feature_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "from data_loader import DataLoader\n",
    "import random\n",
    "import configparser\n",
    "from feature_selector import BaselineSelector, AnalysisSelector\n",
    "from feature_selector import FeatureExtractor\n",
    "import numpy as np\n",
    "from utils import parse_config_features\n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['config.ini']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = configparser.ConfigParser()\n",
    "config.read('config.ini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv(config['Data']['features_path'])\n",
    "y = np.load(config['Data']['labels_path'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'skewness_alpha_0', 'skewness_alpha_1',\n",
       "       'skewness_alpha_2', 'skewness_alpha_3', 'skewness_alpha_4',\n",
       "       'skewness_alpha_5', 'skewness_alpha_6', 'skewness_alpha_7',\n",
       "       'skewness_alpha_8',\n",
       "       ...\n",
       "       'hjorth_complexity_theta_9', 'hjorth_complexity_theta_10',\n",
       "       'hjorth_complexity_theta_11', 'hjorth_complexity_theta_12',\n",
       "       'hjorth_complexity_theta_13', 'hjorth_complexity_theta_14',\n",
       "       'hjorth_complexity_theta_15', 'hjorth_complexity_theta_16',\n",
       "       'hjorth_complexity_theta_17', 'hjorth_complexity_theta_18'],\n",
       "      dtype='object', length=476)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mutual_info = mutual_info_classif(X, y)\n",
    "mutual_info_series = pd.Series(mutual_info, index=X.columns)\n",
    "mutual_info_series.sort_values(ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['hjorth_activity_alpha_9', 'hjorth_activity_alpha_14',\n",
      "       'hjorth_activity_beta_9', 'hjorth_activity_alpha_8',\n",
      "       'hjorth_complexity_alpha_9', 'hjorth_activity_beta_14',\n",
      "       'hjorth_complexity_alpha_8', 'hjorth_complexity_alpha_14',\n",
      "       'hjorth_activity_beta_8', 'hjorth_activity_alpha_6',\n",
      "       'hjorth_activity_alpha_15', 'hjorth_activity_beta_12',\n",
      "       'hjorth_complexity_alpha_15', 'hjorth_activity_beta_6',\n",
      "       'hjorth_activity_beta_15', 'hjorth_complexity_alpha_6',\n",
      "       'hjorth_activity_alpha_18', 'hjorth_complexity_alpha_18',\n",
      "       'hjorth_complexity_alpha_7', 'hjorth_activity_beta_18',\n",
      "       'hjorth_activity_beta_13', 'hjorth_activity_beta_7',\n",
      "       'hjorth_activity_alpha_7', 'hjorth_activity_beta_1',\n",
      "       'hjorth_activity_gamma_0', 'hjorth_activity_beta_0',\n",
      "       'hjorth_activity_gamma_11', 'hjorth_complexity_alpha_1',\n",
      "       'hjorth_activity_alpha_16', 'hjorth_activity_gamma_12'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(mutual_info_series.index[:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 10))\n",
    "    \n",
    "plt.grid()\n",
    "sns.set(style='darkgrid')\n",
    "plt.grid()\n",
    "sns.barplot(x=mutual_info_series.values[:15], y=mutual_info_series.index[:15], alpha=0.5)\n",
    "\n",
    "plt.title(f'Information gain for best 15 features', fontdict = {'fontsize' : 20})\n",
    "plt.savefig(f'data/plots/information_gain/ig_best_15.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}