Body Gestures Training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Motion_capture.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "   frameTime  bodyIndex  SpineBase_positionX  SpineBase_positionY  \\\n",
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       "\n",
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       "0             2.540962             358.7676             180.1466   \n",
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       "\n",
       "   SpineBase_rotationZ  SpineMid_positionX  SpineMid_positionY   ...    \\\n",
       "0             2.819737            0.077674            0.542595   ...     \n",
       "1             1.979240            0.079333            0.548986   ...     \n",
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       "\n",
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       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
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       "\n",
       "                 textfile  output       video_name  gender  \n",
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    "df.head()"
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['frameTime', 'textfile', 'video_name'], axis=1, inplace=True)"
   ]
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  {
   "cell_type": "code",
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       "3            0.085690            0.553738            2.526048   ...     \n",
       "4            0.087480            0.554349            2.524821   ...     \n",
       "\n",
       "   HandTipRight_rotationY  HandTipRight_rotationZ  ThumbRight_positionX  \\\n",
       "0                       0                       0              0.505001   \n",
       "1                       0                       0              0.473728   \n",
       "2                       0                       0              0.421183   \n",
       "3                       0                       0              0.408477   \n",
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       "0              1.357066              2.628572                     0   \n",
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       "3              1.418415              2.645500                     0   \n",
       "4              1.373415              2.632500                     0   \n",
       "\n",
       "   ThumbRight_rotationY  ThumbRight_rotationZ  output  gender  \n",
       "0                     0                     0      Ne       M  \n",
       "1                     0                     0      Ne       M  \n",
       "2                     0                     0      Ne       M  \n",
       "3                     0                     0      Ne       M  \n",
       "4                     0                     0      Ne       M  \n",
       "\n",
       "[5 rows x 153 columns]"
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = df['gender'].map({'M': 0, 'F': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <th>HandTipRight_rotationZ</th>\n",
       "      <th>ThumbRight_positionX</th>\n",
       "      <th>ThumbRight_positionY</th>\n",
       "      <th>ThumbRight_positionZ</th>\n",
       "      <th>ThumbRight_rotationX</th>\n",
       "      <th>ThumbRight_rotationY</th>\n",
       "      <th>ThumbRight_rotationZ</th>\n",
       "      <th>output</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.062298</td>\n",
       "      <td>0.230842</td>\n",
       "      <td>2.540962</td>\n",
       "      <td>358.7676</td>\n",
       "      <td>180.1466</td>\n",
       "      <td>2.819737</td>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.542595</td>\n",
       "      <td>2.547629</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.505001</td>\n",
       "      <td>1.357066</td>\n",
       "      <td>2.628572</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ne</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.069238</td>\n",
       "      <td>0.257058</td>\n",
       "      <td>2.526453</td>\n",
       "      <td>356.7902</td>\n",
       "      <td>179.9677</td>\n",
       "      <td>1.979240</td>\n",
       "      <td>0.079333</td>\n",
       "      <td>0.548986</td>\n",
       "      <td>2.542830</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.473728</td>\n",
       "      <td>1.338728</td>\n",
       "      <td>2.621750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ne</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.084557</td>\n",
       "      <td>0.248516</td>\n",
       "      <td>2.483606</td>\n",
       "      <td>351.4843</td>\n",
       "      <td>183.0456</td>\n",
       "      <td>359.412200</td>\n",
       "      <td>0.083828</td>\n",
       "      <td>0.551884</td>\n",
       "      <td>2.529133</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.421183</td>\n",
       "      <td>1.339403</td>\n",
       "      <td>2.615883</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ne</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.084818</td>\n",
       "      <td>0.249397</td>\n",
       "      <td>2.483649</td>\n",
       "      <td>352.0697</td>\n",
       "      <td>182.5708</td>\n",
       "      <td>359.807800</td>\n",
       "      <td>0.085690</td>\n",
       "      <td>0.553738</td>\n",
       "      <td>2.526048</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.408477</td>\n",
       "      <td>1.418415</td>\n",
       "      <td>2.645500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ne</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.085442</td>\n",
       "      <td>0.250219</td>\n",
       "      <td>2.483902</td>\n",
       "      <td>352.3281</td>\n",
       "      <td>182.3243</td>\n",
       "      <td>0.070354</td>\n",
       "      <td>0.087480</td>\n",
       "      <td>0.554349</td>\n",
       "      <td>2.524821</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.373999</td>\n",
       "      <td>1.373415</td>\n",
       "      <td>2.632500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ne</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 153 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bodyIndex  SpineBase_positionX  SpineBase_positionY  SpineBase_positionZ  \\\n",
       "0          0             0.062298             0.230842             2.540962   \n",
       "1          0             0.069238             0.257058             2.526453   \n",
       "2          0             0.084557             0.248516             2.483606   \n",
       "3          0             0.084818             0.249397             2.483649   \n",
       "4          0             0.085442             0.250219             2.483902   \n",
       "\n",
       "   SpineBase_rotationX  SpineBase_rotationY  SpineBase_rotationZ  \\\n",
       "0             358.7676             180.1466             2.819737   \n",
       "1             356.7902             179.9677             1.979240   \n",
       "2             351.4843             183.0456           359.412200   \n",
       "3             352.0697             182.5708           359.807800   \n",
       "4             352.3281             182.3243             0.070354   \n",
       "\n",
       "   SpineMid_positionX  SpineMid_positionY  SpineMid_positionZ   ...    \\\n",
       "0            0.077674            0.542595            2.547629   ...     \n",
       "1            0.079333            0.548986            2.542830   ...     \n",
       "2            0.083828            0.551884            2.529133   ...     \n",
       "3            0.085690            0.553738            2.526048   ...     \n",
       "4            0.087480            0.554349            2.524821   ...     \n",
       "\n",
       "   HandTipRight_rotationY  HandTipRight_rotationZ  ThumbRight_positionX  \\\n",
       "0                       0                       0              0.505001   \n",
       "1                       0                       0              0.473728   \n",
       "2                       0                       0              0.421183   \n",
       "3                       0                       0              0.408477   \n",
       "4                       0                       0              0.373999   \n",
       "\n",
       "   ThumbRight_positionY  ThumbRight_positionZ  ThumbRight_rotationX  \\\n",
       "0              1.357066              2.628572                     0   \n",
       "1              1.338728              2.621750                     0   \n",
       "2              1.339403              2.615883                     0   \n",
       "3              1.418415              2.645500                     0   \n",
       "4              1.373415              2.632500                     0   \n",
       "\n",
       "   ThumbRight_rotationY  ThumbRight_rotationZ  output  gender  \n",
       "0                     0                     0      Ne       0  \n",
       "1                     0                     0      Ne       0  \n",
       "2                     0                     0      Ne       0  \n",
       "3                     0                     0      Ne       0  \n",
       "4                     0                     0      Ne       0  \n",
       "\n",
       "[5 rows x 153 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['output'], axis=1)\n",
    "Y = df['output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\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:\\Users\\dell\\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",
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression classifier on training set: 0.62\n",
      "Accuracy of Logistic regression classifier on test set: 0.62\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "print('Accuracy of Logistic regression classifier on training set: {:.2f}'\n",
    "     .format(logreg.score(X_train, y_train)))\n",
    "print('Accuracy of Logistic regression classifier on test set: {:.2f}'\n",
    "     .format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 1.00\n",
      "Accuracy of Decision Tree classifier on test set: 0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(clf.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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