2),\n",
    "    'classifier__tol': (0.01, 0.001, 0.0001, 0.00001),\n",
    "    'preprocessor__vect__vectorizer__ngram_range': ((1, 3),), \n",
    "    'preprocessor__vect__vectorizer__sublinear_tf': (True,),\n",
    "}\n",
    "\n",
    "lSVCclf = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', LinearSVC(random_state=42, max_iter=2000) )\n",
    "                      ])\n",
    "\n",
    "\n",
    "grid_search = GridSearchCV(lSVCclf, parameters, cv=5,  scoring='f1_macro', n_jobs=-1, verbose=2)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print(f'Text Preprocessing complete.') # 672 seconds\n",
    "print(f'Time Taken: {round(time.time()-t)} seconds') \n",
    "grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0dfa6754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.85185713433286\n",
      "{'memory': None, 'steps': [('preprocessor', ColumnTransformer(transformers=[('vect',\n",
      "                                 Pipeline(steps=[('vectorizer',\n",
      "                                                  TfidfVectorizer(ngram_range=(1,\n",
      "                                                                               3),\n",
      "                                                                  sublinear_tf=True))]),\n",
      "                                 'text'),\n",
      "                                ('cat',\n",
      "                                 Pipeline(steps=[('onehot',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                 ['user', 'hour', 'weekday', 'month', 'day'])])), ('classifier', LinearSVC(C=0.5, max_iter=2000, random_state=42, tol=0.001))], 'verbose': False, 'preprocessor': ColumnTransformer(transformers=[('vect',\n",
      "                                 Pipeline(steps=[('vectorizer',\n",
      "                                                  TfidfVectorizer(ngram_range=(1,\n",
      "                                                                               3),\n",
      "                                                                  sublinear_tf=True))]),\n",
      "                                 'text'),\n",
      "                                ('cat',\n",
      "                                 Pipeline(steps=[('onehot',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                 ['user', 'hour', 'weekday', 'month', 'day'])]), 'classifier': LinearSVC(C=0.5, max_iter=2000, random_state=42, tol=0.001), 'preprocessor__n_jobs': None, 'preprocessor__remainder': 'drop', 'preprocessor__sparse_threshold': 0.3, 'preprocessor__transformer_weights': None, 'preprocessor__transformers': [('vect', Pipeline(steps=[('vectorizer',\n",
      "                 TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))]), 'text'), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['user', 'hour', 'weekday', 'month', 'day'])], 'preprocessor__verbose': False, 'preprocessor__verbose_feature_names_out': True, 'preprocessor__vect': Pipeline(steps=[('vectorizer',\n",
      "                 TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))]), 'preprocessor__cat': Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), 'preprocessor__vect__memory': None, 'preprocessor__vect__steps': [('vectorizer', TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))], 'preprocessor__vect__verbose': False, 'preprocessor__vect__vectorizer': TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True), 'preprocessor__vect__vectorizer__analyzer': 'word', 'preprocessor__vect__vectorizer__binary': False, 'preprocessor__vect__vectorizer__decode_error': 'strict', 'preprocessor__vect__vectorizer__dtype': <class 'numpy.float64'>, 'preprocessor__vect__vectorizer__encoding': 'utf-8', 'preprocessor__vect__vectorizer__input': 'content', 'preprocessor__vect__vectorizer__lowercase': True, 'preprocessor__vect__vectorizer__max_df': 1.0, 'preprocessor__vect__vectorizer__max_features': None, 'preprocessor__vect__vectorizer__min_df': 1, 'preprocessor__vect__vectorizer__ngram_range': (1, 3), 'preprocessor__vect__vectorizer__norm': 'l2', 'preprocessor__vect__vectorizer__preprocessor': None, 'preprocessor__vect__vectorizer__smooth_idf': True, 'preprocessor__vect__vectorizer__stop_words': None, 'preprocessor__vect__vectorizer__strip_accents': None, 'preprocessor__vect__vectorizer__sublinear_tf': True, 'preprocessor__vect__vectorizer__token_pattern': '(?u)\\\\b\\\\w\\\\w+\\\\b', 'preprocessor__vect__vectorizer__tokenizer': None, 'preprocessor__vect__vectorizer__use_idf': True, 'preprocessor__vect__vectorizer__vocabulary': None, 'preprocessor__cat__memory': None, 'preprocessor__cat__steps': [('onehot', OneHotEncoder(handle_unknown='ignore'))], 'preprocessor__cat__verbose': False, 'preprocessor__cat__onehot': OneHotEncoder(handle_unknown='ignore'), 'preprocessor__cat__onehot__categories': 'auto', 'preprocessor__cat__onehot__drop': None, 'preprocessor__cat__onehot__dtype': <class 'numpy.float64'>, 'preprocessor__cat__onehot__handle_unknown': 'ignore', 'preprocessor__cat__onehot__sparse': True, 'classifier__C': 0.5, 'classifier__class_weight': None, 'classifier__dual': True, 'classifier__fit_intercept': True, 'classifier__intercept_scaling': 1, 'classifier__loss': 'squared_hinge', 'classifier__max_iter': 2000, 'classifier__multi_class': 'ovr', 'classifier__penalty': 'l2', 'classifier__random_state': 42, 'classifier__tol': 0.001, 'classifier__verbose': 0}\n"
     ]
    }
   ],
   "source": [
    "score = f1_score(y_test, grid_search.predict(X_test), average='macro')\n",
    "print(score)\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "\n",
    "print(best_parameters) # C = 0.5, tol = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "27ab4bd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8391595060339134\n"
     ]
    }
   ],
   "source": [
    "#Baseline LogisticRegression\n",
    "logReg = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', LogisticRegression(random_state=42) )\n",
