BMLSwPython/CH02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.datasets.base.Bunch"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iris data is already packed with sklearn\n",
    "# now, loading the data\n",
    "iris = load_iris()\n",
    "\n",
    "type(iris)  # iris var is holding a sklearn dataset which is kind of dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['DESCR', 'feature_names', 'data', 'target', 'target_names'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys() # it is holding, data, target, feature name etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# let's see them one by one\n",
    "\n",
    "# desc\n",
    "print(iris['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris['feature_names'])\n",
    "print(iris['target_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150 150\n"
     ]
    }
   ],
   "source": [
    "# now, extracting the data into variables\n",
    "X = iris['data']\n",
    "y = iris['target']\n",
    "\n",
    "print(len(X), len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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emv4vbVn9gghrQCYNIavENzAA/+/1PWzfv505U+dw1qnnjKr8Mo+malI7KqFMW1bll6tW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\n73gwiKkUNWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x82fd198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(3):\n",
    "    if t == 0:\n",
    "        c = 'r'\n",
    "        marker = '>'\n",
    "    elif t == 1:\n",
    "        c = 'g'\n",
    "        marker = 'o'\n",
    "    elif t == 2:\n",
    "        c = 'b'\n",
    "        marker = 'x'\n",
    "    #plt.figure(figsize=(12,9))\n",
    "    plt.scatter(X[y == t,0],X[y == t,1], marker=marker, color=c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Let's build a model which can identify the type of IRIS flower using simple mathematics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating boolean filtes\n",
    "setosa = y==0\n",
    "versi = y==1\n",
    "virgi = y==2\n",
    "\n",
    "#setosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_setosa, y_setosa = X[setosa,], y[setosa]\n",
    "X_versi, y_versi = X[versi,], y[versi]\n",
    "X_virgi, y_virgi = X[virgi,], y[virgi]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# creating petal length condition\n",
    "print(X_setosa[:,2].max())\n",
    "print(X_setosa[:,2].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.9\n",
      "3.0\n"
     ]
    }
   ],
   "source": [
    "# let's check the same for others but not setosa\n",
    "print(X[~setosa,2].max())\n",
    "print(X[~setosa,2].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1\n",
      "3.0\n",
      "6.9\n",
      "4.5\n"
     ]
    }
   ],
   "source": [
    "# let's check the same for others individually\n",
    "print(X_versi[:,2].max())\n",
    "print(X_versi[:,2].min())\n",
    "\n",
    "print(X_virgi[:,2].max())\n",
    "print(X_virgi[:,2].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    But this way is easy with small datasets which has few features. We need to use advance classification technique to classify large and complex data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cross_validation import cross_val_score, train_test_split, KFold\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** KNN Classifier with 1 neighbors **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 15.26  ,  14.84  ,   0.871 , ...,   2.221 ,   5.22  ,   1.    ],\n",
       "       [ 14.88  ,  14.57  ,   0.8811, ...,   1.018 ,   4.956 ,   1.    ],\n",
       "       [ 14.29  ,  14.09  ,   0.905 , ...,   2.699 ,   4.825 ,   1.    ],\n",
       "       ..., \n",
       "       [ 13.2   ,  13.66  ,   0.8883, ...,   8.315 ,   5.056 ,   3.    ],\n",
       "       [ 11.84  ,  13.21  ,   0.8521, ...,   3.598 ,   5.044 ,   3.    ],\n",
       "       [ 12.3   ,  13.34  ,   0.8684, ...,   5.637 ,   5.063 ,   3.    ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading SEED dataset\n",
    "header = [\"area\", \"perimeter\", \"compactness\", \"kernel_length\", \"kernel_width\", \"asymmetry coefficient\", \n",
    "          \"length of kernel groove\", \"variety\"]\n",
    "seed = sp.genfromtxt(\"data/seeds_dataset.txt\", delimiter=\"|\")\n",
    "seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 15.26    14.84     0.871    5.763    3.312    2.221    5.22  ]\n",
      " [ 14.88    14.57     0.8811   5.554    3.333    1.018    4.956 ]] [ 1.  1.]\n"
     ]
    }
   ],
   "source": [
    "sX = seed[:,0:7]\n",
    "sy = seed[:,7]\n",
    "\n",
    "print(sX[:2], sy[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate the Classifier with neighbors = 1\n",
    "knn1 = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "# fitting the data\n",
    "knn1.fit(sX, sy)\n",
    "\n",
    "# predicting the type\n",
    "knn1.predict([[15.26,14.84,0.871,5.763,3.312,2.221,5.22]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    As we have used all the data to train our model, so training accuracy will be > ~95, but this is not what we want, before procceding futher let's check the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97142857142857142"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sy_pred = knn1.predict(sX)\n",
    "metrics.accuracy_score(sy, sy_pred)  # it will always be around 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** KNN with N=1, splitting the data into train and test **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sX_train, sX_test, sy_train, sy_test = train_test_split(sX, sy, test_size=0.4, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9285714285714286"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn1 = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "knn1.fit(sX_train, sy_train)\n",
    "sy_test_pred = knn1.predict(sX_test)\n",
    "\n",
    "metrics.accuracy_score(sy_test, sy_test_pred)  # 92% accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Simulating the same with KFold **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.cross_validation.KFold(n=210, n_folds=5, shuffle=True, random_state=7)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(len(sX), n_folds=5, shuffle=True, random_state=7)\n",
    "kf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.95238095238095233, 0.8571428571428571, 0.83333333333333337, 0.9285714285714286, 0.90476190476190477]\n",
      "0.895238095238\n"
     ]
    }
   ],
   "source": [
    "mean = []\n",
    "for train, test in kf:\n",
    "    knn1.fit(sX[train], sy[train])\n",
    "    sy_pred = knn1.predict(sX[test])\n",
    "    mean.append(metrics.accuracy_score(sy[test], sy_pred))\n",
    "print(mean)\n",
    "print(np.mean(mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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