MNIST_Classifier_RBFSVM.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"MNIST_Classifier_RBFSVM.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1YY1aBAwX0jow2yExQgdt5AzjViMSTrfd","authorship_tag":"ABX9TyPgr3cqeyxoymYpjtk6VSfS"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"TPU"},"cells":[{"cell_type":"code","metadata":{"id":"INTfktNmKMdn","colab_type":"code","outputId":"237c98db-dc75-49cc-aa7b-b25ceaa75d8e","executionInfo":{"status":"ok","timestamp":1587423534457,"user_tz":300,"elapsed":3287,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"source":["# Import the machine learning libraries.\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn import linear_model\n","from sklearn.model_selection import train_test_split\n","import gc\n","import cv2\n","from sklearn.preprocessing import scale\n","from sklearn import svm\n","from sklearn.svm import SVC\n","from sklearn import metrics\n","from sklearn.metrics import confusion_matrix\n","from mlxtend.plotting import plot_confusion_matrix\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.model_selection import KFold"],"execution_count":1,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"I5z9xS7VKYPG","colab_type":"code","outputId":"996be0e3-af88-43f6-fec2-361fd7d7eeaf","executionInfo":{"status":"ok","timestamp":1587423543649,"user_tz":300,"elapsed":11910,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Load the MNIST dataset.\n","# The dataset can be downloaded here: http://yann.lecun.com/exdb/mnist/.\n","digits = pd.read_csv(\"./drive/My Drive/ML_Code/MNIST SVM/train.csv\")\n","digits.info()"],"execution_count":2,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 42000 entries, 0 to 41999\n","Columns: 785 entries, label to pixel783\n","dtypes: int64(785)\n","memory usage: 251.5 MB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"UiRF86jwOJuh","colab_type":"text"},"source":["### **Data Exploration**"]},{"cell_type":"code","metadata":{"id":"h4p7Hil2Lc4T","colab_type":"code","outputId":"4f3a7c9e-56f6-4f0e-b258-168b60f3cbd5","executionInfo":{"status":"ok","timestamp":1587423543653,"user_tz":300,"elapsed":11789,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":408}},"source":["digits.head(10)"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table 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0         0\n","\n","[10 rows x 785 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"aNTNJ-qDLjat","colab_type":"code","outputId":"ec32a1fe-89c2-41b8-ee4f-dda46c417ba3","executionInfo":{"status":"ok","timestamp":1587423543656,"user_tz":300,"elapsed":11700,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# 7th row. (row index number is 6).\n","seven = digits.iloc[6, 1:]\n","seven.shape"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(784,)"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"yIB2rsRPLnYF","colab_type":"code","outputId":"d113171d-d126-4069-8478-1cef5a8d0197","executionInfo":{"status":"ok","timestamp":1587423544093,"user_tz":300,"elapsed":12037,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["# Plot the single row.\n","seven = seven.values.reshape(28, 28)\n","plt.imshow(seven, cmap='gray')"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f957f4c39b0>"]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"h4ZQto6_L9vI","colab_type":"code","outputId":"d353caaa-8f8b-4559-a09a-bb97bf74dce2","executionInfo":{"status":"ok","timestamp":1587423544099,"user_tz":300,"elapsed":11939,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":493}},"source":["# Print the array.\n","print(seven[:, 5:-5])"],"execution_count":6,"outputs":[{"output_type":"stream","text":["[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n"," [  0  82 152  71  51  51  21  41  51  51  51  51 113 193 152  30   0   0]\n"," [  0 122 253 252 253 252 223 243 253 252 253 252 253 252 233  30   0   0]\n"," [  0 123 102  41 102 102 102 102 102 102 102 162 254 253 142   0   0   0]\n"," [  0 203 102   0   0   0   0   0   0   0   0 183 253 212  20   0   0   0]\n"," [  0 203 142   0   0   0   0   0   0   0  11 213 254  91   0   0   0   0]\n"," [ 41 243 102   0   0   0   0   0   0   0  51 252 172  10   0   0   0   0]\n"," [ 41 223 102   0   0   0   0   0   0   0 214 253 102   0   0   0   0   0]\n"," [  0  20  20   0   0   0   0   0   0   0 253 252 102   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0  62 254 253  41   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0 102 253 171   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0 163 254  91   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0 