day-4-machine-learning-part-I/03 - Cross-validation and Grid Search.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:12:02.182013Z",
     "start_time": "2019-07-03T14:12:02.176041Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:12:03.452387Z",
     "start_time": "2019-07-03T14:12:03.321995Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "digits = load_digits()\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:13:32.381860Z",
     "start_time": "2019-07-03T14:13:32.378228Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:02:08.585427Z",
     "start_time": "2019-07-03T14:02:08.376923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96336996, 0.98154982, 1.        , 0.98507463, 0.9887218 ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(),X_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:18:06.339660Z",
     "start_time": "2019-07-03T14:18:06.335532Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:19:26.564618Z",
     "start_time": "2019-07-03T14:19:26.331824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97037037, 0.97777778, 0.96296296, 0.99259259, 0.97777778,\n",
       "       1.        , 0.99259259, 0.98507463, 1.        , 1.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(),\n",
    "                X_train, \n",
    "                y_train, \n",
    "                cv=KFold(n_splits=10, \n",
    "                         shuffle=True, \n",
    "                         random_state=42))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid Searches\n",
    "================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid-Search with build-in cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:23:11.431768Z",
     "start_time": "2019-07-03T14:23:11.398474Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define parameter grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:23:12.243806Z",
     "start_time": "2019-07-03T14:23:12.238199Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': array([  0.001,   0.01 ,   0.1  ,   1.   ,  10.   , 100.   ]), 'gamma': array([0.00001, 0.0001 , 0.001  , 0.01   , 0.1    ])}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "param_grid = {'C': 10. ** np.arange(-3, 3),\n",
    "              'gamma' : 10. ** np.arange(-5, 0)}\n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "print(param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:23:12.715148Z",
     "start_time": "2019-07-03T14:23:12.710713Z"
    }
   },
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(SVC(), param_grid, verbose=3, cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A GridSearchCV object behaves just like a normal classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:24:11.254521Z",
     "start_time": "2019-07-03T14:23:33.847186Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n",
      "[CV] C=0.001, gamma=1e-05 ............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=0.001, gamma=1e-05, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.001, gamma=1e-05 ............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=0.001, gamma=1e-05, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=1e-05 ............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=0.001, gamma=1e-05, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=1e-05 ............................................\n",
      "[CV] .. C=0.001, gamma=1e-05, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.001, gamma=1e-05 ............................................\n",
      "[CV] .. C=0.001, gamma=1e-05, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.0001 ...........................................\n",
      "[CV] . C=0.001, gamma=0.0001, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.0001 ...........................................\n",
      "[CV] . C=0.001, gamma=0.0001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.0001 ...........................................\n",
      "[CV] . C=0.001, gamma=0.0001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.0001 ...........................................\n",
      "[CV] . C=0.001, gamma=0.0001, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.0001 ...........................................\n",
