NLP_project3.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "!source env/bin/activate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEOgEkSOG2s3"
      },
      "source": [
        "**Importing libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "kCQMxr852yrE"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "from nltk.tokenize import word_tokenize, sent_tokenize, wordpunct_tokenize, TreebankWordTokenizer, TweetTokenizer, MWETokenizer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk import ngrams\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.stem import PorterStemmer\n",
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from os import path\n",
        "sns.set()\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, precision_score, f1_score\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import calendar\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "%matplotlib inline\n",
        "import time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wpKD_So7G-FY"
      },
      "source": [
        "**Importing CSV files**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LeRSkBPZ28rQ",
        "outputId": "37704462-d8ca-4f39-b7d4-f2f9cd01912b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_3767946/2147414233.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
            "\n",
            "\n",
            "  train_df = pd.read_csv(\"project_training_data_with_class_labels.csv\", error_bad_lines=False)\n",
            "b'Skipping line 36926: expected 4 fields, saw 5\\nSkipping line 72352: expected 4 fields, saw 5\\nSkipping line 104352: expected 4 fields, saw 5\\n'\n",
            "b'Skipping line 202373: expected 4 fields, saw 5\\nSkipping line 207923: expected 4 fields, saw 5\\n'\n",
            "b'Skipping line 309632: expected 4 fields, saw 5\\n'\n",
            "b'Skipping line 409346: expected 4 fields, saw 5\\n'\n",
            "b'Skipping line 651860: expected 4 fields, saw 5\\n'\n",
            "b'Skipping line 664720: expected 4 fields, saw 5\\nSkipping line 734905: expected 4 fields, saw 5\\n'\n",
            "b'Skipping line 790510: expected 4 fields, saw 5\\n'\n"
          ]
        }
      ],
      "source": [
        "train_df = pd.read_csv(\"project_training_data_with_class_labels.csv\", error_bad_lines=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Hk_ufL1G3Etx",
        "outputId": "05468c46-6398-4018-cf51-53e83126fe38"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Parent Comments</th>\n",
              "      <th>Class Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ocxtitan</td>\n",
              "      <td>Central Illinois</td>\n",
              "      <td>Jesus; where do you live?</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LeChuckly</td>\n",
              "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
              "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>throwitskrub8</td>\n",
              "      <td>But then again; you have to consider that all ...</td>\n",
              "      <td>agree to that part.It can also mean that gujra...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fresherthanyouuu</td>\n",
              "      <td>ughhhhh</td>\n",
              "      <td>If a guy told you he doesn't use social media ...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>_kushagra</td>\n",
              "      <td>I should've put the</td>\n",
              "      <td>No; it's just a programming bug. After all; th...</td>\n",
              "      <td>sarcastic</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>808085</th>\n",
              "      <td>Arectarius</td>\n",
              "      <td>50% hp/def elgif; impiety orb; bourn jewel che...</td>\n",
              "      <td>Hate to break it to you; but Gloomy can't do a...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>808086</th>\n",
              "      <td>dale1v</td>\n",
              "      <td>Rubicon Mango.</td>\n",
              "      <td>Hey Reddit; if you could only drink one flavor...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>808087</th>\n",
              "      <td>Semyonov</td>\n",
              "      <td>Essentially GAP insurance right?</td>\n",
              "      <td>I'm sure you can pay for it. Most insurance pl...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>808088</th>\n",
              "      <td>futalover99</td>\n",
              "      <td>Yeah; but sometimes that weird kid keeps tryin...</td>\n",
              "      <td>Ive been subscribed to that for a while; and a...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>808089</th>\n",
              "      <td>SuperSpartacus</td>\n",
              "      <td>Petroleum doesn't really require a terroir lik...</td>\n",
              "      <td>It must be very different for oil then. I know...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>808090 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      ID                                           Comments  \\\n",
              "0               ocxtitan                                   Central Illinois   \n",
              "1              LeChuckly  To think - CNN used to be the acronym synonymo...   \n",
              "2          throwitskrub8  But then again; you have to consider that all ...   \n",
              "3       fresherthanyouuu                                            ughhhhh   \n",
              "4              _kushagra                                I should've put the   \n",
              "...                  ...                                                ...   \n",
              "808085        Arectarius  50% hp/def elgif; impiety orb; bourn jewel che...   \n",
              "808086            dale1v                                     Rubicon Mango.   \n",
              "808087          Semyonov                   Essentially GAP insurance right?   \n",
              "808088       futalover99  Yeah; but sometimes that weird kid keeps tryin...   \n",
              "808089    SuperSpartacus  Petroleum doesn't really require a terroir lik...   \n",
              "\n",
              "                                          Parent Comments  Class Labels   \n",
              "0                               Jesus; where do you live?  non-sarcastic  \n",
              "1       Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic  \n",
              "2       agree to that part.It can also mean that gujra...  non-sarcastic  \n",
              "3       If a guy told you he doesn't use social media ...  non-sarcastic  \n",
              "4       No; it's just a programming bug. After all; th...      sarcastic  \n",
              "...                                                   ...            ...  \n",
              "808085  Hate to break it to you; but Gloomy can't do a...      sarcastic  \n",
              "808086  Hey Reddit; if you could only drink one flavor...  non-sarcastic  \n",
              "808087  I'm sure you can pay for it. Most insurance pl...  non-sarcastic  \n",
              "808088  Ive been subscribed to that for a while; and a...  non-sarcastic  \n",
              "808089  It must be very different for oil then. I know...  non-sarcastic  \n",
              "\n",
              "[808090 rows x 4 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02CAARD7AhJv"
      },
      "source": [
        "**Cleaning the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "D_ytyOe33aui"
      },
      "outputs": [],
      "source": [
        "df1 = train_df[train_df.isna().any(axis=1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "vGB6UGwDAYsr",
        "outputId": "88d96206-00ac-4605-9a9b-bb51d8141d52"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Parent Comments</th>\n",
              "      <th>Class Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8917</th>\n",
              "      <td>self_defeating</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Because we have chosen them as sources of mind...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9396</th>\n",
              "      <td>Khvostov_7g-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>woah; thanks.</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9469</th>\n",
              "      <td>roybringus</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I am just shocked an upstanding group like thi...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39976</th>\n",
              "      <td>MObaid27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bebe is a beautiful &amp;amp; wonderfully gifted f...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41499</th>\n",
              "      <td>abdiascoronel</td>\n",
              "      <td>Said the bruised ego.,Pretending to be an idi...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56037</th>\n",
              "      <td>texasboyf150</td>\n",
              "      <td>Going on about they lives...that's an opinion ...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84428</th>\n",
              "      <td>CenaW</td>\n",
              "      <td>NaN</td>\n",
              "      <td>thanks Democrats.</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88537</th>\n",
              "      <td>IndigenousOres</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lol you didn't got it</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104232</th>\n",
              "      <td>roguetroll</td>\n",
              "      <td>But it's wint... *falls over leg* DAMN IT!,*st...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127616</th>\n",
              "      <td>mattindustries</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Droids are just objectively better. Doesn't ta...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134668</th>\n",
              "      <td>Ps3Dave</td>\n",
              "      <td>I don't care how loud your screams are; you're...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161395</th>\n",
              "      <td>Crusty_white_sock</td>\n",
              "      <td>You're nice; but the reason I don't want to da...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162258</th>\n",
              "      <td>butteryrice123</td>\n",
              "      <td>DLC 5 has been announced as a PS4 exclusive,De...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172361</th>\n",
              "      <td>lantech</td>\n",
              "      <td>NaN</td>\n",
              "      <td>We need a sarcasm font. Or perhaps I'm a retard.</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175541</th>\n",
              "      <td>XHo-Oh</td>\n",
              "      <td>This new Shugesh form will have its own rarity...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207469</th>\n",
              "      <td>So12rovv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Will do. EU or NA?</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231580</th>\n",
              "      <td>jhn_rob</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes; using game features is an exploit</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278199</th>\n",
              "      <td>VanDriver1</td>\n",
              "      <td>It will be better when it's one lane each way ...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285272</th>\n",
              "      <td>chamclouder</td>\n",
              "      <td>I cant believe Team: goalie with a bridge cont...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289901</th>\n",
              "      <td>Synsc</td>\n",
              "      <td>NaN</td>\n",
              "      <td>House \"of\" Commons</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293003</th>\n",
              "      <td>ShyBiDude89</td>\n",
              "      <td>It's ok if I do it; it's those other people yo...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312887</th>\n",
              "      <td>BrightIdeaDude</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SERIOUSLY?! THIS IS THE FREE ITEM I GOT FOR PL...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334963</th>\n",
              "      <td>sailingonward</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Are you proposing that the stock market should...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348402</th>\n",
              "      <td>atlacatl</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Doesn't matter if that experience is garbage</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349834</th>\n",
              "      <td>RAGGA_MUFFIN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Uh; what about that looks like a ghetto to you?</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>364333</th>\n",
              "      <td>qubi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No kidding</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>364928</th>\n",
              "      <td>Wemorg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Your favourite lol joke?</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365270</th>\n",
              "      <td>Mdgt_Pope</td>\n",
              "      <td>could've,They could of played 3 bad teams in t...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366544</th>\n",
              "      <td>DJGow</td>\n",
              "      <td>NaN</td>\n",
              "      <td>And thus an universal sarcasm indicator is born.</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>367749</th>\n",
              "      <td>kerplunkerfish</td>\n",
              "      <td>We were just outside Barstow; on the edge of t...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379882</th>\n",
              "      <td>New-Hitler-Hillary</td>\n",
              "      <td>$10;000 cheap,Cheap V-8 Turbo Build! LS-Turbo ...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401222</th>\n",
              "      <td>Trout_Tickler</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nah; that's Tux</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410604</th>\n",
              "      <td>Puffin_fan</td>\n",
              "      <td>give everyone the power to share anything with...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414019</th>\n",
              "      <td>Ryan94985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Even if she is under 18 it isn't pornography. ...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421408</th>\n",
              "      <td>Kiux97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Because with the newer cell it looks stupid?</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>442200</th>\n",
              "      <td>havok0159</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450895</th>\n",
              "      <td>calo9000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valiant sir stands up against the console dron...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487177</th>\n",
              "      <td>TinHao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>integrity?</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>491552</th>\n",
              "      <td>Knorikus</td>\n",
              "      <td>NaN</td>\n",
              "      <td>There probably won't be master chief Might be ...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>507437</th>\n",
              "      <td>ellen_pao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>very true; not all girls like the \"bad boy\"</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>509766</th>\n",
              "      <td>Jobcv314</td>\n",
              "      <td>Oh; sorry your honor; I thought the law was *o...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531255</th>\n",
              "      <td>The_Leedle</td>\n",
              "      <td>But EDM isn't music; it's just sounds *because...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578745</th>\n",
              "      <td>Nessie</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I cant tell if this is sarcasm or not; but so ...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>582988</th>\n",
              "      <td>Ziffelbrixx</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kinda wish the article showed the bridges on t...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>587198</th>\n",
              "      <td>SodokuIRL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LPL shitshow; EU LCS shitshow. What isn't a sh...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>616317</th>\n",
              "      <td>iloveyoujesuschriist</td>\n",
              "      <td>Lend-Lease aid did not arrive in sufficient qu...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>634685</th>\n",
              "      <td>MrOxi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Make sure he doesn't overdose! Too many people...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>635051</th>\n",
              "      <td>TheCavalierLads</td>\n",
              "      <td>NaN</td>\n",
              "      <td>But then no one sees how many active friends y...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638355</th>\n",
              "      <td>RealityIsMyReligion</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pharmacists of Reddit; how much do you judge p...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638916</th>\n",
              "      <td>curbstickle</td>\n",
              "      <td>NaN</td>\n",
              "      <td>if only there was a way to convey sarcasm thro...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653324</th>\n",
              "      <td>Thief39</td>\n",
              "      <td>Something like that',Great minds think alike. ...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>653687</th>\n",
              "      <td>dont_wear_a_C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Thu 2016-08-04 accessing vault............unlo...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>654055</th>\n",
              "      <td>FieldDay</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Fuck you too; buddy.</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711972</th>\n",
              "      <td>waldo686</td>\n",
              "      <td>I don't think we; as law enforcement; can supp...</td>\n",
              "      <td>sarcastic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>739776</th>\n",
              "      <td>insanePower</td>\n",
              "      <td>NaN</td>\n",
              "      <td>jiji still shitstomps solo q against NALCS mid...</td>\n",
              "      <td>non-sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750918</th>\n",
              "      <td>PoochDoobie</td>\n",
              "      <td>NaN</td>\n",
              "      <td>lol I can't believe that your cat slept throug...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753557</th>\n",
              "      <td>Dalmazz</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Best region?</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>778552</th>\n",
              "      <td>wedontlikespaces</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What symbol denotes suck?</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>787209</th>\n",
              "      <td>Untoldstory55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>If only it was that simple; but one fix may im...</td>\n",
              "      <td>sarcastic</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          ID  \\\n",
              "8917          self_defeating   \n",
              "9396          Khvostov_7g-02   \n",
              "9469              roybringus   \n",
              "39976               MObaid27   \n",
              "41499          abdiascoronel   \n",
              "56037           texasboyf150   \n",
              "84428                  CenaW   \n",
              "88537         IndigenousOres   \n",
              "104232            roguetroll   \n",
              "127616        mattindustries   \n",
              "134668               Ps3Dave   \n",
              "161395     Crusty_white_sock   \n",
              "162258        butteryrice123   \n",
              "172361               lantech   \n",
              "175541                XHo-Oh   \n",
              "207469              So12rovv   \n",
              "231580               jhn_rob   \n",
              "278199            VanDriver1   \n",
              "285272           chamclouder   \n",
              "289901                 Synsc   \n",
              "293003           ShyBiDude89   \n",
              "312887        BrightIdeaDude   \n",
              "334963         sailingonward   \n",
              "348402              atlacatl   \n",
              "349834          RAGGA_MUFFIN   \n",
              "364333                  qubi   \n",
              "364928                Wemorg   \n",
              "365270             Mdgt_Pope   \n",
              "366544                 DJGow   \n",
              "367749        kerplunkerfish   \n",
              "379882    New-Hitler-Hillary   \n",
              "401222         Trout_Tickler   \n",
              "410604            Puffin_fan   \n",
              "414019             Ryan94985   \n",
              "421408                Kiux97   \n",
              "442200             havok0159   \n",
              "450895              calo9000   \n",
              "487177                TinHao   \n",
              "491552              Knorikus   \n",
              "507437             ellen_pao   \n",
              "509766              Jobcv314   \n",
              "531255            The_Leedle   \n",
              "578745                Nessie   \n",
              "582988           Ziffelbrixx   \n",
              "587198             SodokuIRL   \n",
              "616317  iloveyoujesuschriist   \n",
              "634685                 MrOxi   \n",
              "635051       TheCavalierLads   \n",
              "638355   RealityIsMyReligion   \n",
              "638916           curbstickle   \n",
              "653324               Thief39   \n",
              "653687         dont_wear_a_C   \n",
              "654055              FieldDay   \n",
              "711972              waldo686   \n",
              "739776           insanePower   \n",
              "750918           PoochDoobie   \n",
              "753557               Dalmazz   \n",
              "778552      wedontlikespaces   \n",
              "787209         Untoldstory55   \n",
              "\n",
              "                                                 Comments  \\\n",
              "8917                                                  NaN   \n",
              "9396                                                  NaN   \n",
              "9469                                                  NaN   \n",
              "39976                                                 NaN   \n",
              "41499    Said the bruised ego.,Pretending to be an idi...   \n",
              "56037   Going on about they lives...that's an opinion ...   \n",
              "84428                                                 NaN   \n",
              "88537                                                 NaN   \n",
              "104232  But it's wint... *falls over leg* DAMN IT!,*st...   \n",
              "127616                                                NaN   \n",
              "134668  I don't care how loud your screams are; you're...   \n",
              "161395  You're nice; but the reason I don't want to da...   \n",
              "162258  DLC 5 has been announced as a PS4 exclusive,De...   \n",
              "172361                                                NaN   \n",
              "175541  This new Shugesh form will have its own rarity...   \n",
              "207469                                                NaN   \n",
              "231580                                                NaN   \n",
              "278199  It will be better when it's one lane each way ...   \n",
              "285272  I cant believe Team: goalie with a bridge cont...   \n",
              "289901                                                NaN   \n",
              "293003  It's ok if I do it; it's those other people yo...   \n",
