hate_speech.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "hate_speech.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/stewieboomhauer/hate-spreadors_detection/blob/main/hate_speech.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVZo4dN2oRcx",
        "outputId": "ad20c608-a7d7-4091-88e0-d07115a77da4"
      },
      "source": [
        "!pip install vaderSentiment\n",
        "!pip install textstat\n",
        "!pip install textblob\n",
        "!pip install demoji"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.7/dist-packages (3.3.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vaderSentiment) (2.23.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2.10)\n",
            "Requirement already satisfied: textstat in /usr/local/lib/python3.7/dist-packages (0.7.0)\n",
            "Requirement already satisfied: pyphen in /usr/local/lib/python3.7/dist-packages (from textstat) (0.10.0)\n",
            "Requirement already satisfied: textblob in /usr/local/lib/python3.7/dist-packages (0.15.3)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.7/dist-packages (from textblob) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (1.15.0)\n",
            "Requirement already satisfied: demoji in /usr/local/lib/python3.7/dist-packages (0.4.0)\n",
            "Requirement already satisfied: requests<3.0.0 in /usr/local/lib/python3.7/dist-packages (from demoji) (2.23.0)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.7/dist-packages (from demoji) (0.4.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0->demoji) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0->demoji) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0->demoji) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0->demoji) (1.24.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2s5c91PsEQCA",
        "outputId": "afbb025f-59d1-40d3-e487-1cf6919c6fde"
      },
      "source": [
        "import pandas as pd\n",
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "import pickle\n",
        "import sys\n",
        "import re\n",
        "import os\n",
        "import xml.etree.ElementTree as et\n",
        "from collections import Counter\n",
        "from textstat.textstat import *\n",
        "from textblob import TextBlob\n",
        "import demoji\n",
        "demoji.download_codes()\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('punkt')\n",
        "nltk.download('vader_lexicon')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import *\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "from nltk.stem.snowball import SnowballStemmer \n",
        "from nltk.util import ngrams\n",
        "\n",
        "import sklearn as sk\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pylab as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading emoji data ...\n",
            "... OK (Got response in 0.38 seconds)\n",
            "Writing emoji data to /root/.demoji/codes.json ...\n",
            "... OK\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
            "  warnings.warn(\"The twython library has not been installed. \"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jEKsBtiR1x3u"
      },
      "source": [
        "-------------------------------------------------------\n",
        "# **1. Loading Data**\n",
        "-------------------------------------------------------\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqZ2DM66f6F4",
        "outputId": "29ac2507-55da-4a0c-96e7-9c2da176b092"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTf0vrNmgOO5"
      },
      "source": [
        "def iter_docs(author):\n",
        "    author_attr = author.attrib\n",
        "    for doc in author.iter('document'):\n",
        "        doc_dict = author_attr.copy()\n",
        "        doc_dict.update(doc.attrib)\n",
        "        doc_dict['tweets'] = doc.text\n",
        "        yield doc_dict"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oinsTodE8Hj"
      },
      "source": [
        "xml ='xml'\n",
        "df = []\n",
        "for filename in os.listdir('/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/en'):\n",
        "  test_xml = open('/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/en/{filename}'.format(filename=filename), \"r\", encoding='utf8')\n",
        "  etree = et.parse(test_xml) #create an ElementTree object \n",
        "  data = pd.DataFrame(list(iter_docs(etree.getroot())))\n",
        "  data['filename'] = filename\n",
        "  df.append(data)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zPArwAia7Uh"
      },
      "source": [
        "def iter_docs(author):\n",
        "    author_attr = author.attrib\n",
        "    for doc in author.iter('documents'):\n",
        "        doc_dict = author_attr.copy()\n",
        "        doc_dict.update(doc.attrib)\n",
        "        doc_dict['tweets'] = doc.text\n",
        "        yield doc_dict"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bR3Qqv5Xa9L-"
      },
      "source": [
        "xml ='xml'\n",
        "df_new = []\n",
        "for filename in os.listdir('/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/en'):\n",
        "  test_xml = open('/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/en/{filename}'.format(filename=filename), \"r\", encoding='utf8')\n",
        "  #mylist = test_xml.readlines()\n",
        "  etree = et.parse(test_xml) #create an ElementTree object \n",
        "  data = pd.DataFrame(list(iter_docs(etree.getroot())))\n",
        "  data['filename'] = filename\n",
        "  df_new.append(data)\n",
        "  #df = pdx.read_xml(mylist, ['author'], ['documents'])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjetJ4GkLaWR"
      },
      "source": [
        "# **2. Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4A226rUbLERs"
      },
      "source": [
        "## **2.1 Automatic Sentiment Analyses of Tweets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "M2L6cqQAK_wS",
        "outputId": "c2109f5c-0abe-42f0-8a38-9b98f7bdcd1b"
      },
      "source": [
        "analyzer = SentimentIntensityAnalyzer()\n",
        "\n",
        "dfs = pd.concat(df, ignore_index=True)\n",
        "\n",
        "dfs['neg'] = dfs['tweets'].apply(lambda x:analyzer.polarity_scores(x)['neg'])\n",
        "dfs['neu'] = dfs['tweets'].apply(lambda x:analyzer.polarity_scores(x)['neu'])\n",
        "dfs['pos'] = dfs['tweets'].apply(lambda x:analyzer.polarity_scores(x)['pos'])\n",
        "dfs['compound'] = dfs['tweets'].apply(lambda x:analyzer.polarity_scores(x)['compound'])\n",
        "dfs"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lang</th>\n",
              "      <th>class</th>\n",
              "      <th>tweets</th>\n",
              "      <th>filename</th>\n",
              "      <th>neg</th>\n",
              "      <th>neu</th>\n",
              "      <th>pos</th>\n",
              "      <th>compound</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>RT #USER#: #USER# VECTIV™ technology is here. ...</td>\n",
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              "    <tr>\n",
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              "      <td>In order to expand the possibilities vs the ou...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>#USER# should find me on LinkedIn</td>\n",
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              "    <tr>\n",
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              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>RT #USER#: Happy Founder’s Day #USER# from #US...</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>0.291</td>\n",
              "      <td>0.5719</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>RT #USER#: Loona debuted the electrifying musi...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>39995</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>RT #USER#: This savage needs to be locked in a...</td>\n",
              "      <td>65042b6a94addf20c6fafd782c5d4347.xml</td>\n",
              "      <td>0.412</td>\n",
              "      <td>0.588</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.8271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39996</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>RT #USER#: Is it really a \"conspiracy theory\" ...</td>\n",
              "      <td>65042b6a94addf20c6fafd782c5d4347.xml</td>\n",
              "      <td>0.209</td>\n",
              "      <td>0.791</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.5709</td>\n",
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              "    <tr>\n",
              "      <th>39997</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>#USER# Jews brought them here</td>\n",
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              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>RT #USER#: Black men harass and assault White ...</td>\n",
              "      <td>65042b6a94addf20c6fafd782c5d4347.xml</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.667</td>\n",
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              "      <td>-0.7906</td>\n",
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              "    <tr>\n",
              "      <th>39999</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>RT #USER#: They hate you, White man. #URL#</td>\n",
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              "      <td>0.346</td>\n",
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            ],
            "text/plain": [
              "      lang class  ...    pos compound\n",
              "0       en     0  ...  0.000   0.0000\n",
              "1       en     0  ...  0.095   0.3182\n",
              "2       en     0  ...  0.000   0.0000\n",
              "3       en     0  ...  0.291   0.5719\n",
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              "39996   en     1  ...  0.000  -0.5709\n",
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              "39998   en     1  ...  0.000  -0.7906\n",
              "39999   en     1  ...  0.000  -0.5719\n",
              "\n",
              "[40000 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-QcwvnYVYOl"
      },
      "source": [
        "## **2.2 Tokens Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZZpC8US68X1"
      },
      "source": [
        "all_tweets =dfs[\"tweets\"]\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_xINQsb82yT"
      },
      "source": [
        "count = 0\n",
        "num_token_inSentence = []\n",
        "for sent in all_tweets:\n",
        "  count = count+1\n",
        "  word_list = len(sent.split())\n",
        "  num_token_inSentence.append(word_list)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJ-UHL09-eLx",
        "outputId": "e8f108f7-03f7-49ad-c322-f581aad1d5d3"
      },
      "source": [
        "avg_tokens =sum(num_token_inSentence)/count\n",
        "print(avg_tokens)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12.273925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCHbQVvQAFOy"
      },
      "source": [
        "dfs[\"tokens\"] = dfs[\"tweets\"].map(lambda text: re.sub(r\"[^a-zA-Z0-9' ]\", ' ', text)).map(lambda text: text.split())"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeIb6eJrAzLj"
      },
      "source": [
        "num = dfs.shape[0]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "M4BBQwsjAPpQ",
        "outputId": "e67491df-a45d-48e9-fdb3-077e640acf49"
      },
      "source": [
        "dfs[\"num_tokens\"] = dfs[\"tokens\"].map(len)\n",
        "ax = dfs[\"num_tokens\"].hist(bins=20, \n",
        "                                 figsize=(4,3),\n",
        "                                 weights=np.ones(num)/num)\n",
        "ax.set_xlabel(\"Number of Tokens\")\n",
        "ax.set_ylabel(\"Ratio\")\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orBlmJFsBeic"
      },
      "source": [
        "#dfs.to_csv(r'/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/sentiment_analyses.csv')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDM3ja54LiaE"
      },
      "source": [
        "## **2.3 Creating Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xj644Ih1Vehl"
      },
      "source": [
        "col_one_list = []\n",
        "for d in df:\n",
        "  #print(dfs['tweets'])\n",
        "  col_one_list.append(d['tweets'].tolist())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "quCCGnbUbjGU",
        "outputId": "28b7d8c0-6864-4cd6-a1c3-544d5e3518a1"
      },
      "source": [
        "data = pd.concat(df_new, ignore_index=True)\n",
        "data.drop(['tweets'], axis=1)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>lang</th>\n",
              "      <th>class</th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>88d8ac4ae2d91e681ee8a8d3b60fd911.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>86a7f84c2dd126dac46270b6c912952c.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>9d58d6313bfb2fba9e1e45bb9d65cf0b.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>f2b1fc84c500c38a93522efbd422b559.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>26644d1348fc1122e8c5ef45d6bc84fa.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>c129b2cf075e1872a86ce8f7473819af.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>8b7608a503048538181399629fe67913.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>5d4a0908d433733c14c38261fc4f4250.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>a41db55c14ec3878c055eb1ece4e5866.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>65042b6a94addf20c6fafd782c5d4347.xml</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    lang class                              filename\n",
              "0     en     0  88d8ac4ae2d91e681ee8a8d3b60fd911.xml\n",
              "1     en     0  86a7f84c2dd126dac46270b6c912952c.xml\n",
              "2     en     0  9d58d6313bfb2fba9e1e45bb9d65cf0b.xml\n",
              "3     en     0  f2b1fc84c500c38a93522efbd422b559.xml\n",
              "4     en     0  26644d1348fc1122e8c5ef45d6bc84fa.xml\n",
              "..   ...   ...                                   ...\n",
              "195   en     1  c129b2cf075e1872a86ce8f7473819af.xml\n",
              "196   en     1  8b7608a503048538181399629fe67913.xml\n",
              "197   en     1  5d4a0908d433733c14c38261fc4f4250.xml\n",
              "198   en     1  a41db55c14ec3878c055eb1ece4e5866.xml\n",
              "199   en     1  65042b6a94addf20c6fafd782c5d4347.xml\n",
              "\n",
              "[200 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElwYQWxMWPex"
      },
      "source": [
        "tweets_to_string = []\n",
        "\n",
        "for elem in col_one_list:\n",
        "    string = ''.join(elem)\n",
        "    tweets_to_string.append(string)\n",
        "    \n",
        "dataframe = pd.DataFrame(tweets_to_string, columns=[\"tweets\"])"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkg8fqz-ecSH"
      },
      "source": [
        "#Create the dataframe, that will be used\n",
        "data.update(dataframe)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "MBfWhKORkq9G",
        "outputId": "fe91d8e5-8811-4b10-aa50-781147e08340"
      },
      "source": [
        "#data = shuffle(data)\n",
        "data"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>lang</th>\n",
              "      <th>class</th>\n",
              "      <th>tweets</th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>RT #USER#: #USER# VECTIV™ technology is here. ...</td>\n",
              "      <td>88d8ac4ae2d91e681ee8a8d3b60fd911.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>Who wants this accAnd to all the people who su...</td>\n",
              "      <td>86a7f84c2dd126dac46270b6c912952c.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>#USER# Wait till you get out...it gets worseIt...</td>\n",
              "      <td>9d58d6313bfb2fba9e1e45bb9d65cf0b.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>If you havent tried Parler yet please give it ...</td>\n",
              "      <td>f2b1fc84c500c38a93522efbd422b559.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>#USER# #USER# \"tell people what to do...\" hmm,...</td>\n",
              "      <td>26644d1348fc1122e8c5ef45d6bc84fa.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>RT #USER#: The four most dangerous people in t...</td>\n",
              "      <td>c129b2cf075e1872a86ce8f7473819af.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>Check out my broadcast from my PlayStation 4! ...</td>\n",
              "      <td>8b7608a503048538181399629fe67913.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>RT #USER#: So Lebron had 30 13 13 goat lmaoWom...</td>\n",
              "      <td>5d4a0908d433733c14c38261fc4f4250.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>Also not frozen or it will explode. #URL#Same ...</td>\n",
              "      <td>a41db55c14ec3878c055eb1ece4e5866.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "      <td>RT #USER#: &amp;gt;\"Billions in COVID relief for b...</td>\n",
              "      <td>65042b6a94addf20c6fafd782c5d4347.xml</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    lang  ...                              filename\n",
              "0     en  ...  88d8ac4ae2d91e681ee8a8d3b60fd911.xml\n",
              "1     en  ...  86a7f84c2dd126dac46270b6c912952c.xml\n",
              "2     en  ...  9d58d6313bfb2fba9e1e45bb9d65cf0b.xml\n",
              "3     en  ...  f2b1fc84c500c38a93522efbd422b559.xml\n",
              "4     en  ...  26644d1348fc1122e8c5ef45d6bc84fa.xml\n",
              "..   ...  ...                                   ...\n",
              "195   en  ...  c129b2cf075e1872a86ce8f7473819af.xml\n",
              "196   en  ...  8b7608a503048538181399629fe67913.xml\n",
              "197   en  ...  5d4a0908d433733c14c38261fc4f4250.xml\n",
              "198   en  ...  a41db55c14ec3878c055eb1ece4e5866.xml\n",
              "199   en  ...  65042b6a94addf20c6fafd782c5d4347.xml\n",
              "\n",
              "[200 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "Ri67RuP9rquT",
        "outputId": "b32b55b9-e16f-4592-c6d5-faa496b0da54"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>lang</th>\n",
              "      <th>class</th>\n",
              "      <th>tweets</th>\n",
              "      <th>filename</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200</td>\n",
              "      <td>200</td>\n",
              "      <td>200</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>200</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "      <td>That shit still fried to me the homie let one ...</td>\n",
              "      <td>d28b60028cf7bcf8a9f145193e261ecf.xml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>200</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       lang  ...                              filename\n",
              "count   200  ...                                   200\n",
              "unique    1  ...                                   200\n",
              "top      en  ...  d28b60028cf7bcf8a9f145193e261ecf.xml\n",
              "freq    200  ...                                     1\n",
              "\n",
              "[4 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "7qHSRQsFnn7C",
        "outputId": "f0224d3a-3035-42c1-d626-d226dde8973c"
      },
      "source": [
        "\n",
        "data['class'].hist()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d82e3aad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHdG5TlqoBTk"
