human_activity_recognition.2.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load train and test data from .csv files\n",
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.288585</td>\n",
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       "      <td>-0.132905</td>\n",
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       "      <td>-0.983111</td>\n",
       "      <td>-0.913526</td>\n",
       "      <td>-0.995112</td>\n",
       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
       "      <td>-0.934724</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
       "      <td>-0.058627</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278419</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
       "      <td>-0.978944</td>\n",
       "      <td>-0.996520</td>\n",
       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.482845</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.012892</td>\n",
       "      <td>0.640011</td>\n",
       "      <td>-0.485366</td>\n",
       "      <td>-0.848649</td>\n",
       "      <td>0.181935</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.276629</td>\n",
       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
       "      <td>-0.980817</td>\n",
       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.699205</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.693578</td>\n",
       "      <td>-0.615971</td>\n",
       "      <td>-0.847865</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>-0.043892</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X    ...     \\\n",
       "0         -0.923527         -0.934724    ...      \n",
       "1         -0.957686         -0.943068    ...      \n",
       "2         -0.977469         -0.938692    ...      \n",
       "3         -0.989302         -0.938692    ...      \n",
       "4         -0.990441         -0.942469    ...      \n",
       "\n",
       "   fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
       "0                        -0.710304                    -0.112754   \n",
       "1                        -0.861499                     0.053477   \n",
       "2                        -0.760104                    -0.118559   \n",
       "3                        -0.482845                    -0.036788   \n",
       "4                        -0.699205                     0.123320   \n",
       "\n",
       "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                              0.030400                         -0.464761   \n",
       "1                             -0.007435                         -0.732626   \n",
       "2                              0.177899                          0.100699   \n",
       "3                             -0.012892                          0.640011   \n",
       "4                              0.122542                          0.693578   \n",
       "\n",
       "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
       "0                             -0.018446             -0.841247   \n",
       "1                              0.703511             -0.844788   \n",
       "2                              0.808529             -0.848933   \n",
       "3                             -0.485366             -0.848649   \n",
       "4                             -0.615971             -0.847865   \n",
       "\n",
       "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity  \n",
       "0              0.179941             -0.058627        1  STANDING  \n",
       "1              0.180289             -0.054317        1  STANDING  \n",
       "2              0.180637             -0.049118        1  STANDING  \n",
       "3              0.181935             -0.047663        1  STANDING  \n",
       "4              0.185151             -0.043892        1  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.257178</td>\n",
       "      <td>-0.023285</td>\n",
       "      <td>-0.014654</td>\n",
       "      <td>-0.938404</td>\n",
       "      <td>-0.920091</td>\n",
       "      <td>-0.667683</td>\n",
       "      <td>-0.952501</td>\n",
       "      <td>-0.925249</td>\n",
       "      <td>-0.674302</td>\n",
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       "      <td>-0.705974</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>0.162920</td>\n",
       "      <td>-0.825886</td>\n",
       "      <td>0.271151</td>\n",
       "      <td>-0.720009</td>\n",
       "      <td>0.276801</td>\n",
       "      <td>-0.057978</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.286027</td>\n",
       "      <td>-0.013163</td>\n",
       "      <td>-0.119083</td>\n",
       "      <td>-0.975415</td>\n",
       "      <td>-0.967458</td>\n",
       "      <td>-0.944958</td>\n",
       "      <td>-0.986799</td>\n",
       "      <td>-0.968401</td>\n",
       "      <td>-0.945823</td>\n",
       "      <td>-0.894088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.594944</td>\n",
       "      <td>-0.083495</td>\n",
       "      <td>0.017500</td>\n",
       "      <td>-0.434375</td>\n",
       "      <td>0.920593</td>\n",
       "      <td>-0.698091</td>\n",
       "      <td>0.281343</td>\n",
       "      <td>-0.083898</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.275485</td>\n",
       "      <td>-0.026050</td>\n",
       "      <td>-0.118152</td>\n",
       "      <td>-0.993819</td>\n",
       "      <td>-0.969926</td>\n",
       "      <td>-0.962748</td>\n",
       "      <td>-0.994403</td>\n",
       "      <td>-0.970735</td>\n",
       "      <td>-0.963483</td>\n",
       "      <td>-0.939260</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.640736</td>\n",
       "      <td>-0.034956</td>\n",
       "      <td>0.202302</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>0.145068</td>\n",
       "      <td>-0.702771</td>\n",
       "      <td>0.280083</td>\n",
       "      <td>-0.079346</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.270298</td>\n",
       "      <td>-0.032614</td>\n",
       "      <td>-0.117520</td>\n",
       "      <td>-0.994743</td>\n",
       "      <td>-0.973268</td>\n",
       "      <td>-0.967091</td>\n",
       "      <td>-0.995274</td>\n",
       "      <td>-0.974471</td>\n",
       "      <td>-0.968897</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.736124</td>\n",
       "      <td>-0.017067</td>\n",
       "      <td>0.154438</td>\n",
       "      <td>0.340134</td>\n",
       "      <td>0.296407</td>\n",
       "      <td>-0.698954</td>\n",
       "      <td>0.284114</td>\n",
       "      <td>-0.077108</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.274833</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>-0.129527</td>\n",
       "      <td>-0.993852</td>\n",
       "      <td>-0.967445</td>\n",
       "      <td>-0.978295</td>\n",
       "      <td>-0.994111</td>\n",
       "      <td>-0.965953</td>\n",
       "      <td>-0.977346</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.846595</td>\n",
       "      <td>-0.002223</td>\n",
       "      <td>-0.040046</td>\n",
       "      <td>0.736715</td>\n",
       "      <td>-0.118545</td>\n",
       "      <td>-0.692245</td>\n",
       "      <td>0.290722</td>\n",
       "      <td>-0.073857</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.257178          -0.023285          -0.014654         -0.938404   \n",
       "1           0.286027          -0.013163          -0.119083         -0.975415   \n",
       "2           0.275485          -0.026050          -0.118152         -0.993819   \n",
       "3           0.270298          -0.032614          -0.117520         -0.994743   \n",
       "4           0.274833          -0.027848          -0.129527         -0.993852   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.920091         -0.667683         -0.952501         -0.925249   \n",
       "1         -0.967458         -0.944958         -0.986799         -0.968401   \n",
       "2         -0.969926         -0.962748         -0.994403         -0.970735   \n",
       "3         -0.973268         -0.967091         -0.995274         -0.974471   \n",
       "4         -0.967445         -0.978295         -0.994111         -0.965953   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X    ...     \\\n",
       "0         -0.674302         -0.894088    ...      \n",
       "1         -0.945823         -0.894088    ...      \n",
       "2         -0.963483         -0.939260    ...      \n",
       "3         -0.968897         -0.938610    ...      \n",
       "4         -0.977346         -0.938610    ...      \n",
       "\n",
       "   fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
       "0                        -0.705974                     0.006462   \n",
       "1                        -0.594944                    -0.083495   \n",