              "312887                                                NaN   \n",
              "334963                                                NaN   \n",
              "348402                                                NaN   \n",
              "349834                                                NaN   \n",
              "364333                                                NaN   \n",
              "364928                                                NaN   \n",
              "365270  could've,They could of played 3 bad teams in t...   \n",
              "366544                                                NaN   \n",
              "367749  We were just outside Barstow; on the edge of t...   \n",
              "379882  $10;000 cheap,Cheap V-8 Turbo Build! LS-Turbo ...   \n",
              "401222                                                NaN   \n",
              "410604  give everyone the power to share anything with...   \n",
              "414019                                                NaN   \n",
              "421408                                                NaN   \n",
              "442200                                                 []   \n",
              "450895                                                NaN   \n",
              "487177                                                NaN   \n",
              "491552                                                NaN   \n",
              "507437                                                NaN   \n",
              "509766  Oh; sorry your honor; I thought the law was *o...   \n",
              "531255  But EDM isn't music; it's just sounds *because...   \n",
              "578745                                                NaN   \n",
              "582988                                                NaN   \n",
              "587198                                                NaN   \n",
              "616317  Lend-Lease aid did not arrive in sufficient qu...   \n",
              "634685                                                NaN   \n",
              "635051                                                NaN   \n",
              "638355                                                NaN   \n",
              "638916                                                NaN   \n",
              "653324  Something like that',Great minds think alike. ...   \n",
              "653687                                                NaN   \n",
              "654055                                                NaN   \n",
              "711972  I don't think we; as law enforcement; can supp...   \n",
              "739776                                                NaN   \n",
              "750918                                                NaN   \n",
              "753557                                                NaN   \n",
              "778552                                                NaN   \n",
              "787209                                                NaN   \n",
              "\n",
              "                                          Parent Comments  Class Labels   \n",
              "8917    Because we have chosen them as sources of mind...      sarcastic  \n",
              "9396                                        woah; thanks.  non-sarcastic  \n",
              "9469    I am just shocked an upstanding group like thi...      sarcastic  \n",
              "39976   Bebe is a beautiful &amp; wonderfully gifted f...      sarcastic  \n",
              "41499                                       non-sarcastic            NaN  \n",
              "56037                                       non-sarcastic            NaN  \n",
              "84428                                   thanks Democrats.      sarcastic  \n",
              "88537                               Lol you didn't got it      sarcastic  \n",
              "104232                                          sarcastic            NaN  \n",
              "127616  Droids are just objectively better. Doesn't ta...      sarcastic  \n",
              "134668                                      non-sarcastic            NaN  \n",
              "161395                                      non-sarcastic            NaN  \n",
              "162258                                      non-sarcastic            NaN  \n",
              "172361   We need a sarcasm font. Or perhaps I'm a retard.      sarcastic  \n",
              "175541                                      non-sarcastic            NaN  \n",
              "207469                                 Will do. EU or NA?  non-sarcastic  \n",
              "231580             Yes; using game features is an exploit      sarcastic  \n",
              "278199                                          sarcastic            NaN  \n",
              "285272                                          sarcastic            NaN  \n",
              "289901                                 House \"of\" Commons  non-sarcastic  \n",
              "293003                                          sarcastic            NaN  \n",
              "312887  SERIOUSLY?! THIS IS THE FREE ITEM I GOT FOR PL...      sarcastic  \n",
              "334963  Are you proposing that the stock market should...      sarcastic  \n",
              "348402       Doesn't matter if that experience is garbage      sarcastic  \n",
              "349834    Uh; what about that looks like a ghetto to you?      sarcastic  \n",
              "364333                                         No kidding      sarcastic  \n",
              "364928                           Your favourite lol joke?      sarcastic  \n",
              "365270                                      non-sarcastic            NaN  \n",
              "366544   And thus an universal sarcasm indicator is born.      sarcastic  \n",
              "367749                                          sarcastic            NaN  \n",
              "379882                                      non-sarcastic            NaN  \n",
              "401222                                    Nah; that's Tux      sarcastic  \n",
              "410604                                      non-sarcastic            NaN  \n",
              "414019  Even if she is under 18 it isn't pornography. ...      sarcastic  \n",
              "421408       Because with the newer cell it looks stupid?      sarcastic  \n",
              "442200                                                NaN      sarcastic  \n",
              "450895  Valiant sir stands up against the console dron...      sarcastic  \n",
              "487177                                         integrity?      sarcastic  \n",
              "491552  There probably won't be master chief Might be ...      sarcastic  \n",
              "507437        very true; not all girls like the \"bad boy\"      sarcastic  \n",
              "509766                                      non-sarcastic            NaN  \n",
              "531255                                          sarcastic            NaN  \n",
              "578745  I cant tell if this is sarcasm or not; but so ...      sarcastic  \n",
              "582988  Kinda wish the article showed the bridges on t...  non-sarcastic  \n",
              "587198  LPL shitshow; EU LCS shitshow. What isn't a sh...      sarcastic  \n",
              "616317                                          sarcastic            NaN  \n",
              "634685  Make sure he doesn't overdose! Too many people...      sarcastic  \n",
              "635051  But then no one sees how many active friends y...      sarcastic  \n",
              "638355  Pharmacists of Reddit; how much do you judge p...  non-sarcastic  \n",
              "638916  if only there was a way to convey sarcasm thro...      sarcastic  \n",
              "653324                                          sarcastic            NaN  \n",
              "653687  Thu 2016-08-04 accessing vault............unlo...  non-sarcastic  \n",
              "654055                               Fuck you too; buddy.  non-sarcastic  \n",
              "711972                                          sarcastic            NaN  \n",
              "739776  jiji still shitstomps solo q against NALCS mid...  non-sarcastic  \n",
              "750918  lol I can't believe that your cat slept throug...      sarcastic  \n",
              "753557                                       Best region?      sarcastic  \n",
              "778552                          What symbol denotes suck?      sarcastic  \n",
              "787209  If only it was that simple; but one fix may im...      sarcastic  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "dHu7ilBxAl0i"
      },
      "outputs": [],
      "source": [
        "df5= train_df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "6kWDOg6cCLtD"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_3767946/4288661854.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df5.rename(columns = {' Class Labels ':'label'}, inplace = True)\n"
          ]
        }
      ],
      "source": [
        "df5.rename(columns = {' Class Labels ':'label'}, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "KpWM6NYqBFuW"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_3767946/864785083.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df5['label'].replace(['non-sarcastic','sarcastic'],[1,0], inplace = True)\n"
          ]
        }
      ],
      "source": [
        "df5['label'].replace(['non-sarcastic','sarcastic'],[1,0], inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bumL72PcDAht",
        "outputId": "dc48720b-00dd-4b03-b081-6d6af75b30b3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Parent Comments</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ocxtitan</td>\n",
              "      <td>Central Illinois</td>\n",
              "      <td>Jesus; where do you live?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LeChuckly</td>\n",
              "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
              "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>throwitskrub8</td>\n",
              "      <td>But then again; you have to consider that all ...</td>\n",
              "      <td>agree to that part.It can also mean that gujra...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fresherthanyouuu</td>\n",
              "      <td>ughhhhh</td>\n",
              "      <td>If a guy told you he doesn't use social media ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>_kushagra</td>\n",
              "      <td>I should've put the</td>\n",
              "      <td>No; it's just a programming bug. After all; th...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 ID                                           Comments  \\\n",
              "0          ocxtitan                                   Central Illinois   \n",
              "1         LeChuckly  To think - CNN used to be the acronym synonymo...   \n",
              "2     throwitskrub8  But then again; you have to consider that all ...   \n",
              "3  fresherthanyouuu                                            ughhhhh   \n",
              "4         _kushagra                                I should've put the   \n",
              "\n",
              "                                     Parent Comments  label  \n",
              "0                          Jesus; where do you live?      1  \n",
              "1  Even The CNN Staff Is Sick Of The Wall-To-Wall...      1  \n",
              "2  agree to that part.It can also mean that gujra...      1  \n",
              "3  If a guy told you he doesn't use social media ...      1  \n",
              "4  No; it's just a programming bug. After all; th...      0  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df5.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iv4UuycbUYgL",
        "outputId": "8cb2f14d-0750-4d72-b357-fe855c740f62"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 808031 entries, 0 to 808089\n",
            "Data columns (total 4 columns):\n",
            " #   Column           Non-Null Count   Dtype \n",
            "---  ------           --------------   ----- \n",
            " 0   ID               808031 non-null  object\n",
            " 1   Comments         808031 non-null  object\n",
            " 2   Parent Comments  808031 non-null  object\n",
            " 3   label            808031 non-null  int64 \n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 30.8+ MB\n"
          ]
        }
      ],
      "source": [
        "df5.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8yFX6sseGms9"
      },
      "source": [
        "**Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jB7HGEorDcvH",
        "outputId": "8409445b-f751-4a81-cb78-f2220d43e05f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    404020\n",
              "1    404011\n",
              "Name: label, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# to check number of sarcastic and non-sarcastic comments\n",
        "df5['label'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uylIXRBuBEdt"
      },
      "source": [
        "Almost equal number of sarcastic and non-sarcastic comments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRYnQalNFYU4",
        "outputId": "1a97f4bf-1365-480f-d030-a864071ae966"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    0.500006\n",
            "1    0.499994\n",
            "Name: label, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(df5['label'].value_counts()/len(df5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "B_pE94BtBSuP",
        "outputId": "2bdb5535-3d8d-4fca-9597-7e711512739c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "ax = sns.countplot(x='label',  data= df5)\n",
        "ax.set(title = \"Distribution of Classes\", xlabel=\"Sarcasm Status\", ylabel = \"Total Count\")\n",
        "total = float(len(train_df))\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x()+p.get_width()/2.,\n",
        "            height + 3,\n",
        "            '{:1.1f}%'.format((height/total)*100),\n",
        "            ha=\"center\") \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "hO5UX2J10MMb",
        "outputId": "d33ec26d-d223-4a3b-c602-17e2201577ea"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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LMWLECDz88MM4fvw40tPT4evrW+7//g8fPlxmLZGRkYiMjMSAAQOQkpKCo0ePStvjzJkz2L59O15//XX07t27Qs/9pZdewldffYWhQ4fihRdekJYHaNCgAXJzc63qltu39+rVV1/FsGHDMGDAAPTv319aHqBWrVoYOXLkXY3p7e2NxYsXY/jw4Xj88cetVuY+cuQItmzZgoiICAClpyyTkpKwaNEivPTSS+jevTuysrKwZs0ahIaG4rHHHrvn51hZ+vXrh23btuHNN9/ETz/9hLZt28JkMiEzMxPbt2/HsmXLEBoaWqExLdtl7ty5iI+Ph4uLC7p164bFixdj37596NKlCxo1aoTr169jzZo1qF+/Ptq1a2ePp0dOhEGJqAzNmjXDY489ho0bN1q1BwQEYMGCBZg3bx7ee+891KlTBwMHDoSfnx8mT55s1fff//43Lly4gGXLluHmzZuIjIxEVFQUXFxcsGjRIkyfPh3z589H3bp18eKLL0Kj0cjOt7hdReqoiKysLMyfP9+mvX///ujatStmzJiBxo0bY+PGjfjvf/+LOnXqICkpyeaP+IQJE+Dm5ob169cjIyMD4eHh0Gq1eO6558o9T+a3337Db7/9ZtM+evRotG/fHm+99RZat26NTz/9FHPnzoVSqUSjRo3w2GOPoW3bthV+7g0aNMAnn3yCGTNmICUlBX5+fnj++efh7u6OGTNmwNXVVeort2/vVefOnbFs2TIsWLAACxYsgEqlQocOHfB///d/VqfCKqpNmzbYvHkztFotvv/+e2zatAkKhQIBAQEYPny41RWJo0aNgp+fH1atWoWZM2fC29sbzzzzDMaNG2e1hpKjKRQKLF68GB999BE2bdqEb7/9Fu7u7mjcuDEGDRpU5qT4OwkLC8Po0aPx6aef4ocffoDZbMbOnTvRvXt3nD9/Hhs2bMCNGzfg6+uLyMhIjBo1qtIvmiDnI4iOmF1IRDWO5SjUmDFj8PLLLzu6nHJ755138Nlnn+HAgQOctEtUA3GOEhFVuqKiIps2y9ymyMjIqi6n3G6v+8aNG/jqq6/Qrl07hiSiGoqn3oio0m3duhUbN25EbGwsPDw8sH//fmzZsgUxMTFOPadjwIABiIyMRGBgIK5du4YNGzYgPz/f5r3QiKjmYFAiokoXHBwMpVIpzeGpXbs2Bg8ejDFjxji6tH/UpUsXfPPNN1i3bh0EQUDLli3xzjvvSO8vRkQ1D+coEREREcngHCUiIiIiGQxKRERERDIYlIiIiIhkcDJ3JRBFEWYzp3pVFYVC4PauBrifnB/3UfXA/WQfCoVQrncKYFCqBGaziJycm44uo0ZQqRTw9fWEXl8Ao9Hs6HJIBveT8+M+qh64n+zHz88TSuWdgxJPvRERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQxe9UZERFXKbDbDZDI6uoxqwWwWUFSkhMFQDJOJSwSUl1KpgkJROceCGJSIiKhKiKIIvT4HhYX5ji6lWrl2TQGzmUsDVJS7uxc0Gr9yrZX0TxiUiIioSlhCkpeXL9Rq13v+A1ZTKJUCjyZVgCiKMBiKkZ9/AwDg7V37nsZjUCIiIrszm01SSPLy0ji6nGpFpVJwsckKUqtdAQD5+TdQq5bvPZ2G42RuIiKyO5PJBODvP2BE9mb5WbvX+XAMSkREVGV4uo2qSmX9rDEoEREREclgUCIiIocShNJ3cnfER009wHXy5HFotSkoKiqyat+6dTNiYtojNzfXMYU5IU7mJiIihxEEwAwBhcUmhzy+u1oJhSBCrGEXlZ08eQIrVqThqacGwM3NTWqPiorB0qUr4OXl5cDqnAuDEhEROYwglIakfUcuodBQtYtQuqtVaN+yPrxclRCdICkVFxfB1dXtzh3tyNfXF76+vg6twdkwKBFVU4LgvBNjLXUJQunpDQtRrHn/uVP5FBqMKCyqPqt1Z2aexpIl83HkyB8oLi7CAw/UQ9++/fD88y/i8OFDWLlyBY4dO4qbN/PRuHFTPPvs8+jd+1Hp/vv370Ny8gjMnj0PW7d+hZ9//gnh4RGYPXse8vLysGzZh0hP/x65uTdQp05d9OjxCEaMGAkA2Lt3D9atW4NTp07CYDCgWbPmSExMQqdOnaXx8/LysGTJfGRk/Ai9XgcfH1+EhoZh+vSZ2Lp1M959dzoAoG/fngCA+vUb4PPPN0u3bdnyX/j4+AAADAYDPvpoGb799htcu3YFPj6+aN8+Eq+/Pq1qNraDMSgRVUOCALgpSiAaCh1dSpkUJgEluflwMRihNP+djAS1O4rMLgxLVO1NnDgOfn5+mDTpDXh5eeHcubO4evUKAODSpYsIDW2Dxx9/Cmq1K37//TfMmvU2RFFEnz59rcaZPfsdPPJIH7z7bn8oFAoYDAaMHj0CFy9exNChwxAY+CCuXbuCgwcPSPe5ePE8oqNjMXDgICgUAv73v734v/8bjfnzP0Tbtu0BAAsXzsFPP+3FiBGjUL9+A1y/fg3/+99eAKWn1158MREff6zFBx8shKenF9RqF9nnOmXKq/j1118waNBQtGoVitzcG9i9e1dlb1KnxaBEVA0JggDRUIiCU7/CZCh2dDk2FIIAV3cXFBeWwPxXKlKqXeHxYDsILmqnOM1BdLdyc3Nx8eJ5jB49HjExsQAgBRQA6Nmzl/S1KIpo0yYCV65cxqZNX9gEpZiYWLzySrL0/VdfbcSJE8exdOlytG4dBqB0wclevf4+GvXUUwOkr81mMyIi2iMrKxNffbVRquPo0T/Qs2dvq8ez1OXr64tGjRoDAIKDQ6QjR2X55Zf/Ye/ePXjzzRmIi+sttd/69f2OQYmoGjMZimEudsKjSgoBotIEs8EAs5mhiO4v3t7eqF+/AVJSFiEvT4927TrggQfqSbfr9XosX56CH37YjWvXrkqLbXp7e9uMFRUVY/X9r7/+jObN/aWQVJYrVy4jNXUJ9u37GdevX5P+8QgODpH6BAW1wLZtW1C7dh106hSFgIAH7+q57tv3C9zc3KzCX03DoERERFQBgiBgzpxFSE1dgjlz3kNhYSGCg0MwatRYhIe3xbvvTsPhw4cwZMhL8PcPhKenJzZu/BzfffetzVh+fn5W3+t0OtSuXVf2sc1mMyZNGof8/Hy89FISGjVqAnd3dyxbthSXL1+S+o0d+yo0mhR89tkqLFkyHw88UA+DBg3FE0/0r9Bz1et1qF27jtPOh6wKDEpEREQV1LRpM8yY8R6MRiN+//03pKYuxsSJY7Fx4zbs3bsHI0eORf/+z0r95U433x5AvL29cfr0KdnHPXfuLE6cOI6ZM9/Hww93ldqLi61PwXt5eWH06PEYPXo8Tp8+hfXr1+KDD2YhICAQbdpElPt5ajTe0lGrmhqWuOAkERHRXVKpVIiIaIfnnx+Cmzdv4vz5czCbzXBx+XtydEHBTezZk16u8dq374gzZ7Lwxx+Hy7zdEohUqr/Hv3TpIn7//TfZMQMDH0Ry8jgAwJkzWVb3N9xhjmP79pEoKioq82hYTVHhI0rZ2dnQarX47bffcPLkSQQEBGDLli3S7efOnUOPHj3KvK9arcbvv//+j/3atGmDdevWWbXt378f7733Ho4ePYratWtj4MCBGDZsmFW6FUURaWlpWLNmDXJychASEoLXXnsN4eHhVmNdvnwZM2bMwJ49e+Di4oK4uDi89tprXFyLiIjK5dSpk1i0aC569HgEjRo1Rn5+PlauXIEGDRoiICAQISEtsWrVR/Dx8YFSqcKqVR/B09MLubk5dxy7V694bNy4Hq++OhpDhw5DQMCDuH79Kvbv34+JE19Hs2bN8cAD9bB06SKYzWYUFhZAq01B3boPWI3z8ssJePjhbggICIRSqcD27V/DxcVFOprUvHlzAMAXX6zHww93hZubGwIDbecxdejQEVFR0Zg58y2cP38OLVu2hl6vx/ff78Rbb828941ZDVQ4KJ08eRK7d+9GmzZtYDabbQ4nPvDAA/jss8+s2kRRxEsvvYROnTrZjDdu3Dh07NhR+t7T09Pq9uzsbCQmJiI6OhpjxozB8ePH8f7770OpVCIxMVHql5aWhgULFmDChAkIDg7G6tWrkZCQgE2bNqFJkyYAgJKSErz00ksAgA8++ABFRUV47733MH78eKSkpFR0UxARUSVxV1f9TJC7fczatWujdu3aWLlyBa5duwpPTy+0aROOqVPfhlKpxJtvvoP//OddvPPONGg03ujf/1kUFhbg009X3XFstVqNefM+RGrqEqxcuQJ6vR4PPFAPPXo8It3+zjuzMWfOe3jjjUl44IF6ePHFBOzfvw/Hjh2RxgkNbYNvvvkaFy5cgEIhICDgQbz33lw0b+4PoHSyd0LCcGzZsglr1nyCBx6oh88/31xmTTNmzMaKFWnYtOkLLF+eCj+/2ujQoWOZfe9HgljB63TNZjMUitIzdpMmTcLhw4etjiiV5aeffsLgwYMxb9489OnTB8DfR5Tmz5+P3r3lLzOcOnUq9uzZg+3bt0OtVgMA5syZg7Vr1+LHH3+EWq1GcXExOnfujOeffx7jxpUeXjQYDOjduzdiY2Mxbdo0AMCWLVswYcIEbN26FQEBAQCAPXv2IDExEevXr0dYmPxVBv/EZDIjJ+fmXd2XKkalUsDX1xM3btyE0Wh2dDkOo1AIUJfokHdkr1Ne9aZQCHBzV6Oo8O+r3hSu7qjVsjMMLt68Es4JVPVrqaTEgOvXL6J27QZwcVFL7dJbmBgc+BYmcO6FUFUqRY3+fXe35H7mLPz8PKFU3nkGUoXjtCUkVcSWLVvg5eWF7t27V/i+6enpiIuLk0ISAMTHxyMlJQUHDhxAx44dsX//fuTn50shDChN3XFxcfj222+txgoODpZCEgBER0fDx8cHu3fvvuugREREd0cUAYUgwstV6aDHd+6QRI5n98ncJSUl2LFjB+Li4uDq6mpz+7Rp0xASEoKoqChMmTLF6h2LCwoKcPHiRatgAwABAQEQBAGZmZkAIH2+vV9gYCAuXLggvTtyZmamTR9BEODv7y+NQUREVUsUAbNZdMgHQxLdid1PCqenpyM3Nxd9+1qvRqpWqzFw4EDExMRAo9Hgt99+w9KlS3H48GGsX78eLi4uyMvLAwBoNBqb+7q7u0On0wEoXdxLrVbbBDGNRgNRFKHT6eDm5ga9Xo9atWrZ1Ojt7S2NdbdUKl5AWBUsh0nLc7j0fiYIAhQmAQpBABTOd8mu8FdNgkKQ/htT/PW+b0qlAgoF/zo5WlW/lsxm5/s5rQ4s1ywJAhjq7pJSKdzT32i7B6XNmzejTp06iIqKsmp/4IEHpLlDABAZGYmHHnoISUlJ+PbbbxEfH2/v0iqNQiHA19fzzh2p0mg07o4uweFKcvPh6u4CUemYuR3l4er69yXMgtoFarUKnj4eDqyIbldVr6WiIiWuXVPc8x+tmqqm/3N4N8xmAQqFAt7eHnBzc7vrcewalG7evIldu3bh6aefhlJ55/PPXbp0gYeHB/744w/Ex8dLR38sR5YsDAYDCgsLpeXgNRoNDAYDiouLrY4q6fV6CIJg1S8/P9/mcXU6HRo0aHDXz9NsFqHXF9z1/an8lEoFNBp36PWFMJlq7uRGQRDgYjCWvpeaweDocmwICgGuri4oLi6BaJnMbVJCbTDiZm4B3+vNCVT1a8lgKIbZbIbJJHJicgUIQum+MpnMPKJUQSaTCLPZDJ2uAIWFtv9QajTu9pnMXRHffvstioqK8K9//euu7u/h4YEGDRrYzB/KysqCKIrSfCPL56ysLLRo0ULql5mZiYYNG0pJMiAgACdOnLAaSxRFZGVlITo6+q5qtOALv2qZTOYavc0VCgFKswizKDrlFWSWXz2i+Zb6/qrVZDI7Zc01VVW9lkwm7vO7IYrWn6ni7jWc2/VY3pYtW9C0aVO0adOmXP137dqFgoIChIaGSm2xsbHYuXMnSkpKpLatW7dCo9EgIqJ04ay2bdvCy8sL27Ztk/pYJpHHxsZajXXs2DGcOXNGasvIyEBubi66dOlyt0+TiIiI7lMVPqJUWFiI3bt3AwDOnz+P/Px8bN++HUDpPCPLG/zl5OQgIyMDw4YNK3OcWbNmQRAEhIeHQ6PR4NChQ0hJSUHr1q3Rs2dPqV9iYiI2b96M8ePHY+DAgThx4gS0Wi3Gjh0rLRng6uqKpKQkLFy4EH5+fggKCsLatWuRm5trtShlr169kJKSglGjRmHcuHEoLCzE7Nmz0bVrVy4NQERERDYqHJSuX7+O0aNHW7VZvv/kk0+kVba3bdsGo9Eoe9otMDAQa9euxbp161BUVIR69eqhf//+SE5Ohkr1d1nNmjWDVqvFrFmzMHz4cPj5+SE5ORkJCQlW4w0bNgyiKGL58uXSW5hotVppVW4AcHFxwbJlyzBjxgyMGzcOKpUKcXFxmDx5ckU3AxEREdUAFV6Zm2xxZe6qw5W5S3FlbrpXzrIyN90ZV+a+O5W1MjevNyQiIocShNJw7YgPgcs7OczFixeg1abg2rWrVu379+9DTEx7q/euc6SqfxdCIiKivwgC4KYogWhwzJFRQe2OIrMLrypzgIsXL2DFijRERz+MOnXqSu3BwS2wdOkKNGvm78Dq/sagREREDiMIAkRDIQpO/QqTobhKH1updoXHg+0guKi5ttdfiouL4Op694szVgZPTy+0bh16545VhEGJiIgczmQodsr5dmV5551pOHbsCMaOfRULF87B2bN/wt8/AOPHv4YWLUIAAMXFxUhNXYz//ncH8vL0aNq0OYYOHYYuXbpVaBw5V65cxsKFc3Hw4H7cvJmP2rXr4OGHuyA5eTwAIDv7DJYvT8Hvvx+CTpeLBg0a4tFH+2HAgOekN7e/ePECnn76MUye/CZ+//037N69C3Xq1MEnn3wGg8GAjz5ahm+//QbXrl2Bj48v2rePxOuvTwMAHD58CCtXrsCxY0dx82Y+GjduimeffR69ez8q1Wg0GpGSshg7d+7AjRs50Gg0CA5uialT38aJE8eQnDwCAPDSS4Ol++zZsw/79+9DcvIILFv2CVq0aAkAMJvNWLduDTZv/hIXLpxHrVoahIWFY9KkN+Dl5XWPe/SfMSgRERFVUE7Odcyf/z6ef34IvLy8kJKyCJMnT8C6dZugUqnw1ltT8NNPGRg+/BU0bdoc27d/jSlTXsXMme8jJqZLuceRM2PGm7h27SrGjJkAX18/XL58CcePH5Vuv3r1Cpo2bY64uD7w8PDAqVMnoNWmoLCwAAkJw63GSklZhKioGEyb9g7M5tJJ41OmvIpff/0FgwYNRatWocjNvYHdu3dJ97l06SJCQ9vg8cefglrtit9//w2zZr0NURTRp0/pe7uuXLkCX365AS+/PAr+/gHQ6XLx88//Q0mJAcHBLTBu3ETMmfMeJk9+E02bNv/H7T137n/w1Vdf4JlnnkOHDh1RUHATe/fuQWFhAYMSERGRs9Hr9Vi4MBUBAYEAADc3NyQnj8AffxyGp6cndu/ehQkTXsPjjz8FAOjUqTMuXbqI5cvTrILSP43Tpk247OMfPfoHkpL+jR49HpHaLAEFANq3j0T79pEASt+BIiwsHEVFRdiwYZ1NUHrwwWBMmvSG9P0vv/wPe/fuwZtvzkBcXG+p/dave/bsJX0tiiLatInAlSuXsWnTF1IdR4/+gcjIjnjyyaelvl279pC+bt68dA5SQECgdOSoLH/+mY0vv/wcw4e/gkGDhpY5lj0xKBEREVVQnTp1pXADAP7+pW+ldfXqZZw6pQMAdO/e0+o+3bvHYeHCOSgsLIS7u/sdxwFKTzkZjX+/zYxSqYQgCAgKaoG1a1dBqVShQ4eOaNy4idVjFRcXY9Wqj7BjxzZcvnwJRqNRuq2goAAeHn+/OXXnztZv4bVv3y9wc3OzCkO30+v1WL48BT/8sBvXrl2FyVT6XmqW91YFgKCgFlizZiW02hR07hyD4OAQ6bRfRezf/wtEUUTfvv0qfN/KwKBERERUQbef7lGpXACUvml7Xp4eKpUKGo23VR8/Pz+Iooj8/DwpKP3TOAAwc+Zb2LZti3T75MlvIj7+X5g+fSZSUxcjNXUJPvhgFpo2bYakpH+jS5fuAIAPP1yIzZs3YujQYQgODkGtWrXwww+78fHHWhgMBqug5Otb26oGvV6H2rXrQPiHtRPefXcaDh8+hCFDXoK/fyA8PT2xcePn+O67b6U+gwcnQBAEbN/+NVasSIOPjy+efPJpDB067B/Hvp1Op4NSqYSvr1+571OZGJSIiIgqkUbjDaPRCL1eD41GI7Xn5ORAEAR4edUq91gJCcPxzDPPwmQqPaLUoEFDAECdOnUwefKbMJvNOH78KD7+WIupU1/DmjUb0KhRY+za9V/06/ckXnhhiDTW3r17ynyM2zOLRuON69evQRTFMgNNcXEx9u7dg5Ejx6J//2el9tuvHFSr1UhMTEJiYhLOnTuLr7/+CsuXp6Jhw0ZWk77vxNvbGyaTCTdu5DgkLHHBSSIiokoUFhYOANi1679W7bt2/RcPPRQsHU0qjwYNGiIkpCVatCj98Pb2sbpdoVAgJKQVhg17BSaTCefPnwNQGmYsR6cAwGQyYefOHeV6zPbtI1FUVGR1dOhWJSUlMJvNcHH5e/yCgpvYsydddszGjZsgKenf0Gi8kZ19BgCk+xcXG/6xnrZtO0AQBHz99Vflqr+y8YgSERFRJXrwwYfQpUs3LFo0F8XFxWjatBl27NiGw4cPYebMD+55/Pz8fIwbNxK9esWjadNmMBpL8Pnn6+DlVQtBQS0AAB06dMTmzV/C3z8A3t4+2LhxPQyGknKN36FDR0RFRWPmzLdw/vw5tGzZGnq9Ht9/vxNvvTUTXl5eCAlpiVWrPoKPjw+UShVWrfoInp5eyM3NkcZ57bXxCA4OkcLhjz+mIy9Pj7Zt2wMAmjRpBqVSia+/3gSlUgmVSlnmpO6mTZuhX7+nkJb2IfR6vRTkMjL2ICFhOOrWfeCet+k/YVAiIiKHU6pd76vHnDr1baSkLMbq1R9Bry9dR2nGjPcQExN7z2Or1WoEBj6IDRs+w+XLl+Dq6oYWLUIwd+4i+Pj4AADGjv0//Oc/MzF37n/g5uaGPn36Ija2G957b0a5HmPGjNlYsSINmzZ9geXLU+HnVxsdOnSUbn/zzXfwn/+8i3femQaNxhv9+z+LwsICfPrpKqlPaGgbfPfdf/Hpp6tgMpnQpEkzTJ36tjSOj48Pxo59FWvWfIJvvtkKk8mEPXv2lVnPuHGvomHDhvjqqy+xbt0aeHt7Izy8rdVcK3vhm+JWAr4pbtXhm+KW4pvi0r1yljfF5VuY3BnfFPfuVNab4vKIEhEROYwoAkVmFwhl/CGrksc3i04dksjxGJSIiMihRNH2iikiZ8Gr3oiIiIhkMCgRERERyWBQIiIiIpLBoERERFWGc5GoqlTWzxqDEhER2Z1SqQQAGAzFDq6EagrLz5pSeW/XrfGqNyIisjuFQgl3dy/k598AAKjVrhV6Y9SazGwWYDLxSFx5iaIIg6EY+fk34O7uBYXi3o4JMSgREVGV0GhK39DUEpaofBQKBcxmLjhZUe7uXtLP3L1gUCIioiohCAK8vWujVi1fmExGR5dTLSiVAry9PaDTFfCoUgUolap7PpJkwaBERERVSqFQQKFwzErc1Y1KpYCbmxsKC018GxMH4WRuIiIiIhkMSkREREQyGJSIiIiIZDAoEREREclgUCIiIiKSwaBEREREJINBiYiIiEgG11EiqqYEoXQBP6d8GwhLTYJwy5d/f01EVF0wKBFVUyKAEpMZJpPzLUInmACTCJiMJljWElaazOC6wkRU3TAoEVVDglD6JpmXrt9EYX6+o8uxIUCASqWA0WiG+Fc8cvcyQ2MSIbgIACMTEVUTDEpE1ZjJZEaJE76tgVB6XhAlJjNEsTQUqZ3wyBcR0Z1wMjcRERGRjAoHpezsbEydOhX