203 253  50   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0  51 253 254  50   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0  51 252 253  50   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0  51 253 254  50   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0  51 252 213  10   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0 152 253  82   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0 233 252   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0  21 255 253   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0  20 253 212   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TC80mMaVMgpX","colab_type":"code","outputId":"7fcf5bca-180f-4ee0-f999-115538da85ce","executionInfo":{"status":"ok","timestamp":1587423544108,"user_tz":300,"elapsed":11897,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Summarize the counts of 'label' to see how many labels of each digit are present.\n","digits.label.astype('category').value_counts()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    4684\n","7    4401\n","3    4351\n","9    4188\n","2    4177\n","6    4137\n","0    4132\n","4    4072\n","8    4063\n","5    3795\n","Name: label, dtype: int64"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"Sd3hKvV9M77c","colab_type":"code","outputId":"1a0cfcdd-3db2-420a-eb4b-fe8bb26f1ce9","executionInfo":{"status":"ok","timestamp":1587423544120,"user_tz":300,"elapsed":11860,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Summarize count in percentage.\n","100*(round(digits.label.astype('category').value_counts()/len(digits.index), 4))"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    11.15\n","7    10.48\n","3    10.36\n","9     9.97\n","2     9.95\n","6     9.85\n","0     9.84\n","4     9.70\n","8     9.67\n","5     9.04\n","Name: label, dtype: float64"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"dNX7KjsfNSmK","colab_type":"code","outputId":"fec1d692-b2d6-4cd8-d4c9-ab54b548e99b","executionInfo":{"status":"ok","timestamp":1587423544122,"user_tz":300,"elapsed":11795,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"source":["# Try to find the missing values.\n","digits.isnull().sum()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["label       0\n","pixel0      0\n","pixel1      0\n","pixel2      0\n","pixel3      0\n","           ..\n","pixel779    0\n","pixel780    0\n","pixel781    0\n","pixel782    0\n","pixel783    0\n","Length: 785, dtype: int64"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"LyRC1HSvNdkt","colab_type":"code","outputId":"6c68aaa4-1417-4997-d294-792175106be5","executionInfo":{"status":"ok","timestamp":1587423546649,"user_tz":300,"elapsed":14242,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":346}},"source":["# Stats and distribution.\n","digits.describe()"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","      <th>pixel0</th>\n","      <th>pixel1</th>\n","      <th>pixel2</th>\n","      <th>pixel3</th>\n","      <th>pixel4</th>\n","      <th>pixel5</th>\n","      <th>pixel6</th>\n","      <th>pixel7</th>\n","      <th>pixel8</th>\n","      <th>pixel9</th>\n","      <th>pixel10</th>\n","      <th>pixel11</th>\n","      <th>pixel12</th>\n","      <th>pixel13</th>\n","      <th>pixel14</th>\n","      <th>pixel15</th>\n","      <th>pixel16</th>\n","      <th>pixel17</th>\n","      <th>pixel18</th>\n","      <th>pixel19</th>\n","      <th>pixel20</th>\n","      <th>pixel21</th>\n","      <th>pixel22</th>\n","      <th>pixel23</th>\n","      <th>pixel24</th>\n","      <th>pixel25</th>\n","      <th>pixel26</th>\n","      <th>pixel27</th>\n","      <th>pixel28</th>\n","      <th>pixel29</th>\n","      <th>pixel30</th>\n","      <th>pixel31</th>\n","      <th>pixel32</th>\n","      <th>pixel33</th>\n","      <th>pixel34</th>\n","      <th>pixel35</th>\n","      <th>pixel36</th>\n","      <th>pixel37</th>\n","      <th>pixel38</th>\n","      <th>...