Day23 -Naive Bayes實作作業.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0MRC0e0KhQ0S"
   },
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LWd1UlMnhT2s"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YvGPUQaHhXfL"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "import codecs\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K1VMqkGvhc3-"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(r'datasets_483_982_spam.csv', encoding = 'latin-1')\n",
    "all_data = []\n",
    "\n",
    "for content,label in dataset[['v2','v1']].values:\n",
    "    if label == 'spam':\n",
    "        label = 1\n",
    "    else :\n",
    "        label = 0\n",
    "    all_data.append([content, label])\n",
    "all_data = np.array(all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 取出訓練內文與標註"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = all_data[:,0]\n",
    "Y = all_data[:,1].astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training Data Examples : \n['Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'\n 'Ok lar... Joking wif u oni...'\n \"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\"\n 'U dun say so early hor... U c already then say...'\n \"Nah I don't think he goes to usf, he lives around here though\"]\n"
     ]
    }
   ],
   "source": [
    "print('Training Data Examples : \\n{}'.format(X[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Labeling Data Examples : \n[0 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "print('Labeling Data Examples : \\n{}'.format(Y[:5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 文字預處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[nltk_data] Downloading package stopwords to\n[nltk_data]     C:\\Users\\zero0\\AppData\\Roaming\\nltk_data...\n[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "import nltk\n",
    "\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Lemmatize with POS Tag\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer \n",
    "\n",
    "## 創建Lemmatizer\n",
    "lemmatizer = WordNetLemmatizer() \n",
    "def get_wordnet_pos(word):\n",
    "    \"\"\"將pos_tag結果mapping到lemmatizer中pos的格式\"\"\"\n",
    "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
    "    tag_dict = {\"adj\": wordnet.ADJ,\n",
    "                \"noun\": wordnet.NOUN,\n",
    "                \"verb\": wordnet.VERB,\n",
    "                \"adv\": wordnet.ADV}\n",
    "\n",
    "    return tag_dict.get(tag, wordnet.NOUN)\n",
    "\n",
    "\n",
    "def clean_content(X):\n",
    "    # remove non-alphabet characters\n",
    "    X_clean = [re.sub('[^a-zA-Z]',' ', x).lower() for x in X]\n",
    "    # tokenize\n",
    "    X_word_tokenize = [nltk.word_tokenize(x) for x in X_clean]\n",
    "    # stopwords_lemmatizer\n",
    "    X_stopwords_lemmatizer = []\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    for content in X_word_tokenize:\n",
    "        content_clean = []\n",
    "        for word in content:\n",
    "            if word not in stop_words:\n",
    "                word = lemmatizer.lemmatize(word, get_wordnet_pos(word))\n",
    "                content_clean.append(word)\n",
    "        X_stopwords_lemmatizer.append(content_clean)\n",
    "    \n",
    "    X_output = [' '.join(x) for x in X_stopwords_lemmatizer]\n",
    "    \n",
    "    return X_output\n",
    "                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = clean_content(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "#max_features是要建造幾個column，會按造字出現的高低去篩選 \n",
    "\"藉由BOW方式將資料轉換為Vector，自行填入\"\n",
    "cv=CountVectorizer(max_features = 4000)\n",
    "X= cv.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(5572, 4000)"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YvxIPVyMhmKp"
   },
   "source": [
    "## Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AVzJWAXIhxoC"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bb6jCOCQiAmP"
   },
   "source": [
    "## Fit the MultinomialNB model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2356,
     "status": "ok",
     "timestamp": 1588492962262,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "e0pFVAmciHQs",
    "outputId": "8cb18c23-669b-452a-9bee-b2f96534f0f5"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf_M=MultinomialNB()\n",
    "clf_M.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yyxW5b395mR2"
   },
   "source": [
    "## Predicting a new result (可與前章節KNN模型比較)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2351,
     "status": "ok",
     "timestamp": 1588492962263,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "f8YOXsQy58rP",
    "outputId": "e248f6c5-4613-4a9e-faed-093c46defda1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Trainset Accuracy: 0.989230424052053\n"
     ]
    }
   ],
   "source": [
    "print('Trainset Accuracy: {}'.format(clf_M.score(X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Testset Accuracy: 0.9838565022421525\n"
     ]
    }
   ],
   "source": [
    "print('Testset Accuracy: {}'.format(clf_M.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vKYVQH-l5NpE"
   },
   "source": [
    "## Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2345,
     "status": "ok",
     "timestamp": 1588492962263,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "p6VMTb2O4hwM",
    "outputId": "14b859cb-16df-4e5d-894b-3bda8e756d3d"
   },
   "outputs": [],
   "source": [
    "y_pred = clf_M.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h4Hwj34ziWQW"
   },
   "source": [
    "## Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3505,
     "status": "ok",
     "timestamp": 1588492963427,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "D6bpZwUiiXic",
    "outputId": "ec9468d5-c478-4ffa-ba1c-535eb56d7304"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[940   9]\n [  9 157]]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9838565022421525"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cm_output(cm, labels=['ham', 'spam']):\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm,cmap=plt.cm.Blues,)\n",
    "    plt.title('Confusion matrix of the classifier')\n",
    "    fig.colorbar(cax)\n",
    "    ax.set_xticklabels([''] + labels)\n",
    "    ax.set_yticklabels([''] + labels)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_cm_output(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyO/71HmJztjHpR9Q3DXpRZQ",
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "k_nearest_neighbors.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}