9+vVDy5Yt0bdvX5s+gwYNQnBwsM3H6dOnrfrl5eVh8uTJiIyMREREBJKTk3HlyhWb8fbv348BAwYgLCwM3bp1Q2pqqvRfqoUoikhNTUXXrl0RFhaGAQMG4ODBgzZjXb58GaNGjUJERAQiIyPx+uuvI98JT10QERGR41X41NvJkyexe/dutGnTBmaz2SawWLRt2xYTJ060amvcuLHV92PGjMGpU6cwbdo0uLq6Yt68eRg2bBg2bNgAlaq0tOzsbCQmJiI6OhpjxozB8ePH8f7770OpVCIxMVEaKy0tDQsWLMCECRMQHByM1atXIyEhAZs2bUKTJk0AACUlJXjppZcAAB988AGKiorw3nvvYfz48UhJSanopiAiIqL7XIWDUvfu3dGzZ08AwKRJk3D48OEy+2k0GoSHh8uOc+DAAezZswdarRYxMTEAAH9/f8THx2PHjh2Ij48HAGi1Wvj6+mLOnDlQq9WIiopCTk4Oli5dikGDBkGtVqO4uBgpKSlISEjAkCFDAADt2rVD7969odVqMW3aNADAN998g5MnT2Lr1q0ICAiQ6kxMTMShQ4cQFhZW0c1BRERE97EKn3pTKCpnWlN6ejo0Gg2io6OltoCAAISEhCA9Pd2qX48ePaBWq6W2+Ph46PV6HDhwAEDpqbn8/Hz06dNH6qNWqxEXF2czVnBwsBSSACA6Oho+Pj7YvXt3pTwvIiIiun/Y7aq3n3/+GeHh4TCZTGjTpg1Gjx6NDh06SLdnZmbC39/fZrG8gIAAZGZmAgAKCgpw8eJFq2Bj6SMIAjIzM9GxY0ep/+39AgMD8fHHH6OoqAhubm7IzMy06SMIAvz9/aUx7pZKxXnxVUGpVFh9rqkUCgGiUHoZvjMuOHnLepMASr8RIAACoFQKEISavf+cAV9L1QP3k+PZJSh16NAB/fr1Q/PmzXHlyhVotVoMHToUK1euREREBABAr9ejVq1aNvf19vaWTufl5eUBKD09diu1Wg13d3fodDppLLVaDVdXV6t+Go0GoihCp9PBzc3tHx/TMtbdUCgE+Pp63vX9qeI0GndHl+BweUW5UKoUcFEpHV2KLJXy79qUKgWUCgVqeXs4sCK6HV9L1QP3k+PYJSglJydbfd+1a1f07dsXS5YsQVpamj0e0qHMZhF6fYGjy6gRlEoFNBp36PWFTrkidVVRKASIZjNMRjNKjCZHl2NDEEpDktFkguV6D7XRDJPZDJ2uAGYzF5x0NL6WqgfuJ/vRaNzLdaSuShac9PDwQJcuXfDNN99IbRqNBpcuXbLpq9Pp4O3tDQDS0R/LkSULg8GAwsJCqZ9Go4HBYEBxcbHVUSW9Xg9BEKz6lbUUgE6nQ4MGDe7pORqdcNG/+5nJZK7R21ypVEAhAiJE2StPHav0dJsoQqpPhAiIgMkk8he+E6npr6XqgvvJcRx20jMgIABZWVk2v+SzsrKkeUQeHh5o0KCBzfwhy/0s/Syfs7KyrPplZmaiYcOGcHNzk/rdPpYoilaPSURERGRRJUGpoKAA33//PUJDQ6W22NhY6HQ6ZGRkSG1ZWVk4cuQIYmNjrfrt3LkTJSUlUtvWrVuh0Wik+U5t27aFl5cXtm3bJvUpKSnBjh07bMY6duwYzpw5I7VlZGQgNzcXXbp0qdTnTERERNVfhU+9FRYWSpfSnz9/Hvn5+di+fTsAIDIyEpmZmVi2bBni4uLQqFEjXLlyBStWrMDVq1cxf/58aZyIiAjExMRg8uTJmDhxIlxdXTF37lwEBwfjkUcekfolJiZi8+bNGD9+PAYOHIgTJ05Aq9Vi7Nix0pIBrq6uSEpKwsKFC+Hn54egoCCsXbsWubm5VotS9urVCykpKRg1ahTGjRuHwsJCzJ49W1rNm4iIiOhWgljBCQ7nzp1Djx49yrztk08+Qf369fHWW2/h+PHjyM3Nhbu7OyIiIjBy5EibMJKXl4eZM2fi22+/hdFoRExMDKZMmYJ69epZ9du/fz9mzZqFo0ePws/PD88//zyGDRtmdVm05S1M1qxZg5ycHISEhOC1116TjjpZXL58GTNmzMCePXugUqkQFxeHyZMnw8vLqyKbwYrJZEZOzs27vj+Vn0qlgK+vJ27cuFmjz9crlQooim7g6Pff4Gae870FjyAIcFEpUWI0SafXPWt5IaRrL5jdfDlHyQnwtVQ9cD/Zj5+fZ7kmc1c4KJEtBqWqw18apRiU6F7xtVQ9cD/ZT3mDElewIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISIbK0QUQUQ0iCBAEAQqF4OhKyk0URYiio6sgIkdhUCKiKqF0cYFapYDZkAuxGiUPQe2OIrMLwxJRDcWgRERVQqFUQSwpws2swzAVFzm6nHJRql3h8WA7CC7qahXuiKjyMCgRUZUyG4phLi50dBlEROXCydxEREREMhiUiIiIiGQwKBERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQwGJSIiIiIZDEpEREREMhiUiIiIiGQwKBERERHJYFAiIiIiklHhoJSdnY2pU6eiX79+aNmyJfr27Wt1e35+PhYuXIj+/fujffv26Ny5M0aMGIHjx49b9Tt37hyCg4NtPp555hmbx9y/fz8GDBiAsLAwdOvWDampqTbv5C2KIlJTU9G1a1eEhYVhwIABOHjwoM1Yly9fxqhRoxAREYHIyEi8/vrryM/Pr+hmICIiohpAVdE7nDx5Ert370abNm1gNpttAsuFCxfw2Wef4amnnsKYMWNQXFyM5cuXY8CAAdiwYQMCAwOt+o8bNw4dO3aUvvf09LS6PTs7G4mJiYiOjsaYMWNw/PhxvP/++1AqlUhMTJT6paWlYcGCBZgwYQKCg4OxevVqJCQkYNOmTWjSpAkAoKSkBC+99BIA4IMPPkBRURHee+89jB8/HikpKRXdFERERHSfq3BQ6t69O3r27AkAmDRpEg4fPmx1e+PGjfHtt9/C3d1dauvUqRO6d++ONWvW4I033rDq36xZM4SHh8s+nlarha+vL+bMmQO1Wo2oqCjk5ORg6dKlGDRoENRqNYqLi5GSkoKEhAQMGTIEANCuXTv07t0bWq0W06ZNAwB88803OHnyJLZu3YqAgAAAgEajQWJiIg4dOoSwsLCKbg4iIiK6j1X41JtC8c938fDwsApJQOlRoqZNm+LKlSsVfTikp6ejR48eUKvVUlt8fDz0ej0OHDgAoPTUXH5+Pvr06SP1UavViIuLQ3p6utVYwcHBUkgCgOjoaPj4+GD37t0Vro2IiIjub1UymVuv1+PkyZNWAcVi2rRpCAkJQVRUFKZMmYLc3FzptoKCAly8eNHmfgEBARAEAZmZmQAgfb69X2BgIC5cuICioiKp3+19BEGAv7+/NAYRERGRRYVPvd2N//znPxAEAQMHDpTa1Go1Bg4ciJiYGGg0Gvz2229YunQpDh8+jPXr18PFxQV5eXkASk+P3UqtVsPd3R06nQ5AaRBTq9VwdXW16qfRaCCKInQ6Hdzc3KDX61GrVi2b+ry9vaWx7pZKxQsIq4JSqbD6XFMpFAJEARAgQBAER5djw1JS6efSbwQIgAAoBAGiwvlqLotCEKBQCFAqFVAoxDvfoRrha6l64H5yPLsHpQ0bNmDdunWYNWsW6tevL7U/8MAD0twhAIiMjMRDDz2EpKQkfPvtt4iPj7d3aZVGoRDg6+t5545UaTQa9zt3us/lFeVCqVLARaV0dCmyVMq/a1OqFBAgQO2mgqhQ/8O9nIegdoFarYKnj4ejS7EbvpaqB+4nx7FrUNq9ezemTp2KV155BU888cQd+3fp0gUeHh74448/EB8fLx39sRxZsjAYDCgsLIS3tzeA0iNHBoMBxcXFVkeV9Ho9BEGw6lfWUgA6nQ4NGjS46+dpNovQ6wvu+v5UfkqlAhqNO/T6QphMZkeX4zAKhQDRbIbJaEaJ0eTocmwIQmlIMppMsFwYazKaIUKEocgIU7HBsQWWk8KkhNpgxM3cApsrfKs7vpaqB+4n+9Fo3Mt1pM5uQengwYMYPXo0Hn/8cYwePfquxvDw8ECDBg1s5g9lZWVBFEVpvpHlc1ZWFlq0aCH1y8zMRMOGDeHm5ib1O3HihNVYoigiKysL0dHRd1WjhdHIH+CqZDKZa/Q2VyoVUIiACNFJ/4CXnloTRUj1iRABETCLIsxmZ6y5DH/VajKZq0/NFVTTX0vVBfeT49jlpOepU6eQlJSETp06Yfr06eW+365du1BQUIDQ0FCpLTY2Fjt37kRJSYnUtnXrVmg0GkRERAAA2rZtCy8vL2zbtk3qU1JSgh07diA2NtZqrGPHjuHMmTNSW0ZGBnJzc9GlS5e7eapERER0H6vwEaXCwkLpUvrz588jPz8f27dvB1A6z0gURSQmJsLV1RUvvvii1TpLXl5eePDBBwEAs2bNgiAICA8Ph0ajwaFDh5CSkoLWrVtL6zQBQGJiIjZv3ozx48dj4MCBOHHiBLRaLcaOHSstGeDq6oqkpCQsXLgQfn5+CAoKwtq1a5Gbm2u1KGWvXr2QkpKCUaNGYdy4cSgsLMTs2bOl1byJiIiIbiWIFTxuf+7cOfTo0aPM2z755BMAwODBg8u8PTIyEitXrgQArF+/HmvXrkV2djaKiopQr1499OzZE8nJyfDy8rK63/79+zFr1iwcPXoUfn5+eP755zFs2DCrq30sb2GyZs0a5OTkICQkBK+99pp01Mni8uXLmDFjBvbs2QOVSoW4uDhMnjzZ5jErwmQyIyfn5l3fn8pPpVLA19cTN27crNGHoZVKBRRFN3D0+29wM8/53oJHEAS4qJQoMZqkU28+desiJCoKecf2w1RcPeb0KVzdUatlFIpV3vfdqTe+lqoH7if78fPzLNccpQoHJbLFoFR1+EujVHUMSr71HkBIxyhcP/ILjEXVIygpXd3hGxoNA4MSOQj3k/2UNyhVyTpKREQKQYBZFHHtRgH0Or2jyykXdy8zNCYRgosA4P4KSkRUPgxKRFSlSkwiSqrJf8ZqXo5NVONxqU8iIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIqHJSys7MxdepU9OvXDy1btkTfvn3L7Ld+/Xr06tULoaGheOyxx7Br1y6bPnl5eZg8eTIiIyMRERGB5ORkXLlyxabf/v37MWDAAISFhaFbt25ITU2FKIpWfURRRGpqKrp27YqwsDAMGDAABw8etBnr8uXLGDVqFCIiIhAZGYnXX38d+fn5Fd0MREREVANUOCidPHkSu3fvRrNmzRAYGFhmn6+//hpvvPEG+vTpg7S0NISHh2PkyJE2wWXMmDH48ccfMW3aNLz//vvIysrCsGHDYDQapT7Z2dlITExE3bp1kZKSghdffBELFizA8uXLrcZKS0vDggULMGTIEKSkpKBu3bpISEjA2bNnpT4lJSV46aWXcObMGXzwwQeYNm0a9uzZg/Hjx1d0MxAREVENoKroHbp3746ePXsCACZNmoTDhw/b9FmwYAEeffRRjBkzBgDQqVMnnDhxAosXL0ZaWhoA4MCBA9izZw+0Wi1iYmIAAP7+/oiPj8eOHTsQHx8PANBqtfD19cWcOXOgVqsRFRWFnJwcLF26FIMGDYJarUZxcTFSUlKQkJCAIUOGAADatWuH3r17Q6vVYtq0aQCAb775BidPnsTWrVsREBAAANBoNEhMTMShQ4cQFhZW0c1BRERE97EKH1FSKP75LmfPnsWZM2fQp08fq/b4+HhkZGTAYDAAANLT06HRaBAdHS31CQgIQEhICNLT06W29PR09OjRA2q12mosvV6PAwcOACg9NZefn2/1mGq1GnFxcTZjBQcHSyEJAKKjo+Hj44Pdu3dXZDMQERFRDVDpk7kzMzMBlB4dulVgYCBKSkqkU2GZmZnw9/eHIAhW/QICAqQxCgoKcPHiRatgY+kjCILUz/L59n6BgYG4cOECioqKpH639xEEAf7+/tIYRERERBYVPvV2JzqdDkDpKa1bWb633K7X61GrVi2b+3t7e0un8/Ly8socS61Ww93d3WostVoNV1dXm8cURRE6nQ5ubm7/+JiWse6WSsULCKuCUqmw+lxTKRQCRAEQINj8s+EMLCWVfhb++lqA8FebM9ZcFgECIABKpQBBuL9+5vhaqh64nxyv0oNSTaRQCPD19XR0GTWKRuPu6BIcLq8oF0qVAi4qpaNLkaVS/l2bUqkABAEqpXPXfCulSgGlQoFa3h6OLsVu+FqqHrifHKfSg5K3tzeA0qNBdevWldr1er3V7RqNBpcuXbK5v06nk/pYjv5YjixZGAwGFBYWWo1lMBhQXFxsdVRJr9dDEASrfmUtBaDT6dCgQYO7e8IAzGYRen3BXd+fyk+pVECjcYdeXwiTyezochxGoRAgms0wGc0oMZocXY4NQSgNSUaTCZaVPEwmMyCKMJqcs+ayqI1mmMxm6HQFMJvFO9+hGuFrqXrgfrIfjca9XEfqKj0oWeYA3T4fKDMzEy4uLmjSpInULyMjA6IoWh2Gz8rKQlBQEADAw8MDDRo0sJk/lJWVBVEUpfEtn7OystCiRQurx2zYsCHc3NykfidOnLAaSxRFZGVlWU0qvxtGI3+Aq5LJZK7R21ypVEAhAiJEmzXFnEPpa1oUIdUniiLE29qcnQgREAGTSbxv/0jV9NdSdcH95DiVftKzSZMmaN68ObZv327VvnXrVkRFRUlXr8XGxkKn0yEjI0Pqk5WVhSNHjiA2NlZqi42Nxc6dO1FSUmI1lkajQUREBACgbdu28PLywrZt26Q+JSUl2LFjh81Yx44dw5kzZ6S2jIwM5ObmokuXLpWzAYiIiOi+UeEjSoWFhdKl9OfPn0d+fr4UiiIjI+Hn54dRo0ZhwoQJaNq0KTp27IitW7fi0KFDWLVqlTROREQEYmJiMHnyZEycOBGurq6YO3cugoOD8cgjj0j9EhMTsXnzZowfPx4DBw7EiRMnoNVqMXbsWCl0ubq6IikpCQsXLoSfnx+CgoKwdu1a5ObmIjExURqrV69eSElJwahRozBu3DgUFhZi9uzZ0mreRERERLcSxAoeAz937hx69OhR5m2ffPIJOnbsCKD0LUzS0tJw4cIF+Pv7Y9y4cejWrZtV/7y8PMycORPffvstjEYjYmJiMGXKFNSrV8+q3/79+zFr1iwcPXoUfn5+eP755zFs2DCrU3aWtzBZs2YNcnJyEBISgtdee0066mRx+fJlzJgxA3v27IFKpUJcXBwmT54MLy+vimwGKyaTGTk5N+/6/lR+KpUCvr6euHHjZo0+DK1UKqAouoGj33+Dm3nO9xY8giDARaVEidEknWarXb8eHurQEUd+2A197r1dZVpVPGt5IaRrL5jdfO+7U298LVUP3E/24+fnWa45ShUOSmSLQanq8JdGKQalqsGgRI7G/WQ/5Q1KXJiBiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGXYJSoMGDUJwcHCZH19//fU/9jl9+rTVWHl5eZg8eTIiIyMRERGB5ORkXLlyxeYx9+/fjwEDBiAsLAzdunVDamoqRFG06iOKIlJTU9G1a1eEhYVhwIABOHjwoD02AREREd0HVPYY9M0330R+fr5V28cff4wdO3YgKipKamvbti0mTpxo1a9x48ZW348ZMwanTp3CtGnT4Orqinnz5mHYsGHYsGEDVKrS8rOzs5GYmIjo6GiMGTMGx48fx/vvvw+lUonExERprLS0NCxYsAATJkxAcHAwVq9ejYSEBGzatAlNmjSp7M1ARERE1ZxdgtKDDz5o0zZ+/HhER0fDz89PatNoNAgPD5cd58CBA9izZw+0Wi1iYmIAAP7+/oiPj8eOHTsQHx8PANBqtfD19cWcOXOgVqsRFRWFnJwcLF26FIMGDYJarUZxcTFSUlKQkJCAIUOGAADatWuH3r17Q6vVYtq0aZX2/ImIiOj+UCVzlPbv349z587hX//6V4Xul56eDo1Gg+joaKktICAAISEhSE9Pt+rXo0cPqNVqqS0+Ph56vR4HDhyQasjPz0efPn2kPmq1GnFxcVZjUc0kCIBCIVSbD0EQHL3JiIhqBLscUbrdli1b4OHhgR49eli1//zzzwgPD4fJZEKbNm0wevRodOjQQbo9MzMT/v7+Nn8UAgICkJmZCQAoKCjAxYsXERAQYNNHEARkZmaiY8eOUv/b+wUGBuLjjz9GUVER3NzcKu05U/UhCIAZAgqLTY4updxcVCI8HF0EEVENYPegZDQasW3bNnTv3h0eHn//au/QoQP69euH5s2b48qVK9BqtRg6dChWrlyJiIgIAIBer0etWrVsxvT29sbhw4cBlE72BkpP491KrVbD3d0dOp1OGkutVsPV1dWqn0ajgSiK0Ol09xSUVCpeQFgVlEqF1efKIAgC8gpL8OuxyygqNlbauPZUz88DbZuoIAjOeXTJUlLpZ+GvrwUIf7U5Y81lESAAAqBUChCE++s1bo/XElU+7ifHs3tQ+vHHH5GTk4O+fftatScnJ1t937VrV/Tt2xdLlixBWlqavcuqVAqFAF9fT0eXUaNoNO6VOl6xucBy/q1Sx7Wb0rQBpVIBF5XS0dXIUin/rk2pVACCAJWT13wrpUoBpUKBWt737/G7yn4tkX1wPzmO3YPSli1b4OPjI03GluPh4YEuXbrgm2++kdo0Gg0uXbpk01en08Hb2xsApCNOliNLFgaDAYWFhVI/jUYDg8GA4uJiq6NKer0egiBI/e6G2SxCry+46/tT+SmVCmg07tDrC2EymStlTEEQYCguQVFRCQqryRGlYk8XQFTCZDKjxOh8pwwFoTQkGU0mWFbpMJnMgCjC6KQ1l0VtNMNkNkOnK4DZLN75DtWIPV5LVPm4n+xHo3Ev15E6uwaloqIi/Pe//8Vjjz0GFxeXCt8/ICAAGRkZEEXR6lB9VlYWgoKCAJQGrAYNGkhzkG7tI4qiNCfJ8jkrKwstWrSQ+mVmZqJhw4b3PD/JaOQPcFUymcyVts0VCgFmETCLYrX5YyiKIkQAogib9cKcQ+nr9db6nL9mWyJEQARMJvG+/SNVma8lsh/uJ8ex63mG7777DgUFBeW62q2goADff/89QkNDpbbY2FjodDpkZGRIbVlZWThy5AhiY2Ot+u3cuRMlJSVS29atW6HRaKT5Tm3btoWXlxe2bdsm9SkpKcGOHTusxiIiIiKysOsRpc2bN6Nhw4Zo166dVfu+ffuwbNkyxMXFoVGjRrhy5QpWrFiBq1evYv78+VK/iIgIxMTEYPLkyZg4cSJcXV0xd+5cBAcH45FHHpH6JSYmYvPmzRg/fjwGDhyIEydOQKvVYuzYsdKSAa6urkhKSsLChQvh5+eHoKAgrF27Frm5uVaLUhIRERFZ2C0o6XQ6/PDDD3jxxRdtrnCpW7cuSkpKMHfuXOTm5sLd3R0RERGYPn06wsLCrPrOmzcPM2fOxNSpU2E0GhETE4MpU6ZIq3IDQLNmzaDVajFr1iwMHz4cfn5+SE5ORkJCgtVYw4YNgyiKWL58OXJychASEgKtVstVuYmIiKhMglhdJgs4MZPJjJycm44uo0ZQqRTw9fXEjRs3K3WOUn6xCT8cPIfCouoxmbthXU9EBbrh+Pc7cPO2CxmcgSAIcFEpUWI0SfORatevh4c6dMSRH3ZDn6tzcIXl41nLCyFde8Hs5nvfzVGyx2uJKh/3k/34+XmWazJ3NbkWmoiIiKjqMSgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDJUji6AyBkIAuDuWn1eDq4uSgiOLoKIqAaoPn8ZiOxEEIBaKiNa1xNgNCkdXU65eHiIcFGIUCgYl4iI7IlBiQgChJJC5J3Yh4KbNx1dTLmY69ZF3bA2EAQGJSIie2JQIvpLSVEhDAUFji6jXIzFRY4ugYioRuBkbiIiIiIZDEpEREREMhiUiIiIiGQwKBERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQwGJSIiIiIZDEpEREREMhiUiIiIiGQwKBERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQy7BKUvvvgCwcHBNh/vv/++Vb/169ejV69eCA0NxWOPPYZdu3bZjJWXl4fJkycjMjISERERSE5OxpUrV2z67d+/HwMGDEBYWBi6deuG1NRUiKJo1UcURaSmpqJr164ICwvDgAEDcPDgwUp97kRERHT/UNlz8GXLlqFWrVrS9/Xq1ZO+/vrrr/HGG29gxIgR6NSpE7Zu3YqRI0di9erVCA8Pl/qNGTMGp06dwrRp0+Dq6op58+Zh2LBh2LBhA1Sq0vKzs7ORmJiI6OhojBkzBsePH8f7778PpVKJxMREaay0tDQsWLAAEyZMQHBwMFavXo2EhARs2rQJTZo0seemICIiomrIrkGpVatW8PPzK/O2BQsW4NFHH8WYMWMAAJ06dcKJEyewePFipKWlAQAOHDiAPXv2QKvVIiYmBgDg7++P+Ph47NixA/Hx8QAArVYLX19fzJkzB2q1GlFRUcjJycHSpUsxaNAgqNVqFBcXIyUlBQkJCRgyZAgAoF27dujduze0Wi2mTZtmz01BRERE1ZBD5iidPXsWZ86cQZ8+faza4+PjkZGRAYPBAABIT0+HRqNBdHS01CcgIAAhISFIT0+X2tLT09GjRw+o1WqrsfR6PQ4cOACg9NRcfn6+1WOq1WrExcVZjUVERERkYdeg1LdvX4SEhKBHjx5ISUmByWQCAGRmZgIoPTp0q8DAQJSUlODs2bNSP39/fwiCYNUvICBAGqOgoAAXL15EQECATR9BEKR+ls+39wsMDMSFCxdQVFRUGU+ZiIiI7iN2OfVWt25djBo1Cm3atIEgCPjuu+8wb948XL58GVOnToVOpwMAaDQaq/tZvrfcrtfrreY4WXh7e+Pw4cMASid7lzWWWq2Gu7u71VhqtRqurq42jymKInQ6Hdzc3O76OatUvICwKiiVCqvPlUGhECAKgADBJpQ7K0EQIAAQBDhlzZaSSj8Lf33t3DWXRYAACIBSKUAQ7q/XuD1eS1T5uJ8czy5B6eGHH8bDDz8sfR8TEwNXV1d8/PHHGDFihD0e0qEUCgG+vp6OLqNG0WjcK3W8vKJcKFUKuKiUlTquvSiVCkAQoFI6d80q5d+1VZeab6VUKaBUKFDL28PRpdhNZb+WyD64nxzHrpO5b9WnTx8sX74cR48ehbe3N4DSo0F169aV+uj1egCQbtdoNLh06ZLNWDqdTupjOeJkObJkYTAYUFhYaDWWwWBAcXGx1VElvV4PQRCkfnfDbBah1xfc9f2p/JRKBTQad+j1hTCZzJUypkIhQDSbYTKaUWI0VcqY9mYymQFRhNHknDULQmlIMppMsKzS4ew1l0VtNMNkNkOnK4DZLN75DtWIPV5LVPm4n+xHo3Ev15G6KgtKt7LME8rMzLSaM5SZmQkXFxfpUv2AgABkZGRAFEWrQ/VZWVkICgoCAHh4eKBBgwbSHKRb+4iiKI1v+ZyVlYUWLVpYPWbDhg3v6bQbABiN/AGuSiaTudK2uVKpgEIERIg2a285K1EUIQIQRThpzaWv11vrc/6abYkQAREwmcT79o9UZb6WyH64nxynyk56bt26FUqlEi1btkSTJk3QvHlzbN++3aZPVFSUdPVabGwsdDodMjIypD5ZWVk4cuQIYmNjpbbY2Fjs3LkTJSUlVmNpNBpEREQAANq2bQsvLy9s27ZN6lNSUoIdO3ZYjUVERERkYZcjSomJiejYsSOCg4MBADt37sS6deswePBg6VTbqFGjMGHCBDRt2hQdO3bE1q1bcejQIaxatUoaJyIiAjExMZg8eTImTpwIV1dXzJ07F8HBwXjkkUesHm/z5s0YP348Bg4ciBMnTkCr1WLs2LFS6HJ1dUVSUhIWLlwIPz8/BAUFYe3atcjNzbValJKIiIjIwi5Byd/fHxs2bMClS5dgNpvRvHlzTJ48GYMGDZL69O3bF4WFhUhLS0Nqair8/f2xaNEi6QiQxbx58zBz5kxMnToVRqMRMTExmDJlirQqNwA0a9YMWq0Ws2bNwvDhw+Hn54fk5GQkJCRYjTVs2DCIoojly5cjJycHISEh0Gq1XJWbiIiIyiSI1WWygBMzmczIybnp6DJqBJVKAV9fT9y4cbNy5ygV3cDR77/Bzbz8ShnT3mrXr4eHOnTEkR92Q5+rc3Q5NgRBgItKiRKjSZqP5Ow1l8WzlhdCuvaC2c33vpujZI/XElU+7if78fPzLNdkbi7MQERERCSDQYmIiIhIBoMSERERkQwGJSIiIiIZDEpEREREMhiUiIiIiGQwKBERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQwGJSIiIiIZDEpEREREMhiUiIiIiGQwKBERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQwGJSIiIiIZDEpEREREMhiUiIiIiGQwKBERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQwGJSIiIiIZDEpEREREMhiUiIiIiGQwKBERERHJYFAiIiIiksGgRERERCSDQYmIiIhIBoMSERERkQy7BKVt27bh5ZdfRmxsLMLDw9GvXz98/vnnEEVR6jNo0CAEBwfbfJw+fdpqrLy8PEyePBmRkZGIiIhAcnIyrly5YvOY+/fvx4ABAxAWFoZu3bohNTXV6vEAQBRFpKamomvXrggLC8OAAQNw8OBBe2wCIiIiug+o7DHoRx99hEaNGmHSpEnw9fXF3r178cYbb+DSpUsYOXKk1K9t27aYOHGi1X0bN25s9f2YMWNw6tQpTJs2Da6urpg3bx6GDRuGDRs2QKUqLT87OxuJiYmIjo7GmDFjcPz4cbz//vtQKpVITEyUxkpLS8OCBQswYcIEBAcHY/Xq1UhISMCmTZvQpEkTe2wKIiIiqsbsEpQ+/PBD+Pn5Sd9HRUUhNzcXK1aswCuvvAKFovRAlkajQXh4uOw4Bw4cwJ49e6DVahETEwMA8Pf3R3x8PHbs2IH4+HgAgFarha+vL+bMmQO1Wo2oqCjk5ORg6dKlGDRoENRqNYqLi5GSkoKEhAQMGTIEANCuXTv07t0bWq0W06ZNs8emICIiomrMLqfebg1JFiEhIcjPz0dBQUG5x0lPT4dGo0F0dLTUFhAQgJCQEKSnp1v169GjB9RqtdQWHx8PvV6PAwcOACg9NZefn48+ffpIfdRqNeLi4qzGIiIiIrKossncv/76K+rVqwcvLy+p7eeff0Z4eDhCQ0Pxwgsv4JdffrG6T2ZmJvz9/SEIglV7QEAAMjMzAQAFBQW4ePEiAgICbPoIgiD1s3y+vV9gYCAuXLiAoqKiynmiREREdN+wy6m32+3btw9bt261mo/UoUMH9OvXD82bN8eVK1eg1WoxdOhQrFy5EhEREQAAvV6PWrVq2Yzn7e2Nw4cPAyid7A2Unsa7lVqthru7O3Q6nTSWWq2Gq6urVT+NRgNRFKHT6eDm5nbXz1Gl4gWEVUGpVFh9rgwKhQBRAAQINqHcWQmCAAGAIMApa7aUVPpZ+Otr5665LAIEQACUSgGCcH+9xu3xWqLKx/3keHYPSpcuXcLYsWPRsWNHDB48WGpPTk626te1a1f07dsXS5YsQVpamr3LqlQKhQBfX09Hl1GjaDTulTpeXlEulCoFXFTKSh3XXpRKBSAIUCmdu2aV8u/aqkvNt1KqFFAqFKjl7eHoUuymsl9LZB/cT45j16Ck1+sxbNgw+Pj4YOHChdIk7rJ4eHigS5cu+Oabb6Q2jUaDS5cu2fTV6XTw9vYGAOmIk+XIkoXBYEBhYaHUT6PRwGAwoLi42Oqokl6vhyAIUr+7YTaL0OvLP/eK7p5SqYBG4w69vhAmk7lSxlQoBIhmM0xGM0qMpkoZ095MJjMgijCanLNmQSgNSUaTCZZVOpy95rKojWaYzGbodAUwm8U736EascdriSof95P9aDTu5TpSZ7egVFRUhKSkJOTl5eGzzz4r8xTanQQEBCAjIwOiKFodqs/KykJQUBCA0oDVoEEDaQ7SrX1EUZTmJFk+Z2VloUWLFlK/zMxMNGzY8J5OuwGA0cgf4KpkMpkrbZsrlQooRECEaLP2lrMSRREiAFGEk9Zc+nq9tT7nr9mWCBEQAZNJvG//SFXma4nsh/vJcexy0tNoNGLMmDHIzMzEsmXLUK9evTvep6CgAN9//z1CQ0OlttjYWOh0OmRkZEhtWVlZOHLkCGJjY6367dy5EyUlJVLb1q1bodFopPlObdu2hZeXF7Zt2yb1KSkpwY4dO6zGonsnCKVHaezxYQnMglD5YxIREd3OLkeUpk+fjl27dmHSpEnIz8+3Wv26ZcuWOHToEJYtW4a4uDg0atQIV65cwYoVK3D16lXMnz9f6hsREYGYmBhMnjwZEydOhKurK+bOnYvg4GA88sgjUr/ExERs3rwZ48ePx8CBA3HixAlotVqMHTtWWjLA1dUVSUlJWLhwIfz8/BAUFIS1a9ciNzfXalFKujeCAJghoLDYPqdWFIIJxeYCGIpLUFlnQlxUIu7fGShERHQv7BKUfvzxRwDArFmzbG7buXMn6tati5KSEsydOxe5ublwd3dHREQEpk+fjrCwMKv+8+bNw8yZMzF16lQYjUbExMRgypQp0qrcANCsWTNotVrMmjULw4cPh5+fH5KTk5GQkGA11rBhwyCKIpYvX46cnByEhIRAq9VyVe5KJAilIWnfkUsoNBgrfXyFIMDNzQVFRSUwV9Lpm/p+Hmjb1AWW00VEREQWglhdJgs4MZPJjJycm44uwykoFALyi0344eA5FBbZISgpBLi7q1FYaKi0ybUN63oiKtANx7/fgZu3XRTgrGrXr4eHOnTEkR92Q5+rc3Q5NgRBgItKiRKjSZqP5Ow1l8WzlhdCuvWC6OYHs7n6zA8RRRF3+s2uUing6+uJGzducu6LE+N+sh8/P0/HTuYmIqruhL/msOUbjDCZqs//lO5qJRTCncMSEd0ZgxIRkQyFIMBkFnHo9FXk5FWP1fvd1Sq0b1kfXq7KanN1IZEzY1AiIrqDQoPJLqeSicj5cU10IiIiIhkMSkREREQyGJSIiIiIZDAoEREREclgUCIiIiKSwaBEREREJINBiYiIiEgGgxIRERGRDAYlIiIiIhkMSkREREQyGJSIiIiIZPC93oiI/oEgCHBTK+HuVj1+Xbq7qiAIjq6C6P5RPV75REQOoHRxgVqlQHAdEcUapaPLKReVUkAtlRGiUD3qJXJ2DEpERDIUShXEkiLknfgVeXq9o8spFw9PT9Tzi4WodAMgOrocomqPQYmI6A5KigphKChwdBnl4qLk1FOiysRXFBEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyeBVb1TpBKF00Tt7UPy1+B/MKpjFyrn02dVFCa7PR0REZWFQokolCEAtlRGt6wkwmip/wTtBEKBSAkaTEmIlBSUPDxEuChEKBeMSERFZY1CiSiZAKClE3ol9KLh50x6jw0WlRInRBLGSFtMz162LumFtIPB9H4iI6DYMSmQX9lqgTxAEiJagVElHlIzFRZUyDhER3X84mZuIiIhIBoMSERERkQwGJSIiIiIZnKPkxAQB1W6CcXWrl4iI6J8wKDkpQQDMEFBYbHJ0KRXiohLh4egiiIiIKgmDkpMShNKQtO/IJRQajI4up9zq+3mgbVMXgEs4EhHRfYBByckVGowoLKo+Qam4xATAxdFlEBERVQpO5iYiIiKSwaBEREREJKPGBaXTp09j6NChCA8PR3R0NGbPng2DweDosoiIiMgJ1ag5SjqdDi+++CKaN2+OhQsX4vLly5g1axaKioowdepUR5dHRERETqZGBaVPP/0UN2/exKJFi+Dj4wMAMJlMmD59OpKSklCvXj3HFkhEREROpUYFpfT0dERFRUkhCQD69OmDN998Ez/++COefPJJxxVXBkEA3F2r1y5ydVFyYQAiR/trsVqFQv7VaFkc9k79qoooiqik97kmqlTV66/wPcrMzMRTTz1l1abRaFC3bl1kZmY6qKqyCQJQS2VE63oCjCalo8spNw8PES4K0Sl+8RLVRIJCgKBQoMBogtkknzwEoxklNwpgMBidIqC4uigggGGJnE+NCkp6vR4ajcam3dvbGzqd7q7HVSgE+Pl53ktpNgQBEM1u8PR0g1iNfnMoFAooXVwQ8a+nYa4mdSsUCqjUaoT37c+a7Yg1Vw2FQgGVqys8zeUMHZ7Ose6ZQqmodm+BJIqlv6urgre3e6WMU5U1VxZ7vfTK+w99jQpK9iIIApRKO/zkKRRQqqrnLlKrnOOXb0Ww5qrBmquGovociKZyUChq3EXqTqNGbXmNRoO8vDybdp1OB29vbwdURERERM6sRgWlgIAAm7lIeXl5uHr1KgICAhxUFRERETmrGhWUYmNjsXfvXuj1eqlt+/btUCgUiI6OdmBlRERE5IwEsTrNFL5HOp0Ojz76KPz9/ZGUlCQtOPmvf/2LC04SERGRjRoVlIDStzB5++23ceDAAXh6eqJfv34YO3Ys1Gq1o0sjIiIiJ1PjghIRERFRedWoOUpEREREFcGgRERERCSDQYmIiIhIBoMSERERkQwGJSIiIiIZDEpEREREMhiUyOlt27YNL7/8MmJjYxEeHo5+/frh888/B1e2cF43b95EbGwsgoOD8fvvvzu6HCrDxo0b8fjjjyM0NBQdO3bESy+9hKKiIkeXRbfYuXMnnn76aURERCAmJgajR4/G2bNnHV1WjVM935qeapSPPvoIjRo1wqRJk+Dr64u9e/fijTfewKVLlzBy5EhHl0dlWLJkCUwmk6PLIBkffvgh0tLSMGLECISHh+PGjRvIyMjgPnMiP/30E0aOHInHH38cY8eORW5uLubPn4+EhARs3rwZbm5uji6xxmBQIqf34Ycfws/PT/o+KioKubm5WLFiBV555RUoFDww6kxOnz6NNWvWYOLEiXjzzTcdXQ7dJjMzE4sWLcKSJUvQpUsXqb1Xr14OrIpu9/XXX6Nhw4Z49913IQgCAMDPzw8vvvgiDh8+jPbt2zu4wpqDf2HI6d0akixCQkKQn5+PgoICB1RE/2TGjBl49tln4e/v7+hSqAxffPEFGjdubBWSyPkYjUZ4enpKIQkAatWqBQCcdlDFGJSoWvr1119Rr149eHl5OboUusX27dtx4sQJ/Pvf/3Z0KSTjt99+Q1BQEJYsWYKoqCi0bt0azz77LH777TdHl0a3ePLJJ3H69GmsXr0aeXl5OHv2LObMmYOWLVuibdu2ji6vRmFQompn37592Lp1KxISEhxdCt2isLAQs2bNwtixYxlgndjVq1exZ88ebNq0CW+++SYWL14MQRCQkJCA69evO7o8+kv79u2xaNEifPDBB2jfvj169uyJ69evIy0tDUql0tHl1SgMSlStXLp0CWPHjkXHjh0xePBgR5dDt/jwww9Ru3ZtPPXUU44uhf6BKIooKCjA/Pnz0bt3b3Tp0gUffvghRFHEqlWrHF0e/WX//v149dVX8cwzz+Djjz/G/PnzYTabMXz4cF6dWMU4mZuqDb1ej2HDhsHHxwcLFy7kJG4ncv78eSxfvhyLFy9GXl4eAEjzxwoKCnDz5k14eno6skT6i0ajgY+PD1q0aCG1+fj4oGXLljh16pQDK6NbzZgxA506dcKkSZOktvDwcHTt2hWbNm3CgAEDHFhdzcKgRNVCUVERkpKSkJeXh88++0ya1EjO4dy5cygpKcHw4cNtbhs8eDDatGmDdevWOaAyut2DDz6IP//8s8zbiouLq7gaknP69Gn06NHDqq1+/frw9fWV3X9kHwxK5PSMRiPGjBmDzMxMrF69GvXq1XN0SXSbkJAQfPLJJ1ZtR48excyZMzF9+nSEhoY6qDK6Xbdu3fDFF1/g6NGjCAkJAQDcuHEDf/zxB4YMGeLY4kjSsGFDHDlyxKrt/PnzuHHjBho1auSgqmomBiVyetOnT8euXbswadIk5Ofn4+DBg9JtLVu2hFqtdlxxBKD0dE7Hjh3LvK1Vq1Zo1apVFVdEcnr27InQ0FAkJydj7NixcHV1RWpqKtRqNZ577jlHl0d/efbZZ/Huu+9ixowZ6N69O3Jzc6V5gH369HF0eTUKgxI5vR9//BEAMGvWLJvbdu7cicaNG1d1SUTVlkKhQGpqKmbOnImpU6eipKQE7du3x+rVq1G3bl1Hl0d/GTx4MNRqNdauXYsNGzbA09MT4eHhmDdvHnx9fR1dXo0iiFy5ioiIiKhMvGyIiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDIYlIiIiIhkMCgRERERyWBQIiIiIpLBoEREREQkg0GJiIiISAaDEhEREZEMBiUiIiIiGQxKRERERDL+H5GZfEkaUhB4AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# To check if there is any relation between the length of the comment and its label\n",
        "#train_df.loc[train_df,'Comments'].str.len().apply(np.log1p).hist(label='sarcastic,alpha=.5)\n",
        "df5.loc[df5['label']==1,'Comments'].str.len().apply(np.log1p).hist(label='sarcastic', alpha=.5)\n",
        "df5.loc[df5['label']==0,'Comments'].str.len().apply(np.log1p).hist(label='non-sarcastic', alpha=.5)\n",
        "plt.legend()\n",
        "plt.title('Natural Log Length of Comments')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9o3RzVnd70t"
      },
      "source": [
        "Sarcastic comments are normally distributed in length. Non-sarcastic comments has a right-skew."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "wOkkCnR6B939",
        "outputId": "fd76ec42-f99d-4da9-f9cd-0d9affd76693"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_3767946/2449768073.py:8: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n",
            "  plt.grid(b= False)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Most common words in the sarcastic comments \n",
        "wordcloud = WordCloud(background_color='black', stopwords = STOPWORDS,\n",
        "                max_words = 200, max_font_size = 100, \n",
        "                random_state = 17, width=800, height=400)\n",
        "\n",
        "plt.figure(figsize=(12, 12))\n",
        "wordcloud.generate(str(df5.loc[df5['label'] == 1, 'Parent Comments']))\n",
        "plt.grid(b= False)\n",
        "plt.imshow(wordcloud);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "G6sZoOaLCuDl"
      },
      "outputs": [],
      "source": [
        "# Comparing sarcastic comments with Parent comments\n",
        "sarcasm_comm_len = np.array(df5.loc[df5['label'] == 1]['Comments'].str.len())\n",
        "parent_comm_len = np.array(df5.loc[df5['label'] == 1]['Parent Comments'].str.len())\n",
        "ratio_len = np.array((df5.loc[df5['label'] == 1]['Comments'].str.len())/(df5.loc[df5['label'] == 1]['Parent Comments'].str.len()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "gRR1wNN9DIFw"
      },
      "outputs": [],
      "source": [
        "d = pd.DataFrame({'Comment Length': sarcasm_comm_len, 'Parent Comment Length': parent_comm_len, 'Ratio Length': ratio_len}, columns=['Comment Length', 'Parent Comment Length', 'Ratio Length'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "c8bNU05FDQeU",
        "outputId": "2ffca103-5cd7-4b6e-8916-762aaba0c5be"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = plt.axes()\n",
        "sns.scatterplot(data=d, x=\"Comment Length\", y=\"Parent Comment Length\",  size=ratio_len)\n",
        "ax.set_title(\"Comparing Sarcastic Comment Length with Parent Comment\")\n",
        "# control x and y limits\n",
        "plt.ylim(0, 12000)\n",
        "plt.xlim(0, 800)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "I9Ks4VjwDoFp"
      },
      "outputs": [],
      "source": [
        "df5 = df5[:10000]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "SF7pD1UxEPN_"
      },
      "outputs": [],
      "source": [
        "corpus = df5['Comments'].values.astype('U')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYUakysLvpYJ",
        "outputId": "dec8e1c3-185d-4902-ff3f-bee88e707b87"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Central Illinois',\n",
              "       'To think - CNN used to be the acronym synonymous with news.',\n",
              "       \"But then again; you have to consider that all husbands suspecting adultery don't or are simply can't afford to go for a paternity test.\",\n",
              "       ..., 'According to what sources',\n",
              "       'Wow you sure nailed it; we girls just love to gossip and anything related to reading; typing; roleplaying or using imagination is boy stuff!',\n",
              "       \"Both are restoring armor; can't they think of something new?\"],\n",
              "      dtype='<U821')"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "corpus"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKBsY-OPAPJA"
      },
      "source": [
        "Tfidf vectorizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Q8Azgz5y7oZ"
      },
      "source": [
        "Stop words removed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miyEGChGu7lM",
        "outputId": "e9b89b68-c4cb-40d5-aeb1-4d9c44011d51"
      },
      "outputs": [],
      "source": [
        "\"\"\"Tfidf vectorizer\n",
        "\n",
        "Stop words removed\n",
        "\"\"\"\n",
        "\n",
        "#method=\"Binary\"\n",
        "\n",
        "\n",
        "\n",
        "#if method==\"Tfidf\":\n",
        "vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,1))\n",
        "#elif method==\"Binary\":\n",
        "    #vectorizer = CountVectorizer(stop_words='english', ngram_range=(1,2), min_df=2)\n",
        "    \n",
        "#tfidf= TfidfVectorizer(stop_words='english', ngram_range=(1,1))\n",
        "\n",
        "X = vectorizer.fit_transform(corpus)\n",
        "#np.shape(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<10000x13173 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 49401 stored elements in Compressed Sparse Row format>"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "oNLdJXV83i_r"
      },
      "outputs": [],
      "source": [
        "labels = df5['label'].to_list()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWkkhY9Ww7N3",
        "outputId": "3b8f9ed3-ea78-460d-d14e-1222668376ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       00  000  0035  00s   01   02   03   04   05   06  ...  zoolocks  \\\n",
