L6 - GaussianNB, KNN, and Cross-Validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict the Sun Hours using Naive Bayes Classifier and KNN Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the same data resource as in L5, i.e., [Daily Weather Observations of Sydney, New South Wales between Aug 2015 and Aug 2016](http://www.bom.gov.au/climate/dwo/IDCJDW0210.shtml).\n",
    "\n",
    "We will handle the missing values, and category Sun_hours into three levels: High(>10), Med(>5 and <=10), and Low(<=5) as we did in L5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column Meanings\n",
    "| Heading         | Meaning                                                  | Units               |\n",
    "|-----------------|----------------------------------------------------------|---------------------|\n",
    "| Day             | Day of the week                                          | first two letters   |\n",
    "| Temps_min       | Minimum temperature in the 24 hours to 9am.              | degrees Celsius     |\n",
    "| Temps_max       | Maximum temperature in the 24 hours from 9am.            | degrees Celsius     |\n",
    "| Rain            | Precipitation (rainfall) in the 24 hours to 9am.         | millimetres         |\n",
    "| Evap            | Class A pan evaporation in the 24 hours to 9am           | millimetres         |\n",
    "| Sun_hours       | Bright sunshine in the 24 hours to midnight              | hours               |\n",
    "| Max_wind_dir    | Direction of strongest gust in the 24 hours to midnight  | 16 compass points   |\n",
    "| Max_wind_spd    | Speed of strongest wind gust in the 24 hours to midnight | kilometres per hour |\n",
    "| Max_wind_time   | Time of strongest wind gust                              | local time hh:mm    |\n",
    "| Temp_at_9am     | Temperature at 9 am                                      | degrees Celsius     |\n",
    "| RH_at_9am       | Relative humidity at 9 am                                | percent             |\n",
    "| CLD_at_9am      | Fraction of sky obscured by cloud at 9 am                | eighths             |\n",
    "| Wind_dir_at_9am | Wind direction averaged over 10 minutes prior to 9 am    | compass points      |\n",
    "| Wind_spd_at_9am | Wind speed averaged over 10 minutes prior to 9 am        | kilometres per hour |\n",
    "| MSLP_at_9am     | Atmospheric pressure reduced to mean sea level at 9 am   | hectopascals        |\n",
    "| Temp_at_3pm     | Temperature at 3 pm                                      | degrees Celsius     |\n",
    "| RH_at_3pm       | Relative humidity at 3 pm                                | percent             |\n",
    "| CLD_at_3pm      | Fraction of sky obscured by cloud at 3 pm                | eighths             |\n",
    "| Wind_dir_at_3pm | Wind direction averaged over 10 minutes prior to 3 pm    | compass points      |\n",
    "| Wind_spd_at_3pm | Wind speed averaged over 10 minutes prior to 3 pm        | kilometres per hour |\n",
    "| MSLP_at_3pm     | Atmospheric pressure reduced to mean sea level at 3 pm   | hectopascals        |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(362, 26)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('./asset/Daily_Weather_Observations.csv', sep=',')\n",
    "data_missing_sun_hours = data[pd.isnull(data['Sun_hours'])]\n",
    "data = data[pd.notnull(data['Sun_hours'])]\n",
    "\n",
    "labels = ['Low','Med','High']\n",
    "data['Sun_level'] = pd.cut(data.Sun_hours, [-1,5,10,25], labels=labels)\n",
    "\n",
    "data = data.dropna(subset = ['CLD_at_9am', 'Max_wind_dir', 'Max_wind_spd', 'Max_wind_dir'])\n",
    "bitmap1 = data.Evap.notnull()\n",
    "bitmap2 = bitmap1.shift(1)\n",
    "bitmap2[0] = True\n",
    "data = data[bitmap1 & bitmap2]\n",
    "\n",
    "data['Temps_diff'] = data['Temps_max'] - data['Temps_min']\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use *CLD_at_9am*, *CLD_at_3pm*, *RH_at_9am*, *RH_at_3pm*, and *Temps_diff* as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_list = ['CLD_at_9am', 'CLD_at_3pm', 'RH_at_9am', 'RH_at_3pm', 'Temps_diff']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate X and y based on the selected features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLD_at_9am</th>\n",
       "      <th>CLD_at_3pm</th>\n",
       "      <th>RH_at_9am</th>\n",
       "      <th>RH_at_3pm</th>\n",
       "      <th>Temps_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>50</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>36</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>38</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "      <td>71</td>\n",
       "      <td>62</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CLD_at_9am  CLD_at_3pm  RH_at_9am  RH_at_3pm  Temps_diff\n",
       "391         7.0           7         57         50        10.0\n",
       "392         1.0           1         57         45        12.5\n",
       "393         1.0           2         50         36        12.1\n",
       "394         7.0           7         57         38        11.2\n",
       "395         7.0           6         71         62         9.2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[feature_list]\n",
    "X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "391     Med\n",
       "392    High\n",
       "393    High\n",
       "394     Med\n",
       "395     Low\n",