      "[CV] . C=0.001, gamma=0.0001, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] .. C=0.001, gamma=0.001, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] .. C=0.001, gamma=0.001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] .. C=0.001, gamma=0.001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] .. C=0.001, gamma=0.001, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] .. C=0.001, gamma=0.001, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] ... C=0.001, gamma=0.01, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] ... C=0.001, gamma=0.01, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] ... C=0.001, gamma=0.01, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] ... C=0.001, gamma=0.01, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] ... C=0.001, gamma=0.01, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] .... C=0.001, gamma=0.1, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] .... C=0.001, gamma=0.1, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] .... C=0.001, gamma=0.1, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] .... C=0.001, gamma=0.1, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] .... C=0.001, gamma=0.1, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.01, gamma=1e-05 .............................................\n",
      "[CV] ... C=0.01, gamma=1e-05, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.01, gamma=1e-05 .............................................\n",
      "[CV] ... C=0.01, gamma=1e-05, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=1e-05 .............................................\n",
      "[CV] ... C=0.01, gamma=1e-05, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=1e-05 .............................................\n",
      "[CV] ... C=0.01, gamma=1e-05, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.01, gamma=1e-05 .............................................\n",
      "[CV] ... C=0.01, gamma=1e-05, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] .. C=0.01, gamma=0.0001, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] .. C=0.01, gamma=0.0001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] .. C=0.01, gamma=0.0001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] .. C=0.01, gamma=0.0001, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] .. C=0.01, gamma=0.0001, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] ... C=0.01, gamma=0.001, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] ... C=0.01, gamma=0.001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] ... C=0.01, gamma=0.001, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] ... C=0.01, gamma=0.001, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] ... C=0.01, gamma=0.001, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] .... C=0.01, gamma=0.01, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] .... C=0.01, gamma=0.01, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] .... C=0.01, gamma=0.01, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] .... C=0.01, gamma=0.01, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] .... C=0.01, gamma=0.01, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ..... C=0.01, gamma=0.1, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ..... C=0.01, gamma=0.1, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ..... C=0.01, gamma=0.1, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ..... C=0.01, gamma=0.1, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ..... C=0.01, gamma=0.1, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] .... C=0.1, gamma=1e-05, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] .... C=0.1, gamma=1e-05, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] .... C=0.1, gamma=1e-05, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] .... C=0.1, gamma=1e-05, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] .... C=0.1, gamma=1e-05, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .... C=0.1, gamma=0.0001, score=0.8644688644688645, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, score=0.837037037037037, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .... C=0.1, gamma=0.0001, score=0.8629629629629629, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .... C=0.1, gamma=0