aaa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: agg\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import seaborn \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0-beta0'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_f = \"a.csv\"\n",
    "data = pd.read_csv(d_f, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add column names \n",
    "head = [\"feature\"+ str(i) for i in range(26)] + [\"label\" + str(i) for i in range(3)]\n",
    "data.columns = head\n",
    "for l in [\"label0\", \"label1\", \"label2\"]:\n",
    "    data[l] = data[l].astype('int8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine labels\n",
    "def combine_labels(x):\n",
    "    t = tuple(str(i) for i in x)\n",
    "    return \".\".join(t)\n",
    "        \n",
    "data['combined_label']= data[['label0','label1','label2']].apply(lambda x: combine_labels(tuple(x)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature0</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "      <th>feature6</th>\n",
       "      <th>feature7</th>\n",
       "      <th>feature8</th>\n",
       "      <th>feature9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature20</th>\n",
       "      <th>feature21</th>\n",
       "      <th>feature22</th>\n",
       "      <th>feature23</th>\n",
       "      <th>feature24</th>\n",
       "      <th>feature25</th>\n",
       "      <th>label0</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "      <th>combined_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.023256</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.455696</td>\n",
       "      <td>1.850746</td>\n",
       "      <td>1.172414</td>\n",
       "      <td>1.058824</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.897010</td>\n",
       "      <td>...</td>\n",
       "      <td>16237.511</td>\n",
       "      <td>16232.0385</td>\n",
       "      <td>16190.1506</td>\n",
       "      <td>0.164010</td>\n",
       "      <td>0.117436</td>\n",
       "      <td>0.716062</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.016611</td>\n",
       "      <td>1.159091</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.659794</td>\n",
       "      <td>0.354430</td>\n",
       "      <td>1.970149</td>\n",
       "      <td>1.206897</td>\n",
       "      <td>1.098039</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>1.063123</td>\n",
       "      <td>...</td>\n",
       "      <td>16239.637</td>\n",
       "      <td>16233.1535</td>\n",
       "      <td>16192.2826</td>\n",
       "      <td>0.404899</td>\n",
       "      <td>1.201513</td>\n",
       "      <td>2.967842</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.003322</td>\n",
       "      <td>1.227273</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.618557</td>\n",
       "      <td>0.455696</td>\n",
       "      <td>1.850746</td>\n",
       "      <td>1.034483</td>\n",
       "      <td>1.019608</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>1.049834</td>\n",
       "      <td>...</td>\n",
       "      <td>16240.850</td>\n",
       "      <td>16234.3210</td>\n",
       "      <td>16194.3224</td>\n",
       "      <td>0.343777</td>\n",
       "      <td>0.131417</td>\n",
       "      <td>0.382220</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.936877</td>\n",
       "      <td>1.227273</td>\n",
       "      <td>1.088</td>\n",
       "      <td>0.577320</td>\n",
       "      <td>0.354430</td>\n",
       "      <td>1.791045</td>\n",
       "      <td>1.310345</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>1.086957</td>\n",
       "      <td>1.016611</td>\n",
       "      <td>...</td>\n",
       "      <td>16242.248</td>\n",
       "      <td>16236.3805</td>\n",
       "      <td>16196.8272</td>\n",
       "      <td>0.702864</td>\n",
       "      <td>2.712132</td>\n",
       "      <td>3.858596</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.923588</td>\n",
       "      <td>1.295455</td>\n",
       "      <td>1.024</td>\n",
       "      <td>0.597938</td>\n",
       "      <td>0.303797</td>\n",
       "      <td>2.238806</td>\n",
       "      <td>1.172414</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>1.069767</td>\n",
       "      <td>...</td>\n",
       "      <td>16242.034</td>\n",
       "      <td>16237.1930</td>\n",
       "      <td>16198.6796</td>\n",
       "      <td>0.367516</td>\n",
       "      <td>0.299500</td>\n",
       "      <td>0.815035</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3968</th>\n",
       "      <td>0.797342</td>\n",
       "      <td>1.159091</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.536082</td>\n",
       "      <td>0.734177</td>\n",
       "      <td>3.462687</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>1.058824</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>1.003322</td>\n",
       "      <td>...</td>\n",
       "      <td>17566.830</td>\n",
       "      <td>17568.5155</td>\n",
       "      <td>17604.5064</td>\n",
       "      <td>0.625931</td>\n",
       "      <td>0.512326</td>\n",
       "      <td>0.818478</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3969</th>\n",
       "      <td>0.803987</td>\n",
       "      <td>1.409091</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.412371</td>\n",
       "      <td>0.759494</td>\n",
       "      <td>3.253731</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>1.023256</td>\n",
       "      <td>...</td>\n",
       "      <td>17568.755</td>\n",
       "      <td>17567.1340</td>\n",
       "      <td>17602.1674</td>\n",
       "      <td>0.500259</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>0.707563</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3970</th>\n",
       "      <td>0.790698</td>\n",
       "      <td>1.431818</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.494845</td>\n",
       "      <td>0.784810</td>\n",
       "      <td>3.074627</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>1.058824</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.883721</td>\n",
       "      <td>...</td>\n",
       "      <td>17569.268</td>\n",
       "      <td>17567.2370</td>\n",
       "      <td>17600.2636</td>\n",
       "      <td>0.576900</td>\n",
       "      <td>0.490804</td>\n",
       "      <td>0.850761</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3971</th>\n",
       "      <td>0.850498</td>\n",
       "      <td>1.420455</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.453608</td>\n",
       "      <td>0.632911</td>\n",
       "      <td>3.194030</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963455</td>\n",
       "      <td>...</td>\n",
       "      <td>17568.296</td>\n",
       "      <td>17568.4610</td>\n",
       "      <td>17598.3458</td>\n",
       "      <td>0.456788</td>\n",
       "      <td>0.284949</td>\n",
       "      <td>0.623838</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3972</th>\n",
       "      <td>0.803987</td>\n",
       "      <td>1.295455</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.494845</td>\n",
       "      <td>0.734177</td>\n",
       "      <td>3.194030</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>1.217391</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>...</td>\n",
       "      <td>17567.338</td>\n",
       "      <td>17570.1880</td>\n",
       "      <td>17596.3168</td>\n",
       "      <td>0.555614</td>\n",
       "      <td>0.963904</td>\n",
       "      <td>1.734802</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3973 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature0  feature1  feature2  feature3  feature4  feature5  feature6  \\\n",
       "0     1.023256  1.125000     0.864  0.639175  0.455696  1.850746  1.172414   \n",
       "1     1.016611  1.159091     0.832  0.659794  0.354430  1.970149  1.206897   \n",
       "2     1.003322  1.227273     0.864  0.618557  0.455696  1.850746  1.034483   \n",
       "3     0.936877  1.227273     1.088  0.577320  0.354430  1.791045  1.310345   \n",
