sentiment_classifier_Logistic_Regression_Lexicon_dataset2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af6db9a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#pip install eli5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a50d083e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#pip install lime dalex neattext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ffa4e94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install seaborn altair streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7bdf3098",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0228ce",
   "metadata": {},
   "source": [
    "+ Emotion Detection In text\n",
    "+ Text Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85c4baee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "698bb737",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d115737",
   "metadata": {},
   "outputs": [],
   "source": [
    "import neattext.functions as nfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2fb9780b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d4f7e45",
   "metadata": {},
   "source": [
    "# Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81c6bfcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"cleaned_dataset_emotions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e24ef22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>i just feel really helpless and heavy hearted</td>\n",
       "      <td>fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ive enjoyed being able to slouch about relax a...</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i gave up my internship with the dmrg and am f...</td>\n",
       "      <td>fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i dont know i feel so lost</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i am a kindergarten teacher and i am thoroughl...</td>\n",
       "      <td>fear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text    label\n",
       "0      i just feel really helpless and heavy hearted     fear\n",
       "1  ive enjoyed being able to slouch about relax a...  sadness\n",
       "2  i gave up my internship with the dmrg and am f...     fear\n",
       "3                         i dont know i feel so lost  sadness\n",
       "4  i am a kindergarten teacher and i am thoroughl...     fear"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9fa2fd86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "joy         140779\n",
       "sadness     120989\n",
       "anger        57235\n",
       "fear         47664\n",
       "love         34497\n",
       "surprise     14959\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e9dc464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='label',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e017481",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2ca2a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BTC_ADDRESS_REGEX',\n",
       " 'CURRENCY_REGEX',\n",
       " 'CURRENCY_SYMB_REGEX',\n",
       " 'Counter',\n",
       " 'DATE_REGEX',\n",
       " 'EMAIL_REGEX',\n",
       " 'EMOJI_REGEX',\n",
       " 'HASTAG_REGEX',\n",
       " 'MASTERCard_REGEX',\n",
       " 'MD5_SHA_REGEX',\n",
       " 'MOST_COMMON_PUNCT_REGEX',\n",
       " 'NUMBERS_REGEX',\n",
       " 'PHONE_REGEX',\n",
       " 'PoBOX_REGEX',\n",
       " 'SPECIAL_CHARACTERS_REGEX',\n",
       " 'STOPWORDS',\n",
       " 'STOPWORDS_de',\n",
       " 'STOPWORDS_en',\n",
       " 'STOPWORDS_es',\n",
       " 'STOPWORDS_fr',\n",
       " 'STOPWORDS_ru',\n",
       " 'STOPWORDS_yo',\n",
       " 'STREET_ADDRESS_REGEX',\n",
       " 'TextFrame',\n",
       " 'URL_PATTERN',\n",
       " 'USER_HANDLES_REGEX',\n",
       " 'VISACard_REGEX',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__generate_text',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numbers_dict',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_lex_richness_herdan',\n",
       " '_lex_richness_maas_ttr',\n",
       " 'clean_text',\n",
       " 'defaultdict',\n",
       " 'digit2words',\n",
       " 'extract_btc_address',\n",
       " 'extract_currencies',\n",
       " 'extract_currency_symbols',\n",
       " 'extract_dates',\n",
       " 'extract_emails',\n",
       " 'extract_emojis',\n",
       " 'extract_hashtags',\n",
       " 'extract_html_tags',\n",
       " 'extract_mastercard_addr',\n",
       " 'extract_md5sha',\n",
       " 'extract_numbers',\n",
       " 'extract_pattern',\n",
       " 'extract_phone_numbers',\n",
       " 'extract_postoffice_box',\n",
       " 'extract_shortwords',\n",
       " 'extract_special_characters',\n",
       " 'extract_stopwords',\n",
       " 'extract_street_address',\n",
       " 'extract_terms_in_bracket',\n",
       " 'extract_urls',\n",
       " 'extract_userhandles',\n",
       " 'extract_visacard_addr',\n",
       " 'fix_contractions',\n",
       " 'generate_sentence',\n",
       " 'hamming_distance',\n",
       " 'inverse_df',\n",
       " 'lexical_richness',\n",
       " 'markov_chain',\n",