       "2                        -0.640736                    -0.034956   \n",
       "3                        -0.736124                    -0.017067   \n",
       "4                        -0.846595                    -0.002223   \n",
       "\n",
       "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                              0.162920                         -0.825886   \n",
       "1                              0.017500                         -0.434375   \n",
       "2                              0.202302                          0.064103   \n",
       "3                              0.154438                          0.340134   \n",
       "4                             -0.040046                          0.736715   \n",
       "\n",
       "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
       "0                              0.271151             -0.720009   \n",
       "1                              0.920593             -0.698091   \n",
       "2                              0.145068             -0.702771   \n",
       "3                              0.296407             -0.698954   \n",
       "4                             -0.118545             -0.692245   \n",
       "\n",
       "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity  \n",
       "0              0.276801             -0.057978        2  STANDING  \n",
       "1              0.281343             -0.083898        2  STANDING  \n",
       "2              0.280083             -0.079346        2  STANDING  \n",
       "3              0.284114             -0.077108        2  STANDING  \n",
       "4              0.290722             -0.073857        2  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LAYING                1407\n",
       "STANDING              1374\n",
       "SITTING               1286\n",
       "WALKING               1226\n",
       "WALKING_UPSTAIRS      1073\n",
       "WALKING_DOWNSTAIRS     986\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Activity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7352, 563)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2947, 563)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will shuffle data to ensure better randomization\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#be careful to shuffle the examples with the labels\n",
    "train = shuffle(train)\n",
    "test = shuffle(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split train into X and y\n",
    "train_data = train.drop('Activity', axis=1).values\n",
    "train_labels = train.Activity.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split test into X and y\n",
    "test_data = test.drop('Activity', axis=1).values\n",
    "test_labels = test.Activity.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will use LabelEncoder to Hot encode the labels into 6 columns of 0/1 values\n",
    "encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder.fit(train_labels)\n",
    "e_train_labels = encoder.transform(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder.fit(test_labels)\n",
    "e_test_labels = encoder.transform(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a classification problem\n",
    "# there are many models we can use for classification problem. We will use many and compare.\n",
    "# we will start with Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.neural_network as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NN classfier with SGD optimizer\n",
    "nnSGD = nn.MLPClassifier(hidden_layer_sizes=(90, ), max_iter=10, alpha=1e-4, solver='sgd', \n",
    "                          verbose=10, tol=1e-19, random_state=1, learning_rate_init=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NN classfier with ADAM optimizer\n",
    "nnADAM = nn.MLPClassifier(hidden_layer_sizes=(90, ), max_iter=10, alpha=1e-4, solver='adam', \n",
    "                          verbose=10, tol=1e-19, random_state=1, learning_rate_init=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NN classfier with LBFGS optimizer\n",
    "nnLBFGS = nn.MLPClassifier(hidden_layer_sizes=(90, ), max_iter=10, alpha=1e-4, solver='lbfgs', \n",
    "                          verbose=10, tol=1e-19, random_state=1, learning_rate_init=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.54518988\n",
      "Iteration 2, loss = 0.99259400\n",
      "Iteration 3, loss = 0.80926879\n",
      "Iteration 4, loss = 0.69453747\n",
      "Iteration 5, loss = 0.61732060\n",
      "Iteration 6, loss = 0.55983363\n",
      "Iteration 7, loss = 0.51584765\n",
      "Iteration 8, loss = 0.47904992\n",
      "Iteration 9, loss = 0.44978712\n",
      "Iteration 10, loss = 0.42473593\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "nnModelSGD = nnSGD.fit(train_data, e_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8754665761791652"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnModelSGD.score(test_data, e_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.97505119\n",
      "Iteration 2, loss = 0.39226816\n",
      "Iteration 3, loss = 0.28111243\n",
      "Iteration 4, loss = 0.21150314\n",
      "Iteration 5, loss = 0.18308846\n",
      "Iteration 6, loss = 0.15683344\n",
      "Iteration 7, loss = 0.13152995\n",
      "Iteration 8, loss = 0.11837213\n",
      "Iteration 9, loss = 0.10688711\n",
      "Iteration 10, loss = 0.09743504\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "nnModelADAM = nnADAM.fit(train_data, e_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9440108585001696"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnModelADAM.score(test_data, e_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "nnModelLBFGS = nnLBFGS.fit(train_data, e_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6891754326433661"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnModelLBFGS.score(test_data, e_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Activity</th>\n",
       "      <th>LAYING</th>\n",
       "      <th>SITTING</th>\n",
       "      <th>STANDING</th>\n",
       "      <th>WALKING</th>\n",
       "      <th>WALKING_DOWNSTAIRS</th>\n",
       "      <th>WALKING_UPSTAIRS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>95</td>\n",
       "      <td>49</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>52</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>57</td>\n",
       "      <td>47</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
       "      <td>48</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>57</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>59</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>51</td>\n",
       "      <td>54</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>45</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>78</td>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>78</td>\n",
       "      <td>61</td>\n",
       "      <td>46</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>89</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>72</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>72</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>59</td>\n",
       "      <td>54</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>58</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>80</td>\n",
       "      <td>72</td>\n",
       "      <td>79</td>\n",
       "      <td>54</td>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>69</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>53</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>70</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>62</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Activity  LAYING  SITTING  STANDING  WALKING  WALKING_DOWNSTAIRS  \\\n",
       "subject                                                            \n",
       "1             50       47        53       95                  49   \n",
       "3             62       52        61       58                  49   \n",
       "5             52       44        56       56                  47   \n",
       "6             57       55        57       57                  48   \n",
       "7             52       48        53       57                  47   \n",
       "8             54       46        54       48                  38   \n",
       "11            57       53        47       59                  46   \n",
       "14            51       54        60       59                  45   \n",
       "15            72       59        53       54                  42   \n",
       "16            70       69        78       51                  47   \n",
       "17            71       64        78       61                  46   \n",
       "19            83       73        73       52                  39   \n",
       "21            90       85        89       52                  45   \n",