            "0     0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "1     0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "2     0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "3     0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "4     0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "...   ...  ...   ...  ...  ...  ...  ...  ...  ...  ...  ...       ...   \n",
            "9995  0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "9996  0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "9997  0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "9998  0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "9999  0.0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...       0.0   \n",
            "\n",
            "      zoomed  zooming  zooted  zoro  zou  zumba  zunino   zx  zxcvbnm  \n",
            "0        0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "1        0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "2        0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "3        0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "4        0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "...      ...      ...     ...   ...  ...    ...     ...  ...      ...  \n",
            "9995     0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "9996     0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "9997     0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "9998     0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "9999     0.0      0.0     0.0   0.0  0.0    0.0     0.0  0.0      0.0  \n",
            "\n",
            "[10000 rows x 13173 columns]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ],
      "source": [
        "cv_dataframe=pd.DataFrame(X.toarray(),columns=vectorizer.get_feature_names())\n",
        "print(cv_dataframe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "B-h47HfR0x2J"
      },
      "outputs": [],
      "source": [
        "# bigram_vectorizer = CountVectorizer(ngram_range=(1, 2),token_pattern=r'\\b\\w+\\b', min_df=1).build_analyzer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "G4Yp5as412tE"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(corpus, labels , test_size= 0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "jCm3sht904hT"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "vMMdQu6m07ik"
      },
      "outputs": [],
      "source": [
        "parameters = {'solver': [ 'lbfgs', 'liblinear', 'saga'], 'verbose': [0, 1, 2]}\n",
        "\n",
        "# multinomial logistic regression a.k.a softmax classifier\n",
        "logit = LogisticRegression(random_state= 42)\n",
        "\n",
        "logit_classifier = GridSearchCV(logit, parameters, cv = 10, n_jobs= 4)\n",
        "\n",
        "# sklearn's pipeline\n",
        "tfidf_logit_pipeline = Pipeline([('vectorizer', vectorizer), \n",
        "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)), ('logit_classifier', logit_classifier)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.20066D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.96812D+01\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     27     29      1     0     0   2.178D-03   3.945D+03\n",
            "  F =   3944.6842050038208     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.97849D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.3s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.07950D+01\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     48     57      1     0     0   4.443D-02   3.945D+03\n",
            "  F =   3945.3797312878819     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.10619D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.5s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.15891D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   50    f=  3.94662D+03    |proj g|=  2.49346D-03\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     50     55      1     0     0   2.493D-03   3.947D+03\n",
            "  F =   3946.6238047910010     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.6s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.95696D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     32     38      1     0     0   3.157D-02   3.948D+03\n",
            "  F =   3948.1929947789904     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.8s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.22876D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     33     37      1     0     0   8.011D-02   3.947D+03\n",
            "  F =   3946.5770176923143     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.11146D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.8s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     36     42      1     0     0   5.347D-03   3.958D+03\n",
            "  F =   3958.1147471059317     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.99592D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.9s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     37     42      1     0     0   2.871D-03   3.956D+03\n",
            "  F =   3955.5306906570268     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    8.4s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.20066D+01\n",
            "\n",
            "At iterate    1    f=  4.63213D+03    |proj g|=  1.37886D+02\n",
            "\n",
            "At iterate    2    f=  4.41743D+03    |proj g|=  3.55168D+02\n",
            "\n",
            "At iterate    3    f=  4.12979D+03    |proj g|=  4.95883D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate    4    f=  4.08684D+03    |proj g|=  1.11463D+01\n",
            "\n",
            "At iterate    5    f=  4.07055D+03    |proj g|=  1.12047D+01\n",
            "\n",
            "At iterate    6    f=  4.05473D+03    |proj g|=  1.93219D+02\n",
            "\n",
            "At iterate    7    f=  3.98095D+03    |proj g|=  8.93533D+01\n",
            "\n",
            "At iterate    8    f=  3.95887D+03    |proj g|=  2.75060D+01\n",
            "\n",
            "At iterate    9    f=  3.95473D+03    |proj g|=  3.46999D+00\n",
            "\n",
            "At iterate   10    f=  3.95387D+03    |proj g|=  3.25330D+00\n",
            "\n",
            "At iterate   11    f=  3.94757D+03    |proj g|=  7.00728D+00\n",
            "\n",
            "At iterate   12    f=  3.94534D+03    |proj g|=  1.21043D+00\n",
            "\n",
            "At iterate   50    f=  3.95764D+03    |proj g|=  5.35285D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     50     58      1     0     0   5.353D-02   3.958D+03\n",
            "  F =   3957.6415867179931     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate   13    f=  3.94509D+03    |proj g|=  6.49172D-01\n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.96812D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.6s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate    1    f=  4.63273D+03    |proj g|=  1.40926D+02\n",
            "\n",
            "At iterate   14    f=  3.94482D+03    |proj g|=  6.30607D-01\n",
            "\n",
            "At iterate    2    f=  4.42398D+03    |proj g|=  3.71159D+02\n",
            "\n",
            "At iterate    3    f=  4.12996D+03    |proj g|=  1.38719D+02\n",
            "\n",
            "At iterate    4    f=  4.08014D+03    |proj g|=  1.31581D+01\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     24     27      1     0     0   6.660D-02   3.950D+03\n",
            "  F =   3949.5575080390627     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate   15    f=  3.94469D+03    |proj g|=  1.31062D+00\n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "At iterate    5    f=  4.07682D+03    |proj g|=  1.12139D+01\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.97849D+01\n",
            "\n",
            "At iterate    1    f=  4.63273D+03    |proj g|=  1.39796D+02\n",
            "\n",
            "At iterate    6    f=  4.06903D+03    |proj g|=  1.44531D+01\n",
            "\n",
            "At iterate    2    f=  4.42187D+03    |proj g|=  3.70638D+02\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.2s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate    7    f=  4.03283D+03    |proj g|=  4.86454D+01\n",
            "\n",
            "At iterate    3    f=  4.12902D+03    |proj g|=  1.31571D+02\n",
            "\n",
            "At iterate   16    f=  3.94469D+03    |proj g|=  3.48484D-02\n",
            "\n",
            "At iterate    4    f=  4.08071D+03    |proj g|=  1.46283D+01\n",
            "\n",
            "At iterate    8    f=  3.99289D+03    |proj g|=  7.01257D+01\n",
            "\n",
            "At iterate    5    f=  4.07725D+03    |proj g|=  1.08789D+01\n",
            "\n",
            "At iterate    9    f=  3.96082D+03    |proj g|=  4.66721D+01\n",
            "\n",
            "At iterate    6    f=  4.06976D+03    |proj g|=  1.54148D+01\n",
            "\n",
            "At iterate   17    f=  3.94469D+03    |proj g|=  2.32043D-01\n",
            "\n",
            "At iterate   10    f=  3.95362D+03    |proj g|=  5.78722D+00\n",
            "\n",
            "At iterate    7    f=  4.04077D+03    |proj g|=  4.88089D+01\n",
            "\n",
            "At iterate    8    f=  4.00314D+03    |proj g|=  7.11714D+01\n",
            "\n",
            "At iterate   11    f=  3.95248D+03    |proj g|=  2.91955D+00\n",
            "\n",
            "At iterate   18    f=  3.94469D+03    |proj g|=  2.67144D-01\n",
            "\n",
            "At iterate   12    f=  3.95077D+03    |proj g|=  2.38913D+00\n",
            "\n",
            "At iterate   19    f=  3.94469D+03    |proj g|=  3.70992D-02\n",
            "\n",
            "At iterate    9    f=  3.96458D+03    |proj g|=  6.61470D+00\n",
            "\n",
            "At iterate   10    f=  3.95530D+03    |proj g|=  3.37379D+01\n",
            "\n",
            "At iterate   11    f=  3.95340D+03    |proj g|=  3.74596D+01\n",
            "\n",
            "At iterate   13    f=  3.94889D+03    |proj g|=  5.52489D+01\n",
            "\n",
            "At iterate   20    f=  3.94468D+03    |proj g|=  1.20044D-01\n",
            "\n",
            "At iterate   14    f=  3.94637D+03    |proj g|=  2.89398D+00\n",
            "\n",
            "At iterate   12    f=  3.95286D+03    |proj g|=  1.34952D+01\n",
            "\n",
            "At iterate   13    f=  3.95262D+03    |proj g|=  7.71085D+00\n",
            "\n",
            "At iterate   15    f=  3.94634D+03    |proj g|=  7.86724D-01\n",
            "\n",
            "At iterate   21    f=  3.94468D+03    |proj g|=  3.15677D-01\n",
            "\n",
            "At iterate   16    f=  3.94633D+03    |proj g|=  1.93736D+00\n",
            "\n",
            "At iterate   17    f=  3.94625D+03    |proj g|=  5.13539D+00\n",
            "\n",
            "At iterate   18    f=  3.94609D+03    |proj g|=  8.42823D+00\n",
            "\n",
            "At iterate   14    f=  3.95101D+03    |proj g|=  1.72643D+01\n",
            "\n",
            "At iterate   19    f=  3.94581D+03    |proj g|=  1.01994D+01\n",
            "\n",
            "At iterate   22    f=  3.94468D+03    |proj g|=  1.12327D-01\n",
            "\n",
            "At iterate   20    f=  3.94555D+03    |proj g|=  7.42669D+00\n",
            "\n",
            "At iterate   21    f=  3.94545D+03    |proj g|=  1.28067D+00\n",
            "\n",
            "At iterate   15    f=  3.94936D+03    |proj g|=  2.83091D+01\n",
            "\n",
            "At iterate   22    f=  3.94545D+03    |proj g|=  4.43209D-01\n",
            "\n",
            "At iterate   23    f=  3.94468D+03    |proj g|=  1.41989D-02\n",
            "\n",
            "\n",
            "At iterate   16    f=  3.94798D+03    |proj g|=  1.78033D+01\n",
            "At iterate   23    f=  3.94544D+03    |proj g|=  9.39757D-01\n",
            "\n",
            "At iterate   17    f=  3.94764D+03    |proj g|=  4.35964D+00\n",
            "\n",
            "At iterate   24    f=  3.94544D+03    |proj g|=  6.77827D-01\n",
            "\n",
            "At iterate   24    f=  3.94468D+03    |proj g|=  1.07015D-02\n",
            "\n",
            "At iterate   25    f=  3.94544D+03    |proj g|=  3.12593D+00\n",
            "\n",
            "At iterate   18    f=  3.94759D+03    |proj g|=  1.50176D+00\n",
            "\n",
            "At iterate   26    f=  3.94542D+03    |proj g|=  2.03086D+00\n",
            "\n",
            "At iterate   25    f=  3.94468D+03    |proj g|=  2.40593D-02\n",
            "\n",
            "At iterate   27    f=  3.94541D+03    |proj g|=  8.94302D-01\n",
            "\n",
            "At iterate   28    f=  3.94540D+03    |proj g|=  2.09510D-01\n",
            "\n",
            "At iterate   19    f=  3.94757D+03    |proj g|=  3.19924D+00\n",
            "\n",
            "At iterate   29    f=  3.94539D+03    |proj g|=  4.17170D-01\n",
            "\n",
            "At iterate   26    f=  3.94468D+03    |proj g|=  7.92333D-02\n",
            "\n",
            "At iterate   30    f=  3.94539D+03    |proj g|=  2.12374D-01\n",
            "\n",
            "At iterate   31    f=  3.94539D+03    |proj g|=  6.81208D-02\n",
            "\n",
            "At iterate   32    f=  3.94538D+03    |proj g|=  2.98425D-01\n",
            "\n",
            "At iterate   33    f=  3.94538D+03    |proj g|=  2.19981D-01\n",
            "\n",
            "At iterate   20    f=  3.94751D+03    |proj g|=  6.64252D+00\n",
            "\n",
            "At iterate   27    f=  3.94468D+03    |proj g|=  2.17783D-03\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     27     29      1     0     0   2.178D-03   3.945D+03\n",
            "  F =   3944.6842050038208     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.7s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   34    f=  3.94538D+03    |proj g|=  3.05186D-01\n",
            "\n",
            "At iterate   21    f=  3.94739D+03    |proj g|=  1.03557D+01\n",
            "\n",
            "At iterate   35    f=  3.94538D+03    |proj g|=  5.98116D-02\n",
            "\n",
            "At iterate   36    f=  3.94538D+03    |proj g|=  4.22201D-02\n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.07950D+01\n",
            "\n",
            "At iterate   37    f=  3.94538D+03    |proj g|=  2.09552D-01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate    1    f=  4.63295D+03    |proj g|=  1.39757D+02\n",
            "\n",
            "At iterate   22    f=  3.94715D+03    |proj g|=  1.44808D+01\n",
            "\n",
            "At iterate   38    f=  3.94538D+03    |proj g|=  1.59809D-01\n",
            "\n",
            "At iterate    2    f=  4.42659D+03    |proj g|=  3.59337D+02\n",
            "\n",
            "At iterate   39    f=  3.94538D+03    |proj g|=  4.25243D-02\n",
            "\n",
            "At iterate    3    f=  4.14156D+03    |proj g|=  9.09080D+01\n",
            "\n",
            "At iterate   40    f=  3.94538D+03    |proj g|=  2.03397D-02\n",
            "\n",
            "At iterate   41    f=  3.94538D+03    |proj g|=  1.71448D-02\n",
            "\n",
            "At iterate    4    f=  4.09658D+03    |proj g|=  1.50951D+01\n",
            "\n",
            "At iterate   42    f=  3.94538D+03    |proj g|=  3.70635D-02\n",
            "\n",
            "At iterate   43    f=  3.94538D+03    |proj g|=  1.85171D-02\n",
            "\n",
            "At iterate   23    f=  3.94701D+03    |proj g|=  3.94228D+00\n",
            "\n",
            "At iterate   44    f=  3.94538D+03    |proj g|=  9.06504D-03\n",
            "\n",
            "At iterate    5    f=  4.09057D+03    |proj g|=  1.18258D+01\n",
            "\n",
            "At iterate   45    f=  3.94538D+03    |proj g|=  8.85258D-03\n",
            "\n",
            "At iterate    6    f=  4.07032D+03    |proj g|=  2.48719D+01\n",
            "\n",
            "At iterate   46    f=  3.94538D+03    |proj g|=  9.99767D-03\n",
            "\n",
            "At iterate   47    f=  3.94538D+03    |proj g|=  3.06029D-03\n",
            "\n",
            "At iterate   24    f=  3.94680D+03    |proj g|=  5.05751D+00\n",
            "\n",
            "At iterate   48    f=  3.94538D+03    |proj g|=  4.44312D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     48     57      1     0     0   4.443D-02   3.945D+03\n",
            "  F =   3945.3797312878819     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.10619D+01\n",
            "\n",
            "At iterate    7    f=  4.03550D+03    |proj g|=  4.54079D+01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.1s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate    1    f=  4.63248D+03    |proj g|=  1.39335D+02\n",
            "\n",
            "At iterate    2    f=  4.42184D+03    |proj g|=  3.60517D+02\n",
            "\n",
            "At iterate    3    f=  4.13464D+03    |proj g|=  8.32432D+01\n",
            "\n",
            "At iterate    4    f=  4.09052D+03    |proj g|=  1.40949D+01\n",
            "\n",
            "At iterate    5    f=  4.08362D+03    |proj g|=  1.17693D+01\n",
            "\n",
            "At iterate    8    f=  3.98818D+03    |proj g|=  7.58162D+01\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     31     36      1     0     0   1.629D-02   3.945D+03\n",
            "  F =   3944.7069829692182     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate    6    f=  4.05642D+03    |proj g|=  2.82974D+01\n",
            "\n",
            "At iterate   25    f=  3.94674D+03    |proj g|=  3.81175D+00\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.8s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate    7    f=  4.01583D+03    |proj g|=  3.68554D+01\n",
            "\n",
            "At iterate    9    f=  3.97134D+03    |proj g|=  1.13990D+02\n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.15891D+01\n",
            "\n",
            "At iterate    8    f=  3.99407D+03    |proj g|=  2.35582D+02\n",
            "\n",
            "At iterate    1    f=  4.63216D+03    |proj g|=  1.38497D+02\n",
            "\n",
            "At iterate    9    f=  3.96401D+03    |proj g|=  1.12731D+02\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate    2    f=  4.41909D+03    |proj g|=  3.55355D+02\n",
            "\n",
            "At iterate   26    f=  3.94673D+03    |proj g|=  6.50134D-01\n",
            "\n",
            "At iterate   10    f=  3.95546D+03    |proj g|=  1.43795D+01\n",
            "\n",
            "At iterate    3    f=  4.13508D+03    |proj g|=  5.10709D+01\n",
            "\n",
            "At iterate   11    f=  3.95476D+03    |proj g|=  2.45292D+00\n",
            "\n",
            "At iterate   12    f=  3.95428D+03    |proj g|=  5.03088D+00\n",
            "\n",
            "At iterate   27    f=  3.94672D+03    |proj g|=  1.48961D+00\n",
            "\n",
            "At iterate    4    f=  4.09338D+03    |proj g|=  1.17344D+01\n",
            "\n",
            "\n",
            "At iterate    5    f=  4.07795D+03    |proj g|=  1.18310D+01\n",
            "At iterate   10    f=  3.96309D+03    |proj g|=  1.33671D+01\n",
            "\n",
            "At iterate    6    f=  4.05551D+03    |proj g|=  2.27784D+02\n",
            "\n",
            "At iterate   13    f=  3.95190D+03    |proj g|=  1.27682D+01\n",
            "\n",
            "At iterate   14    f=  3.94991D+03    |proj g|=  9.28132D+00\n",
            "\n",
            "At iterate   15    f=  3.94905D+03    |proj g|=  2.12652D+00\n",
            "\n",
            "At iterate    7    f=  3.98586D+03    |proj g|=  9.95194D+01\n",
            "\n",
            "At iterate   28    f=  3.94671D+03    |proj g|=  2.84219D+00\n",
            "\n",
            "At iterate   11    f=  3.96213D+03    |proj g|=  2.74758D+00\n",
            "\n",
            "At iterate    8    f=  3.96075D+03    |proj g|=  2.22178D+01\n",
            "\n",
            "At iterate   29    f=  3.94670D+03    |proj g|=  4.77801D+00\n",
            "\n",
            "At iterate   16    f=  3.94871D+03    |proj g|=  4.30765D+00\n",
            "\n",
            "At iterate   17    f=  3.94854D+03    |proj g|=  5.64482D+00\n",
            "\n",
            "At iterate   18    f=  3.94833D+03    |proj g|=  4.22488D+00\n",
            "\n",
            "At iterate   12    f=  3.96174D+03    |proj g|=  3.23845D+00\n",
            "\n",
            "At iterate    9    f=  3.95646D+03    |proj g|=  3.63847D+00\n",
            "\n",
            "At iterate   19    f=  3.94832D+03    |proj g|=  2.48448D-01\n",
            "\n",
            "At iterate   10    f=  3.95552D+03    |proj g|=  3.34534D+00\n",
            "\n",
            "At iterate   20    f=  3.94828D+03    |proj g|=  9.10623D-01\n",
            "\n",
            "At iterate   11    f=  3.95220D+03    |proj g|=  7.40378D+00\n",
            "\n",
            "At iterate   30    f=  3.94667D+03    |proj g|=  5.33744D+00\n",
            "\n",
            "At iterate   21    f=  3.94828D+03    |proj g|=  3.70290D-01\n",
            "\n",
            "At iterate   12    f=  3.94859D+03    |proj g|=  8.69711D+00\n",
            "\n",
            "At iterate   13    f=  3.95950D+03    |proj g|=  1.79231D+01\n",
            "\n",
            "At iterate   31    f=  3.94665D+03    |proj g|=  4.24853D+00\n",
            "\n",
            "At iterate   22    f=  3.94828D+03    |proj g|=  2.16685D-01\n",
            "\n",
            "At iterate   13    f=  3.94703D+03    |proj g|=  2.38075D+00\n",
            "\n",
            "At iterate   14    f=  3.95813D+03    |proj g|=  2.95198D+01\n",
            "\n",
            "At iterate   14    f=  3.94681D+03    |proj g|=  1.40485D+00\n",
            "\n",
            "At iterate   23    f=  3.94827D+03    |proj g|=  3.64208D-01\n",
            "\n",
            "At iterate   32    f=  3.94664D+03    |proj g|=  1.42541D+00\n",
            "\n",
            "At iterate   15    f=  3.95691D+03    |proj g|=  1.08717D+01\n",
            "\n",
            "At iterate   15    f=  3.94674D+03    |proj g|=  2.08486D+00\n",
            "\n",
            "At iterate   33    f=  3.94664D+03    |proj g|=  2.34258D-01\n",
            "\n",
            "At iterate   16    f=  3.94664D+03    |proj g|=  1.41459D+00\n",
            "\n",
            "At iterate   34    f=  3.94664D+03    |proj g|=  3.26492D-01\n",
            "\n",
            "At iterate   24    f=  3.94824D+03    |proj g|=  9.75206D-01\n",
            "\n",
            "At iterate   16    f=  3.95652D+03    |proj g|=  1.09315D+00\n",
            "\n",
            "At iterate   17    f=  3.94662D+03    |proj g|=  5.79713D+00\n",
            "\n",
            "At iterate   25    f=  3.94821D+03    |proj g|=  7.04626D-01\n",
            "\n",
            "At iterate   17    f=  3.95649D+03    |proj g|=  1.59612D+00\n",
            "\n",
            "At iterate   18    f=  3.94660D+03    |proj g|=  1.24913D-01\n",
            "\n",
            "At iterate   35    f=  3.94664D+03    |proj g|=  4.69525D-01\n",
            "\n",
            "At iterate   36    f=  3.94663D+03    |proj g|=  9.22437D-01\n",
            "\n",
            "At iterate   18    f=  3.95630D+03    |proj g|=  9.74633D-01\n",
            "\n",
            "At iterate   26    f=  3.94820D+03    |proj g|=  2.20618D-01\n",
            "\n",
            "At iterate   19    f=  3.94659D+03    |proj g|=  6.65893D-01\n",
            "\n",
            "\n",
            "At iterate   20    f=  3.94659D+03    |proj g|=  4.73828D-01\n",
            "At iterate   27    f=  3.94820D+03    |proj g|=  1.64860D-01\n",
            "\n",
            "At iterate   19    f=  3.95575D+03    |proj g|=  9.21509D+00\n",
            "\n",
            "At iterate   37    f=  3.94663D+03    |proj g|=  1.22206D+00\n",
            "\n",
            "At iterate   21    f=  3.94659D+03    |proj g|=  9.71767D-01\n",
            "\n",
            "At iterate   22    f=  3.94658D+03    |proj g|=  4.89699D-01\n",
            "\n",
            "At iterate   28    f=  3.94819D+03    |proj g|=  1.43541D-01\n",
            "\n",
            "At iterate   29    f=  3.94819D+03    |proj g|=  7.92548D-03\n",
            "\n",
            "At iterate   20    f=  3.95572D+03    |proj g|=  1.41944D+01\n",
            "\n",
            "At iterate   38    f=  3.94663D+03    |proj g|=  5.27855D-01\n",
            "\n",
