Knowledge_area_classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Knowledge-area-classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VictorCampelo/Knowledge-area-classifier-based-on-professional-description/blob/master/Knowledge_area_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PL4RFctxP_mh"
      },
      "source": [
        "## Downloads e Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vjy-XXNEnlyy"
      },
      "source": [
        "!apt install enchant\n",
        "!pip3 install pyenchant\n",
        "!pip3 install parfit\n",
        "!pip install english-words\n",
        "# !pip3 install git+https://github.com/BoseCorp/py-googletrans.git --upgrade\n",
        "# !pip install -U textblob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJmEZ_Vm5O1K"
      },
      "source": [
        "import nltk\n",
        "import re\n",
        "import pandas as pd\n",
        "import enchant  # pip install pyenchant\n",
        "eng_dict = enchant.Dict(\"en_US\")\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "import numpy as np\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn import tree\n",
        "from nltk.stem import RSLPStemmer\n",
        "from graphviz import Source\n",
        "from collections import Counter\n",
        "from IPython.display import display\n",
        "from IPython.display import SVG\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "from tensorflow import keras\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(\"ignore\")\n",
        "\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn.metrics import f1_score, classification_report, log_loss\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.models import Model\n",
        "from keras.layers import Embedding, LSTM, Dense, SpatialDropout1D, Bidirectional, Flatten, Input, Activation\n",
        "from keras.layers import Dropout, Conv1D, GlobalMaxPool1D, MaxPooling1D, GRU, GlobalAvgPool1D\n",
        "from keras.optimizers import Adam, Nadam\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from keras import regularizers      #for l2 regularization\n",
        "from english_words import english_words_set\n",
        "\n",
        "from nltk.corpus import words as englishWords"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AuQ6OWQn2B_",
        "outputId": "648260ac-d43a-43b2-ad60-9f359991ca98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('rslp')\n",
        "nltk.download('punkt')\n",
        "nltk.download('words')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package rslp to /root/nltk_data...\n",
            "[nltk_data]   Unzipping stemmers/rslp.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package words to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/words.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1GKP5EWQJgV"
      },
      "source": [
        "## Funções para pré-processamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tE6crecgDC0"
      },
      "source": [
        "def clean_text(text):\n",
        "\n",
        "    text = re.sub(r'nan', r' ', text)\n",
        "    \n",
        "    regex = r\"(([a-z]{1})([0-9]{1}))\"\n",
        "    subst = \"\\\\2 \\\\3\"\n",
        "    text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "\n",
        "    regex = r\"(([0-9]{1})([a-z]{1}))\"\n",
        "    subst = \"\\\\2 \\\\3\"\n",
        "    text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "\n",
        "    regex = r\"([a-z])([A-Z])\"\n",
        "    subst = \"\\\\1 \\\\2\"\n",
        "    text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "    \n",
        "    text = text.lower()\n",
        "    text = re.sub(r'nome', r' ', text)\n",
        "    text = re.sub(r'empresa', r' ', text)\n",
        "    text = re.sub(r'emprego', r' ', text)\n",
        "    text = re.sub(r'período', r' ', text)\n",
        "    text = re.sub(r'momento', r' ', text)\n",
        "    text = re.sub(r'duração', r' ', text)\n",
        "    text = re.sub(r'total', r' ', text)\n",
        "    text = re.sub(r'duração', r' ', text)\n",
        "    text = re.sub(r'ano[s]*', r' ', text)\n",
        "    text = re.sub(r'dia[s]*', r' ', text)\n",
        "    text = re.sub(r'meses', r' ', text)\n",
        "    text = re.sub(r'\\d+', r' ', text)\n",
        "    text = re.sub(r\"(jan|fev|mar|abr|mai|jun|jul|ago|set|out|nov|dez)\", r' ', text)\n",
        "\n",
        "    regex = r\"\\W+n\"\n",
        "    subst = \" \"\n",
        "    text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "\n",
        "    regex = r\"(\\W+|\\_)\" \n",
        "    subst = \" \"\n",
        "    text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "    \n",
        "    # regex = r\"([^aeiou]{3})([^aeiou])\"\n",
        "    # subst = \"\\\\1 \\\\2\"\n",
        "    # text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "    \n",
        "    # regex = r\"([^aeiou])([^aeiou]{3})\"\n",
        "    # subst = \"\\\\1 \\\\2\"\n",
        "    # text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "    \n",
        "    regex = r\"\\s+[^aeiou]\\s+\"\n",
        "    subst = \" \"\n",
        "    text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "\n",
        "    regex = r\"\\s[^aeiou]\\s\"\n",
        "    subst = \" \"\n",
        "    text = re.sub(regex, subst, text, 0, re.MULTILINE)\n",
        "\n",
        "    text = re.sub('^\\s', '', text)  # remove extra whitespace\n",
        "    text = re.sub('\\s+', ' ', text)  # remove extra whitespace\n",
        "\n",
        "    return text\n",
        "\n",
        "def separate_df(df, pct):\n",
        "    train = pd.DataFrame(dict(text=[], target=[]),  dtype=int)\n",
        "    test = pd.DataFrame(dict(text=[], target=[]),  dtype=int)\n",
        "    targets = df.target.unique().tolist()\n",
        "    for target in targets:\n",
        "        l = round(len(df[df.target == target]) * pct)\n",
        "        for (index, line), count in zip(df[df.target == target].iterrows(), \n",
        "                                        range(len(df[df.target == target]))):\n",
        "            if(count <= l):\n",
        "                train = train.append(dict(text=line.text, target=int(target)), \n",
        "                                     ignore_index=True)\n",
        "            else:\n",
        "                test = test.append(dict(text=line.text, target=int(target)), \n",
        "                                   ignore_index=True)\n",
        "    return train, test\n",
        "\n",
        "def RemoveStopWords(sent):\n",
        "    stp = nltk.corpus.stopwords.words('portuguese')\n",
        "    listW = \"\"\n",
        "    for wrd in sent.split(\" \"):\n",
        "        if wrd not in stp:\n",
        "            listW += str(wrd)+\" \"\n",
        "    return listW\n",
        "\n",
        "def stemming(sent):\n",
        "    stp = nltk.corpus.stopwords.words('portuguese')\n",
        "    stemmer = nltk.stem.RSLPStemmer()\n",
        "    listW = []\n",
        "    for (desc, value) in sent:\n",
        "        desc = word_tokenize(desc)\n",
        "        [desc for char in desc for l in segment_str(char)]\n",
        "        \n",
        "        aux = [str(stemmer.stem(p)) for p in desc if p not in stp]\n",
        "        listW.append((aux, value))\n",
        "    return listW\n",
        "\n",
        "def tokenize(sent):\n",
        "    sent = sent.lower()\n",
        "    sent = nltk.tokenize.word_tokenize(sent, language='portuguese')\n",
        "    return sent\n",
        "\n",
        "def stemmingSimple(sent):\n",
        "    # stp = nltk.corpus.stopwords.words('portuguese')\n",
        "    stemmer = RSLPStemmer()\n",
        "    listS = []\n",
        "    # print(sent)\n",
        "    for i, wrd in enumerate(sent):\n",
        "      listW = []\n",
        "      for p in wrd.split():\n",
        "        # if p not in stp:\n",
        "        stems = stemmer.stem(p.lower())\n",
        "        listW.append(stems)\n",
        "      # print(listW)\n",
        "      listS.append(\" \".join(listW))\n",
        "    # print(listW)\n",
        "    return listS\n",
        "\n",
        "def stemmingSimpleSet(sent):\n",
        "  # stp = nltk.corpus.stopwords.words('portuguese')\n",
        "  stemmer = RSLPStemmer()\n",
        "  listS = []\n",
        "  # print(sent)\n",
        "  for i, wrd in enumerate(sent):\n",
        "    listW = []\n",
        "    for p in wrd.split():\n",
        "      # if p not in stp:\n",
        "      stems = stemmer.stem(p.lower())\n",
        "      listW.append(stems)\n",
        "    # print(listW)\n",
        "    listS.append(\" \".join(set(listW)))\n",
        "  # print(listW)\n",
        "  return listS\n",
        "\n",
        "def addAllWords(sent):\n",
        "    words = []\n",
        "    for (desc, value) in sent:\n",
        "        words.extend(desc)\n",
        "    return words\n",
        "\n",
        "def freqWords(sent):\n",
        "    wrds = nltk.FreqDist(sent)\n",
        "    return wrds\n",
        "\n",
        "def uniqueWords(sent):\n",
        "    freq = sent.keys()\n",
        "    return freq\n",
        "\n",
        "def extractWords(sent):\n",
        "    doc = set(sent)\n",
        "    characteristics = {}\n",
        "    for wdr in u_words_train:\n",
        "        characteristics['%s' % wdr] = (wdr in doc)\n",
        "    return characteristics\n",
        "\n",
        "def makeAcuracy(tree,x_test,y_test):\n",
        "    predictions = clf.predict(x_test)\n",
        "    erro = 0.0\n",
        "    for x in range(len(predictions)):\n",
        "        if predictions[x] != y_test[x]:\n",
        "            erro += 1.\n",
        "    acuracy = (1-(erro/len(predictions)))\n",
        "    return acuracy\n",
        "\n",
        "def segment_str(chars, exclude=None):\n",
        "    words = []\n",
        "\n",
        "    if not chars.isalpha():  # don't check punctuation etc.; needs more work\n",
        "        return [chars]\n",
        "\n",
        "    if not exclude:\n",
        "        exclude = set()\n",
        "\n",
        "    working_chars = chars\n",
        "    while working_chars:\n",
        "        # iterate through segments of the chars starting with the longest segment possible\n",
        "        for i in range(len(working_chars), 1, -1):\n",
        "            segment = working_chars[:i]\n",
        "            if eng_dict.check(segment) and segment not in exclude:\n",
        "                words.append(segment)\n",
        "                working_chars = working_chars[i:]\n",
        "                break\n",
        "        else:  # no matching segments were found\n",
        "            if words:\n",
        "                exclude.add(words[-1])\n",
        "                return segment_str(chars, exclude=exclude)\n",
        "            # let the user know a word was missing from the dictionary,\n",
        "            # but keep the word\n",
        "            # print('\"{chars}\" not in dictionary (so just keeping as one segment)!'\n",
        "            #       .format(chars=chars))\n",
        "            return [chars]\n",
        "    # return a list of words based on the segmentation\n",
        "    return words\n",
        "\n",
        "def translator(sent):\n",
        "  trans_sent = []\n",
        "  for wrd in sent.split(\" \"):\n",
        "    if wrd in dictionary:\n",
        "      trans_sent.append(dictionary[wrd])\n",
        "    else:\n",
        "      trans_sent.append(wrd)\n",
        "  return \" \".join(trans_sent)\n",
        "\n",
        "def find_english_words(sent):\n",
        "  trans_sent = []\n",
        "  for wrd in sent.split(\" \"):\n",
        "    if wrd in english_words_set:\n",
        "      trans_sent.append(wrd)\n",
        "  return \" \".join(trans_sent)\n",
        "  \n",
        "  \n",
        "def models(X_train, Y_train, X_test, Y_test):\n",
        "  from sklearn.linear_model import LogisticRegression\n",
        "  log = LogisticRegression(class_weight='balanced', random_state = 0)\n",
        "  log.fit(X_train, Y_train)\n",
        "  log_pred = log.predict(X_test)\n",
        "\n",
        "  from sklearn.neighbors import KNeighborsClassifier\n",
        "  knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "  knn.fit(X_train, Y_train)\n",
        "  knn_pred = knn.predict(X_test)\n",
        "\n",
        "  from sklearn.svm import SVC\n",
        "  svc_lin = SVC(kernel='linear', random_state = 0, class_weight='balanced')\n",
        "  svc_lin.fit(X_train, Y_train)\n",
        "  svc_lin_pred = svc_lin.predict(X_test)\n",
        "\n",
        "  from sklearn.svm import SVC\n",
        "  svc_rbf = SVC(kernel='rbf', random_state = 0)\n",
        "  svc_rbf.fit(X_train, Y_train)\n",
        "  svc_rbf_pred = svc_rbf.predict(X_test)\n",
        "\n",
        "  from sklearn.tree import DecisionTreeClassifier\n",
        "  tree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0, class_weight='balanced')\n",
        "  tree.fit (X_train, Y_train)\n",
        "  tree_pred = tree.predict(X_test)\n",
        "\n",