    "                      ])\n",
    "logReg.fit(X_train,y_train)\n",
    "logRegscore = f1_score(y_test, logReg.predict(X_test), average='macro')\n",
    "print(logRegscore) # 0.839"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b124ce87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n",
      "Text Preprocessing complete.\n",
      "Time Taken: 1238 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([222.46785231, 223.97017546, 223.60380158, 220.72266297,\n",
       "        222.78245931, 231.73662052, 231.72287531, 234.24976044]),\n",
       " 'std_fit_time': array([1.60538659, 1.03178336, 1.65366063, 2.21643289, 4.72569468,\n",
       "        0.80706992, 0.84134821, 1.44674994]),\n",
       " 'mean_score_time': array([3.00052204, 2.48772206, 2.35959983, 2.75657778, 2.46432137,\n",
       "        2.81428747, 2.839533  , 1.88632083]),\n",
       " 'std_score_time': array([0.49550225, 0.42026457, 0.40000905, 0.49734924, 0.41527411,\n",
       "        0.28018629, 0.46696952, 0.52708113]),\n",
       " 'param_classifier__C': masked_array(data=[1.0, 1.0, 5.0, 5.0, 15, 15, 30, 30],\n",
       "              mask=[False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_classifier__tol': masked_array(data=[0.001, 0.0001, 0.001, 0.0001, 0.001, 0.0001, 0.001,\n",
       "                    0.0001],\n",
       "              mask=[False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_preprocessor__vect__vectorizer__ngram_range': masked_array(data=[(1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3), (1, 3),\n",
       "                    (1, 3)],\n",
       "              mask=[False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_preprocessor__vect__vectorizer__sublinear_tf': masked_array(data=[True, True, True, True, True, True, True, True],\n",
       "              mask=[False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'classifier__C': 1.0,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 1.0,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 5.0,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 5.0,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 15,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 15,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 30,\n",
       "   'classifier__tol': 0.001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True},\n",
       "  {'classifier__C': 30,\n",
       "   'classifier__tol': 0.0001,\n",
       "   'preprocessor__vect__vectorizer__ngram_range': (1, 3),\n",
       "   'preprocessor__vect__vectorizer__sublinear_tf': True}],\n",
       " 'split0_test_score': array([0.8416722 , 0.8416722 , 0.84181811, 0.84181811, 0.84038305,\n",
       "        0.84038305, 0.83796262, 0.83796262]),\n",
       " 'split1_test_score': array([0.84565896, 0.84565896, 0.84700258, 0.84700258, 0.84549382,\n",
       "        0.84549382, 0.84120471, 0.84120471]),\n",
       " 'split2_test_score': array([0.84613113, 0.84613113, 0.84753364, 0.84753364, 0.84655096,\n",
       "        0.84655096, 0.8441337 , 0.8441337 ]),\n",
       " 'split3_test_score': array([0.84217588, 0.84217588, 0.84212904, 0.84212904, 0.84048511,\n",
       "        0.84048511, 0.83860413, 0.83860413]),\n",
       " 'split4_test_score': array([0.84292519, 0.84292519, 0.84628328, 0.84628328, 0.83818043,\n",
       "        0.83818043, 0.84181998, 0.84181998]),\n",
       " 'mean_test_score': array([0.84371267, 0.84371267, 0.84495333, 0.84495333, 0.84221867,\n",
       "        0.84221867, 0.84074503, 0.84074503]),\n",
       " 'std_test_score': array([0.00183207, 0.00183207, 0.00246708, 0.00246708, 0.0032304 ,\n",
       "        0.0032304 , 0.00224389, 0.00224389]),\n",
       " 'rank_test_score': array([3, 3, 1, 1, 5, 5, 7, 7], dtype=int32)}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = time.time()\n",
    "\n",
    "#Hyperparameter tuning for LogisticRegression\n",
    "parameters = {\n",
    "    'classifier__C': (1.0, 3.0, 6.0, 15, 30),\n",
    "    'classifier__tol': (0.001, 0.0001),\n",
    "    'preprocessor__vect__vectorizer__ngram_range': ((1, 3),), \n",
    "    'preprocessor__vect__vectorizer__sublinear_tf': (True,),\n",
    "}\n",
    "\n",
    "logRegClf = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', LogisticRegression(random_state=42, max_iter = 150) )\n",
    "                      ])\n",
    "\n",
    "grid_search = GridSearchCV(logRegClf, parameters, cv=5,  scoring='f1_macro', n_jobs=-1, verbose=2)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(f'Text Preprocessing complete.') # 1230 seconds\n",
    "print(f'Time Taken: {round(time.time()-t)} seconds') \n",
    "grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cebb9939",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8496609555652823\n",
      "{'memory': None, 'steps': [('preprocessor', ColumnTransformer(transformers=[('vect',\n",
      "                                 Pipeline(steps=[('vectorizer',\n",
      "                                                  TfidfVectorizer(ngram_range=(1,\n",
      "                                                                               3),\n",
      "                                                                  sublinear_tf=True))]),\n",
      "                                 'text'),\n",
      "                                ('cat',\n",
      "                                 Pipeline(steps=[('onehot',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                 ['user', 'hour', 'weekday', 'month', 'day'])])), ('classifier', LogisticRegression(C=5.0, max_iter=150, random_state=42, tol=0.001))], 'verbose': False, 'preprocessor': ColumnTransformer(transformers=[('vect',\n",
      "                                 Pipeline(steps=[('vectorizer',\n",
      "                                                  TfidfVectorizer(ngram_range=(1,\n",
      "                                                                               3),\n",