       "22            72       62        63       46                  36   \n",
       "23            72       68        68       59                  54   \n",
       "25            73       65        74       74                  58   \n",
       "26            76       78        74       59                  50   \n",
       "27            74       70        80       57                  44   \n",
       "28            80       72        79       54                  46   \n",
       "29            69       60        65       53                  48   \n",
       "30            70       62        59       65                  62   \n",
       "\n",
       "Activity  WALKING_UPSTAIRS  \n",
       "subject                     \n",
       "1                       53  \n",
       "3                       59  \n",
       "5                       47  \n",
       "6                       51  \n",
       "7                       51  \n",
       "8                       41  \n",
       "11                      54  \n",
       "14                      54  \n",
       "15                      48  \n",
       "16                      51  \n",
       "17                      48  \n",
       "19                      40  \n",
       "21                      47  \n",
       "22                      42  \n",
       "23                      51  \n",
       "25                      65  \n",
       "26                      55  \n",
       "27                      51  \n",
       "28                      51  \n",
       "29                      49  \n",
       "30                      65  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train.subject, train.Activity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#browse subject 1 data\n",
    "sub1 = train.loc[train['subject']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.260974</td>\n",
       "      <td>0.005191</td>\n",
       "      <td>-0.146238</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>-0.170930</td>\n",
       "      <td>-0.309355</td>\n",
       "      <td>-0.085388</td>\n",
       "      <td>-0.211731</td>\n",
       "      <td>-0.304107</td>\n",
       "      <td>0.373082</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.740391</td>\n",
       "      <td>0.191951</td>\n",
       "      <td>0.630972</td>\n",
       "      <td>-0.857481</td>\n",
       "      <td>-0.603588</td>\n",
       "      <td>-0.722410</td>\n",
       "      <td>0.275822</td>\n",
       "      <td>0.091633</td>\n",
       "      <td>1</td>\n",
       "      <td>WALKING_DOWNSTAIRS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.281505</td>\n",
       "      <td>-0.018435</td>\n",
       "      <td>-0.111393</td>\n",
       "      <td>-0.995468</td>\n",
       "      <td>-0.984330</td>\n",
       "      <td>-0.990995</td>\n",
       "      <td>-0.995978</td>\n",
       "      <td>-0.982309</td>\n",
       "      <td>-0.990284</td>\n",
       "      <td>-0.939789</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.853232</td>\n",
       "      <td>-0.143776</td>\n",
       "      <td>-0.235155</td>\n",
       "      <td>0.689866</td>\n",
       "      <td>-0.573561</td>\n",
       "      <td>-0.645146</td>\n",
       "      <td>-0.013061</td>\n",
       "      <td>-0.210430</td>\n",
       "      <td>1</td>\n",
       "      <td>SITTING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.273108</td>\n",
       "      <td>0.006518</td>\n",
       "      <td>-0.109472</td>\n",
       "      <td>-0.990783</td>\n",
       "      <td>-0.935065</td>\n",
       "      <td>-0.990722</td>\n",
       "      <td>-0.992498</td>\n",
       "      <td>-0.928563</td>\n",
       "      <td>-0.989584</td>\n",
       "      <td>-0.939637</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.821055</td>\n",
       "      <td>-0.033284</td>\n",
       "      <td>-0.192609</td>\n",
       "      <td>0.617874</td>\n",
       "      <td>-0.512405</td>\n",
       "      <td>-0.650782</td>\n",
       "      <td>-0.003049</td>\n",
       "      <td>-0.211196</td>\n",
       "      <td>1</td>\n",
       "      <td>SITTING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.278720</td>\n",
       "      <td>-0.014508</td>\n",
       "      <td>-0.115309</td>\n",
       "      <td>-0.998038</td>\n",
       "      <td>-0.985794</td>\n",
       "      <td>-0.998455</td>\n",
       "      <td>-0.998672</td>\n",
       "      <td>-0.983753</td>\n",
       "      <td>-0.999520</td>\n",
       "      <td>-0.938301</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.864215</td>\n",
       "      <td>0.033310</td>\n",
       "      <td>0.193629</td>\n",
       "      <td>-0.079226</td>\n",
       "      <td>-0.140716</td>\n",
       "      <td>-0.672715</td>\n",
       "      <td>0.322105</td>\n",
       "      <td>0.049121</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.278993</td>\n",
       "      <td>-0.014531</td>\n",
       "      <td>-0.106596</td>\n",
       "      <td>-0.998060</td>\n",
       "      <td>-0.986070</td>\n",
       "      <td>-0.993424</td>\n",
       "      <td>-0.998059</td>\n",
       "      <td>-0.985192</td>\n",
       "      <td>-0.995018</td>\n",
       "      <td>-0.942559</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.937483</td>\n",
       "      <td>0.025652</td>\n",
       "      <td>0.066503</td>\n",
       "      <td>-0.226316</td>\n",
       "      <td>-0.225358</td>\n",
       "      <td>-0.762197</td>\n",
       "      <td>0.262090</td>\n",
       "      <td>0.029404</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  \\\n",
       "301           0.260974           0.005191          -0.146238   \n",
       "40            0.281505          -0.018435          -0.111393   \n",
       "39            0.273108           0.006518          -0.109472   \n",
       "196           0.278720          -0.014508          -0.115309   \n",
       "25            0.278993          -0.014531          -0.106596   \n",
       "\n",
       "     tBodyAcc-std()-X  tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  \\\n",
       "301          0.000084         -0.170930         -0.309355         -0.085388   \n",
       "40          -0.995468         -0.984330         -0.990995         -0.995978   \n",
       "39          -0.990783         -0.935065         -0.990722         -0.992498   \n",
       "196         -0.998038         -0.985794         -0.998455         -0.998672   \n",
       "25          -0.998060         -0.986070         -0.993424         -0.998059   \n",
       "\n",
       "     tBodyAcc-mad()-Y  tBodyAcc-mad()-Z  tBodyAcc-max()-X         ...          \\\n",
       "301         -0.211731         -0.304107          0.373082         ...           \n",
       "40          -0.982309         -0.990284         -0.939789         ...           \n",
       "39          -0.928563         -0.989584         -0.939637         ...           \n",
       "196         -0.983753         -0.999520         -0.938301         ...           \n",
       "25          -0.985192         -0.995018         -0.942559         ...           \n",
       "\n",
       "     fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
       "301                        -0.740391                     0.191951   \n",
       "40                         -0.853232                    -0.143776   \n",
       "39                         -0.821055                    -0.033284   \n",
       "196                        -0.864215                     0.033310   \n",
       "25                         -0.937483                     0.025652   \n",
       "\n",
       "     angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
       "301                              0.630972                         -0.857481   \n",
       "40                              -0.235155                          0.689866   \n",
       "39                              -0.192609                          0.617874   \n",
       "196                              0.193629                         -0.079226   \n",
       "25                               0.066503                         -0.226316   \n",
       "\n",
       "     angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
       "301                             -0.603588             -0.722410   \n",
       "40                              -0.573561             -0.645146   \n",
       "39                              -0.512405             -0.650782   \n",
       "196                             -0.140716             -0.672715   \n",
       "25                              -0.225358             -0.762197   \n",
       "\n",
       "     angle(Y,gravityMean)  angle(Z,gravityMean)  subject            Activity  \n",
       "301              0.275822              0.091633        1  WALKING_DOWNSTAIRS  \n",