        "  from sklearn.ensemble import RandomForestClassifier\n",
        "  forest = RandomForestClassifier(criterion = 'entropy', random_state = 0, class_weight='balanced')\n",
        "  forest.fit(X_train, Y_train)\n",
        "  forest_pred = forest.predict(X_test)\n",
        "\n",
        "  from sklearn.linear_model import Perceptron\n",
        "  pcp = Perceptron(random_state = 0, class_weight='balanced')\n",
        "  pcp.fit(X_train, Y_train)\n",
        "  pcp_pred = pcp.predict(X_test)\n",
        "\n",
        "  from sklearn.naive_bayes import BernoulliNB, ComplementNB, MultinomialNB\n",
        "  mnb = MultinomialNB(alpha=.01)\n",
        "  mnb.fit(X_train, Y_train)\n",
        "  mnb_pred = mnb.predict(X_test)\n",
        "\n",
        "  bnb = BernoulliNB(alpha=.01)\n",
        "  bnb.fit(X_train, Y_train)\n",
        "  bnb_pred = bnb.predict(X_test)\n",
        "\n",
        "  cnb = ComplementNB(alpha=.1)\n",
        "  cnb.fit(X_train, Y_train)\n",
        "  cnb_pred = cnb.predict(X_test)\n",
        "  \n",
        "  from sklearn.linear_model import SGDClassifier\n",
        "  sgdc = SGDClassifier(random_state = 0, class_weight='balanced')\n",
        "  sgdc.fit(X_train, Y_train)\n",
        "  sgdc_pred = sgdc.predict(X_test)\n",
        "\n",
        "  print('[0]Logistic Regression Training Accuracy: ', log.score(X_train, Y_train))\n",
        "  print('[0]Logistic Regression Cross_val Accuracy: ', np.mean(cross_val_score(log, X_train, Y_train, cv=10)))\n",
        "  print('[0]Logistic Regression Testing Accuracy: ', accuracy_score(Y_test, log_pred))\n",
        "  print(classification_report(Y_test, log_pred))\n",
        "\n",
        "  # plot_confusion_matrix(log, X_test, Y_test)\n",
        "\n",
        "  print('[1]KNeighborns Training Accuracy: ', knn.score(X_train, Y_train))\n",
        "  print('[1]KNeighborns Training Cross_val Accuracy: ', np.mean(cross_val_score(knn, X_train, Y_train, cv=10)))\n",
        "  print('[1]KNeighborns Testing Accuracy: ', accuracy_score(Y_test, knn_pred))\n",
        "  print(classification_report(Y_test, knn_pred))\n",
        "\n",
        "  print('[2]SVC Linear Training Accuracy: ', svc_lin.score(X_train, Y_train))\n",
        "  print('[2]SVC Linear Training Cross_val Accuracy: ', np.mean(cross_val_score(svc_lin, X_train, Y_train, cv=10)))\n",
        "  print('[2]SVC Linear Testing Accuracy: ', accuracy_score(Y_test, svc_lin_pred))\n",
        "  print(classification_report(Y_test, svc_lin_pred))\n",
        "\n",
        "  print('[3]SVC RBF Training Accuracy: ', svc_rbf.score(X_train, Y_train))\n",
        "  print('[3]SVC RBF Training Cross_val Accuracy: ', np.mean(cross_val_score(svc_rbf, X_train, Y_train, cv=10)))\n",
        "  print('[3]SVC RBF Testing Accuracy: ', accuracy_score(Y_test, svc_rbf_pred))\n",
        "  print(classification_report(Y_test, svc_rbf_pred))\n",
        "\n",
        "  print('[4]Decision Tree Training Accuracy: ', tree.score(X_train, Y_train))\n",
        "  print('[4]Decision Tree Training Cross_val Accuracy: ', np.mean(cross_val_score(tree, X_train, Y_train, cv=10)))\n",
        "  print('[4]Decision Tree Testing Accuracy: ', accuracy_score(Y_test, tree_pred))\n",
        "  print(classification_report(Y_test, tree_pred))\n",
        "\n",
        "  print('[5]Random Forest Training Accuracy: ', forest.score(X_train, Y_train))\n",
        "  print('[5]Random Forest Training Cross_val Accuracy: ', np.mean(cross_val_score(forest, X_train, Y_train, cv=10)))\n",
        "  print('[5]Random Forest Testing Accuracy: ', accuracy_score(Y_test, forest_pred))\n",
        "  print(classification_report(Y_test, forest_pred))\n",
        "\n",
        "  print('[6] Perceptron Training Accuracy: ', pcp.score(X_train, Y_train))\n",
        "  print('[6] Perceptron Training Cross_val Accuracy: ',  np.mean(cross_val_score(pcp, X_train, Y_train, cv=10)))\n",
        "  print(\"[6] Perceptron Testing accuracy: \", accuracy_score(y_test, pcp_pred))\n",
        "  print(classification_report(Y_test, pcp_pred))\n",
        "\n",
        "  print('[7] MultinomialNB Training Accuracy: ', mnb.score(X_train, Y_train))\n",
        "  print('[7] MultinomialNB Training Cross_val Accuracy: ',  np.mean(cross_val_score(mnb, X_train, Y_train, cv=10)))\n",
        "  print(\"[7] MultinomialNB Testing accuracy: \", accuracy_score(Y_test, mnb_pred))\n",
        "  print(classification_report(Y_test, pcp_pred))\n",
        "\n",
        "  print('[8] BernoulliNB Training Accuracy: ', bnb.score(X_train, Y_train))\n",
        "  print('[8] BernoulliNB Training Cross_val Accuracy: ',  np.mean(cross_val_score(bnb, X_train, Y_train, cv=10)))\n",
        "  print(\"[8] BernoulliNB Testing accuracy: \", accuracy_score(Y_test, bnb_pred))\n",
        "  print(classification_report(Y_test, pcp_pred))\n",
        "\n",
        "  print('[9] ComplementNB Training Accuracy: ', cnb.score(X_train, Y_train))\n",
        "  print('[9] ComplementNB Training Cross_val Accuracy: ',  np.mean(cross_val_score(cnb, X_train, Y_train, cv=10)))\n",
        "  print(\"[9] ComplementNB Testing accuracy: \", accuracy_score(Y_test, cnb_pred))\n",
        "  print(classification_report(Y_test, pcp_pred))\n",
        "\n",
        "  print('[10] SGDClassifier Training Accuracy: ', sgdc.score(X_train, Y_train))\n",
        "  print('[10] SGDClassifier Training Cross_val Accuracy: ',  np.mean(cross_val_score(sgdc, X_train, Y_train, cv=10)))\n",
        "  print(\"[10] SGDClassifier Testing accuracy: \", accuracy_score(Y_test, sgdc_pred))\n",
        "  print(classification_report(Y_test, sgdc_pred))\n",
        "\n",
        "  n_list = [np.mean(cross_val_score(knn, X_train, Y_train, cv=10)), \n",
        "            np.mean(cross_val_score(svc_lin, X_train, Y_train, cv=10)), \n",
        "            np.mean(cross_val_score(svc_rbf, X_train, Y_train, cv=10)), \n",
        "            np.mean(cross_val_score(tree, X_train, Y_train, cv=10)),\n",
        "            np.mean(cross_val_score(forest, X_train, Y_train, cv=10)), \n",
        "            np.mean(cross_val_score(pcp, X_train, Y_train, cv=10)),\n",
        "            np.mean(cross_val_score(mnb, X_train, Y_train, cv=10)), \n",
        "            np.mean(cross_val_score(bnb, X_train, Y_train, cv=10)),\n",
        "            np.mean(cross_val_score(cnb, X_train, Y_train, cv=10)), \n",
        "            np.mean(cross_val_score(sgdc, X_train, Y_train, cv=10))]\n",
        "  n_max = max(n_list)\n",
        "  n_pos = n_list.index(n_max)\n",
        "  models = [log, knn, svc_lin, svc_rbf, tree, forest, pcp, mnb, bnb, cnb, sgdc]\n",
        "  print(models[n_pos+1])\n",
        "  return models[n_pos+1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWu5ZpfYQQqn"
      },
      "source": [
        "##  EDA. Exploração dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zLkfYG-5c3l",
        "outputId": "2200a91c-01d7-49a0-87a7-ed92b313fcfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/lkd_complet.csv', error_bad_lines=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b'Skipping line 3: expected 11 fields, saw 18\\nSkipping line 5: expected 11 fields, saw 36\\nSkipping line 45: expected 11 fields, saw 17\\nSkipping line 51: expected 11 fields, saw 19\\nSkipping line 86: expected 11 fields, saw 12\\nSkipping line 171: expected 11 fields, saw 36\\nSkipping line 173: expected 11 fields, saw 14\\nSkipping line 174: expected 11 fields, saw 18\\nSkipping line 185: expected 11 fields, saw 23\\nSkipping line 207: expected 11 fields, saw 34\\nSkipping line 254: expected 11 fields, saw 12\\nSkipping line 258: expected 11 fields, saw 15\\nSkipping line 268: expected 11 fields, saw 13\\nSkipping line 273: expected 11 fields, saw 77\\nSkipping line 274: expected 11 fields, saw 30\\nSkipping line 275: expected 11 fields, saw 17\\nSkipping line 282: expected 11 fields, saw 18\\nSkipping line 291: expected 11 fields, saw 16\\nSkipping line 311: expected 11 fields, saw 34\\nSkipping line 326: expected 11 fields, saw 77\\nSkipping line 328: expected 11 fields, saw 12\\nSkipping line 362: expected 11 fields, saw 19\\nSkipping line 366: expected 11 fields, saw 13\\nSkipping line 377: expected 11 fields, saw 12\\nSkipping line 393: expected 11 fields, saw 12\\nSkipping line 422: expected 11 fields, saw 14\\nSkipping line 426: expected 11 fields, saw 50\\nSkipping line 428: expected 11 fields, saw 14\\nSkipping line 431: expected 11 fields, saw 12\\nSkipping line 443: expected 11 fields, saw 19\\nSkipping line 475: expected 11 fields, saw 22\\nSkipping line 476: expected 11 fields, saw 20\\nSkipping line 487: expected 11 fields, saw 20\\nSkipping line 492: expected 11 fields, saw 13\\nSkipping line 535: expected 11 fields, saw 27\\nSkipping line 536: expected 11 fields, saw 38\\nSkipping line 538: expected 11 fields, saw 15\\nSkipping line 596: expected 11 fields, saw 14\\nSkipping line 638: expected 11 fields, saw 44\\nSkipping line 639: expected 11 fields, saw 15\\nSkipping line 649: expected 11 fields, saw 24\\nSkipping line 650: expected 11 fields, saw 36\\nSkipping line 663: expected 11 fields, saw 17\\nSkipping line 668: expected 11 fields, saw 61\\nSkipping line 728: expected 11 fields, saw 69\\nSkipping line 732: expected 11 fields, saw 18\\nSkipping line 735: expected 11 fields, saw 12\\nSkipping line 743: expected 11 fields, saw 23\\nSkipping line 747: expected 11 fields, saw 14\\nSkipping line 749: expected 11 fields, saw 40\\nSkipping line 757: expected 11 fields, saw 18\\nSkipping line 820: expected 11 fields, saw 14\\nSkipping line 827: expected 11 fields, saw 30\\nSkipping line 829: expected 11 fields, saw 12\\nSkipping line 830: expected 11 fields, saw 14\\nSkipping line 837: expected 11 fields, saw 77\\nSkipping line 848: expected 11 fields, saw 14\\nSkipping line 849: expected 11 fields, saw 13\\nSkipping line 859: expected 11 fields, saw 45\\nSkipping line 863: expected 11 fields, saw 68\\nSkipping line 870: expected 11 fields, saw 12\\nSkipping line 872: expected 11 fields, saw 23\\nSkipping line 874: expected 11 fields, saw 67\\nSkipping line 876: expected 11 fields, saw 43\\nSkipping line 889: expected 11 fields, saw 14\\nSkipping line 891: expected 11 fields, saw 13\\nSkipping line 920: expected 11 fields, saw 35\\nSkipping line 924: expected 11 fields, saw 12\\nSkipping line 929: expected 11 fields, saw 17\\nSkipping line 946: expected 11 fields, saw 15\\nSkipping line 955: expected 11 fields, saw 77\\nSkipping line 969: expected 11 fields, saw 28\\nSkipping line 997: expected 11 fields, saw 77\\nSkipping line 1026: expected 11 fields, saw 12\\nSkipping line 1055: expected 11 fields, saw 14\\nSkipping line 1097: expected 11 fields, saw 39\\nSkipping line 1100: expected 11 fields, saw 16\\nSkipping line 1108: expected 11 fields, saw 14\\nSkipping line 1109: expected 11 fields, saw 33\\nSkipping line 1113: expected 11 fields, saw 50\\nSkipping line 1114: expected 11 fields, saw 20\\nSkipping line 1129: expected 11 fields, saw 77\\nSkipping line 1140: expected 11 fields, saw 16\\nSkipping line 1141: expected 11 fields, saw 17\\nSkipping line 1145: expected 11 fields, saw 21\\nSkipping line 1148: expected 11 fields, saw 31\\nSkipping line 1151: expected 11 fields, saw 19\\nSkipping line 1154: expected 11 fields, saw 22\\nSkipping line 1156: expected 11 fields, saw 30\\nSkipping line 1158: expected 11 fields, saw 18\\nSkipping line 1167: expected 11 fields, saw 12\\nSkipping line 1173: expected 11 fields, saw 56\\nSkipping line 1251: expected 11 fields, saw 12\\nSkipping line 1282: expected 11 fields, saw 77\\nSkipping line 1329: expected 11 fields, saw 13\\nSkipping line 1331: expected 11 fields, saw 14\\nSkipping line 1332: expected 11 fields, saw 14\\nSkipping line 1336: expected 11 fields, saw 17\\nSkipping line 1382: expected 11 fields, saw 15\\nSkipping line 1402: expected 11 fields, saw 12\\nSkipping line 1414: expected 11 fields, saw 16\\nSkipping line 1442: expected 11 fields, saw 17\\nSkipping line 1446: expected 11 fields, saw 35\\nSkipping line 1447: expected 11 fields, saw 18\\nSkipping line 1483: expected 11 fields, saw 17\\nSkipping line 1486: expected 11 fields, saw 35\\nSkipping line 1489: expected 11 fields, saw 18\\nSkipping line 1499: expected 11 fields, saw 12\\nSkipping line 1506: expected 11 fields, saw 20\\nSkipping line 1509: expected 11 fields, saw 77\\nSkipping line 1562: expected 11 fields, saw 17\\nSkipping line 1599: expected 11 fields, saw 24\\nSkipping line 1607: expected 11 fields, saw 19\\nSkipping line 1611: expected 11 fields, saw 14\\nSkipping line 1630: expected 11 fields, saw 23\\nSkipping line 1634: expected 11 fields, saw 13\\nSkipping line 1989: expected 11 fields, saw 77\\nSkipping line 2061: expected 11 fields, saw 34\\nSkipping line 2063: expected 11 fields, saw 20\\nSkipping line 2066: expected 11 fields, saw 27\\nSkipping line 2068: expected 11 fields, saw 20\\n'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RWRQI0k915g",
        "outputId": "10b17ca1-6c57-4ddb-da95-61e3614fbab2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>category</th>\n",
              "      <th>sub_category</th>\n",
              "      <th>url</th>\n",
              "      <th>image_url</th>\n",
