L1b.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Praktyczny Machine Learning w Pythonie\n",
    "<br>\n",
    "<img src=\"figures/L1/boyfriend.png\">\n",
    "\n",
    "## Co to jest Machine Learning? \n",
    "\n",
    "Machine learning zajmuje się tworzeniem modeli na podstawie danych, które potrafią rozwiązywać dany problem, na przykład klasyfikować cyfry lub rozpoznawać czy dany e-mail jest spamem.  Stworzony model (na podstawie zbioru danych, np. e-maili sprzed paru lat) potem jest testowany na nowych przykładach (nowych e-mailach).\n",
    "\n",
    "Praktyczne systemy machine learningu działają wszędzie. Przykłady to Amazon, Google Translate, Filmweb, rozpoznawanie głosu w Androidzie itp.\n",
    "\n",
    "Jak wspominałem celem warsztatów nie jest nauka machine learningu samego w sobie, \n",
    "tylko raczej wykorzystanie go do rozwiązania konkretnego problemu. Pod koniec zamieścimy dobre źródła do samodzielnej nauki.\n",
    "\n",
    "<img src=\"figures/L1/ml-chart-simple.jpg\">\n",
    "\n",
    "\n",
    "** Skupmy się na problemie klasyfikacji. Chcemy nauczyć algorytm który przydzieli każdemu przykładowi (obrazek, zdanie, itp.) jakąś wartość na podstawie jakiś danych. ** Przykładowo:\n",
    "\n",
    "* Jaka to cyfra:\n",
    "    * dane: obrazek cyfry\n",
    "    * możliwe klasy: 0,1,2,3,4,5,6,7,8,9\n",
    "* Czy na obrazku znajduje się motor? (możliwe klasy/wartości: 0/1)\n",
    "* Czy jutro będzie padało (możliwe klasy: 0/1).\n",
    "    * dane: wiatr, temperatura i opady sprzed 10 dni\n",
    "\n",
    "<img src=\"figures/L1/2d_classification.jpg\">\n",
    "\n",
    "## Reprezentacja danych - kwiatek w komputerze?\n",
    "\n",
    "Iris to znany zbiór danych, w których przykładami są różne gatunki irysa (kwiatek!), którym staramy się przyporządkować gatunek (jeden z trzech).\n",
    "\n",
    "Musimy jakoś ten kwiatek zareprezentować w komputerze. W tym celu zwykle opisujemy każdy przykład wektorem liczb (numpy!), np. możemy zmierzyć wielkość płatków.\n",
    "\n",
    "Pakiet scikit-learn (o którym będzie więcej!) ma wbudowany zbiór Iris.\n",
    "\n",
    "Każdy kwiatek jest opisany jako wektor 4 liczb opisujących wymiary kwiatka\n",
    "\n",
    "1. Długość płatka w cm\n",
    "2. Szerokosć płatka w cm\n",
    "3. Długość działki kielicha (??) w cm\n",
    "4. Szerokość działki kielicha (??) w cm\n",
    "\n",
    "Staramy się przewidzieć gatunek, czyli jeden z :\n",
    "\n",
    "1. Iris Setosa\n",
    "2. Iris Versicolour\n",
    "3. Iris Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['target_names', 'data', 'target', 'DESCR', 'feature_names']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Załadowany obiekt jest słownikiem\n",
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='|S10')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Nawet ma ładny opis ocb.\n",
    "_ = iris[\"DESCR\"] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zbiory danych to macierze numpy !\n",
    "\n",
    "Macierz numpy przechowuje swój wymiar w zmiennej *shape*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macierz ma wymiar:  (150, 4)\n"
     ]
    }
   ],
   "source": [
    "n_samples, n_features = iris.data.shape\n",
    "print \"Macierz ma wymiar: \", iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.1,  3.5,  1.4,  0.2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ilość przykładów:  150\n",
      "Każdy przykład ma  4  cechy\n",
      "Pierwszy przykład:  [ 5.1  3.5  1.4  0.2]\n"
     ]
    }
   ],
   "source": [
    "print \"Ilość przykładów: \", n_samples\n",
    "print \"Każdy przykład ma \", n_features, \" cechy\"\n",
    "print \"Pierwszy przykład: \", iris.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cwiczenie 3 (1 pkt): \n",
    "    a) Jaka jest średnia długość płatka w zbiorze danych? (pierwsza cecha przykładu, podpowiedź: macierz przykładów jest w iris.data). \n",
    "    b) Narysuj histogram długości płatka (plt.hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "# plt.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liniowa separowalnosc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10d07cd50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9nvf/j6fBIftg/1zGLruF9/3lis4LMpgNE224OUGYdanK0KCuVLJB5Comsy5l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2fdf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris.data[iris['target']==0, 0], iris.data[iris['target']==0, 1], color='r')\n",
    "plt.scatter(iris.data[iris['target']==1, 0], iris.data[iris['target']==1, 1], color='g')\n",
    "plt.scatter(iris.data[iris['target']==2, 0], iris.data[iris['target']==2, 1], color='b')\n",
    "plt.xlabel(iris['feature_names'][0])\n",
    "plt.ylabel(iris['feature_names'][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ćwiczenie 5 (1 pkt): \n",
    "    a) We wcześniejszym przykładzie narysowałem wykres na podstawie 1 i 2 cechy (2 z 4 cech). Proszę sprawdzić czy jakaś inna kombinacja cech poprawia separowalność przykładów (łatwiej je oddzielić kreską)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": true,
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}