Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81e990ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e29c6f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "423f1efa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1dfb5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Decision Tree Importance</th>\n",
       "      <th>Random Forest Importance</th>\n",
       "      <th>Gradient Boosting Importance</th>\n",
       "      <th>Average Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>gfc_z_temp_min_ws_12</td>\n",
       "      <td>0.242457</td>\n",
       "      <td>2.090304e-02</td>\n",
       "      <td>1.625948e-01</td>\n",
       "      <td>1.419850e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>mag_y_freq_0.0_Hz_ws_66</td>\n",
       "      <td>0.235665</td>\n",
       "      <td>2.411359e-02</td>\n",
       "      <td>8.891304e-02</td>\n",
       "      <td>1.162306e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>mag_z_temp_max_ws_12</td>\n",
       "      <td>0.230544</td>\n",
       "      <td>1.062403e-02</td>\n",
       "      <td>7.526765e-02</td>\n",
       "      <td>1.054787e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>gfc_x_temp_std_ws_12</td>\n",
       "      <td>0.231293</td>\n",
       "      <td>2.977059e-02</td>\n",
       "      <td>3.756270e-03</td>\n",
       "      <td>8.827340e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>mag_y_max_freq</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.120106e-02</td>\n",
       "      <td>1.662364e-01</td>\n",
       "      <td>6.581249e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>gfc_z_freq_1.212_Hz_ws_66</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.913709e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.304570e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>gfc_y_freq_3.232_Hz_ws_66</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.546948e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.848983e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>acc_y_freq_2.222_Hz_ws_66</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.821450e-07</td>\n",
       "      <td>3.743760e-11</td>\n",
       "      <td>2.607275e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>acc_x_freq_1.111_Hz_ws_66</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.779873e-07</td>\n",
       "      <td>6.823900e-09</td>\n",
       "      <td>1.616037e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>inc_x_pse</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>750 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Feature  Decision Tree Importance  \\\n",
       "167       gfc_z_temp_min_ws_12                  0.242457   \n",
       "568    mag_y_freq_0.0_Hz_ws_66                  0.235665   \n",
       "161       mag_z_temp_max_ws_12                  0.230544   \n",
       "180       gfc_x_temp_std_ws_12                  0.231293   \n",
       "565             mag_y_max_freq                  0.000000   \n",
       "..                         ...                       ...   \n",
       "284  gfc_z_freq_1.212_Hz_ws_66                  0.000000   \n",
       "267  gfc_y_freq_3.232_Hz_ws_66                  0.000000   \n",
       "368  acc_y_freq_2.222_Hz_ws_66                  0.000000   \n",
       "320  acc_x_freq_1.111_Hz_ws_66                  0.000000   \n",
       "641                  inc_x_pse                  0.000000   \n",
       "\n",
       "     Random Forest Importance  Gradient Boosting Importance  \\\n",
       "167              2.090304e-02                  1.625948e-01   \n",
       "568              2.411359e-02                  8.891304e-02   \n",
       "161              1.062403e-02                  7.526765e-02   \n",
       "180              2.977059e-02                  3.756270e-03   \n",
       "565              3.120106e-02                  1.662364e-01   \n",
       "..                        ...                           ...   \n",
       "284              9.913709e-07                  0.000000e+00   \n",
       "267              8.546948e-07                  0.000000e+00   \n",
       "368              7.821450e-07                  3.743760e-11   \n",
       "320              4.779873e-07                  6.823900e-09   \n",
       "641              0.000000e+00                  0.000000e+00   \n",
       "\n",
       "     Average Importance  \n",
       "167        1.419850e-01  \n",
       "568        1.162306e-01  \n",
       "161        1.054787e-01  \n",
       "180        8.827340e-02  \n",
