Audio Classification.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the audio features\n",
    "data = pd.read_pickle('audio_features.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_loc_map = {14: 'beach', 11:'bus', 7: 'cafe/restaurant', 2:'car', 3:'city_center', 6:'forest_path', 1:'grocery_store', 8:'home', 12:'library', 13:'metro_station', 15:'office', 9:'park', 4:'residential_area', 5:'train', 10:'tram'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>mfcc_01_mean</th>\n",
       "      <th>mfcc_01_std</th>\n",
       "      <th>mfcc_02_mean</th>\n",
       "      <th>mfcc_02_std</th>\n",
       "      <th>mfcc_03_mean</th>\n",
       "      <th>mfcc_03_std</th>\n",
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       "      <th>mfcc_04_std</th>\n",
       "      <th>mfcc_05_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>rmse_mean</th>\n",
       "      <th>rmse_std</th>\n",
       "      <th>rolloff_mean</th>\n",
       "      <th>rolloff_std</th>\n",
       "      <th>spec_centoid_mean</th>\n",
       "      <th>spec_centoid_std</th>\n",
       "      <th>spec_flux_mean</th>\n",
       "      <th>spec_flux_std</th>\n",
       "      <th>zcr_mean</th>\n",
       "      <th>zcr_std</th>\n",
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       "      <td>8.809357</td>\n",
       "      <td>-53.725513</td>\n",
       "      <td>5.927891</td>\n",
       "      <td>24.895814</td>\n",
       "      <td>5.725864</td>\n",
       "      <td>-3.183241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003790</td>\n",
       "      <td>0.000803</td>\n",
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       "      <td>1.249412</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>-436.539917</td>\n",
       "      <td>26.518017</td>\n",
       "      <td>144.580086</td>\n",
       "      <td>10.041389</td>\n",
       "      <td>-50.765986</td>\n",
       "      <td>6.809961</td>\n",
       "      <td>23.333789</td>\n",
       "      <td>6.239996</td>\n",
       "      <td>-0.538261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005367</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>5121.480013</td>\n",
       "      <td>532.699615</td>\n",
       "      <td>2677.467822</td>\n",
       "      <td>423.000073</td>\n",
       "      <td>1.254992</td>\n",
       "      <td>0.343668</td>\n",
       "      <td>0.056097</td>\n",
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       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>-434.430916</td>\n",
       "      <td>24.856232</td>\n",
       "      <td>137.971707</td>\n",
       "      <td>9.206689</td>\n",
       "      <td>-53.072679</td>\n",
       "      <td>6.447099</td>\n",
       "      <td>24.839483</td>\n",
       "      <td>5.899629</td>\n",
       "      <td>-2.037454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>5450.023734</td>\n",
       "      <td>399.612084</td>\n",
       "      <td>2972.668779</td>\n",
       "      <td>329.724678</td>\n",
       "      <td>1.257049</td>\n",
       "      <td>0.339699</td>\n",
       "      <td>0.069154</td>\n",
       "      <td>0.020514</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>-429.411378</td>\n",
       "      <td>29.635465</td>\n",
       "      <td>130.319024</td>\n",
       "      <td>9.218251</td>\n",
       "      <td>-53.530134</td>\n",
       "      <td>5.817122</td>\n",
       "      <td>25.646563</td>\n",
       "      <td>6.147800</td>\n",
       "      <td>-3.226847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003940</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>5783.338734</td>\n",
       "      <td>405.385235</td>\n",
       "      <td>3230.235048</td>\n",
       "      <td>301.789578</td>\n",
       "      <td>1.271621</td>\n",
       "      <td>0.412785</td>\n",
       "      <td>0.085348</td>\n",
       "      <td>0.019375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>-438.519412</td>\n",
       "      <td>31.680932</td>\n",
       "      <td>129.181425</td>\n",
       "      <td>8.782248</td>\n",
       "      <td>-58.355813</td>\n",
       "      <td>6.728568</td>\n",
       "      <td>25.899696</td>\n",
       "      <td>6.163876</td>\n",
       "      <td>-4.961766</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>5841.843097</td>\n",
       "      <td>325.315548</td>\n",
       "      <td>3342.164413</td>\n",
       "      <td>236.548179</td>\n",
       "      <td>1.270838</td>\n",
       "      <td>0.386981</td>\n",
       "      <td>0.099330</td>\n",
       "      <td>0.013661</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
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      ],
      "text/plain": [
       "   label  mfcc_01_mean  mfcc_01_std  mfcc_02_mean  mfcc_02_std  mfcc_03_mean  \\\n",
       "0     14   -431.829531    31.289929    135.895387     8.809357    -53.725513   \n",
       "1     14   -436.539917    26.518017    144.580086    10.041389    -50.765986   \n",
       "2     14   -434.430916    24.856232    137.971707     9.206689    -53.072679   \n",
       "3     14   -429.411378    29.635465    130.319024     9.218251    -53.530134   \n",
       "4     14   -438.519412    31.680932    129.181425     8.782248    -58.355813   \n",
       "\n",
       "   mfcc_03_std  mfcc_04_mean  mfcc_04_std  mfcc_05_mean    ...     rmse_mean  \\\n",
       "0     5.927891     24.895814     5.725864     -3.183241    ...      0.003790   \n",
       "1     6.809961     23.333789     6.239996     -0.538261    ...      0.005367   \n",
       "2     6.447099     24.839483     5.899629     -2.037454    ...      0.004325   \n",
       "3     5.817122     25.646563     6.147800     -3.226847    ...      0.003940   \n",
       "4     6.728568     25.899696     6.163876     -4.961766    ...      0.003133   \n",
       "\n",
       "   rmse_std  rolloff_mean  rolloff_std  spec_centoid_mean  spec_centoid_std  \\\n",
       "0  0.000803   5579.098051   346.033500        3120.416928        302.216856   \n",
       "1  0.002189   5121.480013   532.699615        2677.467822        423.000073   \n",
       "2  0.001139   5450.023734   399.612084        2972.668779        329.724678   \n",
       "3  0.001392   5783.338734   405.385235        3230.235048        301.789578   \n",
       "4  0.000929   5841.843097   325.315548        3342.164413        236.548179   \n",
       "\n",
       "   spec_flux_mean  spec_flux_std  zcr_mean   zcr_std  \n",
       "0        1.249412       0.333877  0.082505  0.019671  \n",
       "1        1.254992       0.343668  0.056097  0.022570  \n",
       "2        1.257049       0.339699  0.069154  0.020514  \n",
       "3        1.271621       0.412785  0.085348  0.019375  \n",
       "4        1.270838       0.386981  0.099330  0.013661  \n",
       "\n",
       "[5 rows x 51 columns]"
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     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('label', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5.725864</td>\n",
       "      <td>-3.183241</td>\n",
       "      <td>5.771691</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-436.539917</td>\n",
       "      <td>26.518017</td>\n",
       "      <td>144.580086</td>\n",
       "      <td>10.041389</td>\n",
       "      <td>-50.765986</td>\n",
       "      <td>6.809961</td>\n",
       "      <td>23.333789</td>\n",
       "      <td>6.239996</td>\n",
       "      <td>-0.538261</td>\n",
       "      <td>6.159258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005367</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>5121.480013</td>\n",
       "      <td>532.699615</td>\n",
       "      <td>2677.467822</td>\n",
       "      <td>423.000073</td>\n",
       "      <td>1.254992</td>\n",
       "      <td>0.343668</td>\n",
       "      <td>0.056097</td>\n",
       "      <td>0.022570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-434.430916</td>\n",
       "      <td>24.856232</td>\n",
       "      <td>137.971707</td>\n",
       "      <td>9.206689</td>\n",
       "      <td>-53.072679</td>\n",
       "      <td>6.447099</td>\n",
       "      <td>24.839483</td>\n",
       "      <td>5.899629</td>\n",
       "      <td>-2.037454</td>\n",
       "      <td>5.491702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>5450.023734</td>\n",
       "      <td>399.612084</td>\n",
       "      <td>2972.668779</td>\n",
       "      <td>329.724678</td>\n",
       "      <td>1.257049</td>\n",
       "      <td>0.339699</td>\n",
       "      <td>0.069154</td>\n",
       "      <td>0.020514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-429.411378</td>\n",
       "      <td>29.635465</td>\n",
       "      <td>130.319024</td>\n",
       "      <td>9.218251</td>\n",
       "      <td>-53.530134</td>\n",
       "      <td>5.817122</td>\n",
       "      <td>25.646563</td>\n",
       "      <td>6.147800</td>\n",
       "      <td>-3.226847</td>\n",
       "      <td>5.835915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003940</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>5783.338734</td>\n",
       "      <td>405.385235</td>\n",
       "      <td>3230.235048</td>\n",
       "      <td>301.789578</td>\n",
       "      <td>1.271621</td>\n",
       "      <td>0.412785</td>\n",
       "      <td>0.085348</td>\n",
       "      <td>0.019375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-438.519412</td>\n",
       "      <td>31.680932</td>\n",
       "      <td>129.181425</td>\n",
       "      <td>8.782248</td>\n",
       "      <td>-58.355813</td>\n",
       "      <td>6.728568</td>\n",
       "      <td>25.899696</td>\n",
       "      <td>6.163876</td>\n",
       "      <td>-4.961766</td>\n",
       "      <td>6.464432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>5841.843097</td>\n",
       "      <td>325.315548</td>\n",
       "      <td>3342.164413</td>\n",
       "      <td>236.548179</td>\n",
       "      <td>1.270838</td>\n",
       "      <td>0.386981</td>\n",
       "      <td>0.099330</td>\n",
       "      <td>0.013661</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
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      ],
      "text/plain": [
       "   mfcc_01_mean  mfcc_01_std  mfcc_02_mean  mfcc_02_std  mfcc_03_mean  \\\n",
       "0   -431.829531    31.289929    135.895387     8.809357    -53.725513   \n",
       "1   -436.539917    26.518017    144.580086    10.041389    -50.765986   \n",
       "2   -434.430916    24.856232    137.971707     9.206689    -53.072679   \n",
       "3   -429.411378    29.635465    130.319024     9.218251    -53.530134   \n",
       "4   -438.519412    31.680932    129.181425     8.782248    -58.355813   \n",
       "\n",
       "   mfcc_03_std  mfcc_04_mean  mfcc_04_std  mfcc_05_mean  mfcc_05_std  \\\n",
       "0     5.927891     24.895814     5.725864     -3.183241     5.771691   \n",
       "1     6.809961     23.333789     6.239996     -0.538261     6.159258   \n",
       "2     6.447099     24.839483     5.899629     -2.037454     5.491702   \n",
       "3     5.817122     25.646563     6.147800     -3.226847     5.835915   \n",
       "4     6.728568     25.899696     6.163876     -4.961766     6.464432   \n",
       "\n",
       "     ...     rmse_mean  rmse_std  rolloff_mean  rolloff_std  \\\n",
       "0    ...      0.003790  0.000803   5579.098051   346.033500   \n",
       "1    ...      0.005367  0.002189   5121.480013   532.699615   \n",
       "2    ...      0.004325  0.001139   5450.023734   399.612084   \n",
       "3    ...      0.003940  0.001392   5783.338734   405.385235   \n",
       "4    ...      0.003133  0.000929   5841.843097   325.315548   \n",
       "\n",
       "   spec_centoid_mean  spec_centoid_std  spec_flux_mean  spec_flux_std  \\\n",
       "0        3120.416928        302.216856        1.249412       0.333877   \n",
       "1        2677.467822        423.000073        1.254992       0.343668   \n",
       "2        2972.668779        329.724678        1.257049       0.339699   \n",
       "3        3230.235048        301.789578        1.271621       0.412785   \n",
       "4        3342.164413        236.548179        1.270838       0.386981   \n",
       "\n",
       "   zcr_mean   zcr_std  \n",
       "0  0.082505  0.019671  \n",
       "1  0.056097  0.022570  \n",
       "2  0.069154  0.020514  \n",
       "3  0.085348  0.019375  \n",
       "4  0.099330  0.013661  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1356)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mfcc_01_mean</th>\n",
       "      <th>mfcc_01_std</th>\n",
       "      <th>mfcc_02_mean</th>\n",
       "      <th>mfcc_02_std</th>\n",
       "      <th>mfcc_03_mean</th>\n",
       "      <th>mfcc_03_std</th>\n",
       "      <th>mfcc_04_mean</th>\n",
       "      <th>mfcc_04_std</th>\n",
       "      <th>mfcc_05_mean</th>\n",
       "      <th>mfcc_05_std</th>\n",
       "      <th>...</th>\n",
       "      <th>rmse_mean</th>\n",
       "      <th>rmse_std</th>\n",
       "      <th>rolloff_mean</th>\n",
       "      <th>rolloff_std</th>\n",
       "      <th>spec_centoid_mean</th>\n",
       "      <th>spec_centoid_std</th>\n",
       "      <th>spec_flux_mean</th>\n",
       "      <th>spec_flux_std</th>\n",
       "      <th>zcr_mean</th>\n",
       "      <th>zcr_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4065</th>\n",
       "      <td>-440.187558</td>\n",
       "      <td>82.527257</td>\n",
       "      <td>64.392595</td>\n",
       "      <td>30.802939</td>\n",
       "      <td>-35.776806</td>\n",
       "      <td>21.030260</td>\n",
       "      <td>41.708039</td>\n",
       "      <td>11.703217</td>\n",
       "      <td>-0.533726</td>\n",
       "      <td>12.253490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003889</td>\n",
       "      <td>0.003346</td>\n",
       "      <td>9721.266971</td>\n",
       "      <td>1787.999061</td>\n",
       "      <td>5302.897524</td>\n",
       "      <td>1035.480297</td>\n",
       "      <td>1.254196</td>\n",
       "      <td>0.801711</td>\n",
       "      <td>0.167646</td>\n",
       "      <td>0.063456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>-512.741784</td>\n",
       "      <td>19.889379</td>\n",
       "      <td>149.537087</td>\n",
       "      <td>14.668363</td>\n",
       "      <td>-14.520807</td>\n",
       "      <td>9.296644</td>\n",
       "      <td>50.816664</td>\n",
       "      <td>9.956619</td>\n",
       "      <td>16.900872</td>\n",
       "      <td>7.939784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005219</td>\n",
       "      <td>0.001632</td>\n",
       "      <td>4669.332709</td>\n",
       "      <td>938.561235</td>\n",
       "      <td>2167.623639</td>\n",
       "      <td>465.505843</td>\n",
       "      <td>1.220943</td>\n",
       "      <td>0.439326</td>\n",
       "      <td>0.043411</td>\n",
       "      <td>0.022282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>-456.054945</td>\n",
       "      <td>21.662896</td>\n",
       "      <td>205.446998</td>\n",
       "      <td>6.448543</td>\n",
       "      <td>-26.614992</td>\n",
       "      <td>8.533651</td>\n",
       "      <td>31.997193</td>\n",
       "      <td>9.615701</td>\n",
       "      <td>27.096909</td>\n",
       "      <td>6.234048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012295</td>\n",
       "      <td>0.003563</td>\n",
       "      <td>2371.500122</td>\n",
       "      <td>341.708539</td>\n",
       "      <td>1122.420405</td>\n",
       "      <td>137.324864</td>\n",
       "      <td>1.204051</td>\n",
       "      <td>0.364150</td>\n",
       "      <td>0.017761</td>\n",
       "      <td>0.006143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>-549.517751</td>\n",
       "      <td>10.961404</td>\n",
       "      <td>191.978345</td>\n",
       "      <td>8.297782</td>\n",
       "      <td>-4.994147</td>\n",
       "      <td>6.922257</td>\n",
       "      <td>32.207744</td>\n",
       "      <td>6.121659</td>\n",
       "      <td>21.103760</td>\n",
       "      <td>6.014747</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005182</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>2075.805777</td>\n",
       "      <td>480.966629</td>\n",
       "      <td>1216.923590</td>\n",
       "      <td>178.405499</td>\n",
       "      <td>1.221205</td>\n",
       "      <td>0.299320</td>\n",
       "      <td>0.019082</td>\n",
       "      <td>0.007279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>-441.079252</td>\n",
       "      <td>26.010250</td>\n",
       "      <td>172.588627</td>\n",
       "      <td>12.746169</td>\n",
       "      <td>55.294870</td>\n",
       "      <td>16.623113</td>\n",
       "      <td>25.539507</td>\n",
       "      <td>9.572419</td>\n",
       "      <td>25.285492</td>\n",
       "      <td>6.527616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.022036</td>\n",
       "      <td>485.171544</td>\n",
       "      <td>294.009338</td>\n",
       "      <td>398.186692</td>\n",
       "      <td>142.897878</td>\n",
       "      <td>1.113851</td>\n",
       "      <td>0.439893</td>\n",
       "      <td>0.004888</td>\n",
       "      <td>0.002192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mfcc_01_mean  mfcc_01_std  mfcc_02_mean  mfcc_02_std  mfcc_03_mean  \\\n",
       "4065   -440.187558    82.527257     64.392595    30.802939    -35.776806   \n",
       "1416   -512.741784    19.889379    149.537087    14.668363    -14.520807   \n",
       "2261   -456.054945    21.662896    205.446998     6.448543    -26.614992   \n",
       "2173   -549.517751    10.961404    191.978345     8.297782     -4.994147   \n",
       "3326   -441.079252    26.010250    172.588627    12.746169     55.294870   \n",
       "\n",
       "      mfcc_03_std  mfcc_04_mean  mfcc_04_std  mfcc_05_mean  mfcc_05_std  \\\n",
       "4065    21.030260     41.708039    11.703217     -0.533726    12.253490   \n",
       "1416     9.296644     50.816664     9.956619     16.900872     7.939784   \n",
       "2261     8.533651     31.997193     9.615701     27.096909     6.234048   \n",
       "2173     6.922257     32.207744     6.121659     21.103760     6.014747   \n",
       "3326    16.623113     25.539507     9.572419     25.285492     6.527616   \n",
