arv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.util import pr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>classification</th>\n",
       "      <th>label</th>\n",
       "      <th>tweets_pt</th>\n",
       "      <th>tweets_en</th>\n",
       "      <th>pt_lemma</th>\n",
       "      <th>en_lemma</th>\n",
       "      <th>pt_tokens</th>\n",
       "      <th>en_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "      <td>mayasolovely como mulher você não deve reclama...</td>\n",
       "      <td>mayasolovely   woman shouldnt complain cleanin...</td>\n",
       "      <td>mayasolovelyr como mulher você não dever recla...</td>\n",
       "      <td>mayasolovely    woman should not complain clea...</td>\n",
       "      <td>['mayasolovelyr', 'como', 'mulher', 'você', 'n...</td>\n",
       "      <td>['mayasolovely', 'woman', 'should', 'not', 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Offensive</td>\n",
       "      <td>1</td>\n",
       "      <td>menino dats frio  tyga dwn ruim para cuffin da...</td>\n",
       "      <td>boy dats coldtyga dwn bad cuffin dat hoe   place</td>\n",
       "      <td>menino dats frio   tyga dwn ruim para cuffin d...</td>\n",
       "      <td>boy dat coldtyga dwn bad cuffin dat hoe    place</td>\n",
       "      <td>['menino', 'dats', 'frio', 'tyga', 'dwn', 'rui...</td>\n",
       "      <td>['boy', 'dat', 'coldtyga', 'dwn', 'bad', 'cuff...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Offensive</td>\n",
       "      <td>1</td>\n",
       "      <td>urkindofbrand cara   você  fodeu uma cadela  e...</td>\n",
       "      <td>urkindofbrand dawg   ever fuck  bitch start  c...</td>\n",
       "      <td>urkindofbrand cara    você   foder um cadela  ...</td>\n",
       "      <td>urkindofbrand dawg    ever fuck   bitch start ...</td>\n",
       "      <td>['urkindofbrand', 'cara', 'você', 'foder', 'um...</td>\n",
       "      <td>['urkindofbrand', 'dawg', 'ever', 'fuck', 'bit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Offensive</td>\n",
       "      <td>1</td>\n",
       "      <td>cganderson vivabased ela parece uma travesti</td>\n",
       "      <td>cganderson vivabased look like  tranny</td>\n",
       "      <td>Cganderson Vivabased ela parecer um travesti</td>\n",
       "      <td>cganderson vivabase look like   tranny</td>\n",
       "      <td>['Cganderson', 'Vivabased', 'ela', 'parecer', ...</td>\n",
       "      <td>['cganderson', 'vivabase', 'look', 'like', 'tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Offensive</td>\n",
       "      <td>1</td>\n",
       "      <td>shenikaroberts  merda que você ouve sobre mim ...</td>\n",
       "      <td>shenikaroberts shit hear  might  true   might ...</td>\n",
       "      <td>shenikarobert   merdar que você ouve sobre eu ...</td>\n",
       "      <td>shenikarobert shit hear   might   true    migh...</td>\n",
       "      <td>['shenikarobert', 'merdar', 'que', 'você', 'ou...</td>\n",
       "      <td>['shenikarobert', 'shit', 'hear', 'might', 'tr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  classification  label                                          tweets_pt  \\\n",
       "0         Normal      2  mayasolovely como mulher você não deve reclama...   \n",
       "1      Offensive      1  menino dats frio  tyga dwn ruim para cuffin da...   \n",
       "2      Offensive      1  urkindofbrand cara   você  fodeu uma cadela  e...   \n",
       "3      Offensive      1       cganderson vivabased ela parece uma travesti   \n",
       "4      Offensive      1  shenikaroberts  merda que você ouve sobre mim ...   \n",
       "\n",
       "                                           tweets_en  \\\n",
       "0  mayasolovely   woman shouldnt complain cleanin...   \n",
       "1   boy dats coldtyga dwn bad cuffin dat hoe   place   \n",
       "2  urkindofbrand dawg   ever fuck  bitch start  c...   \n",
       "3             cganderson vivabased look like  tranny   \n",
       "4  shenikaroberts shit hear  might  true   might ...   \n",
       "\n",
       "                                            pt_lemma  \\\n",
       "0  mayasolovelyr como mulher você não dever recla...   \n",
       "1  menino dats frio   tyga dwn ruim para cuffin d...   \n",
       "2  urkindofbrand cara    você   foder um cadela  ...   \n",
       "3       Cganderson Vivabased ela parecer um travesti   \n",
