SVM2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn import svm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = datasets.load_iris() #datasets of flowers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris feature data: : [[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]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Iris feature data: :\", iris_dataset['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "print(iris_dataset.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Target :: [0 0 0 0 0 0 0 0 0 0 0 0 0 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": [
    "print (\"Iris Target ::\", iris_dataset['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# since the target is discrete, we will use classification. algorithm svm because the data set has only 150 rows which is less than 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris_dataset.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(iris_dataset.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualise_sepal_data():\n",
    "    iris = datasets.load_iris()\n",
    "    X = iris.data[:,:2] #taking only two features beacuse we can visualise only 2d plots  :2 means taking feature from 0 to 1\n",
    "    Y = iris.target\n",
    "    plt.scatter(X[:,0],X[:,1], c=Y) #c=y because it sets the color according to the features retrieved in target\n",
    "    plt.xlabel('sepal Length')\n",
    "    plt.ylabel('sepal Width')\n",
    "    plt.show()\n",
    "visualise_sepal_data()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualise_petal_data():\n",
    "    iris= datasets.load_iris()\n",
    "    X = iris.data[:,2:]\n",
    "    Y = iris.target\n",
    "    plt.scatter(X[:,0],X[:,1], c=Y)\n",
    "    plt.xlabel('petal length')\n",
    "    plt.ylabel('petal width')\n",
    "    plt.show()\n",
    "visualise_petal_data()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=datasets.load_iris()\n",
    "X=iris.data[:,:2]\n",
    "Y=iris.target\n",
    "C=1.0  #prevents overfitting, if the value is higher, it overfits\n",
    "svc = svm.SVC(kernel = 'linear', C = C).fit(X,Y)\n",
    "rbf_svc=svm.SVC(kernel='rbf', C=C).fit(X,Y)\n",
    "ply_svc=svm.SVC(kernel = 'poly', degree=3, C=C).fit(X,Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = .02 #step size in mesh\n",
    "x_min,x_max = X[:,0].min() - 1, X[:,0].max() + 1\n",
    "y_min,y_max = X[:,1].min() -1, X[:,1].max() + 1 ]"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}