      "                                                                  sublinear_tf=True))]),\n",
      "                                 'text'),\n",
      "                                ('cat',\n",
      "                                 Pipeline(steps=[('onehot',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                 ['user', 'hour', 'weekday', 'month', 'day'])]), 'classifier': LogisticRegression(C=5.0, max_iter=150, random_state=42, tol=0.001), 'preprocessor__n_jobs': None, 'preprocessor__remainder': 'drop', 'preprocessor__sparse_threshold': 0.3, 'preprocessor__transformer_weights': None, 'preprocessor__transformers': [('vect', Pipeline(steps=[('vectorizer',\n",
      "                 TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))]), 'text'), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['user', 'hour', 'weekday', 'month', 'day'])], 'preprocessor__verbose': False, 'preprocessor__verbose_feature_names_out': True, 'preprocessor__vect': Pipeline(steps=[('vectorizer',\n",
      "                 TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))]), 'preprocessor__cat': Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), 'preprocessor__vect__memory': None, 'preprocessor__vect__steps': [('vectorizer', TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True))], 'preprocessor__vect__verbose': False, 'preprocessor__vect__vectorizer': TfidfVectorizer(ngram_range=(1, 3), sublinear_tf=True), 'preprocessor__vect__vectorizer__analyzer': 'word', 'preprocessor__vect__vectorizer__binary': False, 'preprocessor__vect__vectorizer__decode_error': 'strict', 'preprocessor__vect__vectorizer__dtype': <class 'numpy.float64'>, 'preprocessor__vect__vectorizer__encoding': 'utf-8', 'preprocessor__vect__vectorizer__input': 'content', 'preprocessor__vect__vectorizer__lowercase': True, 'preprocessor__vect__vectorizer__max_df': 1.0, 'preprocessor__vect__vectorizer__max_features': None, 'preprocessor__vect__vectorizer__min_df': 1, 'preprocessor__vect__vectorizer__ngram_range': (1, 3), 'preprocessor__vect__vectorizer__norm': 'l2', 'preprocessor__vect__vectorizer__preprocessor': None, 'preprocessor__vect__vectorizer__smooth_idf': True, 'preprocessor__vect__vectorizer__stop_words': None, 'preprocessor__vect__vectorizer__strip_accents': None, 'preprocessor__vect__vectorizer__sublinear_tf': True, 'preprocessor__vect__vectorizer__token_pattern': '(?u)\\\\b\\\\w\\\\w+\\\\b', 'preprocessor__vect__vectorizer__tokenizer': None, 'preprocessor__vect__vectorizer__use_idf': True, 'preprocessor__vect__vectorizer__vocabulary': None, 'preprocessor__cat__memory': None, 'preprocessor__cat__steps': [('onehot', OneHotEncoder(handle_unknown='ignore'))], 'preprocessor__cat__verbose': False, 'preprocessor__cat__onehot': OneHotEncoder(handle_unknown='ignore'), 'preprocessor__cat__onehot__categories': 'auto', 'preprocessor__cat__onehot__drop': None, 'preprocessor__cat__onehot__dtype': <class 'numpy.float64'>, 'preprocessor__cat__onehot__handle_unknown': 'ignore', 'preprocessor__cat__onehot__sparse': True, 'classifier__C': 5.0, 'classifier__class_weight': None, 'classifier__dual': False, 'classifier__fit_intercept': True, 'classifier__intercept_scaling': 1, 'classifier__l1_ratio': None, 'classifier__max_iter': 150, 'classifier__multi_class': 'auto', 'classifier__n_jobs': None, 'classifier__penalty': 'l2', 'classifier__random_state': 42, 'classifier__solver': 'lbfgs', 'classifier__tol': 0.001, 'classifier__verbose': 0, 'classifier__warm_start': False}\n"
     ]
    }
   ],
   "source": [
    "score = f1_score(y_test, grid_search.predict(X_test), average='macro')\n",
    "print(score)\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "\n",
    "print(best_parameters) # C = 6, toll = 0.001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f8608f",
   "metadata": {},
   "source": [
    "More fine tuning hyperparameter for **Logistic regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4deb48c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C value = 1.0: 0.8459458662842196\n",
      "C value = 3.0: 0.8506488496077538\n",
      "C value = 5.0: 0.8514109521076004\n",
      "C value = 6.0: 0.8517412654955099\n",
      "C value = 7.0: 0.8518207783467924\n",
      "C value = 8.0: 0.8519977048434562\n",
      "C value = 10.0: 0.8517860306828324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C value = 12.0: 0.851717862036229\n"
     ]
    }
   ],
   "source": [
    "if flag_tuning:\n",
    "    c_values = [1.0, 3.0, 5.0, 6.0, 7.0, 8.0, 10.0, 12.0]\n",
    "    scores_c_value = np.zeros(len(c_values))\n",
    "    for i in range(len(c_values)):\n",
    "        c_value = c_values[i]\n",
    "        model = make_pipeline(\n",
    "            make_column_transformer(\n",
    "                (TfidfVectorizer(ngram_range=(1, 3), sublinear_tf = True), \"text\"),\n",
    "                (OneHotEncoder(handle_unknown=\"ignore\"), [\"user\", \"hour\", \"weekday\", \"month\", \"day\"]),\n",
    "                remainder=\"passthrough\",\n",
    "                n_jobs=-1\n",
    "            ),\n",
    "            LogisticRegression(C = c_value, max_iter = 1000, n_jobs=-1, tol=0.001)\n",
    "        )\n",
    "        model.fit(X_train, y_train);\n",
    "        scores_c_value[i] = f1_score(y_test, model.predict(X_test), average='macro')\n",
    "        print(f\"C value = {c_value}: {scores_c_value[i]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2503093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if flag_tuning:\n",
    "    #plot C value graph - Logistic regression\n",
    "    plt.grid(True)\n",
    "    plt.xlabel(\"C value\")\n",
    "    plt.ylabel(\"macro f1 score\")\n",
    "    plt.plot(c_values, scores_c_value);\n",
    "    plt.savefig('C_valueLogisticRegression.svg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361a961d",
   "metadata": {},
   "source": [