              "      <th>about</th>\n",
              "      <th>experience</th>\n",
              "      <th>name</th>\n",
              "      <th>title</th>\n",
              "      <th>location</th>\n",
              "      <th>skills</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>47</td>\n",
              "      <td>Administracao</td>\n",
              "      <td>Administração</td>\n",
              "      <td>https://br.linkedin.com/in/leandrovieira</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4E03AQ...</td>\n",
              "      <td>Fundador e CEO do Administradores.com, Mestre ...</td>\n",
              "      <td>Founder &amp; CEONome da empresaAdministradores.co...</td>\n",
              "      <td>Leandro Vieira</td>\n",
              "      <td>Founder and CEO at Administradores.com</td>\n",
              "      <td>João Pessoa e Região, Brasil</td>\n",
              "      <td>['\\nBusiness Strategy\\n', '\\nStart-ups\\n', '\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>Administracao</td>\n",
              "      <td>Administração</td>\n",
              "      <td>https://br.linkedin.com/in/vicentefalconi</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C5603AQ...</td>\n",
              "      <td>Vicente Falconi Campos nasceu em Niterói, Rio ...</td>\n",
              "      <td>Sócio-fundador e Presidente do Conselho de Adm...</td>\n",
              "      <td>Vicente Falconi</td>\n",
              "      <td>Sócio-fundador e Membro do Conselho de Adminis...</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>51</td>\n",
              "      <td>Administracao</td>\n",
              "      <td>Administração</td>\n",
              "      <td>https://br.linkedin.com/in/paulo-neto-201a7b13b</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4D03AQ...</td>\n",
              "      <td>Nossa empresa é especializada em Gestão Comerc...</td>\n",
              "      <td>Diretor de vendas e marketingNome da empresaIk...</td>\n",
              "      <td>Paulo Neto .'.</td>\n",
              "      <td>Diretor de vendas e marketing | Ikarus Adminis...</td>\n",
              "      <td>Santos, São Paulo, Brasil</td>\n",
              "      <td>['\\nLiderança de equipe\\n', '\\nEstratégia empr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52</td>\n",
              "      <td>Administracao</td>\n",
              "      <td>Administração</td>\n",
              "      <td>https://br.linkedin.com/in/ericafialho</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4E03AQ...</td>\n",
              "      <td>Administração do contrato de Parceria Público-...</td>\n",
              "      <td>Diretora ExecutivaNome da empresaHELA Administ...</td>\n",
              "      <td>Érica Fialho</td>\n",
              "      <td>Diretora Executiva na HELA Administração, Gest...</td>\n",
              "      <td>Salvador, Bahia, Brasil</td>\n",
              "      <td>['\\nMicrosoft Project\\n', '\\nPlanejamento estr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>Administracao</td>\n",
              "      <td>Administração</td>\n",
              "      <td>https://br.linkedin.com/in/marcelo-gagliardi-8...</td>\n",
              "      <td>data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP//...</td>\n",
              "      <td>No âmbito da área Educacional, é mestre em Ges...</td>\n",
              "      <td>Coordenador de Cursos (Graduação e Pós-Graduaç...</td>\n",
              "      <td>Marcelo Gagliardi</td>\n",
              "      <td>Coordenador - UniCEUB - Centro Universitário d...</td>\n",
              "      <td>Brasília, Distrito Federal, Brasil</td>\n",
              "      <td>['\\nDocência\\n', '\\nInglês\\n', '\\nPesquisa\\n']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1982</th>\n",
              "      <td>2219</td>\n",
              "      <td>saúde</td>\n",
              "      <td>Nutrição</td>\n",
              "      <td>https://br.linkedin.com/in/nath%C3%A1lia-rodri...</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4E03AQ...</td>\n",
              "      <td>Doutora em Ciência e Tecnologia de Alimentos. ...</td>\n",
              "      <td>Nome da empresaEstácioDuração total1 ano 9 mes...</td>\n",
              "      <td>Nathália Rodrigues</td>\n",
              "      <td>Coordenadora do Curso de Nutrição na Estácio</td>\n",
              "      <td>260 conexões</td>\n",
              "      <td>['\\nDesenvolvimento de produtos\\n', '\\nSeguran...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983</th>\n",
              "      <td>2220</td>\n",
              "      <td>saúde</td>\n",
              "      <td>Nutrição</td>\n",
              "      <td>https://br.linkedin.com/in/gustavo-luiz-53434846</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4D03AQ...</td>\n",
              "      <td>Formado em Educação Física, trabalhou por 5 an...</td>\n",
              "      <td>NutricionistaNome da empresaGustavo Luiz Nutri...</td>\n",
              "      <td>Gustavo Luiz</td>\n",
              "      <td>Nutricionista em São Bernardo | Grande ABC</td>\n",
              "      <td>São Bernardo do Campo, São Paulo, Brasil</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1984</th>\n",
              "      <td>2221</td>\n",
              "      <td>saúde</td>\n",
              "      <td>Nutrição</td>\n",
              "      <td>https://br.linkedin.com/in/ta%C3%ADs-malheiros...</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4D03AQ...</td>\n",
              "      <td>Acredito que a sua saúde vem do equilíbrio ent...</td>\n",
              "      <td>Nome da empresaEstima NutricaoDuração total3 a...</td>\n",
              "      <td>Taís Malheiros</td>\n",
              "      <td>Nutricionista Funcional e Esportiva</td>\n",
              "      <td>São Paulo, São Paulo, Brasil</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1985</th>\n",
              "      <td>2222</td>\n",
              "      <td>saúde</td>\n",
              "      <td>Nutrição</td>\n",
              "      <td>https://br.linkedin.com/in/cinara-oliveira-077...</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4E03AQ...</td>\n",
              "      <td>Experiência nas Áreas: Personal Diet, Saúde Co...</td>\n",
              "      <td>Professora da Faculdade de NutriçãoNome da emp...</td>\n",
              "      <td>Cinara Oliveira</td>\n",
              "      <td>Professora Faculdade de Nutrição na Universida...</td>\n",
              "      <td>Rio de Janeiro, Rio de Janeiro, Brasil</td>\n",
              "      <td>['\\nNutrição\\n', '\\nBem-estar\\n', '\\nNutrition...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1986</th>\n",
              "      <td>2223</td>\n",
              "      <td>saúde</td>\n",
              "      <td>Nutrição</td>\n",
              "      <td>https://br.linkedin.com/in/catiamedeirosatualn...</td>\n",
              "      <td>https://media-exp1.licdn.com/dms/image/C4E03AQ...</td>\n",
              "      <td>Specialties: Especialista em nutrição clínica</td>\n",
              "      <td>Responsável Técnica e NutricionistaNome da emp...</td>\n",
              "      <td>Cátia Medeiros</td>\n",
              "      <td>Responsável Técnica e Nutricionista na Atual N...</td>\n",
              "      <td>São Paulo, São Paulo, Brasil</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1987 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  ...                                             skills\n",
              "0       47  ...  ['\\nBusiness Strategy\\n', '\\nStart-ups\\n', '\\n...\n",
              "1       49  ...                                                 []\n",
              "2       51  ...  ['\\nLiderança de equipe\\n', '\\nEstratégia empr...\n",
              "3       52  ...  ['\\nMicrosoft Project\\n', '\\nPlanejamento estr...\n",
              "4       53  ...     ['\\nDocência\\n', '\\nInglês\\n', '\\nPesquisa\\n']\n",
              "...    ...  ...                                                ...\n",
              "1982  2219  ...  ['\\nDesenvolvimento de produtos\\n', '\\nSeguran...\n",
              "1983  2220  ...                                                 []\n",
              "1984  2221  ...                                                 []\n",
              "1985  2222  ...  ['\\nNutrição\\n', '\\nBem-estar\\n', '\\nNutrition...\n",
              "1986  2223  ...                                                 []\n",
              "\n",
              "[1987 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piFaNCYuQsxA"
      },
      "source": [
        "#### Substituindo dados nulos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qm7se4bk55dI",
        "outputId": "bdf4c2a1-aaa4-4685-ebc3-953ee939adab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                0\n",
              "category          0\n",
              "sub_category      0\n",
              "url               0\n",
              "image_url        34\n",
              "about           580\n",
              "experience      111\n",
              "name             34\n",
              "title            34\n",
              "location         34\n",
              "skills            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUXm5qIHAV91"
      },
      "source": [
        "df.fillna(\" \", inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zaUUPRTM6G48",
        "outputId": "536c3863-f66a-478b-a1b2-71cd35b3e21b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df.category.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Administracao', 'Arte e Desing', 'Ciências Biológicas e da Terra',\n",
              "       'Análise e Desenvolvimento de Sistemas',\n",
              "       'Ciências Sociais e Humanas', 'Comunicação e Informação',\n",
              "       'Engenharia e Produção', 'saúde'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YprH1lpK6QsD",
        "outputId": "4572f538-83d3-4f20-a73d-eb712fdf0dd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(df.sub_category.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "207"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oc5Hr7sN6T7i",
        "outputId": "2a5c3382-30fe-477e-a39d-329c22bdd778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'category', 'sub_category', 'url', 'image_url', 'about',\n",
              "       'experience', 'name', 'title', 'location', 'skills'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6S_LXBAQ4ne"
      },
      "source": [
        "#### Remoção de colunas desnecessárias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HW8pbV0v7sqW"
      },
      "source": [
        "df.drop(columns=['id', 'url', 'image_url', 'name', 'location'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkjQbO34ro7i"
      },
      "source": [
        "# df[['about', 'experience', 'title', 'skills']].apply(lambda x : '{} {} {} {}'.format(x[0],x[1], x[2], x[3]), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZZKbTNsRFyJ"
      },
      "source": [
        "#### Formação do corpus. Concatenação de colunas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coenxQ-cg15o"
      },
      "source": [
        "df['text'] = df[['about', 'experience', 'title', 'skills']].apply(\n",
        "    lambda x : '{} {} {} {}'.format(x[0],x[1], x[2], x[3]), axis=1)\n",
        "df['target'] = df['category']\n",
        "\n",
        "df.drop(columns=['about', 'experience', 'title', 'skills'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SM1J1fA2RNxn"
      },
      "source": [
        "#### Convensão dos valores das classes em valores númericos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jj6EKn81ghvC"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(df.target.unique().tolist())\n",
        "le.classes_\n",
        "df.target = le.transform(df.target)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eadb0cyRh3Vs",
        "outputId": "5579cd7b-1e00-4391-d431-7fb2560681d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "le.classes_[6]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Engenharia e Produção'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F79K351GRY5m"
      },
      "source": [
        "#### Reservando dados para análise futura. Simulação de dados reais."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCBBtY0Zl02C"
      },
      "source": [
        "df_train, df_test = separate_df(df, .90)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rf1iTLf0l5Yp",
        "outputId": "0ddf4ce9-e5fe-4a70-f60b-c5212cdb6e1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(len(df_train))\n",
        "print(len(df_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1796\n",
            "191\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQc-DxzgnC6s",
        "outputId": "d219444e-2f0c-4647-824a-668e141a47f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(len(df_test.target.unique()))\n",
        "print(len(df_train.target.unique()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8\n",
            "8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XLS_HSPq6YP"
      },
      "source": [
        "df_train.target = df_train.target.astype(int)\n",