       "\n",
       "        ...     rmse_mean  rmse_std  rolloff_mean  rolloff_std  \\\n",
       "4065    ...      0.003889  0.003346   9721.266971  1787.999061   \n",
       "1416    ...      0.005219  0.001632   4669.332709   938.561235   \n",
       "2261    ...      0.012295  0.003563   2371.500122   341.708539   \n",
       "2173    ...      0.005182  0.001053   2075.805777   480.966629   \n",
       "3326    ...      0.046476  0.022036    485.171544   294.009338   \n",
       "\n",
       "      spec_centoid_mean  spec_centoid_std  spec_flux_mean  spec_flux_std  \\\n",
       "4065        5302.897524       1035.480297        1.254196       0.801711   \n",
       "1416        2167.623639        465.505843        1.220943       0.439326   \n",
       "2261        1122.420405        137.324864        1.204051       0.364150   \n",
       "2173        1216.923590        178.405499        1.221205       0.299320   \n",
       "3326         398.186692        142.897878        1.113851       0.439893   \n",
       "\n",
       "      zcr_mean   zcr_std  \n",
       "4065  0.167646  0.063456  \n",
       "1416  0.043411  0.022282  \n",
       "2261  0.017761  0.006143  \n",
       "2173  0.019082  0.007279  \n",
       "3326  0.004888  0.002192  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4065     8\n",
       "1416     1\n",
       "2261    13\n",
       "2173    13\n",
       "3326    10\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-process the data\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# standard scaling\n",
    "std_scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "X_train_std = std_scaler.transform(X_train)\n",
    "X_test_std = std_scaler.transform(X_test)\n",
    "\n",
    "# min max scaling\n",
    "minmax_scaler = preprocessing.MinMaxScaler().fit(X_train)\n",
    "X_train_minmax = minmax_scaler.transform(X_train)\n",
    "X_test_minmax = minmax_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb6729dde48>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb672976320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k nearest neighbours\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "scores=[]\n",
    "\n",
    "for i in range(1, 11):\n",
    "    neigh=KNeighborsClassifier(n_neighbors = i)\n",
    "    neigh.fit(X_train_std, y_train)\n",
    "    scores += [neigh.score(X_test_std, y_test)]\n",
    "    \n",
    "plt.plot(np.arange(1, 11), scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb672a69b38>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6729f5160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the same but without preprocessing\n",
    "\n",
    "scores=[]\n",
    "\n",
    "for i in range(1, 11):\n",
    "    neigh=KNeighborsClassifier(n_neighbors = i)\n",
    "    neigh.fit(X_train, y_train)\n",
    "    scores += [neigh.score(X_test, y_test)]\n",
    "    \n",
    "plt.plot(np.arange(1, 11), scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb672af2f60>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb672976e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the same but minmax scaling\n",
    "scores=[]\n",
    "\n",
    "for i in range(1, 11):\n",
    "    neigh=KNeighborsClassifier(n_neighbors = i)\n",
    "    neigh.fit(X_train_minmax, y_train)\n",
    "    scores += [neigh.score(X_test_minmax, y_test)]\n",
    "    \n",
    "plt.plot(np.arange(1, 11), scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9538461538461539\n"
     ]
    }
   ],
   "source": [
    "neigh=KNeighborsClassifier(n_neighbors = 1)\n",
    "neigh.fit(X_train_minmax, y_train)\n",
    "print(neigh.score(X_test_minmax, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc_int_map = {int_loc_map[i]:i for i in int_loc_map.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the mappings to and from for convenience\n",
    "\n",
    "import pickle\n",
    "\n",
    "f1 = open('locintmap.pkl', 'wb')\n",
    "f2 = open('intlocmap.pkl', 'wb')\n",
    "\n",
    "pickle.dump(loc_int_map, f1)\n",
    "pickle.dump(int_loc_map, f2)\n",
    "\n",
    "f1.close()\n",
    "f2.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support as metrics\n",
    "\n",
    "mdf = pd.DataFrame(list(metrics(y_test, neigh.predict(X_test_minmax))))\n",
    "mdf.columns = range(1,16)\n",
    "mdf = mdf.rename(columns=int_loc_map)\n",
    "mdf = mdf.rename(index={0:'precision', 1:'recall', 2:'fscore', 3:'support'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "# create a helper function to print the following\n",
    "def showmetrics(X_test, y_test, classifier):\n",
    "    mdf = pd.DataFrame(list(metrics(y_test, classifier.predict(X_test))))\n",
    "    mdf.columns = range(1,16)\n",
    "    mdf = mdf.rename(columns = int_loc_map)\n",
    "    mdf = mdf.rename(index={0:'precision', 1:'recall', 2:'fscore', 3:'support'})\n",
    "    print(\"Average precision: {:.3f}\".format(mdf.loc['precision'].mean()))\n",
    "    print(\"Average recall: {:.3f}\".format(mdf.loc['recall'].mean()))\n",
    "    print(\"Average fscore: {:.3f}\".format(mdf.loc['fscore'].mean()))\n",
    "    display(mdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
       "      <th>car</th>\n",
       "      <th>city_center</th>\n",
       "      <th>residential_area</th>\n",
       "      <th>train</th>\n",
       "      <th>forest_path</th>\n",
       "      <th>cafe/restaurant</th>\n",
       "      <th>home</th>\n",
       "      <th>park</th>\n",
       "      <th>tram</th>\n",
       "      <th>bus</th>\n",
       "      <th>library</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>beach</th>\n",
       "      <th>office</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.927835</td>\n",
       "      <td>0.982143</td>\n",
       "      <td>0.891566</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972603</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.917526</td>\n",
       "      <td>0.978022</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925373</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.932203</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.806818</td>\n",
       "      <td>0.972603</td>\n",
       "      <td>0.976471</td>\n",
       "      <td>0.956989</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.948052</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.942408</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.930818</td>\n",
       "      <td>0.893082</td>\n",
       "      <td>0.972603</td>\n",
       "      <td>0.988095</td>\n",
       "      <td>0.936842</td>\n",
       "      <td>0.962162</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.980892</td>\n",
       "      <td>0.961240</td>\n",
       "      <td>0.966102</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.980892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.927835   0.982143     0.891566          1.000000   0.972603   \n",
       "recall          0.957447   0.932203     0.973684          0.806818   0.972603   \n",
       "fscore          0.942408   0.956522     0.930818          0.893082   0.972603   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     1.000000         0.917526   0.978022   0.950000   0.901235   \n",
       "recall        0.976471         0.956989   0.946809   0.974359   0.948052   \n",
       "fscore        0.988095         0.936842   0.962162   0.962025   0.924051   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   1.000000   0.925373       0.934426   0.947368   1.000000  \n",
       "recall      0.962500   1.000000       1.000000   0.972973   0.962500  \n",
       "fscore      0.980892   0.961240       0.966102   0.960000   0.980892  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showmetrics(X_test_minmax, y_test, neigh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a helper function that trains, tests and prints the metrics\n",
    "def train_test_report(X_train, y_train, X_test, y_test, classifier):\n",
    "    classifier.fit(X_train, y_train)\n",
    "    print(\"Accuracy: {:.3f}\".format(classifier.score(X_test, y_test)))\n",
    "    showmetrics(X_test, y_test, classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.954\n",
      "Average precision: 0.955\n",
      "Average recall: 0.956\n",
      "Average fscore: 0.955\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
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       "      <th>precision</th>\n",
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       "      <th>fscore</th>\n",
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       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.927835   0.982143     0.891566          1.000000   0.972603   \n",
       "recall          0.957447   0.932203     0.973684          0.806818   0.972603   \n",
       "fscore          0.942408   0.956522     0.930818          0.893082   0.972603   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     1.000000         0.917526   0.978022   0.950000   0.901235   \n",
       "recall        0.976471         0.956989   0.946809   0.974359   0.948052   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   1.000000   0.925373       0.934426   0.947368   1.000000  \n",
       "recall      0.962500   1.000000       1.000000   0.972973   0.962500  \n",
       "fscore      0.980892   0.961240       0.966102   0.960000   0.980892  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the output for the k-nearest neighbours (1 neighbour)\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, neigh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Without preprocessing\n",
      "Accuracy: 0.785\n",
      "Average precision: 0.780\n",
      "Average recall: 0.785\n",
      "Average fscore: 0.778\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.882979   0.852459     0.791209          0.812500   0.600000   \n",
       "recall          0.882979   0.881356     0.947368          0.590909   0.452055   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.918605         0.715686   0.843373   0.747126   0.666667   \n",
       "recall        0.929412         0.784946   0.744681   0.833333   0.649351   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.807692   0.681159       0.700000   0.838710   0.838710  \n",
       "recall      0.787500   0.758065       0.859649   0.702703   0.975000  \n",
       "fscore      0.797468   0.717557       0.771654   0.764706   0.901734  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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    },
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     "text": [
      "\n",
      "Standard scaling\n",
      "Accuracy: 0.857\n",
      "Average precision: 0.853\n",
      "Average recall: 0.859\n",
      "Average fscore: 0.854\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.945055   0.903226     0.873563          0.815789   0.758065   \n",
       "recall          0.914894   0.949153     1.000000          0.704545   0.643836   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.952381         0.819149   0.951220   0.779070   0.753425   \n",
       "recall        0.941176         0.827957   0.829787   0.858974   0.714286   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.892857   0.800000       0.753623   0.885714   0.917647  \n",
       "recall      0.937500   0.838710       0.912281   0.837838   0.975000  \n",
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.806\n",
      "Average precision: 0.800\n",
      "Average recall: 0.805\n",
      "Average fscore: 0.799\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
       "      <th>car</th>\n",
       "      <th>city_center</th>\n",
       "      <th>residential_area</th>\n",
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       "  <tbody>\n",
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       "      <th>precision</th>\n",
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       "      <td>78.000000</td>\n",
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       "      <td>74.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.841584   0.787879     0.902439          0.833333   0.679245   \n",
       "recall          0.904255   0.881356     0.973684          0.681818   0.493151   \n",
       "fscore          0.871795   0.832000     0.936709          0.750000   0.571429   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.952941         0.818182   0.857143   0.764045   0.623377   \n",
       "recall        0.952941         0.774194   0.765957   0.871795   0.623377   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.811765   0.711864       0.675325   0.887097   0.855556  \n",
       "recall      0.862500   0.677419       0.912281   0.743243   0.962500  \n",
       "fscore      0.836364   0.694215       0.776119   0.808824   0.905882  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logre = LogisticRegression(solver='lbfgs', max_iter=200)\n",
    "\n",
    "print(\"Without preprocessing\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, logre)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, logre)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, logre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Without preprocessing\n",
      "Accuracy: 0.669\n",
      "Average precision: 0.683\n",
      "Average recall: 0.665\n",
      "Average fscore: 0.662\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.690000   0.648649     0.736264          0.844444   0.553191   \n",
       "recall          0.734043   0.813559     0.881579          0.431818   0.356164   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.931507         0.629213   0.621622   0.425373   0.691176   \n",
       "recall        0.800000         0.602151   0.734043   0.730769   0.610390   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.777778   0.520000       0.631579   0.700000   0.851351  \n",
       "recall      0.787500   0.629032       0.421053   0.662162   0.787500  \n",
       "fscore      0.782609   0.569343       0.505263   0.680556   0.818182  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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     "text": [
      "\n",
      "Standard scaling\n",
      "Accuracy: 0.662\n",
      "Average precision: 0.676\n",
      "Average recall: 0.656\n",
      "Average fscore: 0.652\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.669903   0.631579     0.770115          0.844444   0.617021   \n",
       "recall          0.734043   0.813559     0.881579          0.431818   0.397260   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.942029         0.582418   0.657407   0.408163   0.650794   \n",
       "recall        0.764706         0.569892   0.755319   0.769231   0.532468   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.741176   0.550000       0.552632   0.671429   0.851852  \n",
       "recall      0.787500   0.532258       0.368421   0.635135   0.862500  \n",
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.662\n",
      "Average precision: 0.676\n",
      "Average recall: 0.656\n",
      "Average fscore: 0.652\n"
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       "      <th>fscore</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.669903   0.631579     0.770115          0.844444   0.617021   \n",
       "recall          0.734043   0.813559     0.881579          0.431818   0.397260   \n",
       "fscore          0.700508   0.711111     0.822086          0.571429   0.483333   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.942029         0.582418   0.657407   0.408163   0.650794   \n",
       "recall        0.764706         0.569892   0.755319   0.769231   0.532468   \n",
       "fscore        0.844156         0.576087   0.702970   0.533333   0.585714   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.741176   0.550000       0.552632   0.671429   0.851852  \n",
       "recall      0.787500   0.532258       0.368421   0.635135   0.862500  \n",
       "fscore      0.763636   0.540984       0.442105   0.652778   0.857143  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# naive-bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "\n",
    "print(\"Without preprocessing\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, gnb)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, gnb)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, gnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Without preprocessing\n",
      "Accuracy: 0.056\n",
      "Average precision: 0.137\n",
      "Average recall: 0.075\n",
      "Average fscore: 0.022\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision            0.0   1.000000          0.0               0.0   1.000000   \n",
       "recall               0.0   0.118644          0.0               0.0   0.013699   \n",
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       "\n",
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      "\n",
      "Standard scaling\n",
      "Accuracy: 0.947\n",
      "Average precision: 0.945\n",
      "Average recall: 0.948\n",
      "Average fscore: 0.946\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.707\n",
      "Average precision: 0.725\n",
      "Average recall: 0.709\n",
      "Average fscore: 0.697\n"
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.804124   0.710145     0.811111          0.866667   0.666667   \n",
       "recall          0.829787   0.830508     0.960526          0.443182   0.273973   \n",
       "fscore          0.816754   0.765625     0.879518          0.586466   0.388350   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.916667         0.766234   0.785714   0.558559   0.560440   \n",
       "recall        0.905882         0.634409   0.702128   0.794872   0.662338   \n",
       "fscore        0.911243         0.694118   0.741573   0.656085   0.607143   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.780488   0.417476       0.511111   0.901961   0.818182  \n",
       "recall      0.800000   0.693548       0.807018   0.621622   0.675000  \n",
       "fscore      0.790123   0.521212       0.625850   0.736000   0.739726  \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# svm\n",
    "from sklearn import svm\n",
    "\n",
    "svm = svm.SVC()\n",
    "\n",
    "print(\"Without preprocessing\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, svm)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, svm)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C parameter: 0.01\n",