</th>\n","      <th>pixel744</th>\n","      <th>pixel745</th>\n","      <th>pixel746</th>\n","      <th>pixel747</th>\n","      <th>pixel748</th>\n","      <th>pixel749</th>\n","      <th>pixel750</th>\n","      <th>pixel751</th>\n","      <th>pixel752</th>\n","      <th>pixel753</th>\n","      <th>pixel754</th>\n","      <th>pixel755</th>\n","      <th>pixel756</th>\n","      <th>pixel757</th>\n","      <th>pixel758</th>\n","      <th>pixel759</th>\n","      <th>pixel760</th>\n","      <th>pixel761</th>\n","      <th>pixel762</th>\n","      <th>pixel763</th>\n","      <th>pixel764</th>\n","      <th>pixel765</th>\n","      <th>pixel766</th>\n","      <th>pixel767</th>\n","      <th>pixel768</th>\n","      <th>pixel769</th>\n","      <th>pixel770</th>\n","      <th>pixel771</th>\n","      <th>pixel772</th>\n","      <th>pixel773</th>\n","      <th>pixel774</th>\n","      <th>pixel775</th>\n","      <th>pixel776</th>\n","      <th>pixel777</th>\n","      <th>pixel778</th>\n","      <th>pixel779</th>\n","      <th>pixel780</th>\n","      <th>pixel781</th>\n","      <th>pixel782</th>\n","      <th>pixel783</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>42000.000000</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.00000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>...</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.00000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.00000</td>\n","      <td>42000.000000</td>\n","      <td>42000.000000</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","      <td>42000.0</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>4.456643</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00300</td>\n","      <td>0.011190</td>\n","      <td>0.005143</td>\n","      <td>0.000214</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000381</td>\n","      <td>0.001310</td>\n","      <td>0.010548</td>\n","      <td>0.027262</td>\n","      <td>0.050905</td>\n","      <td>0.066405</td>\n","      <td>0.129571</td>\n","      <td>...</td>\n","      <td>3.772524</td>\n","      <td>2.748905</td>\n","      <td>1.796452</td>\n","      <td>1.089905</td>\n","      <td>0.563190</td>\n","      <td>0.239571</td>\n","      <td>0.093524</td>\n","      <td>0.024833</td>\n","      <td>0.000857</td>\n","      <td>0.001405</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.006143</td>\n","      <td>0.035833</td>\n","      <td>0.082357</td>\n","      <td>0.114905</td>\n","      <td>0.178714</td>\n","      <td>0.301452</td>\n","      <td>0.413643</td>\n","      <td>0.513667</td>\n","      <td>0.558833</td>\n","      <td>0.677857</td>\n","      <td>0.60281</td>\n","      <td>0.489238</td>\n","      <td>0.340214</td>\n","      <td>0.219286</td>\n","      <td>0.117095</td>\n","      <td>0.059024</td>\n","      <td>0.02019</td>\n","      <td>0.017238</td>\n","      <td>0.002857</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2.887730</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.56812</td>\n","      <td>1.626927</td>\n","      <td>1.053972</td>\n","      <td>0.043916</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.078072</td>\n","      <td>0.232634</td>\n","      <td>1.131661</td>\n","      <td>2.310396</td>\n","      <td>3.121847</td>\n","      <td>3.259128</td>\n","      <td>4.992894</td>\n","      <td>...</td>\n","      <td>26.957829</td>\n","      <td>22.879248</td>\n","      <td>18.595109</td>\n","      <td>14.434439</td>\n","      <td>10.517823</td>\n","      <td>6.469315</td>\n","      <td>3.976306</td>\n","      <td>1.846016</td>\n","      <td>0.139556</td>\n","      <td>0.287891</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.949803</td>\n","      <td>2.350859</td>\n","      <td>3.934280</td>\n","      <td>4.543583</td>\n","      <td>5.856772</td>\n","      <td>7.219742</td>\n","      <td>8.928286</td>\n","      <td>10.004069</td>\n","      <td>10.129595</td>\n","      <td>11.254931</td>\n","      <td>10.69603</td>\n","      <td>9.480066</td>\n","      <td>7.950251</td>\n","      <td>6.312890</td>\n","      <td>4.633819</td>\n","      <td>3.274488</td>\n","      <td>1.75987</td>\n","      <td>1.894498</td>\n","      <td>0.414264</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>2.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>4.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>7.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>9.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>116.00000</td>\n","      <td>254.000000</td>\n","      <td>216.000000</td>\n","      <td>9.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>16.000000</td>\n","      <td>47.000000</td>\n","      <td>157.000000</td>\n","      <td>254.000000</td>\n","      <td>255.000000</td>\n","      <td>243.000000</td>\n","      <td>255.000000</td>\n","      <td>...</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>253.000000</td>\n","      <td>28.000000</td>\n","      <td>59.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>177.000000</td>\n","      <td>231.000000</td>\n","      <td>253.000000</td>\n","      <td>254.000000</td>\n","      <td>254.