task_8_2nd_ed_t.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import our favorite libraries and modules\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import loadmat\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import signal\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import os\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import mannwhitneyu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 468/468 [01:13<00:00,  6.41it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'C:\\\\dina\\\\Computational neuroscience course\\\\proj.8\\\\Alcoholism\\\\SMNI_CMI_TEST\\\\Test'"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kaggle dataset Url: https://www.kaggle.com/datasets/nnair25/Alcoholics\n",
    "#loading whole datasetf\n",
    "filenames_list = os.listdir(r\"C:\\dina\\Computational neuroscience course\\proj.8\\Alcoholism\\SMNI_CMI_TRAIN\\Train\") ## list of file names in the directory\n",
    "EEG_data = pd.DataFrame({}) ## create an empty df that will hold data from each file\n",
    "\n",
    "for file_name in tqdm(filenames_list):\n",
    "    temp_df = pd.read_csv(\"C:/dina/Computational neuroscience course/proj.8/Alcoholism/SMNI_CMI_TRAIN/Train/\" + file_name) ## read from the file to df\n",
    "    EEG_data = pd.concat([EEG_data,temp_df]) ## add the file data to the main df\n",
    "\n",
    "\"C:\\dina\\Computational neuroscience course\\proj.8\\Alcoholism\\SMNI_CMI_TEST\\Test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clarified info: 12 trials were done by 39 subjects and each trial was recorded by 64 channels while sampling 256 times in 1 second(each epoch lasted 3.9 msec) \n",
    "#frequency mentioned in data info.: 256 Hz (3.9-msec epoch) for 1 second\n",
    "fr = 256\n",
    "n_channels = 64\n",
    "total_time = 60 # for each trial per subject\n",
    "sample_num = 256\n",
    "samp_range = np.arange(0, sample_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29952/29952 [00:37<00:00, 803.71it/s] \n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>trial number</th>\n",
       "      <th>matching condition</th>\n",
       "      <th>sensor position</th>\n",
       "      <th>subject identifier</th>\n",
       "      <th>sample_0</th>\n",
       "      <th>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>sample_3</th>\n",
       "      <th>sample_4</th>\n",
       "      <th>...</th>\n",
       "      <th>sample_246</th>\n",
       "      <th>sample_247</th>\n",
       "      <th>sample_248</th>\n",
       "      <th>sample_249</th>\n",
       "      <th>sample_250</th>\n",
       "      <th>sample_251</th>\n",
       "      <th>sample_252</th>\n",
       "      <th>sample_253</th>\n",
       "      <th>sample_254</th>\n",
       "      <th>sample_255</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>co2a0000364</td>\n",
       "      <td>0</td>\n",
       "      <td>S1 obj</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>-8.921</td>\n",
       "      <td>-8.433</td>\n",
       "      <td>-2.574</td>\n",
       "      <td>5.239</td>\n",
       "      <td>11.587</td>\n",
       "      <td>...</td>\n",
       "      <td>10.610</td>\n",
       "      <td>8.169</td>\n",
       "      <td>4.751</td>\n",
       "      <td>2.797</td>\n",
       "      <td>2.797</td>\n",
       "      <td>3.286</td>\n",
       "      <td>3.774</td>\n",
       "      <td>4.262</td>\n",
       "      <td>5.727</td>\n",
       "      <td>8.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>co2a0000364</td>\n",
       "      <td>0</td>\n",
       "      <td>S1 obj</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>0.834</td>\n",
       "      <td>3.276</td>\n",
       "      <td>5.717</td>\n",
       "      <td>7.670</td>\n",
       "      <td>9.623</td>\n",
       "      <td>...</td>\n",
       "      <td>12.065</td>\n",
       "      <td>15.483</td>\n",
       "      <td>12.553</td>\n",
       "      <td>5.229</td>\n",
       "      <td>-2.584</td>\n",
       "      <td>-6.490</td>\n",
       "      <td>-4.049</td>\n",
       "      <td>2.787</td>\n",
       "      <td>9.623</td>\n",
       "      <td>13.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>co2a0000364</td>\n",
       "      <td>0</td>\n",
       "      <td>S1 obj</td>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>-19.847</td>\n",
       "      <td>-12.522</td>\n",
       "      <td>1.149</td>\n",
       "      <td>14.821</td>\n",
       "      <td>20.681</td>\n",
       "      <td>...</td>\n",
       "      <td>19.216</td>\n",
       "      <td>17.751</td>\n",
       "      <td>10.915</td>\n",
       "      <td>2.126</td>\n",
       "      <td>-4.222</td>\n",
       "      <td>-4.710</td>\n",
       "      <td>0.661</td>\n",
       "      <td>7.009</td>\n",
       "      <td>11.403</td>\n",