      },
      "source": [
        "tweets = data[\"tweets\"].copy()"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qyk9dkf4IoQa"
      },
      "source": [
        "# **3. Methods**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7VvlX3C2-Nw"
      },
      "source": [
        "------------------------------------------\n",
        "## **3.1. Feature Engineering**\n",
        "\n",
        "**Vectorization**\n",
        "\n",
        "The following settings for vectorization were used:\n",
        "\n",
        "\n",
        "\n",
        "*   Word representation: TFIDFVectorizer;\n",
        "*   N-grams: unigram & bigram & trigram;\n",
        "*   Convert to lowercase;\n",
        "*   Try binary counter;\n",
        "*   Use stop words;\n",
        "\n",
        "------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhXuovOJMB5l"
      },
      "source": [
        "### **3.1.1 Creating List with \"Stopwords\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oOeTkDgGL2o7",
        "outputId": "082ab202-de81-4984-832d-44d981596484"
      },
      "source": [
        "\n",
        "#Tokenize tweets to count words\n",
        "s = ''.join(tweets_to_string)\n",
        "#get rid of punctuation\n",
        "sentence = re.sub(r'[^\\w\\s]','',s.lower())\n",
        "tokens = nltk.word_tokenize(sentence)\n",
        "\n",
        "#Tokens Distribution (word occurrences)\n",
        "occurrences = Counter(tokens).most_common(63)\n",
        "dictOftokens = dict(occurrences)\n",
        "\n",
        "#Tokens with occurrences more than 1000\n",
        "most_common_tokens = []\n",
        "for k,v in dictOftokens.items():\n",
        "  most_common_tokens.append(k)\n",
        "\n",
        "#Get most common tokens to add them to stopwords\n",
        "print(most_common_tokens) "
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['user', 'the', 'to', 'a', 'i', 'you', 'is', 'and', 'urlrt', 'hashtag', 'of', 'in', 'for', 'this', 'on', 'my', 'it', 'that', 'be', 'urluser', 'me', 'are', 'not', 'with', 'like', 'they', 'just', 'im', 'have', 'your', 'all', 'so', 'dont', 'we', 'its', 'was', 'rt', 'what', 'if', 'do', 'no', 'but', 'get', 'at', 'url', 'up', 'people', 'he', 'by', 'as', 'out', 'will', 'about', 'can', 'from', 'got', 'when', 'how', 'one', 'who', 'know', 'more', 'amp']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoPDgctAP0Jz"
      },
      "source": [
        "#Creating a list with stopwords\n",
        "stopwords=stopwords = nltk.corpus.stopwords.words(\"english\")\n",
        "stopwords.extend(most_common_tokens)\n",
        "stopwords = list(set(stopwords))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6IG8sc578Lst"
      },
      "source": [
        "### **3.1.2 Preprocess Methods**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfElrM0_n3Ai"
      },
      "source": [
        "\n",
        "#Add preprocess Method\n",
        "def preprocess(text_string):\n",
        "    \"\"\"\n",
        "    Accepts a text string and replaces:\n",
        "    1) urls with URLHERE\n",
        "    2) lots of whitespace with one instance\n",
        "    3) mentions with MENTIONHERE\n",
        "\n",
        "    This allows us to get standardized counts of urls and mentions\n",
        "    Without caring about specific people mentioned\n",
        "    \"\"\"\n",
        "    space_pattern = '\\s+'\n",
        "    giant_url_regex = ('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|'\n",
        "        '[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+')\n",
        "    mention_regex = '@[\\w\\-]+'\n",
        "    parsed_text = re.sub(space_pattern, ' ', text_string)\n",
        "    parsed_text = re.sub(giant_url_regex, '', parsed_text)\n",
        "    parsed_text = re.sub(mention_regex, '', parsed_text)\n",
        "    return parsed_text\n",
        "\n",
        "#Add method to get rid of Emojis\n",
        "def remove_emoji(string):\n",
        "    emoji_pattern = re.compile(\"[\"\n",
        "                               u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "                               u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "                               u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "                               u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "                               u\"\\U00002500-\\U00002BEF\"  # chinese char\n",
        "                               u\"\\U00002702-\\U000027B0\"\n",
        "                               u\"\\U00002702-\\U000027B0\"\n",
        "                               u\"\\U000024C2-\\U0001F251\"\n",
        "                               u\"\\U0001f926-\\U0001f937\"\n",
        "                               u\"\\U00010000-\\U0010ffff\"\n",
        "                               u\"\\u2640-\\u2642\"\n",
        "                               u\"\\u2600-\\u2B55\"\n",
        "                               u\"\\u200d\"\n",
        "                               u\"\\u23cf\"\n",
        "                               u\"\\u23e9\"\n",
        "                               u\"\\u231a\"\n",
        "                               u\"\\ufe0f\"  # dingbats\n",
        "                               u\"\\u3030\"\n",
        "                               \"]+\", flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', string)\n",
        "\n",
        "#Add Word Tokenizer without Stemming\n",
        "def basic_tokenize(tweet):\n",
        "    \"\"\"Removes punctuation & excess whitespace, sets to lowercase.\n",
        "      Returns a list of tokens.\"\"\"\n",
        "    list_tokens = []\n",
        "    for t in tweet:\n",
        "      sentence = re.sub(r'[^\\w\\s]','',t.lower())\n",
        "      list_tokens.append(nltk.word_tokenize(sentence))\n",
        "    tokens = [item for sublist in list_tokens for item in sublist]\n",
        "    return tokens\n",
        "\n",
        "counter = CountVectorizer()\n",
        "data1 = counter.fit_transform(tweets)\n",
        "count = pd.DataFrame(data = data1.toarray(), columns = counter.get_feature_names())\n",
        "unique_words = count.columns[count.sum(axis = 0) == 1]\n",
        "\n",
        "#Add method to ged rid of unique words\n",
        "def kick_the_word(s):\n",
        "    p = re.compile(r\"\\w+(?:'\\w+)*|[^\\w\\s]\")\n",
        "    b = p.findall(s)\n",
        "    for word in b:\n",
        "        if word in unique_words:\n",
        "            s = s.replace(word, '')\n",
        "    return s\n",
        "\n",
        "\n",
        "def analyzer(text):\n",
        "    text = text.lower()\n",
        "    tokens = basic_tokenize(text)    \n",
        "    return [''.join(ngram) for token in tokens for ngram in ngrams(token, 2)]\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lBvDkwmNJ3d"
      },
      "source": [
        "#Application of methods\n",
        "tweets = tweets.apply(lambda x: x.replace('#USER#', '').lower())\n",
        "tweets = tweets.apply(remove_emoji)\n",
        "tweets = tweets.apply(kick_the_word)\n",
        "tweets = tweets.str.replace('[^\\w\\s]','')"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KY8pdTiqFXft"
      },
      "source": [
        "## **3.2 N-gram Method**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKx3b3A-GNvI"
      },
      "source": [
        "### **3.2.1 Word N-gram Vectorizer**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRubp_KDnjNX"
      },
      "source": [
        "tfidf_word = TfidfVectorizer(\n",
        "    analyzer='word',\n",
        "    stop_words=stopwords,\n",
        "    preprocessor=preprocess,\n",
        "    ngram_range=(1, 3),\n",
        "    use_idf=True,\n",
        "    smooth_idf=False,\n",
        "    norm=None,\n",
        "    decode_error='replace',\n",
        "    max_features=10000,\n",
        "    min_df=5,\n",
        "    max_df=0.75)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Vx60AuKogSe"
      },
      "source": [
        "#Construct tfidf matrix and get relevant scores\n",
        "tfidf_words =tfidf_word.fit_transform(tweets).toarray()\n",
        "vocab_words = {v:i for i, v in enumerate(tfidf_word.get_feature_names())}\n",
        "idf_vals = tfidf_word.idf_\n",
        "idf_dict = {i:idf_vals[i] for i in vocab_words.values()} #keys are indices; values are IDF scores\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EgIMZ6qGhxr"
      },
      "source": [
        "### **3.2.2 Char N-gram Vectorizer**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mktohBFWGnqG"
      },
      "source": [
        "tfidf_char = TfidfVectorizer(\n",
        "    analyzer='char',\n",
        "    stop_words=stopwords,\n",
        "    preprocessor=preprocess,\n",
        "    ngram_range=(3, 6),\n",
        "    use_idf=True,\n",
        "    smooth_idf=False,\n",
        "    norm=None,\n",
        "    decode_error='replace',\n",
        "    max_features=10000,\n",
        "    min_df=5,\n",
        "    max_df=0.75)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgaNNYf3Gu7x"
      },
      "source": [
        "#Construct tfidf matrix and get relevant scores\n",
        "tfidf_chars =tfidf_char.fit_transform(tweets).toarray()\n",
        "vocab_chars = {v:i for i, v in enumerate(tfidf_char.get_feature_names())}\n",
        "idf_vals = tfidf_char.idf_\n",
        "idf_dict = {i:idf_vals[i] for i in vocab_chars.values()} #keys are indices; values are IDF scores\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-GghH4ITQNDF"
      },
      "source": [
        ""
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yt9BMeZbNgbK"
      },
      "source": [
        "### **3.2.3 POS-TF-IDF Vectorizer**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BKbC1u6_4oH_"
      },
      "source": [
        "#We can use the TFIDF vectorizer to get a token matrix for the POS tags\n",
        "pos_vectorizer = TfidfVectorizer(\n",
        "    tokenizer=None,\n",
        "    lowercase=False,\n",
        "    preprocessor=None,\n",
        "    ngram_range=(1, 3),\n",
        "    stop_words=None,\n",
        "    use_idf=False,\n",
        "    smooth_idf=False,\n",
        "    norm=None,\n",
        "    decode_error='replace',\n",
        "    max_features=5000,\n",
        "    min_df=5,\n",
        "    max_df=0.75,\n",
        "    )"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZwFPpxNNnag"
      },
      "source": [
        "#Get POS tags for tweets and save as a string\n",
        "tweet_tags = []\n",
        "list_tokens = []\n",
        "for t in tweets:\n",
        "    sentence = re.sub(r'[^\\w\\s]','',t.lower())\n",
        "    tokens = nltk.word_tokenize(preprocess(sentence))\n",
        "    tags = nltk.pos_tag(tokens)\n",
        "    tag_list = [x[1] for x in tags]\n",
        "    tag_str = \" \".join(tag_list)\n",
        "    tweet_tags.append(tag_str)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2xkferj4uct"
      },
      "source": [
        "#Construct POS TF matrix and get vocab dict\n",
        "pos = pos_vectorizer.fit_transform(pd.Series(tweet_tags)).toarray()\n",
        "pos_vocab = {v:i for i, v in enumerate(pos_vectorizer.get_feature_names())}"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeipR-maRrb1"
      },
      "source": [
        "## **3.3 Key phrase-based Method**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-w_nuMnJR43"
      },
      "source": [
        "def containsAny(str, set):\n",
        "    \"\"\" Check whether sequence str contains ANY of the items in set. \"\"\"\n",
        "    return 1 in [c in str for c in set]\n",
        "    \n",
        "\n",
        "def proccestxts(col_one_list,txt):\n",
        "    s=set(())\n",
        "    for i in open(txt, 'r'):\n",
        "        s.add(i[:-1])\n",
        "    vectorizer = CountVectorizer(analyzer='char',ngram_range=(3, 3))\n",
        "    x = list(s)\n",
        "    X = vectorizer.fit_transform(x)\n",
        "    gram=vectorizer.get_feature_names()\n",
        "    s=set(())\n",
        "    for i in gram:\n",
        "        s.add(i)\n",
        "    #print(s)\n",
        "    ar=[]\n",
        "    for i in col_one_list:\n",
        "        x=0\n",
        "        for tw in i:\n",
        "            if TextBlob(tw).polarity<-0.2:\n",
        "                if containsAny(tw,s):\n",
        "                    x=x+1\n",
        "        ar.append(x)\n",
        "    return ar \n",
        "\n",
        "\n",
        "\n",
        "hate_words_list=proccestxts(col_one_list, '/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/hate_words.txt')\n",
        "nation_words_list=proccestxts(col_one_list, '/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/nations.txt')\n",
        "lgbt_words_list=proccestxts(col_one_list, '/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/lgbt.txt')\n",
        "religion_words_list=proccestxts(col_one_list, '/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/religion.txt')\n",
        "genders_words_list=proccestxts(col_one_list, '/content/drive/MyDrive/pan21-author-profiling-training-2021-03-14/genders.txt')\n",
        "\n",
        "myM=[]\n",
        "\n",
        "myM.append(genders_words_list)\n",
        "myM.append(lgbt_words_list)\n",
        "myM.append(religion_words_list)\n",
        "myM.append(nation_words_list)\n",
        "myM.append(hate_words_list)\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4kxfC6N4ZIy"
      },
      "source": [
        "## **3.4 Splitting Datasets for Machine Learning Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1VWSsJCqfbO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "263a6589-81e8-41c3-b613-5e3217573b32"
      },
      "source": [
        "key_list = np.array(myM)\n",
        "print(key_list.shape)\n",
        "\n",
        "ngramm_word = np.concatenate([tfidf_words,pos],axis=1)\n",
        "print(ngramm_word.shape)\n",
        "ngramm_char = np.concatenate([tfidf_chars, pos],axis=1)\n",
        "print(ngramm_char.shape)\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5, 200)\n",
            "(200, 11346)\n",
            "(200, 14735)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTG_kKsu0Iy8"
      },
      "source": [
        "#Finally get a list of variable names\n",
        "variables_words = ['']*len(vocab_words)\n",
        "for k,v in vocab_words.items():\n",
        "    variables_words[v] = k\n",
        "\n",
        "variables_chars = ['']*len(vocab_chars)\n",
        "for k,v in vocab_chars.items():\n",
        "    variables_chars[v] = k\n",
        "\n",
        "pos_variables = ['']*len(pos_vocab)\n",
        "for k,v in pos_vocab.items():\n",
        "    pos_variables[v] = k\n",
        "\n",
        "feature_names_words = variables_words+pos_variables\n",
        "feature_names_chars = variables_chars+pos_variables"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eOHBOUWq1c8"
      },
      "source": [
        "X_word = pd.DataFrame(ngramm_word)\n",
        "X_char = pd.DataFrame(ngramm_char)\n",
        "X_keyList = pd.DataFrame(key_list.T)\n",
        "\n",
        "y = data['class'].astype(int)\n",
        "\n"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBOHzB5ZOWB1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "aa52f3c8-0d2f-4725-a6a4-c75385e95c0c"
      },
      "source": [
        "X_train_word, X_test_word, y_train_word, y_test_word = train_test_split(X_word, y, test_size = 0.3, random_state = 42, shuffle=True)\n",
        "X_train_word"
      ],
      "execution_count": 39,
      "outputs": [
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.328025</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.101093</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.24786</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.525729</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4.352407</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>3.900422</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2.427116</td>\n",
              "      <td>4.688879</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.903868</td>\n",
              "      <td>3.253795</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>2.427116</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.506558</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>3.353878</td>\n",
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              "\n",
              "[140 rows x 11346 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZGLk47zLZqI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "60813736-a1fc-4fc9-ac6c-28fba013d9c5"
      },
      "source": [
        "X_train_char, X_test_char, y_train_char, y_test_char = train_test_split(X_char, y, test_size = 0.3, random_state = 42, shuffle=True)\n",
        "X_train_char"
      ],
      "execution_count": 40,
      "outputs": [
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              "      <td>1.400478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.319274</td>\n",
              "      <td>1.980829</td>\n",
              "      <td>2.035637</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.213939</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.482889</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.891598</td>\n",
              "      <td>2.021651</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.021651</td>\n",
              "      <td>4.507580</td>\n",
              "      <td>5.754020</td>\n",
              "      <td>5.53191</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.615770</td>\n",
              "      <td>1.941609</td>\n",
              "      <td>2.07881</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.879477</td>\n",
              "      <td>1.430783</td>\n",
              "      <td>1.84397</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1.597837</td>\n",
              "      <td>2.575364</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.035637</td>\n",
              "      <td>22.407641</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.355149</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.497228</td>\n",
              "      <td>1.855666</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.820908</td>\n",
              "      <td>1.867501</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.488881</td>\n",
              "      <td>1.941609</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.288714</td>\n",
              "      <td>3.641961</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.021651</td>\n",
              "      <td>1.502527</td>\n",
              "      <td>2.877010</td>\n",
              "      <td>3.68794</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.615770</td>\n",
              "      <td>1.941609</td>\n",
              "      <td>2.07881</td>\n",
              "      <td>2.203973</td>\n",
              "      <td>2.491655</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.430783</td>\n",
              "      <td>1.84397</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.287682</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.035637</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.579818</td>\n",
              "      <td>1.980829</td>\n",
              "      <td>2.035637</td>\n",
              "      <td>1.588787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.733969</td>\n",
              "      <td>1.832409</td>\n",