       "40              -0.013061             -0.210430        1             SITTING  \n",
       "39              -0.003049             -0.211196        1             SITTING  \n",
       "196              0.322105              0.049121        1            STANDING  \n",
       "25               0.262090              0.029404        1            STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25    409\n",
       "21    408\n",
       "26    392\n",
       "30    383\n",
       "28    382\n",
       "27    376\n",
       "23    372\n",
       "17    368\n",
       "16    366\n",
       "19    360\n",
       "1     347\n",
       "29    344\n",
       "3     341\n",
       "15    328\n",
       "6     325\n",
       "14    323\n",
       "22    321\n",
       "11    316\n",
       "7     308\n",
       "5     302\n",
       "8     281\n",
       "Name: subject, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.subject.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's plot some figures\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "\n",
    "#let's plot  some figures to show the relationship between some sensors and the activity for subject1\n",
    "\n",
    "fig = plt.figure(figsize=(32,24))\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1 = sb.stripplot(x='Activity', y=sub1.iloc[:,0], data=sub1, jitter=True)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2 = sb.stripplot(x='Activity', y=sub1.iloc[:,1], data=sub1, jitter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2304x1728 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(32,24))\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1 = sb.stripplot(x='Activity', y=sub1.iloc[:,2], data=sub1, jitter=True)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2 = sb.stripplot(x='Activity', y=sub1.iloc[:,3], data=sub1, jitter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2304x1728 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(32,24))\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1 = sb.stripplot(x='Activity', y=sub1.iloc[:,4], data=sub1, jitter=True)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2 = sb.stripplot(x='Activity', y=sub1.iloc[:,5], data=sub1, jitter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reload data\n",
    "train_df = pd.read_csv(\"train.csv\")\n",
    "test_df = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_activities = train_df.Activity.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "replacer = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#an anlternative of LabelEncoder\n",
    "for i, activity in enumerate(unique_activities):\n",
    "    replacer[activity] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.Activity = train_df.Activity.replace(replacer)\n",
    "test_df.Activity = test_df.Activity.replace(replacer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.288585</td>\n",
       "      <td>-0.020294</td>\n",
       "      <td>-0.132905</td>\n",
       "      <td>-0.995279</td>\n",
       "      <td>-0.983111</td>\n",
       "      <td>-0.913526</td>\n",
       "      <td>-0.995112</td>\n",
       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
       "      <td>-0.934724</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
       "      <td>-0.058627</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278419</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
       "      <td>-0.978944</td>\n",
       "      <td>-0.996520</td>\n",
       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.482845</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.012892</td>\n",
       "      <td>0.640011</td>\n",
       "      <td>-0.485366</td>\n",
       "      <td>-0.848649</td>\n",
       "      <td>0.181935</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.276629</td>\n",
       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
       "      <td>-0.980817</td>\n",
       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.699205</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.693578</td>\n",
       "      <td>-0.615971</td>\n",
       "      <td>-0.847865</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>-0.043892</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.277199</td>\n",
       "      <td>-0.010098</td>\n",
       "      <td>-0.105137</td>\n",
       "      <td>-0.997335</td>\n",
       "      <td>-0.990487</td>\n",
       "      <td>-0.995420</td>\n",
       "      <td>-0.997627</td>\n",
       "      <td>-0.990218</td>\n",
       "      <td>-0.995549</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.844619</td>\n",
       "      <td>0.082632</td>\n",
       "      <td>-0.143439</td>\n",
       "      <td>0.275041</td>\n",
       "      <td>-0.368224</td>\n",
       "      <td>-0.849632</td>\n",
       "      <td>0.184823</td>\n",
       "      <td>-0.042126</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.279454</td>\n",
       "      <td>-0.019641</td>\n",
       "      <td>-0.110022</td>\n",
       "      <td>-0.996921</td>\n",
       "      <td>-0.967186</td>\n",
       "      <td>-0.983118</td>\n",
       "      <td>-0.997003</td>\n",
       "      <td>-0.966097</td>\n",
       "      <td>-0.983116</td>\n",
       "      <td>-0.940987</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.564430</td>\n",
       "      <td>-0.212754</td>\n",
       "      <td>-0.230622</td>\n",
       "      <td>0.014637</td>\n",
       "      <td>-0.189512</td>\n",
       "      <td>-0.852150</td>\n",
       "      <td>0.182170</td>\n",
       "      <td>-0.043010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.277432</td>\n",
       "      <td>-0.030488</td>\n",
       "      <td>-0.125360</td>\n",
       "      <td>-0.996559</td>\n",
       "      <td>-0.966728</td>\n",
       "      <td>-0.981585</td>\n",
       "      <td>-0.996485</td>\n",
       "      <td>-0.966313</td>\n",
       "      <td>-0.982982</td>\n",
       "      <td>-0.940987</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.421715</td>\n",
       "      <td>-0.020888</td>\n",
       "      <td>0.593996</td>\n",
       "      <td>-0.561871</td>\n",
       "      <td>0.467383</td>\n",
       "      <td>-0.851017</td>\n",
       "      <td>0.183779</td>\n",
       "      <td>-0.041976</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.277293</td>\n",
       "      <td>-0.021751</td>\n",
       "      <td>-0.120751</td>\n",
       "      <td>-0.997328</td>\n",
       "      <td>-0.961245</td>\n",
       "      <td>-0.983672</td>\n",
       "      <td>-0.997596</td>\n",
       "      <td>-0.957236</td>\n",
       "      <td>-0.984379</td>\n",
       "      <td>-0.940598</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.572995</td>\n",
       "      <td>0.012954</td>\n",
       "      <td>0.080936</td>\n",
       "      <td>-0.234313</td>\n",
       "      <td>0.117797</td>\n",
       "      <td>-0.847971</td>\n",
       "      <td>0.188982</td>\n",
       "      <td>-0.037364</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.280586</td>\n",
       "      <td>-0.009960</td>\n",
       "      <td>-0.106065</td>\n",
       "      <td>-0.994803</td>\n",
       "      <td>-0.972758</td>\n",
       "      <td>-0.986244</td>\n",
       "      <td>-0.995405</td>\n",
       "      <td>-0.973663</td>\n",
       "      <td>-0.985642</td>\n",
       "      <td>-0.940028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140452</td>\n",
       "      <td>-0.020590</td>\n",
       "      <td>-0.127730</td>\n",
       "      <td>-0.482871</td>\n",
       "      <td>-0.070670</td>\n",
       "      <td>-0.848294</td>\n",
       "      <td>0.190310</td>\n",
       "      <td>-0.034417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
       "5           0.277199          -0.010098          -0.105137         -0.997335   \n",
       "6           0.279454          -0.019641          -0.110022         -0.996921   \n",
       "7           0.277432          -0.030488          -0.125360         -0.996559   \n",
       "8           0.277293          -0.021751          -0.120751         -0.997328   \n",
       "9           0.280586          -0.009960          -0.106065         -0.994803   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
       "5         -0.990487         -0.995420         -0.997627         -0.990218   \n",
       "6         -0.967186         -0.983118         -0.997003         -0.966097   \n",
       "7         -0.966728         -0.981585         -0.996485         -0.966313   \n",
       "8         -0.961245         -0.983672         -0.997596         -0.957236   \n",
       "9         -0.972758         -0.986244         -0.995405         -0.973663   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X    ...     \\\n",
       "0         -0.923527         -0.934724    ...      \n",