            "At iterate   23    f=  3.94658D+03    |proj g|=  9.52217D-02\n",
            "\n",
            "At iterate   30    f=  3.94819D+03    |proj g|=  1.70448D-01\n",
            "\n",
            "At iterate   31    f=  3.94819D+03    |proj g|=  2.48923D-02\n",
            "\n",
            "At iterate   24    f=  3.94658D+03    |proj g|=  9.94783D-02\n",
            "\n",
            "At iterate   21    f=  3.95560D+03    |proj g|=  3.68473D-01\n",
            "\n",
            "At iterate   39    f=  3.94663D+03    |proj g|=  1.26655D+00\n",
            "\n",
            "At iterate   25    f=  3.94658D+03    |proj g|=  1.37395D-01\n",
            "\n",
            "At iterate   32    f=  3.94819D+03    |proj g|=  3.15692D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     32     38      1     0     0   3.157D-02   3.948D+03\n",
            "  F =   3948.1929947789904     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate   26    f=  3.94658D+03    |proj g|=  2.89749D-01\n",
            "\n",
            "At iterate   22    f=  3.95559D+03    |proj g|=  1.88071D-01\n",
            "\n",
            "At iterate   40    f=  3.94663D+03    |proj g|=  2.59321D-01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.9s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.9s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   41    f=  3.94663D+03    |proj g|=  4.10297D-02\n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.95696D+01\n",
            "\n",
            "At iterate    1    f=  4.63653D+03    |proj g|=  1.72774D+02\n",
            "\n",
            "At iterate   27    f=  3.94658D+03    |proj g|=  9.93033D-02\n",
            "\n",
            "At iterate    2    f=  4.49778D+03    |proj g|=  3.53455D+02\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   23    f=  3.95559D+03    |proj g|=  1.59636D-01\n",
            "\n",
            "At iterate   42    f=  3.94663D+03    |proj g|=  3.59402D-02\n",
            "\n",
            "At iterate   28    f=  3.94658D+03    |proj g|=  2.85672D-01\n",
            "\n",
            "At iterate    3    f=  4.19770D+03    |proj g|=  2.80676D+02\n",
            "\n",
            "At iterate    4    f=  4.13131D+03    |proj g|=  1.53768D+02\n",
            "\n",
            "At iterate    5    f=  4.08931D+03    |proj g|=  4.49268D+01\n",
            "\n",
            "At iterate   29    f=  3.94658D+03    |proj g|=  5.33791D-02\n",
            "\n",
            "At iterate   43    f=  3.94662D+03    |proj g|=  2.69453D-02\n",
            "\n",
            "At iterate   24    f=  3.95558D+03    |proj g|=  1.25665D+00\n",
            "\n",
            "At iterate    6    f=  4.08490D+03    |proj g|=  2.35648D+01\n",
            "\n",
            "At iterate    7    f=  4.07898D+03    |proj g|=  1.17132D+01\n",
            "\n",
            "At iterate   30    f=  3.94658D+03    |proj g|=  1.66690D-02\n",
            "\n",
            "At iterate   31    f=  3.94658D+03    |proj g|=  1.13014D-01\n",
            "\n",
            "\n",
            "At iterate   44    f=  3.94662D+03    |proj g|=  3.15697D-01\n",
            "At iterate    8    f=  4.04997D+03    |proj g|=  2.52636D+01\n",
            "\n",
            "At iterate   25    f=  3.95556D+03    |proj g|=  2.27945D-01\n",
            "\n",
            "At iterate   32    f=  3.94658D+03    |proj g|=  1.02009D-01\n",
            "\n",
            "At iterate    9    f=  4.01209D+03    |proj g|=  4.99724D+01\n",
            "\n",
            "At iterate   10    f=  3.97443D+03    |proj g|=  4.70518D+01\n",
            "\n",
            "At iterate   45    f=  3.94662D+03    |proj g|=  1.39080D-01\n",
            "\n",
            "At iterate   11    f=  3.96167D+03    |proj g|=  1.72962D+01\n",
            "\n",
            "At iterate   26    f=  3.95555D+03    |proj g|=  1.09032D+00\n",
            "\n",
            "At iterate   12    f=  3.95953D+03    |proj g|=  2.04869D+00\n",
            "\n",
            "At iterate   33    f=  3.94658D+03    |proj g|=  8.01083D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     33     37      1     0     0   8.011D-02   3.947D+03\n",
            "  F =   3946.5770176923143     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate   13    f=  3.95928D+03    |proj g|=  7.09941D-01\n",
            "\n",
            "At iterate   46    f=  3.94662D+03    |proj g|=  6.68072D-02\n",
            "\n",
            "At iterate   14    f=  3.95901D+03    |proj g|=  1.04941D+00\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.1s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   47    f=  3.94662D+03    |proj g|=  5.66443D-02\n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.22876D+01\n",
            "\n",
            "At iterate   15    f=  3.95895D+03    |proj g|=  1.24953D+01\n",
            "\n",
            "At iterate   27    f=  3.95554D+03    |proj g|=  1.57855D+00\n",
            "\n",
            "At iterate   16    f=  3.95861D+03    |proj g|=  6.38742D+00\n",
            "\n",
            "At iterate    1    f=  4.63570D+03    |proj g|=  1.69659D+02\n",
            "\n",
            "At iterate    2    f=  4.49230D+03    |proj g|=  3.43872D+02\n",
            "\n",
            "At iterate    3    f=  4.19968D+03    |proj g|=  2.06480D+02\n",
            "\n",
            "At iterate   28    f=  3.95554D+03    |proj g|=  2.02572D-01\n",
            "\n",
            "At iterate    4    f=  4.10387D+03    |proj g|=  3.53113D+01\n",
            "\n",
            "At iterate    5    f=  4.09759D+03    |proj g|=  1.89862D+01\n",
            "\n",
            "At iterate   48    f=  3.94662D+03    |proj g|=  4.69531D-02\n",
            "\n",
            "At iterate   17    f=  3.95835D+03    |proj g|=  3.95670D-01\n",
            "\n",
            "At iterate    6    f=  4.08529D+03    |proj g|=  1.46684D+01\n",
            "\n",
            "At iterate    7    f=  4.00370D+03    |proj g|=  1.76491D+02\n",
            "\n",
            "At iterate   18    f=  3.95824D+03    |proj g|=  2.61404D+00\n",
            "\n",
            "At iterate   49    f=  3.94662D+03    |proj g|=  2.05931D-02\n",
            "\n",
            "At iterate    8    f=  3.97673D+03    |proj g|=  1.27952D+02\n",
            "\n",
            "At iterate   29    f=  3.95554D+03    |proj g|=  6.80582D-02\n",
            "\n",
            "At iterate   19    f=  3.95819D+03    |proj g|=  1.62139D+00\n",
            "\n",
            "At iterate    9    f=  3.96857D+03    |proj g|=  1.89445D+01\n",
            "\n",
            "At iterate   20    f=  3.95817D+03    |proj g|=  4.12091D-01\n",
            "\n",
            "At iterate   10    f=  3.96803D+03    |proj g|=  6.19791D+00\n",
            "\n",
            "At iterate   30    f=  3.95554D+03    |proj g|=  6.87255D-02\n",
            "\n",
            "At iterate   11    f=  3.96790D+03    |proj g|=  9.66515D+00\n",
            "\n",
            "At iterate   21    f=  3.95816D+03    |proj g|=  7.15942D-01\n",
            "\n",
            "At iterate   50    f=  3.94662D+03    |proj g|=  2.49346D-03\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     50     55      1     0     0   2.493D-03   3.947D+03\n",
            "  F =   3946.6238047910010     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate   12    f=  3.96727D+03    |proj g|=  1.68372D+01\n",
            "\n",
            "At iterate   31    f=  3.95553D+03    |proj g|=  1.07707D-01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   10.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   10.2s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   22    f=  3.95815D+03    |proj g|=  1.15393D+00\n",
            "\n",
            "At iterate   13    f=  3.96549D+03    |proj g|=  2.83741D+01\n",
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  2.11146D+01\n",
            "\n",
            "At iterate   14    f=  3.96267D+03    |proj g|=  3.08283D+01\n",
            "\n",
            "At iterate   23    f=  3.95814D+03    |proj g|=  1.00138D+00\n",
            "\n",
            "At iterate   32    f=  3.95553D+03    |proj g|=  9.88394D-02\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " This problem is unconstrained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   15    f=  3.96256D+03    |proj g|=  4.21876D+01\n",
            "\n",
            "At iterate    1    f=  4.63521D+03    |proj g|=  1.67983D+02\n",
            "\n",
            "At iterate   16    f=  3.95995D+03    |proj g|=  2.66651D+01\n",
            "\n",
            "At iterate    2    f=  4.48519D+03    |proj g|=  3.55572D+02\n",
            "\n",
            "At iterate    3    f=  4.18378D+03    |proj g|=  2.47059D+02\n",
            "\n",
            "At iterate   33    f=  3.95553D+03    |proj g|=  4.49436D-02\n",
            "\n",
            "At iterate   24    f=  3.95813D+03    |proj g|=  1.26889D+00\n",
            "\n",
            "At iterate   17    f=  3.95894D+03    |proj g|=  7.78210D+00\n",
            "\n",
            "At iterate   34    f=  3.95553D+03    |proj g|=  8.28929D-02\n",
            "\n",
            "At iterate    4    f=  4.09248D+03    |proj g|=  1.79428D+02\n",
            "\n",
            "At iterate   25    f=  3.95812D+03    |proj g|=  7.14843D-02\n",
            "\n",
            "At iterate    5    f=  4.07839D+03    |proj g|=  5.08759D+01\n",
            "\n",
            "At iterate   26    f=  3.95812D+03    |proj g|=  8.17047D-02\n",
            "\n",
            "At iterate    6    f=  4.07665D+03    |proj g|=  1.21588D+01\n",
            "\n",
            "At iterate    7    f=  4.07552D+03    |proj g|=  1.19798D+01\n",
            "\n",
            "At iterate   27    f=  3.95812D+03    |proj g|=  2.21672D-01\n",
            "\n",
            "At iterate   18    f=  3.95879D+03    |proj g|=  8.31340D-01\n",
            "\n",
            "At iterate    8    f=  4.06782D+03    |proj g|=  5.09630D+01\n",
            "\n",
            "At iterate   35    f=  3.95553D+03    |proj g|=  1.07785D-02\n",
            "\n",
            "At iterate   28    f=  3.95812D+03    |proj g|=  1.46701D-01\n",
            "\n",
            "At iterate    9    f=  4.05386D+03    |proj g|=  1.04908D+02\n",
            "\n",
            "At iterate   10    f=  4.02313D+03    |proj g|=  1.66742D+02\n",
            "\n",
            "At iterate   19    f=  3.95876D+03    |proj g|=  1.11352D+00\n",
            "\n",
            "At iterate   29    f=  3.95812D+03    |proj g|=  2.71324D-02\n",
            "\n",
            "At iterate   11    f=  3.98381D+03    |proj g|=  1.76038D+02\n",
            "\n",
            "At iterate   30    f=  3.95812D+03    |proj g|=  2.29219D-02\n",
            "\n",
            "At iterate   20    f=  3.95869D+03    |proj g|=  4.95424D+00\n",
            "\n",
            "At iterate   21    f=  3.95849D+03    |proj g|=  3.90290D+00\n",
            "\n",
            "\n",
            "At iterate   12    f=  3.95241D+03    |proj g|=  9.45337D+00\n",
            "At iterate   36    f=  3.95553D+03    |proj g|=  1.25595D-02\n",
            "\n",
            "At iterate   31    f=  3.95812D+03    |proj g|=  1.58966D-01\n",
            "\n",
            "At iterate   32    f=  3.95811D+03    |proj g|=  1.04555D-02\n",
            "\n",
            "At iterate   13    f=  3.94774D+03    |proj g|=  1.24122D+00\n",
            "\n",
            "At iterate   22    f=  3.95843D+03    |proj g|=  1.36543D+01\n",
            "\n",
            "At iterate   14    f=  3.94694D+03    |proj g|=  1.97236D+01\n",
            "\n",
            "At iterate   15    f=  3.94666D+03    |proj g|=  4.27087D+00\n",
            "\n",
            "At iterate   23    f=  3.95819D+03    |proj g|=  1.00047D+01\n",
            "\n",
            "At iterate   37    f=  3.95553D+03    |proj g|=  2.87140D-03\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     37     42      1     0     0   2.871D-03   3.956D+03\n",
            "  F =   3955.5306906570268     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate   16    f=  3.94634D+03    |proj g|=  9.98816D-01\n",
            "\n",
            "At iterate   17    f=  3.94536D+03    |proj g|=  7.26335D-01\n",
            "\n",
            "At iterate   33    f=  3.95811D+03    |proj g|=  4.94139D-03\n",
            "\n",
            "At iterate   24    f=  3.95778D+03    |proj g|=  4.91814D-01\n",
            "\n",
            "At iterate   18    f=  3.94487D+03    |proj g|=  2.20711D+00\n",
            "\n",
            "At iterate   34    f=  3.95811D+03    |proj g|=  1.40546D-03\n",
            "\n",
            "At iterate   19    f=  3.94479D+03    |proj g|=  1.40849D+00\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    9.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    9.2s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "At iterate   20    f=  3.94475D+03    |proj g|=  4.98444D-01\n",
            "\n",
            "At iterate   21    f=  3.94474D+03    |proj g|=  1.43291D-01\n",
            "\n",
            "At iterate   25    f=  3.95776D+03    |proj g|=  3.33097D-01\n",
            "\n",
            "At iterate   22    f=  3.94471D+03    |proj g|=  1.15549D-01\n",
            "\n",
            "At iterate   23    f=  3.94471D+03    |proj g|=  6.49050D-01\n",
            "\n",
            "At iterate   26    f=  3.95773D+03    |proj g|=  2.25376D-01\n",
            "\n",
            "At iterate   24    f=  3.94471D+03    |proj g|=  7.35503D-01\n",
            "\n",
            "At iterate   35    f=  3.95811D+03    |proj g|=  4.31876D-04\n",
            "\n",
            "At iterate   25    f=  3.94471D+03    |proj g|=  1.57150D-02\n",
            "\n",
            "At iterate   27    f=  3.95773D+03    |proj g|=  1.76456D+00\n",
            "\n",
            "At iterate   36    f=  3.95811D+03    |proj g|=  5.34724D-03\n",
            "\n",
            "At iterate   26    f=  3.94471D+03    |proj g|=  2.80681D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "At iterate   28    f=  3.95766D+03    |proj g|=  2.50990D-01\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     36     42      1     0     0   5.347D-03   3.958D+03\n",
            "  F =   3958.1147471059317     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate   27    f=  3.94471D+03    |proj g|=  2.50403D-01\n",
            "\n",
            "At iterate   28    f=  3.94471D+03    |proj g|=  1.36962D-01\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.9s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.9s finished\n",
            " This problem is unconstrained.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.5s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.5s finished\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RUNNING THE L-BFGS-B CODE\n",
            "\n",
            "           * * *\n",
            "\n",
            "Machine precision = 2.220D-16\n",
            " N =        11076     M =           10\n",
            "\n",
            "At X0         0 variables are exactly at the bounds\n",
            "\n",
            "At iterate    0    f=  4.67874D+03    |proj g|=  1.99592D+01\n",
            "\n",
            "At iterate   29    f=  3.94471D+03    |proj g|=  3.63635D-02\n",
            "\n",
            "At iterate   30    f=  3.94471D+03    |proj g|=  4.54740D-03\n",
            "\n",
            "At iterate   31    f=  3.94471D+03    |proj g|=  1.62897D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     31     36      1     0     0   1.629D-02   3.945D+03\n",
            "  F =   3944.7069829692182     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "\n",
            "At iterate    1    f=  4.63574D+03    |proj g|=  1.70246D+02\n",
            "\n",
            "At iterate    2    f=  4.49008D+03    |proj g|=  3.57565D+02\n",
            "\n",
            "At iterate   29    f=  3.95766D+03    |proj g|=  1.06298D+00\n",
            "\n",
            "At iterate    3    f=  4.18756D+03    |proj g|=  2.74110D+02\n",
            "\n",
            "At iterate   30    f=  3.95765D+03    |proj g|=  2.91205D-01\n",
            "\n",
            "At iterate    4    f=  4.14858D+03    |proj g|=  4.61440D+02\n",
            "\n",
            "At iterate   31    f=  3.95765D+03    |proj g|=  8.65127D-02\n",
            "\n",
            "At iterate    5    f=  4.08081D+03    |proj g|=  5.84134D+01\n",
            "\n",
            "At iterate   32    f=  3.95765D+03    |proj g|=  7.65384D-02\n",
            "\n",
            "At iterate    6    f=  4.07874D+03    |proj g|=  2.00660D+01\n",
            "\n",
            "At iterate   33    f=  3.95765D+03    |proj g|=  2.44505D+00\n",
            "\n",
            "At iterate   34    f=  3.95764D+03    |proj g|=  2.58408D-01\n",
            "\n",
            "At iterate    7    f=  4.07752D+03    |proj g|=  1.40610D+01\n",
            "[LibLinear]iter  1 act 7.303e+02 pre 7.201e+02 delta 3.044e+01 f 4.679e+03 |g| 5.442e+01 CG   5\n",
            "iter  2 act 3.622e+00 pre 3.411e+00 delta 3.044e+01 f 3.948e+03 |g| 9.320e+00 CG   3\n",
            "iter  3 act 1.727e-01 pre 1.700e-01 delta 3.044e+01 f 3.945e+03 |g| 1.387e+00 CG   3\n",
            "iter  4 act 2.337e-03 pre 2.336e-03 delta 3.044e+01 f 3.945e+03 |g| 1.156e-01 CG   4\n",
            "iter  5 act 4.227e-06 pre 4.227e-06 delta 3.044e+01 f 3.945e+03 |g| 4.044e-03 CG   4\n",
            "[LibLinear]iter  1 act 7.301e+02 pre 7.200e+02 delta 3.036e+01 f 4.679e+03 |g| 5.391e+01 CG   5\n",
            "iter  2 act 3.122e+00 pre 2.991e+00 delta 3.036e+01 f 3.949e+03 |g| 8.188e+00 CG   3\n",
            "iter  3 act 1.352e-01 pre 1.341e-01 delta 3.036e+01 f 3.946e+03 |g| 1.079e+00 CG   4\n",
            "\n",
            "At iterate   35    f=  3.95764D+03    |proj g|=  5.45551D-02\n",
            "iter  4 act 2.082e-04 pre 2.082e-04 delta 3.036e+01 f 3.945e+03 |g| 3.859e-02 CG   4\n",
            "[LibLinear]iter  1 act 7.289e+02 pre 7.189e+02 delta 3.040e+01 f 4.679e+03 |g| 5.381e+01 CG   5\n",
            "iter  2 act 3.103e+00 pre 2.960e+00 delta 3.040e+01 f 3.950e+03 |g| 8.513e+00 CG   3\n",
            "\n",
            "At iterate    8    f=  4.06831D+03    |proj g|=  1.13835D+01\n",
            "iter  3 act 1.306e-01 pre 1.295e-01 delta 3.040e+01 f 3.947e+03 |g| 1.129e+00 CG   3\n",
            "\n",
            "At iterate   36    f=  3.95764D+03    |proj g|=  9.15157D-02\n",
            "iter  4 act 2.069e-03 pre 2.069e-03 delta 3.040e+01 f 3.947e+03 |g| 1.027e-01 CG   4\n",
            "iter  5 act 4.582e-06 pre 4.582e-06 delta 3.040e+01 f 3.947e+03 |g| 4.253e-03 CG   4\n",
            "[LibLinear]iter  1 act 7.201e+02 pre 7.106e+02 delta 3.025e+01 f 4.679e+03 |g| 5.368e+01 CG   5\n",
            "iter  2 act 2.962e+00 pre 2.825e+00 delta 3.025e+01 f 3.959e+03 |g| 8.544e+00 CG   3\n",
            "iter  3 act 1.360e-01 pre 1.349e-01 delta 3.025e+01 f 3.956e+03 |g| 1.138e+00 CG   4\n",
            "iter  4 act 2.535e-04 pre 2.534e-04 delta 3.025e+01 f 3.956e+03 |g| 4.487e-02 CG   3\n",
            "iter  5 act 4.442e-06 pre 4.442e-06 delta 3.025e+01 f 3.956e+03 |g| 3.853e-03 CG   4\n",
            "\n",
            "At iterate   37    f=  3.95764D+03    |proj g|=  3.48411D-02\n",
            "[LibLinear]\n",
            "At iterate    9    f=  4.04366D+03    |proj g|=  2.39659D+01\n",
            "iter  1 act 7.271e+02 pre 7.169e+02 delta 3.034e+01 f 4.679e+03 |g| 5.413e+01 CG   5\n",
            "\n",
            "At iterate   38    f=  3.95764D+03    |proj g|=  1.66384D-01\n",
            "iter  2 act 3.339e+00 pre 3.175e+00 delta 3.034e+01 f 3.952e+03 |g| 8.474e+00 CG   3\n",
            "iter  3 act 1.501e-01 pre 1.488e-01 delta 3.034e+01 f 3.948e+03 |g| 1.220e+00 CG   3\n",
            "iter  4 act 3.052e-03 pre 3.051e-03 delta 3.034e+01 f 3.948e+03 |g| 1.274e-01 CG   4\n",
            "iter  5 act 7.057e-06 pre 7.057e-06 delta 3.034e+01 f 3.948e+03 |g| 5.437e-03 CG   4\n",
            "[LibLinear][LibLinear]\n",
            "At iterate   39    f=  3.95764D+03    |proj g|=  7.81046D-02\n",
            "\n",
            "At iterate   10    f=  4.00028D+03    |proj g|=  3.86565D+01\n",
            "iter  1 act 7.287e+02 pre 7.186e+02 delta 3.034e+01 f 4.679e+03 |g| 5.444e+01 CG   5\n",
            "iter  1 act 7.176e+02 pre 7.081e+02 delta 3.025e+01 f 4.679e+03 |g| 5.328e+01 CG   5\n",
            "iter  2 act 3.337e+00 pre 3.178e+00 delta 3.034e+01 f 3.950e+03 |g| 8.952e+00 CG   3\n",
            "iter  3 act 1.554e-01 pre 1.540e-01 delta 3.034e+01 f 3.947e+03 |g| 1.242e+00 CG   3\n",
            "\n",
            "At iterate   40    f=  3.95764D+03    |proj g|=  2.12485D-01\n",
            "iter  2 act 2.923e+00 pre 2.799e+00 delta 3.025e+01 f 3.961e+03 |g| 8.510e+00 CG   3\n",
            "iter  4 act 3.251e-03 pre 3.250e-03 delta 3.034e+01 f 3.947e+03 |g| 1.306e-01 CG   4\n",
            "iter  5 act 7.260e-06 pre 7.260e-06 delta 3.034e+01 f 3.947e+03 |g| 5.530e-03 CG   4\n",
            "[LibLinear]\n",
            "At iterate   41    f=  3.95764D+03    |proj g|=  2.71808D-01\n",
            "iter  1 act 7.175e+02 pre 7.079e+02 delta 3.026e+01 f 4.679e+03 |g| 5.400e+01 CG   5\n",
            "iter  2 act 3.425e+00 pre 3.243e+00 delta 3.026e+01 f 3.961e+03 |g| 9.021e+00 CG   3\n",
            "iter  3 act 1.620e-01 pre 1.603e-01 delta 3.026e+01 f 3.958e+03 |g| 1.346e+00 CG   3\n",
            "iter  3 act 1.248e-01 pre 1.239e-01 delta 3.025e+01 f 3.958e+03 |g| 1.080e+00 CG   4\n",
            "iter  4 act 3.044e-03 pre 3.043e-03 delta 3.026e+01 f 3.958e+03 |g| 1.308e-01 CG   4\n",
            "\n",
            "At iterate   42    f=  3.95764D+03    |proj g|=  1.19028D-01\n",
            "iter  5 act 6.862e-06 pre 6.861e-06 delta 3.026e+01 f 3.958e+03 |g| 5.392e-03 CG   4\n",
            "[LibLinear]iter  1 act 7.265e+02 pre 7.154e+02 delta 3.001e+01 f 4.679e+03 |g| 5.419e+01 CG   4\n",
            "\n",
            "At iterate   43    f=  3.95764D+03    |proj g|=  6.60271D-02\n",
            "iter  2 act 7.421e+00 pre 7.240e+00 delta 3.001e+01 f 3.952e+03 |g| 1.075e+01 CG   4\n",
            "\n",
            "At iterate   11    f=  3.96142D+03    |proj g|=  1.83384D+01\n",
            "iter  3 act 9.516e-02 pre 9.448e-02 delta 3.001e+01 f 3.945e+03 |g| 1.247e+00 CG   3\n",
            "iter  4 act 2.408e-03 pre 2.407e-03 delta 3.001e+01 f 3.945e+03 |g| 1.014e-01 CG   4\n",
            "iter  4 act 2.273e-04 pre 2.273e-04 delta 3.025e+01 f 3.958e+03 |g| 3.927e-02 CG   4\n",
            "iter  5 act 3.698e-06 pre 3.698e-06 delta 3.001e+01 f 3.945e+03 |g| 4.183e-03 CG   4\n",
            "[LibLinear][LibLinear]iter  1 act 7.303e+02 pre 7.201e+02 delta 3.044e+01 f 4.679e+03 |g| 5.442e+01 CG   5\n",
            "iter  2 act 3.622e+00 pre 3.411e+00 delta 3.044e+01 f 3.948e+03 |g| 9.320e+00 CG   3\n",
            "iter  3 act 1.727e-01 pre 1.700e-01 delta 3.044e+01 f 3.945e+03 |g| 1.387e+00 CG   3\n",
            "\n",
            "At iterate   44    f=  3.95764D+03    |proj g|=  1.27352D-01\n",
            "iter  4 act 2.337e-03 pre 2.336e-03 delta 3.044e+01 f 3.945e+03 |g| 1.156e-01 CG   4\n",
            "iter  5 act 4.227e-06 pre 4.227e-06 delta 3.044e+01 f 3.945e+03 |g| 4.044e-03 CG   4\n",
            "[LibLinear]iter  1 act 7.261e+02 pre 7.163e+02 delta 3.035e+01 f 4.679e+03 |g| 5.382e+01 CG   5\n",
            "\n",
            "At iterate   45    f=  3.95764D+03    |proj g|=  3.73953D-02\n",
            "\n",
            "At iterate   12    f=  3.95413D+03    |proj g|=  4.46693D+01\n",
            "iter  1 act 7.301e+02 pre 7.200e+02 delta 3.036e+01 f 4.679e+03 |g| 5.391e+01 CG   5\n",
            "\n",
            "At iterate   46    f=  3.95764D+03    |proj g|=  5.11680D-01\n",
            "iter  2 act 2.926e+00 pre 2.792e+00 delta 3.035e+01 f 3.953e+03 |g| 8.138e+00 CG   3\n",
            "iter  2 act 3.122e+00 pre 2.991e+00 delta 3.036e+01 f 3.949e+03 |g| 8.188e+00 CG   3\n",
            "iter  3 act 1.352e-01 pre 1.341e-01 delta 3.036e+01 f 3.946e+03 |g| 1.079e+00 CG   4\n",
            "\n",
            "At iterate   47    f=  3.95764D+03    |proj g|=  1.87174D-01\n",
            "iter  4 act 2.082e-04 pre 2.082e-04 delta 3.036e+01 f 3.945e+03 |g| 3.859e-02 CG   4\n",