      "Standard scaling\n",
      "Accuracy: 0.110\n",
      "Average precision: 0.106\n",
      "Average recall: 0.143\n",
      "Average fscore: 0.066\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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       "           grocery_store        car  city_center  residential_area  train  \\\n",
       "precision            0.0   0.307229          0.0               0.0    0.0   \n",
       "recall               0.0   0.864407          0.0               0.0    0.0   \n",
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       "\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.049\n",
      "Average precision: 0.003\n",
      "Average recall: 0.067\n",
      "Average fscore: 0.006\n"
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       "           grocery_store   car  city_center  residential_area  train  \\\n",
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      "C parameter: 0.1\n",
      "Standard scaling\n",
      "Accuracy: 0.815\n",
      "Average precision: 0.819\n",
      "Average recall: 0.815\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.115\n",
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       "           grocery_store        car  city_center  residential_area  train  \\\n",
       "precision            0.0   0.251185          0.0               0.0    0.0   \n",
       "recall               0.0   0.898305          0.0               0.0    0.0   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home  park  tram   bus  \\\n",
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       "\n",
       "             library  metro_station  beach  office  \n",
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      "C parameter: 1\n",
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      "Accuracy: 0.947\n",
      "Average precision: 0.945\n",
      "Average recall: 0.948\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.707\n",
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      "Average recall: 0.709\n",
      "Average fscore: 0.697\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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      "C parameter: 10\n",
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      "Average precision: 0.964\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978947   0.966667     0.962025          0.952941   0.971831   \n",
       "recall          0.989362   0.983051     1.000000          0.920455   0.945205   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.988095         0.946809   0.988889   0.948052   0.925000   \n",
       "recall        0.976471         0.956989   0.946809   0.935897   0.961039   \n",
       "fscore        0.982249         0.951872   0.967391   0.941935   0.942675   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.949367   0.939394       0.950000   1.000000   0.987342  \n",
       "recall      0.937500   1.000000       1.000000   0.959459   0.975000  \n",
       "fscore      0.943396   0.968750       0.974359   0.979310   0.981132  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.836\n",
      "Average precision: 0.834\n",
      "Average recall: 0.837\n",
      "Average fscore: 0.832\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.906250   0.830769     0.937500          0.863014   0.683333   \n",
       "recall          0.925532   0.915254     0.986842          0.715909   0.561644   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.941176         0.864198   0.880952   0.778947   0.604396   \n",
       "recall        0.941176         0.752688   0.787234   0.948718   0.714286   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "C parameter: 100\n",
      "Standard scaling\n",
      "Accuracy: 0.967\n",
      "Average precision: 0.966\n",
      "Average recall: 0.969\n",
      "Average fscore: 0.967\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978947   0.983051     0.962025          0.952941   0.972973   \n",
       "recall          0.989362   0.983051     1.000000          0.920455   0.986301   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987952         0.946809   0.988889   0.948052   0.937500   \n",
       "recall        0.964706         0.956989   0.946809   0.935897   0.974026   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.974026   0.939394       0.950000   0.986111   0.987342  \n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.909\n",
      "Average precision: 0.907\n",
      "Average recall: 0.908\n",
      "Average fscore: 0.907\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.938144   0.901639     0.915663          0.865854   0.833333   \n",
       "recall          0.968085   0.932203     1.000000          0.806818   0.821918   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976471         0.898990   0.964706   0.864198   0.802632   \n",
       "recall        0.976471         0.956989   0.872340   0.897436   0.792208   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach  office  \n",
       "precision   0.947368   0.865672       0.866667   0.984848   0.975  \n",
       "recall      0.900000   0.935484       0.912281   0.878378   0.975  \n",
       "fscore      0.923077   0.899225       0.888889   0.928571   0.975  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000  "
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     },
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   ],
   "source": [
    "# svm\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# trying various C\n",
    "for c in [0.01, 0.1, 1, 10, 100]:\n",
    "    print(\"C parameter: {}\".format(c))\n",
    "    svm = SVC(C=c)\n",
    "\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, svm)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel: linear\n",
      "Standard scaling\n",
      "Accuracy: 0.909\n",
      "Average precision: 0.911\n",
      "Average recall: 0.912\n",
      "Average fscore: 0.910\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.860\n",
      "Average precision: 0.858\n",
      "Average recall: 0.860\n",
      "Average fscore: 0.857\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "Kernel: rbf\n",
      "Standard scaling\n",
      "Accuracy: 0.947\n",
      "Average precision: 0.945\n",
      "Average recall: 0.948\n",
      "Average fscore: 0.946\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978947   0.918033     0.987013          0.963415   0.885714   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.967742   0.988636   0.903614   0.860759   \n",
       "recall        0.964706         0.967742   0.925532   0.961538   0.883117   \n",
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       "\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.707\n",
      "Average precision: 0.725\n",
      "Average recall: 0.709\n",
      "Average fscore: 0.697\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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       "fscore        0.794872         0.481752   0.352941   0.705882   0.17094   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.00000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.877551   0.587302       0.704545   0.930233   0.852459  \n",
       "recall      0.537500   0.596774       0.543860   0.540541   0.650000  \n",
       "fscore      0.666667   0.592000       0.613861   0.683761   0.737589  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.593\n",
      "Average precision: 0.659\n",
      "Average recall: 0.603\n",
      "Average fscore: 0.580\n"
     ]
    },
    {
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       "      <th>precision</th>\n",
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       "      <th>fscore</th>\n",
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       "      <td>80.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.792208   0.595238     0.687500          1.000000   0.360465   \n",
       "recall          0.648936   0.847458     0.868421          0.125000   0.424658   \n",
       "fscore          0.713450   0.699301     0.767442          0.222222   0.389937   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.818182         0.723077   0.777778   0.534091   0.636364   \n",
       "recall        0.847059         0.505376   0.670213   0.602564   0.181818   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.777778   0.281046       0.320896   0.844828   0.739130  \n",
       "recall      0.787500   0.693548       0.754386   0.662162   0.425000  \n",
       "fscore      0.782609   0.400000       0.450262   0.742424   0.539683  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# svm\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# trying various kernels\n",
    "for k in ['linear','rbf','poly','sigmoid']:\n",
    "    print(\"Kernel: {}\".format(k))\n",
    "    svm = SVC(kernel=k)\n",
    "\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, svm)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Without preprocessing\n",
      "Accuracy: 0.664\n",
      "Average precision: 0.751\n",
      "Average recall: 0.668\n",
      "Average fscore: 0.637\n"
     ]
    },
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.840000   0.836066     0.728155          0.883721   0.636364   \n",
       "recall          0.893617   0.864407     0.986842          0.431818   0.479452   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant      home       park       tram  \\\n",
       "precision     0.860759         0.653846   1.00000   0.870968   0.958333   \n",
       "recall        0.800000         0.913978   0.06383   0.346154   0.298701   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.608696   0.541176       0.935484   0.442029   0.467456  \n",
       "recall      0.875000   0.741935       0.508772   0.824324   0.987500  \n",
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       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
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     "text": [
      "\n",
      "Standard scaling\n",
      "Accuracy: 0.863\n",
      "Average precision: 0.860\n",
      "Average recall: 0.866\n",
      "Average fscore: 0.860\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.915789   0.918033     0.883721          0.859155   0.766667   \n",
       "recall          0.925532   0.949153     1.000000          0.693182   0.630137   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.963855         0.810526   0.987500   0.790698   0.750000   \n",
       "recall        0.941176         0.827957   0.840426   0.871795   0.740260   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.901235   0.779412       0.746479   0.878378   0.951807  \n",
       "recall      0.912500   0.854839       0.929825   0.878378   0.987500  \n",
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      ]
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    },
    {
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.835\n",
      "Average precision: 0.831\n",
      "Average recall: 0.836\n",
      "Average fscore: 0.830\n"
     ]
    },
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       "      <th>beach</th>\n",
       "      <th>office</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.873016</td>\n",
       "      <td>0.880952</td>\n",
       "      <td>0.855072</td>\n",
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       "      <td>0.915663</td>\n",
       "      <td>0.755814</td>\n",
       "      <td>0.712329</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.757576</td>\n",
       "      <td>0.693333</td>\n",
       "      <td>0.893939</td>\n",
       "      <td>0.896552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.925532</td>\n",
       "      <td>0.932203</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.670455</td>\n",
       "      <td>0.561644</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>0.808511</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>0.912281</td>\n",
       "      <td>0.797297</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.915789</td>\n",
       "      <td>0.901639</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.751592</td>\n",
       "      <td>0.640625</td>\n",
       "      <td>0.935673</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>0.858757</td>\n",
       "      <td>0.792683</td>\n",
       "      <td>0.693333</td>\n",
       "      <td>0.880952</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.842857</td>\n",
       "      <td>0.934132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.906250   0.873016     0.880952          0.855072   0.745455   \n",
       "recall          0.925532   0.932203     0.973684          0.670455   0.561644   \n",
       "fscore          0.915789   0.901639     0.925000          0.751592   0.640625   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.930233         0.806452   0.915663   0.755814   0.712329   \n",
       "recall        0.941176         0.806452   0.808511   0.833333   0.675325   \n",
       "fscore        0.935673         0.806452   0.858757   0.792683   0.693333   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.840909   0.757576       0.693333   0.893939   0.896552  \n",
       "recall      0.925000   0.806452       0.912281   0.797297   0.975000  \n",
       "fscore      0.880952   0.781250       0.787879   0.842857   0.934132  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# svm\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svm = LinearSVC()\n",
    "\n",
    "print(\"Without preprocessing\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, svm)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, svm)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C parameter: 0.01\n",
      "Standard scaling\n",
      "Accuracy: 0.831\n",
      "Average precision: 0.826\n",
      "Average recall: 0.830\n",
      "Average fscore: 0.822\n"
     ]
    },
    {
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       "      <th>fscore</th>\n",
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       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.887755   0.811594     0.894118          0.893939   0.782609   \n",
       "recall          0.925532   0.949153     1.000000          0.670455   0.493151   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.891304         0.793814   0.937500   0.779070   0.724638   \n",
       "recall        0.964706         0.827957   0.797872   0.858974   0.649351   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.822222   0.771930       0.712329   0.857143   0.836957  \n",
       "recall      0.925000   0.709677       0.912281   0.810811   0.962500  \n",
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      ]
     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.725\n",
      "Average precision: 0.726\n",
      "Average recall: 0.720\n",
      "Average fscore: 0.707\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.808081   0.500000     0.797872          0.886792   0.647059   \n",
       "recall          0.851064   0.898305     0.986842          0.534091   0.301370   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.824742         0.720000   0.685714   0.666667   0.566038   \n",
       "recall        0.941176         0.774194   0.765957   0.769231   0.389610   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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       "recall      0.787500   0.548387       0.736842   0.635135   0.887500  \n",
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     "text": [
      "C parameter: 0.1\n",
      "Standard scaling\n",
      "Accuracy: 0.858\n",
      "Average precision: 0.856\n",
      "Average recall: 0.860\n",
      "Average fscore: 0.854\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.934783   0.903226     0.883721          0.873239   0.818182   \n",
       "recall          0.914894   0.949153     1.000000          0.704545   0.616438   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.802\n",
      "Average precision: 0.795\n",
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      "Average fscore: 0.791\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "C parameter: 1\n",
      "Standard scaling\n",
      "Accuracy: 0.864\n",
      "Average precision: 0.861\n",
      "Average recall: 0.866\n",
      "Average fscore: 0.861\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.835\n",
      "Average precision: 0.831\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "C parameter: 10\n",
      "Standard scaling\n",
      "Accuracy: 0.857\n",
      "Average precision: 0.856\n",
      "Average recall: 0.860\n",
      "Average fscore: 0.855\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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      "Accuracy: 0.868\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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      "C parameter: 100\n",
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      "Accuracy: 0.824\n",
      "Average precision: 0.822\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "      <th>fscore</th>\n",
       "      <td>0.926316</td>\n",
       "      <td>0.925620</td>\n",
       "      <td>0.931677</td>\n",
       "      <td>0.642336</td>\n",
       "      <td>0.682540</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.881356</td>\n",
       "      <td>0.725490</td>\n",
       "      <td>0.788321</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.808824</td>\n",
       "      <td>0.827068</td>\n",
       "      <td>0.842857</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.916667   0.903226     0.882353          0.897959   0.811321   \n",
       "recall          0.936170   0.949153     0.986842          0.500000   0.589041   \n",
       "fscore          0.926316   0.925620     0.931677          0.642336   0.682540   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.910112         0.815217   0.939759   0.587302   0.900000   \n",
       "recall        0.952941         0.806452   0.829787   0.948718   0.701299   \n",
       "fscore        0.931034         0.810811   0.881356   0.725490   0.788321   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.923077   0.743243       0.723684   0.893939   0.962963  \n",
       "recall      0.900000   0.887097       0.964912   0.797297   0.975000  \n",