       "4     0.923588  1.295455     1.024  0.597938  0.303797  2.238806  1.172414   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "3968  0.797342  1.159091     0.608  0.536082  0.734177  3.462687  0.689655   \n",
       "3969  0.803987  1.409091     0.576  0.412371  0.759494  3.253731  0.655172   \n",
       "3970  0.790698  1.431818     0.512  0.494845  0.784810  3.074627  0.551724   \n",
       "3971  0.850498  1.420455     0.512  0.453608  0.632911  3.194030  0.586207   \n",
       "3972  0.803987  1.295455     0.544  0.494845  0.734177  3.194030  0.620690   \n",
       "\n",
       "      feature7  feature8  feature9  ...  feature20   feature21   feature22  \\\n",
       "0     1.058824  0.913043  0.897010  ...  16237.511  16232.0385  16190.1506   \n",
       "1     1.098039  0.782609  1.063123  ...  16239.637  16233.1535  16192.2826   \n",
       "2     1.019608  0.913043  1.049834  ...  16240.850  16234.3210  16194.3224   \n",
       "3     0.705882  1.086957  1.016611  ...  16242.248  16236.3805  16196.8272   \n",
       "4     0.509804  0.869565  1.069767  ...  16242.034  16237.1930  16198.6796   \n",
       "...        ...       ...       ...  ...        ...         ...         ...   \n",
       "3968  1.058824  0.956522  1.003322  ...  17566.830  17568.5155  17604.5064   \n",
       "3969  0.862745  0.826087  1.023256  ...  17568.755  17567.1340  17602.1674   \n",
       "3970  1.058824  0.956522  0.883721  ...  17569.268  17567.2370  17600.2636   \n",
       "3971  0.823529  1.000000  0.963455  ...  17568.296  17568.4610  17598.3458   \n",
       "3972  0.980392  1.217391  0.976744  ...  17567.338  17570.1880  17596.3168   \n",
       "\n",
       "      feature23  feature24  feature25  label0  label1  label2  combined_label  \n",
       "0      0.164010   0.117436   0.716062       1       1       0           1.1.0  \n",
       "1      0.404899   1.201513   2.967842       1       0       1           1.0.1  \n",
       "2      0.343777   0.131417   0.382220       0       1       0           0.1.0  \n",
       "3      0.702864   2.712132   3.858596       1       0       0           1.0.0  \n",
       "4      0.367516   0.299500   0.815035       0       0       0           0.0.0  \n",
       "...         ...        ...        ...     ...     ...     ...             ...  \n",
       "3968   0.625931   0.512326   0.818478       1       1       0           1.1.0  \n",
       "3969   0.500259   0.353976   0.707563       1       0       1           1.0.1  \n",
       "3970   0.576900   0.490804   0.850761       0       1       0           0.1.0  \n",
       "3971   0.456788   0.284949   0.623838       1       0       0           1.0.0  \n",
       "3972   0.555614   0.963904   1.734802       0       0       1           0.0.1  \n",
       "\n",
       "[3973 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([507., 311., 293.,   0., 488., 649.,   0., 487., 505., 733.]),\n",
       " array([0. , 0.7, 1.4, 2.1, 2.8, 3.5, 4.2, 4.9, 5.6, 6.3, 7. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of combined labels (combined labels as 1 outcome)\n",
    "plt.hist(data['combined_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distributions of each feature\n",
    "nrow, ncol = (4,7)\n",
    "fig, ax = plt.subplots(nrow, ncol, figsize=(15,15))\n",
    "for i in range(25):\n",
    "    ax[i//ncol, i%ncol].hist(data['feature'+str(i)])\n",
    "    ax[i//ncol, i%ncol].title.set_text('feature'+str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1013., 1739.,  765.,  252.,  103.,   58.,   20.,   14.,    4.,\n",
       "           5.]),\n",
       " array([0.02445595, 0.25290634, 0.48135673, 0.70980712, 0.93825751,\n",
       "        1.1667079 , 1.39515828, 1.62360867, 1.85205906, 2.08050945,\n",
       "        2.30895984]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distributions of feature(18,19,24) on log-scale (Check if they are log-normally distributed)\n",
    "# They are not. \n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 6))\n",
    "ax1.hist(data['feature18'].apply(lambda x: np.log(x+1)))\n",
    "ax2.hist(data['feature19'].apply(lambda x: np.log(x+1)))\n",
    "ax3.hist(data['feature24'].apply(lambda x: np.log(x+1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distributions of the features in each category from the combined_label \n",
    "nrow, ncol = (4,7)\n",
    "fig, ax = plt.subplots(nrow, ncol, figsize=(20,20))\n",
    "for i in range(26):\n",
    "    data.pivot(columns=\"combined_label\", values=\"feature\"+str(i) ).plot.hist(bins=50, \\\n",
    "        alpha=1, stacked=True, ax=ax[i//ncol, i%ncol], \\\n",
    "        legend=None)\n",
    "    ax[i//ncol, i%ncol].title.set_text('feature'+str(i))\n",
    "    if i ==25:\n",
    "        handles, labels = ax[i//ncol, i%ncol].get_legend_handles_labels()\n",
    "        fig.legend(handles, labels, loc='lower right', bbox_to_anchor=(0.9, 0.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try tensoflow to model the features & labels "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitted 2 models (nnt & multinomial logit regression).  \n",
    "Results are the same: Features cannot predit the output (combined_label)  \n",
    "The reason can trace to the plot above. All the features are the same cross the 8 output categories   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Fit nnt model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_dataset shape is (3178, 30). \n",
      " test_dataset shape is (795, 30)\n"
     ]
    }
   ],
   "source": [
    "dataset = data.copy()\n",
    "train_dataset = data.sample(frac=0.8, random_state=0)\n",
    "test_dataset = data.drop(train_dataset.index)\n",
    "print(f\"train_dataset shape is {train_dataset.shape}. \\n test_dataset shape is {test_dataset.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature0</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "      <th>feature6</th>\n",
       "      <th>feature7</th>\n",
       "      <th>feature8</th>\n",
       "      <th>feature9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature20</th>\n",
       "      <th>feature21</th>\n",
       "      <th>feature22</th>\n",
       "      <th>feature23</th>\n",
       "      <th>feature24</th>\n",
       "      <th>feature25</th>\n",
       "      <th>label0</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "      <th>combined_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>0.863787</td>\n",
       "      <td>1.443182</td>\n",
       "      <td>1.184</td>\n",
       "      <td>0.783505</td>\n",
       "      <td>0.481013</td>\n",
       "      <td>1.761194</td>\n",
       "      <td>0.793103</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.923588</td>\n",
       "      <td>...</td>\n",
       "      <td>15952.287</td>\n",
       "      <td>15942.4210</td>\n",
       "      <td>15937.3354</td>\n",
       "      <td>0.348803</td>\n",
       "      <td>0.283271</td>\n",
       "      <td>0.812131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287</th>\n",
       "      <td>0.797342</td>\n",
       "      <td>1.409091</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.886598</td>\n",
       "      <td>0.632911</td>\n",
       "      <td>1.134328</td>\n",
       "      <td>1.310345</td>\n",
       "      <td>1.607843</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>0.963455</td>\n",
       "      <td>...</td>\n",
       "      <td>16696.823</td>\n",
       "      <td>16690.0340</td>\n",
       "      <td>16673.9688</td>\n",
       "      <td>0.511194</td>\n",
       "      <td>0.502989</td>\n",
       "      <td>0.983952</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>0.870432</td>\n",
       "      <td>1.590909</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.762887</td>\n",
       "      <td>0.556962</td>\n",
       "      <td>1.373134</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>1.260870</td>\n",
       "      <td>0.777409</td>\n",
       "      <td>...</td>\n",
       "      <td>16034.105</td>\n",
       "      <td>16027.8560</td>\n",
       "      <td>16032.8422</td>\n",
       "      <td>0.427509</td>\n",
       "      <td>0.229233</td>\n",
