dataHacker_blog_005_Iris_dataset_001.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iris dataset i linearni klasifikator LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# uvozimo standardne biblioteke\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importovanje Iris dataseta\n",
    "# u samom Anaconda okruzenju instalirana je i biblioteka sklearn\n",
    "# u okviru nje se nalaze dataset-ovi koji su odlicni za ucenje\n",
    "\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ove tri naredbe su nam dovoljne da ucitamo iris dataset\n",
    "# ne moramo za sada razmisljati sta se nalazi u load_iris()\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis - EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# prvi korak je da pogledamo podatke X i y\n",
    "print(type(X))\n",
    "print(type(y))\n",
    "# Videcemo da su u pitanju numpy nizovi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 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",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "# Uz komentare o samom datasetu koji dati u blogu (opisu dataseta), mozemo potvrditi da se zaista radi o formatu od 150 cvetova. \n",
    "# Svaki cvet predstabljen je sa 4 parametra duzine. \n",
    "# u promenljivoj y nalazi se pripadnost klasi\n",
    "# u ovosm slucaju tri vrste cveta iris kodovane su brojevima 0, 1 i 2\n",
    "print(y)\n",
    "# prvih 50 elemenata su nule, zatim imamo 50 jedinica i 50 dvojki. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.1  3.5  1.4  0.2]\n"
     ]
    }
   ],
   "source": [
    "# Hajde da pogledamo kakve su to dimenzije u pitanju. Pogledajmo prvi, treci i 125-ti cvet. \n",
    "# Potrebno je pristupiti odgovarajucim elementima, ali se moramo podsetiti da indeksiranje krece od nule!\n",
    "print (X[0,0:4]) # ovo je prvi cvet i njegova cetiri elementa. indeks od 0:4 uzima ustvari 0, 1, 2 i 3 !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treci cvet ima dimenzije  [ 4.7  3.2  1.3  0.2]\n",
      "125-ti cvet ima dimenzije  [ 6.7  3.3  5.7  2.1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Treci cvet ima dimenzije \", X[2,:])\n",
    "print(\"125-ti cvet ima dimenzije \", X[124, :])\n",
    "# koriscenje : u ovom slucaju je skraceni oblik za \"od prvog do poslednjeg elementa\". U ovom slucaju to je 0:4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cvetovi 1, 3 i 125 pripadaju redom klasama: 0 0 2\n"
     ]
    }
   ],
   "source": [
    "# Za svaki redni element mozemo i pojedinacno proveriti pripadnost klasi\n",
    "print(\"Cvetovi 1, 3 i 125 pripadaju redom klasama:\" , y[0], y[2] , y[124])\n",
    "# Dakle prva dva cveta iz klase 0, a treci iz klase 2. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA - Vizuelizacija"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xa2297b8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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BzmK4SSTEDXAhjqOph8HkokvHGlp2F1RTveEgZzHcJBLiBrgQxxHiWLt0jnfpWEPjYR0A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\nVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e53630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nacrtajmo sada dimenzije ovih cvetova na jednom grafu. \n",
    "# Odmah se namece pitanje ? Kako iscrtati 4 dimenzije, kada imamo samo mogucnost crtanja u 2D (i 3D) sistemu. \n",
    "# Izabracemo prve dve kolone\n",
    "plt.scatter(X[:,0], X[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xa2646a0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa52c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pazljivim brojanjem vidimo da su zaista sve tacke prisutne :-)\n",
    "# Da bismo shvatili sa koliko lakim ili teskim problemom se suocavamao potrebno je ove tacke iscrtati razlicitim bojama\n",
    "# Kako je ovaj dataset sortiran, mozemo crtati tacke u tri etape po 50\n",
    "plt.scatter(X[0:50,0],    X[0:50,1]) # ovo je default vrednost - plava\n",
    "plt.scatter(X[50:100,0],  X[50:100,1], c=u'r' )\n",
    "plt.scatter(X[100:150,0], X[100:150,1], c = u'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}