.0001, score=0.8582089552238806, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .... C=0.1, gamma=0.0001, score=0.8909774436090225, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..... C=0.1, gamma=0.001, score=0.9560439560439561, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..... C=0.1, gamma=0.001, score=0.9629629629629629, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..... C=0.1, gamma=0.001, score=0.9629629629629629, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..... C=0.1, gamma=0.001, score=0.9477611940298507, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..... C=0.1, gamma=0.001, score=0.9699248120300752, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ..... C=0.1, gamma=0.01, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ..... C=0.1, gamma=0.01, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ..... C=0.1, gamma=0.01, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ..... C=0.1, gamma=0.01, score=0.10820895522388059, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ..... C=0.1, gamma=0.01, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ...... C=0.1, gamma=0.1, score=0.10622710622710622, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ...... C=0.1, gamma=0.1, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ...... C=0.1, gamma=0.1, score=0.10740740740740741, total=   0.2s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ...... C=0.1, gamma=0.1, score=0.10820895522388059, total=   0.3s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ...... C=0.1, gamma=0.1, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=1.0, gamma=1e-05 ..............................................\n",
      "[CV] ..... C=1.0, gamma=1e-05, score=0.8827838827838828, total=   0.2s\n",
      "[CV] C=1.0, gamma=1e-05 ..............................................\n",
      "[CV] ..... C=1.0, gamma=1e-05, score=0.8814814814814815, total=   0.2s\n",
      "[CV] C=1.0, gamma=1e-05 ..............................................\n",
      "[CV] ..... C=1.0, gamma=1e-05, score=0.8814814814814815, total=   0.2s\n",
      "[CV] C=1.0, gamma=1e-05 ..............................................\n",
      "[CV] ..... C=1.0, gamma=1e-05, score=0.8843283582089553, total=   0.2s\n",
      "[CV] C=1.0, gamma=1e-05 ..............................................\n",
      "[CV] ..... C=1.0, gamma=1e-05, score=0.9210526315789473, total=   0.2s\n",
      "[CV] C=1.0, gamma=0.0001 .............................................\n",
      "[CV] .... C=1.0, gamma=0.0001, score=0.9743589743589743, total=   0.1s\n",
      "[CV] C=1.0, gamma=0.0001 .............................................\n",
      "[CV] .... C=1.0, gamma=0.0001, score=0.9703703703703703, total=   0.1s\n",
      "[CV] C=1.0, gamma=0.0001 .............................................\n",
      "[CV] .... C=1.0, gamma=0.0001, score=0.9666666666666667, total=   0.1s\n",
      "[CV] C=1.0, gamma=0.0001 .............................................\n",
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      "[CV] C=1.0, gamma=0.1 ................................................\n",
      "[CV] ...... C=1.0, gamma=0.1, score=0.10989010989010989, total=   0.2s\n",
      "[CV] C=1.0, gamma=0.1 ................................................\n",
      "[CV] ...... C=1.0, gamma=0.1, score=0.10740740740740741, total=   0.2s\n",
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      "[CV] ...... C=1.0, gamma=0.1, score=0.10740740740740741, total=   0.3s\n",
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      "[CV] ...... C=1.0, gamma=0.1, score=0.11194029850746269, total=   0.3s\n",
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      "[CV] ...... C=1.0, gamma=0.1, score=0.10526315789473684, total=   0.2s\n",
      "[CV] C=10.0, gamma=1e-05 .............................................\n",
      "[CV] .... C=10.0, gamma=1e-05, score=0.9706959706959707, total=   0.1s\n",
      "[CV] C=10.0, gamma=1e-05 .............................................\n",
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      "[CV] C=10.0, gamma=1e-05 .............................................\n",
      "[CV] .... C=10.0, gamma=1e-05, score=0.9701492537313433, total=   0.1s\n",