       "      <td>0.536205</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>0.823920</td>\n",
       "      <td>1.272727</td>\n",
       "      <td>0.784</td>\n",
       "      <td>0.845361</td>\n",
       "      <td>0.734177</td>\n",
       "      <td>1.701493</td>\n",
       "      <td>1.310345</td>\n",
       "      <td>1.215686</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.956811</td>\n",
       "      <td>...</td>\n",
       "      <td>15992.782</td>\n",
       "      <td>16003.6475</td>\n",
       "      <td>16013.7170</td>\n",
       "      <td>0.588629</td>\n",
       "      <td>0.873409</td>\n",
       "      <td>1.483770</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3706</th>\n",
       "      <td>1.023256</td>\n",
       "      <td>1.306818</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.721649</td>\n",
       "      <td>0.759494</td>\n",
       "      <td>1.582090</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.745098</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>1.089701</td>\n",
       "      <td>...</td>\n",
       "      <td>17496.445</td>\n",
       "      <td>17485.6955</td>\n",
       "      <td>17511.5180</td>\n",
       "      <td>0.593442</td>\n",
       "      <td>0.487756</td>\n",
       "      <td>0.821912</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>0.910299</td>\n",
       "      <td>1.227273</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.865979</td>\n",
       "      <td>0.531646</td>\n",
       "      <td>1.761194</td>\n",
       "      <td>1.551724</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>1.049834</td>\n",
       "      <td>...</td>\n",
       "      <td>16124.424</td>\n",
       "      <td>16145.2635</td>\n",
       "      <td>16158.0540</td>\n",
       "      <td>0.507461</td>\n",
       "      <td>0.129799</td>\n",
       "      <td>0.255807</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3777</th>\n",
       "      <td>0.897010</td>\n",
       "      <td>1.170455</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.536082</td>\n",
       "      <td>0.582278</td>\n",
       "      <td>2.447761</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.916944</td>\n",
       "      <td>...</td>\n",
       "      <td>17738.275</td>\n",
       "      <td>17754.9660</td>\n",
       "      <td>17766.5730</td>\n",
       "      <td>0.523998</td>\n",
       "      <td>0.518783</td>\n",
       "      <td>0.990072</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>0.863787</td>\n",
       "      <td>1.659091</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.824742</td>\n",
       "      <td>0.455696</td>\n",
       "      <td>1.820896</td>\n",
       "      <td>1.206897</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.823920</td>\n",
       "      <td>...</td>\n",
       "      <td>16262.749</td>\n",
       "      <td>16252.9725</td>\n",
       "      <td>16269.8326</td>\n",
       "      <td>0.243021</td>\n",
       "      <td>0.432455</td>\n",
       "      <td>1.779210</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>1.149502</td>\n",
       "      <td>1.329545</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.556701</td>\n",
       "      <td>0.607595</td>\n",
       "      <td>0.835821</td>\n",
       "      <td>1.586207</td>\n",
       "      <td>1.137255</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>1.016611</td>\n",
       "      <td>...</td>\n",
       "      <td>15821.510</td>\n",
       "      <td>15811.5670</td>\n",
       "      <td>15836.1482</td>\n",
       "      <td>0.727677</td>\n",
       "      <td>1.281185</td>\n",
       "      <td>1.760609</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>0.850498</td>\n",
       "      <td>1.386364</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.680412</td>\n",
       "      <td>0.430380</td>\n",
       "      <td>2.119403</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.897010</td>\n",
       "      <td>...</td>\n",
       "      <td>15912.177</td>\n",
       "      <td>15919.1235</td>\n",
       "      <td>15911.7724</td>\n",
       "      <td>0.680811</td>\n",
       "      <td>0.664449</td>\n",
       "      <td>0.975969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3178 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature0  feature1  feature2  feature3  feature4  feature5  feature6  \\\n",
       "1694  0.863787  1.443182     1.184  0.783505  0.481013  1.761194  0.793103   \n",
       "3287  0.797342  1.409091     0.864  0.886598  0.632911  1.134328  1.310345   \n",
       "1867  0.870432  1.590909     0.768  0.762887  0.556962  1.373134  0.896552   \n",
       "2161  0.823920  1.272727     0.784  0.845361  0.734177  1.701493  1.310345   \n",
       "3706  1.023256  1.306818     0.880  0.721649  0.759494  1.582090  0.827586   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "622   0.910299  1.227273     0.752  0.865979  0.531646  1.761194  1.551724   \n",
       "3777  0.897010  1.170455     0.960  0.536082  0.582278  2.447761  0.965517   \n",
       "409   0.863787  1.659091     0.656  0.824742  0.455696  1.820896  1.206897   \n",
       "2684  1.149502  1.329545     0.720  0.556701  0.607595  0.835821  1.586207   \n",
       "1623  0.850498  1.386364     0.896  0.680412  0.430380  2.119403  0.965517   \n",
       "\n",
       "      feature7  feature8  feature9  ...  feature20   feature21   feature22  \\\n",
       "1694  0.666667  0.565217  0.923588  ...  15952.287  15942.4210  15937.3354   \n",
       "3287  1.607843  0.739130  0.963455  ...  16696.823  16690.0340  16673.9688   \n",
       "1867  0.823529  1.260870  0.777409  ...  16034.105  16027.8560  16032.8422   \n",
       "2161  1.215686  0.826087  0.956811  ...  15992.782  16003.6475  16013.7170   \n",
       "3706  0.745098  0.652174  1.089701  ...  17496.445  17485.6955  17511.5180   \n",
       "...        ...       ...       ...  ...        ...         ...         ...   \n",
       "622   0.784314  0.913043  1.049834  ...  16124.424  16145.2635  16158.0540   \n",
       "3777  0.980392  0.782609  0.916944  ...  17738.275  17754.9660  17766.5730   \n",
       "409   0.588235  0.608696  0.823920  ...  16262.749  16252.9725  16269.8326   \n",
       "2684  1.137255  0.478261  1.016611  ...  15821.510  15811.5670  15836.1482   \n",
       "1623  0.980392  0.869565  0.897010  ...  15912.177  15919.1235  15911.7724   \n",
       "\n",
       "      feature23  feature24  feature25  label0  label1  label2  combined_label  \n",
       "1694   0.348803   0.283271   0.812131       0       0       0           0.0.0  \n",
       "3287   0.511194   0.502989   0.983952       1       1       0           1.1.0  \n",
       "1867   0.427509   0.229233   0.536205       1       1       1           1.1.1  \n",
       "2161   0.588629   0.873409   1.483770       1       0       0           1.0.0  \n",
       "3706   0.593442   0.487756   0.821912       0       0       1           0.0.1  \n",
       "...         ...        ...        ...     ...     ...     ...             ...  \n",
       "622    0.507461   0.129799   0.255807       0       1       1           0.1.1  \n",
       "3777   0.523998   0.518783   0.990072       1       1       1           1.1.1  \n",
       "409    0.243021   0.432455   1.779210       0       1       1           0.1.1  \n",
       "2684   0.727677   1.281185   1.760609       1       1       1           1.1.1  \n",
       "1623   0.680811   0.664449   0.975969       1       1       1           1.1.1  \n",
       "\n",
       "[3178 rows x 30 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate dataset into trainning and test sets\n",
    "train_features = train_dataset.copy().drop(['label'+str(i) for i in range(3)], axis=1)\n",
    "test_features = test_dataset.copy().drop(['label'+str(i) for i in range(3)], axis=1)\n",
    "\n",
    "train_labels = train_features.pop('combined_label')\n",
    "test_labels = test_features.pop('combined_label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label corresponding to factors as below:\n",