        "df_test.target = df_test.target.astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_iIQKzprJsm",
        "outputId": "79e1ad31-25dd-43b8-b784-c6ad1469c6e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "df_train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1796 entries, 0 to 1795\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   text    1796 non-null   object\n",
            " 1   target  1796 non-null   int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 28.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krIm4c6sRnuX"
      },
      "source": [
        "## Criação de um dicionário"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTjsP-n45dQJ",
        "outputId": "38e3b502-9a34-4f93-c545-1e6025502fb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "en, pt = [], []\n",
        "f = open(\"/content/drive/My Drive/Colab Notebooks/dict.txt\", \"r\")\n",
        "for line in f.readlines():\n",
        "  # print(line)\n",
        "  en.append(line.split(\" - \")[0])\n",
        "  pt.append(line.split(\" - \")[1].replace(\"\\n\", \"\"))\n",
        "\n",
        "dictionary = {}\n",
        "zip_iterator = zip(en, pt)\n",
        "\n",
        "dictionary = dict(zip_iterator)\n",
        "print(len(dictionary))\n",
        "# dictionary"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2918\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-1G6iFEosRc",
        "outputId": "d44bc6c6-d894-4362-cee5-1c0edaea2d70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "f = open(\"/content/drive/My Drive/Colab Notebooks/text_en.txt\", \"r\")\n",
        "en1 = f.read().split(\",\")\n",
        "\n",
        "f = open(\"/content/drive/My Drive/Colab Notebooks/text_pt.txt\", \"r\")\n",
        "pt1 = f.read().split(\",\")\n",
        "\n",
        "print(len(en1))\n",
        "print(len(pt1))\n",
        "\n",
        "en2 = [en.replace(\" \", \"\") for en in en1]\n",
        "pt2 = [pt.replace(\" \", \"\") for pt in pt1]\n",
        "\n",
        "en = [en.replace(\"\\n\", \"\") for en in en2]\n",
        "pt = [pt.replace(\"\\n\", \"\") for pt in pt2]\n",
        "\n",
        "dictionary2 = {}\n",
        "zip_iterator = zip(en, pt)\n",
        "\n",
        "dictionary2 = dict(zip_iterator)\n",
        "print(len(dictionary2))\n",
        "\n",
        "dictionary.update(dictionary2)\n",
        "print(len(dictionary))\n",
        "# dictionary"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2723\n",
            "2723\n",
            "1363\n",
            "4226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agoBbxI1KSvC"
      },
      "source": [
        "### Salvando dicionário"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S23Jhf0fskH7"
      },
      "source": [
        "fout = \"/content/drive/My Drive/Colab Notebooks/dictionary.txt\"\n",
        "fo = open(fout, \"w\")\n",
        "\n",
        "for k, v in dictionary.items():\n",
        "    fo.write(str(k) + ' - '+ str(v) + '\\n')\n",
        "\n",
        "fo.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRr_KWiCSJgE"
      },
      "source": [
        "## Aplicação de pré-processamento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLy3kyiFSONQ"
      },
      "source": [
        "#### Aplicando limpeza dos textos, traduzindo as palavras inglês e removendo as stopwords."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyYmxMlS-Zdr"
      },
      "source": [
        "for i, row in df_train.iterrows():\n",
        "  result = RemoveStopWords(translator(clean_text(row['text'])))\n",
        "  df_train.at[i,'text'] = result\n",
        "\n",
        "for i, row in df_test.iterrows():\n",
        "  result = RemoveStopWords(translator(clean_text(row['text'])))\n",
        "  df_test.at[i,'text'] = result\n",
        "\n",
        "for i, row in df.iterrows():\n",
        "  result = RemoveStopWords(translator(clean_text(row['text'])))\n",
        "  df.at[i,'clear_text'] = result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iic-8H8R-82O",
        "outputId": "dbee74db-3847-494f-fbcc-c1d7cf5dc8e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "result = \"\"\n",
        "for i, row in df.iterrows():\n",
        "  result += find_english_words(row['clear_text']) + \" \"\n",
        "result.replace(\"\", \"\")\n",
        "print()\n",
        "# print(set(result.split(\" \")))\n",
        "# print(len(set(result.split(\" \"))))\n",
        "# print(\", \".join(set(result.split(\" \"))))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aboine04S0E7"
      },
      "source": [
        "## Salvando dados tratados em novos datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBaPj7Y84d79"
      },
      "source": [
        "df_train.to_csv('/content/drive/My Drive/Colab Notebooks/df_train_treated.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGk_GxR70sa8"
      },
      "source": [
        "df_test.to_csv('/content/drive/My Drive/Colab Notebooks/df_test_treated.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJ7lLBVVwcha"
      },
      "source": [
        "# print(stemmingSimple(df_train['text'])[1])\n",
        "# print(stemmingSimpleSet(df_train['text'])[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmtydngJS8Mc"
      },
      "source": [
        "## Pré-processamento. Extração de radical."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncBCtK9UGtEY"
      },
      "source": [
        "# applie stemming in each list\n",
        "train = stemmingSimpleSet(df_train['text'])\n",
        "test  = stemmingSimpleSet(df_test['text'])\n",
        "complet = stemmingSimpleSet(df['clear_text'])\n",
        "\n",
        "df_train['text_stem'] = train\n",
        "df_test['text_stem'] = test\n",
        "df['text_stem'] = complet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUnYFI-4Cv8Z"
      },
      "source": [
        "# df_train['text_stem']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDrQ5FI1TOMV"
      },
      "source": [
        "> Número máximo de palavras encontrada nos registros"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rDN-IzFDfxg",
        "outputId": "9226caaa-a35a-4cf3-d6cc-af29b852960c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "MAX_SEQ_LEN = int(df['text'].apply(lambda x : len(x.split(' '))).quantile(0.95))\n",
        "MAX_SEQ_LEN"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "804"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QxAAFp1Ti3O"
      },
      "source": [
        "## Análise das classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7Ipj2C4C19U",
        "outputId": "c9e5c639-7915-4c9e-9a90-efb194c5a839",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# tokenize the sentences\n",
        "tokenizer = Tokenizer(lower=False)\n",
        "tokenizer.fit_on_texts(df_train['text_stem'])\n",
        "train_text_vec = tokenizer.texts_to_sequences(df_train['text_stem'])\n",
        "test_text_vec = tokenizer.texts_to_sequences(df_test['text_stem'])\n",
        "\n",
        "# pad the sequences\n",
        "train_text_vec = pad_sequences(train_text_vec, maxlen=MAX_SEQ_LEN, padding='post')\n",
        "test_text_vec = pad_sequences(test_text_vec, maxlen=MAX_SEQ_LEN, padding='post')\n",
        "\n",
        "print('Number of Tokens:', len(tokenizer.word_index))\n",
        "print(\"Max Token Index:\", train_text_vec.max(), \"\\n\")\n",
        "\n",
        "# print('Sample Before Processing:', df_train[\"text\"].values[0])\n",
        "# print('Sample After Processing:', tokenizer.sequences_to_texts([train_text_vec[0]]), '\\n')\n",
        "\n",
        "print('What the model will interpret:', train_text_vec[0].tolist())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Tokens: 16303\n",
            "Max Token Index: 16303 \n",
            "\n",
            "What the model will interpret: [93, 205, 104, 294, 7078, 57, 794, 184, 31, 19, 1666, 708, 180, 167, 807, 1402, 74, 20, 370, 2469, 1337, 11, 81, 139, 519, 27, 427, 119, 1, 366, 59, 887, 125, 4977, 102, 55, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGUyu--wEJ6p",
        "outputId": "f1466225-67e2-4d81-e29d-70aa8260fcab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# get an idea of the distribution of the text values\n",
        "from collections import Counter\n",
        "ctr = Counter(df_train['target'].values)\n",
        "print('Distribution of Classes:', ctr)\n",
        "\n",
        "# get class weights for the training data, this will be used data\n",
        "cws = class_weight.compute_class_weight('balanced', \n",
        "                              np.unique(df_train['target'].values), \n",
        "                              df_train['target'].values)\n",
        "print(cws)\n",
        "# print(y_train_int)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Distribution of Classes: Counter({6: 618, 0: 217, 4: 187, 3: 178, 5: 165, 2: 146, 1: 144, 7: 141})\n",
            "[1.03456221 1.55902778 1.53767123 1.26123596 1.20053476 1.36060606\n",
            " 0.36326861 1.59219858]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtaItHYYIWri"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0q64zUTLVmv"
      },
      "source": [
        "### Análise do Balanceamento das Classes e distribuição das palavras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEVqiNgNWAnV",
        "outputId": "0ad9da11-0caa-4ea6-ac3d-184b12631574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "df_train['target'].value_counts().plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0eb39bdb00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fal4-T4uTqLv"
      },
      "source": [
        "- Classe 6 (Engenharia e Produção) está desbalanceada."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNB__Y-nL6s2"
      },
      "source": [
        "### Distribuição da quantidade de palavras únicas em cada registro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiVfUJ0BN5aS",
        "outputId": "21f6aafa-fc5b-4ccf-caed-9b228c63623e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "t = [len(set(tokenize(t))) for t in df['text_stem']]\n",
        "print(t)\n",
        "plt.hist(t)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[37, 28, 43, 92, 61, 81, 167, 128, 3, 3, 3, 7, 52, 88, 12, 100, 55, 124, 4, 2, 4, 5, 2, 7, 3, 9, 3, 116, 122, 46, 94, 31, 80, 28, 128, 128, 14, 29, 89, 57, 95, 33, 40, 40, 202, 36, 39, 12, 261, 30, 77, 164, 88, 34, 114, 33, 86, 70, 39, 33, 22, 21, 220, 78, 74, 25, 16, 14, 18, 195, 5, 162, 152, 11, 14, 115, 54, 33, 71, 183, 116, 18, 27, 30, 0, 68, 37, 83, 94, 38, 55, 159, 42, 44, 161, 34, 56, 38, 283, 12, 85, 21, 82, 5, 233, 5, 11, 3, 3, 8, 5, 4, 2, 4, 9, 46, 0, 25, 21, 129, 209, 202, 116, 101, 88, 249, 86, 34, 82, 22, 132, 171, 108, 108, 168, 159, 65, 6, 93, 231, 71, 463, 71, 180, 200, 177, 19, 159, 32, 36, 47, 7, 7, 7, 8, 7, 72, 150, 156, 72, 6, 93, 32, 80, 115, 28, 30, 17, 35, 107, 82, 153, 22, 112, 187, 232, 33, 122, 216, 197, 140, 172, 35, 151, 209, 180, 151, 159, 120, 218, 254, 110, 103, 21, 74, 72, 201, 158, 5, 91, 138, 40, 96, 98, 43, 22, 66, 80, 50, 33, 7, 5, 8, 8, 12, 5, 35, 5, 7, 27, 25, 111, 38, 47, 104, 92, 23, 46, 150, 67, 18, 88, 154, 34, 79, 145, 100, 42, 25, 141, 23, 159, 49, 12, 39, 184, 18, 68, 72, 30, 34, 33, 181, 16, 38, 127, 47, 20, 26, 47, 161, 9, 17, 111, 7, 8, 84, 31, 109, 10, 45, 64, 25, 172, 49, 63, 40, 6, 33, 167, 34, 68, 149, 73, 19, 64, 119, 53, 84, 47, 11, 133, 132, 175, 135, 23, 67, 34, 20, 134, 281, 63, 148, 82, 204, 89, 201, 88, 143, 27, 66, 155, 136, 72, 76, 39, 137, 144, 65, 179, 200, 126, 72, 251, 17, 12, 55, 6, 27, 32, 76, 28, 27, 107, 78, 118, 115, 114, 57, 10, 28, 96, 107, 77, 28, 72, 35, 84, 93, 119, 100, 211, 82, 24, 50, 26, 35, 5, 32, 17, 34, 23, 28, 4, 25, 34, 22, 67, 76, 91, 28, 104, 151, 114, 106, 89, 60, 7, 136, 147, 100, 128, 198, 154, 36, 10, 7, 90, 38, 118, 38, 101, 0, 97, 20, 91, 29, 186, 17, 24, 134, 80, 82, 287, 259, 82, 59, 44, 116, 146, 80, 34, 97, 64, 67, 98, 36, 44, 167, 30, 204, 151, 58, 15, 25, 91, 5, 7, 106, 91, 213, 93, 70, 238, 32, 51, 19, 215, 67, 117, 130, 13, 110, 30, 5, 92, 55, 30, 55, 34, 102, 99, 156, 53, 131, 33, 138, 34, 37, 28, 49, 103, 92, 48, 4, 13, 15, 96, 12, 54, 14, 32, 86, 17, 23, 6, 29, 6, 2, 41, 96, 2, 40, 123, 47, 109, 30, 109, 50, 155, 27, 73, 24, 59, 23, 74, 39, 27, 41, 118, 46, 71, 34, 119, 9, 58, 52, 118, 72, 36, 165, 30, 216, 125, 132, 29, 178, 95, 128, 108, 169, 145, 72, 147, 49, 25, 99, 83, 20, 150, 4, 47, 32, 37, 48, 26, 15, 43, 53, 48, 42, 68, 11, 62, 105, 122, 66, 13, 56, 31, 32, 39, 115, 38, 43, 34, 92, 66, 5, 21, 226, 23, 124, 108, 147, 305, 52, 5, 6, 5, 69, 40, 140, 29, 147, 29, 21, 22, 104, 84, 25, 40, 14, 16, 32, 16, 159, 73, 47, 14, 58, 52, 27, 66, 25, 51, 84, 40, 6, 45, 173, 44, 33, 73, 310, 64, 178, 39, 25, 68, 63, 37, 77, 53, 97, 72, 134, 79, 176, 115, 190, 159, 98, 192, 27, 105, 76, 35, 88, 201, 21, 19, 36, 86, 48, 10, 34, 147, 33, 72, 228, 9, 31, 162, 62, 13, 73, 36, 145, 27, 59, 79, 188, 158, 20, 59, 141, 105, 42, 71, 60, 29, 158, 14, 63, 42, 92, 88, 55, 27, 41, 8, 41, 56, 189, 102, 207, 108, 131, 315, 118, 87, 117, 130, 12, 30, 88, 111, 7, 88, 72, 35, 54, 129, 46, 111, 25, 52, 133, 147, 82, 27, 212, 67, 148, 50, 37, 219, 27, 145, 46, 130, 231, 32, 26, 301, 107, 49, 184, 76, 163, 210, 86, 87, 84, 84, 85, 24, 213, 251, 176, 