       "1         -0.957686         -0.943068    ...      \n",
       "2         -0.977469         -0.938692    ...      \n",
       "3         -0.989302         -0.938692    ...      \n",
       "4         -0.990441         -0.942469    ...      \n",
       "5         -0.995549         -0.942469    ...      \n",
       "6         -0.983116         -0.940987    ...      \n",
       "7         -0.982982         -0.940987    ...      \n",
       "8         -0.984379         -0.940598    ...      \n",
       "9         -0.985642         -0.940028    ...      \n",
       "\n",
       "   fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
       "0                        -0.710304                    -0.112754   \n",
       "1                        -0.861499                     0.053477   \n",
       "2                        -0.760104                    -0.118559   \n",
       "3                        -0.482845                    -0.036788   \n",
       "4                        -0.699205                     0.123320   \n",
       "5                        -0.844619                     0.082632   \n",
       "6                        -0.564430                    -0.212754   \n",
       "7                        -0.421715                    -0.020888   \n",
       "8                        -0.572995                     0.012954   \n",
       "9                         0.140452                    -0.020590   \n",
       "\n",
       "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                              0.030400                         -0.464761   \n",
       "1                             -0.007435                         -0.732626   \n",
       "2                              0.177899                          0.100699   \n",
       "3                             -0.012892                          0.640011   \n",
       "4                              0.122542                          0.693578   \n",
       "5                             -0.143439                          0.275041   \n",
       "6                             -0.230622                          0.014637   \n",
       "7                              0.593996                         -0.561871   \n",
       "8                              0.080936                         -0.234313   \n",
       "9                             -0.127730                         -0.482871   \n",
       "\n",
       "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
       "0                             -0.018446             -0.841247   \n",
       "1                              0.703511             -0.844788   \n",
       "2                              0.808529             -0.848933   \n",
       "3                             -0.485366             -0.848649   \n",
       "4                             -0.615971             -0.847865   \n",
       "5                             -0.368224             -0.849632   \n",
       "6                             -0.189512             -0.852150   \n",
       "7                              0.467383             -0.851017   \n",
       "8                              0.117797             -0.847971   \n",
       "9                             -0.070670             -0.848294   \n",
       "\n",
       "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity  \n",
       "0              0.179941             -0.058627        1         0  \n",
       "1              0.180289             -0.054317        1         0  \n",
       "2              0.180637             -0.049118        1         0  \n",
       "3              0.181935             -0.047663        1         0  \n",
       "4              0.185151             -0.043892        1         0  \n",
       "5              0.184823             -0.042126        1         0  \n",
       "6              0.182170             -0.043010        1         0  \n",
       "7              0.183779             -0.041976        1         0  \n",
       "8              0.188982             -0.037364        1         0  \n",
       "9              0.190310             -0.034417        1         0  \n",
       "\n",
       "[10 rows x 563 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop('subject', axis=1)\n",
    "test_df = test_df.drop('subject', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_data():\n",
    "    train_df = pd.read_csv(\"train.csv\")\n",
    "    test_df = pd.read_csv(\"test.csv\")\n",
    "    train_df = train_df.drop('subject', axis=1)\n",
    "    test_df = test_df.drop('subject', axis=1)\n",
    "    train_values = train_df.values\n",
    "    test_values = test_df.values\n",
    "    np.random.shuffle(train_values)\n",
    "    np.random.shuffle(test_values)\n",
    "\n",
    "    X_train = train_values[:,:-1]\n",
    "    y_train = train_values[:,-1]\n",
    "\n",
    "    X_test = test_values[:,:-1]\n",
    "    y_test = test_values[:,-1]\n",
    "    return X_train, y_train, X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = get_all_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#After NN let's try LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(C=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9619952494061758"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's try LogisticRegression with PCA\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9623345775364778"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, y_train, X_test, y_test = get_all_data()\n",
    "pca = PCA(n_components=200)\n",
    "\n",
    "pca.fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test = pca.transform(X_test)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9667458432304038"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's try LogisticRegression with StandarScaler instead of PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train, y_train, X_test, y_test = get_all_data()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Now we are using Keras library\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train, X_test, y_test = get_all_data()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 6) (2947, 6)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "n_input = X_train.shape[1]\n",
    "n_output = 6 #number of possible situations\n",
    "n_samples = X_train.shape[0]\n",
    "n_hidden_units = 40\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(y_train)\n",
    "y_train = encoder.transform(y_train)\n",
    "y_test = encoder.transform(y_test)\n",
    "\n",
    "Y_train = to_categorical(y_train)\n",
    "Y_test = to_categorical(y_test)\n",
    "print(Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(n_hidden_units,\n",
    "                   input_dim = n_input,\n",
    "                   activation = \"relu\"))\n",
    "    model.add(Dense(n_hidden_units,\n",
    "                   input_dim = n_input,\n",
    "                   activation = \"relu\"))\n",
    "    model.add(Dense(n_output, activation=\"softmax\"))\n",
    "    \n",
    "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Score:  0.9372242894219591\n"
     ]
    }
   ],
   "source": [
    "estimator = KerasClassifier(build_fn=create_model, epochs=20, batch_size=10, verbose=False)\n",
    "estimator.fit(X_train, Y_train)\n",
    "print(\"Score: \", estimator.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9290804207668816"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's try RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X_train, y_train, X_test, y_test = get_all_data()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model = RandomForestClassifier(n_estimators = 500)\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.optimizers import SGD, Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, train_labels, test_data, test_labels = get_all_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:371: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:371: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "encoded_train_labels = label_encoder.fit_transform(train_labels)\n",
    "encoded_test_labels = label_encoder.fit_transform(test_labels)\n",
    "\n",
    "onehot_encoder = OneHotEncoder(sparse=False)\n",
    "\n",
    "encoded_train_labels = encoded_train_labels.reshape(len(encoded_train_labels), 1)\n",
    "encoded_test_labels = encoded_test_labels.reshape(len(encoded_test_labels), 1)\n",
    "\n",
    "onehot_encoded_train_labels = onehot_encoder.fit_transform(encoded_train_labels)\n",
    "onehot_encoded_test_labels = onehot_encoder.fit_transform(encoded_test_labels)\n",
    "\n",
    "train_labels = onehot_encoded_train_labels\n",
    "test_labels = onehot_encoded_test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2947, 561) (2947, 6)\n"
     ]
    }
   ],
   "source": [
    "print(test_data.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "7352/7352 [==============================] - 3s 369us/step - loss: 1.4257 - acc: 0.3787\n",
