Sentiment_Analysis_RF_DT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce8db653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imblearn\n",
      "  Downloading imblearn-0.0-py2.py3-none-any.whl.metadata (355 bytes)\n",
      "Collecting imbalanced-learn (from imblearn)\n",
      "  Downloading imbalanced_learn-0.12.2-py3-none-any.whl.metadata (8.2 kB)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\sync\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.21.5)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\sync\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.9.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\sync\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.0.2)\n",
      "Collecting joblib>=1.1.1 (from imbalanced-learn->imblearn)\n",
      "  Downloading joblib-1.4.0-py3-none-any.whl.metadata (5.4 kB)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\sync\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (2.2.0)\n",
      "Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n",
      "Downloading imbalanced_learn-0.12.2-py3-none-any.whl (257 kB)\n",
      "   -------------------------------------- 258.0/258.0 kB 610.2 kB/s eta 0:00:00\n",
      "Downloading joblib-1.4.0-py3-none-any.whl (301 kB)\n",
      "   -------------------------------------- 301.2/301.2 kB 620.4 kB/s eta 0:00:00\n",
      "Installing collected packages: joblib, imbalanced-learn, imblearn\n",
      "  Attempting uninstall: joblib\n",
      "    Found existing installation: joblib 1.1.0\n",
      "    Uninstalling joblib-1.1.0:\n",
      "      Successfully uninstalled joblib-1.1.0\n",
      "Successfully installed imbalanced-learn-0.12.2 imblearn-0.0 joblib-1.4.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\sync\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\sync\\anaconda3\\lib\\site-packages)\n",
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "ydata-profiling 4.7.0 requires numba<1,>=0.56.0, but you have numba 0.55.1 which is incompatible.\n"
     ]
    }
   ],
   "source": [
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6de17c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "import imblearn\n",
    "from gensim.models import Word2Vec\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score, f1_score, confusion_matrix\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed29b3dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Sync\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Sync\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "stop_words = set(stopwords.words('english'))\n",
    "lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e0a60f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_and_preprocess(text):\n",
    "    text = re.sub(r'@[A-Za-z0-9_]+', '', text)\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    text = re.sub(r'[^A-Za-z0-9]', ' ', text)\n",
    "    words = text.lower().split()\n",
    "    words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]\n",
    "    return ' '.join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8466467f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>author</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1956967341</td>\n",
       "      <td>empty</td>\n",
       "      <td>xoshayzers</td>\n",
       "      <td>@tiffanylue i know  i was listenin to bad habi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1956967666</td>\n",
       "      <td>sadness</td>\n",
       "      <td>wannamama</td>\n",
       "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1956967696</td>\n",
       "      <td>sadness</td>\n",
       "      <td>coolfunky</td>\n",
       "      <td>Funeral ceremony...gloomy friday...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1956967789</td>\n",
       "      <td>enthusiasm</td>\n",
       "      <td>czareaquino</td>\n",
       "      <td>wants to hang out with friends SOON!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1956968416</td>\n",
       "      <td>neutral</td>\n",
       "      <td>xkilljoyx</td>\n",
       "      <td>@dannycastillo We want to trade with someone w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tweet_id   sentiment       author  \\\n",
       "0  1956967341       empty   xoshayzers   \n",
       "1  1956967666     sadness    wannamama   \n",
       "2  1956967696     sadness    coolfunky   \n",
       "3  1956967789  enthusiasm  czareaquino   \n",
       "4  1956968416     neutral    xkilljoyx   \n",
       "\n",
       "                                             content  \n",
       "0  @tiffanylue i know  i was listenin to bad habi...  \n",
       "1  Layin n bed with a headache  ughhhh...waitin o...  \n",
       "2                Funeral ceremony...gloomy friday...  \n",
       "3               wants to hang out with friends SOON!  \n",
       "4  @dannycastillo We want to trade with someone w...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = 'text_emotion.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a9507ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40000 entries, 0 to 39999\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   tweet_id   40000 non-null  int64 \n",
      " 1   sentiment  40000 non-null  object\n",
      " 2   author     40000 non-null  object\n",
      " 3   content    40000 non-null  object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "89367fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\sync\\anaconda3\\lib\\site-packages (3.7)\n",
      "Requirement already satisfied: click in c:\\users\\sync\\anaconda3\\lib\\site-packages (from nltk) (8.0.4)\n",
      "Requirement already satisfied: joblib in c:\\users\\sync\\anaconda3\\lib\\site-packages (from nltk) (1.4.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\sync\\anaconda3\\lib\\site-packages (from nltk) (2022.7.9)\n",