            "[LibLinear]iter  3 act 1.219e-01 pre 1.210e-01 delta 3.035e+01 f 3.950e+03 |g| 1.090e+00 CG   3\n",
            "\n",
            "At iterate   13    f=  3.95284D+03    |proj g|=  1.61484D+01\n",
            "iter  1 act 7.289e+02 pre 7.189e+02 delta 3.040e+01 f 4.679e+03 |g| 5.381e+01 CG   5\n",
            "iter  2 act 3.103e+00 pre 2.960e+00 delta 3.040e+01 f 3.950e+03 |g| 8.513e+00 CG   3\n",
            "\n",
            "At iterate   48    f=  3.95764D+03    |proj g|=  3.53546D-02\n",
            "iter  3 act 1.306e-01 pre 1.295e-01 delta 3.040e+01 f 3.947e+03 |g| 1.129e+00 CG   3\n",
            "iter  4 act 2.545e-03 pre 2.544e-03 delta 3.035e+01 f 3.950e+03 |g| 1.153e-01 CG   4\n",
            "iter  4 act 2.069e-03 pre 2.069e-03 delta 3.040e+01 f 3.947e+03 |g| 1.027e-01 CG   4\n",
            "iter  5 act 4.582e-06 pre 4.582e-06 delta 3.040e+01 f 3.947e+03 |g| 4.253e-03 CG   4\n",
            "[LibLinear]iter  1 act 7.201e+02 pre 7.106e+02 delta 3.025e+01 f 4.679e+03 |g| 5.368e+01 CG   5\n",
            "iter  2 act 2.962e+00 pre 2.825e+00 delta 3.025e+01 f 3.959e+03 |g| 8.544e+00 CG   3\n",
            "iter  3 act 1.360e-01 pre 1.349e-01 delta 3.025e+01 f 3.956e+03 |g| 1.138e+00 CG   4\n",
            "\n",
            "At iterate   49    f=  3.95764D+03    |proj g|=  2.92622D-02\n",
            "iter  4 act 2.535e-04 pre 2.534e-04 delta 3.025e+01 f 3.956e+03 |g| 4.487e-02 CG   3\n",
            "iter  5 act 4.442e-06 pre 4.442e-06 delta 3.025e+01 f 3.956e+03 |g| 3.853e-03 CG   4\n",
            "[LibLinear]\n",
            "At iterate   14    f=  3.95212D+03    |proj g|=  6.64791D+00\n",
            "iter  5 act 4.413e-06 pre 4.413e-06 delta 3.035e+01 f 3.950e+03 |g| 4.257e-03 CG   4\n",
            "[LibLinear]iter  1 act 7.271e+02 pre 7.169e+02 delta 3.034e+01 f 4.679e+03 |g| 5.413e+01 CG   5\n",
            "iter  2 act 3.339e+00 pre 3.175e+00 delta 3.034e+01 f 3.952e+03 |g| 8.474e+00 CG   3\n",
            "iter  3 act 1.501e-01 pre 1.488e-01 delta 3.034e+01 f 3.948e+03 |g| 1.220e+00 CG   3\n",
            "iter  4 act 3.052e-03 pre 3.051e-03 delta 3.034e+01 f 3.948e+03 |g| 1.274e-01 CG   4\n",
            "\n",
            "At iterate   50    f=  3.95764D+03    |proj g|=  5.35285D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     50     58      1     0     0   5.353D-02   3.958D+03\n",
            "  F =   3957.6415867179931     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
            "iter  5 act 7.057e-06 pre 7.057e-06 delta 3.034e+01 f 3.948e+03 |g| 5.437e-03 CG   4\n",
            "[LibLinear][LibLinear]iter  1 act 7.175e+02 pre 7.079e+02 delta 3.026e+01 f 4.679e+03 |g| 5.400e+01 CG   5\n",
            "iter  1 act 7.287e+02 pre 7.186e+02 delta 3.034e+01 f 4.679e+03 |g| 5.444e+01 CG   5\n",
            "iter  2 act 3.425e+00 pre 3.243e+00 delta 3.026e+01 f 3.961e+03 |g| 9.021e+00 CG   3\n",
            "iter  1 act 7.176e+02 pre 7.081e+02 delta 3.025e+01 f 4.679e+03 |g| 5.328e+01 CG   5\n",
            "\n",
            "At iterate   15    f=  3.95181D+03    |proj g|=  3.96098D+00\n",
            "iter  3 act 1.620e-01 pre 1.603e-01 delta 3.026e+01 f 3.958e+03 |g| 1.346e+00 CG   3\n",
            "iter  4 act 3.044e-03 pre 3.043e-03 delta 3.026e+01 f 3.958e+03 |g| 1.308e-01 CG   4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.8s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.8s finished\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "iter  5 act 6.862e-06 pre 6.861e-06 delta 3.026e+01 f 3.958e+03 |g| 5.392e-03 CG   4\n",
            "[LibLinear]iter  1 act 7.265e+02 pre 7.154e+02 delta 3.001e+01 f 4.679e+03 |g| 5.419e+01 CG   4\n",
            "iter  2 act 7.421e+00 pre 7.240e+00 delta 3.001e+01 f 3.952e+03 |g| 1.075e+01 CG   4\n",
            "iter  2 act 2.923e+00 pre 2.799e+00 delta 3.025e+01 f 3.961e+03 |g| 8.510e+00 CG   3\n",
            "iter  2 act 3.337e+00 pre 3.178e+00 delta 3.034e+01 f 3.950e+03 |g| 8.952e+00 CG   3\n",
            "iter  3 act 9.516e-02 pre 9.448e-02 delta 3.001e+01 f 3.945e+03 |g| 1.247e+00 CG   3\n",
            "iter  4 act 2.408e-03 pre 2.407e-03 delta 3.001e+01 f 3.945e+03 |g| 1.014e-01 CG   4\n",
            "iter  5 act 3.698e-06 pre 3.698e-06 delta 3.001e+01 f 3.945e+03 |g| 4.183e-03 CG   4\n",
            "iter  3 act 1.248e-01 pre 1.239e-01 delta 3.025e+01 f 3.958e+03 |g| 1.080e+00 CG   4\n",
            "[LibLinear]iter  1 act 7.261e+02 pre 7.163e+02 delta 3.035e+01 f 4.679e+03 |g| 5.382e+01 CG   5\n",
            "iter  2 act 2.926e+00 pre 2.792e+00 delta 3.035e+01 f 3.953e+03 |g| 8.138e+00 CG   3\n",
            "iter  3 act 1.219e-01 pre 1.210e-01 delta 3.035e+01 f 3.950e+03 |g| 1.090e+00 CG   3\n",
            "iter  4 act 2.545e-03 pre 2.544e-03 delta 3.035e+01 f 3.950e+03 |g| 1.153e-01 CG   4\n",
            "iter  5 act 4.413e-06 pre 4.413e-06 delta 3.035e+01 f 3.950e+03 |g| 4.257e-03 CG   4\n",
            "\n",
            "At iterate   16    f=  3.95066D+03    |proj g|=  3.78545D+00\n",
            "iter  3 act 1.554e-01 pre 1.540e-01 delta 3.034e+01 f 3.947e+03 |g| 1.242e+00 CG   3\n",
            "iter  4 act 3.251e-03 pre 3.250e-03 delta 3.034e+01 f 3.947e+03 |g| 1.306e-01 CG   4\n",
            "iter  4 act 2.273e-04 pre 2.273e-04 delta 3.025e+01 f 3.958e+03 |g| 3.927e-02 CG   4\n",
            "iter  5 act 7.260e-06 pre 7.260e-06 delta 3.034e+01 f 3.947e+03 |g| 5.530e-03 CG   4\n",
            "\n",
            "At iterate   17    f=  3.95030D+03    |proj g|=  3.14211D+00\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 2, change: 0.36420632\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 3, change: 0.22826404\n",
            "Epoch 2, change: 0.45117908\n",
            "Epoch 2, change: 0.37355653\n",
            "Epoch 4, change: 0.22463429\n",
            "Epoch 3, change: 0.22595775\n",
            "Epoch 3, change: 0.22330450\n",
            "Epoch 5, change: 0.16889536\n",
            "Epoch 4, change: 0.18669283\n",
            "Epoch 6, change: 0.12919254\n",
            "Epoch 7, change: 0.08737405\n",
            "Epoch 8, change: 0.13447442\n",
            "Epoch 9, change: 0.09331200\n",
            "Epoch 10, change: 0.02100373\n",
            "Epoch 4, change: 0.18430430\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s finished\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 11, change: 0.02399187\n",
            "Epoch 12, change: 0.07486672\n",
            "Epoch 13, change: 0.01465907\n",
            "Epoch 14, change: 0.00976210\n",
            "Epoch 15, change: 0.00731452\n",
            "Epoch 5, change: 0.18935712\n",
            "Epoch 16, change: 0.00253024\n",
            "Epoch 17, change: 0.00258237\n",
            "Epoch 6, change: 0.12521129\n",
            "Epoch 18, change: 0.00096255\n",
            "Epoch 7, change: 0.08488273\n",
            "Epoch 19, change: 0.00218210\n",
            "Epoch 8, change: 0.13608461\n",
            "Epoch 5, change: 0.17936945\n",
            "Epoch 20, change: 0.00040107\n",
            "Epoch 9, change: 0.09106915\n",
            "Epoch 6, change: 0.12136403\n",
            "Epoch 21, change: 0.00024950\n",
            "Epoch 7, change: 0.08997795\n",
            "Epoch 22, change: 0.00045059\n",
            "Epoch 8, change: 0.12910992\n",
            "Epoch 23, change: 0.00044528\n",
            "Epoch 24, change: 0.00018294\n",
            "convergence after 25 epochs took 0 seconds\n",
            "\n",
            "At iterate   18    f=  3.94971D+03    |proj g|=  1.34907D+00\n",
            "Epoch 9, change: 0.08502255\n",
            "Epoch 10, change: 0.02819738\n",
            "Epoch 10, change: 0.02835997\n",
            "Epoch 11, change: 0.02361680\n",
            "Epoch 11, change: 0.02140991\n",
            "Epoch 12, change: 0.07465572\n",
            "Epoch 13, change: 0.01342733\n",
            "Epoch 14, change: 0.00960266\n",
            "Epoch 15, change: 0.00802343\n",
            "Epoch 16, change: 0.00262487\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 12, change: 0.10240870\n",
            "Epoch 2, change: 0.37750156\n",
            "Epoch 13, change: 0.01437078\n",
            "Epoch 14, change: 0.01821150\n",
            "Epoch 3, change: 0.23342661\n",
            "Epoch 4, change: 0.22723428\n",
            "Epoch 5, change: 0.19435870\n",
            "Epoch 6, change: 0.13216118\n",
            "Epoch 7, change: 0.08961790\n",
            "Epoch 8, change: 0.13706643\n",
            "Epoch 17, change: 0.00245471\n",
            "Epoch 9, change: 0.10034561\n",
            "Epoch 18, change: 0.00084727\n",
            "Epoch 10, change: 0.02210699\n",
            "Epoch 19, change: 0.00217225\n",
            "Epoch 11, change: 0.02402590\n",
            "Epoch 20, change: 0.00042358\n",
            "Epoch 12, change: 0.08069311\n",
            "Epoch 13, change: 0.01600730\n",
            "Epoch 14, change: 0.00850746\n",
            "Epoch 21, change: 0.00026104\n",
            "Epoch 15, change: 0.00827141\n",
            "Epoch 22, change: 0.00041874\n",
            "Epoch 15, change: 0.00879120\n",
            "Epoch 16, change: 0.00249091\n",
            "Epoch 23, change: 0.00044891\n",
            "Epoch 17, change: 0.00237088\n",
            "Epoch 16, change: 0.00242312\n",
            "\n",
            "At iterate   19    f=  3.94959D+03    |proj g|=  1.13623D-01\n",
            "Epoch 24, change: 0.00010570\n",
            "Epoch 18, change: 0.00074014\n",
            "convergence after 25 epochs took 0 seconds\n",
            "Epoch 19, change: 0.00135520\n",
            "Epoch 20, change: 0.00032172\n",
            "Epoch 21, change: 0.00027189\n",
            "Epoch 22, change: 0.00014428\n",
            "Epoch 23, change: 0.00010871\n",
            "Epoch 24, change: 0.00021503\n",
            "Epoch 17, change: 0.00200429\n",
            "convergence after 25 epochs took 0 seconds\n",
            "Epoch 18, change: 0.00071906\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 2, change: 0.39541684\n",
            "Epoch 3, change: 0.22245710\n",
            "Epoch 4, change: 0.17990802\n",
            "Epoch 19, change: 0.00151995\n",
            "Epoch 5, change: 0.17498645\n",
            "Epoch 6, change: 0.09404559\n",
            "Epoch 7, change: 0.08739188\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 8, change: 0.12938302\n",
            "Epoch 2, change: 0.38444621\n",
            "Epoch 3, change: 0.20911497\n",
            "Epoch 9, change: 0.08873388\n",
            "Epoch 20, change: 0.00032776\n",
            "Epoch 10, change: 0.02079986\n",
            "Epoch 4, change: 0.18783627\n",
            "Epoch 11, change: 0.01783258\n",
            "Epoch 5, change: 0.16961799\n",
            "Epoch 12, change: 0.06950891\n",
            "Epoch 6, change: 0.10821149\n",
            "Epoch 21, change: 0.00036697\n",
            "Epoch 13, change: 0.01047607\n",
            "Epoch 7, change: 0.08362588\n",
            "Epoch 14, change: 0.00717779\n",
            "Epoch 8, change: 0.14113076\n",
            "Epoch 22, change: 0.00043572\n",
            "Epoch 15, change: 0.00889164\n",
            "Epoch 9, change: 0.08612131\n",
            "Epoch 16, change: 0.00296689\n",
            "Epoch 10, change: 0.02099888\n",
            "Epoch 17, change: 0.00234632\n",
            "Epoch 11, change: 0.03598183\n",
            "Epoch 12, change: 0.07623205\n",
            "Epoch 18, change: 0.00067331\n",
            "Epoch 19, change: 0.00185351\n",
            "Epoch 13, change: 0.01033047\n",
            "Epoch 20, change: 0.00029524\n",
            "Epoch 14, change: 0.00766932\n",
            "Epoch 23, change: 0.00031335\n",
            "Epoch 15, change: 0.00995291\n",
            "Epoch 21, change: 0.00071479\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 16, change: 0.00388984\n",
            "Epoch 22, change: 0.00016769\n",
            "Epoch 17, change: 0.00257928\n",
            "Epoch 23, change: 0.00031242\n",
            "Epoch 18, change: 0.00056587\n",
            "Epoch 24, change: 0.00013290\n",
            "Epoch 19, change: 0.00067757\n",
            "convergence after 25 epochs took 0 seconds\n",
            "\n",
            "Epoch 20, change: 0.00040024\n",
            "At iterate   20    f=  3.94958D+03    |proj g|=  1.60543D-01\n",
            "Epoch 21, change: 0.00033798\n",
            "Epoch 22, change: 0.00014477\n",
            "Epoch 23, change: 0.00014683\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 2, change: 0.42204602\n",
            "Epoch 3, change: 0.20925365\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 4, change: 0.17235603\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 2, change: 0.32526324\n",
            "Epoch 5, change: 0.15812657\n",
            "Epoch 2, change: 0.46232318\n",
            "Epoch 3, change: 0.15315331\n",
            "Epoch 6, change: 0.12125159\n",
            "Epoch 4, change: 0.14047066\n",
            "Epoch 7, change: 0.14334455\n",
            "Epoch 5, change: 0.13811828\n",
            "Epoch 6, change: 0.14418231\n",
            "Epoch 7, change: 0.12024204\n",
            "Epoch 8, change: 0.12016664\n",
            "Epoch 9, change: 0.06209898\n",
            "Epoch 10, change: 0.03222751\n",
            "Epoch 11, change: 0.01514788\n",
            "Epoch 12, change: 0.05496876\n",
            "Epoch 13, change: 0.00711247\n",
            "Epoch 3, change: 0.18627108\n",
            "Epoch 14, change: 0.00685871\n",
            "Epoch 4, change: 0.15659100\n",
            "Epoch 15, change: 0.00781749\n",
            "Epoch 8, change: 0.15355458\n",
            "Epoch 16, change: 0.00225562\n",
            "Epoch 17, change: 0.00159015\n",
            "Epoch 9, change: 0.07748485\n",
            "Epoch 18, change: 0.00054417\n",
            "Epoch 10, change: 0.04452085\n",
            "Epoch 5, change: 0.13795953\n",
            "Epoch 11, change: 0.03609273\n",
            "Epoch 19, change: 0.00189728\n",
            "Epoch 20, change: 0.00052098\n",
            "Epoch 12, change: 0.07780227\n",
            "Epoch 13, change: 0.01059016\n",
            "Epoch 21, change: 0.00096704\n",
            "Epoch 22, change: 0.00049920\n",
            "Epoch 14, change: 0.00856302\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s finished\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 15, change: 0.01030487\n",
            "Epoch 6, change: 0.09337338\n",
            "Epoch 16, change: 0.00358266\n",
            "Epoch 7, change: 0.12057847\n",
            "Epoch 23, change: 0.00032517\n",
            "Epoch 17, change: 0.00213571\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 8, change: 0.11875426\n",
            "Epoch 9, change: 0.06354791\n",
            "Epoch 18, change: 0.00072161\n",
            "Epoch 19, change: 0.00094069\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 20, change: 0.00041491\n",
            "Epoch 2, change: 0.38106112\n",
            "Epoch 21, change: 0.00099817\n",
            "Epoch 3, change: 0.22480126\n",
            "Epoch 22, change: 0.00023565\n",
            "Epoch 4, change: 0.18030994\n",
            "Epoch 23, change: 0.00012835\n",
            "Epoch 5, change: 0.19097428\n",
            "Epoch 10, change: 0.02308283\n",
            "convergence after 24 epochs took 0 secondsEpoch 6, change: 0.17537174\n",
            "Epoch 7, change: 0.14823720\n",
            "Epoch 8, change: 0.08668792\n",
            "Epoch 9, change: 0.07764628\n",
            "Epoch 10, change: 0.02470623\n",
            "Epoch 11, change: 0.01863197\n",
            "Epoch 12, change: 0.07395125\n",
            "Epoch 11, change: 0.02922881\n",
            "Epoch 13, change: 0.00973574\n",
            "Epoch 12, change: 0.05961761\n",
            "\n",
            "Epoch 14, change: 0.00790966\n",
            "Epoch 15, change: 0.01008815\n",
            "Epoch 16, change: 0.00233776\n",
            "Epoch 17, change: 0.00203519\n",
            "Epoch 13, change: 0.00951565\n",
            "Epoch 18, change: 0.00071966\n",
            "Epoch 19, change: 0.00261648\n",
            "Epoch 20, change: 0.00088160\n",
            "Epoch 21, change: 0.00094923\n",
            "Epoch 14, change: 0.00648685\n",
            "Epoch 22, change: 0.00057636\n",
            "Epoch 15, change: 0.00786903\n",
            "Epoch 23, change: 0.00045243\n",
            "Epoch 16, change: 0.00297331\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 17, change: 0.00189823\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 18, change: 0.00067532\n",
            "Epoch 19, change: 0.00071309\n",
            "Epoch 2, change: 0.45117908\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 3, change: 0.22595775\n",
            "Epoch 4, change: 0.18430430\n",
            "Epoch 2, change: 0.37355653\n",
            "Epoch 3, change: 0.22330450\n",
            "Epoch 4, change: 0.18669283\n",
            "Epoch 20, change: 0.00022852\n",
            "Epoch 5, change: 0.18935712\n",
            "Epoch 6, change: 0.12521129\n",
            "Epoch 7, change: 0.08488273\n",
            "Epoch 21, change: 0.00041751\n",
            "Epoch 8, change: 0.13608461\n",
            "Epoch 9, change: 0.09106915\n",
            "Epoch 10, change: 0.02819738\n",
            "Epoch 11, change: 0.02361680\n",
            "Epoch 12, change: 0.07465572\n",
            "Epoch 13, change: 0.01342733\n",
            "Epoch 22, change: 0.00017287\n",
            "Epoch 5, change: 0.17936945\n",
            "Epoch 14, change: 0.00960266\n",
            "Epoch 23, change: 0.00017632\n",
            "Epoch 6, change: 0.12136403\n",
            "Epoch 15, change: 0.00802343\n",
            "Epoch 16, change: 0.00262487\n",
            "Epoch 17, change: 0.00245471\n",
            "Epoch 18, change: 0.00084727\n",
            "convergence after 24 epochs took 1 seconds\n",
            "Epoch 19, change: 0.00217225\n",
            "Epoch 7, change: 0.08997795\n",
            "\n",
            "At iterate   21    f=  3.94956D+03    |proj g|=  2.07291D-01\n",
            "Epoch 20, change: 0.00042358\n",
            "Epoch 8, change: 0.12910992\n",
            "Epoch 21, change: 0.00026104\n",
            "Epoch 9, change: 0.08502255\n",
            "Epoch 22, change: 0.00041874\n",
            "Epoch 10, change: 0.02835997\n",
            "Epoch 23, change: 0.00044891\n",
            "Epoch 11, change: 0.02140991\n",
            "Epoch 24, change: 0.00010570\n",
            "Epoch 12, change: 0.10240870\n",
            "convergence after 25 epochs took 0 seconds\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 2, change: 0.37750156\n",
            "Epoch 3, change: 0.23342661\n",
            "Epoch 13, change: 0.01437078\n",
            "Epoch 4, change: 0.22723428\n",
            "Epoch 14, change: 0.01821150\n",
            "Epoch 5, change: 0.19435870\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 15, change: 0.00879120\n",
            "Epoch 6, change: 0.13216118\n",
            "Epoch 16, change: 0.00242312\n",
            "Epoch 7, change: 0.08961790\n",
            "Epoch 17, change: 0.00200429\n",
            "Epoch 8, change: 0.13706643\n",
            "Epoch 9, change: 0.10034561\n",
            "Epoch 10, change: 0.02210699\n",
            "Epoch 11, change: 0.02402590\n",
            "Epoch 18, change: 0.00071906\n",
            "Epoch 2, change: 0.36420632\n",
            "Epoch 12, change: 0.08069311\n",
            "Epoch 19, change: 0.00151995\n",
            "Epoch 3, change: 0.22826404\n",
            "Epoch 13, change: 0.01600730\n",
            "Epoch 4, change: 0.22463429\n",
            "Epoch 14, change: 0.00850746\n",
            "Epoch 5, change: 0.16889536\n",
            "Epoch 15, change: 0.00827141\n",
            "Epoch 16, change: 0.00249091\n",
            "Epoch 17, change: 0.00237088\n",
            "Epoch 18, change: 0.00074014\n",
            "Epoch 6, change: 0.12919254\n",
            "Epoch 19, change: 0.00135520\n",
            "Epoch 7, change: 0.08737405\n",
            "Epoch 20, change: 0.00032172\n",
            "Epoch 8, change: 0.13447442\n",
            "Epoch 21, change: 0.00027189\n",
            "Epoch 9, change: 0.09331200\n",
            "Epoch 22, change: 0.00014428\n",
            "Epoch 23, change: 0.00010871\n",
            "Epoch 24, change: 0.00021503\n",
            "convergence after 25 epochs took 0 seconds\n",
            "Epoch 20, change: 0.00032776\n",
            "Epoch 10, change: 0.02100373\n",
            "Epoch 21, change: 0.00036697\n",
            "Epoch 22, change: 0.00043572\n",
            "Epoch 11, change: 0.02399187\n",
            "Epoch 23, change: 0.00031335\n",
            "convergence after 24 epochs took 1 seconds\n",
            "Epoch 12, change: 0.07486672\n",
            "Epoch 13, change: 0.01465907\n",
            "Epoch 14, change: 0.00976210\n",
            "Epoch 15, change: 0.00731452\n",
            "Epoch 16, change: 0.00253024\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 2, change: 0.39541684\n",
            "Epoch 3, change: 0.22245710\n",
            "Epoch 4, change: 0.17990802\n",
            "Epoch 5, change: 0.17498645\n",
            "Epoch 6, change: 0.09404559\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
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            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 7, change: 0.08739188\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 8, change: 0.12938302\n",
            "Epoch 17, change: 0.00258237\n",
            "Epoch 2, change: 0.38444621\n",
            "Epoch 9, change: 0.08873388\n",
            "Epoch 18, change: 0.00096255\n",
            "Epoch 3, change: 0.20911497\n",
            "Epoch 10, change: 0.02079986\n",
            "Epoch 19, change: 0.00218210\n",
            "Epoch 4, change: 0.18783627\n",
            "Epoch 11, change: 0.01783258\n",
            "Epoch 20, change: 0.00040107\n",
            "Epoch 12, change: 0.06950891\n",
            "Epoch 21, change: 0.00024950\n",
            "Epoch 22, change: 0.00045059\n",
            "Epoch 13, change: 0.01047607\n",
            "Epoch 14, change: 0.00717779\n",
            "Epoch 23, change: 0.00044528\n",
            "Epoch 5, change: 0.16961799\n",
            "Epoch 15, change: 0.00889164\n",
            "Epoch 16, change: 0.00296689\n",
            "Epoch 6, change: 0.10821149\n",
            "Epoch 17, change: 0.00234632\n",
            "Epoch 18, change: 0.00067331\n",
            "Epoch 7, change: 0.08362588\n",
            "Epoch 24, change: 0.00018294\n",
            "Epoch 19, change: 0.00185351\n",
            "Epoch 8, change: 0.14113076\n",
            "convergence after 25 epochs took 0 seconds\n",
            "Epoch 20, change: 0.00029524\n",
            "Epoch 9, change: 0.08612131\n",
            "Epoch 10, change: 0.02099888\n",
            "Epoch 21, change: 0.00071479\n",
            "Epoch 22, change: 0.00016769\n",
            "Epoch 11, change: 0.03598183\n",
            "Epoch 23, change: 0.00031242\n",
            "Epoch 12, change: 0.07623205\n",
            "Epoch 24, change: 0.00013290\n",
            "Epoch 13, change: 0.01033047\n",
            "convergence after 25 epochs took 0 seconds\n",
            "Epoch 14, change: 0.00766932\n",
            "Epoch 15, change: 0.00995291\n",
            "Epoch 16, change: 0.00388984\n",
            "Epoch 17, change: 0.00257928\n",
            "Epoch 18, change: 0.00056587\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 19, change: 0.00067757\n",
            "Epoch 2, change: 0.42204602\n",
            "Epoch 2, change: 0.46232318\n",
            "Epoch 20, change: 0.00040024\n",
            "Epoch 3, change: 0.20925365\n",
            "Epoch 3, change: 0.18627108\n",
            "Epoch 21, change: 0.00033798\n",
            "Epoch 4, change: 0.17235603\n",
            "Epoch 4, change: 0.15659100\n",
            "Epoch 22, change: 0.00014477\n",
            "Epoch 5, change: 0.15812657\n",
            "Epoch 23, change: 0.00014683\n",
            "Epoch 6, change: 0.12125159\n",
            "Epoch 7, change: 0.14334455\n",
            "Epoch 8, change: 0.15355458\n",
            "Epoch 9, change: 0.07748485\n",
            "Epoch 10, change: 0.04452085\n",
            "Epoch 5, change: 0.13795953\n",