      "Epoch 2/30\n",
      "7352/7352 [==============================] - 1s 193us/step - loss: 0.8544 - acc: 0.6091\n",
      "Epoch 3/30\n",
      "7352/7352 [==============================] - 1s 200us/step - loss: 0.6553 - acc: 0.6959\n",
      "Epoch 4/30\n",
      "7352/7352 [==============================] - 2s 205us/step - loss: 0.5412 - acc: 0.7459\n",
      "Epoch 5/30\n",
      "7352/7352 [==============================] - 2s 208us/step - loss: 0.5033 - acc: 0.7601\n",
      "Epoch 6/30\n",
      "7352/7352 [==============================] - 1s 203us/step - loss: 0.4499 - acc: 0.7871\n",
      "Epoch 7/30\n",
      "7352/7352 [==============================] - 1s 200us/step - loss: 0.4247 - acc: 0.8017\n",
      "Epoch 8/30\n",
      "7352/7352 [==============================] - 1s 196us/step - loss: 0.3856 - acc: 0.8301\n",
      "Epoch 9/30\n",
      "7352/7352 [==============================] - 1s 193us/step - loss: 0.3633 - acc: 0.8395\n",
      "Epoch 10/30\n",
      "7352/7352 [==============================] - 1s 201us/step - loss: 0.3310 - acc: 0.8599\n",
      "Epoch 11/30\n",
      "7352/7352 [==============================] - 1s 192us/step - loss: 0.3156 - acc: 0.8643\n",
      "Epoch 12/30\n",
      "7352/7352 [==============================] - 1s 194us/step - loss: 0.3074 - acc: 0.8751\n",
      "Epoch 13/30\n",
      "7352/7352 [==============================] - 1s 199us/step - loss: 0.2939 - acc: 0.8840\n",
      "Epoch 14/30\n",
      "7352/7352 [==============================] - 1s 196us/step - loss: 0.2718 - acc: 0.8900\n",
      "Epoch 15/30\n",
      "7352/7352 [==============================] - 1s 197us/step - loss: 0.2630 - acc: 0.8951\n",
      "Epoch 16/30\n",
      "7352/7352 [==============================] - 1s 196us/step - loss: 0.2498 - acc: 0.8979\n",
      "Epoch 17/30\n",
      "7352/7352 [==============================] - 1s 193us/step - loss: 0.2357 - acc: 0.9038\n",
      "Epoch 18/30\n",
      "7352/7352 [==============================] - 1s 202us/step - loss: 0.2257 - acc: 0.9100\n",
      "Epoch 19/30\n",
      "7352/7352 [==============================] - 1s 203us/step - loss: 0.2194 - acc: 0.9125\n",
      "Epoch 20/30\n",
      "7352/7352 [==============================] - 1s 197us/step - loss: 0.2122 - acc: 0.9163\n",
      "Epoch 21/30\n",
      "7352/7352 [==============================] - 1s 200us/step - loss: 0.2140 - acc: 0.9174\n",
      "Epoch 22/30\n",
      "7352/7352 [==============================] - 1s 196us/step - loss: 0.1872 - acc: 0.9276\n",
      "Epoch 23/30\n",
      "7352/7352 [==============================] - 1s 197us/step - loss: 0.1930 - acc: 0.9257\n",
      "Epoch 24/30\n",
      "7352/7352 [==============================] - 1s 197us/step - loss: 0.1923 - acc: 0.9227\n",
      "Epoch 25/30\n",
      "7352/7352 [==============================] - 1s 194us/step - loss: 0.1865 - acc: 0.9257\n",
      "Epoch 26/30\n",
      "7352/7352 [==============================] - 1s 202us/step - loss: 0.1843 - acc: 0.9278\n",
      "Epoch 27/30\n",
      "7352/7352 [==============================] - 1s 193us/step - loss: 0.1862 - acc: 0.9257\n",
      "Epoch 28/30\n",
      "7352/7352 [==============================] - 1s 193us/step - loss: 0.1890 - acc: 0.9266\n",
      "Epoch 29/30\n",
      "7352/7352 [==============================] - 1s 192us/step - loss: 0.1824 - acc: 0.9285\n",
      "Epoch 30/30\n",
      "7352/7352 [==============================] - 1s 196us/step - loss: 0.1850 - acc: 0.9242\n",
      "2947/2947 [==============================] - 1s 278us/step\n",
      "[0.18343061172698247, 0.9233118425517476]\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation=\"relu\", input_dim=561))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(6, activation=\"softmax\"))\n",
    "\n",
    "#SGD optimizer\n",
    "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_data, train_labels, nb_epoch=30, batch_size=128)\n",
    "score = model.evaluate(test_data, test_labels, batch_size=128)\n",
    "print (score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "7352/7352 [==============================] - 3s 396us/step - loss: 0.1786 - acc: 0.9280\n",
      "Epoch 2/30\n",
      "7352/7352 [==============================] - 2s 207us/step - loss: 0.1594 - acc: 0.9378\n",
      "Epoch 3/30\n",
      "7352/7352 [==============================] - 2s 211us/step - loss: 0.1501 - acc: 0.9429\n",
      "Epoch 4/30\n",
      "7352/7352 [==============================] - 1s 203us/step - loss: 0.1602 - acc: 0.9357\n",
      "Epoch 5/30\n",
      "7352/7352 [==============================] - 1s 196us/step - loss: 0.1448 - acc: 0.9448\n",
      "Epoch 6/30\n",
      "7352/7352 [==============================] - 2s 212us/step - loss: 0.1502 - acc: 0.9388\n",
      "Epoch 7/30\n",
      "7352/7352 [==============================] - 1s 200us/step - loss: 0.1500 - acc: 0.9411\n",
      "Epoch 8/30\n",
      "7352/7352 [==============================] - 1s 198us/step - loss: 0.1340 - acc: 0.9490\n",
      "Epoch 9/30\n",
      "7352/7352 [==============================] - 1s 194us/step - loss: 0.1297 - acc: 0.9483\n",
      "Epoch 10/30\n",
      "7352/7352 [==============================] - 1s 199us/step - loss: 0.1406 - acc: 0.9395\n",
      "Epoch 11/30\n",
      "7352/7352 [==============================] - 1s 198us/step - loss: 0.1463 - acc: 0.9381\n",
      "Epoch 12/30\n",
      "7352/7352 [==============================] - 1s 201us/step - loss: 0.1599 - acc: 0.9331\n",
      "Epoch 13/30\n",
      "7352/7352 [==============================] - 1s 198us/step - loss: 0.1582 - acc: 0.9331\n",
      "Epoch 14/30\n",
      "7352/7352 [==============================] - 1s 199us/step - loss: 0.1483 - acc: 0.9414\n",
      "Epoch 15/30\n",
      "7352/7352 [==============================] - 1s 198us/step - loss: 0.1398 - acc: 0.9436\n",
      "Epoch 16/30\n",
      "7352/7352 [==============================] - 1s 195us/step - loss: 0.1484 - acc: 0.9414\n",
      "Epoch 17/30\n",
      "7352/7352 [==============================] - 1s 194us/step - loss: 0.1291 - acc: 0.9478\n",
      "Epoch 18/30\n",
      "7352/7352 [==============================] - 2s 205us/step - loss: 0.1454 - acc: 0.9412\n",
      "Epoch 19/30\n",
      "7352/7352 [==============================] - 1s 200us/step - loss: 0.1482 - acc: 0.9395\n",
      "Epoch 20/30\n",
      "7352/7352 [==============================] - 1s 199us/step - loss: 0.1350 - acc: 0.9423\n",
      "Epoch 21/30\n",
      "7352/7352 [==============================] - 1s 197us/step - loss: 0.1352 - acc: 0.9431\n",
      "Epoch 22/30\n",
      "7352/7352 [==============================] - 2s 206us/step - loss: 0.1390 - acc: 0.9425\n",
      "Epoch 23/30\n",
      "7352/7352 [==============================] - 1s 196us/step - loss: 0.1334 - acc: 0.9427\n",
      "Epoch 24/30\n",
      "7352/7352 [==============================] - 1s 200us/step - loss: 0.1221 - acc: 0.9495\n",
      "Epoch 25/30\n",
      "7352/7352 [==============================] - 1s 197us/step - loss: 0.1359 - acc: 0.9455\n",
      "Epoch 26/30\n",
      "7352/7352 [==============================] - 1s 195us/step - loss: 0.1193 - acc: 0.9524\n",
      "Epoch 27/30\n",
      "7352/7352 [==============================] - 1s 197us/step - loss: 0.1411 - acc: 0.9407\n",
      "Epoch 28/30\n",
      "7352/7352 [==============================] - 1s 199us/step - loss: 0.1190 - acc: 0.9523\n",
      "Epoch 29/30\n",
      "7352/7352 [==============================] - 1s 201us/step - loss: 0.1171 - acc: 0.9512\n",
      "Epoch 30/30\n",
      "7352/7352 [==============================] - 1s 194us/step - loss: 0.1090 - acc: 0.9539\n",
      "2947/2947 [==============================] - 1s 251us/step\n",
      "[0.13777965585083973, 0.9579233118425518]\n"
     ]
    }
   ],
   "source": [
    "#ADAM optimizer\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_data, train_labels, nb_epoch=30, batch_size=128)\n",
    "score = model.evaluate(test_data, test_labels, batch_size=128)\n",
    "print (score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "X_train, y_train, X_test, y_test = get_all_data()\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(y_train)\n",
    "y_train = encoder.fit_transform(y_train)\n",
    "y_test = encoder.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's try support vector machine\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels before shuffle:  [0 2 3 0 5]\n",
      "labels after shuffle:  [3 2 2 1 3]\n"
     ]
    }
   ],
   "source": [
    "#we will calculate processing time\n",
    "t0 = time.clock()\n",
    "\n",
    "svc = SVC(kernel=\"linear\")\n",
    "rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(6), scoring='accuracy')\n",