              "      <td>1.855666</td>\n",
              "      <td>1.776529</td>\n",
              "      <td>3.213939</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.099644</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.891598</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.644357</td>\n",
              "      <td>1.820981</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.021651</td>\n",
              "      <td>1.502527</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>3.250977</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.879477</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.287682</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.043302</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.588787</td>\n",
              "      <td>2.007858</td>\n",
              "      <td>1.733969</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.776529</td>\n",
              "      <td>6.427878</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.494296</td>\n",
              "      <td>2.049822</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.941609</td>\n",
              "      <td>1.891598</td>\n",
              "      <td>4.043302</td>\n",
              "      <td>3.288714</td>\n",
              "      <td>3.641961</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.502527</td>\n",
              "      <td>4.315515</td>\n",
              "      <td>5.53191</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.539424</td>\n",
              "      <td>1.941609</td>\n",
              "      <td>2.07881</td>\n",
              "      <td>8.815891</td>\n",
              "      <td>7.474965</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>7.989185</td>\n",
              "      <td>2.575364</td>\n",
              "      <td>3.177574</td>\n",
              "      <td>1.903868</td>\n",
              "      <td>8.142550</td>\n",
              "      <td>7.002388</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.532723</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.733969</td>\n",
              "      <td>3.664818</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.820908</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.988593</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.744440</td>\n",
              "      <td>13.591260</td>\n",
              "      <td>3.783196</td>\n",
              "      <td>32.346420</td>\n",
              "      <td>4.933071</td>\n",
              "      <td>1.820981</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.502527</td>\n",
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              "      <td>2.615770</td>\n",
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              "      <td>2.203973</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.879477</td>\n",
              "      <td>5.723132</td>\n",
              "      <td>1.84397</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.287682</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.588787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.733969</td>\n",
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              "\n",
              "[140 rows x 14735 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZn6RcpOMmok",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "599f5f06-8fa5-4e11-b430-88df8706352b"
      },
      "source": [
        "X_train_keyList, X_test_keyList, y_train_keyList, y_test_keyList = train_test_split(X_keyList, y, test_size = 0.3, random_state = 42, shuffle=True)\n",
        "X_train_keyList"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>46</td>\n",
              "      <td>61</td>\n",
              "      <td>68</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>17</td>\n",
              "      <td>13</td>\n",
              "      <td>17</td>\n",
              "      <td>27</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>140 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      0   1   2   3   4\n",
              "169   8   9   9  15  15\n",
              "97   22  16  19  32  31\n",
              "31    7   3   5  11  11\n",
              "12   15  14  15  26  24\n",
              "35   46  61  68  77  77\n",
              "..   ..  ..  ..  ..  ..\n",
              "106  17  13  17  27  26\n",
              "14    9   4   9  15  15\n",
              "92    8   3   7  22  21\n",
              "179  10   5   8  16  16\n",
              "102   8   6   7  16  16\n",
              "\n",
              "[140 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vL5Fb7pN5Zr9"
      },
      "source": [
        "# **4. Machine Learning Models**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iq095_DZqd1J"
      },
      "source": [
        "# **4.1 Evaluation Metrics for Machine Learning Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GT-IJjbn390k"
      },
      "source": [
        "def evaluate_model_Xy(model, X, y, y_pred=None, label=\"Training\", model_name=\"model\"):\n",
        "  if y_pred is None:\n",
        "    y_pred = model.predict(X)\n",
        "\n",
        "  print(label + ' Set')\n",
        "  print(\"Accuracy:\", accuracy_score(y, y_pred))\n",
        "  print()\n",
        "\n",
        "  print(classification_report(y, y_pred, digits=4))\n",
        "  disp = plot_confusion_matrix(model, X, y, \n",
        "                               cmap=plt.cm.Blues, normalize='true')\n",
        "  plt.savefig(model_name + \"_\" + label.lower() + \".png\")\n",
        "  plt.show()\n",
        "  print()"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbwrJRon3-CL"
      },
      "source": [
        "def evaluate_model_ngrammWord(model, model_name=\"model\",\n",
        "                   y_train_pred=None, y_dev_pred=None, y_test_pred=None):\n",
        "  evaluate_model_Xy(model, X_train_word, y_train_word, label=\"Training\", model_name=model_name)\n",
        "  evaluate_model_Xy(model, X_test_word, y_test_word, label=\"Testing\", model_name=model_name)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeZJiP8gK0lL"
      },
      "source": [
        "def evaluate_model_ngrammChar(model, model_name=\"model\",\n",
        "                   y_train_pred=None, y_dev_pred=None, y_test_pred=None):\n",
        "  evaluate_model_Xy(model, X_train_char, y_train_char, label=\"Training\", model_name=model_name)\n",
        "  evaluate_model_Xy(model, X_test_char, y_test_char, label=\"Testing\", model_name=model_name)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32mUSKOKK0uw"
      },
      "source": [
        "def evaluate_model_keyList(model, model_name=\"model\",\n",
        "                   y_train_pred=None, y_dev_pred=None, y_test_pred=None):\n",
        "  evaluate_model_Xy(model, X_train_keyList, y_train_keyList, label=\"Training\", model_name=model_name)\n",
        "  evaluate_model_Xy(model, X_test_keyList, y_test_keyList, label=\"Testing\", model_name=model_name)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dMx5i0qp4oP6"
      },
      "source": [
        "### **4.2 LogisticRegression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsRkKrMuy_4k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98e0c61f-f0c0-4376-ba9f-6bec4fe9ab81"
      },
      "source": [
        "select = SelectFromModel(LogisticRegression(C=1, penalty='l1', solver='liblinear'))\n",
        "X_w = select.fit_transform(X_word,y)\n",
        "X_c = select.fit_transform(X_char,y)\n",
        "model = LinearSVC(class_weight='balanced',C=0.01, penalty='l2', loss='squared_hinge',multi_class='ovr').fit(X_w, y)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3sEgwYMzn5E"
      },
      "source": [
        "final_features_words = select.get_support(indices=True) #get indices of features\n",
        "final_feature_list = [str(feature_names_words[i]) for i in final_features_words] #Get list of names corresponding to indices"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCclzKTR0c6C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04ef575b-a703-444e-ef0e-2cce7ec4948e"
      },
      "source": [
        "print(final_feature_list)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['510', 'accountable', 'accounts', 'af', 'agendart', 'aid', 'aide', 'aided', 'aint nobody', 'amendment', 'andy', 'article', 'attach', 'bald', 'bay', 'bbc', 'believe', 'birth', 'birth control', 'brort', 'candidates', 'censor', 'chant', 'chaos', 'clue', 'cole', 'come back', 'committing', 'cos', 'crazy', 'crazyrt', 'cream', 'criticism', 'dad', 'debt', 'denmark', 'details', 'df', 'dhs', 'discussion', 'dms', 'doesnt deserve', 'dort', 'downtown', 'dozen', 'dr', 'enough enough', 'exciting', 'fed', 'feelings', 'forward', 'full', 'gets', 'getting ready', 'goddamn', 'godi', 'halal', 'halloween', 'heaven', 'hillary', 'horrifying', 'ice cream', 'instead', 'intentions', 'investigated', 'investigating', 'july', 'king', 'lady', 'leads', 'leaf', 'lies', 'liked', 'likely', 'linked', 'logging', 'material', 'missouri', 'monsters', 'month', 'monthly', 'murderers', 'nations', 'needa', 'oath', 'obamas', 'often', 'origin', 'page', 'pages', 'pay taxes', 'personally', 'perspective', 'pet', 'pisses', 'pizza', 'point life', 'possibly', 'praises', 'purposely', 'rapists', 'real quick', 'refuses', 'refusing', 'reporting', 'royals', 'rude', 'sarah', 'saving', 'seert', 'senate', 'shall', 'shameful', 'sign petition', 'significant', 'signs', 'skills', 'slammed', 'slams', 'social media', 'steel', 'television', 'tend', 'thanks', 'thanks sharing', 'theres', 'toort', 'trent', 'trial', 'truthrt', 'turnt', 'twitter account', 'twitterrt', 'unarmed', 'urlgood', 'urlmy', 'urlthese', 'vulnerable', 'wage', 'weed', 'weight', 'weird', 'whew', 'wont', 'wont even', 'would better', 'youre saying', 'CC RB VBG', 'CC VBG NNS', 'CC WP VBZ', 'CD CD PRP', 'CD CD TO', 'CD NNS VBG', 'CD PRP RB', 'CD PRP VBD', 'DT FW', 'DT IN VBG', 'DT NN RBR', 'DT PRP DT', 'DT PRP IN', 'DT VBN', 'IN DT JJR', 'IN JJ CD', 'IN JJ DT', 'IN JJ VBP', 'IN PRP VB', 'IN VBG CD', 'IN VBZ NN', 'JJ NN RBR', 'JJ RB VB', 'JJ RB VBD', 'JJ VBD JJ', 'JJ VBP CD', 'JJ VBP VBP', 'JJ VBP VBZ', 'JJ VBP WP', 'JJR JJ', 'JJR JJ RB', 'JJR VBP', 'NN DT VBP', 'NN NN NNP', 'NN PDT DT', 'NN VB VBG', 'NN VBN WRB', 'NNS EX', 'NNS PRP RB', 'NNS RP RB', 'NNS VBP WP', 'NNS VBZ VBN', 'PRP CC RB', 'PRP CC VB', 'PRP JJS JJ', 'PRP MD TO', 'PRP RB CC', 'PRP VBG RP', 'PRP WRB VB', 'RB JJ VBG', 'RB TO NN', 'RB VB TO', 'RB VBG WRB', 'RB VBP CD', 'RB WRB JJ', 'RP IN JJ', 'RP MD VB', 'RP NN CC', 'RP NNS CC', 'RP PRP PRP', 'TO JJ JJ', 'VB PRP VBG', 'VB PRP VBN', 'VB WP IN', 'VBD CD NNS', 'VBD NN DT', 'VBD NN VBZ', 'VBD NN WRB', 'WP CC']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHKH8mzloo-o"
      },
      "source": [
        "#Getting names for each class of features\n",
        "ngram_features_words = final_feature_list[:final_feature_list.index('youre saying')+1]\n",
        "pos_features_words = final_feature_list[final_feature_list.index('youre saying')+1:final_feature_list.index('WP CC')]"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dn6YzPXNp6Uz"
      },
      "source": [
        "new_vocab_words = {v:i for i, v in enumerate(ngram_features_words)}\n",
        "new_vocab_words_to_index = {}\n",
        "for k in ngram_features_words:\n",
        "    new_vocab_words_to_index[k] = vocab_words[k]"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htR7rTdYqYIl"
      },
      "source": [
        "#new_vocab_words"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPXSc67dd8uG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b74f3cd-49de-4aeb-904b-22178a76b998"
      },
      "source": [
        "model = LinearSVC(class_weight='balanced',C=0.01, penalty='l2', loss='squared_hinge',multi_class='ovr').fit(X_c, y)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBOjhUU3eBIz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3bbd11a-36a8-4a24-96af-df207e403020"
      },
      "source": [
        "final_features = select.get_support(indices=True) #get indices of features\n",
        "final_feature_list = [str(feature_names_chars[i]) for i in final_features] #Get list of names corresponding to indices\n",
        "print(final_feature_list)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[' amp', ' ass', ' ass ', ' beca', ' best ', ' bit', ' bitc', ' bitch', ' bor', ' cau', ' cle', ' done', ' eu', ' fuc', ' gone', ' gone ', ' has b', ' i j', ' i ju', ' jew', ' mad ', ' my d', ' nigg', ' nigga', ' plan', ' post', ' presi', ' qui', ' sid', ' stu', ' stup', ' stupi', ' team', ' to se', ' unde', ' urlu', ' w ', ' wann', ' wanna', ' white', ' work ', ' yal', '00000', '2 pe', '2 peo', '2 peop', 'ag ha', 'all s', 'anna', 'ant wa', 'at p', 'auti', 'bam', 'bc ', 'bitc', 'bitch', 'cent', 'cer', 'cks', 'cor', 'cum', 'd k', 'ders', 'dia', 'dly', 'dly ', 'e can ', 'e hash', 'e las', 'e peo', 'e peop', 'e url ', 'e way', 'e way ', 'each', 'eav', 'emo', 'eral', 'eric', 'erica', 'erica ', 'es b', 'esi', 'estr', 'fem', 'femi', 'fina', 'fuc', 'g has', 'g hash', 'g you', 'gga', 'ggas', 'ggas ', 'gone', 'gone ', 'h d', 'hah', 'has b', 'hey a', 'hite', 'hot', 'hur', 'hurt', 'i need', 'igga', 'iggas ', 'illega', 'im b', 'inf', 'ing co', 'ins ', 'int g', 'israe', 'ist ', 'iste', 'it wa', 'itch', 'itch ', 'iva', 'l i ', 'litica', 'lki', 'llega', 'llegal', 'lol ', 'm f', 'mate', 'mate ', 'me to ', 'men ', 'meric', 'merica', 'mind', 'my d', 'n go', 'n pe', 'ne p', 'ne pe', 'nia ', 'nigg', 'nigga', 'nni', 'nt ev', 'nt eve', 'nt wan', 'o one', 'oll ', 'ommy', 'on un', 'one p', 'one pe', 'oooooo', 'ople t', 'ople u', 'ot to ', 'otest', 'ou th', 'oure', 'oure ', 'oy ', 'poli', 'presi', 'presid', 'protes', 'r my ', 'rae', 'rds', 'riot', 'rlhas', 'rlhash', 'rlu', 'rona', 'rotes', 'rotest', 'roug', 'rt f', 'rt he', 'rt the', 's fi', 's ri', 's url ', 'seas', 'sident', 'st c', 't amp ', 't da', 't me ', 't nigg', 't shi', 't shit', 'tag ha', 'tag ur', 'team', 'the fu', 'tica', 'to se', 'tt ', 'tup', 'u got', 'u th', 'um ', 'urg', 'url u', 'url ur', 'urlhas', 'urlu', 'ut u', 'whit', 'white', 'y my ', 'y urlr', 'you th', 'youre', 'youre ', 'JJ VB PRP']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfmdV_lL08By"
      },
      "source": [
        "#Getting names for each class of features\n",
        "ngram_features = final_feature_list[:final_feature_list.index('youre ')+1]\n",
        "pos_features = final_feature_list[final_feature_list.index('youre ')+1:final_feature_list.index('JJ VB PRP')]\n"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sy3oQBdi1grf"
      },
      "source": [
        "new_vocab_chars = {v:i for i, v in enumerate(ngram_features)}\n",
        "new_vocab_chars_to_index = {}\n",
        "for k in ngram_features:\n",
        "    new_vocab_chars_to_index[k] = vocab_chars[k]"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkLHLdqsRJ0Q"
      },
      "source": [
        "#new_vocab_chars"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwXXYnLoRI3M"
      },
      "source": [
        "clf_lr = LogisticRegression(penalty='l2',\n",
        "                            tol=1e-4,\n",
        "                            C=5.0,\n",
        "                            fit_intercept=True,\n",
        "                            class_weight='balanced',\n",
        "                            random_state=0,\n",
        "                            solver='lbfgs',\n",
        "                            max_iter=100,\n",
        "                            multi_class='auto',\n",
        "                            verbose=1,\n",
        "                            n_jobs=-1)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQZoDSPUtEgz"
      },
      "source": [
        "param_grid = [{}]"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0yDoNPks55e"
      },
      "source": [
        "grid_search_lr = GridSearchCV(clf_lr, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKXOKH-8z0SQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4128c43-db81-4d94-f201-d8b1f5c329c8"
      },
      "source": [
        "clf_lr_word = clf_lr.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    1.4s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1Q5jJ6r3ycG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "outputId": "f728bc05-b16d-43a1-f3d8-60d67d75af52"
      },
      "source": [
        "evaluate_model_ngrammWord(clf_lr_word, model_name=\"clf_lr_word\")"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 1.0\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    1.0000    1.0000        67\n",
            "           1     1.0000    1.0000    1.0000        73\n",
            "\n",
            "    accuracy                         1.0000       140\n",
            "   macro avg     1.0000    1.0000    1.0000       140\n",
            "weighted avg     1.0000    1.0000    1.0000       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.6833333333333333\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7500    0.6364    0.6885        33\n",
            "           1     0.6250    0.7407    0.6780        27\n",
            "\n",
            "    accuracy                         0.6833        60\n",
            "   macro avg     0.6875    0.6886    0.6832        60\n",