            "Epoch 11, change: 0.03609273\n",
            "Epoch 6, change: 0.09337338\n",
            "Epoch 12, change: 0.07780227\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 7, change: 0.12057847\n",
            "Epoch 13, change: 0.01059016\n",
            "Epoch 8, change: 0.11875426\n",
            "Epoch 14, change: 0.00856302\n",
            "Epoch 9, change: 0.06354791\n",
            "Epoch 15, change: 0.01030487\n",
            "Epoch 10, change: 0.02308283\n",
            "Epoch 16, change: 0.00358266\n",
            "\n",
            "At iterate   22    f=  3.94956D+03    |proj g|=  9.24085D-02\n",
            "Epoch 11, change: 0.02922881\n",
            "Epoch 17, change: 0.00213571\n",
            "Epoch 12, change: 0.05961761\n",
            "Epoch 18, change: 0.00072161\n",
            "Epoch 19, change: 0.00094069\n",
            "Epoch 20, change: 0.00041491\n",
            "Epoch 21, change: 0.00099817\n",
            "Epoch 22, change: 0.00023565\n",
            "Epoch 23, change: 0.00012835\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 13, change: 0.00951565\n",
            "Epoch 2, change: 0.32526324\n",
            "Epoch 14, change: 0.00648685\n",
            "Epoch 15, change: 0.00786903\n",
            "Epoch 3, change: 0.15315331\n",
            "Epoch 16, change: 0.00297331\n",
            "Epoch 4, change: 0.14047066\n",
            "Epoch 5, change: 0.13811828\n",
            "Epoch 1, change: 1.00000000\n",
            "Epoch 2, change: 0.38106112\n",
            "Epoch 17, change: 0.00189823\n",
            "Epoch 3, change: 0.22480126\n",
            "Epoch 18, change: 0.00067532\n",
            "Epoch 4, change: 0.18030994\n",
            "Epoch 6, change: 0.14418231\n",
            "Epoch 19, change: 0.00071309\n",
            "Epoch 5, change: 0.19097428\n",
            "Epoch 7, change: 0.12024204\n",
            "Epoch 6, change: 0.17537174\n",
            "Epoch 8, change: 0.12016664\n",
            "Epoch 7, change: 0.14823720\n",
            "Epoch 9, change: 0.06209898\n",
            "Epoch 8, change: 0.08668792\n",
            "Epoch 10, change: 0.03222751\n",
            "Epoch 9, change: 0.07764628\n",
            "Epoch 11, change: 0.01514788\n",
            "Epoch 10, change: 0.02470623\n",
            "Epoch 12, change: 0.05496876\n",
            "Epoch 11, change: 0.01863197\n",
            "Epoch 13, change: 0.00711247\n",
            "Epoch 20, change: 0.00022852\n",
            "Epoch 12, change: 0.07395125\n",
            "Epoch 14, change: 0.00685871\n",
            "Epoch 21, change: 0.00041751\n",
            "Epoch 13, change: 0.00973574\n",
            "Epoch 22, change: 0.00017287\n",
            "Epoch 14, change: 0.00790966\n",
            "Epoch 23, change: 0.00017632\n",
            "Epoch 15, change: 0.01008815\n",
            "Epoch 16, change: 0.00233776\n",
            "Epoch 17, change: 0.00203519\n",
            "Epoch 18, change: 0.00071966\n",
            "Epoch 19, change: 0.00261648\n",
            "Epoch 20, change: 0.00088160\n",
            "Epoch 21, change: 0.00094923\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 15, change: 0.00781749\n",
            "Epoch 22, change: 0.00057636\n",
            "Epoch 16, change: 0.00225562\n",
            "Epoch 23, change: 0.00045243\n",
            "Epoch 17, change: 0.00159015\n",
            "convergence after 24 epochs took 0 seconds\n",
            "Epoch 18, change: 0.00054417\n",
            "Epoch 19, change: 0.00189728\n",
            "Epoch 20, change: 0.00052098\n",
            "Epoch 21, change: 0.00096704\n",
            "Epoch 22, change: 0.00049920\n",
            "Epoch 23, change: 0.00032517\n",
            "convergence after 24 epochs took 0 seconds\n",
            "\n",
            "At iterate   23    f=  3.94956D+03    |proj g|=  1.40257D-02\n",
            "\n",
            "At iterate   24    f=  3.94956D+03    |proj g|=  6.66000D-02\n",
            "\n",
            "           * * *\n",
            "\n",
            "Tit   = total number of iterations\n",
            "Tnf   = total number of function evaluations\n",
            "Tnint = total number of segments explored during Cauchy searches\n",
            "Skip  = number of BFGS updates skipped\n",
            "Nact  = number of active bounds at final generalized Cauchy point\n",
            "Projg = norm of the final projected gradient\n",
            "F     = final function value\n",
            "\n",
            "           * * *\n",
            "\n",
            "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
            "11076     24     27      1     0     0   6.660D-02   3.950D+03\n",
            "  F =   3949.5575080390627     \n",
            "\n",
            "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.4s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.4s finished\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "66.14130115509033\n"
          ]
        }
      ],
      "source": [
        "\n",
        "start = time.time()\n",
        "tfidf_logit_pipeline.fit(x_train, y_train)\n",
        "end = time.time()\n",
        "print(end - start) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [],
      "source": [
        "#from numba import jit, cuda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxQ8oB5P0-dg",
        "outputId": "fad517fe-8a10-4853-da86-4e641ce440d9"
      },
      "outputs": [],
      "source": [
        "# @jit(target_backend='cuda')    \n",
        "# def tfidf_pipeline(x_train, y_train):\n",
        "#     start = time.time()\n",
        "#     tfidf_logit_pipeline.fit(x_train, y_train)\n",
        "#     end = time.time()\n",
        "#     print(end - start)  \n",
        "    \n",
        "# tfidf_pipeline(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDRTlTbk7iUm",
        "outputId": "e0f923b0-28d6-4311-e911-dc24e0aae887"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'solver': 'lbfgs', 'verbose': 0}\n",
            "0.6026666666666666\n"
          ]
        }
      ],
      "source": [
        "print(logit_classifier.best_params_)\n",
        "print(logit_classifier.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "I_nYoISO7jPF"
      },
      "outputs": [],
      "source": [
        "# @jit(target_backend='cuda')    \n",
        "# def pred_tfidf(x_test):\n",
        "#     return tfidf_logit_pipeline.predict(x_test)\n",
        "\n",
        "#  pred_tfidf(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5wuGryG7l9F",
        "outputId": "a46b22f1-ade2-4928-947e-b3907fb15214"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6076"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_test, valid_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvNxigMA7ogp",
        "outputId": "509bffb5-49ad-4861-bb41-a8d0b21eae12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Statistic Suite-1: \n",
            "Accuracy:  0.6076\n",
            "Precision:  0.6076\n",
            "Recall:  0.59375\n",
            "F1 Score:  0.6193247962747381\n",
            "Micro f1:  0.6076\n",
            "Sensitivity :  0.6237024221453287\n",
            "Specificity :  0.59375\n"
          ]
        }
      ],
      "source": [
        "# Getting the accuracy metric\n",
        "acc = accuracy_score(valid_pred, y_test)\n",
        "pre = precision_score(valid_pred, y_test, average = 'micro')\n",
        "rec = recall_score(valid_pred, y_test)\n",
        "f1 = f1_score(valid_pred, y_test)\n",
        "micro_f1 = f1_score(valid_pred, y_test, average = 'micro')\n",
        "\n",
        "print ('Model Performance Statistic Suite-1: ')\n",
        "print ('Accuracy: ', acc)\n",
        "print ('Precision: ', pre)\n",
        "print ('Recall: ',rec)\n",
        "print ('F1 Score: ', f1)\n",
        "print('Micro f1: ', micro_f1)\n",
        "\n",
        "cm = confusion_matrix(y_test,valid_pred)\n",
        "\n",
        "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Sensitivity : ', sensitivity )\n",
        "\n",
        "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
        "print('Specificity : ', specificity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "lFhbO3zK7rA4",
        "outputId": "08cc04dd-9047-4fa7-9bd9-b8f0ed903b03"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
        "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
        "ax.set_ylabel('Actual',fontsize=14)\n",
        "ax.set_xlabel('Predicted',fontsize=14)\n",
        "ax.xaxis.set_ticks_position('top')\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.tick_params(labelsize=12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {},
      "outputs": [],
      "source": [
        "# # def stemmed_words(doc):\n",
        "#       return (PorterStemmer().stem(w) for w in analyzer(doc))\n",
        "\n",
        "# def lemmatized_words(doc):\n",
        "#   return (WordNetLemmatizer().lemmatize(w) for w in analyzer(doc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "20 fits failed out of a total of 40.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "20 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\", line 382, in fit\n",
            "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py\", line 251, in fit\n",
            "    fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py\", line 374, in _sparse_fit\n",
            "    ) = libsvm_sparse.libsvm_sparse_train(\n",
            "  File \"sklearn/svm/_libsvm_sparse.pyx\", line 152, in sklearn.svm._libsvm_sparse.libsvm_sparse_train\n",
            "ValueError: b'C <= 0'\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [     nan      nan 0.629625 0.63125 ]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "********* Best Set of Parameters ********* \n",
            "\n",
            "\n",
            "Pipeline(steps=[('vect', CountVectorizer(token_pattern='\\\\b\\\\w+\\\\b')),\n",
            "                ('tfidf', TfidfTransformer()),\n",
            "                ('clf', SVC(C=1, kernel='poly'))])\n"
          ]
        }
      ],
      "source": [
        "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
        "#Classifier\n",
        "\n",
        "clf=SVC()\n",
        "\n",
        "clf_parameters = {'clf__kernel':('linear', 'poly'),#, 'rbf', 'sigmoid'),\n",
        "                  'clf__C' : (0,1),#,3,5,10),\n",
        "                  }    \n",
        "        \n",
        "#Feature Extraction\n",
        "pipeline = Pipeline([\n",
        "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
        "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
        "('clf', clf),]) \n",
        "    \n",
        "\n",
        "\n",
        "#Classificaion\n",
        "parameters={**clf_parameters} \n",
        "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
        "grid.fit(train_x,train_y)     \n",
        "clf= grid.best_estimator_  \n",
        "print('********* Best Set of Parameters ********* \\n\\n')\n",
        "print(clf)\n",
        "\n",
        "predicted = clf.predict(test_x)\n",
        "predicted =list(predicted)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.613"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(test_y, predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Statistic Suite-2: \n",
            "Accuracy:  0.613\n",
            "Precision:  0.613\n",
            "Recall:  0.6054613935969868\n",
            "F1 Score:  0.6242718446601943\n",
            "Micro f1:  0.613\n",
            "Sensitivity :  0.5818363273453094\n",
            "Specificity :  0.6442885771543087\n"
          ]
        }
      ],
      "source": [
        "# Getting the accuracy metric\n",
        "acc = accuracy_score(predicted, test_y)\n",
        "pre = precision_score(predicted, test_y, average = 'micro')\n",
        "rec = recall_score(predicted, test_y)\n",
        "f1 = f1_score(predicted, test_y)\n",
        "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
        "\n",
        "print ('Model Performance Statistic Suite-2: ')\n",
        "print ('Accuracy: ', acc)\n",
        "print ('Precision: ', pre)\n",
        "print ('Recall: ',rec)\n",
        "print ('F1 Score: ', f1)\n",
        "print('Micro f1: ', micro_f1)\n",
        "\n",
        "cm = confusion_matrix(predicted, test_y)\n",
        "\n",
        "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Sensitivity : ', sensitivity )\n",
        "\n",
        "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
        "print('Specificity : ', specificity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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fP7344ov2keZ/at++vdzd3bVz504dO3ZMly9flpeXlx555BH16NFDnTp1uuNuWf+8//z58/Xjjz9q1apVWrdunZycnFSqVCm1bNlS3bt3v+eHp3Kai4uLpk2bpokTJ2r58uXasmWLPD09Va1aNY0dO1axsbHphltJ+uijj+Tj46ONGzdq+fLl9mkC9xpuU+bZJiUlacSIESpbtmyq9iJFimjkyJF65ZVX9M477ygoKCjLu7sByB42I73fzgAAAIAHEHNuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFgAfY6dOnFRgYqKFDh6Y63q1bNwUGBjqoqrsTHBys4OBgR5cBwCJcHF0AADwoTp8+rSZNmqQ65urqKj8/P9WoUUN9+vRRUFCQg6rLXkOHDlVoaKhWr16tYsWKObocAMg0wi0A3CV/f3+1adNGknT9+nXt2bNHixcv1ooVKzRlyhRVr17dwRVKo0aNUlxcnKPLAIAcR7gFgLvk7++vV199NdWx0aNHa8KECfrqq680bdo0B1X2P0WKFHF0CQDgEMy5BYBs0K1bN0nS/v37JUmBgYHq1q2bzp8/r7ffflv16tVTUFCQwsLC7Ods375d/fr1U+3atVWxYkU1b95co0ePTnfENSkpSRMnTlSzZs1UqVIlNWvWTN99950Mw8iwnozm3K5atUq9evVS7dq1ValSJQUHB+utt97S0aNHJd2aAxsaGipJatKkiQIDA+3v558iIiL07rvvqlGjRqpYsaLq16+voUOHKjIyMsP7dujQQZUrV9bjjz+u4cOH68qVK7f7WAHgrjFyCwDZyGaz2f8cHR2tZ599Vnnz5tVTTz2lmzdvytPTU5I0Y8YMffTRR/L29lbjxo3l6+urP/74QxMmTFBYWJh++uknubm52a/13nvvad68eSpWrJi6dOmimzdvavLkydq9e/dd1ffZZ59p8uTJ8vHxUZMmTeTn56ezZ89qy5YtqlChggICAtS9e3eFhobq8OHD6t69u7y9vSVJRYsWtV9n79696t27t+Li4tSoUSOVKFFCkZGRWrRokTZs2KBZs2apePHi9v4LFizQkCFD5OnpqbZt28rLy0vr1q1Tz549FR8fn+q9AsC9INwCQDaYMWOGJKlSpUr2Y0ePHlX79u31ySefyNnZ2X782LFj+vTTTxUYGKgpU6YoX7589raJEyfqiy++0PTp09WrVy9JUlhYmObNm6egoCDNnDlTuXPnliT169dPbdu2zXSNa9eu1eTJkxUQEKCffvop1X0TExMVHR0tSXrhhRd0+PBhHT58WD169EjzQFlCQoIGDx6s5ORkzZkzR48++qi9bceOHerevbs+/fRTTZgwQZJ07do1ffzxx8qdO7fmzp2rUqVKSZIGDx6snj176uLFi6mCMwDcC6YlAMBdCg8P17hx4zRu3DiNGjVKXbp00ddff61cuXJp8ODB9n6urq566623UgVbSfrll1+UmJio9957L1XAlKQXX3xRvr6+Wrx4sf3YggULJEn9+/e3B1tJKlSokLp3757pulMC+Lvvvpvmvi4uLsqfP3+mrrNu3TpFRkaqd+/eqYKtJNWoUUNNmjTR+vXrde3aNUm3piNcu3ZNHTp0sAdb6dbnM2jQoEzXDwCZwcgtANyl8PBwjR8/XtL/lgJ7+umn1bdv31TzXIsVKyZfX9805+/du1eStHHjRm3ZsiVNu4uLi06ePGl/feTIEUm3guO/pXcsI/v27ZObm5tq1aqV6XPSs2fPHknSyZMnNW7cuDTtFy9eVHJysk6ePKlKlSrp8OHDkpTuKhLVqlWTiwv/KgKQffhGAYC7VL9+fU2aNOmO/TIaCU15iCrlZ/s7iYmJkZOTU5rRVkny8/PL1DWkW9MDChUqJCene/vRLqX+RYsW3bZfyoNxMTExktKv1dnZWT4+PvdUDwD8E+EWAO6Tfz5c9k8pD5Xt3LnT/ufb8fLyUnJysv7+++80I8FRUVGZrsfLy8s+qnovATel5gkTJqhx48aZuq+Ufq1JSUmKjo5WoUKFslwPAPwTc24BIIdVrlxZ0v+mJ9xJylSHHTt2pGlL79jt7hsfH69t27bdsW9K+E1OTk73OtL/pifcScqubTt37kzTtnv3biUmJmbqOgCQGYRbAMhhnTt3louLiz7++GOdOXMmTfvVq1d18OBB++uUFRG+/vprXb9+3X78/Pnz+umnnzJ93y5dukiSPv30U/vKCCkSExN16dIl++u8efNKks6ePZvmOk2bNlWRIkU0efJkbd++PU17QkJCqtDdpEkTeXp6at68eanmEickJGjMmDGZrh8AMoNpCQCQwwICAvT+++/rgw8+0JNPPqmGDRuqePHiio2N1enTp7Vt2zaFhIToo48+kiTVqVNH7du31/z589W6dWs1a9ZM8fHxWrJkiapWraq1a9dm6r4NGzZUr1699OOPP6pFixZq2rSp/Pz8dP78eW3ZskW9evXSCy+8YL/njz/+qBEjRqh58+by8PBQkSJF1K5dO7m5uWnMmDHq06ePunbtqjp16iggIEA2m01nzpzRjh075OPjo2XLlkm6NS1h+PDhGjp0qDp27KhWrVrJ09NT69atk7u7uwoUKHBfPmcADyfCLQA4QKdOnRQUFKQpU6Zo+/btWrt2rTw9PVWkSBG98MILateuXar+n3zyiUqVKqXZs2dr+vTpKly4sHr27KmWLVtmOtxK0pAhQ1StWjVNnz5dy5cv182bN1WgQAHVqVNH9erVs/dr2LCh3nrrLc2ZM0eTJ09WQkKCatWqZa+rcuXK+vXXX/XDDz9ow4YN2rVrl9zc3FSoUCE1bdpUrVq1SnXfkJAQeXl56ZtvvlFoaKi8vLzsO6OFhIRk+XMEgH+zGRnt3QgAAAA8YJhzCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3ALI0Lhx4+y7YwGAo3Tr1k3dunVzdBl4QBBuYUrz589XYGCgKlWqpPPnz6dp79atm55++mkHVGY9cXFxGjdunMLCwhxdCoBMOnLkiF577TU1btxYlSpV0hNPPKGePXtq2rRpji4ty44dO6Zx48bp9OnTji4FDzjCLUwtPj5eEydOdHQZlhYXF6fx48dr27Ztadpefvll7du3zwFVAcjIrl271KFDBx0+fFjPPPOMRowYoWeeeUZOTk53tR2z2Rw7dkzjx49XZGRkmrZJkyZp0qRJDqgKDyJ2KIOplS9fXrNnz1bfvn1VqFAhR5dz312/fl25c+d2dBl2Li4ucnHhawIwkwkTJsjLy0tz586Vt7d3qraoqKh7vr5hGLp586bc3d3v+VrZxc3NzdEl4AHCyC1M7aWXXlJycrK+//772/ZLTEzU119/raZNm6pixYoKDg7Wl19+qfj4+FT9goOD9dJLL2nHjh3q2LGjKlWqpCZNmmjBggWZqufatWv69NNPFRwcrIoVK6pu3brq2bOnDhw4YO+zY8cOvfbaa2rUqJEqVqyohg0b6v/+7/9048aNVNcaOnSoqlWrpvDwcPXp00fVqlXTm2++KUlKTk7W1KlT1bp1a1WqVEl16tRR7969tX//fvv58+bNU/fu3VW3bl1VrFhRTz31lGbMmJGm5v3796t3796qXbu2KleurODgYA0bNkySdPr0adWtW1eSNH78eAUGBiowMFDjxo2TlPGc24ULF6pjx46qUqWKatasqS5duuj333/P1GcI4N6Eh4erbNmyaYKtJPn5+dn/nNnviJTvxY0bN6p9+/aqXLmyfvnlF0nS1atX9X//93/277wGDRro7bff1uXLlyXd+nVtzJgxat++vapXr66qVauqc+fO2rp1a5r7/Pbbb2rfvr2qVaumxx57TK1bt9bUqVMl3ZqKNnDgQElS9+7d7d9FKdOl0ptze/PmTY0bN04tWrRQpUqVVL9+fQ0YMEDh4eFZ+VhhIQzJwNSKFSumtm3bavbs2erTp0+Go7fDhw9XaGioWrRooZ49e2rfvn367rvvdPz4cX399dep+p46dUoDBw5Ux44dFRISonnz5mno0KGqUKGCypUrd9t63n//fS1fvlxdu3ZVmTJlFB0drZ07d+r48eOqUKGCJGnZsmW6ceOGnn/+efn4+Gjfvn2aPn26zp07p7Fjx6a6XmJionr37q3q1atryJAh9pGSd999V/Pnz1eDBg3UsWNHJSUlaceOHdq7d68qVaokSZo5c6bKlSun4OBgubi4aO3atfrwww9lGIa6dOki6dYoTu/evZUvXz717dtX3t7eOn36tFauXClJ8vX11QcffKAPPvhAzZo1U7NmzSTptg+RjR8/XuPGjVO1atX02muvydXVVXv37tXWrVtVv379235+AO5d0aJFtXv3bh09elQBAQEZ9svMd0SKkydP6o033tCzzz6rTp06qVSpUoqNjVWXLl10/PhxdejQQY8++qj+/vtvrVmzRufPn5evr6+uXbumOXPm6Omnn9Yzzzyj2NhYzZ07Vy+++KLmzJmj8uXLS5I2bdqk119/XXXr1rX/Jf7EiRPatWuXevTooZo1a6pbt26aNm2a+vXrp9KlS0uSypQpk+57S0pK0ksvvaQtW7aoVatW6t69u2JjY7Vp0yYdPXpU/v7+2fFR40FlACY0b948IyAgwNi3b58RHh5uPProo8bHH39sb+/atavRqlUrwzAM49ChQ0ZAQIDx7rvvprrGZ599ZgQEBBhbtmyxH2vcuLEREBBgbN++3X4sKirKqFixovHZZ5/dsa7q1asbH3744W37xMXFpTn23XffGYGBgUZkZKT92JAhQ4yAgADj888/T9V3y5YtRkBAQKr3myI5Ofm29+nVq5fRpEkT++uVK1faP8eMREVFGQEBAcbYsWPTtI0dO9YICAiwv/7rr7+MoKAgo3///kZSUlKGtQG4f37//XejfPnyRvny5Y1nn33W+M9//mNs3LjRiI+PT9UvM98RhvG/78UNGzakOj5mzBgjICDAWLFiRZrrpPzznpiYaNy8eTNV25UrV4zHH3/cGDZsmP3YJ598Yjz22GNGYmJihu9r6dKlRkBAgLF169Y0bV27djW6du1qfz137lwjICDAmDx5coa14eHFtASYXvHixdWmTRvNnj1bFy5cSNO+fv16SVLPnj1THe/Vq1eq9hRly5ZVjRo17K99fX1VqlQpRURE3LEWb29v7d27N90VHFL8c57a9evXdfnyZVWrVk2GYejgwYNp+j///POpXq9YsUI2m00DBgxI09dms6V7n5iYGF2+fFm1atVSRESEYmJiJEleXl6SpHXr1ikhIeGO7+9OVq1apeTkZPXv319OTqm/Pv5ZG4D7p169evrll18UHBysw4cP64cfflDv3r3VoEEDrV692t4vM98RKYoVK6Ynnngi1bEVK1YoKCjI/ovOP6X88+7s7GyfD5ucnKzo6GglJiaqYsWKqb7vvL29FRcXp02bNt37B/D/a8uXL5+6du2aYW14eDEtAQ+EV155Rb/++qsmTpyo4cOHp2qLjIyUk5NTmp+hChQoIG9v7zRP3j7yyCNprp83b15duXJF0q2fu1Lmk/2z3c3NTW+++aaGDh2qRo0aqUKFCmrYsKHatWun4sWL2/ueOXNGY8eO1Zo1a+zXTHHt2rVUr11cXFS4cOFUx8LDw1WwYEH5+Pjc5hORdu7cqXHjxmnPnj2Ki4tL1RYTEyMvLy/VqlVLLVq00Pjx4zVlyhTVqlVLTZs2VevWrbP0gEZ4eLicnJwy/KkQQM6oXLmyxo8fr/j4eB0+fFirVq3SlClTNHDgQC1YsEBly5bN1HdEimLFiqW5R3h4uJo3b37HWkJDQ/Xjjz/q5MmTqf4S/c9rdu7cWUuXLrVPL6tXr55atmypBg0aZOXtKzw8XKVKleKBV6SL/1fggfDP0du+ffum2yezf1t3dna+bfvZs2fVpEmTVMd++ukn1a5dW0899ZRq1KihlStXatOmTZo0aZK+//57jRs3Tg0bNlRSUpJ69uypK1eu6MUXX1Tp0qWVO3dunT9/XkOHDlVycnKq67q5uaUZAc2M8PBwvfDCCypdurSGDh2qRx55RK6urlq/fr2mTJliv4/NZtPYsWO1Z88erV27Vhs3btQ777yjyZMna9asWcqTJ89d3xuAebi5ualy5cqqXLmySpYsqWHDhmnZsmVq06ZNpr4jUmR1ZYSFCxdq6NChatq0qXr37i0/Pz85Ozvru+++S/VrmJ+fnxYsWKDff/9dGzZs0IYNGzR//ny1a9dOo0aNuqfPAPg3wi0eGC+//LJ+/fXXNCsnFC1aVMnJyTp16lSqEcVLly7p6tWrKlq06F3dp0CBApo8eXKqY0FBQfY/FyxYUF26dFGXLl0UFRWlkJAQTZgwQQ0bNtTRo0f1119/adSoUWrXrp39nLv5Kc7f31+///67oqOjMxy9XbNmjeLj4/Xtt9+qSJEi9uMZbcRQtWpVVa1aVYMHD9aiRYv05ptvasmSJXrmmWfu6ic8f39/JScn6/jx4/YHRQCYQ8WKFSVJFy5cuOvviPT4+/vrzz//vG2f5cuXq3jx4ho/fnyq75J/Pzwr3QriwcHBCg4OVnJysj744APNmjVLr7zyikqUKHHX30V79+5VQkKCXF1dM30eHg7MucUDw9/fX23atNGsWbN08eJF+/GGDRtKkn1JmRQpATWlPbNy5cqlxx9/PNV/8ubNq6SkpDTz1Pz8/FSwYEH7kmMpo7CGYdj7GIZxVwurN2/eXIZhaPz48WnaUq6bMvr8z/vExMRo3rx5qfpfuXIlVR9J9lCaUrOHh4ekW0v+3EnTpk3l5OSkr7/+Os3Iz7/vA+D+2Lp1a7r/vKU8X1C6dOlMf0fcTvPmzXX48GH76ir/dLvvor1792rPnj2p+v/999+pXjs5OdlXZfn3d9G/v2czqu3vv//Wzz//nGFteHgxcosHSr9+/bRw4UKdPHnSvmxXUFCQQkJCNGvWLF29elU1a9bU/v37FRoaqqZNm6pOnTrZcu/Y2Fg1bNhQLVq0UFBQkHLnzq3Nmzdr//79Gjp0qKRb/1Lx9/fXqFGjdP78eXl6emr58uWZCo4p6tSpo7Zt22ratGk6deqUnnjiCSUnJ2vnzp2qXbu2unbtqnr16snV1VX9+vXTc889p9jYWM2ZM0d+fn6pgn9oaKhmzpyppk2byt/fX7GxsZo9e7Y8PT3tc93c3d1VtmxZLV26VCVLlpSPj4/KlSuX7hJDJUqUUL9+/fTNN9+oc+fOat68udzc3LR//34VLFhQb7zxxj1+ygDu5JNPPlFcXJyaNWum0qVLKyEhQbt27dLSpUtVtGhRtW/fXpcuXcrUd8Tt9O7dW8uXL9fAgQPVoUMHVahQQVeuXNGaNWv04YcfKigoSI0aNdKKFSvUv39/NWrUSKdPn9Yvv/yismXL6vr16/ZrDR8+XFeuXFGdOnVUqFAhnTlzRtOnT1f58uXtv7iVL19ezs7O+v777xUTEyM3NzfVqVMn1dq9Kdq1a6cFCxZo5MiR2rdvn6pXr664uDht2bJFzz//vJo2bZo9HzYeSIRbPFBKlCihNm3aKDQ0NNXxTz75RMWKFVNoaKhWrVql/Pnz66WXXkp3xYGscnd31/PPP69NmzZpxYoVMgxD/v7+ev/999W5c2dJkqurqyZMmKBPPvlE3333nXLlyqVmzZqpS5cuatu2babvNXLkSAUGBmru3Ln6z3/+Iy8vL1WsWFHVqlWTdCtEjx07Vl999ZVGjRql/Pnz6/nnn5evr6/eeecd+3Vq1aql/fv3a8mSJbp06ZK8vLxUuXJlff7556kegvvkk0/08ccfa+TIkUpISNCAAQMyXD9z4MCBKlasmKZPn67Ro0fLw8NDgYGBd/X+AGTd22+/rWXLlmn9+vWaNWuWEhISVKRIEXXu3Fkvv/yyvL295e3tnanviNvJkyePfv75Z40bN04rV65UaGio/Pz8VLduXfua4ylBetasWfr9999VtmxZ/fe//9WyZctSbemd8szEjBkzdPXqVRUoUEAtW7bUq6++av/Fq0CBAvrwww/13Xff6d1331VSUpJ++umndMNtSgj+9ttvtXjxYq1YsUI+Pj567LHHbrtONx4ONoPxewAAAFgEc24BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAVyUJMmTdSkSRNHlwEA2YbvNZgN4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBk2wzAMRxcBAAAAZAcXRxdgJoU/auDoEgAg25wbsUE3kq47ugwAyDbuzrnv2IdpCQAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsw9ThdvPmzfryyy8zbB89erS2bNmSgxUBAADAzEwdbr/55hudPXs2w/bz58/r22+/zcGKAAAAYGamDrdHjx5VlSpVMmyvVKmSjhw5koMVAQAAwMxMHW7j4+OVkJBw2/YbN27kYEUAAAAwM1OH23LlymnlypXpthmGoRUrVqhMmTI5XBUAAADMytThtmvXrtq1a5dee+01HTlyRImJiUpMTNThw4c1cOBA7dmzR926dXN0mQAAADAJF0cXcDtt27ZVRESEvvnmG61cuVJOTreyeHJysmw2m15++WWFhIQ4uEoAAACYhanDrSQNGDBAbdq00cqVKxURESFJ8vf3V9OmTeXv7+/g6gAAAGAmpg+30q0w27t3b0eXAQAAAJMz9ZxbAAAA4G6YauQ2KChITk5O2rNnj9zc3BQUFCSbzXbbc2w2mw4ePJhDFQIAAMDMTBVu+/fvL5vNJhcXl1SvAQAAgMywGYZhOLoIsyj8UQNHlwAA2ebciA26kXTd0WUAQLZxd859xz6mnnM7fvx4HT16NMP2P//8U+PHj8/BigAAAGBmpg+3R44cybD9zz//1Ndff52DFQEAAMDMTB1u7yQ6Olqurq6OLgMAAAAmYaoHyiRp+/btCgsLs79euXKlTp06laZfTEyMlixZooCAgJwsDwAAACZmunAbFhZmn0drs9m0YsUKrVixIt2+ZcuW1XvvvZeT5QEAAMDETLdawo0bNxQXFyfDMPT444/rww8/VPPmzVP1sdls8vDwUK5cubL13qyWAMBKWC0BgNVkZrUE043curu7y93dXZK0evVq+fr6ysPDw8FVAQAA4EFgunD7T0WLFk1zLC4uTr/99pvi4+PVsGHDdPsAAADg4WTqcPvOO+9o3759Wrx4sSQpPj5enTp10p9//ilJ8vLy0tSpU/Xoo486skwAAACYhKmXAgsLC1OzZs3srxcvXqw///xTn3/+uRYvXqz8+fOziQMAAADsTB1uL126lGrawapVq1SxYkU9/fTTKlu2rDp16qR9+/Y5sEIAAACYianDrYeHh2JiYiRJiYmJ2rZtm+rXr29vz5Mnj70dAAAAMPWc2woVKmj27NmqXbu21qxZo9jYWAUHB9vbw8PD5efn58AKAQAAYCamDreDBg3Siy++qA4dOsgwDLVo0UKVK1e2t69cuVKPPfaYAysEAACAmZg63FaqVElLly7Vrl275O3trVq1atnbrl69qs6dO6c6BgAAgIeb6XYocyR2KANgJexQBsBqHsgdyjJy7do1Xbt2TcnJyWnaihQp4oCKAAAAYDamD7czZszQlClTFBERkWGfQ4cO5WBFAAAAMCtTLwU2c+ZMffTRR/L399egQYNkGIZ69Oihvn37Kn/+/AoKCtKnn37q6DIBAABgEqYOt9OnT1f9+vX1ww8/qFOnTpKkhg0bavDgwVqyZIliY2MVHR3t2CIBAABgGqYOt+Hh4WrcuLEkydXVVZKUkJAgSfLy8lLHjh01Y8YMh9UHAAAAczF1uPXy8lJSUpIkydPTUx4eHjp37py9PU+ePLp06ZKjygMAAIDJmDrclitXTocPH7a/rlKlimbOnKnz58/r7NmzmjVrlkqWLOm4AgEAAGAqpg63bdq00Z9//qn4+HhJ0quvvqrjx4+rUaNGCg4O1smTJzVo0CDHFgkAAADTeOA2cYiIiNCaNWvk7OysevXqqVSpUtl2bTZxAGAlbOIAwGostYlDiuLFi6tHjx6OLgMAAAAmZOppCQcOHNDPP/+cYfvPP//MBg4AAACwM3W4HT16tLZs2ZJhe1hYmL766qucKwgAAACmZupwe+DAAdWoUSPD9urVq+uPP/7IwYoAAABgZqYOt7GxsXJ2ds6w3cnJSTExMTlYEQAAAMzM1OG2RIkS2rRpU4btGzduVPHixXOwIgAAAJiZqVdL6Nixo0aOHKmRI0eqf//+8vb2liRdvXpV48eP18aNG/X22287uEo8jB4vUVXze4xNt+2pSf20K/KgJMkmm7pVb63u1duqlG9RXY+/of3njurLDT9px+n/TakJLFBSbzbsqcqPBKqAp6/iEm7o6MVT+mbLTK08ujlH3hOAh9uxP49rwtcTdPDgIUVdipK7u7tKlymlHr16qFHjhvZ+770zQr8uWJTm/JKlSmrhb6H215GRZ/RUs1bp3uuzz0eq5VNPZv+bAGTycNu9e3cdPnxYU6dO1bRp01SwYEFJ0oULF5ScnKy2bdvqhRdecGyReKh9HzZXe86kXrHjr8uR9j+/3+wV9av7rObsW64pOxYor7unuj3WRqE9xqrN5P7a/f/PLZa3sPK45dbsvct0LuaSPFzd1ap8Q0177jO9ufi/mr4r7b9IACA7nT1zRrHXr6tN29YqULCAbty4oVUrVmlg/0F674Ph6tipg72vm5ub3v9oRKrzPb08071uy1ZPqv4T9VMdq1Klcva/AeD/M3W4tdlsGjlypNq2basVK1YoIiJCktSkSRM1b95ctWvXdnCFeNiFhe/V4kPr021ztjmre422WnRwrV5d8Kn9+KKD67TttVlqX6mZPdyuPrZVq49tTXX+j9vna0Wf7/VSnU6EWwD33RMNn9ATDZ9Idey5zs/q+Y6dNW3q9FTh1tnZWU+3SX9U9t/Kly+f6b5AdjBtuI2Li9Nbb72l5s2bq02bNqpTp46jSwLSlcfNQzcS4pVkJKU67ursrNyu7rp47e9Uxy/F/q2k5CTdSLx52+smG8k6c/WCqhYJyvaaASAznJ2dVeiRwjqw/0CatqSkJMXFxcnTM/0R23+6fj1Ori4ucnVzvR9lAqmYNtx6eHho8+bNatCALXFhXl+1GSbPXLmVmJyosPB9+mjlt9p79ogk6UZivHaePqBnqz6pHacPKCx8r7zdPfV6gx6KvhGjaTt/TXO93K7ucnfJJS/3PGoRUE/BZWtr4YG1Of22ADzErl+P082bN3Qt5prWrV2vTRs3qcWTzVP1uXHjhh6vVV834m7I29tbLVs9qUGvD1TuPGm3Rp3wzXf68vPRstlserRCeQ0YOECP16ubU28HDyHThlvp1jq2u3fvVqdOnRxdCpBKfFKiFh9cp9XHtirq+hUFFCipl+s+qwUvjFfrya/oj3N/SpL6h36i7zp8oG/av2c/96/LkWozub/Co8+mue77zfqrR422kqSk5CQtObxB7ywdnTNvCgAkffGfLzR39jxJt5bcbNI0WMOGD7W358+fXy/07qHy5csr2UjW5t83a9bM2Tpy5KgmTfleLi63ooWTzaa69eoquEljFSxUUJGnIzVtyjT1f2mAxnz9lRr8awoEkF1shmEYji4iIxEREerdu7datmyp559/XoULF76v9yv8EaPEyLqS+YpqTb/J2npqrzrPeEuSlD9PPr3f9GXF3LyujSd3qqCnr16t10XXE26q3ZQBuhx3JdU1yvr56xHvAirslV9tHm2s+KREDVnyhS7F/p3eLYHbOjdig24kXXd0GXjAnDxxUufPndfFixe1fNlKubq6aviId+SX3y/Dc374bpLGjRl/x1UQrkRfUUjrDvLy9kq1sgKQWe7OaX8d+DdTh9tq1aopKSlJCQkJkm7N/XFzc0vVx2azaefOndlyP8It7tW37UfoqaAGKjWyuWyyaVXfSdp8arfeXTbG3qeUbzGtf3mqJm6do09WT7jt9X7p8oXyunuq5aSX7nfpsCDCLbLDSy++rJiYGP38yzTZbLZ0+9y4cUN1a9RT25A2+uDj9297vTFfjtWPP0zWijXLVKhwoftRMiwsM+HW1NMSWrRokeE/SIAZnbl6Qblc3JTb1V1VigSpfKHSen/l+FR9Tl4+rT8vnlLN4hXveL3Fh9bp86ffUhm/4joeFXG/ygaADDVr3lQff/CJTv11SiVLlUy3j7u7u/L65NWVK1fveL3Cj9z6FfbKlSuEW9wXpg63n332maNLAO5KCZ8iiku4qdj4OBXIk0+S5GxLuxGgq7OLXJwy3lo6hbtLLkmSd647P40MAPfDzZu3VnaJibmWYZ/Y2FhF/x2tfPny3fF6pyNOS5Ly+d65L5AVpt5+FzArv9x50xx7tFAZNQ+sp/UntsuQoROXb420tqvQJFW/SoUDVMavuP2hM0nKn9snzfVcnJz1TOUWup5wQ0cu/pWt9QPAv0VFXU5zLCEhQYsWLpa7u7vKlCmtmzdvKjY2Nk2/id9+L8MwVO+Jx+3HLl9Oe73z5y9owfyFCggspwIFCmTvGwD+P1OP3KY4d+6cDh48qJiYGKU3Rbhdu3Y5XxQeat91+FA3Em9qe8QfuhT7twIKlFS3x1orLuGGPl39nSRp39mjWnd8u56t2lKeufJo/YltKuTpp161OuhG4k1NDJtjv95/nn5TXrnyaOupvTobc1EFPf3UvmIzBRQoofdXjNf1hDhHvVUAD4mPP/hEsddiVb3GYypYqIAuXYrSksVLdfLESb3x9uvKnSe3IiPP6NkOz6nlU0/apyhs2bRFGzf8rnr1H1fj4Eb2643+fIxOR0Sodp3aKlCwgM5EntHc2fMUFxent4e97Zg3iYeCqR8ou3nzpoYMGaIVK1YoOTlZNpvNHm7/ORf30KFDGV3irvBAGTKrd60O6lCpmUrmKyqvXHkUdT1aG0/u1Bfrp+ivv/+3/a67i5tervuc2lVoouL5HlFCUoLCwvdp1NpJOnD+mL1f2wrB6lytlcoXLK18Hnl1Lf669p09oknb5mvF0U2OeIuwAB4ow91YumSZFsxboD+PHtOVK1eUO3duPVqhvJ7v8pwa/f/QevVqjD77dJT2792nCxcvKjkpWcX9i6vV0y3VvWd3ubr+b5OGpb8t1ZxZc3XixEnFXI2Rl5enqlV/TH37vajyj5Z30LvEg+6BXy1h5MiRmj59ugYOHKhq1aqpW7du+uyzz1SwYEFNnTpVFy5c0KhRoxQQEJAt9yPcArASwi0Aq8lMuDX1nNvly5erffv26tu3r8qWLStJKlSokB5//HF999138vLy0s8//+zgKgEAAGAWpg63UVFRqly5sqRby4xIUlzc/+YetmjRQitXrnRIbQAAADAfU4fb/Pnz6++/b+3M5OHhobx58+rkyZP29mvXrtmXKAEAAABMvVpC5cqVtWvXLvvrxo0ba9KkSSpQoICSk5M1ZcoUVa1a1XEFAgAAwFRMHW67deumZcuWKT4+Xm5ubho4cKB2796tt9++tYSIv7+/3n33XQdXCQAAALMw9WoJ6UlOTtbRo0fl5OSk0qVLy8Ul+/I5qyUAsBJWSwBgNZlZLcHUI7fpcXJyUlBQkKPLAAAAgAmZ+oGyQ4cOafHixamObdy4UV26dNEzzzyjqVOnOqgyAAAAmJGpw+1///tfLVmyxP46IiJCAwYM0OnTpyVJn332mWbNmuWo8gAAAGAypg63hw8fVvXq1e2vFy5cKCcnJ4WGhmrOnDlq0aKFfvnlFwdWCAAAADMxdbiNiYmRj4+P/fX69etVr149+fr6SpLq1aunU6dOOag6AAAAmI2pw22BAgV0/PhxSdKFCxd04MAB1atXz94eGxsrJydTvwUAAADkIFOvltCkSRNNnz5d8fHx2rt3r9zc3NSsWTN7+5EjR1S8eHEHVggAAAAzMXW4HTRokC5fvqyFCxfKy8tLI0eOVP78+SXd2np32bJl6tKli4OrBAAAgFk8cJs4pEhOTlZsbKzc3d3l6uqaLddkEwcAVsImDgCsJjObODxQE1YTEhK0fft2xcTEyMnJSV5eXtkWbAEAAPDge6DC7ZUrV9S9e3f98ccfji4FAAAAJvRAhVtJekBnUQAAACAHPHDh1mazOboEAAAAmNQDF24ZuQUAAEBGTL0U2L/5+vpq9erVKlCggKNLAQAAgAk9UOHWyclJRYsWdXQZAAAAMCnTh9vjx49r3rx5On36tK5cuZJmWoLNZtPUqVMdVB0AAADMxNThdsGCBXrnnXfk4uKiUqVKydvbO00f5uACAAAghanD7fjx41W+fHl9//338vX1dXQ5AAAAMDlTr5Zw4cIFdejQgWALAACATDF1uA0MDNSFCxccXQYAAAAeEKYOt0OHDtXcuXO1a9cuR5cCAACAB4Cp59x+//338vLyUpcuXVS2bFk98sgjcnJKncdtNpu+/fZbB1UIAAAAMzF1uD169Kgk6ZFHHlFsbKyOHTuWpg/b8QIAACCFqcPtmjVrHF0CAAAAHiCmnnMLAAAA3A1Tj9ym2LZtm9atW6czZ85IkooUKaJGjRqpVq1aDq4MAAAAZmLqcBsfH6833nhDq1atkmEY9h3Krl69qsmTJ6tZs2b64osv5Orq6uBKAQAAYAamnpbw9ddfa+XKlerZs6d+//13bdu2Tdu2bdOmTZvUq1cvrVixQl9//bWjywQAAIBJmDrcLlq0SCEhIXr77beVP39++3E/Pz+99dZbateunX799VcHVggAAAAzMXW4vXjxoipXrpxhe+XKlXXx4sUcrAgAAABmZupwW7hwYW3bti3D9u3bt6tw4cI5WBEAAADMzNThtl27dlq6dKlGjBihEydOKCkpScnJyTpx4oTef/99LVu2TCEhIY4uEwAAACZh6tUS+vXrp4iICM2ePVtz5syxb72bnJwswzAUEhKifv36ObhKAAAAmIXNMAzD0UXcyeHDh7V+/Xr7OrdFixZVgwYNFBQUlK33KfxRg2y9HgA40rkRG3Qj6bqjywCAbOPunPuOfUw9cpsiKCgo24MsAAAArMd04bZ169Z31d9ms7EcGAAAACSZMNz6+Phkqt+lS5d08uRJ2Wy2+1sQAAAAHhimC7fTpk27bfvFixf1/fffa9asWXJ2dlabNm1yqDIAAACYnenCbUYuXbqkiRMnavbs2UpMTFTr1q318ssvy9/f39GlAQAAwCRMH25TRmr/GWpfeeUVFS9e3NGlAQAAwGRMG24vXryoiRMnas6cOUpMTFSbNm308ssvE2oBAACQIdOF2wsXLthDbVJSktq2bat+/foRagEAAHBHpgu3zZo1U3x8vMqXL6+XXnpJxYoV09WrV3XgwIEMz6lQoUIOVggAAACzMl24vXnzpiTp4MGDGjRo0G37GoYhm82mQ4cO5UBlAAAAMDvThduRI0c6ugQAAAA8oEwXbkNCQhxdAgAAAB5QTo4uAAAAAMguhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZLpnpdObMmSzfoEiRIlk+FwAAALgbmQq3wcHBstlsd31xm82mgwcP3vV5AAAAQFZkKty2a9cuS+EWAAAAyEmZCrefffbZ/a4DAAAAuGc8UAYAAADLINwCAADAMjI1LSE9SUlJWrp0qTZv3qwLFy4oPj4+TR+bzaapU6feU4EAAABAZmUp3F6/fl29evXS3r17ZRiGbDabDMOwt6e85iE0AAAA5KQsTUv49ttvtWfPHr366qvaunWrDMPQgAED9Pvvv2v06NEqXry4nnzySe3fvz+76wUAAAAylKVwu2LFClWtWlWvvPKKfHx87Mfz58+vli1b6qefftKWLVs0adKk7KoTAAAAuKMshduzZ8+qSpUq/7uIk5MSEhLsrwsXLqyGDRsqNDT03isEAAAAMilL4dbDw0NOTv871cvLSxcuXEjVJ3/+/Dp79uy9VQcAAADchSyF26JFi+rMmTP21+XKlVNYWJh9xQTDMLR161YVKFAge6oEAAAAMiFL4bZOnToKCwtTYmKipFvb8545c0bPPvusRo0apeeff16HDh1S8+bNs7VYAAAA4HaytBRYp06d5OPjo8uXL6tgwYLq2LGjDh06pBkzZujQoUOSpObNm+vVV1/N1mIBAACA27EZ/1yg9h5dvnxZERERKlKkyAM5JaHwRw0cXQIAZJtzIzboRtJ1R5cBANnG3Tn3HftkeYey9Pj6+srX1zc7LwkAAABkWpbm3AIAAABmlKWR2yZNmmSqn81m06pVq7JyCwAAAOCuZSncZjRN99q1a7p69aokqUCBAnJ1dc16ZQAAAMBdylK4XbNmTYZtp0+f1meffaZLly5p8uTJWS4MAAAAuFvZPue2WLFiGj16tK5evarRo0dn9+UBAACADN2XB8pcXV31+OOPa+nSpffj8gAAAEC67ttqCTdu3NCVK1fu1+UBAACANO5LuN2xY4d+++03lSpV6n5cHgAAAEhXlh4o6969e7rHk5KSdP78eUVGRkqS+vfvn/XKAAAAgLuUpXC7bdu2dI/bbDZ5e3urXr166tmzp+rVq3dPxQEAAAB3w2ZktGgtAAAA8IDJ0sitVe2OCnN0CQCQbar51ZatWTFHlwEA2cZYefqOfbL0QFmTJk30008/3bbPzz//nOltegEAAIDskKVwGxkZad9mNyNXr17VmTNnslQUAAAAkBX3bZ3bmJgYubm53a/LAwAAAGlkes7t9u3bU72OjIxMc0y6tRzYuXPntGjRIpUsWfKeCwQAAAAyK9Phtlu3brLZbJJuLfm1YMECLViwIN2+hmHIZrPpjTfeyJYiAQAAgMzIdLjt37+/bDabDMPQ119/rZo1a6p27dpp+jk5OSlv3ryqU6eOypQpk63FAgAAALeT6XD76quv2v+8bds2dejQQe3atbsfNQEAAABZkqV1bqdNm5bddQAAAAD3LEurJezatUsjR47UxYsX022/cOGCRo4cqT179txLbQAAAMBdyVK4nTx5stauXasCBQqk216wYEGtW7dOU6ZMuZfaAAAAgLuSpXC7f/9+Va9e/bZ9atSoob1792apKAAAACArshRuo6KiVLBgwdv2yZ8/v6KiorJUFAAAAJAVWQq33t7eOnv27G37nDlzRrlz585SUQAAAEBWZCncVqlSRStXrsww4J45c0arVq1StWrV7qk4AAAA4G5kKdz27NlTN27c0PPPP68FCxbowoULkm6tkhAaGqrnn39eN2/eVK9evbK1WAAAAOB2bIZhGFk5cerUqRo1apRSTk/ZvUy6tUvZO++8oy5dumRfpTlgd1SYo0sAgGxTza+2bM2KOboMAMg2xsrTd+yTpU0cJKlHjx6qXbu2fvnlF+3fv1/Xrl2Tl5eXKleurOeee04BAQGKj4+Xm5tbVm8BAAAA3JUsj9zezoEDBzR37lwtWbJEYWEPzmgoI7cArISRWwBWc19Hbv/t6tWr+vXXXzV37lwdOXJEhmHI3d09uy4PAAAA3NE9h9vNmzdr7ty5Wr16teLj42UYhqpWraoOHTqoZcuW2VEjAAAAkClZCrdnz57VvHnzNH/+fJ09e1aGYahQoUI6f/68QkJCNHLkyOyuEwAAALijTIfbhIQErVq1SnPnztXWrVuVlJQkDw8PtW7dWu3atVOdOnX06KOPysUl22Y6AAAAAHcl00n0iSee0JUrV2Sz2VS7dm21bdtWzZs3ZxcyAAAAmEamw210dLScnJzUo0cP9enTR76+vvezLgAAAOCuZXqHspCQEOXKlUtTpkxRgwYN1K9fPy1dulTx8fH3sz4AAAAg0zI9cjty5EgNHz5cS5Ys0dy5c7Vu3TqtX79enp6eatmypdq0aXM/6wQAAADuKMubOBw/flxz5szRr7/+qsuXL8tms0mSqlevrlGjRqlo0aLZWmhOYBMHAFbCJg4ArCYzmzjc8w5liYmJ9lUUNm/erOTkZDk5OalmzZoKCQlRu3bt7uXyOYpwC8BKCLcArCZHwu0/nTt3TvPmzVNoaKhOnz4tm82mQ4cOZdfl7zvCLQArIdwCsJrMhNtMP1CWGYULF1b//v21atUqTZ48WU899VR2Xh4AAAC4rfu240LdunVVt27d+3V5AAAAII1sHbkFAAAAHIlwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALMPU4TYiIkJr1qzJsH3NmjU6ffp0DlYEAAAAM3NxdAG385///EfXrl1TcHBwuu0///yzvL29NXr06ByuDAAAAGZk6pHb3bt36/HHH8+wvW7dutqxY0cOVgQAAAAzM3W4vXr1qvLkyZNhe+7cuRUdHZ1zBQEAAMDUTB1uH3nkEe3atSvD9p07d6pw4cI5WBEAAADMzNTh9umnn9Zvv/2mn376ScnJyfbjSUlJmjp1qpYsWaKnn37agRUCAADATGyGYRiOLiIj8fHx6tu3r7Zu3SpfX1+VKlVKknTy5EldvnxZtWrV0g8//CA3N7dsud/uqLBsuQ4AmEE1v9qyNSvm6DIAINsYK++8SpapV0twc3PTjz/+qNDQUK1cuVLh4eGSpMqVK6t58+Zq166dnJxMPfgMAACAHGTqkducxsgtACth5BaA1WRm5JZhTwAAAFiGqaYldOvWTU5OTpo0aZJcXFzUvXv3O55js9k0derUHKgOAAAAZmeqcCsp1aoImZkxwawKAAAApGDO7T8w5xaAlTDnFoDVPPBzbrdv367Lly9n2H758mVt3749BysCAACAmZk63Hbv3l2bNm3KsH3r1q2ZmpcLAACAh4Opw+2dZkzEx8fL2dk5h6oBAACA2ZnugbIzZ84oMjLS/vrEiRPpTj24evWqfvnlFxUpUiQnywMAAICJmS7czp8/X+PHj5fNZpPNZtOECRM0YcKENP0Mw5Czs7M+/PBDB1QJAAAAMzJduG3ZsqXKlSsnwzA0aNAgdevWTTVq1EjVx2azycPDQ+XLl1f+/PkdVCkAAADMxnThtkyZMipTpowkaeTIkapRo4aKFy/u4KoAAADwIDBduP2nkJCQdI9HREQoPj7eHoIBAAAAyeSrJUybNk2DBw9OdWzYsGFq3ry5nn76abVv315RUVEOqg4AAABmY+pwO3v2bPn5+dlfb9y4UaGhoerUqZOGDx+u06dPa/z48Q6sEAAAAGZi6mkJZ86cSTX1YOnSpSpWrJh9hYRLly5p4cKFjioPAAAAJmPqkdt/b+KwadMmNWjQwP66aNGiunTpUk6XBQAAAJMydbgtWbKkVq1aJenWlIQLFy6kCrfnzp2Tt7e3o8oDAACAyZh6WkLv3r31xhtvqGbNmoqLi1OZMmVUv359e3tYWJiCgoIcWCHwP6FTftWsiXNVrFRRff7zSPvxvWH7tWV1mI4dOK7IU2fkV9BP4+d/me41zp0+rxnfzNaBnQeUEJ+oUoEl1KlPB1Wo/mhOvQ0AD7lqZSvqg+6vq36FWnJ3y6UTZ09p4pIZGrfgxzR98+bx1tHJG1QwX351/Oglzdv4m73t0RIB+qD766perpIK5yuo6zfjdPDUUf13zgQt3roqJ98SHjKmDretWrWSj4+P1q9fL29vb3Xu3FkuLrdKjo6OVt68edW2bVsHVwlIURcua8FPvyqXR640bZtWbtGWVWEqFVhS+fLny/Aal85H6b2+H8nJyUlPd35Kudxzaf2Sjfq/Qf/V8LFDVL4af5EDcH81q95Aiz6arN3HD+jjn7/StbjrKlOkhIrlfyTd/h/1eFO53T3SbStRqJi8PDw1dcVcnYk6r9zuHupQv6UWfTxFfUcP0fdLfr6fbwUPMZvx74mtD7HdUWGOLgEPqDHvfa2r0TFKTk5WTHRMqpHbyxf/lnc+L7m4uGjUm18o4kRkuiO3P34+VasXrtN/p/+fipS49S+Smzdu6vXnh8jbx1sjJ3+UY+8H1lDNr7ZszYo5ugw8ILxye+ro5A3afHCnOn7UN81zL/9WoWSgdn+7TB9N/0ofv/BWmpHb9Dg5OWnnN0vl7ppL5Xs3ysbq8bAwVp6+Yx9Tz7kFHgSHdh9W2Lrt6jGoS7rtvgXy2X9xuJ3De4+qZEAJe7CVpFzuuVS9/mM6eeQvnY04l201A8C/dQ5up8K+BfXu5FEyDEO53T1ks9ky7D/mlQ8VummZNu7flul7JCcnK+LCGfl48rwM7h9TT0uQpMOHD2v69Ok6ePCgYmJujYz9k81msz90BuS05KRkTR49TcGtG8q/zL1tE52QkKA8XrnTHM/l7iZJOnn4Lz1SvPA93QMAMtK02hO6EntVRf0Ka8EHkxRYvIyuxcVq2qp5Gvzth7qZcNPet2ODVnr80Roq37uRSha6/XdfbncPebi5K28eb7Wp20wtazXWrHWL7vfbwUPM1CO3YWFheuaZZ7Ru3ToVLFhQERERKl68uAoWLKgzZ84od+7cqlmzpqPLxENs5YI1unQuSp36dLjnaxXxf0ThxyMUFxuX6vjhvUcl3ZreAAD3S7mipeTi5KKFH/6o5TvWq/2HffTjsll6uXV3TX7rC3s/dzd3fd73PY2e/71Onb/zT8RfvDRCl+bt1/GfNunzvu8pdNMyDRg//H6+FTzkTD1yO3bsWBUvXlyzZ89WfHy8Hn/8cb300kuqW7eu9u7dqz59+ujNN990dJl4SMVcidGc7+ep/Qtt5Z3v3n9iaxoSrJ2/79aYEV/r2b4dlcsjl1bOX60Th09KkuJvxt/zPQAgI54euZXHI7e+XfSTBn4zQpIU+vtSubm6qt/T3TRi6hc6FnlSQ5/rL1cXF/3fjHGZuu5X83/Q3A2/qYhfIXVq2FrOTs5yc3G9n28FDzlTj9wePHhQHTt2lKenp5ydnSXJPi2hSpUqevbZZzVmzBhHloiH2KyJ8+Tp7aknn2mWLderVreKXni9mw7tOaJhPUfo9eeGaPfmvXrupY6SJPfcaVdiAIDsEnfzhiRp5trUO3/OWLNAklS3/GMqUaiY3nqmn96d/B/F3rieqeseiTiu1bt/17RV89T6vRfk6ZFbiz6ekp2lA6mYeuTW2dlZefLkkSR5e3vLxcVFUVFR9vbixYvr+PHjjioPD7GzEee0euFa9RjYRZcv/W+6QEJ8gpKSknTh7EXlzuMhT2/Pu7rukx2bqVGrBgo/Fi4XVxeVLFdCaxatlyQ9Ujz9pXgAIDucuXxeFUsF6fzfF1MdvxB9ayfQfF4++qjHm4qMOqd1e7eoRKFbK3EU9i0gSSqQ11clChVT+IXI2660MHfDEk0cPEoBxUrr6OkT9+nd4GFm6nDr7++vv/76S9KtB8dKly6tVatWqU2bNpKkdevWKX/+/A6sEA+ryxf/lpFsaMro6Zoyenqa9tc6vKGWnZqrx6Cud31td49cCqhUzv76jx0H5JbLTYGVy93mLAC4NzuP7lfz6g1VNH/hVKGziF8hSdLF6Cj5FyyqckVL6eS0LWnO/3bgrSUQfdo9qiuxVzO8j0cud0m3NoAA7gdTh9uGDRtq3rx5euONN+Ti4qKePXtq2LBhat68uSQpPDxcr7/+uoOrxMOoeOliemPkwDTHZ02cqxvXb6jHoK4qVKzgPd/nyP4/tW39DjULCVZuz7QrKQBAdpm9fpGGPT9AvZ98Xmv3bLYff7FlZyUkJmjdvi0KvxCp/Hl9U51XsWSgPun5tkbN+kZbDu60T1co4OOni9FRqfq6OLuoe9MOun7j1m5lwP1g6nD7yiuvqHv37vb5tiEhIXJyctKKFSvk7Oysfv36qX379g6uEg8jbx8v1WxYPc3xJbOXS1KqtlPHwrVz425J0vnTFxQXe13zJ9+a01ainL+q168mSbp49pK+em+8atR/THn98ur0idNatWCt/MsU13MvPXO/3xKAh9ye4wc0aekv6t3yObk4O2v9vq1qVKWuOjVsrf+bOU5no87rbNT5NOdFX7s1Srv9yF4t3Lzcfvy7gaPkncdTG/aFKTLqnArnK6AuTUJU3r+cXp/wYabn7AJ3y9Th1tXVVfnypd6utG3btmy5iwfKySOnNPv7eamOpbxu8FR9e7j1yOOhfH4+Wj5vpa5djZVvgXx68plmCunRRh550t/eEgCyU78xQxV+IVI9W3RSSL0ndepCpAZ9877GhE6662vNWv+rej/5nF5u3U1+3vkUcz1WO//cpyE//J8WbVl5H6oHbjH19rvR0dE6d+6cgoKC0m0/cuSIChcurLx582bL/dh+F4CVsP0uAKt54LffHTlypEaMGJFh+/vvv69Ro0blYEUAAAAwM1OH261btyo4ODjD9saNG2vLlrRPbAIAAODhZOpwe/ny5TRzbv/Jx8cn1bq3AAAAeLiZOtwWKFBABw8ezLD9wIED8vX1zbAdAAAADxdTh9umTZtq3rx5Wr16dZq2VatWaf78+WratKkDKgMAAIAZmXopsFdffVVbtmzRgAEDFBQUpHLlbu3Q9Oeff+rw4cMqU6aMXnvtNQdXCQAAALMw9citl5eXZs2apZdfflmJiYlavny5li9frsTERL3yyiuaPXu2vL3Zvg8AAAC3mHad25s3b2rWrFkqX768atasmSP3ZJ1bAFbCOrcArOaBXuc2V65c+vzzz3Xy5ElHlwIAAIAHhGnDrSSVK1dOkZGRji4DAAAADwhTh9vBgwfrl19+0ebNmx1dCgAAAB4Apl4tYfr06fLx8VHv3r1VrFgxFStWTLly5UrVx2az6dtvv3VQhQAAADATU4fbo0ePSpIeeeQRJSUl6dSpU2n62Gy2nC4LAAAAJmXqcLtmzRpHlwAAAIAHiKnn3AIAAAB3w9Qjt/907do1Xbt2TcnJyWnaihQp4oCKAAAAYDamD7czZszQlClTFBERkWGfQ4cO5WBFAAAAMCtTT0uYOXOmPvroI/n7+2vQoEEyDEM9evRQ3759lT9/fgUFBenTTz91dJkAAAAwCVOH2+nTp6t+/fr64Ycf1KlTJ0lSw4YNNXjwYC1ZskSxsbGKjo52bJEAAAAwDVOH2/DwcDVu3FiS5OrqKklKSEiQJHl5ealjx46aMWOGw+oDAACAuZg63Hp5eSkpKUmS5OnpKQ8PD507d87enidPHl26dMlR5QEAAMBkTB1uy5Urp8OHD9tfV6lSRTNnztT58+d19uxZzZo1SyVLlnRcgQAAADAVU4fbNm3a6M8//1R8fLwk6dVXX9Xx48fVqFEjBQcH6+TJkxo0aJBjiwQAAIBp2AzDMBxdxN2IiIjQmjVr5OzsrHr16qlUqVLZdu3dUWHZdi0AcLRqfrVla1bM0WUAQLYxVp6+Yx9Tj9z+2/Hjx/Xrr7/q5MmTSkpKUoECBRxdEgAAAEzEdJs4TJ8+XdOmTdPMmTPl6+trP75mzRoNHDjQvlqCzWbT9OnTNWvWrFT9AAAA8PAy3cjtmjVrVLx48VSBNTExUcOHD5ezs7NGjhypRYsW6fXXX9eZM2c0YcIEB1YLAAAAMzFduD127JiqVq2a6lhYWJguX76sHj16KCQkROXKlVOfPn305JNPav369Y4pFAAAAKZjunAbHR2twoULpzq2ZcsW2Ww2NWvWLNXxxx57TGfPns3J8gAAAGBipgu3+fPnT7Mxw44dO+Tu7q6goKBUx93c3Ow7lwEAAACmC7cVK1ZUaGiorl27Jkn6888/tX//fj3xxBNycUn9/NuJEyfSjPICAADg4WW61RL69++vjh07qkWLFipbtqwOHDggm82mvn37pum7cuVK1alTxwFVAgAAwIxMN3IbGBioqVOnqkKFCrpw4YKqVKmiiRMnqmLFiqn6hYWFycPDQ08++aSDKgUAAIDZPHA7lN1P7FAGwErYoQyA1VhuhzIAAADgdgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACzDZhiG4egiAAAAgOzAyC0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAs4/8B01gV1kfWE/8AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
        "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
        "ax.set_ylabel('Actual',fontsize=14)\n",
        "ax.set_xlabel('Predicted',fontsize=14)\n",
        "ax.xaxis.set_ticks_position('top')\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.tick_params(labelsize=12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "********* Best Set of Parameters ********* \n",
            "\n",
            "\n",
            "Pipeline(steps=[('vect', CountVectorizer(token_pattern='\\\\b\\\\w+\\\\b')),\n",
            "                ('tfidf', TfidfTransformer()),\n",
            "                ('clf', MultinomialNB(alpha=2))])\n"
          ]
        }
      ],
      "source": [
        "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
        "#Classifier\n",
        "\n",
        "clf=MultinomialNB(fit_prior=True, class_prior=None)\n",
        "\n",
        "\n",
        "clf_parameters = {'clf__alpha':(0,1,2),}   \n",
        "        \n",
        "#Feature Extraction\n",
        "pipeline = Pipeline([\n",
        "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
        "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
        "('clf', clf),]) \n",
        "    \n",
        "\n",
        "\n",
        "#Classificaion\n",
        "parameters={**clf_parameters} \n",
        "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
        "grid.fit(train_x,train_y)     \n",
        "clf= grid.best_estimator_  \n",
        "print('********* Best Set of Parameters ********* \\n\\n')\n",
        "print(clf)\n",
        "\n",
        "predicted = clf.predict(test_x)\n",
        "predicted =list(predicted)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5985"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(test_y, predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Statistic Suite-3: \n",
            "Accuracy:  0.5985\n",
            "Precision:  0.5985\n",
            "Recall:  0.6162097735399285\n",
            "F1 Score:  0.562874251497006\n",
            "Micro f1:  0.5985\n",
            "Sensitivity :  0.6786427145708582\n",
            "Specificity :  0.5180360721442886\n"
          ]
        }
      ],
      "source": [
        "# Getting the accuracy metric\n",
        "acc = accuracy_score(predicted, test_y)\n",
        "pre = precision_score(predicted, test_y, average = 'micro')\n",
        "rec = recall_score(predicted, test_y)\n",
        "f1 = f1_score(predicted, test_y)\n",
        "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
        "\n",
        "print ('Model Performance Statistic Suite-3: ')\n",
        "print ('Accuracy: ', acc)\n",
        "print ('Precision: ', pre)\n",
        "print ('Recall: ',rec)\n",
        "print ('F1 Score: ', f1)\n",
        "print('Micro f1: ', micro_f1)\n",
        "\n",
        "cm = confusion_matrix(predicted, test_y)\n",
        "\n",
        "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Sensitivity : ', sensitivity )\n",
        "\n",
        "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
        "print('Specificity : ', specificity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
        "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
        "ax.set_ylabel('Actual',fontsize=14)\n",
        "ax.set_xlabel('Predicted',fontsize=14)\n",
        "ax.xaxis.set_ticks_position('top')\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.tick_params(labelsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Stemming applied to comments "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {},
      "outputs": [],
      "source": [
        "analyzer = CountVectorizer().build_analyzer()\n",
        "def stemmed_words(doc):\n",
        "  return (PorterStemmer().stem(w) for w in analyzer(doc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {},
      "outputs": [],
      "source": [
        "clf=MultinomialNB(alpha=0,fit_prior=True, class_prior=None)  \n",
        "vectorizer=TfidfVectorizer(analyzer = stemmed_words,ngram_range=(1,1),token_pattern=r'\\b\\w+\\b')\n",
        "tfidf = vectorizer.fit_transform(corpus)\n",
        "tfidf = tfidf.toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {},
      "outputs": [],
      "source": [
        "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "clf.fit(train_x,train_y)\n",
        "predicted = clf.predict(test_x)\n",
        "predicted =list(predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.571"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(test_y, predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Statistic Suite-4: \n",
            "Accuracy:  0.571\n",
            "Precision:  0.571\n",
            "Recall:  0.5704225352112676\n",
            "F1 Score:  0.5692771084337349\n",
            "Micro f1:  0.571\n",
            "Sensitivity :  0.5738522954091816\n",
            "Specificity :  0.5681362725450901\n"
          ]
        }
      ],
      "source": [
        "# Getting the accuracy metric\n",
        "acc = accuracy_score(predicted, test_y)\n",
        "pre = precision_score(predicted, test_y, average = 'micro')\n",
        "rec = recall_score(predicted, test_y)\n",
        "f1 = f1_score(predicted, test_y)\n",
        "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
        "\n",
        "print ('Model Performance Statistic Suite-4: ')\n",
        "print ('Accuracy: ', acc)\n",
        "print ('Precision: ', pre)\n",
        "print ('Recall: ',rec)\n",
        "print ('F1 Score: ', f1)\n",
        "print('Micro f1: ', micro_f1)\n",
        "\n",
        "cm = confusion_matrix(predicted, test_y)\n",
        "\n",
        "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Sensitivity : ', sensitivity )\n",
        "\n",
        "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
        "print('Specificity : ', specificity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
        "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
        "ax.set_ylabel('Actual',fontsize=14)\n",
        "ax.set_xlabel('Predicted',fontsize=14)\n",
        "ax.xaxis.set_ticks_position('top')\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.tick_params(labelsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lemmatization applied to comments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {},
      "outputs": [],
      "source": [
        "from nltk.stem import WordNetLemmatizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {},
      "outputs": [],
      "source": [
        "analyzer = CountVectorizer().build_analyzer()\n",
        "def lemmatized_words(doc):\n",
        "  return (WordNetLemmatizer().lemmatize(w) for w in analyzer(doc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "clf=MultinomialNB(alpha=0,fit_prior=True, class_prior=None)  \n",
        "vectorizer=TfidfVectorizer(analyzer = lemmatized_words,ngram_range=(1,1),token_pattern=r'\\b\\w+\\b')\n",
        "tfidf = vectorizer.fit_transform(corpus)\n",
        "tfidf = tfidf.toarray()\n",
        "# Training and Test Split           \n",
        "  \n",
        "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
        "\n",
        "#Classificaion    \n",
        "clf.fit(train_x,train_y)\n",
        "predicted = clf.predict(test_x)\n",
        "predicted =list(predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.568"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(test_y, predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Statistic Suite-5: \n",
            "Accuracy:  0.568\n",
            "Precision:  0.568\n",
            "Recall:  0.5682281059063137\n",
            "F1 Score:  0.5636363636363636\n",
            "Micro f1:  0.568\n",
            "Sensitivity :  0.5768463073852296\n",
            "Specificity :  0.5591182364729459\n"
          ]
        }
      ],
      "source": [
        "# Getting the accuracy metric\n",
        "acc = accuracy_score(predicted, test_y)\n",
        "pre = precision_score(predicted, test_y, average = 'micro')\n",
        "rec = recall_score(predicted, test_y)\n",
        "f1 = f1_score(predicted, test_y)\n",
        "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
        "\n",
        "print ('Model Performance Statistic Suite-5: ')\n",
        "print ('Accuracy: ', acc)\n",
        "print ('Precision: ', pre)\n",
        "print ('Recall: ',rec)\n",
        "print ('F1 Score: ', f1)\n",
        "print('Micro f1: ', micro_f1)\n",
        "\n",
        "cm = confusion_matrix(predicted, test_y)\n",
        "\n",
        "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Sensitivity : ', sensitivity )\n",
        "\n",
        "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
        "print('Specificity : ', specificity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
        "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
        "ax.set_ylabel('Actual',fontsize=14)\n",
        "ax.set_xlabel('Predicted',fontsize=14)\n",
        "ax.xaxis.set_ticks_position('top')\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.tick_params(labelsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Without removing stopwords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/naive_bayes.py:591: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "clf=MultinomialNB(alpha=0,fit_prior=True, class_prior=None)  \n",
        "vectorizer=TfidfVectorizer(ngram_range=(1,1),token_pattern=r'\\b\\w+\\b')\n",
        "tfidf = vectorizer.fit_transform(corpus)\n",
        "tfidf = tfidf.toarray()\n",
        "# Training and Test Split           \n",
        "  \n",
        "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
        "\n",
        "#Classificaion    \n",
        "clf.fit(train_x,train_y)\n",
        "predicted = clf.predict(test_x)\n",
        "predicted =list(predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.562"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(test_y, predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Performance Statistic Suite-6: \n",
            "Accuracy:  0.562\n",
            "Precision:  0.562\n",
            "Recall:  0.561\n",
            "F1 Score:  0.5615615615615616\n",
            "Micro f1:  0.562\n",
            "Sensitivity :  0.56187624750499\n",
            "Specificity :  0.5621242484969939\n"
          ]
        }
      ],
      "source": [
        "# Getting the accuracy metric\n",
        "acc = accuracy_score(predicted, test_y)\n",
        "pre = precision_score(predicted, test_y, average = 'micro')\n",
        "rec = recall_score(predicted, test_y)\n",
        "f1 = f1_score(predicted, test_y)\n",
        "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
        "\n",
        "print ('Model Performance Statistic Suite-6: ')\n",
        "print ('Accuracy: ', acc)\n",
        "print ('Precision: ', pre)\n",
        "print ('Recall: ',rec)\n",
        "print ('F1 Score: ', f1)\n",
        "print('Micro f1: ', micro_f1)\n",
        "\n",
        "cm = confusion_matrix(predicted, test_y)\n",
        "\n",
        "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
        "print('Sensitivity : ', sensitivity )\n",
        "\n",
        "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
        "print('Specificity : ', specificity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
        "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
        "ax.set_ylabel('Actual',fontsize=14)\n",
        "ax.set_xlabel('Predicted',fontsize=14)\n",
        "ax.xaxis.set_ticks_position('top')\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.tick_params(labelsize=12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {},
      "outputs": [],
      "source": [
        "# !deactivate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.8.2 64-bit",
      "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": "3.10.4"
    },
    "vscode": {
      "interpreter": {
        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}