    "np.random.seed(1)\n",
    "print(\"labels before shuffle: \", y_test[0:5])\n",
    "y_train = shuffle(y_train)\n",
    "y_test = shuffle(y_test)\n",
    "print(\"labels after shuffle: \", y_test[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfecv.fit(X_train, y_train)\n",
    "\n",
    "print(\"Optimal number of features: \", rfecv.n_features_)\n",
    "print(\"Preprocesing time sec: \", time.clock()-t0)\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score = nb. of correct classifications\")\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print (\"SVM accruacy: \", rfecv.score(X_test, y_test))\n",
    "print(\"Features rancing: \", rfecv.ranking_)\n",
    "\n",
    "best_features = []\n",
    "for ix, val in enumerate(rfecv.support_):\n",
    "    if val==True:\n",
    "        best_features.append(X_test[:,ix])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'best_features' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-70-19cb38e1fd59>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotting\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mscatter_matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mvisualize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbest_features\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mvisualize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mscatter_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvisualize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiagonal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"kde\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'best_features' is not defined"
     ]
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "visualize = pd.DataFrame(np.asarray(best_features).T)\n",
    "print (visualize.shape)\n",
    "scatter_matrix(visualize.iloc[:, 0:5], alpha=0.2, figsize=(6,6), diagonal=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining the function again in case I want to run the notebook from the middle\n",
    "def get_all_data():\n",
    "    train_df = pd.read_csv(\"train.csv\")\n",
    "    test_df = pd.read_csv(\"test.csv\")\n",
    "    train_df = train_df.drop('subject', axis=1)\n",
    "    test_df = test_df.drop('subject', axis=1)\n",
    "    train_values = train_df.values\n",
    "    test_values = test_df.values\n",
    "    np.random.shuffle(train_values)\n",
    "    np.random.shuffle(test_values)\n",
    "\n",
    "    X_train = train_values[:,:-1]\n",
    "    y_train = train_values[:,-1]\n",
    "\n",
    "    X_test = test_values[:,:-1]\n",
    "    y_test = test_values[:,-1]\n",
    "    return X_train, y_train, X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "#Let's use tensorflow\n",
    "import tensorflow as tf\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['WALKING' 'WALKING' 'WALKING' ... 'LAYING' 'WALKING_UPSTAIRS'\n",
      " 'WALKING_UPSTAIRS'] ['WALKING' 'SITTING' 'STANDING' ... 'LAYING' 'STANDING'\n",
      " 'WALKING_DOWNSTAIRS']\n",
      "[[0 0 0 1 0 0]\n",
      " [0 0 0 1 0 0]\n",
      " [0 0 0 1 0 0]\n",
      " ...\n",
      " [1 0 0 0 0 0]\n",
      " [0 0 0 0 0 1]\n",
      " [0 0 0 0 0 1]] [[0 0 0 1 0 0]\n",
      " [0 1 0 0 0 0]\n",
      " [0 0 1 0 0 0]\n",
      " ...\n",
      " [1 0 0 0 0 0]\n",
      " [0 0 1 0 0 0]\n",
      " [0 0 0 0 1 0]]\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train, X_test, y_test = get_all_data()\n",
    "\n",
    "print(y_train, y_test)\n",
    "binarizer = LabelBinarizer().fit(y_train)\n",
    "y_train = binarizer.transform(y_train)\n",
    "y_test = binarizer.transform(y_test)\n",
    "\n",
    "print(y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weight_variable(shape):\n",
    "    return tf.Variable(tf.truncated_normal(shape, stddev=0.1))\n",
    "\n",
    "def bias_variable(shape):\n",
    "    return tf.Variable(tf.truncated_normal(shape, stddev=0.1))\n",
    "\n",
    "def add_layer(inputs, input_size, output_size, activation= None):\n",
    "    W = weight_variable([input_size, output_size])\n",
    "    b = bias_variable([output_size])\n",
    "    wxb = tf.matmul(inputs, W) + b\n",
    "    if activation:\n",
    "        return activation(wxb)\n",
    "    else:\n",
    "        return wxb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "X = tf.placeholder(tf.float32, [None, 561])\n",
    "layer1 = add_layer(X, 561, 1000, tf.nn.relu)\n",
    "layer2 = add_layer(layer1, 1000, 300, tf.nn.relu)\n",
    "layer3 = add_layer(layer2, 300, 50, tf.nn.relu)\n",
    "output = add_layer(layer3, 50, 6)\n",
    "\n",
    "y_ = tf.placeholder(tf.float32, [None, 6])\n",
    "#logit_q = tf.placeholder(tf.float32, [None, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_, logits=output))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.001)\n",
    "train_step = optimizer.minimize(loss)\n",
    "\n",
    "correct = tf.equal(tf.argmax(output,1), tf.argmax(y_,1))\n",
    "score = tf.reduce_mean(tf.cast(correct, \"float\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:193: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "init = tf.initialize_all_variables()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 561)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 561) (100, 6)\n",
      "0.6079863\n",
      "(100, 561) (100, 6)\n",
      "0.6314866\n",
      "(100, 561) (100, 6)\n",
      "0.6186188\n",
      "(100, 561) (100, 6)\n",
      "0.5956061\n",
      "(100, 561) (100, 6)\n",
      "0.6045017\n",
      "(100, 561) (100, 6)\n",
      "0.6074314\n",
      "(100, 561) (100, 6)\n",
      "0.5759426\n",
      "(100, 561) (100, 6)\n",
      "0.58273023\n",
      "(100, 561) (100, 6)\n",
      "0.5493043\n",
      "(100, 561) (100, 6)\n",
      "0.5626909\n",
      "(100, 561) (100, 6)\n",
      "0.5562915\n",
      "(100, 561) (100, 6)\n",
      "0.58794695\n",
      "(100, 561) (100, 6)\n",
      "0.57435536\n",
      "(100, 561) (100, 6)\n",
      "0.5295721\n",
      "(100, 561) (100, 6)\n",
      "0.5294671\n",
      "(100, 561) (100, 6)\n",
      "0.53516585\n",
      "(100, 561) (100, 6)\n",
      "0.5454706\n",
      "(100, 561) (100, 6)\n",
      "0.5390203\n",
      "(100, 561) (100, 6)\n",
      "0.5160403\n",
      "(100, 561) (100, 6)\n",
      "0.54017323\n",
      "(100, 561) (100, 6)\n",
      "0.49034145\n",
      "(100, 561) (100, 6)\n",
      "0.5328322\n",
      "(100, 561) (100, 6)\n",
      "0.50434023\n",
      "(100, 561) (100, 6)\n",
      "0.5054143\n",
      "(100, 561) (100, 6)\n",
      "0.48953298\n",
      "(100, 561) (100, 6)\n",
      "0.48809078\n",
      "(100, 561) (100, 6)\n",
      "0.48309103\n",
      "(100, 561) (100, 6)\n",
      "0.5090535\n",
      "(100, 561) (100, 6)\n",
      "0.47127086\n",
      "(100, 561) (100, 6)\n",
      "0.48596272\n",
      "(100, 561) (100, 6)\n",
      "0.469871\n",
      "(100, 561) (100, 6)\n",
      "0.48590097\n",
      "(100, 561) (100, 6)\n",
      "0.46727774\n",
      "(100, 561) (100, 6)\n",
      "0.47145367\n",
      "(100, 561) (100, 6)\n",
      "0.46024576\n",
      "(100, 561) (100, 6)\n",
      "0.47220066\n",
      "(100, 561) (100, 6)\n",
      "0.4762862\n",
      "(100, 561) (100, 6)\n",
      "0.45987707\n",
      "(100, 561) (100, 6)\n",
      "0.4422375\n",
      "(100, 561) (100, 6)\n",
      "0.4639961\n",
      "(100, 561) (100, 6)\n",
      "0.44702056\n",
      "(100, 561) (100, 6)\n",
      "0.45964447\n",
      "(100, 561) (100, 6)\n",
      "0.4596109\n",
      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
      "0.43263316\n",
      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
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      "(100, 561) (100, 6)\n",
      "0.4292635\n",
      "(100, 561) (100, 6)\n",
      "0.4123772\n",
      "(100, 561) (100, 6)\n",
      "0.3905384\n",
      "(100, 561) (100, 6)\n",
      "0.41605225\n",
      "(100, 561) (100, 6)\n",
      "0.40761077\n",
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      "(100, 561) (100, 6)\n",
      "0.29484394\n",
      "(100, 561) (100, 6)\n",
      "0.3000171\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "0.28088352\n",
      "(100, 561) (100, 6)\n",
      "0.2901501\n",
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      "0.2779629\n",
      "(100, 561) (100, 6)\n",
      "0.28245363\n",
      "(100, 561) (100, 6)\n",
      "0.2714302\n",
      "(100, 561) (100, 6)\n",
      "0.2806847\n",
      "(100, 561) (100, 6)\n",
      "0.27126712\n",
      "(100, 561) (100, 6)\n",
      "0.26153654\n",
      "(100, 561) (100, 6)\n",
      "0.27206594\n",
      "(100, 561) (100, 6)\n",
      "0.27993414\n",
      "(100, 561) (100, 6)\n",
      "0.2893311\n",
      "(100, 561) (100, 6)\n",
      "0.27388114\n",
      "(100, 561) (100, 6)\n",
      "0.28062394\n",
      "(100, 561) (100, 6)\n",