       "Name: Sun_level, dtype: category\n",
       "Categories (3, object): [Low < Med < High]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data.Sun_level\n",
    "y.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Gaussian) Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73204419889502759"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB() # Note that there is no parameter allowed in GaussianNB()\n",
    "gnb.fit(X, y)\n",
    "gnb.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get the probability estimates for the input vector X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.79281852e-02,   2.18950571e-03,   9.79882309e-01],\n",
       "       [  9.08261588e-02,   9.89759965e-04,   9.08184081e-01],\n",
       "       [  8.92937811e-01,   8.73826443e-13,   1.07062189e-01],\n",
       "       ..., \n",
       "       [  9.02419655e-01,   1.84466699e-11,   9.75803449e-02],\n",
       "       [  1.35234568e-03,   7.09322475e-02,   9.27715407e-01],\n",
       "       [  3.73481033e-04,   7.76129501e-01,   2.23497018e-01]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.predict_proba(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before classification, we need to normalize the data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(362, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "X_scaled = min_max_scaler.fit_transform(X)\n",
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following parameters affects the performance (accuracy) of the classifier\n",
    " * *n_neighbors*: Number of neighbors to use\n",
    " * *weights*: weight function of the points. Possible values are:\n",
    "   * ‘uniform’ : uniform weights.\n",
    "   * ‘distance’ : weight points by the inverse of their distance.\n",
    "   * [callable] : a user-defined weight function.\n",
    " * *metric*: The distance metric to use. The default metric is minkowski.\n",
    " * *p*: Power parameter for the Minkowski metric (assume we use the default metric). Usual choices are:\n",
    "   * p = 2: Euclidean Distance\n",
    "   * p = 1: Manhattan Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82320441988950277"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=3,weights='uniform',p=2)\n",
    "neigh.fit(X_scaled, y)\n",
    "neigh.score(X_scaled, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use K-fold cross validation to get a reliable estimate of how well a model performs on unseen data (i.e., the generalization error). This helps to (1) determine which model works well, or (2) how to set the values for the hyper parameters for a model. We will see an example of the latter usage in finding the best $k$ for kNN classifiers.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_folds = 10\n",
    "kf = KFold(n=len(X), n_folds=n_folds, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_Gaussian_NB(train_X, train_y, test_X, test_y, debug_flag = False):\n",
    "    gnb = GaussianNB()\n",
    "    gnb.fit(train_X, train_y)\n",
    "    train_error = gnb.score(train_X, train_y)\n",
    "    test_error = gnb.score(test_X, test_y)\n",
    "    if debug_flag:\n",
    "        print('=============')\n",
    "        print('training error:\\t{}'.format(train_error))\n",
    "        print('testing error:\\t{}'.format(test_error))\n",
    "    return train_error, test_error\n",
    "\n",
    "def test_KNN(train_X, train_y, test_X, test_y, n_neighbors=3, weights='uniform', p=2, debug_flag = False):\n",
    "    neigh = KNeighborsClassifier(n_neighbors=n_neighbors, weights=weights, p=p)\n",
    "    neigh.fit(train_X, train_y)\n",
    "    train_error = neigh.score(train_X, train_y)\n",
    "    test_error = neigh.score(test_X, test_y)\n",
    "    if debug_flag:\n",
    "        print('=============')\n",
    "        print('training error:\\t{}'.format(train_error))\n",
    "        print('testing error:\\t{}'.format(test_error))\n",
    "    return train_error, test_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CV on Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================\n",
      "avg. training error (Gaussian NB):\t0.73756866446437\n",
      "avg. testing error (Gaussian NB):\t0.7318318318318319\n"
     ]
    }
   ],
   "source": [
    "train_error_total = 0\n",
    "test_error_total = 0\n",
    "for train, test in kf:\n",
    "    train_X = X.iloc[train]\n",
    "    test_X = X.iloc[test]\n",
    "    train_y = y.iloc[train]\n",
    "    test_y = y.iloc[test]\n",
    "    train_error, test_error = test_Gaussian_NB(train_X, train_y, test_X, test_y)\n",
    "    train_error_total += train_error\n",
    "    test_error_total += test_error\n",
    "\n",
    "print('===================')\n",
    "print('avg. training error (Gaussian NB):\\t{}'.format(train_error_total/n_folds))\n",
    "print('avg. testing error (Gaussian NB):\\t{}'.format(test_error_total/n_folds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CV on KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cv(n_neighbors=3,weights='uniform',p=2):\n",
    "    train_error_total = 0\n",
    "    test_error_total = 0\n",
    "    for train, test in kf:\n",
    "        train_X = X_scaled[train]\n",
    "        test_X = X_scaled[test]\n",
    "        train_y = y.iloc[train]\n",
    "        test_y = y.iloc[test]\n",
    "        train_error, test_error = test_KNN(train_X, train_y, test_X, test_y, n_neighbors, weights, p)\n",