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>255.00000</td>\n","      <td>255.000000</td>\n","      <td>255.000000</td>\n","      <td>254.000000</td>\n","      <td>254.000000</td>\n","      <td>253.000000</td>\n","      <td>253.00000</td>\n","      <td>254.000000</td>\n","      <td>62.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8 rows × 785 columns</p>\n","</div>"],"text/plain":["              label   pixel0   pixel1  ...  pixel781  pixel782  pixel783\n","count  42000.000000  42000.0  42000.0  ...   42000.0   42000.0   42000.0\n","mean       4.456643      0.0      0.0  ...       0.0       0.0       0.0\n","std        2.887730      0.0      0.0  ...       0.0       0.0       0.0\n","min        0.000000      0.0      0.0  ...       0.0       0.0       0.0\n","25%        2.000000      0.0      0.0  ...       0.0       0.0       0.0\n","50%        4.000000      0.0      0.0  ...       0.0       0.0       0.0\n","75%        7.000000      0.0      0.0  ...       0.0       0.0       0.0\n","max        9.000000      0.0      0.0  ...       0.0       0.0       0.0\n","\n","[8 rows x 785 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"Y8OuCvEfOhlc","colab_type":"text"},"source":["### **Data Preparation**"]},{"cell_type":"code","metadata":{"id":"gGlRrMSONpvw","colab_type":"code","colab":{}},"source":["# Split a dataset into X and y.\n","X = digits.iloc[:, 1:]\n","y = digits.iloc[:, 0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lMI4phFfO70W","colab_type":"code","colab":{}},"source":["# Scale the features. Standardize a dataset.\n","X_scaled = scale(X)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RV1d926fO8qn","colab_type":"code","outputId":"56638745-b324-4229-d44e-1e4fe6a70af2","executionInfo":{"status":"ok","timestamp":1587423548029,"user_tz":300,"elapsed":15513,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Split the dataset into the training set and test subsets.\n","X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=0)\n","print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["(29400, 784) (12600, 784) (29400,) (12600,)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Ii1dxe4ESxUs","colab_type":"text"},"source":["### **Linear Support Vector Machine (SVM)**"]},{"cell_type":"code","metadata":{"id":"R8Y3WiybPTMB","colab_type":"code","outputId":"f6cdcf48-671d-4331-8c2d-df4c7959b510","executionInfo":{"status":"ok","timestamp":1587423691289,"user_tz":300,"elapsed":158700,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# SVM model with linear kernel.\n","%%time\n","svm_linear = svm.SVC(kernel='linear')\n","\n","# Fit the model.\n","svm_linear.fit(X_train, y_train)"],"execution_count":14,"outputs":[{"output_type":"stream","text":["CPU times: user 2min 23s, sys: 102 ms, total: 2min 23s\n","Wall time: 2min 23s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xxUUvtzpP7Sm","colab_type":"code","outputId":"581bbe5f-dc40-4553-be06-c7d9c67aeba8","executionInfo":{"status":"ok","timestamp":1587423783287,"user_tz":300,"elapsed":250646,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predict.\n","%%time\n","y_pred = svm_linear.predict(X_test)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["CPU times: user 1min 31s, sys: 10.6 ms, total: 1min 31s\n","Wall time: 1min 31s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"0QDYcT6LQ-bb","colab_type":"code","outputId":"0ad0dcc0-19c4-4763-93ed-4adcd24293dd","executionInfo":{"status":"ok","timestamp":1587423783289,"user_tz":300,"elapsed":250579,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["y_pred.shape"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(12600,)"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"RKqtAQyQSG7L","colab_type":"code","outputId":"c2c55dfc-bf43-4a96-89a5-6612c08812a5","executionInfo":{"status":"ok","timestamp":1587423783290,"user_tz":300,"elapsed":250529,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["y_pred"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([3, 6, 9, ..., 1, 6, 5])"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"Dx_Br3CMRfI5","colab_type":"code","outputId":"82c697ff-5a91-4e62-9bc7-2e1c16c75dc8","executionInfo":{"status":"ok","timestamp":1587423783292,"user_tz":300,"elapsed":250471,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["y_pred[:10]"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([3, 6, 9, 5, 6, 5, 6, 0, 0, 1])"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"xJ-7UEZuQNZS","colab_type":"code","outputId":"019c97e6-ac45-4aaf-b0fa-08a5069e51fd","executionInfo":{"status":"ok","timestamp":1587423783934,"user_tz":300,"elapsed":251046,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":723}},"source":["# Plot the confusion matrix.