    "More fine tuning hyperparameter for **LinearSVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ce7c306b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C value = 0.1: 0.8476742462424939\n",
      "C value = 0.2: 0.8511630575687361\n",
      "C value = 0.3: 0.8520640867131453\n",
      "C value = 0.4: 0.8522751697204503\n",
      "C value = 0.5: 0.8520915517047729\n",
      "C value = 0.6: 0.851925726067774\n",
      "C value = 0.7: 0.8519311158675982\n",
      "C value = 0.8: 0.8515922420438387\n",
      "C value = 0.9: 0.8513010555627882\n",
      "C value = 1: 0.851175425848105\n",
      "C value = 2: 0.8505968070376245\n"
     ]
    }
   ],
   "source": [
    "if flag_tuning:\n",
    "    c_values = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2]\n",
    "    scores_c_value = np.zeros((len(c_values), ))\n",
    "    for i in range(len(c_values)):\n",
    "        c_value = c_values[i]\n",
    "        model = make_pipeline(\n",
    "            make_column_transformer( \n",
    "                (TfidfVectorizer(ngram_range=(1, 3), sublinear_tf = True ), \"text\"),\n",
    "                (OneHotEncoder(handle_unknown=\"ignore\"), [\"user\", \"hour\", \"weekday\", \"month\", \"day\"]),\n",
    "                remainder=\"passthrough\",\n",
    "                n_jobs=-1\n",
    "            ),\n",
    "            LinearSVC(C = c_value, random_state=42, max_iter = 100000, tol = 0.01)\n",
    "        )\n",
    "        model.fit(X_train, y_train);\n",
    "        scores_c_value[i] = f1_score(y_test, model.predict(X_test), average='macro')\n",
    "        print(f\"C value = {c_value}: {scores_c_value[i]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "901db895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if flag_tuning:\n",
    "    #plot C value graph - linearSVC\n",
    "    plt.grid(True)\n",
    "    plt.xlabel(\"C value\")\n",
    "    plt.ylabel(\"macro f1 score\")\n",
    "    plt.plot(c_values, scores_c_value);\n",
    "    plt.savefig('C_valueLinearSVC.svg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62715b78",
   "metadata": {},
   "source": [
    "# Prediction and final result on eval dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a40063c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/.local/lib/python3.8/site-packages/dateutil/parser/_parser.py:1207: UnknownTimezoneWarning: tzname PDT identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.\n",
      "  warnings.warn(\"tzname {tzname} identified but not understood.  \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation dataset size: 74999\n"
     ]
    }
   ],
   "source": [
    "eval_dataset = pd.read_csv(dir_path + \"evaluation.csv\",  infer_datetime_format=True, parse_dates=[\"date\"])\n",
    "print(f\"Evaluation dataset size: {eval_dataset['text'].size}\")\n",
    "\n",
    "eval_dataset[\"hour\"] = eval_dataset[\"date\"].apply(lambda x: x.hour)\n",
    "eval_dataset[\"weekday\"] = eval_dataset[\"date\"].apply(lambda x: x.weekday())\n",
    "eval_dataset[\"month\"] = eval_dataset[\"date\"].apply(lambda x: x.month)\n",
    "eval_dataset[\"day\"] = eval_dataset[\"date\"].apply(lambda x: x.day)\n",
    "\n",
    "df_eval_done = eval_dataset.drop(columns=[\"flag\", \"ids\", \"date\"])\n",
    "\n",
    "df_eval_done[\"text\"] = df_eval_done[\"text\"].apply(lambda tweet: preprocess_text_tweet_for_tfidfVect(tweet))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dd0fe3b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1score : 0.8522751697204503\n"
     ]
    }
   ],
   "source": [
    "model = make_pipeline(\n",
    "            make_column_transformer(\n",
    "                (TfidfVectorizer(ngram_range= (1, 3), sublinear_tf = True), \"text\"),\n",
    "                (OneHotEncoder(handle_unknown=\"ignore\"), [\"user\", \"hour\", \"weekday\", \"month\", \"day\"]),\n",
    "                remainder=\"passthrough\",\n",
    "                n_jobs=-1\n",
    "            ),\n",
    "#         LogisticRegression(C = 8, max_iter = 1000, n_jobs=-1, tol=0.001)\n",
    "        LinearSVC(C = 0.4, max_iter = 100000, random_state=42, tol = 0.01)\n",
    "    )\n",
    "model.fit(X_train, y_train);\n",
    "\n",
    "F1score = f1_score(y_test, model.predict(X_test), average='macro')\n",
    "print(f\"F1score : {F1score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d82c167f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File written....\n"
     ]
    }
   ],
   "source": [
    "# evaluation of the model and write to file\n",
    "y_eval = model.predict(df_eval_done)\n",
    "submit = pd.Series(y_eval, range(len(y_eval)))\n",
    "submit.to_csv(\"submit-final.csv\", index_label=\"Id\", header=[\"Predicted\"])\n",
    "print(\"File written....\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e330ef8b",
   "metadata": {},
   "source": [
    "### ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4ab2b109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test) #[:,1]\n",
    "\n",
    "# compute the false positive and true positive rate for the class 'neg'\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "fig, ax = plt.subplots(dpi=200)\n",
    "plt.plot(fpr, tpr, lw=1, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.xlim(0.0, 1.0)\n",
    "plt.ylim(0.0, 1.0)\n",
    "fig.suptitle('ROC curve for negative tweet classifier')\n",
    "ax.grid()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "ax.plot(np.linspace(0, 1, 100), np.linspace(0, 1, 100), linestyle='dashed')\n",
    "plt.savefig('Roc_curve.svg', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e4cae3",
   "metadata": {},
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e08368e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15825  3001]\n",