    "        train_error_total += train_error\n",
    "        test_error_total += test_error\n",
    "    return train_error_total/n_folds, test_error_total/n_folds\n",
    "#     print('===================')\n",
    "#     print('avg. training error (kNN):\\t{}'.format(train_error_total/n_folds))\n",
    "#     print('avg. testing error (kNN):\\t{}'.format(test_error_total/n_folds))\n",
    "#     print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cv_plot(weights='uniform',p=2):\n",
    "    cv_res = []\n",
    "    for i in range(1,50):\n",
    "        train_error, test_error = cv(i, weights, p)\n",
    "        cv_res.append([i, train_error, test_error])\n",
    "    cv_res_arr = np.array(cv_res)\n",
    "    plt.figure(figsize=(16,9)) \n",
    "    plt.title('Errors vs k for kNN classifiers')\n",
    "    plot_train, = plt.plot(cv_res_arr[:,0], cv_res_arr[:,1], label='training')\n",
    "    plot_test, = plt.plot(cv_res_arr[:,0], cv_res_arr[:,2], label='testing')\n",
    "    plt.legend(handles=[plot_train, plot_test])\n",
    "    plt.ylim((min(min(cv_res_arr[:,1]), min(cv_res_arr[:,2])) - 0.05, max(max(cv_res_arr[:,1]), max(cv_res_arr[:,2]))+0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LWwzprLO8n3dprfTtt9L+/S60BapSu26dq85OmSINHOjmnfboUfnr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\n/w+Hy2eyYUiftAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2752c6a8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_plot('uniform',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MpVRKAQAAACA6EEoBAAAAAJ6J21BK+y4AAAAAeM9YayN3MmNsJM9Xl717pbZt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\nA7ucoOeK2fmvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2752f1e3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_plot('uniform',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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flK7Z+bOV2CdRKYkpumbMNep5bM8m6SsAoHUjrCI4G7Zv0Ml/PFnp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2752f82db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_plot('distance',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A5Fdsz2pFoWcVAAAAAKqm8rp1DQAAAAAAFYqwCgAAAAAIHMIqAAAAACBwCKsA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\nZdKKSYqqEqXkAcmqXKlyWI4JAAAAABVVhQ6r+4/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2752f83c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_plot('distance',1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the above results, we decide to use *k=27* and *uniform* weight with *p=1*. Then we use these parameters to train a classifier over all the data (i.e., X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72928176795580113"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=27,weights='uniform',p=1)\n",
    "neigh.fit(X_scaled, y)\n",
    "neigh.score(X_scaled, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to normalize data, but be aware that we need to make sure that testing data and training data are normalized in the same way (e.g., use ```min_max_scaler.transform()```)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLD_at_9am</th>\n",
       "      <th>CLD_at_3pm</th>\n",
       "      <th>RH_at_9am</th>\n",
       "      <th>RH_at_3pm</th>\n",
       "      <th>Temps_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>44</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>52</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>52</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>43</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>78</td>\n",
       "      <td>33</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>33</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CLD_at_9am  CLD_at_3pm  RH_at_9am  RH_at_3pm  Temps_diff\n",
       "116         1.0           0         62         44        15.4\n",
       "126         1.0           1         56         52         9.4\n",
       "141         1.0           1         61         52        10.4\n",
       "166         1.0           7         50         43        16.5\n",
       "334         1.0           7         78         33        11.1\n",
       "335         7.0           1         58         33         9.0\n",
       "369         6.0           7         90         60         7.3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_missing_sun_hours['Temps_diff'] = data_missing_sun_hours['Temps_max'] - data_missing_sun_hours['Temps_min']\n",
    "test_data = data_missing_sun_hours[feature_list]\n",
    "test_data_scaled = min_max_scaler.transform(test_data)\n",
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's predict the sun_level using both KNN classifier and Naive Bayes classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Temps_min</th>\n",
       "      <th>Temps_max</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Evap</th>\n",
       "      <th>Sun_hours</th>\n",
       "      <th>Max_wind_dir</th>\n",
       "      <th>...</th>\n",
       "      <th>Wind_spd_at_9am</th>\n",
       "      <th>MSLP_at_9am</th>\n",
       "      <th>Temp_at_3pm</th>\n",
       "      <th>RH_at_3pm</th>\n",
       "      <th>CLD_at_3pm</th>\n",
       "      <th>Wind_dir_at_3pm</th>\n",
       "      <th>Wind_spd_at_3pm</th>\n",
       "      <th>MSLP_at_3pm</th>\n",
       "      <th>Temps_diff</th>\n",
       "      <th>Sun_level_pred_knn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>We</td>\n",