       "4  shenikarobert   merdar que você ouve sobre eu ...   \n",
       "\n",
       "                                            en_lemma  \\\n",
       "0  mayasolovely    woman should not complain clea...   \n",
       "1   boy dat coldtyga dwn bad cuffin dat hoe    place   \n",
       "2  urkindofbrand dawg    ever fuck   bitch start ...   \n",
       "3             cganderson vivabase look like   tranny   \n",
       "4  shenikarobert shit hear   might   true    migh...   \n",
       "\n",
       "                                           pt_tokens  \\\n",
       "0  ['mayasolovelyr', 'como', 'mulher', 'você', 'n...   \n",
       "1  ['menino', 'dats', 'frio', 'tyga', 'dwn', 'rui...   \n",
       "2  ['urkindofbrand', 'cara', 'você', 'foder', 'um...   \n",
       "3  ['Cganderson', 'Vivabased', 'ela', 'parecer', ...   \n",
       "4  ['shenikarobert', 'merdar', 'que', 'você', 'ou...   \n",
       "\n",
       "                                           en_tokens  \n",
       "0  ['mayasolovely', 'woman', 'should', 'not', 'co...  \n",
       "1  ['boy', 'dat', 'coldtyga', 'dwn', 'bad', 'cuff...  \n",
       "2  ['urkindofbrand', 'dawg', 'ever', 'fuck', 'bit...  \n",
       "3  ['cganderson', 'vivabase', 'look', 'like', 'tr...  \n",
       "4  ['shenikarobert', 'shit', 'hear', 'might', 'tr...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importando os dados\n",
    "df = pd.read_csv('./Dados/twitter.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(df[\"tweets_pt\"])\n",
    "y = np.array(df[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "X = cv.fit_transform(x) # Fit the Data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ein = 0.0011\n",
      "Eout = 0.2754\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.33      0.37      1677\n",
      "           1       0.80      0.83      0.81      5198\n",
      "           2       0.71      0.81      0.75      1302\n",
      "\n",
      "    accuracy                           0.72      8177\n",
      "   macro avg       0.65      0.66      0.65      8177\n",
      "weighted avg       0.71      0.72      0.71      8177\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)\n",
    "\n",
    "print('Ein = %0.4f' % (1 - clf.score(X_train,y_train)))\n",
    "print('Eout = %0.4f' % (1 - clf.score(X_test,y_test)))\n",
    "print(classification_report(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "\n",
    "for feature, importance in zip(cv.get_feature_names_out(), clf.feature_importances_):\n",
    "    print(\"{}:{}\".format(feature, importance))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'CountVectorizer' object has no attribute 'get_feature_names'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m feature_names \u001b[39m=\u001b[39m cv\u001b[39m.\u001b[39;49mget_feature_names()\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'CountVectorizer' object has no attribute 'get_feature_names'"
     ]
    }
   ],
   "source": [
    "feature_names = cv.get_feature_names()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'CountVectorizer' object has no attribute 'get_feature_names'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 7\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m      4\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m50\u001b[39m,\u001b[39m20\u001b[39m))\n\u001b[0;32m      5\u001b[0m plot_tree(clf,\n\u001b[0;32m      6\u001b[0m           filled\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m----> 7\u001b[0m           feature_names\u001b[39m=\u001b[39mcv\u001b[39m.\u001b[39;49mget_feature_names(),\n\u001b[0;32m      8\u001b[0m           class_names\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39m0\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m1\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m      9\u001b[0m plt\u001b[39m.\u001b[39msavefig(\u001b[39m'\u001b[39m\u001b[39marvore.png\u001b[39m\u001b[39m'\u001b[39m, dpi\u001b[39m=\u001b[39m\u001b[39m300\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'CountVectorizer' object has no attribute 'get_feature_names'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(50, 20))\n",
    "tree.plot_tree(clf,\n",
    "               filled=True,\n",
    "               feature_names=range(X.shape[1]),\n",
    "               class_names=['0', '1'])\n",
    "plt.savefig('arvore.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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