      " [ 3450 22346]]\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Op5zKIbVqUaVKFbqd0ZNPZn9csH/r1m1UrqyZ1GU3BZ9SZP2mLWRmGBWzs5j04ed0bdeQnIMqk3NQZbq2a8ikDz/f55xVazZyaqujAOjU9mgWLVsDQNNz76fJOffR5Jz7ePmtudz04BjGvxOZ2ajhYbWZt/jr1N2YlEo5NWqwMz+frVu30qVrd6ZMnsT6detYv24dUyZPokvX7nG31aVrd+bPncvmzZvJy8vjvWnv0LhJUwDcnW+/+ZrDj2iQpDspv1R2k4R586Mv6HB8A96asYi/PDGFacOvA+De4ZMLvtnzyO3n8tjL05m1YAXX/WUUD9x8FlmZmWzbvoMBfy1+zrbT2hzF6+/pE9oCnbt0Y/r70+h0elduHXgnnU89EYDbbr+LGjUjn+m4/tf9ufLq/rRq05bxY8fwu9/cyJo1q7ng3D4c1+J4Ro97jZwaNbjuhps4/dQTMTO6ndGDM3pGPtsxe9ZM2rZrX/BtIEmckMtuSZvb7UBobjdo2agu1190Clfd/ULSrjHpkV9x/u+eZv2mkj1fKms0txvM/ngWj/zrYYY9/lTSrvG7395Mr95nFjz/Ka8SPbdb1XqN/dhr/5OQtnLv6lym5naTAzD785W8PWsxGRnGzp2J/4dBrZyqDBn5brkPPBLRslVrTu3Yifz8/IKh0Yl27LHNyn3gSZaAEx8Fn9Lo6Vdyiz/oAK1Z/0PBsx8RgJ9fntwZLy6/8uqktl9uWdhlNw04EBGRlFPmIyISoMh7PunuxYFT8BERCZI+qSAiIlIiynxERAIVcOKj4CMiEiqV3UREREpAmY+ISIjSOC9bIij4iIgESJ9UEBERKSEFHxGRQJlZQpY4rtPDzBaa2SIzG7ifYy4ws/lmNs/MniuuTZXdREQClYqqm5llAkOBbsByYIaZjXP3+THHNARuB05293Vmdmhx7SrzERGRorQDFrn7YnffDowE+u51zC+Boe6+DsDdvy2uUQUfEZFAJbDsVsvMcmOW/jGXqQcsi1lfHt0WqxHQyMzeM7PpZtajuL6r7CYiEqLEDrVe8yM/JpcFNAQ6AfWBd8zsOHdfX9QJIiISGEvdxKIrgMNi1utHt8VaDnzo7juAJWb2OZFgNGN/jarsJiIiRZkBNDSzI80sG7gIGLfXMWOIZD2YWS0iZbjFRTWqzEdEJFCpSHzcPc/MBgATgUxguLvPM7O7gVx3Hxfd193M5gP5wK3u/l1R7Sr4iIgEKiNFMxy4+wRgwl7bBsX87MAt0SUuKruJiEjKKfMREQlUwFO7KfiIiITITBOLioiIlIgyHxGRQGWEm/go+IiIhEplNxERkRJQ5iMiEqiAEx8FHxGREBmR+d1CpbKbiIikXLHBx8xujGebiIikVoYlZklL3+M45vJCtl2R4H6IiEhJJOhDcukaMbffZz5mdjFwCXCkmcVOn30QsDbZHRMRkbKrqAEH7wOrgFrA32K2bwI+TWanRESkeGVytJu7fwl8CZyUuu6IiEg8jNR9UiEZ4hlwcI6ZfWFmG8xso5ltMrONqeiciIiUTfG853M/cJa7f5bszoiISPwCTnziCj7fKPCIiJQ+Ic/tFk/wyTWz54ExwLZdG919dLI6JSIiZVs8wedgYDPQPWabAwo+IiJpEvmYXLp7ceCKDT7u/otUdEREREqmrI92a2Rmk81sbnS9hZndlfyuiYhIWRXP9Dr/BW4HdgC4+6fARcnslIiIFM8StKRDPM98qrj7R3uNqshLUn9ERCROIY92iyfzWWNmRxMZZICZnUdk2h0REZEDEk/mcx0wDGhiZiuAJcClSe2ViIgUKTK9Trp7ceDiGe22GOhqZlWBDHfflPxuiYhIkdL4OYREKDb4mFkOcBnQAMjadbPufkMyOyYiImVXPGW3CcB0YA6wM7ndERGReAWc+MQVfCq5+y1J74mIiJRIyGW3eEa7PWNmvzSzOmZWc9eS9J6JiEiZFU/msx14ALiT6HDr6K9HJatTIiJStDI/2g34DXCMu69JdmdERCR+Zb3stojIrNYiIiIJEU/m8wMw28zeYs/v+WiotYhIGoWb98QXfMZEFxERKSXMwv6kQjwzHDyVio6IiEj5sd/gY2YvuPsFZjaH3aPcCrh7i6T2TEREihRw4lNk5nNj9NczU9EREREpmTI52s3dd3024Vp3/zJ2Aa5NTfdERKQsimeodbdCtvVMdEdERKRkzBKzpENRz3x+TSTDOcrMPo3ZdRDwXrI7JiIi+2dY0KPdisp8ngPOAsZFf921tHF3fUxORKScMLMeZrbQzBaZ2cBC9l9hZqvNbHZ0ubq4Nveb+bj7BmADcLGZZQI/iR5fzcyquftXP+JeRETkx0hRySz69/9QIo9glgMzzGycu8/f69Dn3X1AvO3G8zG5AcAfgW/Y/T0fBzTUWkQkjVI02q0dsCj6VWvMbCTQF9g7+JRIPDMc3AQ0dvfvfsyF4tGqcT3ee/evyb6MCAA1Toj7H2kiP9q2hcEWi+oBy2LWlwPtCznuXDPrCHwO3Ozuywo5pkA8o92WESm/iYhIKZKRoAWoZWa5MUv/EnZlPNAgOvnAJKDYmXHiyXwWA1PN7FX2nFj07yXsnIiIJIiR0LLbGndvu599K4DDYtbrR7cV2Ksy9hhwf3EXjCf4fBVdsqOLiIiUHzOAhmZ2JJGgcxFwSewBZlYnZmKCPsBnxTUaz8Sif4o2XsXd9V0fEZFSIhVfMnX3vOjAs4lAJjDc3eeZ2d1ArruPA24wsz5AHrAWuKK4duMZ7XYS8DhQDTjczI4HfuXummJHRCSNUvUZbXefAEzYa9ugmJ9vB24vSZvxDDh4CDgD+C56kU+AjiW5iIiIJFZkahxLyJIO8QQfChkyl5+EvoiISDkRz4CDZWbWAXAzq0DkUwvFPkwSEZHkSlXZLRniCT7XAA8TedFoBfAGcF0yOyUiIsULeF7RuEa7rQH6paAvIiJSThT7zMfM7jezg82sgplNjs5cqlmtRUTSyIAMs4Qs6RDPgIPu7r6RyOe0lwLHALcms1MiIlK8BE6vk3LxXHdXaa438GL0UwsiIiIHLJ4BB6+Y2QJgC/BrM6sNbE1ut0REpDhlfcDBQDO7H9jg7vlmtpnItxxERCRNLI3PaxIhnswHd18b8/MPwA9J65GIiJR5cQUfEREpfQJOfBR8RERCFfIMB/G852NmdqmZDYquH25m7ZLfNRERKaviGWr9CHAScHF0fRMwNGk9EhGRYoX+kmk8Zbf27t7azD4GcPd1ZqYvmoqIpFnIz3ziyXx2mFkm4ADR93x2JrVXIiJSpsWT+QwBXgYONbPBwHnAXUntlYiIFM3CHnAQz0um/zOzmUAXImXGs91d3/MREUkzI9zoU2zwMbPDgc3A+Nht7v5VMjsmIiJlVzxlt1eJPO8xoBJwJLAQaJbEfomISBEio93S3YsDF0/Z7bjYdTNrDVybtB6JiEhcQg4+Jf6Ug7vPAtonoS8iIlJOxPPM55aY1QygNbAyaT0SEZG4WMAv+sTzzOegmJ/ziDwDGpWc7oiISDzK9DOf6MulB7n7b1PUHxERKQf2G3zMLMvd88zs5FR2SERE4mBhT69TVObzEZHnO7PNbBzwIjEfkXP30Unum4iIFKGsf8m0EvAdcDq73/dxQMFHREQOSFHB59DoSLe57A46u3hSeyUiIkUqywMOMoFqUOjkQQo+IiJpFnDVrcjgs8rd705ZT0REpNwoKvgEHFNFRMo6IyPgv6aLCj5dUtYLEREpESPsstt+53Zz97Wp7IiIiJQf8Qy1FhGR0qasf8lURERKp5BfMi3xJxVERER+LGU+IiIBCn3AgYKPiEigVHYTEREpAWU+IiKBCjjxUfAREQmREXbpKuS+i4hICphZDzNbaGaLzGxgEceda2ZuZm2La1OZj4hIiAwsBXU3M8sEhgLdgOXADDMb5+7z9zruIOBG4MN42lXmIyISKEvQUox2wCJ3X+zu24GRQN9CjrsHuA/YGk/fFXxERKSWmeXGLP1j9tUDlsWsL49uK2BmrYHD3P3VeC+ospuISIAiXzJNWNltjbsX+5ym0H6YZQB/B64oyXnKfEREApWistsK4LCY9frRbbscBDQHpprZUuBEYFxxgw4UfEREpCgzgIZmdqSZZQMXAeN27XT3De5ey90buHsDYDrQx91zi2pUZTcRkUCl4iVTd88zswHARCATGO7u88zsbiDX3ccV3ULhFHxERIJkKRlqDeDuE4AJe20btJ9jO8XTpspuIiKScsp8REQCFPr0Ogo+IiKBSlXZLRkUfEREAhVu6Ak7axMRkUAp8xERCVGKJhZNFgUfEZEAhT7gIOS+i4hIoJT5iIgESmU3ERFJuXBDj8puIiKSBgo+pdDWrVs55aR2tGt9PK2Pb8Y9f/rDHvtvuekGauVUK1h/5qknOaxObdq3aUn7Ni154vHHCvY9+/RTNG/akOZNG/Ls00+l7B6kdKv/kxxeH3YDs0bdycyX7uS6izsBcO9NZzN79F189PztPP+3X1K9WmUA2jY7gukjBzJ95EA+fH4gfTq32KO9jAzjgxG/Y9TD1+yx/Y/XncWnYwbx8ai7uPbi01Jyb+WJWWKWdFDZrRSqWLEir0+aQrVq1dixYwenn3YK3c/oSfsTT2Rmbi7r163b55xzz7+Qh4b8a49ta9euZfCf/8R703MxMzq0b0Pvs/pQo0aNVN2KlFJ5+TsZ+PfRzF6wnGpVKvL+c79j8ocLmDx9Ab//5zjy83fy5xv6cuuV3blryFjm/d9KTu53P/n5O/lprYP58PnbefWdueTn7wRgwCWdWbjkGw6qWqngGj/vcyL1f5rD8T+7B3endo1q++uOHIDIaLdwC2/KfEohM6Natcgf1B07dpC3YwdmRn5+PncMvJXBf70/rnYmvTGRLl26UbNmTWrUqEGXLt14Y+Lryey6BOLrNRuZvWA5AN9v3saCJV9Tt3YOk6cvKAgoH81ZQr2f5ACwZeuOgu0Vsyvg7gVt1Ts0hx6nNOOJl9/f4xr9zz+Fe4e9VnDs6nXfJ/u2JCAKPqVUfn4+7du05PC6h3J61260a9+eR4f+i95n9qFOnTr7HD/25VGc0KoFF194HsuWRT63vnLlCuoftvsDhPXq12flyhX7nCvl2+F1atKycX1mzF26x/bL+p7ExPfmF6yf0PwIZr50J7kv3sENg0cWBKMHbj2XOx8ew86dvsf5R9avzXnd2zDtf7cx5l+/5ujDayf9XsqbkMtuCj6lVGZmJh/OnM2ipcvJnfER0959h9GjXuTaAdfvc2yvM89iwaKlzPj4U7p06cYvr7w8DT2WEFWtnM2IB6/m1gdHsemHrQXbb7vqDPLzdzJywoyCbTPmfkmb8wZzyqX3c+uV3amYnUXPU5vz7dpNfPzZsn3arpidxbbtOzil3/08Mfp9/vOHfim5p/LDEva/dFDwKeVycnI4rVNn3p76Fov/bxHNmhxD42MasHnzZpo1OQaAQw45hIoVKwLwi6uu5uNZMwGoW7cey5ft/kthxfLl1K1bL/U3IaVSVlYGIx78Jc+/lsvYKZ8UbL/0rPb06ticK+58stDzFi75hu83b6PZMXU5qeVRnHnacSx49U88/ddf0OmERgz/82UArPhmHWMmR9odO+UTmjfU7z3ZTcGnFFq9ejXr168HYMuWLUx+cxKtWrdh6fKvWbhoKQsXLaVKlSrMW7AIgFWrVhWc+8r4cTRu0hSAbt3P4M0332DdunWsW7eON998g27dz0j5/Ujp9O8/9GPhkq8Z8uyUgm3dOjTlliu6ct5N/2HL1h0F24+oewiZmZG/Lg6vU4PGR/6UL1d+x6B/juOYHr+nSe8/cNnAJ5g643OuvOtpAMZP/ZTTTmgIwKltGrLoq29TeHflQ8hlt6SNdjOz4cCZwLfu3jxZ1ymLvl61il9eeTn5+fns9J2ce94F9Op95n6Pf+RfQ3j1lXFkZWZRo2ZN/vv4kwDUrFmT2+/4PaecdAIAd9w5iJo1a6biFqSU69DyKPqd2Z45n69g+siBAPzhX+P4263nUzE7i1ceHQDAR3OWcsPgkXRodRS//UV