       " 'math',\n",
       " 'nlargest',\n",
       " 'normalize',\n",
       " 'num2words',\n",
       " 'random',\n",
       " 're',\n",
       " 'read_txt',\n",
       " 'remove_accents',\n",
       " 'remove_bad_quotes',\n",
       " 'remove_btc_address',\n",
       " 'remove_currencies',\n",
       " 'remove_currency_symbols',\n",
       " 'remove_custom_pattern',\n",
       " 'remove_custom_words',\n",
       " 'remove_dates',\n",
       " 'remove_emails',\n",
       " 'remove_emojis',\n",
       " 'remove_hashtags',\n",
       " 'remove_html_tags',\n",
       " 'remove_mastercard_addr',\n",
       " 'remove_md5sha',\n",
       " 'remove_multiple_spaces',\n",
       " 'remove_non_ascii',\n",
       " 'remove_numbers',\n",
       " 'remove_phone_numbers',\n",
       " 'remove_postoffice_box',\n",
       " 'remove_puncts',\n",
       " 'remove_punctuations',\n",
       " 'remove_shortwords',\n",
       " 'remove_special_characters',\n",
       " 'remove_stopwords',\n",
       " 'remove_street_address',\n",
       " 'remove_terms_in_bracket',\n",
       " 'remove_urls',\n",
       " 'remove_userhandles',\n",
       " 'remove_visacard_addr',\n",
       " 'replace_bad_quotes',\n",
       " 'replace_currencies',\n",
       " 'replace_currency_symbols',\n",
       " 'replace_dates',\n",
       " 'replace_emails',\n",
       " 'replace_emojis',\n",
       " 'replace_numbers',\n",
       " 'replace_phone_numbers',\n",
       " 'replace_special_characters',\n",
       " 'replace_term',\n",
       " 'replace_urls',\n",
       " 'string',\n",
       " 'term_freq',\n",
       " 'to_txt',\n",
       " 'unicodedata',\n",
       " 'word_freq',\n",
       " 'word_length_freq']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Cleaning\n",
    "dir(nfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2bcc6012",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Clean_Text'] = df['text'].apply(nfx.remove_userhandles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de0743ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Clean_Text'] = df['Clean_Text'].apply(nfx.remove_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "123f63b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Clean_Text'] = df['Clean_Text'].apply(nfx.remove_special_characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "af0ebd26",
   "metadata": {
    "scrolled": true
   },
   "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>text</th>\n",
       "      <th>label</th>\n",
       "      <th>Clean_Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>i just feel really helpless and heavy hearted</td>\n",
       "      <td>fear</td>\n",
       "      <td>feel helpless heavy hearted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ive enjoyed being able to slouch about relax a...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>ive enjoyed able slouch relax unwind frankly n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i gave up my internship with the dmrg and am f...</td>\n",
       "      <td>fear</td>\n",
       "      <td>gave internship dmrg feeling distraught</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i dont know i feel so lost</td>\n",
       "      <td>sadness</td>\n",
       "      <td>dont know feel lost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i am a kindergarten teacher and i am thoroughl...</td>\n",
       "      <td>fear</td>\n",
       "      <td>kindergarten teacher thoroughly weary job take...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416118</th>\n",
       "      <td>i feel like telling these horny devils to find...</td>\n",
       "      <td>love</td>\n",
       "      <td>feel like telling horny devils find site suite...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416119</th>\n",
       "      <td>i began to realize that when i was feeling agi...</td>\n",
       "      <td>anger</td>\n",
       "      <td>began realize feeling agitated restless though...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416120</th>\n",
       "      <td>i feel very curious be why previous early dawn...</td>\n",
       "      <td>surprise</td>\n",
       "      <td>feel curious previous early dawn time seek tro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416121</th>\n",
       "      <td>i feel that becuase of the tyranical nature of...</td>\n",
       "      <td>anger</td>\n",
       "      <td>feel becuase tyranical nature government el sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416122</th>\n",
       "      <td>i think that after i had spent some time inves...</td>\n",
       "      <td>surprise</td>\n",
       "      <td>think spent time investigating surroundings th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416123 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     text     label  \\\n",
       "0           i just feel really helpless and heavy hearted      fear   \n",
       "1       ive enjoyed being able to slouch about relax a...   sadness   \n",