       "      <td>17.3</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNE</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1013.1</td>\n",
       "      <td>28.7</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>24</td>\n",
       "      <td>1007.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Sa</td>\n",
       "      <td>15.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1022.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>NE</td>\n",
       "      <td>20</td>\n",
       "      <td>1018.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>Su</td>\n",
       "      <td>19.9</td>\n",
       "      <td>30.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>28.5</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>NNE</td>\n",
       "      <td>31</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>10.4</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>Th</td>\n",
       "      <td>22.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1011.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>ENE</td>\n",
       "      <td>15</td>\n",
       "      <td>1008.1</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>Th</td>\n",
       "      <td>5.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>1021.2</td>\n",
       "      <td>14.5</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>NNW</td>\n",
       "      <td>24</td>\n",
       "      <td>1015.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>Med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Fr</td>\n",
       "      <td>7.7</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>1015.7</td>\n",
       "      <td>16.4</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>WSW</td>\n",
       "      <td>20</td>\n",
       "      <td>1016.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>Th</td>\n",
       "      <td>10.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>1024.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>SSW</td>\n",
       "      <td>19</td>\n",
       "      <td>1025.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Month  Date Day  Temps_min  Temps_max  Rain  Evap  Sun_hours  \\\n",
       "116  2015     11    25  We       17.3       32.7   0.0   9.0        NaN   \n",
       "126  2015     12     5  Sa       15.5       24.9   0.0   8.0        NaN   \n",
       "141  2015     12    20  Su       19.9       30.3   0.0  13.0        NaN   \n",
       "166  2016      1    14  Th       22.7       39.2   0.0   8.4        NaN   \n",
       "334  2016      6    30  Th        5.4       16.5   0.0   1.8        NaN   \n",
       "335  2016      7     1  Fr        7.7       16.7   0.0   4.0        NaN   \n",
       "369  2016      8     4  Th       10.8       18.1  61.0   9.4        NaN   \n",
       "\n",
       "    Max_wind_dir         ...          Wind_spd_at_9am MSLP_at_9am  \\\n",
       "116          NNE         ...                        9      1013.1   \n",
       "126           NE         ...                        7      1022.6   \n",
       "141           NE         ...                       11      1013.4   \n",
       "166            S         ...                        6      1011.7   \n",
       "334           NW         ...                       11      1021.2   \n",
       "335           NW         ...                       13      1015.7   \n",
       "369           SE         ...                       15      1024.3   \n",
       "\n",
       "     Temp_at_3pm  RH_at_3pm  CLD_at_3pm Wind_dir_at_3pm  Wind_spd_at_3pm  \\\n",
       "116         28.7         44           0             NNE               24   \n",
       "126         24.1         52           1              NE               20   \n",
       "141         28.5         52           1             NNE               31   \n",
       "166         32.4         43           7             ENE               15   \n",
       "334         14.5         33           7             NNW               24   \n",
       "335         16.4         33           1             WSW               20   \n",
       "369         15.5         60           7             SSW               19   \n",
       "\n",
       "     MSLP_at_3pm  Temps_diff  Sun_level_pred_knn  \n",
       "116       1007.4        15.4                High  \n",
       "126       1018.9         9.4                High  \n",
       "141       1008.7        10.4                High  \n",
       "166       1008.1        16.5                 Med  \n",
       "334       1015.1        11.1                 Med  \n",
       "335       1016.2         9.0                High  \n",
       "369       1025.1         7.3                 Low  \n",
       "\n",
       "[7 rows x 26 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_missing_sun_hours['Sun_level_pred_knn'] = neigh.predict(test_data_scaled)\n",
    "data_missing_sun_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Temps_min</th>\n",
       "      <th>Temps_max</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Evap</th>\n",
       "      <th>Sun_hours</th>\n",
       "      <th>Max_wind_dir</th>\n",
       "      <th>...</th>\n",
       "      <th>MSLP_at_9am</th>\n",
       "      <th>Temp_at_3pm</th>\n",
       "      <th>RH_at_3pm</th>\n",
       "      <th>CLD_at_3pm</th>\n",
       "      <th>Wind_dir_at_3pm</th>\n",
       "      <th>Wind_spd_at_3pm</th>\n",