       "fscore      0.911392   0.808824       0.827068   0.842857   0.968944  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# svm\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# trying various C\n",
    "for c in [0.01, 0.1, 1, 10, 100]:\n",
    "    print(\"C parameter: {}\".format(c))\n",
    "    svm = LinearSVC(C=c)\n",
    "\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, svm)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C parameter: 0.01\n",
      "Standard scaling\n",
      "Accuracy: 0.831\n",
      "Average precision: 0.826\n",
      "Average recall: 0.830\n",
      "Average fscore: 0.822\n"
     ]
    },
    {
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       "      <th>precision</th>\n",
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       "      <th>fscore</th>\n",
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.887755   0.811594     0.894118          0.893939   0.782609   \n",
       "recall          0.925532   0.949153     1.000000          0.670455   0.493151   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.891304         0.793814   0.937500   0.779070   0.724638   \n",
       "recall        0.964706         0.827957   0.797872   0.858974   0.649351   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.822222   0.771930       0.712329   0.857143   0.836957  \n",
       "recall      0.925000   0.709677       0.912281   0.810811   0.962500  \n",
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      ]
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.725\n",
      "Average precision: 0.726\n",
      "Average recall: 0.720\n",
      "Average fscore: 0.707\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.808081   0.500000     0.797872          0.886792   0.647059   \n",
       "recall          0.851064   0.898305     0.986842          0.534091   0.301370   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.824742         0.720000   0.685714   0.666667   0.566038   \n",
       "recall        0.941176         0.774194   0.765957   0.769231   0.389610   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.777778   0.708333       0.656250   0.886792   0.763441  \n",
       "recall      0.787500   0.548387       0.736842   0.635135   0.887500  \n",
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     "text": [
      "C parameter: 0.1\n",
      "Standard scaling\n",
      "Accuracy: 0.858\n",
      "Average precision: 0.856\n",
      "Average recall: 0.860\n",
      "Average fscore: 0.854\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.934783   0.903226     0.883721          0.873239   0.818182   \n",
       "recall          0.914894   0.949153     1.000000          0.704545   0.616438   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "\n",
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      "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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      "C parameter: 1\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.914894   0.933333     0.873563          0.845070   0.742424   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "\n",
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      "Accuracy: 0.863\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "C parameter: 100\n",
      "Standard scaling\n",
      "Accuracy: 0.856\n",
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      "Average fscore: 0.854\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.925532   0.918033     0.894118          0.849315   0.750000   \n",
       "recall          0.925532   0.949153     1.000000          0.704545   0.657534   \n",
       "fscore          0.925532   0.933333     0.944099          0.770186   0.700730   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.963855         0.817204   0.987500   0.800000   0.763158   \n",
       "recall        0.941176         0.817204   0.840426   0.871795   0.753247   \n",
       "fscore        0.952381         0.817204   0.908046   0.834356   0.758170   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.935065   0.774648       0.739726   0.863014   0.963415  \n",
       "recall      0.900000   0.887097       0.947368   0.851351   0.987500  \n",
       "fscore      0.917197   0.827068       0.830769   0.857143   0.975309  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# svm\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# trying various C\n",
    "for c in [0.01, 0.1, 1, 10, 100]:\n",
    "    print(\"C parameter: {}\".format(c))\n",
    "    svm = LinearSVC(C=c, dual=False)\n",
    "\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, svm)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.949\n",
      "Average precision: 0.949\n",
      "Average recall: 0.951\n",
      "Average fscore: 0.949\n"
     ]
    },
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
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       "      <td>73.000000</td>\n",
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       "      <td>93.000000</td>\n",
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       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978723   0.948276     0.938272          0.975000   0.932432   \n",
       "recall          0.978723   0.932203     1.000000          0.886364   0.945205   \n",
       "fscore          0.978723   0.940171     0.968153          0.928571   0.938776   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.952941         0.907216   0.987952   0.948718   0.925000   \n",
       "recall        0.952941         0.946237   0.872340   0.948718   0.961039   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.961538   0.884058       0.964912   0.972973   0.951220  \n",
       "recall      0.937500   0.983871       0.964912   0.972973   0.975000  \n",
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       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.879\n",
      "Average precision: 0.878\n",
      "Average recall: 0.881\n",
      "Average fscore: 0.879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
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       "</table>\n",
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.957447   0.904762     0.937500          0.817073   0.823529   \n",
       "recall          0.957447   0.966102     0.986842          0.761364   0.767123   \n",
       "fscore          0.957447   0.934426     0.961538          0.788235   0.794326   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.815217   0.921348   0.795181   0.772152   \n",
       "recall        0.964706         0.806452   0.872340   0.846154   0.792208   \n",
       "fscore        0.970414         0.810811   0.896175   0.819876   0.782051   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "            bus    library  metro_station      beach     office  \n",
       "precision   0.9   0.830769       0.877193   0.914286   0.928571  \n",
       "recall      0.9   0.870968       0.877193   0.864865   0.975000  \n",
       "fscore      0.9   0.850394       0.877193   0.888889   0.951220  \n",
       "support    80.0  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# neural network\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# def is relu activation, 1 hidden layer with 100 neurons\n",
    "nn = MLPClassifier()\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.947\n",
      "Average precision: 0.947\n",
      "Average recall: 0.949\n",
      "Average fscore: 0.948\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.989247   0.949153     0.961538          0.929412   0.943662   \n",
       "recall          0.978723   0.949153     0.986842          0.897727   0.917808   \n",
       "fscore          0.983957   0.949153     0.974026          0.913295   0.930556   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.965116         0.934066   0.965517   0.913580   0.912500   \n",
       "recall        0.976471         0.913978   0.893617   0.948718   0.948052   \n",
       "fscore        0.970760         0.923913   0.928177   0.930818   0.929936   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.961538   0.869565       0.982759   0.972222   0.951220  \n",
       "recall      0.937500   0.967742       1.000000   0.945946   0.975000  \n",
       "fscore      0.949367   0.916031       0.991304   0.958904   0.962963  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.885\n",
      "Average precision: 0.887\n",
      "Average recall: 0.884\n",
      "Average fscore: 0.884\n"
     ]
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.937500   0.890625     0.914634          0.793478   0.929825   \n",
       "recall          0.957447   0.966102     0.986842          0.829545   0.726027   \n",
       "fscore          0.947368   0.926829     0.949367          0.811111   0.815385   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987654         0.847826   0.932584   0.827160   0.765432   \n",
       "recall        0.941176         0.838710   0.882979   0.858974   0.805195   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.842697   0.820896        0.88000   0.970588   0.962963  \n",
       "recall      0.937500   0.887097        0.77193   0.891892   0.975000  \n",
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       "support    80.000000  62.000000       57.00000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 50 neurons\n",
    "nn = MLPClassifier(hidden_layer_sizes=(50,50))\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hidden layers: 27\n",
      "Standard scaling\n",
      "Accuracy: 0.915\n",
      "Average precision: 0.914\n",
      "Average recall: 0.919\n",
      "Average fscore: 0.915\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
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       "           grocery_store        car  city_center  residential_area     train  \\\n",
       "precision       0.965909   0.949153     0.892857          0.878049   0.90411   \n",
       "recall          0.904255   0.949153     0.986842          0.818182   0.90411   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.00000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.869565   0.951220   0.935897   0.884615   \n",
       "recall        0.964706         0.860215   0.829787   0.935897   0.896104   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.974026   0.824324       0.820896   0.929577   0.950617  \n",
       "recall      0.937500   0.983871       0.964912   0.891892   0.962500  \n",
       "fscore      0.955414   0.897059       0.887097   0.910345   0.956522  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.809\n",
      "Average precision: 0.801\n",
      "Average recall: 0.808\n",
      "Average fscore: 0.802\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "Hidden layers: 18\n",
      "Standard scaling\n",
      "Accuracy: 0.891\n",
      "Average precision: 0.888\n",
      "Average recall: 0.894\n",
      "Average fscore: 0.889\n"
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       "      <td>0.869565</td>\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.977528   0.875000     0.891566          0.876712   0.808219   \n",
       "recall          0.925532   0.949153     0.973684          0.727273   0.808219   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975309         0.852632   0.952381   0.843373   0.853333   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.717\n",
      "Average precision: 0.709\n",
      "Average recall: 0.718\n",
      "Average fscore: 0.707\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "Hidden layers: 13\n",
      "Standard scaling\n",
      "Accuracy: 0.878\n",
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      "Average recall: 0.879\n",
      "Average fscore: 0.876\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
       "             bus    library  metro_station      beach     office  \n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "      <td>0.939759</td>\n",
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       "      <td>0.730337</td>\n",
       "      <td>0.728324</td>\n",
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       "      <td>0.809816</td>\n",
       "      <td>0.626866</td>\n",
       "      <td>0.705882</td>\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.847826   0.768116     0.870588          0.686567   0.561404   \n",
       "recall          0.829787   0.898305     0.973684          0.522727   0.438356   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.962963         0.702381   0.773810   0.663158   0.594595   \n",
       "recall        0.917647         0.634409   0.691489   0.807692   0.571429   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "Hidden layers: 7\n",
      "Standard scaling\n",
      "Accuracy: 0.772\n",
      "Average precision: 0.767\n",
      "Average recall: 0.772\n",
      "Average fscore: 0.767\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.616\n",
      "Average precision: 0.612\n",
      "Average recall: 0.620\n",
      "Average fscore: 0.610\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "\n",
       "                 bus    library  metro_station      beach  office  \n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
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      "Hidden layers: 6\n",
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      "Accuracy: 0.735\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
       "      <th>car</th>\n",
       "      <th>city_center</th>\n",
       "      <th>residential_area</th>\n",
       "      <th>train</th>\n",
       "      <th>forest_path</th>\n",
       "      <th>cafe/restaurant</th>\n",
       "      <th>home</th>\n",
       "      <th>park</th>\n",
       "      <th>tram</th>\n",
       "      <th>bus</th>\n",
       "      <th>library</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>beach</th>\n",
       "      <th>office</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.720430</td>\n",
       "      <td>0.611940</td>\n",
       "      <td>0.509091</td>\n",
       "      <td>0.819149</td>\n",
       "      <td>0.771084</td>\n",
       "      <td>0.731343</td>\n",
       "      <td>0.610169</td>\n",
       "      <td>0.605634</td>\n",
       "      <td>0.827160</td>\n",
       "      <td>0.691176</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>0.552083</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.840426</td>\n",
       "      <td>0.881356</td>\n",
       "      <td>0.881579</td>\n",
       "      <td>0.465909</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.905882</td>\n",
       "      <td>0.688172</td>\n",
       "      <td>0.521277</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.558442</td>\n",
       "      <td>0.837500</td>\n",
       "      <td>0.758065</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.716216</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.831579</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.792899</td>\n",
       "      <td>0.529032</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.860335</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.525547</td>\n",
       "      <td>0.581081</td>\n",
       "      <td>0.832298</td>\n",
       "      <td>0.723077</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.623529</td>\n",
       "      <td>0.747253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.822917   0.800000     0.720430          0.611940   0.509091   \n",
       "recall          0.840426   0.881356     0.881579          0.465909   0.383562   \n",
       "fscore          0.831579   0.838710     0.792899          0.529032   0.437500   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.819149         0.771084   0.731343   0.610169   0.605634   \n",
       "recall        0.905882         0.688172   0.521277   0.461538   0.558442   \n",
       "fscore        0.860335         0.727273   0.608696   0.525547   0.581081   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.827160   0.691176       0.534247   0.552083   0.666667  \n",
       "recall      0.837500   0.758065       0.684211   0.716216   0.850000  \n",
       "fscore      0.832298   0.723077       0.600000   0.623529   0.747253  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.626\n",
      "Average precision: 0.613\n",
      "Average recall: 0.627\n",
      "Average fscore: 0.612\n"
     ]
    },
    {
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       "      <th>fscore</th>\n",
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       "      <td>0.818898</td>\n",
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       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.0000</td>\n",
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.802326   0.764706     0.711538          0.437500   0.457627   \n",
       "recall          0.734043   0.881356     0.973684          0.159091   0.369863   \n",
       "fscore          0.766667   0.818898     0.822222          0.233333   0.409091   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.811765         0.632911   0.744681   0.543478   0.591549   \n",
       "recall        0.811765         0.537634   0.744681   0.641026   0.545455   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "               bus    library  metro_station      beach     office  \n",
       "precision   0.6375   0.466667       0.358025   0.432099   0.795181  \n",
       "recall      0.6375   0.564516       0.508772   0.472973   0.825000  \n",
       "fscore      0.6375   0.510949       0.420290   0.451613   0.809816  \n",
       "support    80.0000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "for alpha in range(2, 11):\n",
    "    # using the formula samples / (alpha * (input + output)), 1 hidden layer\n",
    "    nn = MLPClassifier(hidden_layer_sizes=(3510 // (65*alpha),))\n",
    "    \n",
    "    print(\"Hidden layers: {}\".format(3510 // (65*alpha)))\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.922\n",
      "Average precision: 0.921\n",
      "Average recall: 0.924\n",
      "Average fscore: 0.922\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area     train  \\\n",
       "precision       0.946809   0.947368     0.938272          0.937500   0.90411   \n",
       "recall          0.946809   0.915254     1.000000          0.852273   0.90411   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.00000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.952941         0.904255   0.962963   0.935065   0.833333   \n",