      "Requirement already satisfied: tqdm in c:\\users\\sync\\anaconda3\\lib\\site-packages (from nltk) (4.64.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\sync\\anaconda3\\lib\\site-packages (from click->nltk) (0.4.5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\sync\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\sync\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "559febc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package omw to\n",
      "[nltk_data]     C:\\Users\\Sync\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download()\n",
    "nltk.download('omw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8dea1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 39987 entries, 0 to 39999\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   sentiment        39987 non-null  object\n",
      " 1   author           39987 non-null  object\n",
      " 2   content          39987 non-null  object\n",
      " 3   cleaned_content  39987 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.drop(columns=['tweet_id'], inplace=True)\n",
    "data.drop_duplicates(keep=\"first\", inplace=True)\n",
    "data.dropna(inplace=True)\n",
    "\n",
    "data['cleaned_content'] = data['content'].apply(clean_and_preprocess)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf727890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>author</th>\n",
       "      <th>content</th>\n",
       "      <th>cleaned_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>empty</td>\n",
       "      <td>xoshayzers</td>\n",
       "      <td>@tiffanylue i know  i was listenin to bad habi...</td>\n",
       "      <td>know listenin bad habit earlier started freaki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sadness</td>\n",
       "      <td>wannamama</td>\n",
       "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
       "      <td>layin n bed headache ughhhh waitin call</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sadness</td>\n",
       "      <td>coolfunky</td>\n",
       "      <td>Funeral ceremony...gloomy friday...</td>\n",
       "      <td>funeral ceremony gloomy friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>enthusiasm</td>\n",
       "      <td>czareaquino</td>\n",
       "      <td>wants to hang out with friends SOON!</td>\n",
       "      <td>want hang friend soon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neutral</td>\n",
       "      <td>xkilljoyx</td>\n",
       "      <td>@dannycastillo We want to trade with someone w...</td>\n",
       "      <td>want trade someone houston ticket one</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>95</th>\n",
       "      <td>sadness</td>\n",
       "      <td>yellowduckx</td>\n",
       "      <td>@sweeetnspicy hiii im on my ipod...i cant fall...</td>\n",
       "      <td>hiii im ipod cant fall asleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>sadness</td>\n",
       "      <td>KaKuBby</td>\n",
       "      <td>dont wanna work 11-830 tomorrow  but i get paid</td>\n",
       "      <td>dont wanna work 11 830 tomorrow get paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>sadness</td>\n",
       "      <td>benchidol</td>\n",
       "      <td>feels sad coz i wasnt able to play with the gu...</td>\n",
       "      <td>feel sad coz wasnt able play guy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>neutral</td>\n",
       "      <td>outrAjusCharmer</td>\n",
       "      <td>PrinceCharming</td>\n",
       "      <td>princecharming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>hate</td>\n",
       "      <td>M0anique</td>\n",
       "      <td>@ cayogial i wanted to come to BZ this summer ...</td>\n",
       "      <td>cayogial wanted come bz summer sure anymore te...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sentiment           author  \\\n",
       "0        empty       xoshayzers   \n",
       "1      sadness        wannamama   \n",
       "2      sadness        coolfunky   \n",
       "3   enthusiasm      czareaquino   \n",
       "4      neutral        xkilljoyx   \n",
       "..         ...              ...   \n",
       "95     sadness      yellowduckx   \n",
       "96     sadness          KaKuBby   \n",
       "97     sadness        benchidol   \n",
       "98     neutral  outrAjusCharmer   \n",
       "99        hate         M0anique   \n",
       "\n",
       "                                              content  \\\n",
       "0   @tiffanylue i know  i was listenin to bad habi...   \n",
       "1   Layin n bed with a headache  ughhhh...waitin o...   \n",
       "2                 Funeral ceremony...gloomy friday...   \n",
       "3                wants to hang out with friends SOON!   \n",
       "4   @dannycastillo We want to trade with someone w...   \n",
       "..                                                ...   \n",
       "95  @sweeetnspicy hiii im on my ipod...i cant fall...   \n",
       "96    dont wanna work 11-830 tomorrow  but i get paid   \n",
       "97  feels sad coz i wasnt able to play with the gu...   \n",
       "98                                     PrinceCharming   \n",
       "99  @ cayogial i wanted to come to BZ this summer ...   \n",
       "\n",
       "                                      cleaned_content  \n",
       "0   know listenin bad habit earlier started freaki...  \n",
       "1             layin n bed headache ughhhh waitin call  \n",
       "2                      funeral ceremony gloomy friday  \n",
       "3                               want hang friend soon  \n",
       "4               want trade someone houston ticket one  \n",
       "..                                                ...  \n",
       "95                      hiii im ipod cant fall asleep  \n",
       "96           dont wanna work 11 830 tomorrow get paid  \n",
       "97                   feel sad coz wasnt able play guy  \n",