358, 147, 112, 77, 281, 127, 37, 14, 39, 106, 40, 166, 35, 10, 132, 103, 4, 64, 182, 4, 38, 21, 47, 18, 93, 74, 49, 21, 100, 171, 27, 78, 97, 4, 87, 42, 106, 44, 162, 75, 39, 18, 41, 157, 57, 4, 110, 50, 88, 36, 98, 5, 24, 53, 64, 134, 53, 4, 6, 136, 32, 243, 211, 81, 55, 121, 34, 132, 123, 40, 288, 122, 155, 6, 62, 86, 172, 8, 14, 33, 25, 31, 27, 45, 43, 96, 111, 46, 395, 91, 215, 37, 55, 55, 74, 62, 47, 69, 77, 52, 37, 187, 64, 198, 175, 167, 92, 39, 47, 127, 95, 40, 119, 66, 189, 74, 33, 243, 76, 84, 96, 82, 52, 51, 41, 37, 107, 117, 136, 109, 44, 29, 82, 81, 64, 29, 118, 102, 55, 99, 29, 34, 40, 96, 132, 57, 10, 22, 137, 12, 95, 73, 86, 80, 101, 50, 139, 22, 39, 107, 112, 156, 71, 77, 95, 48, 103, 27, 198, 61, 52, 76, 36, 83, 145, 207, 96, 155, 45, 103, 86, 124, 53, 28, 21, 123, 29, 123, 66, 429, 40, 42, 146, 213, 249, 74, 25, 100, 175, 52, 41, 9, 49, 43, 0, 39, 22, 57, 24, 33, 25, 39, 34, 121, 86, 44, 12, 21, 15, 61, 43, 68, 91, 86, 34, 27, 42, 138, 71, 48, 123, 56, 112, 28, 122, 113, 225, 80, 174, 51, 37, 69, 57, 82, 109, 28, 107, 46, 53, 143, 172, 55, 42, 6, 2, 3, 7, 10, 5, 5, 6, 8, 124, 78, 204, 62, 144, 103, 72, 222, 56, 31, 22, 222, 260, 116, 96, 38, 92, 65, 153, 138, 200, 260, 145, 139, 80, 231, 223, 30, 103, 36, 64, 43, 147, 126, 53, 105, 56, 272, 56, 58, 98, 112, 171, 76, 79, 50, 30, 156, 134, 33, 7, 96, 52, 245, 76, 0, 251, 5, 320, 228, 193, 179, 181, 120, 97, 162, 16, 202, 209, 35, 91, 23, 199, 43, 37, 185, 94, 204, 25, 87, 216, 36, 5, 72, 33, 197, 15, 46, 23, 116, 11, 129, 89, 12, 96, 105, 129, 6, 63, 37, 22, 143, 37, 104, 73, 95, 62, 13, 55, 44, 6, 12, 84, 149, 27, 40, 13, 40, 38, 37, 16, 15, 4, 74, 13, 7, 5, 5, 5, 8, 4, 4, 6, 166, 127, 5, 4, 11, 2, 8, 3, 3, 4, 4, 171, 19, 123, 21, 99, 79, 142, 185, 225, 179, 14, 109, 184, 47, 29, 99, 54, 20, 40, 104, 18, 53, 55, 124, 12, 68, 89, 30, 24, 63, 3, 112, 42, 32, 20, 154, 19, 136, 172, 104, 67, 97, 26, 5, 83, 59, 8, 104, 18, 141, 4, 89, 65, 204, 132, 131, 26, 54, 5, 101, 88, 83, 21, 86, 42, 13, 5, 115, 4, 35, 54, 14, 36, 43, 61, 29, 87, 14, 8, 152, 160, 136, 39, 78, 54, 62, 137, 28, 77, 83, 19, 68, 10, 35, 66, 18, 149, 50, 153, 68, 42, 148, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 117, 72, 81, 70, 19, 91, 142, 21, 119, 226, 51, 21, 43, 7, 21, 41, 65, 8, 26, 66, 96, 193, 104, 20, 82, 36, 195, 68, 23, 183, 31, 31, 119, 3, 107, 49, 196, 87, 170, 80, 59, 101, 31, 125, 3, 47, 79, 19, 0, 3, 27, 75, 91, 30, 72, 124, 31, 55, 96, 22, 194, 28, 122, 58, 112, 46, 38, 22, 29, 88, 100, 108, 155, 46, 41, 103, 109, 134, 122, 46, 220, 157, 26, 74, 137, 109, 60, 176, 155, 175, 104, 40, 274, 233, 96, 22, 194, 5, 9, 7, 6, 4, 6, 3, 22, 29, 88, 100, 108, 155, 46, 41, 103, 109, 134, 220, 122, 46, 157, 137, 60, 26, 74, 56, 176, 218, 155, 175, 104, 40, 233, 7, 89, 188, 27, 70, 24, 4, 55, 164, 60, 66, 125, 61, 82, 42, 26, 233, 151, 83, 35, 24, 13, 91, 11, 8, 52, 103, 30, 25, 47, 21, 28, 77, 8, 4, 54, 3, 164, 245, 45, 130, 33, 44, 31, 245, 50, 210, 48, 153, 13, 224, 273, 29, 242, 172, 329, 80, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 187, 84, 47, 69, 69, 198, 152, 103, 48, 49, 113, 63, 203, 106, 158, 110, 109, 61, 21, 154, 134, 112, 145, 108, 180, 114, 39, 98, 148, 77, 18, 100, 19, 48, 6, 73, 41, 23, 69, 154, 79, 228, 5, 107, 72, 101, 26, 294, 0, 53, 58, 125, 179, 146, 12, 70, 25, 25, 173, 28, 176, 86, 88, 161, 130, 72, 52, 50, 5, 34, 180, 269, 72, 174, 30, 37, 172, 39, 59, 37, 71, 92, 39, 66, 198, 92, 207, 22, 65, 168, 39, 33, 34, 179, 48, 30, 13, 70, 2, 5, 6, 5, 3, 2, 67, 256, 22, 47, 5, 133, 164, 124, 166, 32, 56, 15, 112, 88, 56, 60, 134, 278, 15, 115, 66, 18, 193, 5, 0, 207, 98, 32, 4, 7, 30, 122, 15, 137, 156, 164, 81, 32, 83, 116, 137, 211, 68, 83, 68, 70, 116, 20, 164, 86, 85, 5, 13, 155, 77, 118, 149, 42, 72, 6, 0, 212, 96, 182, 175, 148, 246, 149, 235, 113, 324, 137, 19, 30, 40, 14, 49, 67, 12, 159, 122, 171, 225, 182, 138, 192, 45, 261, 110, 108, 75, 30, 5, 29, 11, 13, 5, 5, 61, 50, 32, 200, 126, 59, 38, 117, 151, 25, 24, 23, 4, 14, 5, 324, 4, 82, 52, 39, 8, 130, 86, 0, 22, 7, 38, 207, 168, 54, 106, 113, 0, 17, 60, 84, 241, 13, 73, 79, 75, 43, 59, 283, 178, 48, 118, 276, 15, 119, 124, 221, 79, 22, 14, 185, 71, 144, 23, 23, 6, 58, 87, 59, 111, 16, 19, 90, 29, 33, 145, 70, 63, 67, 87, 7, 213, 85, 142, 3, 19, 190, 137, 80, 352, 121, 145, 67, 171, 243, 8, 29, 116, 190, 183, 24, 42, 81, 156, 34, 110, 80, 80, 120, 91, 112, 111, 106, 56, 125, 237, 111, 317, 81, 111, 86, 204, 261, 44, 148, 60, 58, 37, 174, 44, 130, 133, 161, 40, 112, 73, 122, 16, 4, 29, 42, 0, 26, 72, 94, 164, 7, 33, 85, 33, 37, 118, 21, 83, 93, 81, 14, 64, 8, 82, 198, 20, 130, 264, 180, 47, 224, 150, 125, 154, 103, 113, 164, 0, 0, 155, 79, 141, 78, 362, 76, 17, 127, 13, 170, 36, 227, 156, 162, 92, 105, 22, 84, 74, 33, 146, 68, 145, 48, 74, 39, 46, 61, 95, 116, 13, 23, 97, 16, 43, 123, 177, 86, 82, 173, 37, 23, 69, 108, 140, 98, 68, 99, 107, 0, 93, 4, 23, 173, 36, 59, 127, 21, 39, 88, 75, 7, 75, 13, 119, 67, 25, 67, 41, 101, 51, 2, 42, 20, 39, 46, 73, 50, 44, 46, 52, 95, 62, 41, 17, 44, 26, 113, 86, 87, 52, 128, 27, 40, 0, 123, 128, 106, 197, 64, 24, 102, 7, 26, 29, 0, 137, 44, 6, 22, 85, 19, 61, 17, 28, 89, 118, 5, 65, 224, 33, 163, 61, 171, 14, 35, 16, 50, 114, 63, 78, 27, 223, 11, 96, 63, 72, 42, 79, 57, 23, 73, 73, 34, 77, 103, 87, 109, 114, 118, 34, 92, 66, 65, 67, 27, 65, 146, 73, 33, 97, 46, 70, 69, 78, 72, 13]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([787., 505., 336., 202.,  98.,  36.,  16.,   4.,   1.,   2.]),\n",
              " array([  0. ,  46.3,  92.6, 138.9, 185.2, 231.5, 277.8, 324.1, 370.4,\n",
              "        416.7, 463. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjRkxFhjRf2b"
      },
      "source": [
        "# print(df['text_stem'][0])\n",
        "# print(set(tokenize(df['text_stem'][0])))\n",
        "# print(len(tokenize(df['text_stem'][0])))\n",
        "# print(len(set(tokenize(df['text_stem'][0]))))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAnbjZ-tMUn5"
      },
      "source": [
        "# Separação dos dados em treino e teste:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lo6Pbk_nHoQl"
      },
      "source": [
        "sentences_train, sentences_test, y_train, y_test = \\\n",
        "train_test_split(df_train['text_stem'], df_train['target'], test_size=0.25, \n",
        "                 random_state=41)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMBdofZkUcjK"
      },
      "source": [
        "#### Calculando o número máximo de palavras dentre os registros da base de treinamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHZKq8NV-Rq_"
      },
      "source": [
        "maxlen = 0\n",
        "for sent in sentences_train:\n",
        "  maxlen += len(sent)\n",
        "maxlen = int(maxlen/len(sentences_train))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6X48kjkrUrBq"
      },
      "source": [
        "#### Aplicação do TF-IDF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ql_ZHlCzbJh4"
      },
      "source": [
        "vectorizer = TfidfVectorizer()\n",
        "vectorizer.fit(sentences_train)\n",
        "\n",
        "X_train_tf = vectorizer.transform(sentences_train)\n",
        "X_test_tf  = vectorizer.transform(sentences_test)\n",
        "\n",
        "# print(vectorizer.vocabulary_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqwlXh9cUz_c"
      },
      "source": [
        "#### Número de classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwaPZKV2lFCv",
        "outputId": "6304c1ab-d02d-4328-ada0-038bb6cdcd69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "num_classes = np.max(y_train) + 1\n",
        "num_classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQZQbwm0UTVY"
      },
      "source": [
        "# Modelo simple de Rede Neural utilizando o Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgUtsp3EZQNo"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4qcgqXtU44y"
      },
      "source": [
        "#### Criação da matriz de categorias das classes utilizando o Keras "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9phuNO_grzg"
      },
      "source": [
        "y_train_enc = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test_enc = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJ845hP8VM1p"
      },
      "source": [
        "#### Rede neural simples\n",
        "- Input = Número de features\n",
        "- 1 Hidden Layer\n",
        "- Output = Número de classes\n",
        "- Ativação Hidden Layer = Relu\n",
        "- Ativação output = Softmax"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEVV-bVgRvf8",
        "outputId": "ba8ccf68-dc1b-486b-af51-e73fc3e98911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "n_layers = 1 \n",
        "n_densers = 126\n",
        "learning_rate = 0.001\n",
        "epochs = 50\n",
        "batch = 32\n",
        "activation = 'relu'\n",
        "opt = Nadam(lr=learning_rate)\n",
        "model = Sequential()\n",
        "model.add(layers.Dense(n_densers, input_dim=X_train_tf.shape[1], activation=activation))\n",
        "for i in range(1):\n",
        "  model.add(layers.Dense(n_densers,  activation=activation))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer = opt,\n",
        "              loss = \"categorical_crossentropy\",\n",
        "              metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 126)               1709946   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 126)               16002     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 8)                 1016      \n",
            "=================================================================\n",
            "Total params: 1,726,964\n",
            "Trainable params: 1,726,964\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ie6q9O_UWHT_"
      },
      "source": [
        "#### Treinamento do modelo de Rede Neural criado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTQz-UrvRyFF",
        "outputId": "05b5cbf3-b997-48cc-e929-c317a4e323fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        }
      },
      "source": [
        "earlyStopping = EarlyStopping(monitor='val_loss', patience=10, verbose=0, mode='min')\n",
        "mcp_save = ModelCheckpoint('.mdl_wts.hdf5', save_best_only=True, monitor='val_loss', mode='min')\n",
        "reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=7, verbose=1, epsilon=1e-4, mode='min')\n",
        "\n",
        "history = model.fit(X_train_tf.toarray(), y_train_enc,\n",
        "                    epochs=epochs,\n",
        "                    verbose=True,\n",
        "                    validation_data=(X_test_tf.toarray(), y_test_enc),\n",
        "                    batch_size=batch,\n",
        "                    callbacks=[earlyStopping, mcp_save, reduce_lr_loss])\n",
        "loss, accuracy = model.evaluate(X_train_tf.toarray(), y_train_enc, verbose=False)\n",
        "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
        "loss, accuracy = model.evaluate(X_test_tf.toarray(), y_test_enc, verbose=False)\n",
        "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))\n",
        "# plot_history(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
            "Epoch 1/50\n",
            "43/43 [==============================] - 1s 29ms/step - loss: 1.8923 - accuracy: 0.3259 - val_loss: 1.5517 - val_accuracy: 0.4232\n",
            "Epoch 2/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 1.1641 - accuracy: 0.6756 - val_loss: 1.0069 - val_accuracy: 0.7060\n",
            "Epoch 3/50\n",
            "43/43 [==============================] - 1s 26ms/step - loss: 0.3931 - accuracy: 0.9592 - val_loss: 0.7540 - val_accuracy: 0.7840\n",
            "Epoch 4/50\n",
            "43/43 [==============================] - 1s 26ms/step - loss: 0.1191 - accuracy: 0.9903 - val_loss: 0.6919 - val_accuracy: 0.7862\n",
            "Epoch 5/50\n",
            "43/43 [==============================] - 1s 25ms/step - loss: 0.0632 - accuracy: 0.9918 - val_loss: 0.6850 - val_accuracy: 0.7884\n",
            "Epoch 6/50\n",
            "43/43 [==============================] - 1s 25ms/step - loss: 0.0461 - accuracy: 0.9926 - val_loss: 0.7565 - val_accuracy: 0.7684\n",
            "Epoch 7/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0401 - accuracy: 0.9911 - val_loss: 0.7957 - val_accuracy: 0.7572\n",
            "Epoch 8/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0360 - accuracy: 0.9926 - val_loss: 0.7312 - val_accuracy: 0.7817\n",
            "Epoch 9/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0283 - accuracy: 0.9933 - val_loss: 0.7597 - val_accuracy: 0.7684\n",
            "Epoch 10/50\n",
            "43/43 [==============================] - 1s 25ms/step - loss: 0.0309 - accuracy: 0.9918 - val_loss: 0.6745 - val_accuracy: 0.7973\n",
            "Epoch 11/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0243 - accuracy: 0.9948 - val_loss: 0.8040 - val_accuracy: 0.7728\n",
            "Epoch 12/50\n",
            "43/43 [==============================] - 1s 23ms/step - loss: 0.0254 - accuracy: 0.9941 - val_loss: 0.6997 - val_accuracy: 0.7996\n",
            "Epoch 13/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0233 - accuracy: 0.9948 - val_loss: 0.7414 - val_accuracy: 0.7951\n",