       "565        6.581249e-02  \n",
       "..                  ...  \n",
       "284        3.304570e-07  \n",
       "267        2.848983e-07  \n",
       "368        2.607275e-07  \n",
       "320        1.616037e-07  \n",
       "641        0.000000e+00  \n",
       "\n",
       "[750 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most important feature is: gfc_z_temp_min_ws_12\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "# Assuming 'X' is your feature matrix and 'y' is the target variable\n",
    "# Replace 'X' and 'y' with your actual data\n",
    "df = pd.read_csv('gestures_feat_engi.csv')\n",
    "df= df.dropna()\n",
    "X = df.drop(['label', 'time'], axis=1)\n",
    "y = df['label']\n",
    "\n",
    "# Create an instance of the decision tree classifier\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "# Fit the decision tree model to your data\n",
    "dt.fit(X, y)\n",
    "\n",
    "# Get the feature importances from the decision tree model\n",
    "dt_feature_importances = dt.feature_importances_\n",
    "\n",
    "# Create an instance of the random forest classifier\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# Fit the random forest model to your data\n",
    "rf.fit(X, y)\n",
    "\n",
    "# Get the feature importances from the random forest model\n",
    "rf_feature_importances = rf.feature_importances_\n",
    "\n",
    "# Create an instance of the gradient boosting classifier\n",
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "# Fit the gradient boosting model to your data\n",
    "gb.fit(X, y)\n",
    "\n",
    "# Get the feature importances from the gradient boosting model\n",
    "gb_feature_importances = gb.feature_importances_\n",
    "\n",
    "# # Print the feature importances\n",
    "# for feature, dt_importance, rf_importance, gb_importance in zip(X.columns, dt_feature_importances, rf_feature_importances, gb_feature_importances):\n",
    "#     print(f\"Feature: {feature}, Decision Tree Importance: {dt_importance}, Random Forest Importance: {rf_importance}, Gradient Boosting Importance: {gb_importance}\")\n",
    "# Create a dictionary to hold the feature importance data\n",
    "data = {\n",
    "    'Feature': X.columns,\n",
    "    'Decision Tree Importance': dt_feature_importances,\n",
    "    'Random Forest Importance': rf_feature_importances,\n",
    "    'Gradient Boosting Importance': gb_feature_importances\n",
    "}\n",
    "\n",
    "# Create a pandas DataFrame from the dictionary\n",
    "result_df = pd.DataFrame(data)\n",
    "\n",
    "\n",
    "# Calculate the average importance across the three classifiers\n",
    "result_df['Average Importance'] = result_df[['Decision Tree Importance', 'Random Forest Importance', 'Gradient Boosting Importance']].mean(axis=1)\n",
    "\n",
    "# Sort the DataFrame by average importance in descending order\n",
    "result_df = result_df.sort_values('Average Importance', ascending=False)\n",
    "\n",
    "display(result_df)\n",
    "\n",
    "result_df.to_csv('feature_importance.csv', index=False)\n",
    "\n",
    "\n",
    "# Get the most important feature (top row)\n",
    "most_important_feature = result_df.iloc[0]['Feature']\n",
    "\n",
    "# Print the most important feature\n",
    "print(\"The most important feature is:\", most_important_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96278fe5",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecc5ebe4",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Ranking</th>\n",
       "      <th>Selected</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>mag_z_temp_max_ws_8</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>-4.455415e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>inc_y_temp_min_ws_4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1.305602e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>mag_z_temp_min_ws_12</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.353312e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>mag_z_temp_max_ws_12</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>-4.512491e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>inc_y_temp_max_ws_8</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>-6.054810e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>gyr_y_freq_2.828_Hz_ws_66</td>\n",
       "      <td>737</td>\n",
       "      <td>False</td>\n",
       "      <td>7.468857e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>gyr_y_freq_2.222_Hz_ws_66</td>\n",
       "      <td>738</td>\n",
       "      <td>False</td>\n",