       "98                                     princecharming  \n",
       "99  cayogial wanted come bz summer sure anymore te...  \n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(n=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba643a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_data = [text.split() for text in data['cleaned_content']]\n",
    "\n",
    "w2v_model = Word2Vec(w2v_data, vector_size=150, window=10, min_count=2, workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df82dfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_to_vector(text):\n",
    "    words = text.split()\n",
    "    word_vectors = [w2v_model.wv[word] for word in words if word in w2v_model.wv.key_to_index]\n",
    "    return np.mean(word_vectors, axis=0) if word_vectors else np.zeros(w2v_model.vector_size)\n",
    "\n",
    "\n",
    "vectorized_texts = np.array([text_to_vector(text) for text in data['cleaned_content']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0bba6a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=42)\n",
    "X_resampled, y_resampled = ros.fit_resample(vectorized_texts, data['sentiment'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46bb3d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ouput_result(y_pred):\n",
    "  print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "  print('F1 Score:', f1_score(y_test, y_pred, average='weighted'))\n",
    "  print('Classification Report:\\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8fc70659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.1763369995992341\n",
      "F1 Score: 0.1585035969175728\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.14      0.34      0.19      1745\n",
      "     boredom       0.18      0.42      0.25      1784\n",
      "       empty       0.18      0.12      0.15      1756\n",
      "  enthusiasm       0.20      0.10      0.13      1701\n",
      "         fun       0.14      0.16      0.15      1686\n",
      "   happiness       0.17      0.12      0.14      1733\n",
      "        hate       0.19      0.25      0.21      1699\n",
      "        love       0.34      0.38      0.36      1716\n",
      "     neutral       0.18      0.02      0.04      1779\n",
      "      relief       0.16      0.14      0.15      1732\n",
      "     sadness       0.21      0.06      0.09      1710\n",
      "    surprise       0.13      0.11      0.12      1716\n",
      "       worry       0.13      0.05      0.07      1700\n",
      "\n",
      "    accuracy                           0.18     22457\n",
      "   macro avg       0.18      0.18      0.16     22457\n",
      "weighted avg       0.18      0.18      0.16     22457\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svm.predict(X_test)\n",
    "ouput_result(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8bacb2eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.12610767244066437\n",
      "F1 Score: 0.10227369577335252\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.11      0.23      0.15      1745\n",
      "     boredom       0.12      0.53      0.20      1784\n",
      "       empty       0.20      0.10      0.13      1756\n",
      "  enthusiasm       0.11      0.03      0.04      1701\n",
      "         fun       0.12      0.04      0.06      1686\n",
      "   happiness       0.12      0.04      0.06      1733\n",
      "        hate       0.09      0.18      0.12      1699\n",
      "        love       0.25      0.24      0.25      1716\n",
      "     neutral       0.12      0.02      0.04      1779\n",
      "      relief       0.14      0.11      0.13      1732\n",
      "     sadness       0.12      0.04      0.06      1710\n",
      "    surprise       0.09      0.04      0.06      1716\n",
      "       worry       0.13      0.03      0.04      1700\n",
      "\n",
      "    accuracy                           0.13     22457\n",
      "   macro avg       0.13      0.12      0.10     22457\n",
      "weighted avg       0.13      0.13      0.10     22457\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'var_smoothing': np.logspace(0, -9, num=100)  # Логарифмическое пространство между 1 и 1e-9\n",
    "}\n",
    "\n",
    "\n",
    "grid_search = GridSearchCV(GaussianNB(), param_grid, cv=3, scoring='f1_weighted', n_jobs=8)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "best_gnb = grid_search.best_estimator_\n",
    "y_pred = best_gnb.predict(X_test)\n",
    "ouput_result(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc662026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.1671193837110923\n",
      "F1 Score: 0.1466642436127481\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.31      0.34      0.33      1745\n",
      "     boredom       0.22      0.49      0.30      1784\n",
      "       empty       0.11      0.10      0.10      1756\n",
      "  enthusiasm       0.14      0.13      0.13      1701\n",
      "         fun       0.14      0.11      0.12      1686\n",
      "   happiness       0.14      0.11      0.12      1733\n",
      "        hate       0.13      0.20      0.16      1699\n",
      "        love       0.17      0.37      0.23      1716\n",
      "     neutral       0.13      0.08      0.10      1779\n",
      "      relief       0.14      0.09      0.11      1732\n",
      "     sadness       0.14      0.04      0.06      1710\n",
      "    surprise       0.13      0.04      0.06      1716\n",
      "       worry       0.09      0.05      0.07      1700\n",
      "\n",
      "    accuracy                           0.17     22457\n",
      "   macro avg       0.15      0.17      0.15     22457\n",
      "weighted avg       0.15      0.17      0.15     22457\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# AdaBoost \n",
    "param_grid = {'n_estimators': [25, 100], 'learning_rate': [0.01, 0.1, 1]}\n",