            "Epoch 14/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0234 - accuracy: 0.9941 - val_loss: 0.7561 - val_accuracy: 0.7884\n",
            "Epoch 15/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0234 - accuracy: 0.9941 - val_loss: 0.7148 - val_accuracy: 0.8040\n",
            "Epoch 16/50\n",
            "43/43 [==============================] - 1s 23ms/step - loss: 0.0232 - accuracy: 0.9941 - val_loss: 1.0853 - val_accuracy: 0.6993\n",
            "Epoch 17/50\n",
            "43/43 [==============================] - ETA: 0s - loss: 0.0332 - accuracy: 0.9948\n",
            "Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0332 - accuracy: 0.9948 - val_loss: 0.7035 - val_accuracy: 0.7996\n",
            "Epoch 18/50\n",
            "43/43 [==============================] - 1s 25ms/step - loss: 0.0254 - accuracy: 0.9948 - val_loss: 0.7057 - val_accuracy: 0.7973\n",
            "Epoch 19/50\n",
            "43/43 [==============================] - 1s 24ms/step - loss: 0.0246 - accuracy: 0.9948 - val_loss: 0.7117 - val_accuracy: 0.8040\n",
            "Epoch 20/50\n",
            "43/43 [==============================] - 1s 26ms/step - loss: 0.0243 - accuracy: 0.9948 - val_loss: 0.7128 - val_accuracy: 0.7996\n",
            "Training Accuracy: 0.9948\n",
            "Testing Accuracy:  0.7996\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5_qRk_wWTUN"
      },
      "source": [
        "#### Análise de classificação da Rede Neural criada"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApSApIU-L7de",
        "outputId": "6b59ec5f-31fa-475a-bf2f-33ab04bd4545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "test = vectorizer.transform(df_test['text_stem'].values)\n",
        "for i in range(0, 100, 10):\n",
        "  prediction = model.predict(test[i].toarray())\n",
        "  # print(df_test.loc[i, 'text'][:50], \"...\")\n",
        "  print('Actual label:' + str(le.classes_[df_test.loc[i, 'target']]))\n",
        "  print(\"Predicted label: \" + str(le.classes_[np.argmax(prediction)]) + \"\\n\") "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actual label:Administracao\n",
            "Predicted label: Administracao\n",
            "\n",
            "Actual label:Administracao\n",
            "Predicted label: Administracao\n",
            "\n",
            "Actual label:Administracao\n",
            "Predicted label: Administracao\n",
            "\n",
            "Actual label:Arte e Desing\n",
            "Predicted label: Arte e Desing\n",
            "\n",
            "Actual label:Ciências Biológicas e da Terra\n",
            "Predicted label: Engenharia e Produção\n",
            "\n",
            "Actual label:Ciências Biológicas e da Terra\n",
            "Predicted label: Ciências Biológicas e da Terra\n",
            "\n",
            "Actual label:Análise e Desenvolvimento de Sistemas\n",
            "Predicted label: Análise e Desenvolvimento de Sistemas\n",
            "\n",
            "Actual label:Análise e Desenvolvimento de Sistemas\n",
            "Predicted label: Comunicação e Informação\n",
            "\n",
            "Actual label:Ciências Sociais e Humanas\n",
            "Predicted label: Ciências Sociais e Humanas\n",
            "\n",
            "Actual label:Ciências Sociais e Humanas\n",
            "Predicted label: Comunicação e Informação\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_CxOs5HX57-"
      },
      "source": [
        "## Escolhendo melhores parametros da rede neural simples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EacvbCFTZ3NJ"
      },
      "source": [
        "input_shape = X_train_tf.shape[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZJ08YOfYCpv"
      },
      "source": [
        "def create_model(learning_rate = 0.01, activation = 'relu', n_densers = 100, n_layers = 0):\n",
        "  opt = Nadam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, schedule_decay=0.004)\n",
        "  model = Sequential()\n",
        "  model.add(layers.Dense(n_densers, input_dim=X_train_tf.shape[1], activation=activation))\n",
        "  for i in range(n_layers):\n",
        "    model.add(layers.Dense(n_densers,  activation=activation))\n",
        "  model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "  model.compile(optimizer = opt,\n",
        "                loss = \"categorical_crossentropy\",\n",
        "                metrics=['accuracy'])\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RImdggF_YegL"
      },
      "source": [
        "params = {'activation': [\"relu\"],\n",
        "          'batch_size': [16, 32, 64], \n",
        "          'epochs': [50],\n",
        "          'learning_rate': [0.01, 0.001, 0.0001],\n",
        "          'n_densers': [100, 126, 200, 256],\n",
        "          'n_layers': [0, 1, 2]\n",
        "         }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9ijd0iFYYNm"
      },
      "source": [
        "model = KerasClassifier(build_fn = create_model, verbose = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLX3shhKYjiH"
      },
      "source": [
        "random_search = RandomizedSearchCV(model,\n",
        "                                   param_distributions = params,\n",
        "                                   cv=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhBYIpR7Y5eo",
        "outputId": "6b734f86-efdc-41c6-c20e-c09e0eb20ff2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "grid_result = random_search.fit(X_train_tf.toarray(), \n",
        "                                y_train_enc, \n",
        "                                validation_data=(X_test_tf.toarray(), y_test_enc), \n",
        "                                callbacks=[earlyStopping, mcp_save, reduce_lr_loss])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00040: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00030: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00034: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00044: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00034: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00038: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00035: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00036: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00036: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00029: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00024: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00026: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00030: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00024: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00023: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00025: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00027: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00025: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00031: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00025: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00025: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00022: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00025: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00029: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00022: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00021: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00019: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
            "\n",
            "Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gUnpjTaZADV",
        "outputId": "b5b0131c-0d43-4b3f-936f-032b3cd9eb6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print(random_search.best_score_)\n",
        "print(random_search.best_params_)\n",
        "# print(random_search.best_estimator_)\n",
        "# print(random_search.cv_results_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7921337783336639\n",
            "{'n_layers': 0, 'n_densers': 126, 'learning_rate': 0.0001, 'epochs': 50, 'batch_size': 16, 'activation': 'relu'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaIQHMNgbZwA",
        "outputId": "fb32ab3f-1452-443d-dce4-544b166e2359",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "print(classification_report(y_test, random_search.best_estimator_.predict(X_test_tf.toarray())))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/wrappers/scikit_learn.py:241: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.79      0.75        42\n",
            "           1       0.86      0.46      0.60        39\n",
            "           2       1.00      0.61      0.76        31\n",
            "           3       0.75      0.46      0.57        39\n",
            "           4       0.75      0.73      0.74        49\n",
            "           5       0.68      0.81      0.74        37\n",
            "           6       0.77      0.96      0.86       170\n",
            "           7       0.94      0.79      0.86        42\n",
            "\n",
            "    accuracy                           0.78       449\n",
            "   macro avg       0.81      0.70      0.74       449\n",
            "weighted avg       0.79      0.78      0.77       449\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51pYkHzWX2YT"
      },
      "source": [
        "best_model = random_search.best_estimator_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9IFoNdLdUDf"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUbQzO7gdJT3",
        "outputId": "7e9d0b0a-4beb-4608-8890-07a4ed8a0b68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "cm = confusion_matrix(y_test, random_search.predict(X_test_tf.toarray()))\n",
        "# or\n",
        "#cm = np.array([[1401,    0],[1112, 0]])\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
        "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues_r', \n",
        "            xticklabels=le.classes_, yticklabels=le.classes_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ea7cf6b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DA9PUDs0MPXg"
      },
      "source": [
        "# Experimentos: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7Tta5xM8FHr"
      },
      "source": [
        "# # The maximum number of words to be used. (most frequent)\n",
        "# MAX_NB_WORDS = 1000\n",
        "# # Max number of words in each complaint.\n",
        "# MAX_SEQUENCE_LENGTH = maxlen\n",
        "# # This is fixed.\n",
        "# EMBEDDING_DIM = 50\n",
        "\n",
        "# tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\n",
        "# tokenizer.fit_on_texts(sentences_train)\n",
        "\n",
        "# X_train = tokenizer.texts_to_sequences(sentences_train)\n",
        "# X_test = tokenizer.texts_to_sequences(sentences_test)\n",
        "\n",
        "# vocab_size = len(tokenizer.word_index) + 1  # Adding 1 because of reserved 0 index\n",
        "\n",
        "# print(sentences_train[2])\n",
        "# print(X_train[2])\n",
        "\n",
        "# X_train = pad_sequences(X_train, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "# X_test = pad_sequences(X_test, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "\n",
        "# print(X_train[2, :])\n",
        "\n",
        "# def RNN():\n",
        "#   inputs= Input(name='inputs',shape=[MAX_SEQUENCE_LENGTH])\n",
        "#   layer = Embedding(MAX_NB_WORDS,EMBEDDING_DIM,input_length=MAX_SEQUENCE_LENGTH)(inputs)\n",
        "#   # layer = Bidirectional(LSTM(n_densers))(layer)\n",
        "#   layer = LSTM(126)(layer)\n",
        "#   layer = Dense(n_densers,name='FC1')(layer)\n",
        "#   layer = Activation('relu')(layer)\n",
        "#   layer = Dense(num_classes,name='out_layer')(layer)\n",
        "#   layer = Activation('softmax')(layer)\n",
        "#   model = Model(inputs=inputs,outputs=layer)\n",
        "#   return model\n",
        "\n",
        "# opt = Nadam(lr=learning_rate)\n",
        "# model = RNN()\n",
        "# model.summary()\n",
        "# model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])\n",
        "\n",
        "# earlyStopping = EarlyStopping(monitor='val_loss', patience=10, verbose=0, mode='min')\n",
        "# mcp_save = ModelCheckpoint('.mdl_wts.hdf5', save_best_only=True, monitor='val_loss', mode='min')\n",
        "# reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=7, verbose=1, epsilon=1e-4, mode='min')\n",
        "\n",
        "# history = model.fit(X_train, y_train,\n",
        "#                     epochs=epochs,\n",
        "#                     verbose=True,\n",
        "#                     validation_data=(X_test, y_test),\n",
        "#                     batch_size=100,\n",
        "#                     callbacks=[earlyStopping, mcp_save, reduce_lr_loss])\n",
        "# loss, accuracy = model.evaluate(X_train, y_train, verbose=False)\n",
        "# print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
        "# loss, accuracy = model.evaluate(X_test, y_test, verbose=False)\n",
        "# print(\"Testing Accuracy:  {:.4f}\".format(accuracy))\n",
        "# # plot_history(history)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1EBeRYBIaxC"
      },
      "source": [
        "# vectorizer = CountVectorizer()\n",
        "# vectorizer.fit(sentences_train)\n",
        "\n",
        "# X_train_ct = vectorizer.transform(sentences_train)\n",