            "weighted avg     0.6937    0.6833    0.6838        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATUAAAEGCAYAAAAE8QIHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAasklEQVR4nO3de5xVdb3/8dd7BhARkLsiF0FFgaN4ATFvaF4KtZ/mJfNy/OXpYmakYenROpna6fLTX5oZ/srM0soL5o2KJNNjqGGCRigghpjcRC6CpiA6M5/fH3vPuMFhZi2YPXvNmvezx3o89lr7O9/1GbA337XWd62liMDMLC+qKl2AmVlLcqiZWa441MwsVxxqZpYrDjUzy5UOlS6gVNX23aNDt36VLsNS2GPnbpUuwVJYvnQx615fo23po7r7rhE1GxK1jQ2rpkXE+G3ZX1qZCrUO3frR99RrK12GpfDL/zy60iVYCueceMQ29xE1G9hur9MTtX1n9qQ+27zDlDIVambWFgiU3TNXDjUzS0dAVXWlq9gih5qZpadtOi1XVg41M0vJh59mljceqZlZbgiP1MwsT+SRmpnljK9+mll++EKBmeWJ8OGnmeWMR2pmlh8+/DSzPBFQ7QsFZpYnGT6nlt0xpJllVPHwM8nSXE/SeEkLJC2UdFkj318vaXZxeVHSuub69EjNzNJrgZGapGpgEnAssBSYKWlKRMyrbxMRE0vafwnYv7l+PVIzs/RaZqQ2FlgYEYsi4l3gLuCkJtqfCdzZXKcONTNLR0q+QB9Js0qW80p6GgAsKVlfWtzWyC61KzAUeLS58nz4aWbpJb9NanVEjGmBPZ4B/CYiaptr6FAzs5RabJ7aMmBQyfrA4rbGnAF8MUmnPvw0s/SSH342ZSYwTNJQSZ0oBNeUD+5Kw4GewIwkpTnUzCyd+uepbeOFgoioASYA04D5wOSImCvpakknljQ9A7grIiJJeT78NLOUWu42qYiYCkzdbNsVm61fmaZPh5qZpefnqZlZrmT4NimHmpmlIz+lw8zyxiM1M8sTOdTMLC8KT/N2qJlZXkioyqFmZjnikZqZ5YpDzcxyxaFmZvmh4pJRDjUzS0XIIzUzy5eqKt9RYGY54pGameWHz6mZWd54pGZmueELBWaWO75NyszyQz78NLOccaiZWa441MwsN3yhwMzyJ7uZ5lAzs5Tk26TMLGd8+Glm+ZLdTHOobYtxI/rxjVNHUV0l7p7xCj95+MUPtDl+/wFceNxwAnhh2RtMvG1Ww3ddO3fgoa8dw8PPLeeqe+a0YuXt11PPvsgNP/sddXV1fOyYAznn1CM2+f6Bh/7KfX94iqqqKrbv3IlLL/g4QwftBMDCf77Ktf/vAd7esJEqiZ9eewHbdepYiV+j4trtSE3SeOAGoBq4JSK+V879taYqwZWf2JdPTXqSFes2cP8lH+aR515l4Yp/NbQZ0ncHzj92T06/fjpvbniP3l07bdLHxBNGMPOl1a1dertVW1vHdTdP4forP02/3t357KU3cdjY4Q2hBXDsuH35+PiDAHji6fnc+POpXHfFf1BTW8u3fnAP/3XRJxg2tD9vvLmeDtXVlfpVKkrK9tXPsp3tk1QNTAKOA0YCZ0oaWa79tbZ9d+3FK6vfZsma9bxXG/zumaUcs0//Tdp88pAh/OrxRby54T0A1rz1bsN3ew/qQZ9unXnihZWtWnd7Nv8fSxnYvzcDdu5Fx44dOOawUTzx9PxN2uzQpXPD5w0b30XF46yZsxey+647M2xo4e94x+5dqK7O7snycqsPtuaWSijnSG0ssDAiFgFIugs4CZhXxn22mp16dObVtRsa1les28C+Q3pu0mZov64ATJ44jiqJH/5hPtPnr0SCy0/em6/cPotD9+rXqnW3Z6tef4N+fXZsWO/be0fmvbjkA+3unTqDu6c8SU1NLTdc/RkAlixfjQQXX/Vz1r35NkcfNoqzTx7XarVnTZbv/SznPzUDgNL/YpYWt21C0nmSZkmaVbfhzTKW0/qqq6oY0rcrZ93wOF++bSbfOXN/um3fkX8/fDf+PPc1Vqx7p9IlWiNOPf5gJv/4q5z/vz/Kbff8DwA1tXXMmf8KV0w8nZu+cx7Tn5rLrDkLK1xp5bTXkVoiEXEzcDNAp357RIXLSey1de/Qv+f2Des799ie1zYLqRXrNjD7n69TUxcsXbOel1e+xZC+O7D/kF4cuHtvzj58KF2260DH6irWb6zl2ilzW/vXaFf69tqRlavfaFhfteYN+vbuvsX2xxw2iu//5EEA+vXuzr4jh9Cj+w4AHDx6L158aTljRu1R3qKzKOM3tJdzpLYMGFSyPrC4LRfmLF7LkL5dGdi7Cx2rxcdGD+SR517dpM3Dc5bzoWF9Aei5QyeG9uvKktXrufj2WRz+zWkcceUf+d4Dz3P/zMUOtFYwfNgAlry6muWvvc5779XwpyfmcOiBIzZps2T5+xdu/vLMAgb27wPA2P33ZNHi13hn47vU1Nbyt7kvM2RQ+zx1IEBKtlRCOUdqM4FhkoZSCLMzgLPKuL9WVVsXXHXP3/nFBYdSJfjNU6/wjxX/4svHj+C5xWt55PkVTJ+/ksOG78RDXzuaugi+98DzrFv/bvOdW1l0qK7m4s+dyMVX/Zy6uuCEo0ez2+CduOWOhxm+x0AOGzuCe6fOYNacl+hQXU23rp35+oWnAdC96/Z88n8dymcvuQlRGKkdMmZ4ZX+hisn21U9FlO+IT9LxwA8oTOm4NSK+3VT7Tv32iL6nXlu2eqzlTfnPoytdgqVwzolHMG/O37YpkTrvvGfs+qkbE7V98Zrxz0TEmG3ZX1plPacWEVOBqeXch5m1sgoeWibRfifamNlWEVBVpURLs31J4yUtkLRQ0mVbaHO6pHmS5kq6o7k+K37108zanpYYqZVM0D+WwpSvmZKmRMS8kjbDgMuBQyNiraRmr854pGZmqbXQPLWGCfoR8S5QP0G/1OeASRGxFiAimr0Fx6FmZukknM5RzLQ+9ZPri8t5JT0lmaC/J7CnpCclPVW8n7xJPvw0s1SE0jwkcvU2Xv3sAAwDjqQw13W6pH0iYt2WfsAjNTNLrYUm3yaZoL8UmBIR70XEy8CLFEJuixxqZpZaC51Ta5igL6kThQn6UzZr8wCFURqS+lA4HF3UVKcONTNLJ905tS2KiBpgAjANmA9Mjoi5kq6WdGKx2TRgjaR5wP8Al0TEmqb69Tk1M0ulcO9ny8y+bWyCfkRcUfI5gIuLSyIONTNLLct3FDjUzCy1JHcLVIpDzczSyfjz1BxqZpZK/fPUssqhZmYpZft5ag41M0stw5nmUDOzlOQLBWaWIy05T60cHGpmlppDzcxyJcOZ5lAzs/Q8UjOz/Mj4i1ccamaWSuEhkdlNNYeamaVWleGhmkPNzFLLcKY51MwsHfmGdjPLmwyfUttyqEm6EYgtfR8RF5alIjPLvLZ6oWBWq1VhZm2GKFwBzaothlpE3Fa6LqlLRKwvf0lmlnUZHqg1/zYpSQcX3+TyQnF9X0k3lb0yM8umhK/Hq9TFhCSvyPsB8FFgDUBE/B0YV86izCzbWuhlxmWR6OpnRCzZLHVry1OOmWWdaPuTb5dIOgQISR2Biyi8eNTM2qksX/1Mcvh5PvBFYACwHNivuG5m7VDSQ8/MHn5GxGrg7FaoxczaiCwffia5+rmbpN9KWiVppaQHJe3WGsWZWTYp4VIJSQ4/7wAmA/2BXYB7gDvLWZSZZVtbn9LRJSJ+GRE1xeVXQOdyF2Zm2VS4+plsqYSm7v3sVfz4B0mXAXdRuBf0k8DUVqjNzLJIbfchkc9QCLH66j9f8l0Al5erKDPLtjb56KGIGNqahZhZ21B/+JlVie4okLQ3MJKSc2kRcXu5ijKzbGuTI7V6kr4JHEkh1KYCxwFPAA41s3Yqu5GW7OrnacDRwIqI+A9gX2DHslZlZpklQXWVEi2VkOTwc0NE1EmqkdQdWAkMKnNdZpZhWT78TDJSmyWpB/BTCldEnwVmlLUqM8u0lrr3U9J4SQskLSxOHdv8+3OLdzPNLi6fba7PJPd+XlD8+GNJDwHdI2JO8+WaWR4Jtci9n5KqgUnAscBSYKakKRExb7Omd0fEhKT9NjX59oCmvouIZ5PuxMxypOWewDEWWBgRiwAk3QWcBGweaqk0NVL7fhPfBXDUtuy4MfsM6sGTN57c0t1aGfU8MPE/oJYBGxcua5F+UpxT6yOp9CVON0fEzcXPA4AlJd8tBQ5qpI9TJY0DXgQmRsSSRto0aGry7YeT1Wxm7YmA6uShtjoixmzD7n4L3BkRGyV9HriNZgZUSS4UmJltooVuaF/GpjMpBha3NYiINRGxsbh6CzC62dqS/xpmZgUtFGozgWGShkrqBJwBTCltIKl/yeqJJHiVQKLbpMzM6hWma2z7lYKIqJE0AZgGVAO3RsRcSVcDsyJiCnChpBOBGuB14Nzm+k1ym5QoPM57t4i4WtJgYOeIeHrrfx0za8ta6maBiJjKZo8yi4grSj5fTsonAiU5/LwJOBg4s7j+LwpzS8ysnWrTL14BDoqIAyT9DSAi1haPf82sHRLQIcO3SSUJtfeKM38DQFJfoK6sVZlZpmU40xKF2g+B+4F+kr5N4akd/1XWqswss6SWuU2qXJLc+/lrSc9QePyQgI9HhN/QbtaOZTjTEl39HAyspzCzt2FbRCwuZ2Fmll1t/XHev+f9F7B0BoYCC4B/K2NdZpZRgoo9ADKJJIef+5SuF5/eccEWmptZ3lXwnZ5JpL6jICKeldTYnfRm1k4ow28pSHJO7eKS1SrgAGB52Soys0zLwyvyupV8rqFwju3e8pRjZm1Bmw214qTbbhHx1Vaqx8zagCy/eKWpx3l3KN5Ff2hrFmRm2VZ4RV6lq9iypkZqT1M4fzZb0hTgHuDt+i8j4r4y12ZmGdWm7yigMDdtDYVH6NbPVwvAoWbWDrXlCwX9ilc+n+f9MKsXZa3KzDItwwO1JkOtGugKjU5IcaiZtVuiqo3OU3s1Iq5utUrMrE0QbXekluGyzaxiBB0yfFKtqVA7utWqMLM2o82O1CLi9dYsxMzajrY+pcPMbBMZzjSHmpmlI7L9FnSHmpmlIx9+mlmOFO4ocKiZWY5kN9Icama2FTI8UHOomVlaapvPUzMza4yvfppZ7vhCgZnlh9ro47zNzBrjw08zyx2P1MwsV7IbaQ41M0tJQLVHamaWJxnOtEyf7zOzTFLi/zXbkzRe0gJJCyVd1kS7UyWFpDHN9elQM7PUpGRL032oGpgEHAeMBM6UNLKRdt2Ai4C/JqnNoWZmqRSmdCjR0oyxwMKIWBQR7wJ3ASc10u5bwP8B3klSn0PNzNJJOEorjtT6SJpVspxX0tMAYEnJ+tLitvd3JR0ADIqI3yctzxcKzCy1FLdJrY6IZs+DNUZSFXAdcG6an3OomVkqhYdEtkhXy4BBJesDi9vqdQP2Bh4rTvbdGZgi6cSImLWlTh1qZpZakiubCcwEhkkaSiHMzgDOqv8yIt4A+jTsU3oM+GpTgQY+p2ZmW6Elrn5GRA0wAZgGzAcmR8RcSVdLOnFra/NIbRv86S/zuPz7v6G2ro5zTjqEied+ZJPvJ/36EX754Ayqq6vo06MrN17x7wzu3wuAJSte56L/voNlr61FEpN/8AUG79K7Er9Gu3L0wSP47ldOo7qqil8++Bd+cNvDm3z/7YmncPiYPQHYfrtO9O3VlSFHXdrwfbcdOjPj7q8z9c9zuPTae1q19ixpoZEaETEVmLrZtiu20PbIJH2WLdQk3Qp8DFgZEXuXaz+VUltbxyXXTOb+H01gl516cNSnruW4cfswfLf+DW1G7TWIR28/nC6dO/Gz3zzOlT98gFu/+2kAvvDN2/nKpz/Khw8awVvrN1LVQicpbMuqqsS1l57OyRN+xPLX1vHobZfwh+nPseDlFQ1tvn79fQ2fP3f6EYzaa+AmfXzt/BOY8beXWq3mLGrBc2plUc7Dz18A48vYf0U9M/ef7DaoD0MG9qFTxw6ccuwBTP3znE3aHD5mT7p07gTAgfsMYdnKdQC8sOhVamrr+PBBIwDo2mW7hnZWPqP/bQiLlqzmlWVreK+mlvsefpbjjxi1xfanfXQ09057pmF93+GD6NerO4/+dX5rlJtdElUJl0ooW6hFxHTg9XL1X2mvrnqDATv1bFjfZaeevLrqjS22/+WDMzj2kMJk6ZcWr2THbttzziU/ZdzZ3+MbN9xPbW1d2Wtu7/r33ZFlr61tWF/+2lr6992x0baDdu7J4F16M33WAqDwqJ3//vIpfOOG+1ul1qxTwqUSKn6hQNJ59RPzVq1eVelyyuLuqU8ze/5ivnTO0QDU1NYx428v8a2LTubR2y7hlWWrueN3T1W4Sit1ykdGM+WR2dTVBQCfPe1wHn5yLsuLo+32rP69n1kdqVX8QkFE3AzcDDB69JiocDmJJf1X/7G/vsB1P5/G737yZbbr1BGAXfr1YJ89BzJkYOFq9fFH7sus517mnMZuELEWk2Z0fcpHRnPJNZMb1g8cNZSD99udz5x2ODt02Y6OHap5e8NGrvrRlLLXnUUZPqVW+VBrqw4YuSsvLV7FK8tW079fD+57+Fl++q1zN2kzZ8ESJn73Ln7zwwvo26vbJj/7xlsbWL32X/Tp2Y3HZy5gvxGDW/cXaIeenfcKuw/uy+BdevPqynWccuwBfO4bv/hAu2G77kSPbl14es7LDdvO+8ZtDZ/P/NhB7D9icLsNNCDTqeZQ20odOlRzzaWnc+qFk6itDc4+8UOM2L0/3/nx79hvxGCOP2IUV9zwAG9v2Mi5l/0MgIE79+TO686nurqKb130cU664EYigv2GD+ZTJx9a4d8o/2pr67j0msnc+8MvUl0tfj3lKV5YtILLP38Cs+cv5g/TnwMKo7T7Hn6mmd7atyy/TUoR5Tnik3QncCSFGcGvAd+MiJ819TOjR4+JJ//a5GRhy5ieB06odAmWwsYFk6lbv3KbEmnEPvvH7Q8+lqjt2N17PLO1935urbKN1CLizHL1bWYVlt2Bmg8/zSydwnSN7KaaQ83M0klwX2clOdTMLLUMZ5pDzczSkl9mbGb5kuFMc6iZWTqVvK8zCYeamaWX4VRzqJlZap7SYWa54nNqZpYfnqdmZnnjw08zyw3hkZqZ5UyGM82hZmZbIcOp5lAzs9Sy/JBIh5qZpZbdSHOomdnWyHCqOdTMLBU/JNLM8sWTb80sbzKcaQ41M0vLD4k0s5zJcKY51MwsHT8k0szyJ8Op5lAzs9Q8pcPMcsXn1MwsPwRVGQ61qkoXYGZtkRIuzfQijZe0QNJCSZc18v35kp6TNFvSE5JGNtenQ83MUql/SGSSpcl+pGpgEnAcMBI4s5HQuiMi9omI/YBrgOuaq8+hZmaptcw4jbHAwohYFBHvAncBJ5U2iIg3S1Z3AKK5Tn1OzcxSS3GhoI+kWSXrN0fEzcXPA4AlJd8tBQ764L70ReBioBNwVHM7dKiZWWopbpNaHRFjtmVfETEJmCTpLOC/gE811d6Hn2aWWgsdfi4DBpWsDyxu25K7gI8316lDzcxSSXqRIMFgbiYwTNJQSZ2AM4Apm+5Lw0pWTwD+0VynPvw0s9Ra4o6CiKiRNAGYBlQDt0bEXElXA7MiYgowQdIxwHvAWpo59ASHmpltjRaafBsRU4Gpm227ouTzRWn7dKiZWWoZvqHAoWZmacmvyDOz/Ki/oyCrfPXTzHLFIzUzSy3LIzWHmpml5odEmll++L2fZpYnWb9Q4FAzs9R8+GlmueKRmpnlSoYzzaFmZlshw6nmUDOzVASZvk1KEc0+8rvVSFoFvFLpOsqgD7C60kVYKnn9O9s1IvpuSweSHqLw55PE6ogYvy37SytToZZXkmZt6yONrXX576zt8r2fZpYrDjUzyxWHWuu4ufkmljH+O2ujfE7NzHLFIzUzyxWHmpnlikOtjCSNl7RA0kJJl1W6HmuepFslrZT0fKVrsa3jUCsTSdXAJOA4YCRwpqSRla3KEvgF0KqTRa1lOdTKZyywMCIWRcS7wF3ASRWuyZoREdOB1ytdh209h1r5DACWlKwvLW4zszJyqJlZrjjUymcZMKhkfWBxm5mVkUOtfGYCwyQNldQJOAOYUuGazHLPoVYmEVEDTACmAfOByRExt7JVWXMk3QnMAPaStFTSZypdk6Xj26TMLFc8UjOzXHGomVmuONTMLFccamaWKw41M8sVh1obIqlW0mxJz0u6R1KXbejrF5JOK36+pamb7SUdKemQrdjHPyV94K1DW9q+WZu3Uu7rSklfTVuj5Y9DrW3ZEBH7RcTewLvA+aVfStqq97hGxGcjYl4TTY4EUoeaWSU41Nqux4E9iqOoxyVNAeZJqpZ0raSZkuZI+jyACn5UfL7bn4B+9R1JekzSmOLn8ZKelfR3SY9IGkIhPCcWR4mHS+or6d7iPmZKOrT4s70l/VHSXEm3kOA93pIekPRM8WfO2+y764vbH5HUt7htd0kPFX/mcUnDW+IP0/LDb2hvg4ojsuOAh4qbDgD2joiXi8HwRkQcKGk74ElJfwT2B/ai8Gy3nYB5wK2b9dsX+CkwrthXr4h4XdKPgbci4v8W290BXB8RT0gaTOGuiRHAN4EnIuJqSScASWbjf7q4j+2BmZLujYg1wA7ArIiYKOmKYt8TKLwQ5fyI+Iekg4CbgKO24o/Rcsqh1rZsL2l28fPjwM8oHBY+HREvF7d/BBhVf74M2BEYBowD7oyIWmC5pEcb6f9DwPT6viJiS88VOwYYKTUMxLpL6lrcxynFn/29pLUJfqcLJZ1c/DyoWOsaoA64u7j9V8B9xX0cAtxTsu/tEuzD2hGHWtuyISL2K91Q/D/326WbgC9FxLTN2h3fgnVUAR+KiHcaqSUxSUdSCMiDI2K9pMeAzltoHsX9rtv8z8CslM+p5c804AuSOgJI2lPSDsB04JPFc279gQ838rNPAeMkDS3+bK/i9n8B3Ura/RH4Uv2KpPqQmQ6cVdx2HNCzmVp3BNYWA204hZFivSqgfrR5FoXD2jeBlyV9orgPSdq3mX1YO+NQy59bKJwve7b48pCfUBiR3w/8o/jd7RSeRLGJiFgFnEfhUO/vvH/491vg5PoLBcCFwJjihYh5vH8V9ioKoTiXwmHo4mZqfQjoIGk+8D0KoVrvbWBs8Xc4Cri6uP1s4DPF+ubiR6TbZvyUDjPLFY/UzCxXHGpmlisONTPLFYeameWKQ83McsWhZma54lAzs1z5/+IzrT4PYoOwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zOB_W-qOETN"
      },
      "source": [
        "grid_search_lr = GridSearchCV(clf_lr, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_char, y_train_char), \n",
        "                           verbose=2)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZn55sF2OEfL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7122199a-31dc-4292-c696-6b8ef6a5a636"
      },
      "source": [
        "clf_lr_char= clf_lr.fit(X_train_char, y_train_char)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    1.5s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Nhyi2zVOEqU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "outputId": "eea01d69-5363-41ef-9446-31f8fd405585"
      },
      "source": [
        "evaluate_model_ngrammChar(clf_lr_char, model_name=\"clf_lr_char\")"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 1.0\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    1.0000    1.0000        67\n",
            "           1     1.0000    1.0000    1.0000        73\n",
            "\n",
            "    accuracy                         1.0000       140\n",
            "   macro avg     1.0000    1.0000    1.0000       140\n",
            "weighted avg     1.0000    1.0000    1.0000       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cyilTDi27DCvgPZfNKbuEjlDdSGtjqEXEue1q28xKVuFU8+mnmRVSu1xW3VRzqJlZMRWfT813P82ssFbd/JQ0VdLDklZIunQ7339Y0nJJD0r6saQDm7XpUDOzgoSUb2nYitQNzASmAROBcyVN7Lfb/cDkiDgc+DZwdbPqHGpmVpiUb2niKGBFRKyMiOeBucBp9TtExE8jom8w7r3AmGaNOtTMrJC8p55ZpvVIWly3TK9rajSwqm59dbZtIBcAP2hWn28UmFlx+W8UbIiIyTt9OOldwGTgLc32daiZWWEteqRjDTC2bn1Mtu3lx5JOAv4FeEtE/KlZoz79NLPCWnRNbREwQdJ4ScOAc4Delx9HRwBfBk6NiHV5anNPzcyKadFzahGxRdJFwAKgG5gdEcskzQAWR0Qv8BlgL+DG7G7qExFxaqN2HWpmVlirRhRExHxgfr9tl9d9Pqlomw41MytEVHtEgUPNzAqrcKY51MxsB1Q41RxqZlbYoJwk0szSVd1Ic6iZ2Y6ocKo51MysEE8SaWZpqfgkkQ41MyuswpnmUDOzoppPAFkmh5qZFVbhTHOomVkxJb58PReHmpkVV+FUc6iZWWF+pMPMkuJramaWDkGXQ83M0lLdVHOomVkhniTSzJJT4UxzqJlZce6pmVlSPEzKzJJS3UhzqJlZQTlfVFwah5qZFeYRBWaWlupmmkPNzIqrcKY51MysKPkVeWaWjqqPKOgquwAzs1ZyT83MCqtyT82hZmaF+ZEOM0uHH741s5RU/UaBQ83MCvPpp5klpco9NT/SYWaFKefStB1pqqSHJa2QdOl2vt9N0rey7++TdFCzNh1qZlZcC1JNUjcwE5gGTATOlTSx324XAJsi4jXANcCnm5XmUDOzQgR0SbmWJo4CVkTEyoh4HpgLnNZvn9OAr2Wfvw2cqCYzVFbqmtrSpUs27DFUj5ddRxv0ABvKLsIKSfXv7MCdbWDp0iUL9hiqnpy77y5pcd36rIiYlX0eDayq+241cHS/32/bJyK2SNoM7EeDv5tKhVpEjCy7hnaQtDgiJpddh+Xnv7OBRcTUsmtoxKefZlaWNcDYuvUx2bbt7iNpCDAC2NioUYeamZVlETBB0nhJw4BzgN5++/QCf5d9Pgv4SUREo0YrdfqZsFnNd7GK8d9Zm2XXyC4CFgDdwOyIWCZpBrA4InqBrwLfkLQCeJpa8DWkJqFnZtZRfPppZklxqJlZUhxqbdRsCIhVj6TZktZJeqjsWmzHONTaJOcQEKueOUCln8Oyxhxq7ZNnCIhVTETcTu0um3Uoh1r7bG8IyOiSajEbNBxqZpYUh1r75BkCYmYt5lBrnzxDQMysxRxqbRIRW4C+ISC/BOZFxLJyq7JmJN0A3AO8TtJqSReUXZMV42FSZpYU99TMLCkONTNLikPNzJLiUDOzpDjUzCwpDrUOImmrpAckPSTpRkl77kRbcySdlX2+rtFge0knSDpuB47xmPT/3zo00PZ++/yh4LGulPSRojVaehxqneXZiHhTRBwGPA+8r/7L7MUUhUXEeyJieYNdTgAKh5pZGRxqnesO4DVZL+oOSb3Ackndkj4jaZGkByW9F0A1X8zmd/sRsH9fQ5JukzQ5+zxV0lJJv5D0Y0kHUQvPD2W9xD+XNFLSTdkxFkmakv12P0kLJS2TdB1N39ENkr4raUn2m+n9vrsm2/5jSSOzba+WdGv2mzskvb4Vf5iWDr94pQNlPbJpwK3ZpknAYRHxaBYMmyPiSEm7AXdJWggcAbyO2txuBwDLgdn92h0JfAU4PmvrlRHxtKQvAX+IiH/P9vsmcE1E3ClpHLVRE4cAVwB3RsQMSX8J5Hka/++zY+wBLJJ0U0RsBF5B7eUbH5J0edb2RdReiPK+iPiNpKOBa4G37cAfoyXKodZZ9pD0QPb5Dmpv2jkO+HlEPJptPwU4vO96GbX3JE4AjgduiIitwJOSfrKd9o8Bbu9rKyIGmlfsJGCitK0jtrekvbJjnJH99vuSNuX4b7pY0unZ57FZrRuBF4FvZduvB27OjnEccGPdsXfLcQwbRBxqneXZiHhT/Ybsf+4/1m8CPhARC/rt9/YW1tEFHBMRz22nltwknUAtII+NiGck3QbsPsDukR33t/3/DMzq+ZpaehYA/yhpKICk10p6BXA78I7smtso4K3b+e29wPGSxme/fWW2/ffA8Lr9FgIf6FuR1BcytwPvzLZNA/ZtUusIYFMWaK+n1lPs00Xt5bVkbd4ZEb8DHpX019kxJOmNTY5hg4xDLT3XUbtetjR7eciXqfXIvwP8Jvvu69RmoniZiFgPTKd2qvcLXjr9+x5wet+NAuBiYHJ2I2I5L92F/VdqobiM2mnoE01qvRUYIumXwFXUQrXPH4Gjsv+GtwEzsu3nARdk9S3DU6RbP56lw8yS4p6amSXFoWZmSXGomVlSHGpmlhSHmpklxaFmZklxqJlZUv4P1u7gR9WxxTIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.7166666666666667\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7857    0.6667    0.7213        33\n",
            "           1     0.6562    0.7778    0.7119        27\n",
            "\n",
            "    accuracy                         0.7167        60\n",
            "   macro avg     0.7210    0.7222    0.7166        60\n",
            "weighted avg     0.7275    0.7167    0.7171        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yg7LrUtOE3Y"
      },
      "source": [
        "grid_search_lr = GridSearchCV(clf_lr, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_keyList, y_train_keyList), \n",
        "                           verbose=2)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yH5mTPlTOnmt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f086f9b-c00e-4e7f-bc78-3d9116f8c83d"
      },
      "source": [
        "clf_lr.fit(X_train_keyList, y_train_keyList)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=5.0, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=1,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wEz1GD3Opbt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "a5d8ba3d-46ba-4900-b603-63211fac0785"
      },
      "source": [
        "evaluate_model_keyList(clf_lr, model_name=\"clf_lr_keyList\")"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.5428571428571428\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.5205    0.5672    0.5429        67\n",
            "           1     0.5672    0.5205    0.5429        73\n",
            "\n",
            "    accuracy                         0.5429       140\n",
            "   macro avg     0.5439    0.5439    0.5429       140\n",
            "weighted avg     0.5449    0.5429    0.5429       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.6166666666666667\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6316    0.7273    0.6761        33\n",
            "           1     0.5909    0.4815    0.5306        27\n",
            "\n",
            "    accuracy                         0.6167        60\n",
            "   macro avg     0.6112    0.6044    0.6033        60\n",
            "weighted avg     0.6133    0.6167    0.6106        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WObBkbO13ypx"
      },
      "source": [
        "del clf_lr"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHGngHlA4vUm"
      },
      "source": [
        "### **4.3 Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIPSDU75EQCq"
      },
      "source": [
        "clf_rf = RandomForestClassifier(n_estimators=500,\n",
        "                                criterion=\"gini\",\n",
        "                                max_depth=None, \n",
        "                                min_samples_split=2,\n",
        "                                min_samples_leaf=10, \n",
        "                                max_features=\"auto\", \n",
        "                                n_jobs=-1,\n",
        "                                verbose=1,\n",
        "                                random_state=0,\n",
        "                                class_weight='balanced')"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZW6qLTd-jji"
      },
      "source": [
        "grid_search_rf = GridSearchCV(clf_rf, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EkLq1MmyMha",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2787c7f4-f137-4aaa-c13b-ad19cd96dd90"
      },
      "source": [
        "clf_rf_word= grid_search_rf.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.9s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.5s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.9s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.5s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.9s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.5s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.9s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.5s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.9s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.5s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    7.5s finished\n",
            "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:    0.4s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:    0.9s\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGlaAofi51l6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cb99b9ed-cba5-4827-d19f-d3d78a13dc44"
      },
      "source": [
        "evaluate_model_ngrammWord(clf_rf_word, model_name=\"clf_rf_word\")"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 1.0\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    1.0000    1.0000        67\n",
            "           1     1.0000    1.0000    1.0000        73\n",
            "\n",
            "    accuracy                         1.0000       140\n",
            "   macro avg     1.0000    1.0000    1.0000       140\n",
            "weighted avg     1.0000    1.0000    1.0000       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Set\n",
            "Accuracy: 0.65\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6765    0.6970    0.6866        33\n",
            "           1     0.6154    0.5926    0.6038        27\n",
            "\n",
            "    accuracy                         0.6500        60\n",
            "   macro avg     0.6459    0.6448    0.6452        60\n",
            "weighted avg     0.6490    0.6500    0.6493        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hR2yXGnMPTCW"
      },
      "source": [
        "grid_search_rf = GridSearchCV(clf_rf, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_char, y_train_char), \n",
        "                           verbose=2)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2FH56xWPTQt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06caf5c9-15ad-4941-c351-d213d11a8823"
      },
      "source": [
        "clf_rf_char= grid_search_rf.fit(X_train_char, y_train_char)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    8.0s finished\n",
            "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:    0.4s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:    1.0s\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.1s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8lJ6iiLPTdM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "35cfe11c-312f-405e-cfc9-5422f5989c04"
      },
      "source": [
        "evaluate_model_ngrammChar(clf_rf_char, model_name=\"clf_rf_char\")"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 1.0\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    1.0000    1.0000        67\n",
            "           1     1.0000    1.0000    1.0000        73\n",
            "\n",
            "    accuracy                         1.0000       140\n",
            "   macro avg     1.0000    1.0000    1.0000       140\n",
            "weighted avg     1.0000    1.0000    1.0000       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cyilTDi27DCvgPZfNKbuEjlDdSGtjqEXEue1q28xKVuFU8+mnmRVSu1xW3VRzqJlZMRWfT813P82ssFbd/JQ0VdLDklZIunQ7339Y0nJJD0r6saQDm7XpUDOzgoSUb2nYitQNzASmAROBcyVN7Lfb/cDkiDgc+DZwdbPqHGpmVpiUb2niKGBFRKyMiOeBucBp9TtExE8jom8w7r3AmGaNOtTMrJC8p55ZpvVIWly3TK9rajSwqm59dbZtIBcAP2hWn28UmFlx+W8UbIiIyTt9OOldwGTgLc32daiZWWEteqRjDTC2bn1Mtu3lx5JOAv4FeEtE/KlZoz79NLPCWnRNbREwQdJ4ScOAc4Delx9HRwBfBk6NiHV5anNPzcyKadFzahGxRdJFwAKgG5gdEcskzQAWR0Qv8BlgL+DG7G7qExFxaqN2HWpmVlirRhRExHxgfr9tl9d9Pqlomw41MytEVHtEgUPNzAqrcKY51MxsB1Q41RxqZlbYoJwk0szSVd1Ic6iZ2Y6ocKo51MysEE8SaWZpqfgkkQ41MyuswpnmUDOzoppPAFkmh5qZFVbhTHOomVkxJb58PReHmpkVV+FUc6iZWWF+pMPMkuJramaWDkGXQ83M0lLdVHOomVkhniTSzJJT4UxzqJlZce6pmVlSPEzKzJJS3UhzqJlZQTlfVFwah5qZFeYRBWaWlupmmkPNzIqrcKY51MysKPkVeWaWjqqPKOgquwAzs1ZyT83MCqtyT82hZmaF+ZEOM0uHH741s5RU/UaBQ83MCvPpp5klpco9NT/SYWaFKefStB1pqqSHJa2QdOl2vt9N0rey7++TdFCzNh1qZlZcC1JNUjcwE5gGTATOlTSx324XAJsi4jXANcCnm5XmUDOzQgR0SbmWJo4CVkTEyoh4HpgLnNZvn9OAr2Wfvw2cqCYzVFbqmtrSpUs27DFUj5ddRxv0ABvKLsIKSfXv7MCdbWDp0iUL9hiqnpy77y5pcd36rIiYlX0eDayq+241cHS/32/bJyK2SNoM7EeDv5tKhVpEjCy7hnaQtDgiJpddh+Xnv7OBRcTUsmtoxKefZlaWNcDYuvUx2bbt7iNpCDAC2NioUYeamZVlETBB0nhJw4BzgN5++/QCf5d9Pgv4SUREo0YrdfqZsFnNd7GK8d9Zm2XXyC4CFgDdwOyIWCZpBrA4InqBrwLfkLQCeJpa8DWkJqFnZtZRfPppZklxqJlZUhxqbdRsCIhVj6TZktZJeqjsWmzHONTaJOcQEKueOUCln8Oyxhxq7ZNnCIhVTETcTu0um3Uoh1r7bG8IyOiSajEbNBxqZpYUh1r75BkCYmYt5lBrnzxDQMysxRxqbRIRW4C+ISC/BOZFxLJyq7JmJN0A3AO8TtJqSReUXZMV42FSZpYU99TMLCkONTNLikPNzJLiUDOzpDjUzCwpDrUOImmrpAckPSTpRkl77kRbcySdlX2+rtFge0knSDpuB47xmPT/3zo00PZ++/yh4LGulPSRojVaehxqneXZiHhTRBwGPA+8r/7L7MUUhUXEeyJieYNdTgAKh5pZGRxqnesO4DVZL+oOSb3Ackndkj4jaZGkByW9F0A1X8zmd/sRsH9fQ5JukzQ5+zxV0lJJv5D0Y0kHUQvPD2W9xD+XNFLSTdkxFkmakv12P0kLJS2TdB1N39ENkr4raUn2m+n9vrsm2/5jSSOzba+WdGv2mzskvb4Vf5iWDr94pQNlPbJpwK3ZpknAYRHxaBYMmyPiSEm7AXdJWggcAbyO2txuBwDLgdn92h0JfAU4PmvrlRHxtKQvAX+IiH/P9vsmcE1E3ClpHLVRE4cAVwB3RsQMSX8J5Hka/++zY+wBLJJ0U0RsBF5B7eUbH5J0edb2RdReiPK+iPiNpKOBa4G37cAfoyXKodZZ9pD0QPb5Dmpv2jkO+HlEPJptPwU4vO96GbX3JE4AjgduiIitwJOSfrKd9o8Bbu9rKyIGmlfsJGCitK0jtrekvbJjnJH99vuSNuX4b7pY0unZ57FZrRuBF4FvZduvB27OjnEccGPdsXfLcQwbRBxqneXZiHhT/Ybsf+4/1m8CPhARC/rt9/YW1tEFHBMRz22nltwknUAtII+NiGck3QbsPsDukR33t/3/DMzq+ZpaehYA/yhpKICk10p6BXA78I7smtso4K3b+e29wPGSxme/fWW2/ffA8Lr9FgIf6FuR1BcytwPvzLZNA/ZtUusIYFMWaK+n1lPs00Xt5bVkbd4ZEb8DHpX019kxJOmNTY5hg4xDLT3XUbtetjR7eciXqfXIvwP8Jvvu69RmoniZiFgPTKd2qvcLXjr9+x5wet+NAuBiYHJ2I2I5L92F/VdqobiM2mnoE01qvRUYIumXwFXUQrXPH4Gjsv+GtwEzsu3nARdk9S3DU6RbP56lw8yS4p6amSXFoWZmSXGomVlSHGpmlhSHmpklxaFmZklxqJlZUv4P1u7gR9WxxTIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Set\n",