    "grid_search = GridSearchCV(AdaBoostClassifier(), param_grid, cv=3, scoring='f1_weighted', n_jobs=8)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Оценка модели\n",
    "best_ada = grid_search.best_estimator_\n",
    "y_pred = best_ada.predict(X_test)\n",
    "ouput_result(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4d4e7dce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7960101527363406\n",
      "F1 Score: 0.7753258400671666\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.99      0.99      0.99      1745\n",
      "     boredom       0.97      1.00      0.99      1784\n",
      "       empty       0.85      0.98      0.91      1756\n",
      "  enthusiasm       0.89      0.99      0.94      1701\n",
      "         fun       0.85      0.97      0.91      1686\n",
      "   happiness       0.67      0.64      0.66      1733\n",
      "        hate       0.86      0.99      0.92      1699\n",
      "        love       0.76      0.81      0.79      1716\n",
      "     neutral       0.38      0.22      0.28      1779\n",
      "      relief       0.84      0.97      0.90      1732\n",
      "     sadness       0.66      0.64      0.65      1710\n",
      "    surprise       0.81      0.94      0.87      1716\n",
      "       worry       0.39      0.23      0.29      1700\n",
      "\n",
      "    accuracy                           0.80     22457\n",
      "   macro avg       0.77      0.80      0.78     22457\n",
      "weighted avg       0.77      0.80      0.78     22457\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_params = {'max_depth': [10, 60]}\n",
    "dt_model = GridSearchCV(DecisionTreeClassifier(), dt_params, cv=5, scoring='f1_weighted')\n",
    "dt_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = dt_model.predict(X_test)\n",
    "ouput_result(y_pred)\n",
    "\n",
    "# Confusion matrix for Decision Tree\n",
    "dt_conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(dt_conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=dt_model.classes_, yticklabels=dt_model.classes_)\n",
    "plt.title('Confusion Matrix for Decision Tree')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0674238c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sync\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Sync\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Sync\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Sync\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Sync\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7446675869439373\n",
      "F1 Score: 0.7112992147165907\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.90      1.00      0.95      1745\n",
      "     boredom       0.97      1.00      0.99      1784\n",
      "       empty       0.86      0.94      0.90      1756\n",
      "  enthusiasm       0.84      0.99      0.91      1701\n",
      "         fun       0.75      0.96      0.84      1686\n",
      "   happiness       0.51      0.63      0.57      1733\n",
      "        hate       0.82      0.98      0.89      1699\n",
      "        love       0.68      0.74      0.71      1716\n",
      "     neutral       0.29      0.19      0.23      1779\n",
      "      relief       0.81      0.93      0.87      1732\n",
      "     sadness       0.57      0.45      0.50      1710\n",
      "    surprise       0.78      0.81      0.79      1716\n",
      "       worry       0.38      0.06      0.10      1700\n",
      "\n",
      "    accuracy                           0.74     22457\n",
      "   macro avg       0.70      0.74      0.71     22457\n",
      "weighted avg       0.71      0.74      0.71     22457\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn_params = {'n_neighbors': [2, 50]}\n",
    "knn_model = GridSearchCV(KNeighborsClassifier(), knn_params, cv=2, scoring='f1_weighted')\n",
    "knn_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knn_model.predict(X_test)\n",
    "ouput_result(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "adad3b0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8124415549717238\n",
      "F1 Score: 0.7997676102629627\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       anger       1.00      0.99      0.99      1745\n",
      "     boredom       0.99      1.00      1.00      1784\n",
      "       empty       0.89      0.98      0.93      1756\n",
      "  enthusiasm       0.94      0.99      0.97      1701\n",
      "         fun       0.88      0.97      0.92      1686\n",
      "   happiness       0.69      0.66      0.68      1733\n",
      "        hate       0.89      0.99      0.94      1699\n",
      "        love       0.77      0.83      0.80      1716\n",
      "     neutral       0.43      0.28      0.34      1779\n",
      "      relief       0.87      0.97      0.92      1732\n",
      "     sadness       0.67      0.65      0.66      1710\n",
      "    surprise       0.85      0.93      0.89      1716\n",
      "       worry       0.41      0.34      0.37      1700\n",
      "\n",
      "    accuracy                           0.81     22457\n",
      "   macro avg       0.79      0.81      0.80     22457\n",
      "weighted avg       0.79      0.81      0.80     22457\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_params = {'n_estimators': [100, 200], 'max_depth': [10, 20]}\n",
    "rf_model = GridSearchCV(RandomForestClassifier(), rf_params, cv=3, scoring='f1_weighted')\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_model.predict(X_test)\n",
    "ouput_result(y_pred)\n",
    "\n",
    "rf_conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(rf_conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=rf_model.classes_, yticklabels=rf_model.classes_)\n",
    "plt.title('Confusion Matrix for Random Forest')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4534350a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}