3duTls3Onc+O9z/Pd+h+KvMaDwyfxxL2Xc32/0/lhyzZ+ffdzSb+v8iT00W4WO2oloQ2bdQS+B56ON/i0adPW3/swNyn9EdlbjRMGpLsLUo5sW/gCOzd/m7Bo0ahZSx/ywqSEtNWz+aEz3b1tQhqLU9IyH3d/x8waJKt9EZFyLY0ls0RI+0umZtYf6A9w2OGHp7k3IiLhCDn4pH3AgbsPc/e27t62di29ByAiUh6kPfjInrZs2UK3008jPz8/rnnZRr30Iq2Pb0aV7Axm5u5+Xjbjo48K5npr1/p4xo55GYDt27fTtXNH8vLyUnI/UrpVqliBNx67kYwMo99Z7ZkzdhBzxg6i31ntCz2+RaN6vP3Ub5g+ciDT/ncbbZsdAcDNl3UpmPst98U7+D53CDUOrkKFrEwmPX5TwUg5SayQ3/NJe9lN9vTUE8Ppe/Y5bNiwIa552Zo1a87IF0Yz4Npf7bm9eXPe+zCXrKwsVq1aRfs2x9P7zLPIzs6m8+ldePGF57n4Er30V95d3vckxk7+hOrVKnNn/56c3O9+3J33n/sdr079lPWbtuxx/OCbzmbwsNd44735nHHKsQy+6WzO+OXD/OPpyfzj6ckA9OrYnOv7dWbdxs0AvPXhQs7v3pqRr2kwUSIZkKGy277MbATwAdDYzJab2VXJulZZMnLE/zirT9+452Vr0rQpjRo33md7lSpVyMqK/Nti29ate3x06qw+Z/P8iP8l7yYkGBf1asv4qZ/SrUNTJk9fwLqNm1m/aQuTpy+g+8nH7nO8OxwcnTy0erXKrFq9YZ9jLujRlhden1mwPn7qp1zY64Tk3YQEKWnBx90vdvc67l7B3eu7++PJulZZsX37dpYuWcwRDRokZF62jz78kNbHN6Ntq+MYMvTfBcGoWfPmzMydUczZUtZVyMqkQb1afLVqLXVr57D8m92zpa/4dj11a+fsc86tD77EvTedzRev3cNfbv4Zg/45do/9lStVoFuHpoyZPLtg27xFK2nTTIOJkiHkspsKsaXImjVrqJ6Tk7D22rVvz6xP5jHtgxk8cN9f2Lo1MndXZmYmFbKz2bRpU8KuJeGpVaMaGzZtLtE5/c8/ldv+NpqGPX/PbQ+O4tG95mvr3fE4Ppi9uKDkBrBzp7NjRz7VqlRMSL9lt5BnOFDwKUUqV65cECASOS9bk6ZNqVatGvPmzi3Ytn3bNipVqlTEWVLWbdm6nUoVKwCwcvV66v9k9/PEeofmsHL1+n3O6Xdm+4KsZtSkjwsGHOxy/hlteDGm5LZLdoUstm7fsc92Kb8UfEqRGjVqkJ+fz9atW3/0vGxLlywpGNH25ZdfsnDhAo5o0ACA7777jkNq1aJChQrJuA0JxPpNW8jMyKBidhaT3v+Mric1IeegyuQcVJmuJzVh0vuf7XPOqtUbOLVNZL62Tu0aseir1QX7Dq5WiVPaHMP4qZ/ucU7N6lX5bv335OXtTO4NlUMhl9002q2U6dq1O++/N43Tu3Td77xsv+5/NVf3v4Y2bdsydszL3HLT9axZvZpz+vamxfEtGT9hIu+/N40HH/grFbIqkJGRwcP/fIRatWoB8PbUt+jRs3fa7lFKjzenf0aHVkfz1ocL+ct/X2fas7cBcO+w1wtKZ48MuoTHXprGrPlfcd09z/HAreeRlZXBtm15DPjziIK2+nQ+nsnTF7B56/Y9rnHaCQ15fdq81N1UORH6aLekze12IDS3G3w8axb/fPgfDH/qmaRd48Lzz+HPg/9Kw0aNknaNEGhuN2jZpD7X9zudq37/dNKuMfLBq7lryLhyP6t1oud2a9K8pQ8bPaX4A+NwWuNDys7cbnJgWrVuzWmdOpOfn09mZmbC29++fTt9+pxd7gOPRMxesJy3cz8nI8P2+RJpIlTIymTc1E/LfeBJjvSVzBJBwacUuvwXVyat7ezsbPr9/LKktS/heXrs9KS1vSMvn+de+Shp7ZdrgU8sqgEHIiKScsp8REQCFXDio+AjIhKiyGi3cMOPym4iIpJyynxERAIVbt6j4CMiEq6Ao4/KbiIiknLKfEREAqWXTEVEJOUCHuymspuIiBTNzHqY2UIzW2RmAwvZf42ZzTGz2WY2zcz2/QzuXhR8REQCZQlairyGWSYwFOgJHAtcXEhwec7dj3P3lsD9wN+L67uCj4hIqFIRfaAdsMjdF7v7dmAk0Df2AHffGLNaFSh2llo98xERkVpmFvs9m2HuPiz6cz1gWcy+5UD7vRsws+uAW4Bs4PTiLqjgIyISoEjSkrARB2t+7Pd83H0oMNTMLgHuAi4v6ngFHxGREKXukworgMNi1utHt+3PSODR4hrVMx8RESnKDKChmR1pZtnARcC42APMrGHMam/gi+IaVeYjIhKoVCQ+7p5nZgOAiUAmMNzd55nZ3UCuu48DBphZV2AHsI5iSm6g4CMiEq4UvWTq7hOACXttGxTz840lbVNlNxERSTllPiIiQTLN7SYiIqmnud1ERERKQJmPiEiA4psZp/RS8BERCVXA0UfBR0QkUCEPONAzHxERSTllPiIigQp5tJuCj4hIoAKOPSq7iYhI6inzEREJUeBjrRV8REQCpdFuIiIiJaDMR0QkQIZGu4mISBoEHHtUdhMRkdRT5iMiEqqAUx8FHxGRQGm0m4iISAko8xERCZRGu4mISMoFHHtUdhMRkdRT5iMiEqqAUx8FHxGRAEXmFQ03+qjsJiIiKafMR0QkRKbRbiIikgYBxx6V3UREJPWU+YiIhCrg1EfBR0QkSKbRbiIiIiWhzEdEJFAa7SYiIillBP3IR2U3ERFJPWU+IiKhCjj1UfAREQmURruJiIiUgDIfEZFAabSbiIikXMCxR2U3ERFJPWU+IiIh0icVREQkPcKNPiq7iYhIkcysh5ktNLNFZjawkP23mNl8M/vUzCab2RHFtangIyISICNSdkvEUuR1zDKBoUBP4FjgYjM7dq/DPgbaunsL4CXg/uL6r+AjIhIoS9BSjHbAIndf7O7bgZFA39gD3P0td98cXZ0O1C+uUQUfERGpZWa5MUv/mH31gGUx68uj2/bnKuC14i5YqgYczJo1c03lCvZluvsRoFrAmnR3QsoN/X47MMU+BympBI52W+PubX9sI2Z2KdAWOK24Y0tV8HH32unuQ4jMLDcRv3FE4qHfb6VHiuZ2WwEcFrNeP7ptz76YdQXuBE5z923FNaqym4iIFGUG0NDMjjSzbOAiYFzsAWbWCvgP0Mfdv42n0VKV+YiISAmkIPFx9zwzGwBMBDKB4e4+z8zuBnLdfRzwAFANeNEitcCv3L1PUe0q+JQNw9LdASlX9PutlEjVK6buPgGYsNe2QTE/dy1pmyq7lQHurr8MJGX0+00SQZmPiEiA4nlBtDRT8BERCZS+ZCppUdx8SyKJZGbDzexbM5ub7r5I+BR8AhXnfEsiifQk0CPdnZAYKZpfJxkUfMJV7HxLIonk7u8Aa9PdD9kt4Nij4BOwks63JCJSamjAgYhIoDTaTdIhrvmWRKSssqBHuyn4hKtgviUiQeci4JL0dklEUmXXx+RCpWc+gXL3PGDXfEufAS+4+7z09krKMjMbAXwANDaz5WZ2Vbr7JOFS5hOwwuZbEkkWd7843X2QskPBR0QkUCq7iYiIlIAyHxGRQGm0m4iIpFbgs1qr7CYiIimn4CMHxMzyzWy2mc01sxfNrMqPaOtJMzsv+vNjRU2QamadzKzDAVxjqZnVOtA+FtN2AzO7JGa9rZkNSca1Yq7R0sx6JfMaUrolal43ze0modni7i3dvTmwHbgmdqeZHVBJ192vdvf5RRzSCShx8EmyBsS84Ovuue5+Q5Kv2RJQ8CnvAo4+Cj6SCO8Cx0SzknfNbBww38wyzewBM5thZp+a2a8ALOJf0W8RvQkcuqshM5tqZm2jP/cws1lm9omZTTazBkSC3M3RrOtUM6ttZqOi15hhZidHzz3EzN4ws3lm9hiF/BGL9u/JaPY2x8xujm4/2sxeN7OZ0ftpEt3+pJkNMbP3zWzxrmwN+CtwarRPN0f/O7wSPeePZvZUtJ0vzewcM7s/er3XzaxC9Lg2ZvZ29JoTzaxOzH+P+8zsIzP7PHrP2cDdwIXRa16Y4P8/RZJOAw7kR4lmOD2B16ObWgPN3X2JmfUHNrj7CWZWEXjPzN4AWgGNiXyH6CfAfGD4Xu3WBv4LdIy2VdPd15rZv4Hv3f3B6HHPAf9w92lmdjiRGR+aAn8Aprn73WbWGyjsbfyWQL1o9oaZ5US3DwOucfcvzKw98AhwenRfHeAUoAkwDngJGAj81t3PjLbTaa/rHA10jt7vB8C57n6bmb0M9DazV4F/An3dfXU0mAwGroyen+Xu7aJltj+4e1czGwS0dfcBhdyXlBMa7SblUWUzmx39+V3gcSLlsI/cfUl0e3egRUyGUB1oCHQERrh7PrDSzKYU0v6JwDu72nL3/X1HpitwrO0e9nOwmVWLXuOc6Lmvmtm6Qs5dDBxlZv8EXgXeiJ7bAXgxps2KMeeMcfedRDK7n+ynT3t7zd13mNkcIJPdgXoOkZJdY6A5MCl6zUxgVcz5o6O/zoweLwKEPdpNwUcO1BZ3bxm7IfoX5w+xm4Dr3X3iXscl8llFBnCiu28tpC9Fcvd1ZnY8cAaRct4FwE3A+r3vLca22MvE2cdt0evtNLMd7u7R7TuJ/Bk0YJ67n1TMNfPRn1kpI/TMR5JpIvDrmOcajcysKvAOkecVmdFnG50LOXc60NEis3ZjZjWj2zcBB8Uc9wZw/a4VM2sZ/fEdooMAzKwnUGPvC1hk9FuGu48C7gJau/tGYImZnR89xqIBqih796mkFgK1zeyk6DUrmFmzJF9TyoCAxxso+EhSPUbkec4sM5sL/IfIv9xfBr6I7nuayHOQPbj7aqA/MNrMPgGej+4aD/xs14AD4AagrUUGNMxn96i7PxEJXvOIlN++KqR/9YCp0fLhs8Dt0e39gKui151H8Z8n/xTIjw6MuLmYY/cR/Qz6ecB90WvOpvgRfW8RKTdqwEF5FnD0sd0VABERCUXrNm192vQZCWmranbGTHdvm5DG4qT6sYhIoDTaTUREUkpfMhURESkhPfMREQmQmb0OJGq+wjXu3iNBbcVFwUdERFJOZTcREUk5BR8REUk5BR8REUk5BR8REUk5BR8REUm5/wed4SjV8CBz8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix=confusion_matrix(y_test, y_pred)\n",
    "print(conf_matrix)\n",
    "print('\\n')\n",
    "\n",
    "fig, ax = plot_confusion_matrix(conf_mat=conf_matrix,figsize=(7, 7),\n",
    "                                show_absolute=True,\n",
    "                                show_normed=True,\n",
    "                                colorbar=True)\n",
    "ax.set_xlabel('Predicted sentiment');ax.set_ylabel('True sentiment');\n",
    "plt.savefig('ConfusionMatrix.svg', bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc15ef32",
   "metadata": {},
   "source": [
    "# Correlations\n",
    "\n",