        "# X_test_ct  = vectorizer.transform(sentences_test)\n",
        "\n",
        "# print(vectorizer.vocabulary_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iT5UI-10AxBV"
      },
      "source": [
        "vectorizer = TfidfVectorizer()\n",
        "vectorizer.fit(sentences_train)\n",
        "\n",
        "X_train_tf = vectorizer.transform(sentences_train)\n",
        "X_test_tf  = vectorizer.transform(sentences_test)\n",
        "\n",
        "# print(vectorizer.vocabulary_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWva5KWvhBdu"
      },
      "source": [
        "## Execusão de varios algoritmos de ML para a escolha do melhor modelo "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0QNswccXNiG",
        "outputId": "acc4f919-5a7d-432d-8c3f-b2b8dbd4000a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "mod = models(X_train_tf, y_train, X_test_tf, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]Logistic Regression Training Accuracy:  0.9673348181143281\n",
            "[0]Logistic Regression Cross_val Accuracy:  0.7958098396904367\n",
            "[0]Logistic Regression Testing Accuracy:  0.7906458797327395\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.69      0.67        42\n",
            "           1       0.76      0.67      0.71        39\n",
            "           2       0.90      0.87      0.89        31\n",
            "           3       0.72      0.59      0.65        39\n",
            "           4       0.68      0.65      0.67        49\n",
            "           5       0.70      0.86      0.77        37\n",
            "           6       0.86      0.90      0.88       170\n",
            "           7       0.85      0.79      0.81        42\n",
            "\n",
            "    accuracy                           0.79       449\n",
            "   macro avg       0.77      0.75      0.76       449\n",
            "weighted avg       0.79      0.79      0.79       449\n",
            "\n",
            "[1]KNeighborns Training Accuracy:  0.370452858203415\n",
            "[1]KNeighborns Training Cross_val Accuracy:  0.33110558319513544\n",
            "[1]KNeighborns Testing Accuracy:  0.37861915367483295\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.33      0.02      0.04        42\n",
            "           1       0.00      0.00      0.00        39\n",
            "           2       0.00      0.00      0.00        31\n",
            "           3       0.00      0.00      0.00        39\n",
            "           4       0.00      0.00      0.00        49\n",
            "           5       0.00      0.00      0.00        37\n",
            "           6       0.38      0.99      0.55       170\n",
            "           7       0.00      0.00      0.00        42\n",
            "\n",
            "    accuracy                           0.38       449\n",
            "   macro avg       0.09      0.13      0.07       449\n",
            "weighted avg       0.17      0.38      0.21       449\n",
            "\n",
            "[2]SVC Linear Training Accuracy:  0.985894580549369\n",
            "[2]SVC Linear Training Cross_val Accuracy:  0.8091708126036483\n",
            "[2]SVC Linear Testing Accuracy:  0.8106904231625836\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.76      0.71        42\n",
            "           1       0.83      0.64      0.72        39\n",
            "           2       0.92      0.77      0.84        31\n",
            "           3       0.73      0.62      0.67        39\n",
            "           4       0.74      0.76      0.75        49\n",
            "           5       0.73      0.86      0.79        37\n",
            "           6       0.85      0.93      0.89       170\n",
            "           7       0.97      0.76      0.85        42\n",
            "\n",
            "    accuracy                           0.81       449\n",
            "   macro avg       0.81      0.76      0.78       449\n",
            "weighted avg       0.82      0.81      0.81       449\n",
            "\n",
            "[3]SVC RBF Training Accuracy:  0.9925760950259837\n",
            "[3]SVC RBF Training Cross_val Accuracy:  0.5716307352128248\n",
            "[3]SVC RBF Testing Accuracy:  0.6458797327394209\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.64      0.67        42\n",
            "           1       1.00      0.18      0.30        39\n",
            "           2       1.00      0.39      0.56        31\n",
            "           3       0.62      0.13      0.21        39\n",
            "           4       0.68      0.55      0.61        49\n",
            "           5       0.68      0.70      0.69        37\n",
            "           6       0.58      0.98      0.73       170\n",
            "           7       1.00      0.48      0.65        42\n",
            "\n",
            "    accuracy                           0.65       449\n",
            "   macro avg       0.78      0.51      0.55       449\n",
            "weighted avg       0.72      0.65      0.61       449\n",
            "\n",
            "[4]Decision Tree Training Accuracy:  0.9948032665181886\n",
            "[4]Decision Tree Training Cross_val Accuracy:  0.5159646213377557\n",
            "[4]Decision Tree Testing Accuracy:  0.5300668151447662\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.37      0.43      0.40        42\n",
            "           1       0.41      0.31      0.35        39\n",
            "           2       0.50      0.35      0.42        31\n",
            "           3       0.35      0.36      0.35        39\n",
            "           4       0.52      0.49      0.51        49\n",
            "           5       0.27      0.38      0.32        37\n",
            "           6       0.71      0.75      0.73       170\n",
            "           7       0.56      0.43      0.49        42\n",
            "\n",
            "    accuracy                           0.53       449\n",
            "   macro avg       0.46      0.44      0.44       449\n",
            "weighted avg       0.53      0.53      0.53       449\n",
            "\n",
            "[5]Random Forest Training Accuracy:  0.9948032665181886\n",
            "[5]Random Forest Training Cross_val Accuracy:  0.5790105030403538\n",
            "[5]Random Forest Testing Accuracy:  0.6035634743875279\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.43      0.51        42\n",
            "           1       0.83      0.13      0.22        39\n",
            "           2       0.82      0.45      0.58        31\n",
            "           3       0.50      0.13      0.20        39\n",
            "           4       0.66      0.51      0.57        49\n",
            "           5       0.65      0.65      0.65        37\n",
            "           6       0.56      0.96      0.71       170\n",
            "           7       0.89      0.38      0.53        42\n",
            "\n",
            "    accuracy                           0.60       449\n",
            "   macro avg       0.69      0.46      0.50       449\n",
            "weighted avg       0.65      0.60      0.56       449\n",
            "\n",
            "[6] Perceptron Training Accuracy:  0.9948032665181886\n",
            "[6] Perceptron Training Cross_val Accuracy:  0.779502487562189\n",
            "[6] Perceptron Testing accuracy:  0.779510022271715\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.83      0.72        42\n",
            "           1       0.67      0.74      0.71        39\n",
            "           2       0.79      0.74      0.77        31\n",
            "           3       0.73      0.49      0.58        39\n",
            "           4       0.79      0.69      0.74        49\n",
            "           5       0.65      0.76      0.70        37\n",
            "           6       0.87      0.89      0.88       170\n",
            "           7       0.84      0.74      0.78        42\n",
            "\n",
            "    accuracy                           0.78       449\n",
            "   macro avg       0.75      0.74      0.74       449\n",
            "weighted avg       0.79      0.78      0.78       449\n",
            "\n",
            "[7] MultinomialNB Training Accuracy:  0.9910913140311804\n",
            "[7] MultinomialNB Training Cross_val Accuracy:  0.7334328358208955\n",
            "[7] MultinomialNB Testing accuracy:  0.7505567928730512\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.83      0.72        42\n",
            "           1       0.67      0.74      0.71        39\n",
            "           2       0.79      0.74      0.77        31\n",
            "           3       0.73      0.49      0.58        39\n",
            "           4       0.79      0.69      0.74        49\n",
            "           5       0.65      0.76      0.70        37\n",
            "           6       0.87      0.89      0.88       170\n",
            "           7       0.84      0.74      0.78        42\n",
            "\n",
            "    accuracy                           0.78       449\n",
            "   macro avg       0.75      0.74      0.74       449\n",
            "weighted avg       0.79      0.78      0.78       449\n",
            "\n",
            "[8] BernoulliNB Training Accuracy:  0.8960653303637713\n",
            "[8] BernoulliNB Training Cross_val Accuracy:  0.6332393587617469\n",
            "[8] BernoulliNB Testing accuracy:  0.6570155902004454\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.83      0.72        42\n",
            "           1       0.67      0.74      0.71        39\n",
            "           2       0.79      0.74      0.77        31\n",
            "           3       0.73      0.49      0.58        39\n",
            "           4       0.79      0.69      0.74        49\n",
            "           5       0.65      0.76      0.70        37\n",
            "           6       0.87      0.89      0.88       170\n",
            "           7       0.84      0.74      0.78        42\n",
            "\n",
            "    accuracy                           0.78       449\n",
            "   macro avg       0.75      0.74      0.74       449\n",
            "weighted avg       0.79      0.78      0.78       449\n",
            "\n",
            "[9] ComplementNB Training Accuracy:  0.9762435040831478\n",
            "[9] ComplementNB Training Cross_val Accuracy:  0.7646213377556661\n",
            "[9] ComplementNB Testing accuracy:  0.7661469933184856\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.83      0.72        42\n",
            "           1       0.67      0.74      0.71        39\n",
            "           2       0.79      0.74      0.77        31\n",
            "           3       0.73      0.49      0.58        39\n",
            "           4       0.79      0.69      0.74        49\n",
            "           5       0.65      0.76      0.70        37\n",
            "           6       0.87      0.89      0.88       170\n",
            "           7       0.84      0.74      0.78        42\n",
            "\n",
            "    accuracy                           0.78       449\n",
            "   macro avg       0.75      0.74      0.74       449\n",
            "weighted avg       0.79      0.78      0.78       449\n",
            "\n",
            "[10] SGDClassifier Training Accuracy:  0.994060876020787\n",
            "[10] SGDClassifier Training Cross_val Accuracy:  0.8195854063018242\n",
            "[10] SGDClassifier Testing accuracy:  0.8129175946547884\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.71      0.71        42\n",
            "           1       0.73      0.69      0.71        39\n",
            "           2       0.93      0.81      0.86        31\n",
            "           3       0.83      0.51      0.63        39\n",
            "           4       0.80      0.76      0.78        49\n",
            "           5       0.73      0.86      0.79        37\n",
            "           6       0.84      0.95      0.89       170\n",
            "           7       0.89      0.79      0.84        42\n",
            "\n",
            "    accuracy                           0.81       449\n",
            "   macro avg       0.81      0.76      0.78       449\n",
            "weighted avg       0.82      0.81      0.81       449\n",
            "\n",
            "SGDClassifier(alpha=0.0001, average=False, class_weight='balanced',\n",
            "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
            "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
            "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
            "              power_t=0.5, random_state=0, shuffle=True, tol=0.001,\n",
            "              validation_fraction=0.1, verbose=0, warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHWfZdv9gLSA"
      },
      "source": [
        "- O modelo que apresentou os melhores resultados foi o Stochastic Gradient Descent (SGD)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o79qgZCWgUqz"
      },
      "source": [