       "recall        0.952941         0.913978   0.829787   0.923077   0.909091   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.936709   0.845070       0.881356   0.945205   0.951220  \n",
       "recall      0.925000   0.967742       0.912281   0.932432   0.975000  \n",
       "fscore      0.930818   0.902256       0.896552   0.938776   0.962963  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.852\n",
      "Average precision: 0.850\n",
      "Average recall: 0.855\n",
      "Average fscore: 0.851\n"
     ]
    },
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       "      <td>0.714286</td>\n",
       "      <td>0.825000</td>\n",
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       "      <td>0.877193</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.928962</td>\n",
       "      <td>0.890625</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.771084</td>\n",
       "      <td>0.746479</td>\n",
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       "      <td>0.813187</td>\n",
       "      <td>0.870056</td>\n",
       "      <td>0.814371</td>\n",
       "      <td>0.705128</td>\n",
       "      <td>0.835443</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.907801</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
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       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.955056   0.826087     0.890244          0.820513   0.768116   \n",
       "recall          0.904255   0.966102     0.960526          0.727273   0.726027   \n",
       "fscore          0.928962   0.890625     0.924051          0.771084   0.746479   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.831461   0.927711   0.764045   0.696203   \n",
       "recall        0.952941         0.795699   0.819149   0.871795   0.714286   \n",
       "fscore        0.964286         0.813187   0.870056   0.814371   0.705128   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.846154   0.791045       0.793651   0.955224   0.905882  \n",
       "recall      0.825000   0.854839       0.877193   0.864865   0.962500  \n",
       "fscore      0.835443   0.821705       0.833333   0.907801   0.933333  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 40, 30 neurons\n",
    "nn = MLPClassifier(hidden_layer_sizes=(40,30), max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.934\n",
      "Average precision: 0.934\n",
      "Average recall: 0.937\n",
      "Average fscore: 0.935\n"
     ]
    },
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       "      <th>precision</th>\n",
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       "      <td>80.000000</td>\n",
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.928571   0.965517     0.937500          0.939024   0.868421   \n",
       "recall          0.968085   0.949153     0.986842          0.875000   0.904110   \n",
       "fscore          0.947917   0.957265     0.961538          0.905882   0.885906   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.953488         0.890110   0.976744   0.935897   0.897436   \n",
       "recall        0.964706         0.870968   0.893617   0.935897   0.909091   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.948052   0.897059       0.932203   0.945946   1.000000  \n",
       "recall      0.912500   0.983871       0.964912   0.945946   0.987500  \n",
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       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.862\n",
      "Average precision: 0.859\n",
      "Average recall: 0.862\n",
      "Average fscore: 0.860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.925532   0.865672     0.902439          0.839506   0.750000   \n",
       "recall          0.925532   0.983051     0.973684          0.772727   0.739726   \n",
       "fscore          0.925532   0.920635     0.936709          0.804734   0.744828   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.963415         0.844444   0.838710   0.862500   0.791667   \n",
       "recall        0.929412         0.817204   0.829787   0.884615   0.740260   \n",
       "fscore        0.946108         0.830601   0.834225   0.873418   0.765101   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.876543   0.787879       0.839286   0.876712   0.913580  \n",
       "recall      0.887500   0.838710       0.824561   0.864865   0.925000  \n",
       "fscore      0.881988   0.812500       0.831858   0.870748   0.919255  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 45 neurons, logistic activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(45,45),activation='logistic', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.921\n",
      "Average precision: 0.920\n",
      "Average recall: 0.925\n",
      "Average fscore: 0.921\n"
     ]
    },
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       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.967742   0.931034     0.925000          0.876543   0.905405   \n",
       "recall          0.957447   0.915254     0.973684          0.806818   0.917808   \n",
       "fscore          0.962567   0.923077     0.948718          0.840237   0.911565   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.901099   0.965517   0.862500   0.896104   \n",
       "recall        0.952941         0.881720   0.893617   0.884615   0.896104   \n",
       "fscore        0.964286         0.891304   0.928177   0.873418   0.896104   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.936709   0.885714       0.861538   0.957143   0.951220  \n",
       "recall      0.925000   1.000000       0.982456   0.905405   0.975000  \n",
       "fscore      0.930818   0.939394       0.918033   0.930556   0.962963  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.874\n",
      "Average precision: 0.874\n",
      "Average recall: 0.876\n",
      "Average fscore: 0.872\n"
     ]
    },
    {
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       "      <th>precision</th>\n",
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       "      <th>fscore</th>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.901639</td>\n",
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       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.957447   0.873016     0.901235          0.848485   0.857143   \n",
       "recall          0.957447   0.932203     0.960526          0.636364   0.739726   \n",
       "fscore          0.957447   0.901639     0.929936          0.727273   0.794118   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.847826   0.930233   0.696078   0.786667   \n",
       "recall        0.964706         0.838710   0.851064   0.910256   0.766234   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach   office  \n",
       "precision   0.901235   0.788732       0.825397   0.956522   0.9625  \n",
       "recall      0.912500   0.903226       0.912281   0.891892   0.9625  \n",
       "fscore      0.906832   0.842105       0.866667   0.923077   0.9625  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 45 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(45,45),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.930\n",
      "Average precision: 0.929\n",
      "Average recall: 0.932\n",
      "Average fscore: 0.929\n"
     ]
    },
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       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
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       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978261   0.964286     0.949367          0.951220   0.862500   \n",
       "recall          0.957447   0.915254     0.986842          0.886364   0.945205   \n",
       "fscore          0.967742   0.939130     0.967742          0.917647   0.901961   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.964286         0.914894   0.963415   0.947368   0.873418   \n",
       "recall        0.952941         0.924731   0.840426   0.923077   0.896104   \n",
       "fscore        0.958580         0.919786   0.897727   0.935065   0.884615   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.973684   0.833333       0.885246   0.945205   0.928571  \n",
       "recall      0.925000   0.967742       0.947368   0.932432   0.975000  \n",
       "fscore      0.948718   0.895522       0.915254   0.938776   0.951220  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.902\n",
      "Average precision: 0.902\n",
      "Average recall: 0.904\n",
      "Average fscore: 0.902\n"
     ]
    },
    {
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       "      <td>88.000000</td>\n",
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       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.937500   0.933333     0.902439          0.883117   0.880597   \n",
       "recall          0.957447   0.949153     0.973684          0.772727   0.808219   \n",
       "fscore          0.947368   0.941176     0.936709          0.824242   0.842857   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.828283   0.952941   0.823529   0.868421   \n",
       "recall        0.952941         0.881720   0.861702   0.897436   0.857143   \n",
       "fscore        0.964286         0.854167   0.905028   0.858896   0.862745   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.890244   0.869565       0.881356   0.970588   0.939024  \n",
       "recall      0.912500   0.967742       0.912281   0.891892   0.962500  \n",
       "fscore      0.901235   0.916031       0.896552   0.929577   0.950617  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 45 neurons, tanh activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(45,45),activation='tanh', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.932\n",
      "Average precision: 0.930\n",
      "Average recall: 0.935\n",
      "Average fscore: 0.932\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.988636   0.932203     0.925926          0.962025   0.888889   \n",
       "recall          0.925532   0.932203     0.986842          0.863636   0.876712   \n",
       "fscore          0.956044   0.932203     0.955414          0.910180   0.882759   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987952         0.913043   0.988636   0.891566   0.848101   \n",
       "recall        0.964706         0.903226   0.925532   0.948718   0.870130   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.974026   0.873239       0.861538   0.932432   0.987342  \n",
       "recall      0.937500   1.000000       0.982456   0.932432   0.975000  \n",
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       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.870\n",
      "Average precision: 0.870\n",
      "Average recall: 0.871\n",
      "Average fscore: 0.870\n"
     ]
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.918367   0.933333     0.880952          0.812500   0.803030   \n",
       "recall          0.957447   0.949153     0.973684          0.738636   0.726027   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.922222         0.865169   0.939024   0.802469   0.764706   \n",
       "recall        0.976471         0.827957   0.819149   0.833333   0.844156   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.851852   0.793651       0.928571   0.955224   0.886364  \n",
       "recall      0.862500   0.806452       0.912281   0.864865   0.975000  \n",
       "fscore      0.857143   0.800000       0.920354   0.907801   0.928571  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# try about 3 * hidden layers w/ 40 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(40,40,40),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.944\n",
      "Average precision: 0.944\n",
      "Average recall: 0.946\n",
      "Average fscore: 0.945\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.957447   0.933333     0.962025          0.963415   0.956522   \n",
       "recall          0.957447   0.949153     1.000000          0.897727   0.904110   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.965517         0.895833   0.988235   0.921053   0.888889   \n",
       "recall        0.988235         0.924731   0.893617   0.897436   0.935065   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach  office  \n",
       "precision   0.961538   0.884058       0.948276   0.960526   0.975  \n",
       "recall      0.937500   0.983871       0.964912   0.986486   0.975  \n",
       "fscore      0.949367   0.931298       0.956522   0.973333   0.975  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000  "
      ]
     },
     "metadata": {},
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    },
    {
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.891\n",
      "Average precision: 0.892\n",
      "Average recall: 0.893\n",
      "Average fscore: 0.892\n"
     ]
    },
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       "      <td>0.987654</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.910112</td>\n",
       "      <td>0.804598</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.852941</td>\n",
       "      <td>0.852459</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.898305</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.794521</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.827957</td>\n",
       "      <td>0.861702</td>\n",
       "      <td>0.897436</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.912281</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>0.954839</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.963855</td>\n",
       "      <td>0.836957</td>\n",
       "      <td>0.885246</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.840764</td>\n",
       "      <td>0.905660</td>\n",
       "      <td>0.892308</td>\n",
       "      <td>0.881356</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.980892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.892157   0.929825     0.936709          0.853659   0.865672   \n",
       "recall          0.968085   0.898305     0.973684          0.795455   0.794521   \n",
       "fscore          0.928571   0.913793     0.954839          0.823529   0.828571   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987654         0.846154   0.910112   0.804598   0.825000   \n",
       "recall        0.941176         0.827957   0.861702   0.897436   0.857143   \n",
       "fscore        0.963855         0.836957   0.885246   0.848485   0.840764   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.911392   0.852941       0.852459   0.914286   1.000000  \n",
       "recall      0.900000   0.935484       0.912281   0.864865   0.962500  \n",
       "fscore      0.905660   0.892308       0.881356   0.888889   0.980892  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 3 * hidden layers w/ 75 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(75,75,75),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.941\n",
      "Average precision: 0.941\n",
      "Average recall: 0.942\n",
      "Average fscore: 0.941\n"
     ]
    },
    {
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       "      <th>precision</th>\n",
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       "      <th>fscore</th>\n",
       "      <td>0.962162</td>\n",
       "      <td>0.956522</td>\n",
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       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978022   0.982143     0.950000          0.941860   0.910256   \n",
       "recall          0.946809   0.932203     1.000000          0.920455   0.972603   \n",
       "fscore          0.962162   0.956522     0.974359          0.931034   0.940397   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976471         0.886598   0.953488   0.912500   0.922078   \n",
       "recall        0.976471         0.924731   0.872340   0.935897   0.922078   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach   office  \n",
       "precision   0.974026   0.910448       0.898305   0.957746   0.9625  \n",
       "recall      0.937500   0.983871       0.929825   0.918919   0.9625  \n",
       "fscore      0.955414   0.945736       0.913793   0.937931   0.9625  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.0000  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.925\n",
      "Average precision: 0.926\n",
      "Average recall: 0.928\n",
      "Average fscore: 0.926\n"
     ]
    },
    {
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       "      <th>fscore</th>\n",
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       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
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       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.956522   0.949153     0.914634          0.863636   0.853659   \n",
       "recall          0.936170   0.949153     0.986842          0.863636   0.958904   \n",
       "fscore          0.946237   0.949153     0.949367          0.863636   0.903226   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987805         0.884211   0.975904   0.931507   0.881579   \n",
       "recall        0.952941         0.903226   0.861702   0.871795   0.870130   \n",
       "fscore        0.970060         0.893617   0.915254   0.900662   0.875817   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.924051   0.869565       0.947368   0.985915   0.963415  \n",
       "recall      0.912500   0.967742       0.947368   0.945946   0.987500  \n",
       "fscore      0.918239   0.916031       0.947368   0.965517   0.975309  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 3 * hidden layers w/ 75 neurons, tanh activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(75,75,75),activation='tanh', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.948\n",
      "Average precision: 0.948\n",
      "Average recall: 0.950\n",
      "Average fscore: 0.949\n"
     ]
    },
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.957895   0.950000     0.950000          0.975309   0.971831   \n",
       "recall          0.968085   0.966102     1.000000          0.897727   0.945205   \n",
       "fscore          0.962963   0.957983     0.974359          0.934911   0.958333   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.953488         0.915789   0.976190   0.912500   0.923077   \n",