    "**Correlation** between some column and sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d719ae1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.039638183420255255"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #calculate correlation between lenght of tweet and sentiment\n",
    "dev_dataset['lenght'] = dev_dataset[\"text\"].apply(lambda tweet: len(tweet))\n",
    "correlation = dev_dataset['sentiment'].corr(dev_dataset['lenght'])\n",
    "correlation # -0.036"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89f33a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024509395593694545"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #calculate correlation between capital letter count inside tweet and sentiment\n",
    "dev_dataset['countCapital'] = dev_dataset[\"text\"].apply(lambda tweet: len(re.findall(r'[A-Z]',tweet)))\n",
    "correlation = dev_dataset['sentiment'].corr(dev_dataset['countCapital'])\n",
    "correlation # 0.025"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "98fb6afa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.06716264581023598"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate correlation between hour and sentiment\n",
    "correlation = df_done['sentiment'].corr(df_done['hour'])\n",
    "correlation # 0.067"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3a864bdc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028373686590151534"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate correlation between weekday and sentiment\n",
    "correlation = df_done['sentiment'].corr(df_done['weekday'])\n",
    "correlation # 0.028"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8f4e77df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.14652841509906894"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate correlation between month and sentiment\n",
    "correlation = df_done['sentiment'].corr(df_done['month'])\n",
    "correlation # -0.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "acc90d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.06699386121255457"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate correlation between day and sentiment\n",
    "correlation = df_done['sentiment'].corr(df_done['day'])\n",
    "correlation # -0.066"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b05ca3",
   "metadata": {},
   "source": [
    "**Correlation** between tweet text word and sentiment. The following code will show the 20 most negative correlated and 20 most positive correlated word. The relative frequency is showed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c250064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most negative correlated word to sentiment with relative frequency\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>not</td>\n",
       "      <td>-0.186428</td>\n",
       "      <td>47793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>i</td>\n",
       "      <td>-0.157089</td>\n",
       "      <td>157802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3665</th>\n",
       "      <td>sad</td>\n",
       "      <td>-0.118118</td>\n",
       "      <td>3093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>sorry</td>\n",
       "      <td>-0.097013</td>\n",
       "      <td>4424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2899</th>\n",
       "      <td>my</td>\n",
       "      <td>-0.096358</td>\n",
       "      <td>38746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>no</td>\n",
       "      <td>-0.092470</td>\n",
       "      <td>11186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>hate</td>\n",
       "      <td>-0.091922</td>\n",
       "      <td>2644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809</th>\n",
       "      <td>miss</td>\n",
       "      <td>-0.090108</td>\n",
       "      <td>3902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>bad</td>\n",
       "      <td>-0.081616</td>\n",
       "      <td>3477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4838</th>\n",
       "      <td>wish</td>\n",
       "      <td>-0.079345</td>\n",
       "      <td>3555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>but</td>\n",
       "      <td>-0.075138</td>\n",
       "      <td>19658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3868</th>\n",
       "      <td>sick</td>\n",
       "      <td>-0.073647</td>\n",
       "      <td>1399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4574</th>\n",
       "      <td>ugh</td>\n",
       "      <td>-0.072964</td>\n",
       "      <td>1173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4153</th>\n",
       "      <td>sucks</td>\n",
       "      <td>-0.072812</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2613</th>\n",
       "      <td>lost</td>\n",
       "      <td>-0.071243</td>\n",
       "      <td>1531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2730</th>\n",
       "      <td>me</td>\n",
       "      <td>-0.069838</td>\n",
       "      <td>25445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>am</td>\n",
       "      <td>-0.069581</td>\n",
       "      <td>39014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>poor</td>\n",
       "      <td>-0.066701</td>\n",
       "      <td>1238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>feel</td>\n",
       "      <td>-0.066200</td>\n",
       "      <td>4188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>hurts</td>\n",
       "      <td>-0.064824</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Word  Correlation  Counts\n",
       "3009    not    -0.186428   47793\n",
       "2162      i    -0.157089  157802\n",
       "3665    sad    -0.118118    3093\n",
       "3993  sorry    -0.097013    4424\n",
       "2899     my    -0.096358   38746\n",
       "2989     no    -0.092470   11186\n",
       "1979   hate    -0.091922    2644\n",
       "2809   miss    -0.090108    3902\n",
       "433     bad    -0.081616    3477\n",
       "4838   wish    -0.079345    3555\n",