       "      <th>MSLP_at_3pm</th>\n",
       "      <th>Temps_diff</th>\n",
       "      <th>Sun_level_pred_knn</th>\n",
       "      <th>Sun_level_pred_nb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>We</td>\n",
       "      <td>17.3</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNE</td>\n",
       "      <td>...</td>\n",
       "      <td>1013.1</td>\n",
       "      <td>28.7</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>24</td>\n",
       "      <td>1007.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Sa</td>\n",
       "      <td>15.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>1022.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>NE</td>\n",
       "      <td>20</td>\n",
       "      <td>1018.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>Su</td>\n",
       "      <td>19.9</td>\n",
       "      <td>30.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>28.5</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>NNE</td>\n",
       "      <td>31</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>10.4</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>Th</td>\n",
       "      <td>22.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>...</td>\n",
       "      <td>1011.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>ENE</td>\n",
       "      <td>15</td>\n",
       "      <td>1008.1</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Med</td>\n",
       "      <td>Med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>Th</td>\n",
       "      <td>5.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>1021.2</td>\n",
       "      <td>14.5</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>NNW</td>\n",
       "      <td>24</td>\n",
       "      <td>1015.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>Med</td>\n",
       "      <td>Med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Fr</td>\n",
       "      <td>7.7</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>1015.7</td>\n",
       "      <td>16.4</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>WSW</td>\n",
       "      <td>20</td>\n",
       "      <td>1016.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>High</td>\n",
       "      <td>Med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>Th</td>\n",
       "      <td>10.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>1024.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>SSW</td>\n",
       "      <td>19</td>\n",
       "      <td>1025.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Month  Date Day  Temps_min  Temps_max  Rain  Evap  Sun_hours  \\\n",
       "116  2015     11    25  We       17.3       32.7   0.0   9.0        NaN   \n",
       "126  2015     12     5  Sa       15.5       24.9   0.0   8.0        NaN   \n",
       "141  2015     12    20  Su       19.9       30.3   0.0  13.0        NaN   \n",
       "166  2016      1    14  Th       22.7       39.2   0.0   8.4        NaN   \n",
       "334  2016      6    30  Th        5.4       16.5   0.0   1.8        NaN   \n",
       "335  2016      7     1  Fr        7.7       16.7   0.0   4.0        NaN   \n",
       "369  2016      8     4  Th       10.8       18.1  61.0   9.4        NaN   \n",
       "\n",
       "    Max_wind_dir        ...          MSLP_at_9am Temp_at_3pm  RH_at_3pm  \\\n",
       "116          NNE        ...               1013.1        28.7         44   \n",
       "126           NE        ...               1022.6        24.1         52   \n",
       "141           NE        ...               1013.4        28.5         52   \n",
       "166            S        ...               1011.7        32.4         43   \n",
       "334           NW        ...               1021.2        14.5         33   \n",
       "335           NW        ...               1015.7        16.4         33   \n",
       "369           SE        ...               1024.3        15.5         60   \n",
       "\n",
       "     CLD_at_3pm  Wind_dir_at_3pm Wind_spd_at_3pm  MSLP_at_3pm  Temps_diff  \\\n",
       "116           0              NNE              24       1007.4        15.4   \n",
       "126           1               NE              20       1018.9         9.4   \n",
       "141           1              NNE              31       1008.7        10.4   \n",
       "166           7              ENE              15       1008.1        16.5   \n",
       "334           7              NNW              24       1015.1        11.1   \n",
       "335           1              WSW              20       1016.2         9.0   \n",
       "369           7              SSW              19       1025.1         7.3   \n",
       "\n",
       "     Sun_level_pred_knn  Sun_level_pred_nb  \n",
       "116                High               High  \n",
       "126                High               High  \n",
       "141                High               High  \n",
       "166                 Med                Med  \n",
       "334                 Med                Med  \n",
       "335                High                Med  \n",
       "369                 Low                Low  \n",
       "\n",
       "[7 rows x 27 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_missing_sun_hours['Sun_level_pred_nb'] = gnb.predict(test_data)\n",
    "data_missing_sun_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": []
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