      "0.26743826\n",
      "(100, 561) (100, 6)\n",
      "0.2824486\n",
      "(100, 561) (100, 6)\n",
      "0.26825622\n",
      "(100, 561) (100, 6)\n",
      "0.28401226\n",
      "(100, 561) (100, 6)\n",
      "0.278379\n",
      "(100, 561) (100, 6)\n",
      "0.29309368\n",
      "(100, 561) (100, 6)\n",
      "0.28967625\n",
      "(100, 561) (100, 6)\n",
      "0.2755519\n",
      "(100, 561) (100, 6)\n",
      "0.27547505\n",
      "(100, 561) (100, 6)\n",
      "0.28379095\n",
      "(100, 561) (100, 6)\n",
      "0.27968735\n",
      "(100, 561) (100, 6)\n",
      "0.2755604\n"
     ]
    }
   ],
   "source": [
    "for i in range(1000):\n",
    "    batch = np.random.choice(X_train.shape[0], 100)\n",
    "    print(X_train[batch].shape, y_train[batch].shape)\n",
    "    _, cost = sess.run([train_step, loss], feed_dict = {X:X_train[batch], y_:y_train[batch]})\n",
    "    print (cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7183576\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(score, feed_dict={X: X_test, y_: y_test}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = get_all_data()\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "encoder.fit(y_train)\n",
    "y_train = encoder.transform(y_train)\n",
    "y_test = encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trying KNeighborsClassifier and checking accuracy\n",
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for distance (k=5) is: 0.900238\n",
      "Score for distance (k=6) is: 0.905667\n",
      "Score for distance (k=7) is: 0.904309\n",
      "Score for distance (k=8) is: 0.909739\n",
      "Score for distance (k=9) is: 0.906006\n",
      "Score for distance (k=10) is: 0.905667\n",
      "Score for uniform (k=5) is: 0.900238\n",
      "Score for uniform (k=6) is: 0.901934\n",
      "Score for uniform (k=7) is: 0.902273\n",
      "Score for uniform (k=8) is: 0.906006\n",
      "Score for uniform (k=9) is: 0.905327\n",
      "Score for uniform (k=10) is: 0.906685\n"
     ]
    }
   ],
   "source": [
    "knnScoreDistance = np.zeros(11)\n",
    "knnScoreUniform = np.zeros(11)\n",
    "\n",
    "#try many K numbers between 5 and 10, weights: [distance, uniform]\n",
    "\n",
    "for num in range(5, 11):\n",
    "    knnclf = knn(n_neighbors = num, weights='distance')\n",
    "    knnModel = knnclf.fit(X_train, y_train)\n",
    "    knnScoreDistance[num] = knnModel.score(X_test, y_test)\n",
    "    print (\"Score for distance (k=%d) is: %f\" %(num, knnScoreDistance[num]))\n",
    "    \n",
    "for num in range(5, 11):\n",
    "    knnclf = knn(n_neighbors = num, weights='uniform')\n",
    "    knnModel = knnclf.fit(X_train, y_train)\n",
    "    knnScoreUniform[num] = knnModel.score(X_test, y_test)\n",
    "    print (\"Score for uniform (k=%d) is: %f\" %(num, knnScoreUniform[num]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6,)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(range(5, 11))\n",
    "print(x.shape)\n",
    "plt.plot(x, knnScoreDistance[5:])\n",
    "plt.plot(x, knnScoreUniform[5:])\n",
    "plt.xlabel(\"Num of neighbors\")\n",
    "plt.ylabel(\"Test accuracy\")\n",
    "plt.legend([\"KNN-Distance\", \"KNN-Uniform\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we try DecisionTreeClassifier and check the accuracy\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = get_all_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LAYING' 'SITTING' 'STANDING' 'WALKING' 'WALKING_DOWNSTAIRS'\n",
      " 'WALKING_UPSTAIRS']\n",
      "Accuracy = 0.843230\n",
      "[[537   0   0   0   0   0]\n",
      " [  0 382 109   0   0   0]\n",
      " [  0 103 429   0   0   0]\n",
      " [  0   0   0 452  31  13]\n",
      " [  0   0   0  23 351  46]\n",
      " [  0   0   0  79  58 334]]\n"
     ]
    }
   ],
   "source": [
    "dt_clf = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "model  = dt_clf.fit(X_train, y_train)\n",
    "\n",
    "predict = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predict)\n",
    "\n",
    "labels = np.unique(y_train)\n",
    "print(labels)\n",
    "cf_matrix = confusion_matrix(y_test, predict, labels)\n",
    "\n",
    "print (\"Accuracy = %f\" %accuracy)\n",
    "print(cf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plottinng a hot map of the confusion matrix. This is a great way to test accuracy.\n",
    "fig = plt.figure(figsize = (10,7))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(cf_matrix)\n",
    "fig.colorbar(cax)\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "\n",
    "for (i, j), z in np.ndenumerate(cf_matrix):\n",
    "    ax.text(j, i, '{:0.1f}'.format(z), ha='center', va='center')\n",
    "    \n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()\n",
    "#sn.heatmap(cf_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are going to implement some lines of code to classify the data using multiple classifiers and compare the result accuracies.\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = [\n",
    "    DecisionTreeClassifier(),\n",
    "    KNeighborsClassifier(7),\n",
    "    SVC(),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    }
   ],
   "source": [
    "names = []\n",
    "scores = [] \n",
    "\n",
    "for classifier in classifiers:\n",
    "    model = classifier.fit(X_train, y_train)\n",
    "    predict = model.predict(X_test)\n",
    "    \n",
    "    names.append(model.__class__.__name__)\n",
    "    scores.append(accuracy_score(predict, y_test))\n",
    "    \n",
    "score_df = pd.DataFrame({\"Model\": names, \"Score\": scores}).set_index(\"Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.859179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <td>0.902273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>0.940278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.770275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QuadraticDiscriminantAnalysis</th>\n",
       "      <td>0.802511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Score\n",
       "Model                                  \n",
       "DecisionTreeClassifier         0.859179\n",
       "KNeighborsClassifier           0.902273\n",
       "SVC                            0.940278\n",
       "GaussianNB                     0.770275\n",
       "QuadraticDiscriminantAnalysis  0.802511"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'DecisionTreeClassifier'),\n",
       " Text(0,0,'KNeighborsClassifier'),\n",
       " Text(0,0,'SVC'),\n",
       " Text(0,0,'GaussianNB'),\n",
       " Text(0,0,'QuadraticDiscriminantAnalysis')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot accuracies as bar for better compare\n",
    "ax = score_df.plot.bar()\n",
    "ax.set_xticklabels(score_df.index, rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using GridSearchCV to classify the data using multiple classifiers and compare the result accuracies.\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter found:  {'C': 100, 'kernel': 'rbf'}\n",
      "Detailed grid scores: \n",
      "0.9804134929270947 0.0013324158910901552 {'C': 100, 'kernel': 'linear'}\n",
      "\n",
      "0.9857181719260065 0.001516735893738445 {'C': 100, 'kernel': 'rbf'}\n",
      "\n",
      "0.9821817192600653 0.0019565866383907276 {'C': 20, 'kernel': 'linear'}\n",
      "\n",
      "0.9827257889009793 0.0007627628743330216 {'C': 20, 'kernel': 'rbf'}\n",
      "\n",
      "0.9846300326441785 0.0012566659833289032 {'C': 1, 'kernel': 'linear'}\n",
      "\n",
      "0.9396082698585418 0.0035564493938648432 {'C': 1, 'kernel': 'rbf'}\n",
      "\n",
      "0.9825897714907508 0.0010078076094764195 {'C': 0.1, 'kernel': 'linear'}\n",
      "\n",
      "0.8711915125136017 0.008218283430494303 {'C': 0.1, 'kernel': 'rbf'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    \"kernel\": [\"linear\", \"rbf\"],\n",
    "    \"C\": [100, 20, 1, 0.1]\n",
    "}\n",
    "\n",
    "selector = GridSearchCV(SVC(), parameters, scoring=\"accuracy\")\n",
    "selector.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best parameter found: \", selector.best_params_)\n",
    "print(\"Detailed grid scores: \")\n",
    "means = selector.cv_results_[\"mean_test_score\"]\n",
    "stds = selector.cv_results_[\"std_test_score\"]\n",
    "for mean, std, params in zip(means, stds, selector.cv_results_[\"params\"]):\n",
    "    print(mean, std, params)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# As we notice: the first best classifiers for our problem are:\n",
    "## 1- SVM(kernel=rbf, C=100)\n",
    "## 2- SVM(kernel=linear, C=100)\n",
    "## 3- LinearRegression with PCA.\n",
    "## 4- The second best classifier is NN with ADAM optimizer.\n",
    "# Of course we can increase iterations but this is only for experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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