      "[CV] C=10.0, gamma=1e-05 .............................................\n",
      "[CV] .... C=10.0, gamma=1e-05, score=0.9736842105263158, total=   0.1s\n",
      "[CV] C=10.0, gamma=0.0001 ............................................\n",
      "[CV] ... C=10.0, gamma=0.0001, score=0.9853479853479854, total=   0.0s\n",
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      "[CV] C=10.0, gamma=0.0001 ............................................\n",
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      "[CV] C=10.0, gamma=0.0001 ............................................\n",
      "[CV] ... C=10.0, gamma=0.0001, score=0.9850746268656716, total=   0.0s\n",
      "[CV] C=10.0, gamma=0.0001 ............................................\n",
      "[CV] ... C=10.0, gamma=0.0001, score=0.9849624060150376, total=   0.0s\n",
      "[CV] C=10.0, gamma=0.001 .............................................\n",
      "[CV] ..... C=10.0, gamma=0.001, score=0.989010989010989, total=   0.1s\n",
      "[CV] C=10.0, gamma=0.001 .............................................\n",
      "[CV] .... C=10.0, gamma=0.001, score=0.9851851851851852, total=   0.1s\n",
      "[CV] C=10.0, gamma=0.001 .............................................\n",
      "[CV] .... C=10.0, gamma=0.001, score=0.9925925925925926, total=   0.1s\n",
      "[CV] C=10.0, gamma=0.001 .............................................\n",
      "[CV] .... C=10.0, gamma=0.001, score=0.9888059701492538, total=   0.1s\n",
      "[CV] C=10.0, gamma=0.001 .............................................\n",
      "[CV] .... C=10.0, gamma=0.001, score=0.9887218045112782, total=   0.1s\n",
      "[CV] C=10.0, gamma=0.01 ..............................................\n",
      "[CV] ..... C=10.0, gamma=0.01, score=0.8608058608058609, total=   0.2s\n",
      "[CV] C=10.0, gamma=0.01 ..............................................\n",
      "[CV] ..... C=10.0, gamma=0.01, score=0.7851851851851852, total=   0.2s\n",
      "[CV] C=10.0, gamma=0.01 ..............................................\n",
      "[CV] ..... C=10.0, gamma=0.01, score=0.7777777777777778, total=   0.2s\n",
      "[CV] C=10.0, gamma=0.01 ..............................................\n",
      "[CV] ..... C=10.0, gamma=0.01, score=0.7873134328358209, total=   0.2s\n",
      "[CV] C=10.0, gamma=0.01 ..............................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..... C=10.0, gamma=0.01, score=0.7744360902255639, total=   0.2s\n",
      "[CV] C=10.0, gamma=0.1 ...............................................\n",
      "[CV] ..... C=10.0, gamma=0.1, score=0.10989010989010989, total=   0.3s\n",
      "[CV] C=10.0, gamma=0.1 ...............................................\n",
      "[CV] ..... C=10.0, gamma=0.1, score=0.10740740740740741, total=   0.3s\n",
      "[CV] C=10.0, gamma=0.1 ...............................................\n",
      "[CV] ..... C=10.0, gamma=0.1, score=0.10740740740740741, total=   0.3s\n",
      "[CV] C=10.0, gamma=0.1 ...............................................\n",
      "[CV] ..... C=10.0, gamma=0.1, score=0.11194029850746269, total=   0.3s\n",
      "[CV] C=10.0, gamma=0.1 ...............................................\n",
      "[CV] ..... C=10.0, gamma=0.1, score=0.10526315789473684, total=   0.3s\n",
      "[CV] C=100.0, gamma=1e-05 ............................................\n",
      "[CV] ... C=100.0, gamma=1e-05, score=0.9816849816849816, total=   0.0s\n",
      "[CV] C=100.0, gamma=1e-05 ............................................\n",
      "[CV] ... C=100.0, gamma=1e-05, score=0.9740740740740741, total=   0.0s\n",
      "[CV] C=100.0, gamma=1e-05 ............................................\n",
      "[CV] ... C=100.0, gamma=1e-05, score=0.9777777777777777, total=   0.0s\n",
      "[CV] C=100.0, gamma=1e-05 ............................................\n",
      "[CV] ... C=100.0, gamma=1e-05, score=0.9776119402985075, total=   0.0s\n",
      "[CV] C=100.0, gamma=1e-05 ............................................\n",
      "[CV] ... C=100.0, gamma=1e-05, score=0.9699248120300752, total=   0.0s\n",
      "[CV] C=100.0, gamma=0.0001 ...........................................\n",
      "[CV] .. C=100.0, gamma=0.0001, score=0.9853479853479854, total=   0.0s\n",
