NaiveBayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "data = fetch_20newsgroups()\n",
    "data.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ['talk.religion.misc', 'soc.religion.christian',\n",
    "              'sci.space', 'comp.graphics']\n",
    "train = fetch_20newsgroups(subset='train', categories=categories)\n",
    "test = fetch_20newsgroups(subset='test', categories=categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)\n",
      "Subject: Federal Hearing\n",
      "Originator: dmcgee@uluhe\n",
      "Organization: School of Ocean and Earth Science and Technology\n",
      "Distribution: usa\n",
      "Lines: 10\n",
      "\n",
      "\n",
      "Fact or rumor....?  Madalyn Murray O'Hare an atheist who eliminated the\n",
      "use of the bible reading and prayer in public schools 15 years ago is now\n",
      "going to appear before the FCC with a petition to stop the reading of the\n",
      "Gospel on the airways of America.  And she is also campaigning to remove\n",
      "Christmas programs, songs, etc from the public schools.  If it is true\n",
      "then mail to Federal Communications Commission 1919 H Street Washington DC\n",
      "20054 expressing your opposition to her request.  Reference Petition number\n",
      "\n",
      "2493.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(train.data[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers. For this we will use the TF-IDF vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this pipeline, we can apply the model to the training data, and predict labels for the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train.data, train.target)\n",
    "labels = model.predict(test.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator. For example, here is the confusion matrix between the true and predicted labels for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(test.target, labels)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
    "            xticklabels=train.target_names, yticklabels=train.target_names)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator. For"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_category(s, train=train, model=model):\n",
    "    pred = model.predict([s])\n",
    "    return train.target_names[pred[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sci.space'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('sending a payload to the ISS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'soc.religion.christian'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('discussing islam vs atheism')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'comp.graphics'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('determining the screen resolution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}