        "## Escolhendo os melhores parâmetros para o Stochastic Gradient Descent (SGD) Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "452HHs2KArIs"
      },
      "source": [
        "# load libraries\n",
        "from sklearn import decomposition, datasets\n",
        "from sklearn import tree\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# sc = StandardScaler()\n",
        "\n",
        "# pca = decomposition.PCA()\n",
        "\n",
        "# pipe = Pipeline(steps=[\n",
        "#                       ('sc', sc),\n",
        "#                       #   ('pca', pca),\n",
        "#                       ('SGD', mod)])\n",
        "\n",
        "parameters = {\n",
        "    'alpha': [1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3],\n",
        "    'loss' : ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'],\n",
        "    'max_iter': [1000, 2000], \n",
        "    'penalty': ['l2'],\n",
        "    'n_jobs': [-1],\n",
        "    'class_weight': ['balanced']\n",
        "}\n",
        "\n",
        "clf = GridSearchCV(mod, parameters, cv=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gf0uJFNxqdwY",
        "outputId": "5c6442fe-1c48-4491-fc25-5e8ac06ee291",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Fit the grid search\n",
        "clf.fit(X_train_tf,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=SGDClassifier(alpha=0.0001, average=False,\n",
              "                                     class_weight='balanced',\n",
              "                                     early_stopping=False, epsilon=0.1,\n",
              "                                     eta0=0.0, fit_intercept=True,\n",
              "                                     l1_ratio=0.15, learning_rate='optimal',\n",
              "                                     loss='hinge', max_iter=1000,\n",
              "                                     n_iter_no_change=5, n_jobs=None,\n",
              "                                     penalty='l2', power_t=0.5, random_state=0,\n",
              "                                     shuffle=True, tol=0.001,\n",
              "                                     validation_fraction=...0,\n",
              "                                     warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0,\n",
              "                                   1000.0],\n",
              "                         'class_weight': ['balanced'],\n",
              "                         'loss': ['hinge', 'log', 'modified_huber',\n",
              "                                  'squared_hinge', 'perceptron'],\n",
              "                         'max_iter': [1000, 2000], 'n_jobs': [-1],\n",
              "                         'penalty': ['l2']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlNY8vqhu2pC",
        "outputId": "965d2776-f9d9-4216-ee89-814b9a97a559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print(clf.best_estimator_.get_params())\n",
        "print(clf.best_score_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'alpha': 0.001, 'average': False, 'class_weight': 'balanced', 'early_stopping': False, 'epsilon': 0.1, 'eta0': 0.0, 'fit_intercept': True, 'l1_ratio': 0.15, 'learning_rate': 'optimal', 'loss': 'modified_huber', 'max_iter': 1000, 'n_iter_no_change': 5, 'n_jobs': -1, 'penalty': 'l2', 'power_t': 0.5, 'random_state': 0, 'shuffle': True, 'tol': 0.001, 'validation_fraction': 0.1, 'verbose': 0, 'warm_start': False}\n",
            "0.8403482587064677\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUdjF8r0u4hw"
      },
      "source": [
        "# from sklearn.model_selection import cross_val_score\n",
        "# CV_Result = cross_val_score(clf, X_train_tf, y_train, cv=10, n_jobs=-1)\n",
        "# print(CV_Result)\n",
        "# print(CV_Result.mean())\n",
        "# print(CV_Result.std())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RYav4hZwjTw",
        "outputId": "e5b54b80-163a-4081-b266-4ff75d2f3e28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "print(classification_report(y_test, clf.best_estimator_.predict(X_test_tf.toarray())))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.71      0.73        42\n",
            "           1       0.78      0.72      0.75        39\n",
            "           2       0.90      0.87      0.89        31\n",
            "           3       0.73      0.56      0.64        39\n",
            "           4       0.77      0.73      0.75        49\n",
            "           5       0.70      0.86      0.77        37\n",
            "           6       0.86      0.94      0.90       170\n",
            "           7       0.91      0.76      0.83        42\n",
            "\n",
            "    accuracy                           0.82       449\n",
            "   macro avg       0.80      0.77      0.78       449\n",
            "weighted avg       0.82      0.82      0.81       449\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYexeL3BgfXe"
      },
      "source": [
        "## Matriz de confusão  aplicada aos dados de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-Ic0n6Fwr1V",
        "outputId": "35afdb09-bbe9-4a14-9e07-48649a0278b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "cm = confusion_matrix(y_test, clf.best_estimator_.predict(X_test_tf.toarray()))\n",
        "# or\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
        "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues_r',\n",
        "            xticklabels=le.classes_, yticklabels=le.classes_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ea67332e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZT0B1kSgyrG"
      },
      "source": [
        "## Matriz de confusão aplicado a dados novos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yaG_O25yDdM",
        "outputId": "61c43baa-1da4-4acc-e39b-2a3b0aa31a87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "test = vectorizer.transform(df_test['text_stem'].values)\n",
        "cm = confusion_matrix(df_test['target'].values, clf.best_estimator_.predict(test.toarray()))\n",
        "# or\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
        "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues_r', \n",
        "            xticklabels=le.classes_, yticklabels=le.classes_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ea7cd5fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfqS-OGFzFqL",
        "outputId": "60457892-5392-4f9a-dc87-53a31a844403",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "clf.best_estimator_.predict(test.toarray())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 6, 0, 0, 5, 4, 4, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,\n",
              "       0, 5, 5, 2, 2, 2, 2, 6, 2, 2, 5, 2, 2, 2, 2, 5, 6, 6, 6, 6, 0, 0,\n",
              "       0, 0, 6, 0, 3, 3, 3, 3, 6, 3, 3, 6, 0, 6, 1, 1, 1, 1, 6, 1, 1, 6,\n",
              "       6, 2, 6, 1, 5, 1, 6, 4, 4, 0, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "       4, 4, 5, 4, 6, 5, 0, 0, 6, 5, 0, 5, 0, 6, 0, 6, 0, 5, 5, 0, 0, 7,\n",
              "       6, 6, 6, 7, 6, 6, 6, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 3, 6, 7, 6,\n",
              "       7, 6, 6, 5, 6, 6, 1, 6, 6, 6, 6, 1, 6, 1, 6, 6, 6, 6, 6, 6, 6, 6,\n",
              "       6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6, 6, 0, 0, 0, 0, 3, 0, 0, 6, 0,\n",
              "       0, 7, 7, 6, 7, 7, 0, 7, 0, 7, 3, 7, 7, 7, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6v94L-QRwtuk"
      },
      "source": [
        "prediction = clf.best_estimator_.predict(test.toarray())\n",
        "for i in range(100):\n",
        "  # print(df_test.loc[i, 'text'][:150], \"...\")\n",
        "  print('Actual label:' + str(le.classes_[df_test.loc[i, 'target']]))\n",
        "  print(\"Predicted label: \" + str(le.classes_[prediction[i]]) + \"\\n\") "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhPsTXHRcYA_"
      },
      "source": [
        "#### Escolhendo o melhores parâmentros para o SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHFercT24wFB"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "mod1 = SVC(kernel='linear', random_state = 0, class_weight='balanced')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cuz5jR-w41vB",
        "outputId": "e6907e19-774f-4016-a281-f062576ab8f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "mod1.get_params"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method BaseEstimator.get_params of SVC(C=1.0, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
              "    verbose=False)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkbuIzIUID0z"
      },
      "source": [
        "from scipy.stats import uniform"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBTYbYwn53gi"
      },
      "source": [
        "parameters = {\n",
        "    'class_weight': ['balanced'],\n",
        "    'C': [0.01, 0.1, 1, 10],\n",
        "    'kernel': ['linear', 'sigmoid'],\n",
        "    'coef0': [1, 0],\n",
        "    'decision_function_shape': ['ovo', 'ovr']\n",
        "}\n",
        "\n",
        "clf = GridSearchCV(mod1, parameters, cv=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuAD_KBPIEyp",
        "outputId": "fa41d146-e1c5-4ed2-d32a-3bffff75a380",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "clf.fit(X_train_tf,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
              "                           class_weight='balanced', coef0=0.0,\n",
              "                           decision_function_shape='ovr', degree=3,\n",
              "                           gamma='scale', kernel='linear', max_iter=-1,\n",
              "                           probability=False, random_state=0, shrinking=True,\n",
              "                           tol=0.001, verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'C': [0.01, 0.1, 1, 10], 'class_weight': ['balanced'],\n",
              "                         'coef0': [1, 0],\n",
              "                         'decision_function_shape': ['ovo', 'ovr'],\n",
              "                         'kernel': ['linear', 'sigmoid']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Iv_4Y8_JQUy",
        "outputId": "466ac59a-f736-4b7d-a97b-c8e6fe3deae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print(clf.best_estimator_.get_params())\n",
        "print(clf.best_score_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 1, 'break_ties': False, 'cache_size': 200, 'class_weight': 'balanced', 'coef0': 0, 'decision_function_shape': 'ovo', 'degree': 3, 'gamma': 'scale', 'kernel': 'sigmoid', 'max_iter': -1, 'probability': False, 'random_state': 0, 'shrinking': True, 'tol': 0.001, 'verbose': False}\n",
            "0.811420674405749\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9hEtco4JYhb",
        "outputId": "f5869edd-1918-4db0-b120-0a32131c8e7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "print(classification_report(y_test, clf.best_estimator_.predict(X_test_tf.toarray())))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.76      0.72        42\n",
            "           1       0.78      0.74      0.76        39\n",
            "           2       0.89      0.77      0.83        31\n",
            "           3       0.73      0.62      0.67        39\n",
            "           4       0.78      0.78      0.78        49\n",
            "           5       0.71      0.86      0.78        37\n",
            "           6       0.87      0.91      0.89       170\n",
            "           7       0.94      0.76      0.84        42\n",
            "\n",
            "    accuracy                           0.81       449\n",
            "   macro avg       0.80      0.78      0.78       449\n",
            "weighted avg       0.82      0.81      0.81       449\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OuIvkScJb_e",
        "outputId": "cea4595f-d421-4184-9bfb-428c0738980b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "cm2 = confusion_matrix(y_test, clf.best_estimator_.predict(X_test_tf.toarray()))\n",
        "# or\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
        "sns.heatmap(cm/np.sum(cm2), annot=True, fmt='.2%', cmap='Blues_r',\n",
        "            xticklabels=le.classes_, yticklabels=le.classes_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e9f2af828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmPnRM6yc_A3"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(211)\n",
        "ax2 = fig.add_subplot(212)\n",
        "\n",
        "# Tell pointplot to plot on ax1 with the ax argument\n",
        "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues_r',\n",
        "            xticklabels=le.classes_, yticklabels=le.classes_, ax=ax1)\n",
        "\n",
        "# Tell the factorplot to plot on ax2 with the ax argument\n",
        "# Also store the FacetGrid in 'g'\n",
        "# g=sns.factorplot(x=\"x\", y=\"y\", data=data, ax=ax2)\n",
        "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues_r',\n",
        "            xticklabels=le.classes_, yticklabels=le.classes_, ax=ax2)\n",
        "\n",
        "# Close the FacetGrid figure which we don't need (g.fig)\n",
        "# plt.close(g.fig)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}