      " {'0.0.0': 0, '1.0.0': 1, '1.0.1': 2, '1.1.0': 3, '0.1.0': 4, '1.1.1': 5, '0.0.1': 6, '0.1.1': 7}\n"
     ]
    }
   ],
   "source": [
    "# recode output labels from 0, 1, 2, 3, 4, 5, 6, 7\n",
    "uniqe_labels = set(data['combined_label'])\n",
    "l_f = {label:factor for (factor, label) in zip(set(range(8)), uniqe_labels) }\n",
    "print(f\"Label corresponding to factors as below:\\n {l_f}\")\n",
    "\n",
    "train_labels_f = train_labels.apply(lambda x: l_f[x] )\n",
    "test_labels_f = test_labels.apply(lambda x: l_f[x] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model specification\n",
    "normalizer = tf.keras.layers.LayerNormalization(input_shape=(train_features.shape[1], ))\n",
    "\n",
    "nnt_model = tf.keras.Sequential()  # Sequential nnt: each layer is placed on its previous layer\n",
    "nnt_model.add(normalizer)  # 1st layer, normalization into mean=0, sd=1 for each feature\n",
    "nnt_model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
    "nnt_model.add(tf.keras.layers.Dense(32, activation='tanh')) # 2nd layer, 32 nodes, activation function as 'relu'\n",
    "nnt_model.add(tf.keras.layers.Dense(64, activation='relu')) # 3rd layer, 32 nodes, activation function as 'relu'\n",
    "nnt_model.add(tf.keras.layers.Dense(8,activation='sigmoid')) \n",
    "# 4th layer (output layer), activation function 'sigmoid', since output is categorical (8 categories)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model specifying loss function, optimizing method\n",
    "nnt_model.compile(loss='sparse_categorical_crossentropy', \\\n",
    "                  optimizer='adam', metrics=['accuracy', \"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2542 samples, validate on 636 samples\n",
      "Epoch 1/50\n",
      "2542/2542 [==============================] - 1s 421us/sample - loss: 2.0451 - accuracy: 0.1833 - mse: 14.7744 - val_loss: 2.0558 - val_accuracy: 0.1792 - val_mse: 14.9888\n",
      "Epoch 2/50\n",
      "2542/2542 [==============================] - 1s 272us/sample - loss: 2.0419 - accuracy: 0.1833 - mse: 14.5518 - val_loss: 2.0518 - val_accuracy: 0.1792 - val_mse: 14.8106\n",
      "Epoch 3/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0389 - accuracy: 0.1865 - mse: 14.4805 - val_loss: 2.0540 - val_accuracy: 0.1792 - val_mse: 14.7068\n",
      "Epoch 4/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0393 - accuracy: 0.1865 - mse: 14.2933 - val_loss: 2.0476 - val_accuracy: 0.1792 - val_mse: 14.5791\n",
      "Epoch 5/50\n",
      "2542/2542 [==============================] - 1s 279us/sample - loss: 2.0374 - accuracy: 0.1865 - mse: 14.3027 - val_loss: 2.0496 - val_accuracy: 0.1792 - val_mse: 14.4700\n",
      "Epoch 6/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0374 - accuracy: 0.1865 - mse: 14.2790 - val_loss: 2.0475 - val_accuracy: 0.1792 - val_mse: 14.4419\n",
      "Epoch 7/50\n",
      "2542/2542 [==============================] - 1s 281us/sample - loss: 2.0372 - accuracy: 0.1865 - mse: 14.2099 - val_loss: 2.0487 - val_accuracy: 0.1792 - val_mse: 14.4594\n",
      "Epoch 8/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0373 - accuracy: 0.1865 - mse: 14.3404 - val_loss: 2.0513 - val_accuracy: 0.1792 - val_mse: 14.5750\n",
      "Epoch 9/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0384 - accuracy: 0.1865 - mse: 14.2794 - val_loss: 2.0484 - val_accuracy: 0.1792 - val_mse: 14.4674\n",
      "Epoch 10/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0376 - accuracy: 0.1865 - mse: 14.2360 - val_loss: 2.0496 - val_accuracy: 0.1792 - val_mse: 14.5694\n",
      "Epoch 11/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0369 - accuracy: 0.1865 - mse: 14.2538 - val_loss: 2.0477 - val_accuracy: 0.1792 - val_mse: 14.5516\n",
      "Epoch 12/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0366 - accuracy: 0.1865 - mse: 14.2983 - val_loss: 2.0489 - val_accuracy: 0.1792 - val_mse: 14.4918\n",
      "Epoch 13/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0358 - accuracy: 0.1865 - mse: 14.2853 - val_loss: 2.0570 - val_accuracy: 0.1792 - val_mse: 14.5975\n",
      "Epoch 14/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0368 - accuracy: 0.1865 - mse: 14.2752 - val_loss: 2.0507 - val_accuracy: 0.1792 - val_mse: 14.5309\n",
      "Epoch 15/50\n",
      "2542/2542 [==============================] - 1s 274us/sample - loss: 2.0367 - accuracy: 0.1865 - mse: 14.2746 - val_loss: 2.0488 - val_accuracy: 0.1792 - val_mse: 14.5024\n",
      "Epoch 16/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0372 - accuracy: 0.1865 - mse: 14.2508 - val_loss: 2.0483 - val_accuracy: 0.1792 - val_mse: 14.6071\n",
      "Epoch 17/50\n",
      "2542/2542 [==============================] - 1s 279us/sample - loss: 2.0365 - accuracy: 0.1865 - mse: 14.2803 - val_loss: 2.0483 - val_accuracy: 0.1792 - val_mse: 14.4211\n",
      "Epoch 18/50\n",
      "2542/2542 [==============================] - 1s 279us/sample - loss: 2.0367 - accuracy: 0.1865 - mse: 14.2012 - val_loss: 2.0475 - val_accuracy: 0.1792 - val_mse: 14.4334\n",
      "Epoch 19/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0359 - accuracy: 0.1865 - mse: 14.2380 - val_loss: 2.0473 - val_accuracy: 0.1792 - val_mse: 14.5886\n",
      "Epoch 20/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0357 - accuracy: 0.1865 - mse: 14.2808 - val_loss: 2.0504 - val_accuracy: 0.1792 - val_mse: 14.5014\n",
      "Epoch 21/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0363 - accuracy: 0.1865 - mse: 14.2995 - val_loss: 2.0478 - val_accuracy: 0.1792 - val_mse: 14.6326\n",
      "Epoch 22/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0355 - accuracy: 0.1865 - mse: 14.3549 - val_loss: 2.0497 - val_accuracy: 0.1792 - val_mse: 14.3656\n",
      "Epoch 23/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0359 - accuracy: 0.1865 - mse: 14.1798 - val_loss: 2.0496 - val_accuracy: 0.1792 - val_mse: 14.4650\n",
      "Epoch 24/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0359 - accuracy: 0.1865 - mse: 14.2714 - val_loss: 2.0479 - val_accuracy: 0.1792 - val_mse: 14.5952\n",
      "Epoch 25/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0358 - accuracy: 0.1865 - mse: 14.2805 - val_loss: 2.0477 - val_accuracy: 0.1792 - val_mse: 14.5966\n",
      "Epoch 26/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0355 - accuracy: 0.1865 - mse: 14.2754 - val_loss: 2.0478 - val_accuracy: 0.1792 - val_mse: 14.6264\n",
      "Epoch 27/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0356 - accuracy: 0.1865 - mse: 14.3124 - val_loss: 2.0477 - val_accuracy: 0.1792 - val_mse: 14.6043\n",
      "Epoch 28/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0360 - accuracy: 0.1865 - mse: 14.3184 - val_loss: 2.0480 - val_accuracy: 0.1792 - val_mse: 14.5960\n",
      "Epoch 29/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0356 - accuracy: 0.1865 - mse: 14.3285 - val_loss: 2.0488 - val_accuracy: 0.1792 - val_mse: 14.5756\n",
      "Epoch 30/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0356 - accuracy: 0.1865 - mse: 14.2974 - val_loss: 2.0482 - val_accuracy: 0.1792 - val_mse: 14.5945\n",
      "Epoch 31/50\n",
      "2542/2542 [==============================] - 1s 279us/sample - loss: 2.0355 - accuracy: 0.1865 - mse: 14.3289 - val_loss: 2.0478 - val_accuracy: 0.1792 - val_mse: 14.5743\n",
      "Epoch 32/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0353 - accuracy: 0.1865 - mse: 14.3300 - val_loss: 2.0483 - val_accuracy: 0.1792 - val_mse: 14.5778\n",
      "Epoch 33/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0355 - accuracy: 0.1865 - mse: 14.3193 - val_loss: 2.0480 - val_accuracy: 0.1792 - val_mse: 14.6096\n",
      "Epoch 34/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0354 - accuracy: 0.1865 - mse: 14.3134 - val_loss: 2.0477 - val_accuracy: 0.1792 - val_mse: 14.6195\n",
      "Epoch 35/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0353 - accuracy: 0.1865 - mse: 14.3281 - val_loss: 2.0477 - val_accuracy: 0.1792 - val_mse: 14.6397\n",
      "Epoch 36/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0354 - accuracy: 0.1865 - mse: 14.3367 - val_loss: 2.0482 - val_accuracy: 0.1792 - val_mse: 14.6086\n",
