{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Try a bit of sklearn tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data and set up **feature** matrix `X` and the **response** vector `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           n_neighbors=5, p=2, weights='uniform')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict([[3,5,4,2], [1,2,2,1], [3,3,3,3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay!\n",
    "\n",
    "Let's do some cross-validation, randomly leaving out records for the training dataset, then predicting the omitted records and seeing what sort of reliability we achieve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           n_neighbors=5, p=2, weights='uniform')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "k_range = range(1, 26)\n",
    "results = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    results.append(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112db0c10>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T0/oIMtwsZNXKmdwx6H6CPhwEzVR3EOwH3ivxrgGX96WjVrWc4Wql4xyDw5Y1cxwEzVTb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\nzGzG/X+Ud+nEzLeqiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112beb450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ls: cannot access data: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}