       "2       i gave up my internship with the dmrg and am f...      fear   \n",
       "3                              i dont know i feel so lost   sadness   \n",
       "4       i am a kindergarten teacher and i am thoroughl...      fear   \n",
       "...                                                   ...       ...   \n",
       "416118  i feel like telling these horny devils to find...      love   \n",
       "416119  i began to realize that when i was feeling agi...     anger   \n",
       "416120  i feel very curious be why previous early dawn...  surprise   \n",
       "416121  i feel that becuase of the tyranical nature of...     anger   \n",
       "416122  i think that after i had spent some time inves...  surprise   \n",
       "\n",
       "                                               Clean_Text  \n",
       "0                             feel helpless heavy hearted  \n",
       "1       ive enjoyed able slouch relax unwind frankly n...  \n",
       "2                 gave internship dmrg feeling distraught  \n",
       "3                                     dont know feel lost  \n",
       "4       kindergarten teacher thoroughly weary job take...  \n",
       "...                                                   ...  \n",
       "416118  feel like telling horny devils find site suite...  \n",
       "416119  began realize feeling agitated restless though...  \n",
       "416120  feel curious previous early dawn time seek tro...  \n",
       "416121  feel becuase tyranical nature government el sa...  \n",
       "416122  think spent time investigating surroundings th...  \n",
       "\n",
       "[416123 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d393afbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features & Labels\n",
    "Xfeatures = df['Clean_Text']\n",
    "Ylabels = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7a9686d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(Xfeatures,Ylabels,test_size=0.3,random_state=65)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf573c0e",
   "metadata": {},
   "source": [
    "# Build pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9555c591",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5a3ab4",
   "metadata": {},
   "source": [
    "### LogisticRegression Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "21ee0871",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_lr = Pipeline(steps=[('cv',CountVectorizer()),('lr',LogisticRegression(max_iter=1000))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "43eae7b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()),\n",
       "                (&#x27;lr&#x27;, LogisticRegression(max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;cv&#x27;, CountVectorizer()),\n",
       "                (&#x27;lr&#x27;, LogisticRegression(max_iter=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('cv', CountVectorizer()),\n",
       "                ('lr', LogisticRegression(max_iter=1000))])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train and fit data\n",
    "pipe_lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c938af88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8845374368176102"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check accuracy\n",
    "pipe_lr.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a94f34",
   "metadata": {},
   "source": [
    "# Test the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "79f02912",
   "metadata": {},
   "outputs": [],
   "source": [
    "ex1 = \"they're so pretty\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "12f8c24f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['joy'], dtype=object)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr.predict([ex1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f0d31aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "ex2 = \"Why are you so moody today\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "d2e125b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['joy'], dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr.predict([ex2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7e8712d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0119171 , 0.01496671, 0.53285229, 0.18311078, 0.10032502,\n",
       "        0.14242331, 0.00129425, 0.01311055]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction prob\n",
    "pipe_lr.predict_proba([ex1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "30417747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'shame',\n",
       "       'surprise'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0f654c",
   "metadata": {},
   "source": [
    "# Save Model & Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "baebbef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "pipeline_file = open(\"emotion_classifier_pipe_lr_Mar29_2024.pkl\",\"wb\")\n",
    "joblib.dump(pipe_lr,pipeline_file)\n",
    "pipeline_file.close()"
   ]
  }
 ],
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