      "Epoch 37/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0353 - accuracy: 0.1865 - mse: 14.3369 - val_loss: 2.0478 - val_accuracy: 0.1792 - val_mse: 14.6141\n",
      "Epoch 38/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0351 - accuracy: 0.1865 - mse: 14.3450 - val_loss: 2.0479 - val_accuracy: 0.1792 - val_mse: 14.5831\n",
      "Epoch 39/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0361 - accuracy: 0.1865 - mse: 14.3412 - val_loss: 2.0482 - val_accuracy: 0.1792 - val_mse: 14.6128\n",
      "Epoch 40/50\n",
      "2542/2542 [==============================] - 1s 274us/sample - loss: 2.0354 - accuracy: 0.1865 - mse: 14.3184 - val_loss: 2.0479 - val_accuracy: 0.1792 - val_mse: 14.6141\n",
      "Epoch 41/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0353 - accuracy: 0.1865 - mse: 14.3461 - val_loss: 2.0490 - val_accuracy: 0.1792 - val_mse: 14.6029\n",
      "Epoch 42/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0355 - accuracy: 0.1865 - mse: 14.3357 - val_loss: 2.0480 - val_accuracy: 0.1792 - val_mse: 14.6053\n",
      "Epoch 43/50\n",
      "2542/2542 [==============================] - 1s 277us/sample - loss: 2.0352 - accuracy: 0.1865 - mse: 14.3445 - val_loss: 2.0489 - val_accuracy: 0.1792 - val_mse: 14.6002\n",
      "Epoch 44/50\n",
      "2542/2542 [==============================] - 1s 279us/sample - loss: 2.0355 - accuracy: 0.1865 - mse: 14.3368 - val_loss: 2.0480 - val_accuracy: 0.1792 - val_mse: 14.6267\n",
      "Epoch 45/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0353 - accuracy: 0.1865 - mse: 14.3622 - val_loss: 2.0485 - val_accuracy: 0.1792 - val_mse: 14.6595\n",
      "Epoch 46/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0353 - accuracy: 0.1865 - mse: 14.3752 - val_loss: 2.0484 - val_accuracy: 0.1792 - val_mse: 14.6114\n",
      "Epoch 47/50\n",
      "2542/2542 [==============================] - 1s 276us/sample - loss: 2.0356 - accuracy: 0.1865 - mse: 14.3642 - val_loss: 2.0482 - val_accuracy: 0.1792 - val_mse: 14.6048\n",
      "Epoch 48/50\n",
      "2542/2542 [==============================] - 1s 275us/sample - loss: 2.0355 - accuracy: 0.1865 - mse: 14.3566 - val_loss: 2.0482 - val_accuracy: 0.1792 - val_mse: 14.6343\n",
      "Epoch 49/50\n",
      "2542/2542 [==============================] - 1s 279us/sample - loss: 2.0354 - accuracy: 0.1865 - mse: 14.3606 - val_loss: 2.0479 - val_accuracy: 0.1792 - val_mse: 14.6370\n",
      "Epoch 50/50\n",
      "2542/2542 [==============================] - 1s 278us/sample - loss: 2.0352 - accuracy: 0.1865 - mse: 14.3419 - val_loss: 2.0483 - val_accuracy: 0.1792 - val_mse: 14.6161\n"
     ]
    }
   ],
   "source": [
    "# Train model \n",
    "history = nnt_model.fit(train_features, train_labels_f, epochs=50, batch_size=5, validation_split=0.2, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1278d4c550>]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'],label='loss')\n",
    "plt.plot(history.history['val_loss'], label='val_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred = nnt_model.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(795,)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred.argmax(axis=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnt_model.predict(test_features).argmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Relative simpler (than nnt) multinomial logistic regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model specification\n",
    "normalizer = tf.keras.layers.LayerNormalization(input_shape=(train_features.shape[1], ))\n",
    "\n",
    "glm_model = tf.keras.Sequential()  \n",
    "glm_model.add(normalizer)  \n",
    "glm_model.add(tf.keras.layers.Dense(8, input_shape=(train_features.shape[1],))) \n",
    "glm_model.add(tf.keras.layers.Dense(8,activation='sigmoid')) \n",
    "\n",
    "glm_model.compile(loss='sparse_categorical_crossentropy', \\\n",
    "                  optimizer='adam', metrics=['accuracy', \"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2542 samples, validate on 636 samples\n",
      "Epoch 1/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0375 - accuracy: 0.1802 - mse: 14.6958 - val_loss: 2.0509 - val_accuracy: 0.1792 - val_mse: 14.9711\n",
      "Epoch 2/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0383 - accuracy: 0.1865 - mse: 14.6755 - val_loss: 2.0473 - val_accuracy: 0.1792 - val_mse: 14.9411\n",
      "Epoch 3/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0380 - accuracy: 0.1865 - mse: 14.6545 - val_loss: 2.0514 - val_accuracy: 0.1792 - val_mse: 14.9760\n",
      "Epoch 4/50\n",
      "2542/2542 [==============================] - 1s 229us/sample - loss: 2.0371 - accuracy: 0.1865 - mse: 14.6428 - val_loss: 2.0511 - val_accuracy: 0.1792 - val_mse: 14.9165\n",
      "Epoch 5/50\n",
      "2542/2542 [==============================] - 1s 229us/sample - loss: 2.0381 - accuracy: 0.1865 - mse: 14.6264 - val_loss: 2.0498 - val_accuracy: 0.1792 - val_mse: 14.8780\n",
      "Epoch 6/50\n",
      "2542/2542 [==============================] - 1s 229us/sample - loss: 2.0378 - accuracy: 0.1865 - mse: 14.6077 - val_loss: 2.0522 - val_accuracy: 0.1792 - val_mse: 14.8473\n",
      "Epoch 7/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0378 - accuracy: 0.1865 - mse: 14.5831 - val_loss: 2.0486 - val_accuracy: 0.1792 - val_mse: 14.8389\n",
      "Epoch 8/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0375 - accuracy: 0.1865 - mse: 14.5724 - val_loss: 2.0487 - val_accuracy: 0.1792 - val_mse: 14.8525\n",
      "Epoch 9/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0373 - accuracy: 0.1833 - mse: 14.5520 - val_loss: 2.0513 - val_accuracy: 0.1792 - val_mse: 14.8834\n",
      "Epoch 10/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0371 - accuracy: 0.1865 - mse: 14.5636 - val_loss: 2.0513 - val_accuracy: 0.1792 - val_mse: 14.7883\n",
      "Epoch 11/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0382 - accuracy: 0.1865 - mse: 14.5284 - val_loss: 2.0483 - val_accuracy: 0.1792 - val_mse: 14.7657\n",
      "Epoch 12/50\n",
      "2542/2542 [==============================] - 1s 228us/sample - loss: 2.0371 - accuracy: 0.1865 - mse: 14.5172 - val_loss: 2.0508 - val_accuracy: 0.1792 - val_mse: 14.7515\n",
      "Epoch 13/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0375 - accuracy: 0.1865 - mse: 14.4997 - val_loss: 2.0479 - val_accuracy: 0.1792 - val_mse: 14.7482\n",
      "Epoch 14/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0373 - accuracy: 0.1865 - mse: 14.4778 - val_loss: 2.0485 - val_accuracy: 0.1792 - val_mse: 14.7593\n",
      "Epoch 15/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0372 - accuracy: 0.1865 - mse: 14.4772 - val_loss: 2.0483 - val_accuracy: 0.1792 - val_mse: 14.7446\n",
      "Epoch 16/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0364 - accuracy: 0.1865 - mse: 14.4805 - val_loss: 2.0486 - val_accuracy: 0.1792 - val_mse: 14.6769\n",
      "Epoch 17/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0374 - accuracy: 0.1865 - mse: 14.4479 - val_loss: 2.0477 - val_accuracy: 0.1792 - val_mse: 14.6858\n",
      "Epoch 18/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0364 - accuracy: 0.1865 - mse: 14.4374 - val_loss: 2.0504 - val_accuracy: 0.1792 - val_mse: 14.6969\n",
      "Epoch 19/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0365 - accuracy: 0.1865 - mse: 14.4306 - val_loss: 2.0506 - val_accuracy: 0.1792 - val_mse: 14.7067\n",
      "Epoch 20/50\n",
      "2542/2542 [==============================] - 1s 229us/sample - loss: 2.0366 - accuracy: 0.1865 - mse: 14.4277 - val_loss: 2.0490 - val_accuracy: 0.1792 - val_mse: 14.6960\n",
      "Epoch 21/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0373 - accuracy: 0.1865 - mse: 14.4038 - val_loss: 2.0480 - val_accuracy: 0.1792 - val_mse: 14.6820\n",
      "Epoch 22/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0368 - accuracy: 0.1865 - mse: 14.3941 - val_loss: 2.0471 - val_accuracy: 0.1792 - val_mse: 14.6460\n",