       "recall        0.964706         0.935484   0.872340   0.935897   0.935065   \n",
       "fscore        0.959064         0.925532   0.921348   0.924051   0.929032   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.962025   0.895522       0.965517   0.945946   0.963415  \n",
       "recall      0.950000   0.967742       0.982456   0.945946   0.987500  \n",
       "fscore      0.955975   0.930233       0.973913   0.945946   0.975309  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.902\n",
      "Average precision: 0.900\n",
      "Average recall: 0.904\n",
      "Average fscore: 0.900\n"
     ]
    },
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.958763   0.931034     0.904762          0.873418   0.850746   \n",
       "recall          0.989362   0.915254     1.000000          0.784091   0.780822   \n",
       "fscore          0.973822   0.923077     0.950000          0.826347   0.814286   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987952         0.846154   0.975904   0.907895   0.835443   \n",
       "recall        0.964706         0.827957   0.861702   0.884615   0.857143   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.910256   0.786667       0.857143   0.920000   0.951220  \n",
       "recall      0.887500   0.951613       0.947368   0.932432   0.975000  \n",
       "fscore      0.898734   0.861314       0.900000   0.926174   0.962963  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 100 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(100,100),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.945\n",
      "Average precision: 0.946\n",
      "Average recall: 0.946\n",
      "Average fscore: 0.945\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.958763   0.964912     0.950000          0.939759   0.958333   \n",
       "recall          0.989362   0.932203     1.000000          0.886364   0.945205   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.906250   0.988235   0.912500   0.869048   \n",
       "recall        0.964706         0.935484   0.893617   0.935897   0.948052   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.961538   0.909091       0.946429   0.985915   0.962963  \n",
       "recall      0.937500   0.967742       0.929825   0.945946   0.975000  \n",
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       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.895\n",
      "Average precision: 0.893\n",
      "Average recall: 0.898\n",
      "Average fscore: 0.894\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.927835   0.916667     0.903614          0.833333   0.845070   \n",
       "recall          0.957447   0.932203     0.986842          0.795455   0.821918   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.964286         0.946667   0.965909   0.855263   0.840000   \n",
       "recall        0.952941         0.763441   0.904255   0.833333   0.818182   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.901235   0.808219       0.797101   0.943662   0.939759  \n",
       "recall      0.912500   0.951613       0.964912   0.905405   0.975000  \n",
       "fscore      0.906832   0.874074       0.873016   0.924138   0.957055  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 150 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(150,150),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No scaling\n",
      "Accuracy: 0.588\n",
      "Average precision: 0.623\n",
      "Average recall: 0.580\n",
      "Average fscore: 0.570\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
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       "      <th>city_center</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.689655</td>\n",
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       "      <th>fscore</th>\n",
       "      <td>0.662983</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.424242</td>\n",
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       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.689655   0.739130     0.850000          0.636364   0.345865   \n",
       "recall          0.638298   0.864407     0.671053          0.318182   0.630137   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.593750         0.560345   0.581395   0.436975   0.634146   \n",
       "recall        0.894118         0.698925   0.797872   0.666667   0.337662   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.683333   0.812500       0.500000   0.529412   0.750000  \n",
       "recall      0.512500   0.209677       0.508772   0.243243   0.712500  \n",
       "fscore      0.585714   0.333333       0.504348   0.333333   0.730769  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
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     "text": [
      "\n",
      "Standard scaling\n",
      "Accuracy: 0.930\n",
      "Average precision: 0.929\n",
      "Average recall: 0.933\n",
      "Average fscore: 0.930\n"
     ]
    },
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       "      <td>80.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.956522   0.964912     0.891566          0.913580   0.931507   \n",
       "recall          0.936170   0.932203     0.973684          0.840909   0.931507   \n",
       "fscore          0.946237   0.948276     0.930818          0.875740   0.931507   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.965116         0.911111   0.988235   0.890244   0.876543   \n",
       "recall        0.976471         0.881720   0.893617   0.935897   0.922078   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.974026   0.910448       0.846154   0.956522   0.963415  \n",
       "recall      0.937500   0.983871       0.964912   0.891892   0.987500  \n",
       "fscore      0.955414   0.945736       0.901639   0.923077   0.975309  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
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    },
    {
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.884\n",
      "Average precision: 0.883\n",
      "Average recall: 0.885\n",
      "Average fscore: 0.883\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.967033   0.875000     0.892857          0.850000   0.797101   \n",
       "recall          0.936170   0.949153     0.986842          0.772727   0.753425   \n",
       "fscore          0.951351   0.910569     0.937500          0.809524   0.774648   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.797980   1.000000   0.804598   0.816901   \n",
       "recall        0.964706         0.849462   0.851064   0.897436   0.753247   \n",
       "fscore        0.970414         0.822917   0.919540   0.848485   0.783784   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.869048   0.838235       0.864407   0.984375   0.918605  \n",
       "recall      0.912500   0.919355       0.894737   0.851351   0.987500  \n",
       "fscore      0.890244   0.876923       0.879310   0.913043   0.951807  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 60 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(60,60),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"No scaling\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, nn)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No scaling\n",
      "Accuracy: 0.560\n",
      "Average precision: 0.629\n",
      "Average recall: 0.556\n",
      "Average fscore: 0.532\n"
     ]
    },
    {
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       "      <th>train</th>\n",
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       "      <th>tram</th>\n",
       "      <th>bus</th>\n",
       "      <th>library</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>beach</th>\n",
       "      <th>office</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.679012</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.559322</td>\n",
       "      <td>0.609091</td>\n",
       "      <td>0.552632</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.284615</td>\n",
       "      <td>0.494118</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.654545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.585106</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>0.452055</td>\n",
       "      <td>0.788235</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.298246</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.755906</td>\n",
       "      <td>0.738095</td>\n",
       "      <td>0.103093</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.687179</td>\n",
       "      <td>0.497041</td>\n",
       "      <td>0.670391</td>\n",
       "      <td>0.437870</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>0.717241</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.365591</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.757895</td>\n",
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       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.679012   0.705882     0.673913          0.555556   0.559322   \n",
       "recall          0.585106   0.813559     0.815789          0.056818   0.452055   \n",
       "fscore          0.628571   0.755906     0.738095          0.103093   0.500000   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.609091         0.552632   0.705882   0.284615   0.494118   \n",
       "recall        0.788235         0.451613   0.638298   0.948718   0.545455   \n",
       "fscore        0.687179         0.497041   0.670391   0.437870   0.518519   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.800000   0.692308       0.472222   1.000000   0.654545  \n",
       "recall      0.650000   0.290323       0.298246   0.108108   0.900000  \n",
       "fscore      0.717241   0.409091       0.365591   0.195122   0.757895  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
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    },
    {
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     "text": [
      "\n",
      "Standard scaling\n",
      "Accuracy: 0.941\n",
      "Average precision: 0.939\n",
      "Average recall: 0.943\n",
      "Average fscore: 0.940\n"
     ]
    },
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       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978495   0.931034     0.938272          0.950000   0.920000   \n",
       "recall          0.968085   0.915254     1.000000          0.863636   0.945205   \n",
       "fscore          0.973262   0.923077     0.968153          0.904762   0.932432   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.988095         0.935484   0.988095   0.911392   0.909091   \n",
       "recall        0.976471         0.935484   0.882979   0.923077   0.909091   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.961039   0.897059       0.888889   0.934211   0.951220  \n",
       "recall      0.925000   0.983871       0.982456   0.959459   0.975000  \n",
       "fscore      0.942675   0.938462       0.933333   0.946667   0.962963  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.880\n",
      "Average precision: 0.879\n",
      "Average recall: 0.883\n",
      "Average fscore: 0.879\n"
     ]
    },
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978022   0.876923     0.902439          0.818182   0.890625   \n",
       "recall          0.946809   0.966102     0.973684          0.715909   0.780822   \n",
       "fscore          0.962162   0.919355     0.936709          0.763636   0.832117   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.808511   0.964706   0.786517   0.753247   \n",
       "recall        0.964706         0.817204   0.872340   0.897436   0.753247   \n",
       "fscore        0.970414         0.812834   0.916201   0.838323   0.753247   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "               bus    library  metro_station      beach     office  \n",
       "precision   0.9125   0.828125       0.815385   0.942029   0.928571  \n",
       "recall      0.9125   0.854839       0.929825   0.878378   0.975000  \n",
       "fscore      0.9125   0.841270       0.868852   0.909091   0.951220  \n",
       "support    80.0000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# try about 1 * hidden layers w/ 100 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(100,),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"No scaling\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, nn)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No scaling\n",
      "Accuracy: 0.509\n",
      "Average precision: 0.585\n",
      "Average recall: 0.528\n",
      "Average fscore: 0.513\n"
     ]
    },
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       "      <th>fscore</th>\n",
       "      <td>0.369231</td>\n",
       "      <td>0.820896</td>\n",
       "      <td>0.645503</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.430000</td>\n",
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       "      <td>0.601307</td>\n",
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       "      <td>0.523810</td>\n",
       "      <td>0.537037</td>\n",
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       "      <td>88.000</td>\n",
       "      <td>73.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.666667   0.733333     0.539823             0.500   0.338583   \n",
       "recall          0.255319   0.932203     0.802632             0.125   0.589041   \n",
       "fscore          0.369231   0.820896     0.645503             0.200   0.430000   \n",
       "support        94.000000  59.000000    76.000000            88.000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.894737         0.511111   0.857143   0.236486   0.755556   \n",
       "recall        0.800000         0.247312   0.446809   0.448718   0.441558   \n",
       "fscore        0.844720         0.333333   0.587413   0.309735   0.557377   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.630137   0.240741       0.478261   0.852941   0.541667  \n",
       "recall      0.575000   0.629032       0.578947   0.391892   0.650000  \n",
       "fscore      0.601307   0.348214       0.523810   0.537037   0.590909  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
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    {
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     "text": [
      "\n",
      "Standard scaling\n",
      "Accuracy: 0.932\n",
      "Average precision: 0.932\n",
      "Average recall: 0.934\n",
      "Average fscore: 0.932\n"
     ]
    },
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       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.00</td>\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.967391   0.949153     0.961538          0.927711   0.957143   \n",
       "recall          0.946809   0.949153     0.986842          0.875000   0.917808   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987952         0.885417   0.953488   0.910256   0.864198   \n",
       "recall        0.964706         0.913978   0.872340   0.910256   0.909091   \n",
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       "\n",
       "                 bus    library  metro_station      beach  office  \n",
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       "recall      0.937500   0.967742       0.929825   0.972973    0.95  \n",
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.828\n",
      "Average precision: 0.826\n",
      "Average recall: 0.828\n",
      "Average fscore: 0.825\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.911111   0.870968     0.860465          0.779070   0.796610   \n",
       "recall          0.872340   0.915254     0.973684          0.761364   0.643836   \n",
       "fscore          0.891304   0.892562     0.913580          0.770115   0.712121   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.952941         0.802198   0.873563   0.776471   0.691358   \n",
       "recall        0.952941         0.784946   0.808511   0.846154   0.727273   \n",
       "fscore        0.952941         0.793478   0.839779   0.809816   0.708861   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.870130   0.775862       0.694444   0.876923   0.860465  \n",
       "recall      0.837500   0.725806       0.877193   0.770270   0.925000  \n",
       "fscore      0.853503   0.750000       0.775194   0.820144   0.891566  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 30 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(30,30),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"No scaling\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, nn)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hidden neurons: 35\n",
      "Standard scaling\n",
      "Accuracy: 0.928\n",
      "Average precision: 0.927\n",
      "Average recall: 0.931\n",
      "Average fscore: 0.928\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision        0.93617   0.903226     0.903614          0.917647   0.942029   \n",
       "recall           0.93617   0.949153     0.986842          0.886364   0.890411   \n",
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       "support         94.00000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.968421   0.946429     0.936709          0.890244   0.929577   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "\n",
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      "Accuracy: 0.890\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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      "\n",
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      "Standard scaling\n",
      "Accuracy: 0.936\n",
      "Average precision: 0.934\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
       "                 bus    library  metro_station      beach  office  \n",
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      "Accuracy: 0.940\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "      <th>precision</th>\n",
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       "      <td>0.761905</td>\n",
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       "      <td>0.767296</td>\n",
       "      <td>0.890323</td>\n",
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       "</table>\n",
       "</div>"
      ],
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.946809   0.814286     0.935065          0.800000   0.818182   \n",
       "recall          0.946809   0.966102     0.947368          0.727273   0.739726   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.953488         0.810526   0.861702   0.781609   0.743902   \n",