       "      <td>5.883189e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>gfc_y_pse</td>\n",
       "      <td>739</td>\n",
       "      <td>False</td>\n",
       "      <td>-2.063796e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>gyr_y_freq_2.727_Hz_ws_66</td>\n",
       "      <td>740</td>\n",
       "      <td>False</td>\n",
       "      <td>3.369427e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>inc_x_pse</td>\n",
       "      <td>741</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>750 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Feature  Ranking  Selected   Coefficient\n",
       "101        mag_z_temp_max_ws_8        1      True -4.455415e-04\n",
       "58         inc_y_temp_min_ws_4        1      True  1.305602e-04\n",
       "176       mag_z_temp_min_ws_12        1      True -1.353312e-04\n",
       "161       mag_z_temp_max_ws_12        1      True -4.512491e-04\n",
       "103        inc_y_temp_max_ws_8        1      True -6.054810e-04\n",
       "..                         ...      ...       ...           ...\n",
       "485  gyr_y_freq_2.828_Hz_ws_66      737     False  7.468857e-07\n",
       "479  gyr_y_freq_2.222_Hz_ws_66      738     False  5.883189e-07\n",
       "234                  gfc_y_pse      739     False -2.063796e-07\n",
       "484  gyr_y_freq_2.727_Hz_ws_66      740     False  3.369427e-07\n",
       "641                  inc_x_pse      741     False  0.000000e+00\n",
       "\n",
       "[750 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "# Load the data\n",
    "# df = pd.read_csv('gestures.csv')\n",
    "df = df.dropna()\n",
    "X = df.drop(['label', 'time'], axis=1)\n",
    "y = df['label']\n",
    "\n",
    "# Create an instance of the logistic regression model\n",
    "estimator = LogisticRegression()\n",
    "\n",
    "# Create an instance of RFE with the logistic regression model and specify the number of features to select\n",
    "rfe = RFE(estimator, n_features_to_select=10)\n",
    "\n",
    "# Fit RFE to your data\n",
    "rfe.fit(X, y)\n",
    "\n",
    "# Get the ranking of features (higher ranking means less important)\n",
    "feature_ranking = rfe.ranking_\n",
    "\n",
    "# Get the mask of selected features (True represents selected features)\n",
    "selected_features = rfe.support_\n",
    "\n",
    "# Fit the logistic regression model to your data\n",
    "estimator.fit(X, y)\n",
    "\n",
    "# Get the coefficients of the logistic regression model\n",
    "coefficients = estimator.coef_\n",
    "\n",
    "# Create a dictionary to hold the feature ranking, selection, and coefficient data\n",
    "data = {\n",
    "    'Feature': X.columns,\n",
    "    'Ranking': feature_ranking,\n",
    "    'Selected': selected_features,\n",
    "    'Coefficient': coefficients[0]\n",
    "}\n",
    "\n",
    "# Create a pandas DataFrame from the dictionary\n",
    "result_df = pd.DataFrame(data)\n",
    "\n",
    "# Sort the DataFrame based on the ranking in ascending order\n",
    "result_df = result_df.sort_values('Ranking')\n",
    "\n",
    "# Print the resulting DataFrame\n",
    "result_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e193fc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df.to_csv('result_df_750_10.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9adf14d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>gfc_x</th>\n",
       "      <th>gfc_y</th>\n",
       "      <th>gfc_z</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>gyr_x</th>\n",
       "      <th>gyr_y</th>\n",
       "      <th>gyr_z</th>\n",
       "      <th>mag_x</th>\n",
       "      <th>mag_y</th>\n",
       "      <th>mag_z</th>\n",
       "      <th>inc_x</th>\n",
       "      <th>inc_y</th>\n",
       "      <th>inc_z</th>\n",
       "      <th>label</th>\n",
       "      <th>gfc_x_temp_mean_ws_4</th>\n",
       "      <th>gfc_y_temp_mean_ws_4</th>\n",
       "      <th>gfc_z_temp_mean_ws_4</th>\n",
       "      <th>acc_x_temp_mean_ws_4</th>\n",
       "      <th>acc_y_temp_mean_ws_4</th>\n",
       "      <th>acc_z_temp_mean_ws_4</th>\n",
       "      <th>gyr_x_temp_mean_ws_4</th>\n",
       "      <th>gyr_y_temp_mean_ws_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-06-08 16:44:55.950</td>\n",
       "      <td>0.020082</td>\n",
       "      <td>0.014773</td>\n",
       "      <td>0.980182</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>-0.031082</td>\n",
       "      <td>-0.206045</td>\n",
       "      <td>0.006595</td>\n",
       "      <td>-0.001950</td>\n",