      "Epoch 23/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0369 - accuracy: 0.1865 - mse: 14.3649 - val_loss: 2.0487 - val_accuracy: 0.1792 - val_mse: 14.6702\n",
      "Epoch 24/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0368 - accuracy: 0.1865 - mse: 14.3730 - val_loss: 2.0496 - val_accuracy: 0.1792 - val_mse: 14.5939\n",
      "Epoch 25/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0368 - accuracy: 0.1865 - mse: 14.3343 - val_loss: 2.0487 - val_accuracy: 0.1792 - val_mse: 14.6041\n",
      "Epoch 26/50\n",
      "2542/2542 [==============================] - 1s 229us/sample - loss: 2.0366 - accuracy: 0.1865 - mse: 14.3271 - val_loss: 2.0482 - val_accuracy: 0.1792 - val_mse: 14.6013\n",
      "Epoch 27/50\n",
      "2542/2542 [==============================] - 1s 230us/sample - loss: 2.0363 - accuracy: 0.1865 - mse: 14.3271 - val_loss: 2.0491 - val_accuracy: 0.1792 - val_mse: 14.5601\n",
      "Epoch 28/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0366 - accuracy: 0.1865 - mse: 14.2919 - val_loss: 2.0484 - val_accuracy: 0.1792 - val_mse: 14.5844\n",
      "Epoch 29/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0360 - accuracy: 0.1865 - mse: 14.3082 - val_loss: 2.0479 - val_accuracy: 0.1792 - val_mse: 14.5727\n",
      "Epoch 30/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0367 - accuracy: 0.1865 - mse: 14.2939 - val_loss: 2.0478 - val_accuracy: 0.1792 - val_mse: 14.5361\n",
      "Epoch 31/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0369 - accuracy: 0.1865 - mse: 14.2765 - val_loss: 2.0490 - val_accuracy: 0.1792 - val_mse: 14.5144\n",
      "Epoch 32/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0367 - accuracy: 0.1865 - mse: 14.2589 - val_loss: 2.0484 - val_accuracy: 0.1792 - val_mse: 14.5089\n",
      "Epoch 33/50\n",
      "2542/2542 [==============================] - 1s 234us/sample - loss: 2.0368 - accuracy: 0.1865 - mse: 14.2619 - val_loss: 2.0486 - val_accuracy: 0.1792 - val_mse: 14.4936\n",
      "Epoch 34/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0364 - accuracy: 0.1865 - mse: 14.2307 - val_loss: 2.0485 - val_accuracy: 0.1792 - val_mse: 14.5289\n",
      "Epoch 35/50\n",
      "2542/2542 [==============================] - 1s 234us/sample - loss: 2.0362 - accuracy: 0.1865 - mse: 14.2470 - val_loss: 2.0486 - val_accuracy: 0.1792 - val_mse: 14.4906\n",
      "Epoch 36/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0364 - accuracy: 0.1865 - mse: 14.2579 - val_loss: 2.0480 - val_accuracy: 0.1792 - val_mse: 14.4651\n",
      "Epoch 37/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0360 - accuracy: 0.1865 - mse: 14.2261 - val_loss: 2.0485 - val_accuracy: 0.1792 - val_mse: 14.5009\n",
      "Epoch 38/50\n",
      "2542/2542 [==============================] - 1s 234us/sample - loss: 2.0360 - accuracy: 0.1865 - mse: 14.2353 - val_loss: 2.0485 - val_accuracy: 0.1792 - val_mse: 14.4755\n",
      "Epoch 39/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0362 - accuracy: 0.1865 - mse: 14.2147 - val_loss: 2.0481 - val_accuracy: 0.1792 - val_mse: 14.4908\n",
      "Epoch 40/50\n",
      "2542/2542 [==============================] - 1s 234us/sample - loss: 2.0357 - accuracy: 0.1865 - mse: 14.2188 - val_loss: 2.0484 - val_accuracy: 0.1792 - val_mse: 14.4972\n",
      "Epoch 41/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0359 - accuracy: 0.1865 - mse: 14.2349 - val_loss: 2.0481 - val_accuracy: 0.1792 - val_mse: 14.4716\n",
      "Epoch 42/50\n",
      "2542/2542 [==============================] - 1s 234us/sample - loss: 2.0358 - accuracy: 0.1865 - mse: 14.2224 - val_loss: 2.0475 - val_accuracy: 0.1792 - val_mse: 14.4972\n",
      "Epoch 43/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0360 - accuracy: 0.1865 - mse: 14.2395 - val_loss: 2.0473 - val_accuracy: 0.1792 - val_mse: 14.4765\n",
      "Epoch 44/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0359 - accuracy: 0.1865 - mse: 14.2226 - val_loss: 2.0485 - val_accuracy: 0.1792 - val_mse: 14.4745\n",
      "Epoch 45/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0356 - accuracy: 0.1865 - mse: 14.2057 - val_loss: 2.0480 - val_accuracy: 0.1792 - val_mse: 14.4981\n",
      "Epoch 46/50\n",
      "2542/2542 [==============================] - 1s 231us/sample - loss: 2.0358 - accuracy: 0.1865 - mse: 14.2241 - val_loss: 2.0485 - val_accuracy: 0.1792 - val_mse: 14.4887\n",
      "Epoch 47/50\n",
      "2542/2542 [==============================] - 1s 234us/sample - loss: 2.0361 - accuracy: 0.1865 - mse: 14.2299 - val_loss: 2.0490 - val_accuracy: 0.1792 - val_mse: 14.4478\n",
      "Epoch 48/50\n",
      "2542/2542 [==============================] - 1s 233us/sample - loss: 2.0358 - accuracy: 0.1865 - mse: 14.2036 - val_loss: 2.0498 - val_accuracy: 0.1792 - val_mse: 14.4666\n",
      "Epoch 49/50\n",
      "2542/2542 [==============================] - 1s 232us/sample - loss: 2.0359 - accuracy: 0.1865 - mse: 14.1974 - val_loss: 2.0489 - val_accuracy: 0.1792 - val_mse: 14.4566\n",
      "Epoch 50/50\n",
      "2542/2542 [==============================] - 1s 234us/sample - loss: 2.0358 - accuracy: 0.1865 - mse: 14.2148 - val_loss: 2.0477 - val_accuracy: 0.1792 - val_mse: 14.4349\n"
     ]
    }
   ],
   "source": [
    "glm_history = glm_model.fit(train_features, train_labels_f, epochs=50, batch_size=5, validation_split=0.2, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f126afcc3c8>]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T0RXliq9jkOWN7auqkgOU3mNp0zz3Hs4TtY9vam//kdKb5rpSQ0mD0OO7V1/JIcPrqRTudfY43/1RrayEAXFblrmp2PvfcOD/6oQ7oFGi62FSVFjxP9PUXN/91f3dnfVo4L6Yjq4dcO77bmqbamDBwSemo7up5KQdvD3rELqxhqtJW7cwT2k9ltLnuy6ndRqHf/7icyzl57nPxauUfOKPrb6Ff9IXuN5HDeLCO85XtVQZ03hPeghqNYQhfz6wrVY9OP1hF8zmvV/xP9PUTMu/g+X/g5P/Enym5yH3uUk8q6lrqwUHn2DrSe9Y697DHQBXHpFR7ueUVq0UbiOtvwbxrgrJ1yi9ebFbXzpYcIjrXn0L/2QsKF/pqLKqllZPcaWRIfdA/WKzuRx7gRul/dO/wp86vLAAxo2EL2+t3BtAYUHZaUzV2Lcbvr0PmneFgWOCp6vTCE79h5uyvhrWdbfg4OOr7y/+1J61zk1a1yC+avLh67FUkrwdXiNtOYODyMHTaARrjPaJ90oaVT2NRn6em148lMFvJel1CRTkQfKbFZOfoiL44e+uV9vxJYxZEYGz/uMGUf76ZHjnnvGSCzzz3oPfnq2I3AbauxOe7w3f/OmwGFx1VCsqct1Us9bD2U+5lRpL0/ty1zNx0kOlr8dSCSw4+DRsCdH1Auv7s9a7wWkRVfRPFeONdSjpjzicabqDiT3G1Z2ruuBQPzb44L7YblTLwj+bl7j5qMobBFv3dz0+pj1dMYsALfjYNZCf+lDwCQAT+sFxl8GMl0Mb5Q6wfQ1M/j/XM6XHH1zJY9XPh57f4lLGufnBkt+E356r+POb0BQWwITbYM5brq2q/UllHxMR4dokctIOLB5WRSw4+EREHJhjyd+OdVVTpeTTrIOrytmZHrivPNNmFBfXzXWXzdnkGrpa9S25MRpcfXqzDlVfctjfI6ucJQeAYX93Ja3fXzy0vOTnwc//dqWrYy8oPe1p/4DIWvBDCIPiVN2TfESUe4IcOdYF7s+uO1CVWREK8+H3l6DtYBeAfvwHLPmq7ONMxcrfA59e7dqlTvkbnB5GL6S2g9y6JbNfr9L2PwsO/kpaT7qqxjjsz4PfVB6q7ua0eztkp8H6ma5qo16z8p/f1yi9cTZsXR68SsknvhoW/klf4BrcDyUot+rjlhD9fSzsCpi3MXQzXnZPbWf8v7JLjw1bwEl3u4bGWa+Vnnb+R26ahNP+AY0T3GqAl7znZv/9+ApXL10RFn8JORvdk+qol9zswONvgo1zKub8pmx7d8L7F8Kyb9w8ayf/OfgDWTDH3+hqFFZPrpw8lsCCg7+YTu6prTDffd+T454+q6Kn0v48eG0f754PDzeFR1rA4+3hme7uppPQt/Tjy+LrzrrgE3cjKis4xFXgwj+q7qb5ZBdY+WPwdBneNN2HusbTKX9zpbBpweaLLIOqq5JpPwTanRjaMYNuh64jYOK98OtTJVcP7trq6p0Tj4ek6w9sj+kIf3gdMhZVTPuAKkx/3jV8dj7DDbIa/aHrAfbh6APdtIvbsgw+ux5eGuRG7y6bWHr1XFEhbE2FNVMPqymnDwu7t8M758G66XD+q6HNs1aS7iNdFfDs1ys2f6Wo5MmCjjDNOrq67qz17g+1vFN1H4rGrV2Rc1emawOJquPeo+u6V7sQ6ilLU6+Z6yLqGwdQ1rTf8d0PLPxTUiDZmeEaj9udVPqTdXYafHWre/KJrAXf/hnazwhskCsscNVY/jfN8ortCsdd6gLSwDHuCT0cmxe7nlqDA5ZNDy6qFlw8Dr4c49oQ8rLc/6d/oPvur+5p8rznA//NupwBpzwAkx9xDwLlvZkArPnFBdrzXjjwcxrEwmWfwhunwweXwHXfHZi7assymPo4LBrvSjIJ/Vw1yOzXXPVXYn8330/L3u7fZfMi92+0ZakLwgDHnOOuPyIytDyqHvpDQFUJN685m9xD3vY1MPr9A9P3l0dUbeh7tXvg2FE1tRkWHPztr9JJ9YKDN8ahKquVRMK7GZVH3DGuh0zDVq4qpNS0fgv/FA8Oe3LgnXNh6wo3/Uj/G6HPFVC3yYE0qrDwM5h4jyuRnf2069f9wcWugXTgzQefc9tKN97kUNob/J38F1dKmvoEnPtseMcunQCIu+GFIzIazv+vu+lOfx72ZMM5z7gb5sofYeEnLl++UlxxJ93rOgt8/4CbkbbtCeH9fJ/pL0D9OOh58cHb446Bi9+B9y50bRynP+xuOr6gcOKfYNBtrstuwV7YMMs1lK/62TWg45Vo6jaDFse69dLje7ib4eRH4H/3uOst7Ua6ezt8chWsneZ+ZnQ9916rgXtv1h6G3u9+rw4H6fPh/YvcyPjel5adPmsDvH22u84rPg+t8bksSde6UnDym+7/rJJZcPBXfOru/SWHdtWSnUoT190Fh7KqlCD4wj9FRa7uetsq1xd7xfdu2uHJj7iupMff5ALPN39y00skHg/nv+KCrip0GAq/PArHXeLGXviEs4ZDKJq2dX9UyW/CCbcfqLYLxdKv3Y25PLPSRkTAiCdd28mvT7mSwjlPu3+P5l3gpHtKP/b8V+C1YfDRZXDF+PCrEzcvcYsfDXuw5B5WHYe5hvBv7oLUSe6m7B8UfKJquxtb+5Nc+8iura4UGdPJde8uHgDy89wNrGELd3MvSdZ6N935jrUwyJvMeV+u66rpey2Z4NpLhtzr2kuCdflUdSWkhZ+6sSbHXeqCc0Wb+qSbRufLW1ztQp8rgqf1BYa8LLjqK0jsVzF5aJwIx5ztqjqH/rX0pXMrgAUHf/WauT9mX6P0jnUQXf/QGoAPR74n1lCCQ0Ske9IsPsfSlP/Aim9h+BMw4CbXEJs+H2a+CvM+cN31ajVwT56nPgQn3HlgynMR18D7yknuj+7MRw6cN32+q0pr3qVirhXck3jKuzDlUbigjIZin62priH+rMfK/3NF3LXXaez6qa/91VUXXvtd2VMx12kMl3/qBsi9cx5c9jG0Gxz6z57+gnsaT7oueJqka111UF6WW0eg+OC+ktRvDvVLaX859SG3et+U/7g68v7FqgczFrnG2X274covgrflZKe5kcE//9t1JT77Kdf241Owzw0M+/1F2LzQjUWa+54LxEP+7B5QKmqK/W2r3IPCwFvd78RXt7mq1r5XBab1DwxXflFxgcGn/40uL4vHQ+/LKvbcxViDtD8RbxCaFxx8PZWOlDrRUCX2d7OJhlrUjSu28M+SCa5uus8VrheFT8vjYNSLcPdSOO2fbjKxG392T8nF/1Bb9IQ+l8OsV12Dt0/GAldFUZFrZzSMd9VXCz8NvVvu0gnuvVuYVUolGXwnnPuce+pOuq7kNUNK0qyDCySNWsJ7fyi9Ed9fziZ3rX2uLPvBZtCtMOxvoQWGUIjAOc9Bl7Nco/ySCQf2rfkV3hoOCFz3bemN/I0T4JJ34fLP3APGO+e6kurWlW4Njed6wZc3Q1G+a1O5fz1c+hHUbgRfjYEX+8O8DytmZPj0F1w72eA74dIPXalrwu2Q/NbB6bI2wDvnVF5gABcgm3d1fzeVzIJDcc06HrhZVfUYh6oS1w3+vMbNQxSK+O7uaTA30wWJL26GhCTXflBS4Kwf46ooLnq79LaDU/7uGjp//Kf7rnqgp1JFG3ynu3H8/EjZacE9nSX0Cz7vTbj6XQN3LXBVTeFonADXTHSLO304OrQ5o2b+11V9DCplaobKFBkFF77lfkc+v8G1Kywa7wJco1Zww6QDU8KXpfPpcOtMN8/QovEwNsn9vsR2hcs/hzEz3BN8dB3X4PvHqTD6A9du8eXN8OLxbvGc8srd4krCvS91DxnRdd35O5/hquR8vYd8gWH3jsoLDOD+3o6/0bVJVXJ3ZAsOxcV0cqNJ8/OqbpGf6uDfaFwW3x/yumnw0aVulbVL3jv0VaoatYTBd7lBWetnuJLanuyKa4z2V7cpDL7DdQfeMLv0tFkbYFOKm0q5IjVpE3ovHn8NYuHqb1y7w2fXupk6g9m70z3Rdh9ZvY25teq5qrCm7eD9i13Dd0KS6x0VbsCNrusGNY753b3fPM3V5Xc+LfDhRMTVy9809cB6Gx9c4rrZlsesV91iWINu98tPHXfuLme5xvcpj1ZNYPDpdYmrsp0dYhVpOVlwKM7XYJk2B/btrNqeSocrX4+lr25zVRaXvOdu7BXhhNvc1CXf/+3ACPBw1nAIx4CbXe+dSQ+VPoZg2Tfuvdt5lZOP8qjbxN142p/sqk2mPePqwn1jcnxSxrkR8CfcXuJpqlS9ZnDleDeuoscol3//zgfhat7ZlSBCWVM8IsIF9+t/cNVzH4yGjcnh/by9ua4b9DFnu7Xg/UXVdl12u45w7StVFRjA9YI77lLX5lLawmCHyIJDcb7gkPqTe6+J1UrhahDrGhf35bpqkdYBS3+XX636rkdNWjJMeQwk8sCEfxWtdgM45a+wfjos/zZ4uiUTXEAMp2dTVahV3z2NH3OOq1p5oS/8v3h4rrfr/TPxPpg+1k2VkVAFN6lQNE6EO+a6KsZK7l1TonrN4Kov3e/wexe4BvFQzX0X9mS50m1JomrDRe+4399rvq6awODT/wZXokkZV2k/woJDcb7ZWX0ToNXUaqVwJV3n+ub3u7riz33cpe5pcMtiV5fsWy6xMvS5yvWE+vEfJTdW5m6B9b9XfJVSRYmqDRe/C9f9ACNfcm07rfq4J8j5H8HOTa7n2OGkujt0NGzhqqGi67lBaaFMjFiY73pCtTnBTeQYTFQt1932UOY7K4+4Y1zjdPKblbbIVJnBQURai8hkEVkiIotFJGCEljjPi0iqiCwQkb7F9jcSkY0iMtb73lBE5vm9torIs96+a0Qk02/fDRV0raGp08hVPfiqOKxayTnlAfeqDBERcIbXUFwZjdH+IqPcQKatK2BuCU9dy74BFLofRlVKxUVEQJsBrrfXqQ/CRW/BH39xPXb+luFmpDUHa9rOlSC00HUPzt5YevrFX7q2x8F3VEHmyqn/jS6PK76rlNOHUnIoAO5R1e7AQOBWESle7h8OdPZeNwEvF9v/b2B/i5Cq7lTV3r4XsA4Y75f+Y7/9VTeZiE9MJ0ChTpPyrbhmwtfhZFdl5RsUVZm6DndPhJP/4xpw/S392tVRx1VS1VZlEqncUteRLrarG1C4J9sFCP+11P2puqnNm3eFzmdWbR7D0XWEm4gznKqyMJQZHFQ1XVVTvM87gaVA8UlqRgLj1JkBNBGRlgAi0g+IB34o6fwi0gWIA34t91VUNF9ds5UaqtbxN1ZOT6XiROCMf7vuudPHHtiet8P1aul2XvVXhZjK0aq3G1yYswn+OwR+edx99rfqZzewbvAdVbeOS3lERrluvkP/UimnD+vKRaQd0AeYWWxXAuA/xeNGIEFEIoCngHtLOe1oXEnBv/vIBV711Gci0jpIXm4SkWQRSc7MDPIEUF6+4GDtDTVXYhL0ON/NfbQzw21b/p1bNvVw6qVkKl6bgXDllxDbxU338kwP15tp+XeuHWr6825yyp4XVXdOy1arXqWdOuTgICINgM+Bu1Q11OW1xgATVbW0Cr7RwId+378G2qlqL2ASUOLyR6r6qqomqWpSbGw55r4pjW+OJeupVLOd+pBreJzyH/d96dduVbxQphUxR7Y2A1wj9R1zXW+kTSnw4SUuUKyeAgNvOfRxPEe4kIKDiETjAsP7qjq+hCRpgP8TfqK3bRBwm4isBZ4ErhKRR/3OexwQpar7h/qp6jZV3et9fR2o+j55MZ3d++EyI6SpHM06uC6BKePcaNNVP7leSodzVYKpWM06uAkF/7TYjd9pcazrzZZ0bXXnrNqVOYGNiAjwBrBUVYOtmjIBFwQ+AgYA2aqaDlzud55rgCRV9Z+q8VIOLjUgIi29YwHOw7VxVK3YrjDqFTf4xdRsQ+5zaxZ8ONpNFX64dmE1lSsy2v3f2///fqHMbjYYuBJYKCLzvG0PAG0AVPUVYCIwAkgFdgOhht2LveP83SEi5+F6SW0HrgnxXBVHJLQ5282Rr36MGxfw4z+hXnNoE+KkeMbUcKKHuhThYSApKUmTk8McGm+MT34evHyCmyvnrP9Ud26MqTIiMkdVk0raZ+s5GBNd183uGVEJi8QYc4Sy4GAMHPU9U4wpzrplGGOMCWDBwRhjTAALDsYYYwJYcDDGGBPAgoMxxpgAFhyMMcYEsOBgjDEmgAUHY4wxASw4GGOMCWDBwRhjTAALDsYYYwJYcDDGGBPAgoMxxpgAFhyMMcYEsOBgjDEmgAUHY4wxASw4GGOMCWDBwRhjTAALDsYYYwKUGRxEpLWITBaRJSKyWETuLCGNiMjzIpIqIgtEpG+x/Y1EZKOIjPXbNkVElovIPO8V522vLSIfe+eaKSLtKuA6jTHGhCEqhDQFwD2qmiIiDYE5IjJJVZf4pRkOdPZeA4CXvXeffwNTSzj35aqaXGzb9cAOVe0kIqOBx4BLQrscY4wxFaHMkoOqpqtqivd5J7AUSCiWbCQwTp0ZQBMRaQkgIv2AeOCHEPM0EnjH+/wZcKqISIjHGmOMqQBhtTl4VTx9gJnFdiUAG/y+bwQSRCQCeAq4N8gp3/KqlB70CwD7z6WqBUA2EFNCXm4SkWQRSc7MzAznMowxxpQh5OAgIg2Az4G7VDUnxMPGABNVdWMJ+y5X1Z7ASd7rylDzAqCqr6pqkqomxcbGhnOoMcaYMoTS5oCIROMCw/uqOr6EJGlAa7/vid62QcBJIjIGaADUEpFcVb1fVdPAVVWJyAfA8cA4v3NtFJEooDGwrVxXZ4wxplxC6a0kwBvAUlV9OkiyCcBVXq+lgUC211Zxuaq2UdV2uKqlcap6v4hEiUhz7/zRwDnAIr9zXe19vhD4WVW1vBdojDEmfKGUHAbjqnwWisg8b9sDQBsAVX0FmAiMAFKB3cC1ZZyzNvC9FxgigR+B17x9bwDvikgqsB0YHerFGGOMqRhSEx7Kk5KSNDm5eI9YY4wxpRGROaqaVNI+GyFtjDEmgAUHY4wxASw4GGOMCWDBwRhjTAALDsYYYwJYcDDGGBPAgoMxxpgAFhyMMcYEsOBgjDEmgAUHY4wxASw4GGOMCWDBwRhjTAALDsYYYwJYcDDGGBPAgoMxxpgAFhyMMcYEsOBgjDEmgAUHY4wxASw4GGOMCWDBwRhjTAALDsYYYwKUGRxEpLWITBaRJSKyWETuLCGNiMjzIpIqIgtEpG+x/Y1EZKOIjPW+1xOR/4nIMu+cj/qlvUZEMkVknve6oSIu1BhjTOiiQkhTANyjqiki0hCYIyKTVHWJX5rhQGfvNQB42Xv3+Tcwtdh5n1TVySJSC/hJRIar6rfevo9V9bbyXJAxxphDV2bJQVXTVTXF+7wTWAokFEs2EhinzgygiYi0BBCRfkA88IPfOXer6mTv8z4gBUisgOsxxhhTAcJqcxCRdkAfYGaxXQnABr/vG4EEEYkAngLuLeWcTYBzgZ/8Nl/gVU99JiKtgxx3k4gki0hyZmZmOJdhjDGmDCEHBxFpAHwO3KWqOSEeNgaYqKobg5wzCvgQeF5VV3ubvwbaqWovYBLwTknHquqrqpqkqkmxsbGhXoYxxpgQhNLmgIhE4wLD+6o6voQkaYD/E36it20QcJKIjAEaALVEJFdV7/fSvQqsVNVnfQeq6ja/87wOPB7itRhjjKkgZQYHERHgDWCpqj4dJNkE4DYR+QjXEJ2tqunA5X7nuQZI8gUGEfl/QGPgoN5IItLSOxbgPFwbhzHGmCoUSslhMHAlsFBE5nnbHgDaAKjqK8BEYASQCuwGri3thCKSCPwNWAakuPjDWFV9HbhDRM7D9ZLaDlwT1hUZY4w5ZKKq1Z2HQ5aUlKTJycnVnQ1jjDmiiMgcVU0qaZ+NkDbGGBPAgoMxxpgAFhyMMcYEsOBgjDEmgAUHY4wxASw4GGOMCWDBwRhjTAALDsYYYwJYcDDGGBPAgoMxxpgAFhyMMcYEsOBgjDEmgAUHY4wxASw4GGOMCWDBwRhjTAALDsYYYwJYcDDGGBPAgoMxxpgAFhyMMcYEsOBgjDEmQJnBQURai8hkEVkiIotF5M4S0oiIPC8iqSKyQET6FtvfSEQ2ishYv239RGShd8zzIiLe9mYiMklEVnrvTSviQo0xxoQulJJDAXCPqnYHBgK3ikj3YmmGA529103Ay8X2/xuYWmzby8CNfsed5W2/H/hJVTsDP3nfjTHGVKEyg4Oqpqtqivd5J7AUSCiWbCQwTp0ZQBMRaQmuhADEAz/4Env7GqnqDFVVYBwwyu9c73if3/HbbowxpoqE1eYgIu2APsDMYrsSgA1+3zcCCSISATwF3FtC+o3F03uf41U13fucgQssJeXlJhFJFpHkzMzMcC7DGGNMGUIODiLSAPgcuEtVc0I8bAwwUVU3lpmyBF6pQoPse1VVk1Q1KTY2tjynN8YYE0RUKIlEJBoXGN5X1fElJEkDWvt9T/S2DQJOEpExQAOglojkAs95aYqnB9gsIi1VNd2rftoSzgUZY4w5dKH0VhLgDWCpqj4dJNkE4Cqv19JAINtrq7hcVduoajtc1dI4Vb3fqzbKEZGB3vmvAr7yO9fV3uer/bYbY4ypIqGUHAYDVwILRWSet+0BoA2Aqr4CTARGAKnAbuDaEM47BngbqAt8670AHgU+EZHrgXXAxSGcyxhjTAUSV61/ZEtKStLk5OTqzoYxxhxRRGSOqiaVtM9GSBtjjAlgwcEYY0wACw7GGGMCWHAwxhgTwIKDMcaYABYcjDHGBLDgYIwxJoAFB2OMMQEsOBhjjAlgwcEYY0wACw7GGGMCWHAwxhgTwIKDMcaYABYcjDHGBLDgUIKCwiLe/m0NSzaFuhqqMcbULCEtE3q0eXnKKp6atAIROLdXK+4+vQvtmtev7mxVGFVl+qptDOwQQ2SEVHd2jDGHISs5FLNwYzbP/bSS4ce24JaTO/LDkgxOe/oXHvhiIZtz9oR9vuzd+UxevoXCosNnUaUJ8zdx+eszeW/GuurOijHmMGUlBz978gu56+O5NG9Qm//8oSdN6tXimhPaMXZyKh/OWs/nczZyzQntuHVYJxrViQ7pnH/9YgETF2bQOa4B95zRhTN7tMAtm109ioqUsT+nAvDKL6sYfXxrakdFVlt+jDGHJys5+Hnsu2WsytzFExf1okm9WgDENarDv0Yey093D+Xsni159dfV3PbBXEJZXjVl/Q4mLszg7J4tKVLl5vdSOG/sb/yyIjOk4yvDD0syWLkll4v6JZKevYfP56RVSz6MMYc3Kzl4pq3cylu/reWaE9pxUufYgP1tYurx9CW96ZnYmIe/XsK3izIY0bNl0POpKv+ZuJTYhrV5/MJe1ImO5Iu5aTz74wqufnMWx7drxu2ndqJl4zoUFkGRKkWq+GJG1xYNiY6s2Nitqrzwcyrtm9fn0Qt6sWLzTl6akspFSYkV/rOMMUc2Cw64doH7PptPh9j6/OWsY0pNe+XAtnyavJF/fb2EIV1iaVC75H/CSUs2M3vtDh45/1jqe2ku7JfIece14uPZ63n+51SufGNW0J/Tplk97ji1M6N6tyKqgm7cU5ZnsnhTDo9f2IvICOH2YZ25YVwyX85N46Kk1hXyM4wxNYOUVb0hIq2BcUA8oMCrqvpcsTQCPAeMAHYD16hqioi0Bb7AVV9FAy+o6isi0hD41e8UicB7qnqXiFwDPAH46jvGqurrpeUxKSlJk5OTQ7neEt350Vz+tyCd8WNOoFdikzLTp6zfwR9ems5NQzrwwIhuAfsLCos449mpAPxw15ASb+55+wr5ZcUW8guVCBEiBMR7z91bwJu/rWFRWg7tm9fnzlM7c+5xrQ6pZ5GqcsHL09mcs5cp9w0lOjICVeXs56eRl1/Ij3efbD2XjDnKiMgcVU0qaV8oJYcC4B7vZt8QmCMik1R1iV+a4UBn7zUAeNl7TwcGqepeEWkALBKRCaq6Cejtn0FgvN/5PlbV20K/xPL7ev4mvpq3ibtP7xJSYADo26Ypo/u35o1pa7igbyJdWzQ8aP/HyRtYnbmLV6/sF/Spv26tSM46Nni11Pl9EvhhyWaembSCuz6ex9jJqdx1WmdGHNuSiHLcxH9fvY2U9Vn8e9Sx+6uQRITbh3XilvdT+GbBJkb2Tgj7vMaYmqnM+gpVTVfVFO/zTmApUPwuMhIYp84MoImItFTVfaq610tTu6SfJyJdgDgOLklUiYzsPfz9y0X0bt2EMUM7hnXsn886hoZ1onjwy0UHNS7v2lvAM5NW0r9dU07vHl/uvIkIZ/ZowcQ7TuLFy/oiwG0fzGXok1N47seVbNyxO6zzjf05lbiGtbmoX+JB28/s0YIu8Q0Y+3MqRaV0t1XVw6o7rjGmcoVVmS0i7YA+wMxiuxKADX7fN3rbEJHWIrLA2/+YV2rwNxpXUvC/81wgIgtE5DOvWqukvNwkIskikpyZmRnOZew3b8MOAJ6++Liw6/Wb1a/F/Wcdw6y12xmfcqDHz2u/rmZr7l7+OqJbhXRZjYgQzu7Vku/uGsILl/YhsWldnvlxBSc+NpnLXpvBF3M3krevsNRzzFm3nemrtnHTkA7UiT6422pEhHDrKZ1YuSWX7xZnlHh8yvodDHvqF3o//AM3vDOb139dzZJNOaUGk/JIz85jX0FRhZ7TGFM+ZbY57E/oqoV+AR5R1fHF9n0DPKqq07zvPwF/UdVkvzStgC+Bc1V1s9/2JcCVqjrH+x4D5HpVUX8ELlHVYaXl7VDaHHbuyadhiGMWiisqUi54ZTobtu/mp7uHsrewkKFPTGFo11heurxfuc4Zig3bdzM+JY3PUjawYXseDWpHcX6fBG4Z2pFWTeoGpL/2rVnM35jNtL+cQr1agTWJhUXK6U//Qu3oSCbeceL+oFZQWMSLk1fx/M8radGoDid1bs6M1dtYu82VWprWi2ZghxgGd2rOqd3iaNk48GeHKiN7D6c8OYXTusfzwqV9yn0eY0zoDrXNARGJBj4H3i8eGDxpgP8TfiIHGpQBUNVNIrIIOAn4zDvvcUCULzB46bb5HfY68HgoeSyv8gYGcE/d/x55LOeNncaTPyynSJV9BUXcd2bpPZ4OVetm9bjztM7cPqwTs9Zu55PkDXw0ez0fz97AZQPaMGZoR+Ia1QFgUVo2k5dnct+ZXUsMDACRXunhnk/n8+PSLZzePZ4N23fzp4/nkbxuB6N6t+Jfo47dP/BvU1Yev6/axvRV2/h91Va+XZTB37+EHq0acVq3eE7rFs+xCY3CKjk9++MK8vIL+Xr+Jv44pAPHJjQ+5H+n4rbs3MO9ny7Y32vMGBNcKL2VBHgH2K6qdwVJczZwG6630gDgeVU9XkQSgW2qmiciTXHVUReo6kLvuEeBvar6D79ztVTVdO/z+bgSyMDS8niovZUO1T8nLOad39cSIcIVA9rw8MhjqzwPG3fs5oWfUvksZSPRkcJVg9rxxyEd+NsXi/ht1VZ+u39YqaO6CwqLGPbULzSpF811g9vz4JeLAPj3qGMZ1Sd4Q7Wqsiozl0lLtvDT0s3MWb8DVYhvVJuze7biL8O7ljkCO3XLTs54ZioX9E3khyWb6dumCW9de3z5/iGCyC8s4vLXZzJrzXYA7juzK2OGdqzW0erGVLdDLTkMBq4EForIPG/bA0AbAFV9BZiICwypuK6s13rpugFPiYgCAjzpCwyei73j/N0hIufhekltB64JIY/V6u4zuvDNgnT25Bdy+6mdqyUPiU3r8diFvbhlaEee/2klr/26mvdnrGPXvkLuCGG6j6jICMYM7cj94xdy18fzSGrblGcu6U3rZvVKPU5E6BTXkE5xDbllaEe25e5l8vJMJi3J4M3f1hAdJfx1eGB3X3+Pf7ecerWi+OuIbnSMa8Cj3y5j9trt9G/XLOx/h2Ae+3YZs9Zs5/ELejF91Vae+H45G7bvPqj3ljHmgJDbHA5n1V1yAFi5eSe5ewvo06ZptebDJ3XLTp75cSVLNuUw/pYTaFq/VpnH7Cso4tYPUuiV0JhbhnY85MF3fx2/kI9mr+fDGwcysENMiWnmrNvOBS//zr1ndOG2YZ3J21fIyU9Mpm1MPT7546AKebL/ZsEmbvtgLlcPasvDI49FVXnqhxWMnZzKkC6xvHR536CDGY2pyUorOVhwMJVm974Czn5+GvsKiph450k0rntw6UVVufi/v7N2225+uW/o/jaRd2es48EvF/H2tf0Z2jXukPKwcvNORr74G91aNuLDGwdSK+pAwPto1nr+9uUiusQ35K1r+tOicZ39+V64MZt5G7KYtyGL/MIieiU2oXfrJhzXuknAdRhzpLLgYKrNvA1ZXPDydM7t1ZJnRx/cC+mnpZu5/p1k/t+oY7liYNv92/cVFHHq01NoVCear287sVyD/sD1RBv54m/k5BXwvztOJN5rpPf3y4pMxrw3h4Z1ohnWLY5567NYvnnn/jEdbZrVo1ZUBKsyc/fPe9Uxtj69WzelS3wDIkRQlCJl/9xYtSIjGNGrJQkl9ByrKqpK6pZcZqzZzszV25i7PosOsfW5oG8iZ/SID9o5wRxdLDiYavXcjyt55scVPDe69/5R2IVFyvDnppJfqPzwpyEB9f7jUzZy9yfzeenyvqVOcBiMqnLLeylMWrqZ928YELRaC2DJphxuejeZ7Lx8erductArpkFtAHL25LNgQzbzNuzYX6LYmrsv6DkjI4Rze7XkxiEd6NGq4ntelaSwSPl49gamrshk1trtbN/l8hffqDZ92zRlwcZs0rLyqF8rkuE9W/KHvgkMbB9DRIRQVKSkZeWxND2HZRk7WZ6xk9rREdw//BjiGgYGVVMzWHAw1aqgsIiL/vs7qVty+e6uISQ0qcunyRu477MFQW/+hUXKWc9OpVA16PxUpfnvL6v4z7fL+PvZ3bjhpA5lplfvqT/UUoqqkru3ABFBgAgRRNz75pw9vD19LR/NWs+ufYWc2Kk5Nw3pwEmdm4fVhrInv5A9+YX7p48vy9OTVvD8TytJbFqXAe1jGNC+GQM6NKNNs3qIuADgBm1uZOLCDHL3FtCqcR1aNK7Dis255O4t2H+utjH1yMjeQ4PaUTxxUS+GHVP+0f7m8GXBwVS7ddt2Mfy5X+mV2Ji3rjmeYU9NIa5RHb4cc0LQG+Z3izK4+b05PH5hLy4OMmusqpKevYfULbmkbsll5ZZcVm3JJXnddob3bMnYS/tUW3fV7N35vD9rHW/9tpbMnXvp1rIRfz6rK6eE0I4yc/U27vl0Prv2FjB+zGDal7FM7e+rtnH56zMY1SeBpy/uXeb58/YV8sOSDL6at4ncPQUc07Ihx7RoxDEtG9I1viH1a0excvNO7vhoHkvTc7h6UFv+OqJbwAh7c2Sz4GAOC5/M3sCfP19Az4TGLEzL5sMbBzKoY/DqHlVl1Iu/sTV3Hz/fezK1oyLZuSefueuzmL12O7PXbmdRWs5BT7xN6kXTOa4BPROacM8ZXfZPl16d9hYU8tXcTbz8yyrWbN3FGd3jeejc7iQ2DewmvLegkKcnreDVqatp3bQeuXsLaFw3utQeZ9t37WP4c1OpVyuKb24/sUKveW9BIY9/t5w3pq2ha3xDnr+0T8BEk+bIZcHBHBZUlZvfm8P3izcztGssb4cw0G3ayq1c8cZMhnSJZVvuXpam51CkECHQo1Vj+rRpQuf4hnSKbUDn+AbE1K912A5s21dQxOvTVvPCT6koyu3DOnPjSR3296BalpHDXR/NY1nGTi49vg1/P7sbS9NzuOy1mfRu04T3rh9wUG8rcP+mN45LZuqKrYwfc0KljCwHmLJ8C/d+uoCcPfncf9YxXDagTZmliL0FhYxPSePNaWsY2CGGh87tbmNKDjMWHMxhY/uufTz27TJuHtqxzKoScDe/a9+ezczV2+nTpglJ7ZrRv11T+rRpesSOTUjLyuNfXy/m+8Wb6RBbn4fP68Gy9J088f1yGtWN5rELenJqtwN1/F/NS+POj+bxh74JPHXRcQcFv7d+W8PDXy/hoXO6c92J7Ss131tz93Lfp/OZvDyTxnWjOb9PAhclJQY0uO/JL+SjWev579TVpGfvoX3z+qzZuoshXWJ58bI+hzRljalYFhzMEa2oyC2hWlEr4h0uJi/fwj8nLGadN5HhmT3i+b/ze+7vIeXP1+PrntO77B+Fvygtmz+8NJ2TOjfn9auTqqTEpKpMS93KJ8kb+X5RBvsKi+jRqhEXJ7XmtO7xfDN/E6/9uoatuXvp364ptw/rzEmdm/NJ8gYe+GIRneMa8Na1/Q9pkkZTcSw4GHOY2pNfyDvT1xLfqA4je7cKeoNXVe75ZD7j56bx3OjenNYtnnNemMbufQV8e+cQmoUwAr6iZe3ex1fzNvFJ8gYWb8rZv/2kzs257ZRODCjWffjXlZmMeS+FerUjefOa/iF38VVVFqZl892iDFZl5nLPGV3pEn/ktntk7d5Hdl4+bWPKLjlXNgsOxtQAewsKufL1WczbmEX/dk2ZvmobH9xQeqN+VVmUls2U5VsY3Kl5qVPILMvI4bq3ZpOdl8/Yy/pyyjEl99wqLFLmrNvBd4sy+H5xBmlZeURGCHWjI4mOFMZdN4CeiRXfvqKq7CssKnOyyPIoKCzig1nrefL75ezeV8ifTu/CzSd3rNbleS04GFND7Ni1j/Nf+o2123Zzx7BO3H1G1+rOUtg25+zh+ndms2RTDqP6JCAIewoK2bOv0L3nF7Fu2y625u6jVlQEQzo358weLTi9ezzZeflc/vpMsnbn8+Y1/Tm+fcVMzrg6M5cJ8zcxYf4m1m7dRY9WjRnYoRkDO8TQv32zMieuLMusNdt56KtFLMvYyQkdY2hSL5qJCzM4vn0znrmkd7WNprfgYEwNsmH7bn5YspmrB7U9Ytthdu0t4C+fL+D3VduoEx1J7egI6kZHUic6kjrRETRvUJvTusVzyjFxAR0P0rPzuOL1maRl5fHKFf2Czr+VvTufr+ankZOXT1yjOsQ1rE289960Xi0ycvbwzQIXEBal5SACA9o3o3frpsxdv4O567PYV1hEhMCxCY0Z1DGGa09ov38OrlBkZO/hP98u5at5m2jVuA5/P6c7w49tAcDnKWn846tFREYI//eHnpzTq+rXGLHgYIypUbbm7uWqN2axcstOnhvd56BR9ks25fDujLV8MTeNPfklLzsbHSnkF7p733Gtm3Bur5ac06vVQTf+PfmFpKzfwYzV25mxehtz1+8gOjKCO07tzHWD2wd0K/aXtXsf435fxyu/rKKgSLl5SAduGdqJurUOrq5at20Xd340z81B1jeRh0f2oLBQWZKew1LfKyOHtVt307JxHbq2aMgxLRrSJd4NWkxsWrfcc4+BBQdjTA2UnZfPdW/PZu76Hfzf+T2pVzuKd39fy+y1O6gTHcGo3glcOagtHWMbkLlzL5tz9rBl51625Oxh8869NKwTxdk9W4bcMLx+227+9c0Sfly6mQ7N6/OP83pwcpfYg9Kszszlzd/W8NmcjezJL+L07vE8eHZ32sQEXxclv7CIF35aydjJqdSOiiQv/8Ca8DH1a9GtZSM6xNZnU9Yelm/OYcP2vP3769WK5NZTOnHrKZ3C/NdzLDgYY2qk3fsK+OO7c/h15VbAzaJ71aC2XNSvNY3rVc54isnLtvDw14tZu203Z3SP58FzurNhx27enLaGH5duoVZkBKP6tOL6EzuENZp89trtjE9Jo02zenRr2ZDuLRsR27B2QA+23L0FrNi8kxUZO1mWsZOBHWI4y6uqCpcFB2NMjbW3oJDXf11D91aNOLlz7CFVs4T7M8f+nMqegkJU3VP+FQPbcsXAtsQ2DByrcjiy4GCMMZVgU1Yeb09fS/vm9Tm/T8IRNzHhoa4hbYwxpgStmtTlgRGlr5F+pDoy+8EZY4ypVBYcjDHGBCgzOIhIaxGZLCJLRGSxiNxZQhoRkedFJFVEFohIX297WxFJEZF53rE3+x0zRUSWe/vmiUict722iHzsnWumiLSrwOs1xhgTglDaHAqAe1Q1RUQaAnNEZJKqLvFLMxzo7L0GAC977+nAIFXdKyINgEUiMkFVN3nHXa6qxVuSrwd2qGonERkNPAZcUu4rNMYYE7YySw6qmq6qKd7nncBSIKFYspHAOHVmAE1EpKWq7lPVvV6a2qH8PO9c73ifPwNOlcN19RZjjKmhwmpz8Kp4+gAzi+1KADb4fd/obfNVSy3w9j/mV2oAeMurUnrQLwDsP5eqFgDZQMC0kyJyk4gki0hyZmZmOJdhjDGmDCEHB69a6HPgLlXNKSu9j6puUNVeQCfgahHxLXF1uar2BE7yXleGnm1Q1VdVNUlVk2JjY8s+wBhjTMhCCg4iEo0LDO+r6vgSkqQBrf2+J3rb9vNKDItwgQBVTfPedwIfAL4FhfefS0SigMbAttAuxxhjTEUos0Haq+55A1iqqk8HSTYBuE1EPsI1RGerarqIJALbVDVPRJoCJwLPeDf9Jqq61Qs85wA/+p3rauB34ELgZy1jGPecOXO2isi6Mq+2ZM2BreU89kh3tF67XffRxa47uLbBdpQ5fYaInAj8CiwEfPPfPgC0AVDVV7wAMhY4C9gNXKuqySJyOvAUoIAAY1X1VRGpD0wFooFIXGC4W1ULRaQO8C6ubWM7MFpVV5dxgeUmIsnBho/XdEfrtdt1H13susunzJKDqk7D3dhLS6PArSVsnwT0KmH7LqBfkHPtAS4qK1/GGGMqj42QNsYYE8CCA7xa3RmoRkfrtdt1H13susuhRkzZbYwxpmJZycEYY0wACw7GGGMCHNXBQUTO8maGTRWR+6s7P5VFRN4UkS0isshvWzMRmSQiK733ptWZx8oQbEbhmn7tIlJHRGaJyHzvuh/2trf3ZjpO9WY+rlXdea0MIhIpInNF5Bvve42/bhFZKyILvemIkr1th/R7ftQGBxGJBF7EzSjbHbhURLpXb64qzdu4MSj+7gd+UtXOwE/e95rGN6Nwd2AgcKv3f1zTr30vMExVjwN6A2eJyEDcDMfPqGonYAduBuSa6E7cBKE+R8t1n6Kqvf3GNhzS7/lRGxxw03WkqupqVd0HfISbEbbGUdWpuAGF/vxnv30HGFWVeaoKpcwoXKOv3ZsdOdf7Gu29FBiGm+kYauB1A3izMpwNvO59F46C6w7ikH7Pj+bgEHQm2aNEvKqme58zgPjSEh/pis0oXOOv3atamQdsASYBq4Asb6ZjqLm/788Cf+bAbA4xHB3XrcAPIjJHRG7yth3S73koi/2YGk5VVURqbJ/m4jMK+y8PUlOvXVULgd4i0gT4AjimenNU+UTkHGCLqs4RkaHVnJ2qdqKqpnkrak4SkWX+O8vze340lxzKnEm2htssIi0BvPct1ZyfShFkRuGj4toBVDULmAwMwi3C5XsgrIm/74OB80RkLa6aeBjwHDX/uv1nud6Cexg4nkP8PT+ag8NsoLPXk6EWMBo3I+zRwjf7Ld77V9WYl0pRyozCNfraRSTWKzEgInWB03HtLZNxMx1DDbxuVf2rqiaqajvc3/PPqno5Nfy6RaS+uCWc8SY1PQO3PMIh/Z4f1SOkRWQEro4yEnhTVR+p3hxVDhH5EBiKm8J3M/AP4EvgE9zsuuuAi1W1eKP1Ea2UGYVnUoOvXUR64RogI3EPgJ+o6r9EpAPuiboZMBe4wm8Z3xrFq1a6V1XPqenX7V3fF97XKOADVX1ERGI4hN/zozo4GGOMKdnRXK1kjDEmCAsOxhhjAlhwMMYYE8CCgzHGmAAWHIwxxgSw4GCMMSaABQdjjDEB/j9FpEAIBvdkPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(glm_history.history['loss'],label='loss')\n",
    "plt.plot(glm_history.history['val_loss'], label='val_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f126ac03240>]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This cannot even predict **trainning dataset** (let alone test dataset), \n",
    "# because the features are almost \n",
    "# all the same in different categories of the combined_label\n",
    "# In the prediction. All results are category \"5\", because this is the most abundant \n",
    "# category in the dataset . See histgram plot of the output \"combined_label\" above \n",
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "plt.plot(range(train_features.shape[0]), train_labels_f, label=\"truth\")\n",
    "plt.plot(range(train_features.shape[0]), glm_model.predict(train_features).argmax(axis=1), label=\"predicted label\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicted result on test dataset is always category \"5\" as well\n",
    "glm_model.predict(test_features).argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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