            "Accuracy: 0.7333333333333333\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7179    0.8485    0.7778        33\n",
            "           1     0.7619    0.5926    0.6667        27\n",
            "\n",
            "    accuracy                         0.7333        60\n",
            "   macro avg     0.7399    0.7205    0.7222        60\n",
            "weighted avg     0.7377    0.7333    0.7278        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBMV_BDTPTqB"
      },
      "source": [
        "grid_search_rf = GridSearchCV(clf_rf, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_keyList, y_train_keyList), \n",
        "                           verbose=2)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvlS6zGaPT09",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b4515dd-b1b2-4b22-bbff-8973d98d7755"
      },
      "source": [
        "clf_rf_keyList= grid_search_rf.fit(X_train_keyList, y_train_keyList)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.8s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.3s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.3s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.8s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.3s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.8s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.3s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.2s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.8s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.2s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    6.3s finished\n",
            "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:    0.4s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:    0.9s\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdC1f63jPUBg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "82c8033f-e582-454a-8cc5-3aabf427a3a1"
      },
      "source": [
        "evaluate_model_keyList(clf_rf_keyList, model_name=\"clf_rf_keyList\")"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.7214285714285714\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6795    0.7910    0.7310        67\n",
            "           1     0.7742    0.6575    0.7111        73\n",
            "\n",
            "    accuracy                         0.7214       140\n",
            "   macro avg     0.7268    0.7243    0.7211       140\n",
            "weighted avg     0.7289    0.7214    0.7206       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Set\n",
            "Accuracy: 0.7\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7143    0.7576    0.7353        33\n",
            "           1     0.6800    0.6296    0.6538        27\n",
            "\n",
            "    accuracy                         0.7000        60\n",
            "   macro avg     0.6971    0.6936    0.6946        60\n",
            "weighted avg     0.6989    0.7000    0.6986        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ie9EHRVp6aGv"
      },
      "source": [
        "del clf_rf"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rr8Kiu8w4zht"
      },
      "source": [
        "### **4.4 SGDClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEdjr7LmyUAS"
      },
      "source": [
        "clf_sgd = make_pipeline(StandardScaler(with_mean=False),\n",
        "                        SGDClassifier(loss='hinge',\n",
        "                                      penalty='l2',\n",
        "                                      alpha=30,\n",
        "                                      max_iter=1000, \n",
        "                                      tol=1e-3,\n",
        "                                      shuffle=True,\n",
        "                                      verbose=1,\n",
        "                                      n_jobs=-1,\n",
        "                                      random_state=0,\n",
        "                                      learning_rate='optimal',\n",
        "                                      early_stopping=True,\n",
        "                                      class_weight='balanced'))\n",
        "\n"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UO95MUN5yXFP"
      },
      "source": [
        "grid_search_sgd = GridSearchCV(clf_sgd, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCaLXVnw_C-i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "105781c0-63da-4d2c-aa5f-4f7a43fbc7c9"
      },
      "source": [
        "clf_sgd_word = grid_search_sgd.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 11021, Bias: 0.394678, T: 100, Avg. loss: 1.489562\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.19, NNZs: 11259, Bias: 0.393487, T: 200, Avg. loss: 0.248516\n",
            "Total training time: 0.31 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 11274, Bias: 0.393388, T: 300, Avg. loss: 0.035225\n",
            "Total training time: 0.34 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.13, NNZs: 11274, Bias: 0.393171, T: 400, Avg. loss: 0.041554\n",
            "Total training time: 0.36 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 11273, Bias: 0.393330, T: 500, Avg. loss: 0.064625\n",
            "Total training time: 0.39 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 11274, Bias: 0.392998, T: 600, Avg. loss: 0.043838\n",
            "Total training time: 0.42 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.12, NNZs: 11274, Bias: 0.392802, T: 700, Avg. loss: 0.029259\n",
            "Total training time: 0.45 seconds.\n",
            "Convergence after 7 epochs took 0.46 seconds\n",
            "[CV] ................................................. , total=   0.6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 10977, Bias: 0.406276, T: 100, Avg. loss: 1.100123\n",
            "Total training time: 0.03 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.18, NNZs: 11255, Bias: 0.406406, T: 200, Avg. loss: 0.285553\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.13, NNZs: 11268, Bias: 0.405860, T: 300, Avg. loss: 0.032402\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.13, NNZs: 11268, Bias: 0.405806, T: 400, Avg. loss: 0.055674\n",
            "Total training time: 0.11 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 11268, Bias: 0.405723, T: 500, Avg. loss: 0.068602\n",
            "Total training time: 0.13 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 11268, Bias: 0.405657, T: 600, Avg. loss: 0.044701\n",
            "Total training time: 0.17 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.12, NNZs: 11268, Bias: 0.405411, T: 700, Avg. loss: 0.034748\n",
            "Total training time: 0.21 seconds.\n",
            "Convergence after 7 epochs took 0.22 seconds\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 10971, Bias: 0.379966, T: 100, Avg. loss: 1.414108\n",
            "Total training time: 0.03 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.18, NNZs: 11246, Bias: 0.380044, T: 200, Avg. loss: 0.272859\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.13, NNZs: 11257, Bias: 0.379821, T: 300, Avg. loss: 0.031949\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.13, NNZs: 11257, Bias: 0.379213, T: 400, Avg. loss: 0.060156\n",
            "Total training time: 0.11 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 11257, Bias: 0.379365, T: 500, Avg. loss: 0.063752\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 11257, Bias: 0.379125, T: 600, Avg. loss: 0.034128\n",
            "Total training time: 0.14 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.12, NNZs: 11257, Bias: 0.378973, T: 700, Avg. loss: 0.031841\n",
            "Total training time: 0.16 seconds.\n",
            "-- Epoch 8\n",
            "Norm: 0.11, NNZs: 11257, Bias: 0.378982, T: 800, Avg. loss: 0.025703\n",
            "Total training time: 0.19 seconds.\n",
            "Convergence after 8 epochs took 0.20 seconds\n",
            "[CV] ................................................. , total=   0.3s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.31, NNZs: 11071, Bias: -0.426470, T: 100, Avg. loss: 8.340307\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.18, NNZs: 11262, Bias: -0.426060, T: 200, Avg. loss: 0.233224\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 11270, Bias: -0.425651, T: 300, Avg. loss: 0.019185\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.13, NNZs: 11270, Bias: -0.425724, T: 400, Avg. loss: 0.056826\n",
            "Total training time: 0.07 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 11270, Bias: -0.425683, T: 500, Avg. loss: 0.038638\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 11270, Bias: -0.425457, T: 600, Avg. loss: 0.046181\n",
            "Total training time: 0.11 seconds.\n",
            "Convergence after 6 epochs took 0.12 seconds\n",
            "[CV] ................................................. , total=   0.3s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 10961, Bias: -0.428437, T: 100, Avg. loss: 6.100394\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.18, NNZs: 11247, Bias: -0.427356, T: 200, Avg. loss: 0.255426\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.13, NNZs: 11259, Bias: -0.427717, T: 300, Avg. loss: 0.042253\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.13, NNZs: 11259, Bias: -0.427731, T: 400, Avg. loss: 0.050261\n",
            "Total training time: 0.14 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 11259, Bias: -0.427569, T: 500, Avg. loss: 0.054105\n",
            "Total training time: 0.16 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 11259, Bias: -0.427618, T: 600, Avg. loss: 0.041742\n",
            "Total training time: 0.18 seconds.\n",
            "Convergence after 6 epochs took 0.18 seconds\n",
            "[CV] ................................................. , total=   0.4s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    2.0s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "-- Epoch 1\n",
            "Norm: 0.27, NNZs: 11211, Bias: 0.391480, T: 126, Avg. loss: 1.246708\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.17, NNZs: 11331, Bias: 0.391790, T: 252, Avg. loss: 0.233613\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 11332, Bias: 0.391407, T: 378, Avg. loss: 0.053133\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.14, NNZs: 11332, Bias: 0.391369, T: 504, Avg. loss: 0.084322\n",
            "Total training time: 0.10 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 11332, Bias: 0.391058, T: 630, Avg. loss: 0.053260\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 11332, Bias: 0.391126, T: 756, Avg. loss: 0.046054\n",
            "Total training time: 0.15 seconds.\n",
            "Convergence after 6 epochs took 0.16 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71okRdfS560s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "274fa7ba-303a-4b4d-abb4-b47bd423ff73"
      },
      "source": [
        "evaluate_model_ngrammWord(clf_sgd_word, model_name=\"clf_sgd_word\")"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.9428571428571428\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9683    0.9104    0.9385        67\n",
            "           1     0.9221    0.9726    0.9467        73\n",
            "\n",
            "    accuracy                         0.9429       140\n",
            "   macro avg     0.9452    0.9415    0.9426       140\n",
            "weighted avg     0.9442    0.9429    0.9427       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RMUvSlZJO2Nja/NxPM0tENF9vKCImABMarBu1gbZHFLNNh5qZJaN0jyhwqJlZIrkRBQ41M8uQ9EaaQ83MNkKKO2oONTNLSq1zPjUzs0/TnFc/S8GhZmaJ+UKBmWWHWul03mZmn8aHn2aWOe6pmVmmpDfSHGpmlpCASvfUzCxLUpxpDjUzS0ooxQegDjUzS8w9NTPLjNwtHelNNYeamSVTxKy25eRQM7PEPEzKzDIjN0lkuavYMIeamSXmq59mlikpPvp0qCX1tydnc/nP/0zN+vWcdeLBXPy1Y+p9/tHadZx/xe3MfGkB3bt05JaffJ1+vbfmH8/M4UfXj2ftumo236wdV170RQ7bbyCr3/+QY//jF3XfX7L0HU4bvh9Xf+fUlv5pbcLRB+3K1d85lcqKCm6//0l+eesj9T7vu203rhv1Zaq6bsXKdz/g3FG3smTpOxy67wB+8u1T6toN2L4nZ3//f5nwz+db+iekQpvsqUm6BTgeWBoRu5dqPy2ppmY9l44Zx33Xj6R3z64c9dVrGH7YHnxmx151bW6//ym6dO7AjPtGc8/D0xh93f3ccvXX2brrVtx57bn06tGV2fOWcOpFY5k94So6dWzPlD9eXvf9I876GccfuXc5fl7mVVSIay47jZNGXs+St97h77deysTJLzD31Tfr2lz5rZO468F/cdeDz/DZIbsw6oITOO+K23h8+isc9qWfAtC185bMuPcK/vH0nHL9lLJK+zm1Us4g8ntgWAm33+Kmz3qNHftWscN2VWy+WTtOHjr4E/9ST5z8PCOOOwCAE4/ah39OnUtEsOfAvvTq0RWAXXfqxZqP1vHR2nX1vjvv9bdY9vZqDt5np5b5QW3MvrvtwPyFy3l98QrWVddw7yMzOPbwPeu1GbhjL6ZMmwvAlGkvM/ywPT6xnROP3oe/PTWbNR+t+8RnbYJERZGvcihZqEXEZODtUm2/HN5Ytoo+PbvVLffu2Y03lq2q12bJ0o/btGtXSeetOvD2qvfrtRn/95nsNbAvW2y+Wb319z48g5OHDk71tC6tWa8eXVj81sq65SVvraRXjy712sx6eXFdT/n4I/ei81Yd6NalY702Jw8dzD2Tppe+4BRTka9yKPtcb5LOkTRN0rRly5eVu5ySm/N/bzD6uvv5xX+d8YnP7n1kOqd8fkgZqrJaP/zVfRwyeGf++YfvcsjgnVn81kpqatbXfd5z684M2rk3jz41u4xVllftcz/T2lMr+4WCiLgRuBFg332HRJnLaVQx/9L33ibXpk/PblRX1/Due2vonv+XfvFbKznrshv5nx+dRf/tetT73gsvL6K6poa9d+1X+h/SRhXT035z+Sq+ctnNAHTssDlfOHJv3n1vTd3nXxw6mL8+9jzVBUHXFqX5WKLsPbXWZPCg7fm/Bct4ffFy1q6r5t5HZjD8sPrnZIZ9dg/ufPAZAO7/+7Mctt8uSGLV6g84/eIbuOKCEzlwr0+eM7tn0nROOca9tFKaMft1durXg369t2azdpWcPHQwEyfXPyfavUvHusP/i7/2ee544Ol6n59yzL7cM2lai9WcWik+/ix7T601adeukjGXncYpF42lpib40gkHsutOvfjJDX9l7137cezhe3LWiQdz3hW3Mfik0XTr3JHfXfXvANw0bjKvLlzGmJsnMubmiQDce/1IenTvBMBf/jaDcb86v2y/rS2oqVnPZWPGcc+vL6CyUtwx/mlemv8ml597HDPnLGDi5Bc4dN8BjLrgBCLgyWfncemYcXXf79urO316duOJGfPK+CvSIc3DpBRRmiM+SXcCRwBVwFvAFRHxu8a+s+++Q+KJZ/yvYGvSbb+R5S7BEvho7jjWf7B0kxJp1z32idvuf6yotvvv1HV6RLToIUjJemoRMaJU2zazMktvR82Hn2aWTO50WXpTzaFmZsl4PjUzy5oUZ5pDzcySUqpHvTjUzCyxFGeaQ83MkinnuM5ieESBmSXXTCMKJA2TNFfSPEnf+5TPvy1ptqTnJT0qafumtulQM7PEVOT/Gt2GVAmMBYYDg4ARkgY1aPYsMCQi9gT+DIxpqjaHmpklJhX3asL+wLyImB8Ra4G7gBMLG0TEPyLig/zi08B2TW3UoWZmyRQZaPlQq6qdWiz/OqdgS32AhQXLi/LrNuRsYGJT5flCgZkllmBEwfLmGPsp6cvAEODwpto61MwsEdFst3QsBvoWLG+XX1d/f9LngO8Dh0fER01t1IefZpZYM138nAoMkNRf0ubAGcD4evuR9gF+C5wQEUuLqc2hZmbJNUOqRUQ1MBKYBMwBxkXELElXSjoh3+waYCvgbkkzJY3fwObq+PDTzBJrrkkiI2ICMKHBulEF7z+XdJsONTNLLM0jChxqZpZcilPNoWZmiXiSSDPLFk8SaWZZk+JMc6iZWVKeJNLMMibFmeZQM7Nk0j5JpEPNzJJLcao51MwsMd/SYWaZ4nNqZpYdggqHmpllS3pTzaFmZok04ySRJeFQM7PEUpxpDjUzS849NTPLFA+TMrNMSW+kOdTMLKEiH1RcNg41M0vMIwrMLFvSm2kONTNLLsWZ5lAzs6TUbI/IKwWHmpklkvYRBX5Cu5llintqZpZYmntqDjUzS8y3dJhZdvjmWzPLkrRfKHComVliPvw0s0xxT83MMiXFmeZQM7ONkOJUc6iZWSKCVA+TUkSUu4Y6kpYBr5e7jhKoApaXuwhLJKt/Z9tHRI9N2YCkh8j9+RRjeUQM25T9JZWqUMsqSdMiYki567Di+e+s9fLYTzPLFIeamWWKQ61l3FjuAiwx/521Uj6nZmaZ4p6amWWKQ83MMsWhVkKShkmaK2mepO+Vux5rmqRbJC2V9GK5a7GN41ArEUmVwFhgODAIGCFpUHmrsiL8HmjRm0WteTnUSmd/YF5EzI+ItcBdwIllrsmaEBGTgbfLXYdtPIda6fQBFhYsL8qvM7MScqiZWaY41EpnMdC3YHm7/DozKyGHWulMBQZI6i9pc+AMYHyZazLLPIdaiURENTASmATMAcZFxKzyVmVNkXQn8BQwUNIiSWeXuyZLxsOkzCxT3FMzs0xxqJlZpjjUzCxTHGpmlikONTPLFIdaKyKpRtJMSS9KulvSlpuwrd9LOjX//ubGBttLOkLSwRuxj9ckfeKpQxta36DNewn3NVrSJUlrtOxxqLUuayJi74jYHVgLnFf4oaSNeo5rRHwjImY30uQIIHGomZWDQ631mgLsnO9FTZE0HpgtqVLSNZKmSnpe0rkAyrk+P7/b34Btajck6TFJQ/Lvh0maIek5SY9K2oFceF6c7yV+VlIPSffk9zFV0iH5724t6WFJsyTdTBHP8Zb0F0nT8985p8Fnv8ivf1RSj/y6nSQ9lP/OFEmfaY4/TMsOP6G9Fcr3yIYDD+VXDQZ2j4hX88GwKiL2k7QF8ISkh4F9gIHk5nbrCcwGbmmw3R7ATcBh+W11j4i3Jd0AvBcR/51v90fgFxHxuKR+5EZN7ApcATweEVdKOg4o5m78r+f30QGYKumeiFgBdASmRcTFkkbltz2S3ANRzouIVyQdAPwGOGoj/hgtoxxqrUsHSTPz76cAvyN3WPiviHg1v/4YYM/a82VAF2AAcBhwZ0TUAEsk/f1Ttn8gMLl2WxGxoXnFPgcMkuo6Yp0lbZXfx8n57z4oaWURv+kiSSfl3/fN17oCWA/8Kb/+D8C9+X0cDNxdsO8titiHtSEOtdZlTUTsXbgi/x/3+4WrgAsjYlKDdsc2Yx0VwIER8eGn1FI0SUeQC8iDIuIDSY8B7TfQPPL7fafhn4FZIZ9Ty55JwPmSNgOQtIukjsBk4PT8ObdewJGf8t2ngcMk9c9/t3t+/WqgU0G7h4ELaxck1YbMZODM/LrhQLcmau0CrMwH2mfI9RRrVQC1vc0zyR3Wvgu8Kunf8vuQpL2a2Ie1MQ617LmZ3PmyGfmHh/yWXI/8PuCV/Ge3kZuJop6IWAacQ+5Q7zk+Pvx7ADip9kIBcBEwJH8hYjYfX4X9EblQnEXuMHRBE7U+BLSTNAf4KblQrfU+sH/+NxwFXJlf/yXg7Hx9s/AU6daAZ+kws0xxT83MMsWhZmaZ4lAzs0xxqJlZpjjUzCxTHGpmlikONTPLlP8HT36MDmAWdaQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.6333333333333333\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7200    0.5455    0.6207        33\n",
            "           1     0.5714    0.7407    0.6452        27\n",
            "\n",
            "    accuracy                         0.6333        60\n",
            "   macro avg     0.6457    0.6431    0.6329        60\n",
            "weighted avg     0.6531    0.6333    0.6317        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_K03RM4RPhn"
      },
      "source": [
        "grid_search_sgd = GridSearchCV(clf_sgd, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_char, y_train_char), \n",
        "                           verbose=2)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMjIlH0JRPuM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0485869-aeab-4868-c9ab-d9522a521633"
      },
      "source": [
        "clf_sgd_char = grid_search_sgd.fit(X_train_char, y_train_char)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 14521, Bias: 0.396480, T: 100, Avg. loss: 3.510345\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.20, NNZs: 14690, Bias: 0.395669, T: 200, Avg. loss: 0.499618\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.16, NNZs: 14709, Bias: 0.395640, T: 300, Avg. loss: 0.121349\n",
            "Total training time: 0.07 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.15, NNZs: 14716, Bias: 0.395649, T: 400, Avg. loss: 0.098359\n",
            "Total training time: 0.10 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 14716, Bias: 0.395694, T: 500, Avg. loss: 0.029629\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14716, Bias: 0.395640, T: 600, Avg. loss: 0.057681\n",
            "Total training time: 0.15 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.12, NNZs: 14716, Bias: 0.395490, T: 700, Avg. loss: 0.049243\n",
            "Total training time: 0.17 seconds.\n",
            "Convergence after 7 epochs took 0.18 seconds\n",
            "[CV] ................................................. , total=   0.3s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 14510, Bias: 0.407404, T: 100, Avg. loss: 2.189178\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.20, NNZs: 14677, Bias: 0.407791, T: 200, Avg. loss: 0.386586\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.16, NNZs: 14708, Bias: 0.407430, T: 300, Avg. loss: 0.163877\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.14, NNZs: 14712, Bias: 0.407473, T: 400, Avg. loss: 0.113523\n",
            "Total training time: 0.11 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 14712, Bias: 0.407543, T: 500, Avg. loss: 0.073381\n",
            "Total training time: 0.13 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14713, Bias: 0.407498, T: 600, Avg. loss: 0.039420\n",
            "Total training time: 0.16 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.12, NNZs: 14714, Bias: 0.407405, T: 700, Avg. loss: 0.048815\n",
            "Total training time: 0.19 seconds.\n",
            "-- Epoch 8\n",