       "      <td>-0.032759</td>\n",
       "      <td>185.340</td>\n",
       "      <td>83.839091</td>\n",
       "      <td>-32.2500</td>\n",
       "      <td>300.827559</td>\n",
       "      <td>-1.049591</td>\n",
       "      <td>1.106823</td>\n",
       "      <td>rest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-06-08 16:44:56.100</td>\n",
       "      <td>0.020082</td>\n",
       "      <td>0.014773</td>\n",
       "      <td>0.980182</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>-0.031082</td>\n",
       "      <td>-0.206045</td>\n",
       "      <td>0.006595</td>\n",
       "      <td>-0.001950</td>\n",
       "      <td>-0.032759</td>\n",
       "      <td>185.340</td>\n",
       "      <td>83.839091</td>\n",
       "      <td>-32.2500</td>\n",
       "      <td>300.827559</td>\n",
       "      <td>-1.049591</td>\n",
       "      <td>1.106823</td>\n",
       "      <td>rest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-06-08 16:44:56.250</td>\n",
       "      <td>0.011158</td>\n",
       "      <td>0.027933</td>\n",
       "      <td>0.995408</td>\n",
       "      <td>-0.037542</td>\n",
       "      <td>0.034142</td>\n",
       "      <td>-0.057250</td>\n",
       "      <td>0.006312</td>\n",
       "      <td>0.007354</td>\n",
       "      <td>-0.002104</td>\n",
       "      <td>185.975</td>\n",
       "      <td>82.922500</td>\n",
       "      <td>-32.1125</td>\n",
       "      <td>294.368688</td>\n",
       "      <td>-1.397988</td>\n",
       "      <td>0.908512</td>\n",
       "      <td>rest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      time     gfc_x     gfc_y     gfc_z     acc_x     acc_y  \\\n",
       "0  2023-06-08 16:44:55.950  0.020082  0.014773  0.980182  0.006200 -0.031082   \n",
       "1  2023-06-08 16:44:56.100  0.020082  0.014773  0.980182  0.006200 -0.031082   \n",
       "2  2023-06-08 16:44:56.250  0.011158  0.027933  0.995408 -0.037542  0.034142   \n",
       "\n",
       "      acc_z     gyr_x     gyr_y     gyr_z    mag_x      mag_y    mag_z  \\\n",
       "0 -0.206045  0.006595 -0.001950 -0.032759  185.340  83.839091 -32.2500   \n",
       "1 -0.206045  0.006595 -0.001950 -0.032759  185.340  83.839091 -32.2500   \n",
       "2 -0.057250  0.006312  0.007354 -0.002104  185.975  82.922500 -32.1125   \n",
       "\n",
       "        inc_x     inc_y     inc_z label  gfc_x_temp_mean_ws_4  \\\n",
       "0  300.827559 -1.049591  1.106823  rest                   NaN   \n",
       "1  300.827559 -1.049591  1.106823  rest                   NaN   \n",
       "2  294.368688 -1.397988  0.908512  rest                   NaN   \n",
       "\n",
       "   gfc_y_temp_mean_ws_4  gfc_z_temp_mean_ws_4  acc_x_temp_mean_ws_4  \\\n",
       "0                   NaN                   NaN                   NaN   \n",
       "1                   NaN                   NaN                   NaN   \n",
       "2                   NaN                   NaN                   NaN   \n",
       "\n",
       "   acc_y_temp_mean_ws_4  acc_z_temp_mean_ws_4  gyr_x_temp_mean_ws_4  \\\n",
       "0                   NaN                   NaN                   NaN   \n",
       "1                   NaN                   NaN                   NaN   \n",
       "2                   NaN                   NaN                   NaN   \n",
       "\n",
       "   gyr_y_temp_mean_ws_4  \n",
       "0                   NaN  \n",
       "1                   NaN  \n",
       "2                   NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the sensory data into a pandas DataFrame\n",
    "df1 = pd.read_csv('gestures_feat_engi.csv')\n",
    "df = df1.iloc[:, :25]\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f37fc575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        rest\n",
       "1        rest\n",
       "2        rest\n",
       "3        rest\n",
       "4        rest\n",
       "         ... \n",
       "39289    rest\n",
       "39290    rest\n",
       "39291    rest\n",
       "39292    rest\n",
       "39293    rest\n",
       "Name: label, Length: 39294, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['label']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42df774e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "Selected features: ['mag_y']\n",
      "Best score: 0.624252449421046\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x']\n",
      "Best score: 0.8294948466726046\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z']\n",
      "Best score: 0.9368876447385164\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x']\n",