\n","%%time\n","cm = metrics.confusion_matrix(y_test, y_pred)\n","plot_confusion_matrix(conf_mat=cm,\n","                      show_absolute=True,\n","                      show_normed=True,\n","                      colorbar=True,\n","                      figsize=(12,12))\n","plt.show()"],"execution_count":19,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x864 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["CPU times: user 581 ms, sys: 32 ms, total: 613 ms\n","Wall time: 618 ms\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"UH0m8qWkRt6-","colab_type":"code","outputId":"7954002c-4d31-4543-b809-bf80bb625868","executionInfo":{"status":"ok","timestamp":1587423783935,"user_tz":300,"elapsed":250890,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Print accuracy.\n","print(\"Accuracy:\", metrics.accuracy_score(y_true=y_test, y_pred=y_pred), \"\\n\")"],"execution_count":20,"outputs":[{"output_type":"stream","text":["Accuracy: 0.919047619047619 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TG2DsZEBSU46","colab_type":"code","outputId":"45a42483-dc56-487d-89a2-4d3e5fb9b19f","executionInfo":{"status":"ok","timestamp":1587423783937,"user_tz":300,"elapsed":250806,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":306}},"source":["# Print classification report.\n","class_wise = metrics.classification_report(y_true=y_test, y_pred=y_pred)\n","print(class_wise)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      0.97      0.96      1242\n","           1       0.95      0.98      0.96      1429\n","           2       0.89      0.91      0.90      1276\n","           3       0.88      0.89      0.88      1298\n","           4       0.91      0.93      0.92      1236\n","           5       0.90      0.89      0.89      1119\n","           6       0.95      0.95      0.95      1243\n","           7       0.94      0.93      0.93      1334\n","           8       0.92      0.86      0.89      1204\n","           9       0.90      0.87      0.89      1219\n","\n","    accuracy                           0.92     12600\n","   macro avg       0.92      0.92      0.92     12600\n","weighted avg       0.92      0.92      0.92     12600\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"YrDGb4i5SgB7","colab_type":"code","outputId":"d396f135-0d46-469d-e3f7-8c890034f2c1","executionInfo":{"status":"ok","timestamp":1587423783939,"user_tz":300,"elapsed":250750,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Run garbage collection to free up memory.\n","gc.collect()"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9052"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"r7kkarHOS9VT","colab_type":"text"},"source":["### **Non-linear Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel**"]},{"cell_type":"code","metadata":{"id":"tvG9sb9oSsSE","colab_type":"code","outputId":"2292568a-068f-4a25-f574-986d2cdc7150","executionInfo":{"status":"ok","timestamp":1587424073109,"user_tz":300,"elapsed":539885,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Non-linear regression model using RBF SVM kernel, C=1, default value of gamma.\n","%%time\n","non_linear_model = SVC(kernel='rbf')\n","non_linear_model.fit(X_train, y_train)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["CPU times: user 4min 48s, sys: 79.6 ms, total: 4min 48s\n","Wall time: 4min 49s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nqrf7egETXOT","colab_type":"code","outputId":"0c7698b0-9363-4770-b47f-e385ebbe5a30","executionInfo":{"status":"ok","timestamp":1587424231867,"user_tz":300,"elapsed":698527,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predict.\n","%%time\n","y_pred = non_linear_model.predict(X_test)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["CPU times: user 2min 38s, sys: 20.9 ms, total: 2min 38s\n","Wall time: 2min 38s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Zzw2xxnWTul3","colab_type":"code","outputId":"87cd3de0-ca70-4b72-cc03-94675b838aa5","executionInfo":{"status":"ok","timestamp":1587424231870,"user_tz":300,"elapsed":698452,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Print accuracy.\n","print(\"Accuracy:\", metrics.accuracy_score(y_true=y_test, y_pred=y_pred), \"\\n\")"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Accuracy: 0.9573809523809523 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hY8barmsT0j4","colab_type":"code","outputId":"c9ceeed8-9f5c-4327-f525-c6c1175e53ca","executionInfo":{"status":"ok","timestamp":1587424231872,"user_tz":300,"elapsed":698389,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Run garbage collection to free up memory.