            "Norm: 0.11, NNZs: 14714, Bias: 0.407427, T: 800, Avg. loss: 0.017800\n",
            "Total training time: 0.20 seconds.\n",
            "Convergence after 8 epochs took 0.22 seconds\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.29, NNZs: 14544, Bias: 0.386882, T: 100, Avg. loss: 3.993093\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.20, NNZs: 14702, Bias: 0.386848, T: 200, Avg. loss: 0.476481\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.15, NNZs: 14715, Bias: 0.387163, T: 300, Avg. loss: 0.133617\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.14, NNZs: 14718, Bias: 0.387142, T: 400, Avg. loss: 0.078835\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 14718, Bias: 0.386993, T: 500, Avg. loss: 0.050047\n",
            "Total training time: 0.14 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14718, Bias: 0.386893, T: 600, Avg. loss: 0.042452\n",
            "Total training time: 0.16 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.11, NNZs: 14718, Bias: 0.386863, T: 700, Avg. loss: 0.034083\n",
            "Total training time: 0.19 seconds.\n",
            "Convergence after 7 epochs took 0.21 seconds\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.31, NNZs: 14590, Bias: -0.417664, T: 100, Avg. loss: 34.892807\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.20, NNZs: 14699, Bias: -0.417203, T: 200, Avg. loss: 0.477528\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.16, NNZs: 14710, Bias: -0.417093, T: 300, Avg. loss: 0.086118\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.14, NNZs: 14711, Bias: -0.417338, T: 400, Avg. loss: 0.108129\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 14712, Bias: -0.417281, T: 500, Avg. loss: 0.067147\n",
            "Total training time: 0.15 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14712, Bias: -0.417164, T: 600, Avg. loss: 0.049823\n",
            "Total training time: 0.17 seconds.\n",
            "Convergence after 6 epochs took 0.18 seconds\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 14586, Bias: -0.423981, T: 100, Avg. loss: 26.209305\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.21, NNZs: 14700, Bias: -0.423562, T: 200, Avg. loss: 0.409001\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.17, NNZs: 14706, Bias: -0.423543, T: 300, Avg. loss: 0.124469\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.14, NNZs: 14710, Bias: -0.423667, T: 400, Avg. loss: 0.067904\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 14710, Bias: -0.423618, T: 500, Avg. loss: 0.060017\n",
            "Total training time: 0.15 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14710, Bias: -0.423626, T: 600, Avg. loss: 0.045520\n",
            "Total training time: 0.18 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.12, NNZs: 14710, Bias: -0.423560, T: 700, Avg. loss: 0.050249\n",
            "Total training time: 0.21 seconds.\n",
            "Convergence after 7 epochs took 0.22 seconds\n",
            "[CV] ................................................. , total=   0.4s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.9s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "-- Epoch 1\n",
            "Norm: 0.27, NNZs: 14673, Bias: 0.393071, T: 126, Avg. loss: 2.807795\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.19, NNZs: 14728, Bias: 0.393419, T: 252, Avg. loss: 0.411285\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.16, NNZs: 14729, Bias: 0.393365, T: 378, Avg. loss: 0.146996\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.14, NNZs: 14729, Bias: 0.393407, T: 504, Avg. loss: 0.086335\n",
            "Total training time: 0.10 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 14730, Bias: 0.393387, T: 630, Avg. loss: 0.062842\n",
            "Total training time: 0.12 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14730, Bias: 0.393405, T: 756, Avg. loss: 0.063759\n",
            "Total training time: 0.14 seconds.\n",
            "Convergence after 6 epochs took 0.15 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XF8ue0e5RP6V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "61b30c23-4631-489d-e6e1-b7c94620c5bf"
      },
      "source": [
        "evaluate_model_ngrammChar(clf_sgd_char, model_name=\"clf_sgd_char\")"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.95\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9688    0.9254    0.9466        67\n",
            "           1     0.9342    0.9726    0.9530        73\n",
            "\n",
            "    accuracy                         0.9500       140\n",
            "   macro avg     0.9515    0.9490    0.9498       140\n",
            "weighted avg     0.9507    0.9500    0.9499       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.6666666666666666\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8421    0.4848    0.6154        33\n",
            "           1     0.5854    0.8889    0.7059        27\n",
            "\n",
            "    accuracy                         0.6667        60\n",
            "   macro avg     0.7137    0.6869    0.6606        60\n",
            "weighted avg     0.7266    0.6667    0.6561        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATUAAAEGCAYAAAAE8QIHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcH0lEQVR4nO3de7xVdZ3/8df7HASUmyCCyEXR8EJeATF18lJaaI+fZpaCaTZjY02ijU2Z/fKnxUz9mmqyG41ZMVbewlueksQpMy+jCZKpoCiCCshVEEEROOd85o+9Du5zhLP3gr3PXmed99PHejz2Wuu7v+uz4eGH73d913d9FRGYmeVFXa0DMDOrJCc1M8sVJzUzyxUnNTPLFSc1M8uVbrUOoNiufftHv0FDax2GpdDk0fNOZf2qV3jr9bXamTrq++4T0bixrLKxcdXMiJiwM9dLK1NJrd+goXz8u7fVOgxLYf2mxlqHYCncecU5O11HNG6kx4Fnl1X2rSemDtzpC6aUqaRmZp2BQNm9c+WkZmbpCKirr3UU2+WkZmbpaaduy1WVk5qZpeTup5nljVtqZpYbwi01M8sTuaVmZjnj0U8zyw8PFJhZngh3P80sZ9xSM7P8cPfTzPJEQL0HCswsT3xPzczyw91PM8sbt9TMLFfcUjOz3JCnSZlZ3nialJnlR7YHCrIbmZllV0sXtNRWshpNkDRf0gJJV2zj/AhJf5L0V0lPSjqtVJ1OamaWTsv71MrZ2qtGqgemAqcCo4FJkka3KXYlMD0ijgQmAj8uFZ6TmpmlpIokNWA8sCAiFkbEZuAW4Iw2ZQLom3zuB7xSqlLfUzOz9MofKBgoaXbR/nURcV3yeSiwuOjcEuDoNt//KnCvpEuAXsDJpS7opGZm6ZX/SMfqiBi3E1eaBFwfEf8h6RjgV5IOiYjm7X3BSc3M0lHFRj+XAsOL9oclx4pdCEwAiIhHJPUEBgIrt1ep76mZWXqVGf2cBYySNFJSdwoDAQ1tyrwMvL9wSR0M9ARWtVepW2pmlpoqMKMgIholTQZmAvXAtIiYK2kKMDsiGoB/AX4q6TIKgwafjIhor14nNTNLpfA278pMk4qIGcCMNseuKvo8DzguTZ1OamaWjoTqPPfTzHKkUi21anBSM7PUnNTMLFec1MwsP5RsGeWkZmapCLmlZmb5UleX3ef2ndTMLDW31MwsP3xPzczyxi01M8sNDxSYWe54mpSZ5Yfc/TSznHFSM7NccVIzs9zwQIGZ5U92c5qTmpmlJE+TMrOcyXL3M7vp1syyS2VupaqRJkiaL2mBpCu2cf4aSU8k23OSXitVp1tqO+nl51/iod8/SHMEo8eMZsx7x26z3AvzFjDz1/fw0Ys+xqChg2lqauL+u+5j1bJVNDcHBx5+IGOP35k1X60cBw/qzUcOG0Kd4JGX1vKH51a3Oj9+xO58+JC9eG3jFgAeXLiGR15ay9B+PTn7iL3p2a2O5oB756/kr0tfr8VPyIRKtNQk1QNTgVMorM4+S1JDstgKABFxWVH5S4AjS9Vb1aQmaQLwfQrLX/0sIr5Zzet1tObmZh64+8/8n0+cQe++vbntuunse+BIBgwa0Krc5k2befLRJxk8bPDWYy/MXUBTUzMTLz6XLZu3cMvUmxh16AH07d+3o39GlyHgY4fvzdSHF/Haxka+cNJ+PL1sPcvXb2pVbs6Sddz25LJWxzY3NXPD7CWsemMzfXt244sn7c+zKzewcct2FwrPLalio5/jgQURsTCp9xbgDGDedspPAq4uVWnVup9FWfhUYDQwSdLoal2vFlYuXUG/Af3oN6Af9d3qedcho1j07MJ3lHvsvr9w5N+Nob5b/dZjktiyeQvNTc00NTZSV19H9x7dOzL8LmefAbuy6o1NvPrmFpoimLNkHYcO6VPWd1dt2MyqNzYD8PpbjWzY1Ejv7l23o9OS2EptwEBJs4u2i4qqGQosLtpfkhzb1vX2AUYC95WKrZp/K2mzcKfzxutv0Lvf2/9T9O7XmxVLVrQqs+qVlWxYt559D9iXJx6es/X4fqP3Z9Gzi7j+O9No3NLIcRP+jp679eyw2Lui3XvusrVbCfDaxkb26b/rO8odPrQv+w/sxaoNm7jjqeWtvgMwov+u1NeJ1UmS64pSzP1cHRGVuK8yEbgtIppKFaxmUttWFj66baEkc18E0GfPvasYTseL5uDhmQ/xvg+f/I5zK5euRBIXfOHv2bRxE7+ZdgfD9htOvwH9ahCptXh6+XrmLFlHY3Nw7L79OW/sUH700Itbz/ft0Y3zxw7jxseX0O4y4TlXoe7nUmB40f6w5Ni2TAQuLqfSmo9+RsR1ETEuIsbt1rd/rcNJpVffXmxYt37r/oZ1G+jVp9fW/c2bN7Nm5Rruuv5OfnXNL1ixZAUzbr6blUtX8PyTzzFi1Ajq6+vZrfdu7DViCKteWVmLn9FlvPbWFnbfdZet+7vv2o11b7Vuhb25uYnG5kK6euTFtQzf/e2WXM9udXz62H24e94KXly7sWOCziKl6n62ZxYwStJISd0pJK6Gd1xOOgjoDzxSTnjVTGppsnCnNGjvwaxbs47X175OU2MTC55+npEHjdx6vkfPHvzDlz7F+ZddwPmXXcDgYYM5bdKHGDR0ML379WbpwiUAbNm8hRVLlrP7wM6V1Dubl9duZM/ePRiw2y7US4wZ1o+nlq1vVaZvj7c7L4cO6cOKZBChXuLCo0cw6+XXeOKVrjvqCcnTGipva09ENAKTgZnAM8D0iJgraYqk04uKTgRuiYiyGsfV7H5uzcIUktlE4NwqXq/D1dXX8d7Tjue3v7qLaA4OOnI0AwbtwWP3/YU99x7UKsG1dej4Q7nvN3/k5h/dBAQHHXEwA/ca2HHBd0HNAbf97RU+e9y+1CEefWkty9dv4rSDB/Hy2o08vXw9J+y/B4cM6UNzBG9ubuKGxwv/8Bw5rC/vGtiLXt3rGT9idwBunLOUpevequVPqpHKzf2MiBnAjDbHrmqz/9U0darM5LdDJJ0GfI/CIx3TIuLr7ZXf612HxMe/e1vV4rHKW7+psdYhWAp3XnEOq16Yu1MZqedeB8Q+F/ywrLLPfWvC4xUaKChbVcekt5WFzayTK6NrWUtd90EbM9shAur8Om8zyxO31MwsV7L8lg4nNTNLx/fUzCxPhPySSDPLF7fUzCxXfE/NzPLD99TMLE8Kcz+zm9Wc1MwstQznNCc1M0vPMwrMLD/k7qeZ5UjL+9SyyknNzFKq3PvUqsFJzcxSy3BOc1Izs5TkgQIzy5GsP6eW3VmpZpZZFVpNCkkTJM2XtEDSFdspc7akeZLmSrqpVJ1uqZlZapVoqEmqB6YCp1BYF3iWpIaImFdUZhTwZeC4iFgraVCpet1SM7PUKtRSGw8siIiFEbEZuAU4o02ZfwSmRsRagIgouTiuk5qZpVPmmp9JThsoaXbRdlFRTUOBxUX7S5JjxQ4ADpD0sKRHJU0oFZ67n2aWSuElkWX3P1fv5BJ53YBRwIkUFkR/QNKhEfFae18wM0ulrjKjn0uB4UX7w5JjxZYAf4mILcAiSc9RSHKzthtbJSIzs64lRfezPbOAUZJGSuoOTAQa2pT5DYVWGpIGUuiOLmyvUrfUzCwVVWhCe0Q0SpoMzATqgWkRMVfSFGB2RDQk5z4gaR7QBHwxIl5tr14nNTNLrVITCiJiBjCjzbGrij4H8PlkK8t2k5qkHwLRTjCXlnsRM8uXzjpNanaHRWFmnYYojIBm1XaTWkT8onhf0m4R8Wb1QzKzrMtwQ6306KekY5KbdM8m+4dL+nHVIzOzbCpzNkGtJr2X80jH94APAq8CRMTfgOOrGZSZZVuFHumoirJGPyNicZus21SdcMws60TFHr6tinKS2mJJxwIhaRfgc8Az1Q3LzLIsy6Of5XQ/PwNcTGGi6SvAEcm+mXVB5XY9M9v9jIjVwMc7IBYz6ySy3P0sZ/RzP0m/lbRK0kpJd0naryOCM7NsUplbLZTT/bwJmA4MAfYGbgVurmZQZpZtnf2Rjt0i4lcR0ZhsNwA9qx2YmWVTYfSzvK0W2pv7OSD5+PtkQYRbKMwFPYc2E1DNrAtRqpdEdrj2Bgoep5DEWqL/dNG5oLAYgpl1QVleIq+9uZ8jOzIQM+scWrqfWVXWjAJJhwCjKbqXFhG/rFZQZpZtnbKl1kLS1RRepzuawr20U4GHACc1sy4quymtvNHPjwLvB5ZHxN8DhwP9qhqVmWWWBPV1KmurhXK6nxsjollSo6S+wEparwBjZl1Mlruf5bTUZkvaHfgphRHROcAjVY3KzDKtUnM/JU2QNF/SguTRsbbnP5nMZnoi2T5Vqs5y5n5+Nvl4raR7gL4R8WTpcM0sj4QqMvdTUj0wFTiFwvqesyQ1RMS8NkV/HRGTy623vYdvx7R3LiLmlHsRM8uRyr2BYzywICIWAki6BTgDaJvUUmmvpfYf7ZwL4H07c+FtGdqvJ18/7aBKV2tV1P+osv8BtQzYtGxVRepJcU9toKTiRZyui4jrks9DgcVF55YAR2+jjrMkHQ88B1wWEYu3UWar9h6+Pam8mM2sKxFQX35SWx0R43bicr8Fbo6ITZI+DfyCEg2qcgYKzMxaqdCE9qW0fpJiWHJsq4h4NSI2Jbs/A8aWjK38n2FmVlChpDYLGCVppKTuwESgobiApCFFu6dTxlICZU2TMjNrUXhcY+dHCiKiUdJkYCZQD0yLiLmSpgCzI6IBuFTS6UAjsAb4ZKl6y5kmJQqv894vIqZIGgHsFRGP7fjPMbPOrFKTBSJiBm1eZRYRVxV9/jIp3whUTvfzx8AxwKRkfz2FZ0vMrIvq1AuvAEdHxBhJfwWIiLVJ/9fMuiAB3TI8TaqcpLYlefI3ACTtCTRXNSozy7QM57SyktoPgDuBQZK+TuGtHVdWNSozyyypMtOkqqWcuZ83SnqcwuuHBHw4IrxCu1kXluGcVtbo5wjgTQpP9m49FhEvVzMwM8uuzv4677t5ewGWnsBIYD7w7irGZWYZJajZCyDLUU7389Di/eTtHZ/dTnEzy7sarulZjtQzCiJijqRtzaQ3sy5CGV6loJx7ap8v2q0DxgCvVC0iM8u0PCyR16focyOFe2y3VyccM+sMOm1SSx667RMRX+igeMysE8jywivtvc67WzKL/riODMjMsq2wRF6to9i+9lpqj1G4f/aEpAbgVuCNlpMRcUeVYzOzjOrUMwooPJv2KoVX6LY8rxaAk5pZF9SZBwoGJSOfT/N2MmsRVY3KzDItww21dpNaPdAbtvlAipOaWZcl6jrpc2rLImJKh0ViZp2C6LwttQyHbWY1I+iW4Ztq7Q3Mvr/DojCzTqOlpVaJ13lLmiBpvqQFkq5op9xZkkJSyTVE21vMeE3pkMysK6rEIx3Jw/1TgVMorM4+S1JDRMxrU64P8DngL2XFttORmVmXU6GW2nhgQUQsjIjNwC3AGdso96/AvwNvlRObk5qZpSIKiaOcDRgoaXbRdlFRVUOBxUX7S5Jjb1+r8Kqz4RFxd7nxeTFjM0tHqbqfqyOi5H2wbV5GqgO+SxkLGBdzUjOzVAozCioy+rkUGF60Pyw51qIPcAhwfzKBfi+gQdLpETF7e5U6qZlZahV6oGMWMErSSArJbCJwbsvJiFgHDNx6Tel+4AvtJTTwPTUz2wGVGCiIiEZgMjATeAaYHhFzJU2RdPqOxuaWmpmlpIq9Ty0iZgAz2hy7ajtlTyynTic1M0ulZfQzq5zUzCy1zv4+NTOzt6mTvs7bzGxb3P00s9xxS83MciW7Kc1JzcxSElDvlpqZ5UmGc5qTmpmlJZThDqiTmpml5paameVG4ZGO7GY1JzUzS6fM9QdqxUnNzFLzNCkzy43CSyJrHcX2OamZWWoe/TSzXMlw7zPT81Iz7w//M4+jzprCmDO/yjXX3/uO8w/PWcAJ532Tge+5lLv++NdW5z56yVT2OemLnHPZf3ZUuAa8/5iDeey2/8fjd1zNP19wyjvODxvcn4b/vJQ/3/AlHrrpy5xy7GgAdulWz4+uOo+Hb/6/PHjjFRw3ZlRHh54pKvO/WqhaUpM0TdJKSU9X6xq11NTUzBe/NZ1bv/9ZHp1+Jbff+zjPLlzWqszwvfoz9erz+egH37mYziXnn8y1X/tER4VrQF2d+PblZ/Oxz/2Y95z9b5z1gbEcOHKvVmX+5cIJ/OYPczjhvH/nwq/8F9/50jkAXHDmcQAcN+kbnDn5R/zbP5+Z6Und1dRyT62crRaq2VK7HphQxfpr6vG5L7Lf8IHsO2wg3XfpxkdOGcOMPz/ZqsyIvffgkFFDtzlSdML4A+nTq0dHhWvA2Hfvy8LFq3lp6atsaWzijv+ew2knHNa6UAR9evUEoG/vXVm+eh0AB47ciwdnzQdg9doNrNuwkSMPHtGh8WeGRF2ZWy1ULalFxAPAmmrVX2vLVq1j6OD+W/f3HtyfZavW1TAiK2XInv1YumLt1v1XVqxlyJ79WpX55nUzOPvU8Tz9u39l+vf+icu/fSsATz+/lAnHH0p9fR0j9t6DIw4a3urvv6tRmVvJeqQJkuZLWiDpim2c/4ykpyQ9IekhSaNL1VnzgYJkxeaLAIaP6KL/8llmnPXBcdz0u0eZeuN9HHXoSK792ic4duI3uKHhEQ7YdzB/+uXlLF62hseeXERTc3Otw62JSq37KakemAqcQmF19lmSGiJiXlGxmyLi2qT86RQWN263B1jzpBYR1wHXAYwdOy5qHE7ZyvlX37KlnNb1eWccw8cunQrArKcW0bPHLuyxey9Wr93AV665Y2u5mT//PC+8vLJjAs+gCnUsxwMLImIhgKRbgDOArUktIl4vKt8LKJkjPPq5g8aM3ocXXl7FS0tXs3lLI3f89xxOPf6w0l+0mpkz7yX2H7EnI/beg1261fORU8bw+wda3wddunwNxx91IAAH7DuYHt13YfXaDezaYxd269kdgBPHH0RjYzPzFy3v8N+QGeX3PwdKml20XVRUy1BgcdH+kuRY60tJF0t6AfgWcGmp0GreUuusunWr51uXn81Zl06lqSn4+Onv4eD9h/CNa3/HEQeP4LQTDmPO3Jc4//Kf8trrb3LPQ0/xzZ/czSPTrwTg1H+8hudfXMEbGzfx7g9dyQ+uPJf3H1PydoHthKamZi7/1nRu/8HF1NeLGxse5dmFy/nypz/EE8+8zO8feIorv3cn3//KJD476SQCuPhrvwJg4IA+3P7Di2luDpateo3PXP2L2v6YGkvR/VwdEe8c/k8hIqYCUyWdC1wJXNBeeUVUp8cn6WbgRArLxq8Aro6In7f3nbFjx8XDf2l3RXnLmP5HTa51CJbCpvnTaX5z5U71Hg8+9Mj45V33l1V2/P67P769pCbpGOCrEfHBZP/LABHx/7dTvg5YGxHt3uepWkstIiZVq24zq7HK3FSbBYySNBJYCkwEzm11GWlURDyf7H4IeJ4S3P00s1QKt8t2PqtFRKOkycBMoB6YFhFzJU0BZkdEAzBZ0snAFmAtJbqe4KRmZmlV8H1qETEDmNHm2FVFnz+Xtk4nNTNLLcsTxJzUzCwlZXreq5OamaWW4ZzmpGZm6ZQ7r7NWnNTMLL0MZzUnNTNLza/zNrNc8T01M8sPr/tpZnnj7qeZ5YZwS83McibDOc1Jzcx2QIazmpOamaVWq5WiyuGkZmapZTelOamZ2Y7IcFZzUjOzVCr1kshqcVIzs3T88K2Z5U2Gc5qTmpmlle2XRHoxYzNLTSpvK12PJkiaL2mBpCu2cf7zkuZJelLSHyXtU6pOJzUzS6XcxdlL5TRJ9cBU4FRgNDBJUtsVvf8KjIuIw4DbKKzS3i4nNTNLrxJZDcYDCyJiYURsBm4BziguEBF/iog3k91HgWGlKnVSM7PUVOZ/wEBJs4u2i4qqGQosLtpfkhzbnguB35eKzQMFZpZainGC1RExbuevp/OAccAJpco6qZlZOoK6ygx+LgWGF+0PS461vlxhhfavACdExKZSlbr7aWY7oCI31WYBoySNlNQdmAg0tLqKdCTwE+D0iFhZTmRuqZlZKpV6SWRENEqaDMwE6oFpETFX0hRgdkQ0AN8GegO3Js/GvRwRp7dXr5OamaVWqUdvI2IGMKPNsauKPp+ctk4nNTNLLcMTCpzUzCy9LE+TclIzs9Sym9Kc1MwspXLnddaKk5qZpeaXRJpZvmQ3pzmpmVl6Gc5pTmpmlpa8RJ6Z5UelZhRUi+d+mlmuuKVmZqlluaXmpGZmqfmRDjPLDz98a2Z5kvWBAic1M0vN3U8zyxW31MwsVzKc05zUzGwHZDirOamZWSqCTE+TUkTUOoatJK0CXqp1HFUwEFhd6yAslbz+ne0TEXvuTAWS7qHw51OO1RExYWeul1amklpeSZpdiQVdreP476zz8txPM8sVJzUzyxUntY5xXa0DsNT8d9ZJ+Z6ameWKW2pmlitOamaWK05qVSRpgqT5khZIuqLW8VhpkqZJWinp6VrHYjvGSa1KJNUDU4FTgdHAJEmjaxuVleF6oEMfFrXKclKrnvHAgohYGBGbgVuAM2ock5UQEQ8Aa2odh+04J7XqGQosLtpfkhwzsypyUjOzXHFSq56lwPCi/WHJMTOrIie16pkFjJI0UlJ3YCLQUOOYzHLPSa1KIqIRmAzMBJ4BpkfE3NpGZaVIuhl4BDhQ0hJJF9Y6JkvH06TMLFfcUjOzXHFSM7NccVIzs1xxUjOzXHFSM7NccVLrRCQ1SXpC0tOSbpW0207Udb2kjyaff9beZHtJJ0o6dgeu8aKkd6w6tL3jbcpsSHmtr0r6QtoYLX+c1DqXjRFxREQcAmwGPlN8UtIOreMaEZ+KiHntFDkRSJ3UzGrBSa3zehB4V9KKelBSAzBPUr2kb0uaJelJSZ8GUMGPkve7/QEY1FKRpPsljUs+T5A0R9LfJP1R0r4UkudlSSvxvZL2lHR7co1Zko5LvruHpHslzZX0M8pYx1vSbyQ9nnznojbnrkmO/1HSnsmx/SXdk3znQUkHVeIP0/LDK7R3QkmL7FTgnuTQGOCQiFiUJIZ1EXGUpB7Aw5LuBY4EDqTwbrfBwDxgWpt69wR+Chyf1DUgItZIuhbYEBHfScrdBFwTEQ9JGkFh1sTBwNXAQxExRdKHgHKexv+H5Bq7ArMk3R4RrwK9gNkRcZmkq5K6J1NYEOUzEfG8pKOBHwPv24E/RsspJ7XOZVdJTySfHwR+TqFb+FhELEqOfwA4rOV+GdAPGAUcD9wcEU3AK5Lu20b97wEeaKkrIrb3XrGTgdHS1oZYX0m9k2t8JPnu3ZLWlvGbLpV0ZvJ5eBLrq0Az8Ovk+A3AHck1jgVuLbp2jzKuYV2Ik1rnsjEijig+kPzP/UbxIeCSiJjZptxpFYyjDnhPRLy1jVjKJulECgnymIh4U9L9QM/tFI/kuq+1/TMwK+Z7avkzE/gnSbsASDpAUi/gAeCc5J7bEOCkbXz3UeB4SSOT7w5Ijq8H+hSVuxe4pGVHUkuSeQA4Nzl2KtC/RKz9gLVJQjuIQkuxRR3Q0to8l0K39nVgkaSPJdeQpMNLXMO6GCe1/PkZhftlc5LFQ35CoUV+J/B8cu6XFN5E0UpErAIuotDV+xtvd/9+C5zZMlAAXAqMSwYi5vH2KOzXKCTFuRS6oS+XiPUeoJukZ4BvUkiqLd4Axie/4X3AlOT4x4ELk/jm4lekWxt+S4eZ5YpbamaWK05qZpYrTmpmlitOamaWK05qZpYrTmpmlitOamaWK/8L0IVxQqnsVzYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwUc8B7YRQFB"
      },
      "source": [
        "grid_search_sgd = GridSearchCV(clf_sgd, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_keyList, y_train_keyList), \n",
        "                           verbose=2)"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAtMCeMIRQRC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd6bf0f9-2a06-48c6-9157-e1b333dad6b1"
      },
      "source": [
        "clf_sgd_keyList = grid_search_sgd.fit(X_train_keyList, y_train_keyList)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.384593, T: 100, Avg. loss: 0.882770\n",
            "Total training time: 0.01 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.385277, T: 200, Avg. loss: 0.867571\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.385449, T: 300, Avg. loss: 0.868199\n",
            "Total training time: 0.03 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.385540, T: 400, Avg. loss: 0.868590\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.385513, T: 500, Avg. loss: 0.868434\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.385537, T: 600, Avg. loss: 0.868108\n",
            "Total training time: 0.06 seconds.\n",
            "Convergence after 6 epochs took 0.07 seconds\n",
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.01, NNZs: 5, Bias: 0.418529, T: 100, Avg. loss: 0.861732\n",
            "Total training time: 0.01 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.419176, T: 200, Avg. loss: 0.867823\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.419194, T: 300, Avg. loss: 0.866453\n",
            "Total training time: 0.03 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.419313, T: 400, Avg. loss: 0.863820\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.419317, T: 500, Avg. loss: 0.864208\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.419279, T: 600, Avg. loss: 0.864752\n",
            "Total training time: 0.06 seconds.\n",
            "Convergence after 6 epochs took 0.07 seconds\n",
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.01, NNZs: 5, Bias: 0.378549, T: 100, Avg. loss: 0.878925\n",
            "Total training time: 0.01 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.378625, T: 200, Avg. loss: 0.872616\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.378846, T: 300, Avg. loss: 0.868824\n",
            "Total training time: 0.03 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.378939, T: 400, Avg. loss: 0.868583\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.378930, T: 500, Avg. loss: 0.869584\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.378926, T: 600, Avg. loss: 0.868538\n",
            "Total training time: 0.07 seconds.\n",
            "Convergence after 6 epochs took 0.08 seconds\n",
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.464792, T: 100, Avg. loss: 1.348763\n",
            "Total training time: 0.01 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.465130, T: 200, Avg. loss: 0.905909\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.465143, T: 300, Avg. loss: 0.903623\n",
            "Total training time: 0.03 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.465028, T: 400, Avg. loss: 0.901965\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.464981, T: 500, Avg. loss: 0.901988\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.464951, T: 600, Avg. loss: 0.902417\n",
            "Total training time: 0.08 seconds.\n",
            "Convergence after 6 epochs took 0.09 seconds\n",
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.453630, T: 100, Avg. loss: 1.050333\n",
            "Total training time: 0.01 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.454077, T: 200, Avg. loss: 0.909253\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.454163, T: 300, Avg. loss: 0.907390\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.454074, T: 400, Avg. loss: 0.904366\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.454082, T: 500, Avg. loss: 0.905163\n",
            "Total training time: 0.10 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.02, NNZs: 5, Bias: -0.454074, T: 600, Avg. loss: 0.904949\n",
            "Total training time: 0.12 seconds.\n",
            "Convergence after 6 epochs took 0.12 seconds\n",
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.6s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "-- Epoch 1\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.390783, T: 126, Avg. loss: 0.887418\n",