      "[CV] C=100.0, gamma=0.0001 ...........................................\n",
      "[CV] .. C=100.0, gamma=0.0001, score=0.9851851851851852, total=   0.0s\n",
      "[CV] C=100.0, gamma=0.0001 ...........................................\n",
      "[CV] .. C=100.0, gamma=0.0001, score=0.9814814814814815, total=   0.0s\n",
      "[CV] C=100.0, gamma=0.0001 ...........................................\n",
      "[CV] .. C=100.0, gamma=0.0001, score=0.9888059701492538, total=   0.0s\n",
      "[CV] C=100.0, gamma=0.0001 ...........................................\n",
      "[CV] .. C=100.0, gamma=0.0001, score=0.9736842105263158, total=   0.0s\n",
      "[CV] C=100.0, gamma=0.001 ............................................\n",
      "[CV] .... C=100.0, gamma=0.001, score=0.989010989010989, total=   0.1s\n",
      "[CV] C=100.0, gamma=0.001 ............................................\n",
      "[CV] ... C=100.0, gamma=0.001, score=0.9851851851851852, total=   0.1s\n",
      "[CV] C=100.0, gamma=0.001 ............................................\n",
      "[CV] ... C=100.0, gamma=0.001, score=0.9925925925925926, total=   0.1s\n",
      "[CV] C=100.0, gamma=0.001 ............................................\n",
      "[CV] ... C=100.0, gamma=0.001, score=0.9888059701492538, total=   0.1s\n",
      "[CV] C=100.0, gamma=0.001 ............................................\n",
      "[CV] ... C=100.0, gamma=0.001, score=0.9887218045112782, total=   0.1s\n",
      "[CV] C=100.0, gamma=0.01 .............................................\n",
      "[CV] .... C=100.0, gamma=0.01, score=0.8608058608058609, total=   0.2s\n",
      "[CV] C=100.0, gamma=0.01 .............................................\n",
      "[CV] .... C=100.0, gamma=0.01, score=0.7851851851851852, total=   0.2s\n",
      "[CV] C=100.0, gamma=0.01 .............................................\n",
      "[CV] .... C=100.0, gamma=0.01, score=0.7777777777777778, total=   0.2s\n",
      "[CV] C=100.0, gamma=0.01 .............................................\n",
      "[CV] .... C=100.0, gamma=0.01, score=0.7873134328358209, total=   0.2s\n",
      "[CV] C=100.0, gamma=0.01 .............................................\n",
      "[CV] .... C=100.0, gamma=0.01, score=0.7744360902255639, total=   0.2s\n",
      "[CV] C=100.0, gamma=0.1 ..............................................\n",
      "[CV] .... C=100.0, gamma=0.1, score=0.10989010989010989, total=   0.3s\n",
      "[CV] C=100.0, gamma=0.1 ..............................................\n",
      "[CV] .... C=100.0, gamma=0.1, score=0.10740740740740741, total=   0.3s\n",
      "[CV] C=100.0, gamma=0.1 ..............................................\n",
      "[CV] .... C=100.0, gamma=0.1, score=0.10740740740740741, total=   0.3s\n",
      "[CV] C=100.0, gamma=0.1 ..............................................\n",
      "[CV] .... C=100.0, gamma=0.1, score=0.11194029850746269, total=   0.3s\n",
      "[CV] C=100.0, gamma=0.1 ..............................................\n",
      "[CV] .... C=100.0, gamma=0.1, score=0.10526315789473684, total=   0.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:   37.3s finished\n",
      "/Users/lgaalves/miniconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'C': array([  0.001,   0.01 ,   0.1  ,   1.   ,  10.   , 100.   ]), 'gamma': array([0.00001, 0.0001 , 0.001  , 0.01   , 0.1    ])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:24:38.666795Z",
     "start_time": "2019-07-03T14:24:38.634011Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 0, 5, 5, 8, 0, 4, 8, 7, 4, 7, 3, 8, 5, 3, 0, 7, 0, 0, 2, 8, 2,\n",
       "       2, 6, 5, 0, 8, 6, 0, 9, 5, 5, 3, 2, 1, 1, 3, 4, 0, 5, 8, 3, 8, 3,\n",
       "       8, 9, 5, 2, 8, 7, 5, 7, 3, 0, 4, 8, 4, 6, 5, 8, 7, 5, 6, 3, 0, 7,\n",
       "       1, 2, 6, 7, 2, 1, 8, 4, 0, 7, 0, 1, 0, 6, 2, 3, 0, 4, 5, 6, 7, 6,\n",
       "       0, 2, 2, 5, 6, 8, 0, 2, 0, 6, 2, 7, 2, 1, 0, 6, 2, 8, 8, 0, 8, 6,\n",
       "       5, 7, 3, 2, 1, 3, 4, 1, 9, 6, 2, 6, 2, 4, 9, 1, 1, 0, 4, 9, 2, 2,\n",
       "       0, 5, 2, 8, 2, 5, 6, 0, 6, 3, 5, 1, 8, 9, 0, 5, 1, 7, 5, 1, 5, 9,\n",
       "       0, 7, 1, 9, 1, 9, 0, 6, 9, 1, 5, 7, 3, 5, 3, 9, 4, 1, 8, 9, 9, 3,\n",