\n","gc.collect()"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"y0m1YDc2UHVV","colab_type":"text"},"source":["### **Hyperparameter Tuning using Grid Search Cross-Validation**"]},{"cell_type":"code","metadata":{"id":"SZXQ5xKaUDBT","colab_type":"code","outputId":"ca2cd425-f4b2-4c97-ea90-91820e9217a3","colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Create a KFold object with 10 splits.\n","%%time\n","folds = KFold(n_splits=5, shuffle=True, random_state=0)\n","\n","# Specify range of hyperparameters.\n","# Set the parameters by cross-validation\n","hyper_params = {'C':[1, 10, 100], 'gamma': [1e-2, 1e-3, 1e-4]}\n","\n","# Non-linear regression model using RBF SVM kernel.\n","svc_grid_search = SVC(kernel=\"rbf\")\n","\n","# create a classifier to perform grid search\n","clf = GridSearchCV(svc_grid_search,\n","                   param_grid = hyper_params,\n","                   scoring= 'accuracy',\n","                   cv = folds,\n","                   verbose = 1,\n","                   return_train_score=True)\n","# Fit the model.\n","clf.fit(X_train, y_train)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"PzZMrP5rV5at","colab_type":"code","colab":{}},"source":["# Cross-Validation results.\n","cv_results = pd.DataFrame(clf.cv_results_)\n","cv_results"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uy70YyF1WGQo","colab_type":"code","colab":{}},"source":["# Convert C to numeric type for plotting on x-axis.\n","%%time\n","cv_results['param_C'] = cv_results['param_C'].astype('int')\n","\n","plt.figure(figsize=(18,6))\n","\n","plt.subplot(131)\n","gamma_01 = cv_results[cv_results['param_gamma']==0.01]\n","plt.plot(gamma_01[\"param_C\"], gamma_01[\"mean_test_score\"], linewidth=2.5)\n","plt.plot(gamma_01[\"param_C\"], gamma_01[\"mean_train_score\"], linewidth=2.5)\n","plt.xlabel('C')\n","plt.ylabel('Accuracy')\n","plt.title(\"Gamma = 0.01\")\n","plt.ylim([0.60, 1])\n","plt.legend(['Test accuracy', 'Train accuracy'], loc='upper left')\n","plt.xscale('log')\n","\n","plt.subplot(132)\n","gamma_001 = cv_results[cv_results['param_gamma']==0.001]\n","plt.plot(gamma_001[\"param_C\"], gamma_001[\"mean_test_score\"], linewidth=2.5)\n","plt.plot(gamma_001[\"param_C\"], gamma_001[\"mean_train_score\"], linewidth=2.5)\n","plt.xlabel('C')\n","plt.ylabel('Accuracy')\n","plt.title(\"Gamma = 0.001\")\n","plt.ylim([0.60, 1])\n","plt.legend(['Test accuracy', 'Train accuracy'], loc='upper left')\n","plt.xscale('log')\n","\n","plt.subplot(133)\n","gamma_0001 = cv_results[cv_results['param_gamma']==0.0001]\n","plt.plot(gamma_0001[\"param_C\"], gamma_0001[\"mean_test_score\"], linewidth=2.5)\n","plt.plot(gamma_0001[\"param_C\"], gamma_0001[\"mean_train_score\"], linewidth=2.5)\n","plt.xlabel('C')\n","plt.ylabel('Accuracy')\n","plt.title(\"Gamma = 0.0001\")\n","plt.ylim([0.60, 1])\n","plt.legend(['Test accuracy', 'Train accuracy'], loc='upper left')\n","plt.xscale('log')\n","\n","plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oqqPFjEzWw7e","colab_type":"code","colab":{}},"source":["# Print the optimal accuracy score and hyperparameters.\n","%%time\n","best_score = clf.best_score_\n","best_hyperparams = clf.best_params_\n","print(\"The best accuracy is {0} corresponding to hyperparameters {1}\".format(best_score, best_hyperparams))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TagjbCvCv52l","colab_type":"code","colab":{}},"source":["# Run garbage collection to free up memory.\n","gc.collect()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rVSjmtE9W_aN","colab_type":"code","colab":{}},"source":["# Non-linear regression model using RBF SVM kernel with the optimal hyperparameters.\n","%%time\n","model = SVC(C=1000, gamma=0.01, kernel=\"rbf\")\n","\n","model.fit(X_train, y_train)\n","y_pred = model.predict(X_test)\n","\n","# Print accuracy.\n","print(\"accuracy\", metrics.accuracy_score(y_test, y_pred), \"\\n\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Gx4Rkf4_XCUd","colab_type":"code","colab":{}},"source":["# Plot the confusion matrix.\n","%%time\n","cm = metrics.confusion_matrix(y_test, y_pred)\n","plot_confusion_matrix(conf_mat=cm,\n","                      show_absolute=True,\n","                      show_normed=True,\n","                      colorbar=True,\n","                      figsize=(18,18))\n","plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8bbFB6mJaYNC","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}