            "Total training time: 0.01 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.391011, T: 252, Avg. loss: 0.874227\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.391085, T: 378, Avg. loss: 0.873866\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.391088, T: 504, Avg. loss: 0.873223\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.391140, T: 630, Avg. loss: 0.872630\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.02, NNZs: 5, Bias: 0.391168, T: 756, Avg. loss: 0.873208\n",
            "Total training time: 0.08 seconds.\n",
            "Convergence after 6 epochs took 0.08 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1FVXRScRQcn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3ae00806-e9f5-4b46-b429-a1d18af6ecbc"
      },
      "source": [
        "evaluate_model_keyList(clf_sgd_keyList, model_name=\"clf_sgd_keyList\")"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.5214285714285715\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.0000    0.0000    0.0000        67\n",
            "           1     0.5214    1.0000    0.6854        73\n",
            "\n",
            "    accuracy                         0.5214       140\n",
            "   macro avg     0.2607    0.5000    0.3427       140\n",
            "weighted avg     0.2719    0.5214    0.3574       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.45\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.0000    0.0000    0.0000        33\n",
            "           1     0.4500    1.0000    0.6207        27\n",
            "\n",
            "    accuracy                         0.4500        60\n",
            "   macro avg     0.2250    0.5000    0.3103        60\n",
            "weighted avg     0.2025    0.4500    0.2793        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-Ii8dGa6Sf4"
      },
      "source": [
        "del clf_sgd"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UfgxLqHj46JP"
      },
      "source": [
        "### **4.5 Neural Network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEYfAkeHqcfu"
      },
      "source": [
        "clf_nn = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(15,), random_state=1)"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfM_ewcoT_6x"
      },
      "source": [
        "grid_search_nn = GridSearchCV(clf_nn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "incNx66n_1e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ffc8577-15d0-4c26-8ca5-b96d842c7f59"
      },
      "source": [
        "clf_nn_word = grid_search_nn.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   1.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   1.4s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
            "ABNORMAL_TERMINATION_IN_LNSRCH.\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",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   3.3s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   1.1s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   1.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    8.1s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYyFZP0xyUME",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "outputId": "780281f0-925d-49e2-ceba-dbbeea56517c"
      },
      "source": [
        "evaluate_model_ngrammWord(clf_nn_word, model_name=\"clf_nn_word\")"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.9928571428571429\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    0.9851    0.9925        67\n",
            "           1     0.9865    1.0000    0.9932        73\n",
            "\n",
            "    accuracy                         0.9929       140\n",
            "   macro avg     0.9932    0.9925    0.9928       140\n",
            "weighted avg     0.9930    0.9929    0.9929       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.75\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8214    0.6970    0.7541        33\n",
            "           1     0.6875    0.8148    0.7458        27\n",
            "\n",
            "    accuracy                         0.7500        60\n",
            "   macro avg     0.7545    0.7559    0.7499        60\n",
            "weighted avg     0.7612    0.7500    0.7503        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5_oAXrTUBXe"
      },
      "source": [
        "grid_search_nn = GridSearchCV(clf_nn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_char, y_train_char), \n",
        "                           verbose=2)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOA-K1MOUBfY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44a11dd9-294b-432f-e8fd-9decf8517ec5"
      },
      "source": [
        "\n",
        "clf_nn_char = grid_search_nn.fit(X_train_char, y_train_char)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.3s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.4s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    2.1s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLTitSEeUBo3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "5bc2d06e-ed93-4ed9-e6a8-30e2770b22d5"
      },
      "source": [
        "evaluate_model_ngrammChar(clf_nn_char, model_name=\"clf_nn_char\")"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.44285714285714284\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.4421    0.6269    0.5185        67\n",
            "           1     0.4444    0.2740    0.3390        73\n",
            "\n",
            "    accuracy                         0.4429       140\n",
            "   macro avg     0.4433    0.4504    0.4288       140\n",
            "weighted avg     0.4433    0.4429    0.4249       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.6666666666666666\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6585    0.8182    0.7297        33\n",
            "           1     0.6842    0.4815    0.5652        27\n",
            "\n",
            "    accuracy                         0.6667        60\n",
            "   macro avg     0.6714    0.6498    0.6475        60\n",
            "weighted avg     0.6701    0.6667    0.6557        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfBPPAN6UBx4"
      },
      "source": [
        "grid_search_nn = GridSearchCV(clf_nn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_keyList, y_train_keyList), \n",
        "                           verbose=2)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmfCPgZbUB6T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72500a44-034b-485d-f245-f33abd1350af"
      },
      "source": [
        "\n",
        "clf_nn_keyList = grid_search_nn.fit(X_train_keyList, y_train_keyList)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: 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",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: 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",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: 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",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: 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",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.7s finished\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: 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",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WeKzTGuUCED",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "75783316-d818-49e9-df63-66eb57c38ea6"
      },
      "source": [
        "evaluate_model_keyList(clf_nn_keyList, model_name=\"clf_nn_keyList\")"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.5928571428571429\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.5781    0.5522    0.5649        67\n",
            "           1     0.6053    0.6301    0.6174        73\n",
            "\n",
            "    accuracy                         0.5929       140\n",
            "   macro avg     0.5917    0.5912    0.5912       140\n",
            "weighted avg     0.5923    0.5929    0.5923       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.5666666666666667\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6129    0.5758    0.5938        33\n",
            "           1     0.5172    0.5556    0.5357        27\n",
            "\n",
            "    accuracy                         0.5667        60\n",
            "   macro avg     0.5651    0.5657    0.5647        60\n",
            "weighted avg     0.5699    0.5667    0.5676        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2lQ9qrayUQK"
      },
      "source": [
        "del clf_nn"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8AqkvOlAWOf"
      },
      "source": [
        "### **4.6 KNeighborsClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lv3JDSb8EQCu"
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors= 18)"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7n-Gx4xU9AU"
      },
      "source": [
        "grid_search_knn = GridSearchCV(knn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGzxAEiLAzaq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d228828b-c577-4f2f-8ca4-b5daa6459c97"
      },
      "source": [
        "\n",
        "knn_word = grid_search_knn.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.7s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76lbTBq5EQCv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "09ed4fde-6ba8-4180-9bde-ca17786e95ca"
      },
      "source": [
        "evaluate_model_ngrammWord(knn_word, model_name=\"knn_word\")"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.6214285714285714\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6842    0.3881    0.4952        67\n",
            "           1     0.5980    0.8356    0.6971        73\n",
            "\n",
            "    accuracy                         0.6214       140\n",
            "   macro avg     0.6411    0.6118    0.5962       140\n",
            "weighted avg     0.6393    0.6214    0.6005       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.5666666666666667\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7692    0.3030    0.4348        33\n",
            "           1     0.5106    0.8889    0.6486        27\n",
            "\n",
            "    accuracy                         0.5667        60\n",
            "   macro avg     0.6399    0.5960    0.5417        60\n",
            "weighted avg     0.6529    0.5667    0.5310        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dk2jpzHwU-UV"
      },
      "source": [
        "grid_search_knn = GridSearchCV(knn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_char, y_train_char), \n",
        "                           verbose=2)"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woV5TuSTU-dh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f387e77-697d-411a-960b-e15d750b82a7"
      },
      "source": [
        "\n",
        "knn_char = grid_search_knn.fit(X_train_char, y_train_char)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.2s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.2s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.8s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fd0rCvmU-ne",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        },
        "outputId": "f1a7087d-8553-4d95-df8a-a9002e14a38a"
      },
      "source": [
        "evaluate_model_ngrammChar(knn_char, model_name=\"knn_char\")"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.6142857142857143\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6226    0.4925    0.5500        67\n",
            "           1     0.6092    0.7260    0.6625        73\n",
            "\n",
            "    accuracy                         0.6143       140\n",
            "   macro avg     0.6159    0.6093    0.6062       140\n",
            "weighted avg     0.6156    0.6143    0.6087       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.7\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7419    0.6970    0.7188        33\n",
            "           1     0.6552    0.7037    0.6786        27\n",
            "\n",
            "    accuracy                         0.7000        60\n",
            "   macro avg     0.6986    0.7003    0.6987        60\n",
            "weighted avg     0.7029    0.7000    0.7007        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsKV8gcrU-yN"
      },
      "source": [
        "grid_search_knn = GridSearchCV(knn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_keyList, y_train_keyList), \n",
        "                           verbose=2)"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPF3L2JFU-8M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60323f6d-d9ea-4972-fb33-14f280ab20f1"
      },
      "source": [
        "\n",
        "knn_keyList = grid_search_knn.fit(X_train_keyList, y_train_keyList)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.0s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.0s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.0s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.0s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.0s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8N_k4X3SU_Ek",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "add5b520-87f9-48bd-de77-8aa0aecfab14"
      },
      "source": [
        "evaluate_model_keyList(knn_keyList, model_name=\"knn_keyList\")"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.6285714285714286\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.5843    0.7761    0.6667        67\n",
            "           1     0.7059    0.4932    0.5806        73\n",
            "\n",
            "    accuracy                         0.6286       140\n",
            "   macro avg     0.6451    0.6346    0.6237       140\n",
            "weighted avg     0.6477    0.6286    0.6218       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.5666666666666667\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6000    0.6364    0.6176        33\n",
            "           1     0.5200    0.4815    0.5000        27\n",
            "\n",
            "    accuracy                         0.5667        60\n",
            "   macro avg     0.5600    0.5589    0.5588        60\n",
            "weighted avg     0.5640    0.5667    0.5647        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUn_xfDEBaSb"
      },
      "source": [
        "del knn"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5G3DhkMNqrn"
      },
      "source": [
        "# **5. Other Features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEMyD2APTq0o"
      },
      "source": [
        "def other_features(tweet):\n",
        "    \"\"\"This function takes a string and returns a list of features.\n",
        "    These include Sentiment scores, Text and Readability scores,\n",
        "    as well as Twitter specific features\"\"\"\n",
        "    words = basic_tokenize(tweet) #Get text only\n",
        "    syllables = []\n",
        "    for word in words:\n",
        "      syllables.append(textstat.syllable_count(word))\n",
        "\n",
        "    num_chars = sum(len(w) for w in words)\n",
        "    num_chars_total = len(tweet)\n",
        "    #num_terms = len(tweets.split())\n",
        "    num_words = len(words)\n",
        "    num_unique_terms = len(set(words))\n",
        "\n",
        "    avg_syl = []\n",
        "    for s in syllables:\n",
        "      avg_syl.append(int(s+0.001)/int(num_words+0.001))\n",
        "    avg = sum(avg_syl) / len(avg_syl)\n",
        "    ###Modified FK grade, where avg words per sentence is just num words/1\n",
        "    FKRA = round(float(0.39 * float(num_words)/1.0) + float(11.8 * avg) - 15.59,1)\n",
        "    ##Modified FRE score, where sentence fixed to 1\n",
        "    FRE = round(206.835 - 1.015*(float(num_words)/1.0) - (84.6*float(avg)),2)\n",
        "\n",
        "    twitter_objs = []\n",
        "    retweet = 0\n",
        "    if \"rt\" in words:\n",
        "      retweet = 1\n",
        "    features = [FKRA, FRE, avg, num_chars, num_chars_total, num_words,\n",
        "                num_unique_terms,  retweet]\n",
        "        #features = pandas.DataFrame(features)\n",
        "    return features\n",
        "\n",
        "\n",
        "def get_feature_array(tweets):\n",
        "  feats = []\n",
        "  for t in tweets:\n",
        "    feats.append(other_features(t))\n",
        "  return np.array(feats)\n",
        "\n",
        "\n",
        "  \n",
        "other_features_names = [\"FKRA\", \"FRE\",\"avg_syl_per_word\", \"num_chars\", \"num_chars_total\", \\\n",
        "                        \"num_words\", \"num_unique_words\", \"is_retweet\"]\n",
        "\n",
        "                       \n",
        "\n",
        "feats = get_feature_array(tweets)"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_M05gIqmUCFp"
      },
      "source": [
        "### **5.1 Adding New Features to Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHVrujFCUJRC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0bc3cb4-f4fc-47f8-9e3e-bc8a3c35030d"
      },
      "source": [
        "M_new = np.concatenate([tfidf_words,pos,feats],axis=1)\n",
        "M_new.shape"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 11354)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJbEphrJV_Sl"
      },
      "source": [
        "X_new = pd.DataFrame(M_new)\n",
        "y_new = data['class'].astype(int)\n",
        "X_train_word, X_test_word, y_train_word, y_test_word = train_test_split(X_new, y_new, test_size = 0.3, random_state = 42, shuffle=True)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0l_HNnmBWWkT"
      },
      "source": [
        "c_lr = LogisticRegression(penalty='l2',\n",
        "                            tol=1e-4,\n",
        "                            C=5.0,\n",
        "                            fit_intercept=True,\n",
        "                            class_weight='balanced',\n",
        "                            random_state=0,\n",
        "                            solver='lbfgs',\n",
        "                            max_iter=100,\n",
        "                            multi_class='auto',\n",
        "                            verbose=1,\n",
        "                            n_jobs=-1)"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ob7e5oCrWbPd"
      },
      "source": [
        "param_grid = [{}]\n",
        "grid_search_lr = GridSearchCV(c_lr, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4vAmSI-We4B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02756e5d-a7ed-4b47-dc86-e2ca725332f1"
      },
      "source": [
        "c_lr.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    0.9s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=5.0, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=1,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7q7eIb3WiBa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "outputId": "2dee79db-9eb7-4c51-ad10-2805da02980a"
      },
      "source": [
        "evaluate_model_ngrammWord(c_lr, model_name=\"lr\")"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 1.0\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    1.0000    1.0000        67\n",
            "           1     1.0000    1.0000    1.0000        73\n",
            "\n",
            "    accuracy                         1.0000       140\n",
            "   macro avg     1.0000    1.0000    1.0000       140\n",
            "weighted avg     1.0000    1.0000    1.0000       140\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.6833333333333333\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7188    0.6970    0.7077        33\n",