       "       0, 7, 9, 7, 9, 1, 0, 5, 0, 7, 2, 4, 1, 0, 1, 4, 3, 6, 6, 9, 3, 5,\n",
       "       3, 5, 7, 7, 0, 0, 5, 3, 8, 7, 7, 8, 4, 1, 8, 2, 6, 0, 3, 3, 9, 7,\n",
       "       5, 7, 1, 3, 9, 6, 5, 2, 3, 3, 3, 3, 3, 2, 3, 4, 6, 5, 2, 7, 7, 4,\n",
       "       4, 2, 6, 8, 3, 3, 3, 9, 0, 1, 1, 4, 2, 9, 2, 7, 8, 4, 0, 0, 5, 5,\n",
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       "       7, 9, 0, 8, 1, 0, 8, 2, 9, 3, 6, 9, 6, 8, 5, 7, 8, 4, 9, 7, 5, 8,\n",
       "       1, 5, 6, 3, 2, 5, 6, 0, 4, 7, 6, 2, 3, 7, 3, 7, 4, 1, 5, 7, 1, 8,\n",
       "       6, 1, 4, 9, 8, 6, 5, 2, 2, 8, 7, 0, 7, 7, 4, 3, 9, 1, 6, 3, 4, 8,\n",
       "       7, 0, 9, 9, 9, 3, 6, 2, 3, 4, 6, 7, 7, 3, 4, 3, 9, 5, 5, 7, 1, 9,\n",
       "       5, 0, 8, 1, 0, 7, 8, 9, 1, 1, 3, 7, 1, 8, 6, 6, 3, 0, 0, 3, 1, 9,\n",
       "       4, 1, 6, 8, 0, 7, 5, 9, 0, 0, 9, 0, 4, 2, 1, 8, 4, 8, 1, 3, 4, 3,\n",
       "       6, 4, 8, 9, 7, 6, 3, 9, 1, 7, 5, 7, 8, 9, 5, 4, 3, 8, 4, 2, 2, 5,\n",
       "       2, 4, 3, 2, 0, 5, 0, 4, 5, 7])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.predict(X_test)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:24:45.429361Z",
     "start_time": "2019-07-03T14:24:45.398316Z"
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   "outputs": [
    {
     "data": {
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "grid_search.score(X_test, y_test)"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:24:47.223509Z",
     "start_time": "2019-07-03T14:24:47.216917Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10.0, 'gamma': 0.001}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:24:49.126368Z",
     "start_time": "2019-07-03T14:24:49.119742Z"
    }
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888641425389755"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "grid_search.best_score_"
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  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:24:49.636581Z",
     "start_time": "2019-07-03T14:24:49.630348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:24:54.675249Z",
     "start_time": "2019-07-03T14:24:54.445122Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x345.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We extract just the scores\n",
    "\n",
    "scores = grid_search.cv_results_['mean_test_score']\n",
    "scores = np.array(scores).reshape(6, 5)\n",
    "\n",
    "plt.matshow(scores)\n",
    "plt.xlabel('gamma')\n",
    "plt.ylabel('C')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(5), param_grid['gamma'])\n",
    "plt.yticks(np.arange(6), param_grid['C']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "Use GridSearchCV to adjust n_neighbors of KNeighborsClassifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:27:25.145005Z",
     "start_time": "2019-07-03T14:27:25.141312Z"
    }
   },
   "outputs": [],
   "source": [
    "# %load solutions/grid_search_k_neighbors.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:35:55.280031Z",
     "start_time": "2019-07-03T14:35:51.119058Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters: {'n_neighbors': 5}\n",
      "Training set accuracy: 0.9903489235337788\n",
      "Test set accuracy: 0.9755555555555555\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "param_grid = {'n_neighbors': [1, 3, 5, 7, 10]}\n",
    "\n",
    "grid = GridSearchCV(KNeighborsClassifier(), param_grid=param_grid,\n",
    "                    return_train_score=True, cv=5)\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print(\"best parameters: %s\" % grid.best_params_)\n",
    "print(\"Training set accuracy: %s\" % grid.score(X_train, y_train))\n",
    "print(\"Test set accuracy: %s\" % grid.score(X_test, y_test))\n",
    "\n",
    "results = grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T14:29:42.892310Z",
     "start_time": "2019-07-03T14:29:42.693619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a182c07f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(param_grid['n_neighbors'], results['mean_train_score'], label=\"train\")\n",
    "plt.plot(param_grid['n_neighbors'], results['mean_test_score'], label=\"test\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.8"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}