       "recall        0.964706         0.827957   0.861702   0.871795   0.792208   \n",
       "fscore        0.959064         0.819149   0.861702   0.824242   0.767296   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach   office  \n",
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       "recall      0.862500   0.790323       0.824561   0.878378   0.9625  \n",
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      "\n",
      "Hidden neurons: 65\n",
      "Standard scaling\n",
      "Accuracy: 0.945\n",
      "Average precision: 0.944\n",
      "Average recall: 0.947\n",
      "Average fscore: 0.945\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.968421   0.918033     0.938272          0.928571   0.958333   \n",
       "recall          0.978723   0.949153     1.000000          0.886364   0.945205   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987654         0.925532   0.988372   0.933333   0.890244   \n",
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       "\n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.885\n",
      "Average precision: 0.884\n",
      "Average recall: 0.889\n",
      "Average fscore: 0.884\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.937500   0.850746     0.925926          0.901408   0.888889   \n",
       "recall          0.957447   0.966102     0.986842          0.727273   0.767123   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.844444   0.940476   0.802198   0.791667   \n",
       "recall        0.952941         0.817204   0.840426   0.935897   0.740260   \n",
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       "\n",
       "            bus    library  metro_station      beach  office  \n",
       "precision   0.9   0.791667       0.794118   0.958333    0.95  \n",
       "recall      0.9   0.919355       0.947368   0.932432    0.95  \n",
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for hn in range(35, 66, 5):\n",
    "    nn = MLPClassifier(hidden_layer_sizes=(hn,hn),activation='relu', max_iter=500)\n",
    "    \n",
    "    print(\"Hidden neurons: {}\".format(hn))\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No scaling\n",
      "Accuracy: 0.478\n",
      "Average precision: 0.542\n",
      "Average recall: 0.474\n",
      "Average fscore: 0.438\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
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       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.542955</td>\n",
       "      <td>0.754386</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.256410</td>\n",
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       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>74.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.401015   0.781818     0.657895          0.517241   0.328947   \n",
       "recall          0.840426   0.728814     0.657895          0.170455   0.342466   \n",
       "fscore          0.542955   0.754386     0.657895          0.256410   0.335570   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.933333         0.736842   0.533898   0.461538   0.321053   \n",
       "recall        0.329412         0.150538   0.670213   0.615385   0.792208   \n",
       "fscore        0.486957         0.250000   0.594340   0.527473   0.456929   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.600000   0.578947            0.0   0.340541   0.937500  \n",
       "recall      0.037500   0.354839            0.0   0.851351   0.562500  \n",
       "fscore      0.070588   0.440000            0.0   0.486486   0.703125  \n",
       "support    80.000000  62.000000           57.0  74.000000  80.000000  "
      ]
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     "text": [
      "\n",
      "Standard scaling\n",
      "Accuracy: 0.951\n",
      "Average precision: 0.951\n",
      "Average recall: 0.954\n",
      "Average fscore: 0.952\n"
     ]
    },
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       "      <td>80.000000</td>\n",
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       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.957895   0.965517     0.950000          0.962963   0.946667   \n",
       "recall          0.968085   0.949153     1.000000          0.886364   0.972603   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.964286         0.916667   1.000000   0.935897   0.935897   \n",
       "recall        0.952941         0.946237   0.872340   0.935897   0.948052   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.896\n",
      "Average precision: 0.894\n",
      "Average recall: 0.899\n",
      "Average fscore: 0.895\n"
     ]
    },
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.967742   0.861538     0.948052          0.831325   0.913793   \n",
       "recall          0.957447   0.949153     0.960526          0.784091   0.726027   \n",
       "fscore          0.962567   0.903226     0.954248          0.807018   0.809160   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987952         0.903614   0.954023   0.827160   0.761905   \n",
       "recall        0.964706         0.806452   0.882979   0.858974   0.831169   \n",
       "fscore        0.976190         0.852273   0.917127   0.842767   0.795031   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "               bus    library  metro_station      beach  office  \n",
       "precision   0.9125   0.808219       0.835821   0.921053   0.975  \n",
       "recall      0.9125   0.951613       0.982456   0.945946   0.975  \n",
       "fscore      0.9125   0.874074       0.903226   0.933333   0.975  \n",
       "support    80.0000  62.000000      57.000000  74.000000  80.000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 120 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(120,120),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"No scaling\")\n",
    "train_test_report(X_train, y_train, X_test, y_test, nn)\n",
    "print()\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.949\n",
      "Average precision: 0.949\n",
      "Average recall: 0.951\n",
      "Average fscore: 0.949\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.968421   0.950000     0.926829          0.974684   0.985294   \n",
       "recall          0.978723   0.966102     1.000000          0.875000   0.917808   \n",
       "fscore          0.973545   0.957983     0.962025          0.922156   0.950355   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.987805         0.927083   0.976471   0.935065   0.891566   \n",
       "recall        0.952941         0.956989   0.882979   0.923077   0.961039   \n",
       "fscore        0.970060         0.941799   0.927374   0.929032   0.925000   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.961538   0.895522       0.982456   0.924051   0.951220  \n",
       "recall      0.937500   0.967742       0.982456   0.986486   0.975000  \n",
       "fscore      0.949367   0.930233       0.982456   0.954248   0.962963  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.885\n",
      "Average precision: 0.884\n",
      "Average recall: 0.889\n",
      "Average fscore: 0.884\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
       "      <th>car</th>\n",
       "      <th>city_center</th>\n",
       "      <th>residential_area</th>\n",
       "      <th>train</th>\n",
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       "      <th>office</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.956989</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
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       "      <td>0.891892</td>\n",
       "      <td>0.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.951872</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.814371</td>\n",
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       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.956989   0.918033     0.903614          0.860759   0.717647   \n",
       "recall          0.946809   0.949153     0.986842          0.772727   0.835616   \n",
       "fscore          0.951872   0.933333     0.943396          0.814371   0.772152   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.902439   0.953488   0.831325   0.830769   \n",
       "recall        0.952941         0.795699   0.872340   0.884615   0.701299   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.902439   0.830769       0.777778   0.942857   0.950617  \n",
       "recall      0.925000   0.870968       0.982456   0.891892   0.962500  \n",
       "fscore      0.913580   0.850394       0.868217   0.916667   0.956522  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 130 neurons, relu activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(130,130),activation='relu', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.871\n",
      "Average precision: 0.871\n",
      "Average recall: 0.874\n",
      "Average fscore: 0.871\n"
     ]
    },
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       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.945055   0.901639     0.903614          0.819277   0.818182   \n",
       "recall          0.914894   0.932203     0.986842          0.772727   0.739726   \n",
       "fscore          0.929730   0.916667     0.943396          0.795322   0.776978   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.963855         0.812500   0.941176   0.844156   0.702381   \n",
       "recall        0.941176         0.838710   0.851064   0.833333   0.766234   \n",
       "fscore        0.952381         0.825397   0.893855   0.838710   0.732919   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.933333   0.794521       0.818182   0.898551   0.961538  \n",
       "recall      0.875000   0.935484       0.947368   0.837838   0.937500  \n",
       "fscore      0.903226   0.859259       0.878049   0.867133   0.949367  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.845\n",
      "Average precision: 0.845\n",
      "Average recall: 0.851\n",
      "Average fscore: 0.844\n"
     ]
    },
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.904255   0.873016     0.925000          0.794521   0.682927   \n",
       "recall          0.904255   0.932203     0.973684          0.659091   0.767123   \n",
       "fscore          0.904255   0.901639     0.948718          0.720497   0.722581   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.825000   0.916667   0.717391   0.854545   \n",
       "recall        0.952941         0.709677   0.819149   0.846154   0.610390   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.901235   0.763158       0.768116   0.897059   0.877778  \n",
       "recall      0.912500   0.935484       0.929825   0.824324   0.987500  \n",
       "fscore      0.906832   0.840580       0.841270   0.859155   0.929412  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 120 neurons, identity activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(120,120),activation='identity', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.950\n",
      "Average precision: 0.950\n",
      "Average recall: 0.953\n",
      "Average fscore: 0.950\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
       "      <th>car</th>\n",
       "      <th>city_center</th>\n",
       "      <th>residential_area</th>\n",
       "      <th>train</th>\n",
       "      <th>forest_path</th>\n",
       "      <th>cafe/restaurant</th>\n",
       "      <th>home</th>\n",
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       "      <th>tram</th>\n",
       "      <th>bus</th>\n",
       "      <th>library</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>beach</th>\n",
       "      <th>office</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.968421</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.974684</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>0.965116</td>\n",
       "      <td>0.926316</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.961039</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.885246</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.951807</td>\n",
       "    </tr>\n",
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       "      <th>recall</th>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.966102</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875000</td>\n",
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       "      <td>0.976471</td>\n",
       "      <td>0.946237</td>\n",
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       "      <td>0.935897</td>\n",
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       "      <td>0.925000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.972973</td>\n",
       "      <td>0.987500</td>\n",
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       "      <th>fscore</th>\n",
       "      <td>0.973545</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.968153</td>\n",
       "      <td>0.922156</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>0.970760</td>\n",
       "      <td>0.936170</td>\n",
       "      <td>0.937853</td>\n",
       "      <td>0.948052</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.942675</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.915254</td>\n",
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       "      <td>0.969325</td>\n",
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       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.968421   0.982759     0.938272          0.974684   0.958904   \n",
       "recall          0.978723   0.966102     1.000000          0.875000   0.958904   \n",
       "fscore          0.973545   0.974359     0.968153          0.922156   0.958904   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.965116         0.926316   1.000000   0.960526   0.888889   \n",
       "recall        0.976471         0.946237   0.882979   0.935897   0.935065   \n",
       "fscore        0.970760         0.936170   0.937853   0.948052   0.911392   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.961039   0.911765       0.885246   0.972973   0.951807  \n",
       "recall      0.925000   1.000000       0.947368   0.972973   0.987500  \n",
       "fscore      0.942675   0.953846       0.915254   0.972973   0.969325  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.916\n",
      "Average precision: 0.917\n",
      "Average recall: 0.918\n",
      "Average fscore: 0.917\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.976744   0.950000     0.903614          0.882353   0.901408   \n",
       "recall          0.893617   0.966102     0.986842          0.852273   0.876712   \n",
       "fscore          0.933333   0.957983     0.943396          0.867052   0.888889   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.952381         0.836538   0.975904   0.920000   0.896104   \n",
       "recall        0.941176         0.935484   0.861702   0.884615   0.896104   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.924051   0.904762       0.815385   0.958333   0.951807  \n",
       "recall      0.912500   0.919355       0.929825   0.932432   0.987500  \n",
       "fscore      0.918239   0.912000       0.868852   0.945205   0.969325  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 120 neurons, tanh activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(120,120),activation='tanh', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaling\n",
      "Accuracy: 0.948\n",
      "Average precision: 0.947\n",
      "Average recall: 0.949\n",
      "Average fscore: 0.948\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.978947   0.901639     0.926829          0.930233   0.958333   \n",
       "recall          0.989362   0.932203     1.000000          0.909091   0.945205   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.964286         0.946237   1.000000   0.935065   0.896104   \n",
       "recall        0.952941         0.946237   0.882979   0.923077   0.896104   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.948718   0.938462       0.949153   0.960000   0.963855  \n",
       "recall      0.925000   0.983871       0.982456   0.972973   1.000000  \n",
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       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.844\n",
      "Average precision: 0.843\n",
      "Average recall: 0.847\n",
      "Average fscore: 0.843\n"
     ]
    },
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.905263   0.904762     0.882353          0.826087   0.757143   \n",
       "recall          0.914894   0.966102     0.986842          0.647727   0.726027   \n",
       "fscore          0.910053   0.934426     0.931677          0.726115   0.741259   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.839080   0.862069   0.744444   0.696203   \n",
       "recall        0.952941         0.784946   0.797872   0.858974   0.714286   \n",
       "fscore        0.964286         0.811111   0.828729   0.797619   0.705128   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.864198   0.764706       0.816667   0.898551   0.904762  \n",
       "recall      0.875000   0.838710       0.859649   0.837838   0.950000  \n",
       "fscore      0.869565   0.800000       0.837607   0.867133   0.926829  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# try about 2 * hidden layers w/ 120 neurons, logistic activation\n",
    "nn = MLPClassifier(hidden_layer_sizes=(120,120),activation='logistic', max_iter=500)\n",
    "\n",
    "print(\"Standard scaling\")\n",
    "train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "print()\n",
    "print(\"Minmax scaling\")\n",
    "train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activation: relu\n",
      "Standard scaling\n",
      "Accuracy: 0.821\n",
      "Average precision: 0.816\n",
      "Average recall: 0.823\n",
      "Average fscore: 0.817\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.914894   0.788732     0.848837          0.756098   0.783333   \n",
       "recall          0.914894   0.949153     0.960526          0.704545   0.643836   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.940476         0.858824   0.895349   0.727273   0.733333   \n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.829268   0.739130       0.720588   0.805970   0.892857  \n",
       "recall      0.850000   0.822581       0.859649   0.729730   0.937500  \n",
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       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.742\n",