            "           1     0.6429    0.6667    0.6545        27\n",
            "\n",
            "    accuracy                         0.6833        60\n",
            "   macro avg     0.6808    0.6818    0.6811        60\n",
            "weighted avg     0.6846    0.6833    0.6838        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avffFQxr_Y3i"
      },
      "source": [
        "### **5.2 TF-IDF without stopwords**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuYH5P5h_Xsi"
      },
      "source": [
        "tfidf_word_no_stopwords = TfidfVectorizer(\n",
        "    analyzer='word',\n",
        "    preprocessor=preprocess,\n",
        "    ngram_range=(1, 3),\n",
        "    use_idf=True,\n",
        "    smooth_idf=False,\n",
        "    norm=None,\n",
        "    decode_error='replace',\n",
        "    max_features=10000,\n",
        "    min_df=5,\n",
        "    max_df=0.75)"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTqkaPzz_5dk"
      },
      "source": [
        "#Construct tfidf matrix and get relevant scores\n",
        "tfidf =tfidf_word_no_stopwords.fit_transform(tweets).toarray()\n",
        "vocab = {v:i for i, v in enumerate(tfidf_word_no_stopwords.get_feature_names())}\n",
        "idf_vals = tfidf_word_no_stopwords.idf_\n",
        "idf_dict = {i:idf_vals[i] for i in vocab.values()} #keys are indices; values are IDF scores\n"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX8U6lyW_5o4"
      },
      "source": [
        "array = np.concatenate([tfidf,pos],axis=1)"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HCa_yzV_5y-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "928cb399-b63a-4dcf-f105-589a0fb833d9"
      },
      "source": [
        "array.shape"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 14735)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mzrrt-LoAmL_"
      },
      "source": [
        "X_new = pd.DataFrame(array)\n",
        "y_new = data['class'].astype(int)\n",
        "X_train_word, X_test_word, y_train_word, y_test_word = train_test_split(X_new, y_new, test_size = 0.2, random_state = 42, shuffle=True)"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0FH8DvkAvJv"
      },
      "source": [
        "clf_nn = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(15,), random_state=1)\n",
        "\n",
        "grid_search_nn = GridSearchCV(clf_nn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)\n",
        "\n",
        "\n"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkviYvxrAvPd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f34114d1-0187-468a-c06d-01b54daece5d"
      },
      "source": [
        "clf_nn_words = grid_search_nn.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   2.0s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   2.3s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   3.0s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   1.5s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   2.5s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   11.5s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDZYfd-pAvVA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "outputId": "808b1557-cfeb-4bda-b79b-54bdebb8ed07"
      },
      "source": [
        "evaluate_model_ngrammWord(clf_nn_words, model_name=\"clf_nn_words\")"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 1.0\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    1.0000    1.0000        78\n",
            "           1     1.0000    1.0000    1.0000        82\n",
            "\n",
            "    accuracy                         1.0000       160\n",
            "   macro avg     1.0000    1.0000    1.0000       160\n",
            "weighted avg     1.0000    1.0000    1.0000       160\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.75\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.7500    0.8182    0.7826        22\n",
            "           1     0.7500    0.6667    0.7059        18\n",
            "\n",
            "    accuracy                         0.7500        40\n",
            "   macro avg     0.7500    0.7424    0.7442        40\n",
            "weighted avg     0.7500    0.7500    0.7481        40\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5B5xWgLEsW_6"
      },
      "source": [
        ""
      ],
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXr0nSA8sXQW"
      },
      "source": [
        "clf_rf = RandomForestClassifier(n_estimators=500,\n",
        "                                criterion=\"gini\",\n",
        "                                max_depth=None, \n",
        "                                min_samples_split=2,\n",
        "                                min_samples_leaf=10, \n",
        "                                max_features=\"auto\", \n",
        "                                n_jobs=-1,\n",
        "                                verbose=1,\n",
        "                                random_state=0,\n",
        "                                class_weight='balanced')"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaXdoukasXc-"
      },
      "source": [
        "grid_search_rf = GridSearchCV(clf_rf, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sc0LppbsXl2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e36cdda2-d7f2-490f-c11f-b545e37df731"
      },
      "source": [
        "clf_rf_words= grid_search_rf.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.9s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.5s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.5s\n",
            "[CV]  ................................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.0s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................................. , total=   1.6s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    7.8s finished\n",
            "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:    0.4s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:    1.0s\n",
            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.1s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQlwYTXhsq0g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6160a519-b3cc-44d1-c197-98099a4f6129"
      },
      "source": [
        "evaluate_model_ngrammWord(clf_rf_words, model_name=\"clf_rf_words\")"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.99375\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9873    1.0000    0.9936        78\n",
            "           1     1.0000    0.9878    0.9939        82\n",
            "\n",
            "    accuracy                         0.9938       160\n",
            "   macro avg     0.9937    0.9939    0.9937       160\n",
            "weighted avg     0.9938    0.9938    0.9938       160\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Testing Set\n",
            "Accuracy: 0.65\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6667    0.7273    0.6957        22\n",
            "           1     0.6250    0.5556    0.5882        18\n",
            "\n",
            "    accuracy                         0.6500        40\n",
            "   macro avg     0.6458    0.6414    0.6419        40\n",
            "weighted avg     0.6479    0.6500    0.6473        40\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwhqwGT2yiL_"
      },
      "source": [
        ""
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIe8MhxNyiZD"
      },
      "source": [
        "clf_sgd = make_pipeline(StandardScaler(with_mean=False),\n",
        "                        SGDClassifier(loss='hinge',\n",
        "                                      penalty='l2',\n",
        "                                      alpha=30,\n",
        "                                      max_iter=1000, \n",
        "                                      tol=1e-3,\n",
        "                                      shuffle=True,\n",
        "                                      verbose=1,\n",
        "                                      n_jobs=-1,\n",
        "                                      random_state=0,\n",
        "                                      learning_rate='optimal',\n",
        "                                      early_stopping=True,\n",
        "                                      class_weight='balanced'))\n",
        "\n"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Tr-HlJvyihv"
      },
      "source": [
        "grid_search_sgd = GridSearchCV(clf_sgd, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)\n"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsbVg1Nhyimv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f8255cc-d6b7-482e-efa6-fa817566a99c"
      },
      "source": [
        "clf_sgd = grid_search_sgd.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.33, NNZs: 14626, Bias: -0.430110, T: 115, Avg. loss: 1.472512\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.19, NNZs: 14717, Bias: -0.430494, T: 230, Avg. loss: 0.198127\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 14725, Bias: -0.430430, T: 345, Avg. loss: 0.022475\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.13, NNZs: 14725, Bias: -0.430424, T: 460, Avg. loss: 0.036905\n",
            "Total training time: 0.10 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.12, NNZs: 14725, Bias: -0.430486, T: 575, Avg. loss: 0.050098\n",
            "Total training time: 0.14 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14725, Bias: -0.430410, T: 690, Avg. loss: 0.043326\n",
            "Total training time: 0.17 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.11, NNZs: 14725, Bias: -0.430394, T: 805, Avg. loss: 0.025517\n",
            "Total training time: 0.19 seconds.\n",
            "-- Epoch 8\n",
            "Norm: 0.11, NNZs: 14725, Bias: -0.430341, T: 920, Avg. loss: 0.023323\n",
            "Total training time: 0.21 seconds.\n",
            "-- Epoch 9\n",
            "Norm: 0.11, NNZs: 14725, Bias: -0.430306, T: 1035, Avg. loss: 0.026430\n",
            "Total training time: 0.23 seconds.\n",
            "Convergence after 9 epochs took 0.24 seconds\n",
            "[CV] ................................................. , total=   0.4s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 14482, Bias: -0.433745, T: 115, Avg. loss: 1.083786\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.19, NNZs: 14707, Bias: -0.434625, T: 230, Avg. loss: 0.290140\n",
            "Total training time: 0.05 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 14713, Bias: -0.433753, T: 345, Avg. loss: 0.048980\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.12, NNZs: 14714, Bias: -0.434113, T: 460, Avg. loss: 0.033279\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.12, NNZs: 14714, Bias: -0.433861, T: 575, Avg. loss: 0.042928\n",
            "Total training time: 0.11 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14714, Bias: -0.433920, T: 690, Avg. loss: 0.043826\n",
            "Total training time: 0.13 seconds.\n",
            "Convergence after 6 epochs took 0.13 seconds\n",
            "[CV] ................................................. , total=   0.3s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 14551, Bias: -0.417617, T: 115, Avg. loss: 1.866547\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.19, NNZs: 14717, Bias: -0.418489, T: 230, Avg. loss: 0.271085\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 14718, Bias: -0.418317, T: 345, Avg. loss: 0.034530\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.12, NNZs: 14722, Bias: -0.418300, T: 460, Avg. loss: 0.035100\n",
            "Total training time: 0.11 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.12, NNZs: 14721, Bias: -0.418354, T: 575, Avg. loss: 0.043267\n",
            "Total training time: 0.14 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14722, Bias: -0.418267, T: 690, Avg. loss: 0.033799\n",
            "Total training time: 0.16 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.11, NNZs: 14722, Bias: -0.418172, T: 805, Avg. loss: 0.024687\n",
            "Total training time: 0.18 seconds.\n",
            "Convergence after 7 epochs took 0.19 seconds\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.35, NNZs: 14682, Bias: -0.404938, T: 115, Avg. loss: 6.688773\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.19, NNZs: 14715, Bias: -0.405071, T: 230, Avg. loss: 0.123798\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.13, NNZs: 14718, Bias: -0.405166, T: 345, Avg. loss: 0.011960\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.12, NNZs: 14718, Bias: -0.405169, T: 460, Avg. loss: 0.022339\n",
            "Total training time: 0.07 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.12, NNZs: 14718, Bias: -0.405076, T: 575, Avg. loss: 0.055086\n",
            "Total training time: 0.09 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14718, Bias: -0.405058, T: 690, Avg. loss: 0.040007\n",
            "Total training time: 0.11 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.11, NNZs: 14718, Bias: -0.405035, T: 805, Avg. loss: 0.015830\n",
            "Total training time: 0.13 seconds.\n",
            "-- Epoch 8\n",
            "Norm: 0.11, NNZs: 14718, Bias: -0.405094, T: 920, Avg. loss: 0.023928\n",
            "Total training time: 0.15 seconds.\n",
            "-- Epoch 9\n",
            "Norm: 0.11, NNZs: 14718, Bias: -0.405111, T: 1035, Avg. loss: 0.021230\n",
            "Total training time: 0.17 seconds.\n",
            "Convergence after 9 epochs took 0.18 seconds\n",
            "[CV] ................................................. , total=   0.4s\n",
            "[CV]  ................................................................\n",
            "-- Epoch 1\n",
            "Norm: 0.33, NNZs: 14633, Bias: -0.399190, T: 115, Avg. loss: 8.379515\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.19, NNZs: 14712, Bias: -0.399639, T: 230, Avg. loss: 0.188719\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 14719, Bias: -0.398806, T: 345, Avg. loss: 0.041154\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.12, NNZs: 14719, Bias: -0.398926, T: 460, Avg. loss: 0.020546\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.12, NNZs: 14719, Bias: -0.398783, T: 575, Avg. loss: 0.065909\n",
            "Total training time: 0.10 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14719, Bias: -0.398706, T: 690, Avg. loss: 0.038827\n",
            "Total training time: 0.13 seconds.\n",
            "-- Epoch 7\n",
            "Norm: 0.11, NNZs: 14719, Bias: -0.398643, T: 805, Avg. loss: 0.024428\n",
            "Total training time: 0.16 seconds.\n",
            "Convergence after 7 epochs took 0.17 seconds\n",
            "[CV] ................................................. , total=   0.4s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.9s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "-- Epoch 1\n",
            "Norm: 0.30, NNZs: 14719, Bias: 0.399045, T: 144, Avg. loss: 1.441160\n",
            "Total training time: 0.02 seconds.\n",
            "-- Epoch 2\n",
            "Norm: 0.18, NNZs: 14731, Bias: 0.398204, T: 288, Avg. loss: 0.185306\n",
            "Total training time: 0.04 seconds.\n",
            "-- Epoch 3\n",
            "Norm: 0.14, NNZs: 14731, Bias: 0.398240, T: 432, Avg. loss: 0.045801\n",
            "Total training time: 0.06 seconds.\n",
            "-- Epoch 4\n",
            "Norm: 0.13, NNZs: 14731, Bias: 0.397994, T: 576, Avg. loss: 0.069907\n",
            "Total training time: 0.08 seconds.\n",
            "-- Epoch 5\n",
            "Norm: 0.13, NNZs: 14731, Bias: 0.397723, T: 720, Avg. loss: 0.058817\n",
            "Total training time: 0.11 seconds.\n",
            "-- Epoch 6\n",
            "Norm: 0.12, NNZs: 14731, Bias: 0.397649, T: 864, Avg. loss: 0.039111\n",
            "Total training time: 0.14 seconds.\n",
            "Convergence after 6 epochs took 0.15 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jxd18N8yirj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "c90853c2-9acb-4f28-a0f5-059bfcbefb1d"
      },
      "source": [
        "evaluate_model_ngrammWord(clf_sgd, model_name=\"sgd\")"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.98125\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9870    0.9744    0.9806        78\n",
            "           1     0.9759    0.9878    0.9818        82\n",
            "\n",
            "    accuracy                         0.9812       160\n",
            "   macro avg     0.9815    0.9811    0.9812       160\n",
            "weighted avg     0.9813    0.9812    0.9812       160\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.6\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.6071    0.7727    0.6800        22\n",
            "           1     0.5833    0.3889    0.4667        18\n",
            "\n",
            "    accuracy                         0.6000        40\n",
            "   macro avg     0.5952    0.5808    0.5733        40\n",
            "weighted avg     0.5964    0.6000    0.5840        40\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "be3iY9qxyi3f"
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors= 18)\n",
        "grid_search_knn = GridSearchCV(knn, \n",
        "                           param_grid,\n",
        "                           cv=StratifiedKFold(n_splits=5, shuffle=True,\n",
        "                                              random_state=42).split(X_train_word, y_train_word), \n",
        "                           verbose=2)\n"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3oIdhjlyi9F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b17f940c-f59c-48b6-bef7-9d7cbfb7bddd"
      },
      "source": [
        "\n",
        "knn = grid_search_knn.fit(X_train_word, y_train_word)"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.2s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.2s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.2s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.2s\n",
            "[CV]  ................................................................\n",
            "[CV] ................................................. , total=   0.2s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.1s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LOHJt9syjB-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        },
        "outputId": "c47a93da-174d-4ef3-f4e2-984b14d48275"
      },
      "source": [
        "evaluate_model_ngrammWord(knn, model_name=\"knn\")"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set\n",
            "Accuracy: 0.58125\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.8235    0.1795    0.2947        78\n",
            "           1     0.5524    0.9634    0.7022        82\n",
            "\n",
            "    accuracy                         0.5813       160\n",
            "   macro avg     0.6880    0.5715    0.4985       160\n",
            "weighted avg     0.6846    0.5813    0.5036       160\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Set\n",
            "Accuracy: 0.525\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     1.0000    0.1364    0.2400        22\n",
            "           1     0.4865    1.0000    0.6545        18\n",
            "\n",
            "    accuracy                         0.5250        40\n",
            "   macro avg     0.7432    0.5682    0.4473        40\n",
            "weighted avg     0.7689    0.5250    0.4265        40\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}