       "733     but    -0.075138   19658\n",
       "3868   sick    -0.073647    1399\n",
       "4574    ugh    -0.072964    1173\n",
       "4153  sucks    -0.072812    1510\n",
       "2613   lost    -0.071243    1531\n",
       "2730     me    -0.069838   25445\n",
       "243      am    -0.069581   39014\n",
       "3331   poor    -0.066701    1238\n",
       "1595   feel    -0.066200    4188\n",
       "2156  hurts    -0.064824     800"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "frequencyWord = Counter([word for text in df_done['text'] for word in text.split()])\n",
    "mostFrequentWord = pd.DataFrame(frequencyWord.most_common(5000),columns=['Word','Counts'])\n",
    "\n",
    "# for memory size problem take only first 30000 tweet\n",
    "word_corr = df_done[:30000].text.str.get_dummies(sep=' ').corrwith(df_done.sentiment/df_done.sentiment.max())\n",
    "\n",
    "dictionary = {\"Word\": word_corr.index, \"Correlation\": word_corr.values}\n",
    "corrDataframe = pd.DataFrame(dictionary)\n",
    "\n",
    "corrDataframe.set_index('Word')\n",
    "mostFrequentWord.set_index('Word')\n",
    "\n",
    "joinedDataframe = corrDataframe.merge(mostFrequentWord)\n",
    "print(\"Most negative correlated word to sentiment with relative frequency\")\n",
    "joinedDataframe.sort_values(by=['Correlation'])[:20]\n",
    "# joinedDataframe[\"corr\"].round(1)\n",
    "# joinedDataframe.to_csv(\"frequency_corr.csv\")\n",
    "# joinedDataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13e801fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most positive correlated word to sentiment with relative frequency\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>Word</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4957</th>\n",
       "      <td>you</td>\n",
       "      <td>0.140061</td>\n",
       "      <td>66674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4316</th>\n",
       "      <td>thanks</td>\n",
       "      <td>0.113575</td>\n",
       "      <td>7747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2621</th>\n",
       "      <td>love</td>\n",
       "      <td>0.089250</td>\n",
       "      <td>9909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>good</td>\n",
       "      <td>0.086734</td>\n",
       "      <td>15332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4311</th>\n",
       "      <td>thank</td>\n",
       "      <td>0.086303</td>\n",
       "      <td>4083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>great</td>\n",
       "      <td>0.070314</td>\n",
       "      <td>4614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4907</th>\n",
       "      <td>ww</td>\n",
       "      <td>0.069370</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>awesome</td>\n",
       "      <td>0.068133</td>\n",
       "      <td>2966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>your</td>\n",
       "      <td>0.062857</td>\n",
       "      <td>15026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>add</td>\n",
       "      <td>0.060972</td>\n",
       "      <td>1543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>cool</td>\n",
       "      <td>0.058043</td>\n",
       "      <td>2727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4766</th>\n",
       "      <td>welcome</td>\n",
       "      <td>0.057788</td>\n",
       "      <td>1389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4673</th>\n",
       "      <td>vip</td>\n",
       "      <td>0.056139</td>\n",
       "      <td>1126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>com</td>\n",
       "      <td>0.055621</td>\n",
       "      <td>2341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>are</td>\n",
       "      <td>0.055513</td>\n",
       "      <td>20043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>hey</td>\n",
       "      <td>0.052458</td>\n",
       "      <td>3471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>followers</td>\n",
       "      <td>0.052103</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4931</th>\n",
       "      <td>yay</td>\n",
       "      <td>0.050934</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>glad</td>\n",
       "      <td>0.050140</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>followfriday</td>\n",
       "      <td>0.050136</td>\n",
       "      <td>811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Word  Correlation  Counts\n",
       "4957           you     0.140061   66674\n",
       "4316        thanks     0.113575    7747\n",
       "2621          love     0.089250    9909\n",
       "1855          good     0.086734   15332\n",
       "4311         thank     0.086303    4083\n",
       "1889         great     0.070314    4614\n",
       "4907            ww     0.069370    1731\n",
       "408        awesome     0.068133    2966\n",
       "4963          your     0.062857   15026\n",
       "160            add     0.060972    1543\n",
       "1028          cool     0.058043    2727\n",
       "4766       welcome     0.057788    1389\n",
       "4673           vip     0.056139    1126\n",
       "956            com     0.055621    2341\n",
       "328            are     0.055513   20043\n",
       "2044           hey     0.052458    3471\n",
       "1679     followers     0.052103    2007\n",
       "4931           yay     0.050934    1979\n",
       "1828          glad     0.050140    1881\n",
       "1680  followfriday     0.050136     811"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Most positive correlated word to sentiment with relative frequency\")\n",
    "joinedDataframe.sort_values(by=['Correlation'], ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf7a911b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3329534e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb13d573",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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