      "Best score: 0.9670441532001527\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x']\n",
      "Best score: 0.9798956610255758\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z']\n",
      "Best score: 0.9846036391398397\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x']\n",
      "Best score: 0.988039190736735\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z']\n",
      "Best score: 0.990711286423209\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4']\n",
      "Best score: 0.9918564702888408\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4']\n",
      "Best score: 0.992619926199262\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x']\n",
      "Best score: 0.9930016541544726\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4']\n",
      "Best score: 0.9936378674131569\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4']\n",
      "Best score: 0.9937651100648938\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4']\n",
      "Best score: 0.9941468380201043\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4']\n",
      "Best score: 0.9942740806718412\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4']\n",
      "Best score: 0.9945285659753149\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4', 'gyr_y']\n",
      "Best score: 0.9946558086270518\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4', 'gyr_y', 'acc_z']\n",
      "Best score: 0.9950375365822623\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4', 'gyr_y', 'acc_z', 'gfc_y']\n",
      "Best score: 0.9946558086270518\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4', 'gyr_y', 'acc_z', 'gfc_y', 'acc_y']\n",
      "Best score: 0.9946558086270518\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4', 'gyr_y', 'acc_z', 'gfc_y', 'acc_y', 'acc_y_temp_mean_ws_4']\n",
      "Best score: 0.9946558086270518\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4', 'gyr_y', 'acc_z', 'gfc_y', 'acc_y', 'acc_y_temp_mean_ws_4', 'gyr_z']\n",
      "Best score: 0.9942740806718412\n",
      "--------\n",
      "Selected features: ['mag_y', 'mag_x', 'mag_z', 'gyr_x', 'inc_x', 'inc_z', 'acc_x', 'gfc_z', 'acc_x_temp_mean_ws_4', 'gyr_x_temp_mean_ws_4', 'gfc_x', 'gfc_y_temp_mean_ws_4', 'gfc_x_temp_mean_ws_4', 'gfc_z_temp_mean_ws_4', 'acc_z_temp_mean_ws_4', 'gyr_y_temp_mean_ws_4', 'gyr_y', 'acc_z', 'gfc_y', 'acc_y', 'acc_y_temp_mean_ws_4', 'gyr_z', 'inc_y']\n",
      "Best score: 0.9945285659753149\n",
      "--------\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the sensory data into a pandas DataFrame\n",
    "df1 = pd.read_csv('gestures_feat_engi.csv')\n",
    "# df=df.drop(['index'],axis=1)\n",
    "df = df1.iloc[:, :25]\n",
    "df = df.dropna()\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df.drop(['label', 'time'], axis=1)\n",
    "y = df['label']\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize an empty set for selected features\n",
    "selected_features = []\n",
    "\n",
    "# Initialize the Random Forest classifier\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "print(X.shape[1])\n",
    "# Perform forward feature selection\n",
    "for _ in range(X.shape[1]):\n",
    "    best_feature = None\n",
    "    best_score = 0\n",
    "    \n",
    "    for feature in X_train.columns:\n",
    "        if feature not in selected_features:\n",
    "            # Add the feature to the selected features\n",
    "            selected_features.append(feature)\n",
    "            \n",
    "            # Train the classifier on the selected features\n",
    "            clf.fit(X_train[selected_features], y_train)\n",
    "            \n",
    "            # Evaluate the classifier on the test set\n",
    "            score = clf.score(X_test[selected_features], y_test)\n",
    "            \n",
    "            # Check if the score is better than the previous best score\n",
    "            if score > best_score:\n",
    "                best_score = score\n",
    "                best_feature = feature\n",
    "            \n",
    "            # Remove the feature from the selected features for the next iteration\n",
    "            selected_features.remove(feature)\n",
    "    \n",
    "    # Add the best feature to the selected features\n",
    "    selected_features.append(best_feature)\n",
    "    \n",
    "    print(\"Selected features:\", selected_features)\n",
    "    print(\"Best score:\", best_score)\n",
    "    print(\"--------\")\n"
   ]
  }
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