      "Average precision: 0.738\n",
      "Average recall: 0.745\n",
      "Average fscore: 0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.858696   0.818182     0.774194          0.708333   0.553846   \n",
       "recall          0.840426   0.915254     0.947368          0.579545   0.493151   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.950000         0.728395   0.733333   0.662921   0.661972   \n",
       "recall        0.894118         0.634409   0.702128   0.756410   0.610390   \n",
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       "\n",
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     "text": [
      "\n",
      "Activation: logistic\n",
      "Standard scaling\n",
      "Accuracy: 0.809\n",
      "Average precision: 0.806\n",
      "Average recall: 0.813\n",
      "Average fscore: 0.806\n"
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       "</div>"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.916667   0.881356     0.818182          0.746032   0.701493   \n",
       "recall          0.936170   0.881356     0.947368          0.534091   0.643836   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.906977         0.869048   0.878378   0.800000   0.704545   \n",
       "recall        0.917647         0.784946   0.691489   0.820513   0.805195   \n",
       "fscore        0.912281         0.824859   0.773810   0.810127   0.751515   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.851351   0.675000       0.784615   0.753247   0.808989  \n",
       "recall      0.787500   0.870968       0.894737   0.783784   0.900000  \n",
       "fscore      0.818182   0.760563       0.836066   0.768212   0.852071  \n",
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      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.695\n",
      "Average precision: 0.691\n",
      "Average recall: 0.695\n",
      "Average fscore: 0.688\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.822917   0.790323     0.768421          0.758621   0.412698   \n",
       "recall          0.840426   0.830508     0.960526          0.500000   0.356164   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.865854         0.828947   0.707317   0.666667   0.510638   \n",
       "recall        0.835294         0.677419   0.617021   0.717949   0.623377   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.785714   0.558824       0.526316   0.625000   0.739130  \n",
       "recall      0.687500   0.612903       0.701754   0.608108   0.850000  \n",
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      "\n",
      "Activation: tanh\n",
      "Standard scaling\n",
      "Accuracy: 0.821\n",
      "Average precision: 0.819\n",
      "Average recall: 0.822\n",
      "Average fscore: 0.819\n"
     ]
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.913043   0.870968     0.875000          0.688172   0.793103   \n",
       "recall          0.893617   0.915254     0.921053          0.727273   0.630137   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park      tram  \\\n",
       "precision     0.962500         0.833333   0.852273   0.788732   0.74026   \n",
       "recall        0.905882         0.806452   0.797872   0.717949   0.74026   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.00000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.804598   0.728571       0.724638   0.785714   0.927711  \n",
       "recall      0.875000   0.822581       0.877193   0.743243   0.962500  \n",
       "fscore      0.838323   0.772727       0.793651   0.763889   0.944785  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.778\n",
      "Average precision: 0.773\n",
      "Average recall: 0.778\n",
      "Average fscore: 0.773\n"
     ]
    },
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      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.863158   0.836066     0.848837          0.759494   0.500000   \n",
       "recall          0.872340   0.864407     0.960526          0.681818   0.397260   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.938272         0.772727   0.851852   0.783133   0.627907   \n",
       "recall        0.894118         0.731183   0.734043   0.833333   0.701299   \n",
       "fscore        0.915663         0.751381   0.788571   0.807453   0.662577   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.728261   0.637681       0.746269   0.850000   0.845238  \n",
       "recall      0.837500   0.709677       0.877193   0.689189   0.887500  \n",
       "fscore      0.779070   0.671756       0.806452   0.761194   0.865854  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# try about 1 * hidden layers w/ 8 neurons\n",
    "for activation in [\"relu\", \"logistic\", \"tanh\"]:\n",
    "    nn = MLPClassifier(hidden_layer_sizes=(8,),activation=activation, max_iter=500)\n",
    "    \n",
    "    print(\"Activation: {}\".format(activation))\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activation: relu\n",
      "Standard scaling\n",
      "Accuracy: 0.928\n",
      "Average precision: 0.927\n",
      "Average recall: 0.930\n",
      "Average fscore: 0.928\n"
     ]
    },
    {
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       "      <td>0.915254</td>\n",
       "      <td>0.929032</td>\n",
       "      <td>0.874172</td>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.931298</td>\n",
       "      <td>0.924370</td>\n",
       "      <td>0.944444</td>\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.988889   0.932203     0.882353          0.912500   0.868421   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.965116         0.888889   0.975904   0.935065   0.891892   \n",
       "recall        0.976471         0.946237   0.861702   0.923077   0.857143   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "\n",
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      "Accuracy: 0.875\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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       "\n",
       "                 bus    library  metro_station      beach     office  \n",
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      "\n",
      "Activation: logistic\n",
      "Standard scaling\n",
      "Accuracy: 0.928\n",
      "Average precision: 0.928\n",
      "Average recall: 0.930\n",
      "Average fscore: 0.929\n"
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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      "Minmax scaling\n",
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       "      <th>fscore</th>\n",
       "      <td>0.925532</td>\n",
       "      <td>0.897638</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.746479</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.817680</td>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.809816</td>\n",
       "      <td>0.728477</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.830508</td>\n",
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       "      <td>85.000000</td>\n",
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       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.0</td>\n",
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       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.925532   0.838235     0.914634          0.800000   0.768116   \n",
       "recall          0.925532   0.966102     0.986842          0.727273   0.726027   \n",
       "fscore          0.925532   0.897638     0.949367          0.761905   0.746479   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.840909   0.909091   0.776471   0.743243   \n",
       "recall        0.952941         0.795699   0.851064   0.846154   0.714286   \n",
       "fscore        0.964286         0.817680   0.879121   0.809816   0.728477   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "            bus    library  metro_station      beach     office  \n",
       "precision   0.9   0.805970       0.803279   0.914286   0.962963  \n",
       "recall      0.9   0.870968       0.859649   0.864865   0.975000  \n",
       "fscore      0.9   0.837209       0.830508   0.888889   0.968944  \n",
       "support    80.0  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
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    {
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     "text": [
      "\n",
      "Activation: tanh\n",
      "Standard scaling\n",
      "Accuracy: 0.909\n",
      "Average precision: 0.910\n",
      "Average recall: 0.912\n",
      "Average fscore: 0.909\n"
     ]
    },
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       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.934783   0.963636     0.915663          0.886076   0.915493   \n",
       "recall          0.914894   0.898305     1.000000          0.795455   0.890411   \n",
       "fscore          0.924731   0.929825     0.955975          0.838323   0.902778   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.975904         0.851064   0.965517   0.944444   0.811765   \n",
       "recall        0.952941         0.860215   0.893617   0.871795   0.896104   \n",
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       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.925926   0.840580       0.805970   0.943662   0.962963  \n",
       "recall      0.937500   0.935484       0.947368   0.905405   0.975000  \n",
       "fscore      0.931677   0.885496       0.870968   0.924138   0.968944  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
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     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.886\n",
      "Average precision: 0.886\n",
      "Average recall: 0.889\n",
      "Average fscore: 0.886\n"
     ]
    },
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      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.988889   0.903226     0.913580          0.828947   0.888889   \n",
       "recall          0.946809   0.949153     0.973684          0.715909   0.767123   \n",
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       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.812500   0.953488   0.802326   0.779221   \n",
       "recall        0.964706         0.838710   0.872340   0.884615   0.779221   \n",
       "fscore        0.970414         0.825397   0.911111   0.841463   0.779221   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.891566   0.808219       0.828125   0.969697   0.939759  \n",
       "recall      0.925000   0.951613       0.929825   0.864865   0.975000  \n",
       "fscore      0.907975   0.874074       0.876033   0.914286   0.957055  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# try about 1 * hidden layers w/ 30 neurons\n",
    "for activation in [\"relu\", \"logistic\", \"tanh\"]:\n",
    "    nn = MLPClassifier(hidden_layer_sizes=(30,),activation=activation, max_iter=750)\n",
    "    \n",
    "    print(\"Activation: {}\".format(activation))\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activation: relu\n",
      "Standard scaling\n",
      "Accuracy: 0.930\n",
      "Average precision: 0.929\n",
      "Average recall: 0.932\n",
      "Average fscore: 0.930\n"
     ]
    },
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.956522   0.949153     0.914634          0.915663   0.894737   \n",
       "recall          0.936170   0.949153     0.986842          0.863636   0.931507   \n",
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       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
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       "\n",
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      "\n",
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       "           grocery_store        car  city_center  residential_area      train  \\\n",
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       "\n",
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      "Average precision: 0.932\n",
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       "\n",
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       "      <th>precision</th>\n",
       "      <td>0.957895</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.927711</td>\n",
       "      <td>0.864198</td>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.891304</td>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.961039</td>\n",
       "      <td>0.925373</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.946667</td>\n",
       "      <td>0.951807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.915254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.881720</td>\n",
       "      <td>0.872340</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.896104</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.959459</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>0.980645</td>\n",
       "      <td>0.900585</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.970414</td>\n",
       "      <td>0.886486</td>\n",
       "      <td>0.921348</td>\n",
       "      <td>0.935065</td>\n",
       "      <td>0.907895</td>\n",
       "      <td>0.942675</td>\n",
       "      <td>0.961240</td>\n",
       "      <td>0.907563</td>\n",
       "      <td>0.953020</td>\n",
       "      <td>0.969325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.957895   0.947368     0.962025          0.927711   0.864198   \n",
       "recall          0.968085   0.915254     1.000000          0.875000   0.958904   \n",
       "fscore          0.962963   0.931034     0.980645          0.900585   0.909091   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.891304   0.976190   0.947368   0.920000   \n",
       "recall        0.964706         0.881720   0.872340   0.923077   0.896104   \n",
       "fscore        0.970414         0.886486   0.921348   0.935065   0.907895   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.961039   0.925373       0.870968   0.946667   0.951807  \n",
       "recall      0.925000   1.000000       0.947368   0.959459   0.987500  \n",
       "fscore      0.942675   0.961240       0.907563   0.953020   0.969325  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minmax scaling\n",
      "Accuracy: 0.874\n",
      "Average precision: 0.870\n",
      "Average recall: 0.876\n",
      "Average fscore: 0.872\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grocery_store</th>\n",
       "      <th>car</th>\n",
       "      <th>city_center</th>\n",
       "      <th>residential_area</th>\n",
       "      <th>train</th>\n",
       "      <th>forest_path</th>\n",
       "      <th>cafe/restaurant</th>\n",
       "      <th>home</th>\n",
       "      <th>park</th>\n",
       "      <th>tram</th>\n",
       "      <th>bus</th>\n",
       "      <th>library</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>beach</th>\n",
       "      <th>office</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.913580</td>\n",
       "      <td>0.784810</td>\n",
       "      <td>0.840580</td>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>0.783133</td>\n",
       "      <td>0.819444</td>\n",
       "      <td>0.890244</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.768116</td>\n",
       "      <td>0.884058</td>\n",
       "      <td>0.951220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.949153</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.704545</td>\n",
       "      <td>0.794521</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.795699</td>\n",
       "      <td>0.861702</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.766234</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.929825</td>\n",
       "      <td>0.824324</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fscore</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.925620</td>\n",
       "      <td>0.942675</td>\n",
       "      <td>0.742515</td>\n",
       "      <td>0.816901</td>\n",
       "      <td>0.970414</td>\n",
       "      <td>0.817680</td>\n",
       "      <td>0.895028</td>\n",
       "      <td>0.807453</td>\n",
       "      <td>0.791946</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.861538</td>\n",
       "      <td>0.841270</td>\n",
       "      <td>0.853147</td>\n",
       "      <td>0.962963</td>\n",
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       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           grocery_store        car  city_center  residential_area      train  \\\n",
       "precision       0.947368   0.903226     0.913580          0.784810   0.840580   \n",
       "recall          0.957447   0.949153     0.973684          0.704545   0.794521   \n",
       "fscore          0.952381   0.925620     0.942675          0.742515   0.816901   \n",
       "support        94.000000  59.000000    76.000000         88.000000  73.000000   \n",
       "\n",
       "           forest_path  cafe/restaurant       home       park       tram  \\\n",
       "precision     0.976190         0.840909   0.931034   0.783133   0.819444   \n",
       "recall        0.964706         0.795699   0.861702   0.833333   0.766234   \n",
       "fscore        0.970414         0.817680   0.895028   0.807453   0.791946   \n",
       "support      85.000000        93.000000  94.000000  78.000000  77.000000   \n",
       "\n",
       "                 bus    library  metro_station      beach     office  \n",
       "precision   0.890244   0.823529       0.768116   0.884058   0.951220  \n",
       "recall      0.912500   0.903226       0.929825   0.824324   0.975000  \n",
       "fscore      0.901235   0.861538       0.841270   0.853147   0.962963  \n",
       "support    80.000000  62.000000      57.000000  74.000000  80.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# try about 1 * hidden layers w/ 45 neurons\n",
    "for activation in [\"relu\", \"logistic\", \"tanh\"]:\n",
    "    nn = MLPClassifier(hidden_layer_sizes=(45,),activation=activation, max_iter=750)\n",
    "    \n",
    "    print(\"Activation: {}\".format(activation))\n",
    "    print(\"Standard scaling\")\n",
    "    train_test_report(X_train_std, y_train, X_test_std, y_test, nn)\n",
    "    print()\n",
    "    print(\"Minmax scaling\")\n",
    "    train_test_report(X_train_minmax, y_train, X_test_minmax, y_test, nn)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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