       "      <td>12.868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 261 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name  trial number matching condition  sensor position  \\\n",
       "0  co2a0000364             0             S1 obj                0   \n",
       "1  co2a0000364             0             S1 obj                1   \n",
       "2  co2a0000364             0             S1 obj                2   \n",
       "\n",
       "  subject identifier  sample_0  sample_1  sample_2  sample_3  sample_4  ...  \\\n",
       "0                  a    -8.921    -8.433    -2.574     5.239    11.587  ...   \n",
       "1                  a     0.834     3.276     5.717     7.670     9.623  ...   \n",
       "2                  a   -19.847   -12.522     1.149    14.821    20.681  ...   \n",
       "\n",
       "   sample_246  sample_247  sample_248  sample_249  sample_250  sample_251  \\\n",
       "0      10.610       8.169       4.751       2.797       2.797       3.286   \n",
       "1      12.065      15.483      12.553       5.229      -2.584      -6.490   \n",
       "2      19.216      17.751      10.915       2.126      -4.222      -4.710   \n",
       "\n",
       "   sample_252  sample_253  sample_254  sample_255  \n",
       "0       3.774       4.262       5.727       8.169  \n",
       "1      -4.049       2.787       9.623      13.041  \n",
       "2       0.661       7.009      11.403      12.868  \n",
       "\n",
       "[3 rows x 261 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transposed_df_list = []\n",
    "\n",
    "for group_df in tqdm(EEG_data.groupby(['name', 'trial number', 'matching condition', 'channel', 'subject identifier'])):\n",
    "    _df = pd.DataFrame(group_df[1]['sensor value']).T\n",
    "    _df.columns = [f'sample_{idx}' for idx in range(256)]\n",
    "    _df['name'] = group_df[0][0]\n",
    "    _df['trial number'] = group_df[0][1]\n",
    "    _df['matching condition'] = group_df[0][2]\n",
    "    _df['sensor position'] = group_df[0][3]\n",
    "    _df['subject identifier'] = group_df[0][4]\n",
    "    transposed_df_list.append(_df)\n",
    "    \n",
    "df = pd.concat(transposed_df_list)\n",
    "df = df[[*df.columns[-5:],*df.columns[0:-5]]]\n",
    "df = df.reset_index(drop=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Org = df.drop(['name'], axis=1)\n",
    "df_Org = df_Org.drop(['trial number'], axis=1)\n",
    "df_Org = df_Org.drop(['matching condition'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>sensor position</th>\n",
       "      <th>subject identifier</th>\n",
       "      <th>sample_0</th>\n",
       "      <th>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>sample_3</th>\n",
       "      <th>sample_4</th>\n",
       "      <th>sample_5</th>\n",
       "      <th>sample_6</th>\n",
       "      <th>sample_7</th>\n",
       "      <th>...</th>\n",
       "      <th>sample_246</th>\n",
       "      <th>sample_247</th>\n",
       "      <th>sample_248</th>\n",
       "      <th>sample_249</th>\n",
       "      <th>sample_250</th>\n",
       "      <th>sample_251</th>\n",
       "      <th>sample_252</th>\n",
       "      <th>sample_253</th>\n",
       "      <th>sample_254</th>\n",
       "      <th>sample_255</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>-8.921</td>\n",
       "      <td>-8.433</td>\n",
       "      <td>-2.574</td>\n",
       "      <td>5.239</td>\n",
       "      <td>11.587</td>\n",
       "      <td>14.028</td>\n",
       "      <td>11.587</td>\n",
       "      <td>6.704</td>\n",
       "      <td>...</td>\n",
       "      <td>10.610</td>\n",
       "      <td>8.169</td>\n",
       "      <td>4.751</td>\n",
       "      <td>2.797</td>\n",
       "      <td>2.797</td>\n",
       "      <td>3.286</td>\n",
       "      <td>3.774</td>\n",
       "      <td>4.262</td>\n",
       "      <td>5.727</td>\n",
       "      <td>8.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>0.834</td>\n",
       "      <td>3.276</td>\n",
       "      <td>5.717</td>\n",
       "      <td>7.670</td>\n",
       "      <td>9.623</td>\n",
       "      <td>9.623</td>\n",
       "      <td>8.647</td>\n",
       "      <td>5.229</td>\n",
       "      <td>...</td>\n",
       "      <td>12.065</td>\n",
       "      <td>15.483</td>\n",
       "      <td>12.553</td>\n",
       "      <td>5.229</td>\n",
       "      <td>-2.584</td>\n",
       "      <td>-6.490</td>\n",
       "      <td>-4.049</td>\n",
       "      <td>2.787</td>\n",
       "      <td>9.623</td>\n",
       "      <td>13.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>-19.847</td>\n",
       "      <td>-12.522</td>\n",
       "      <td>1.149</td>\n",
       "      <td>14.821</td>\n",
       "      <td>20.681</td>\n",
       "      <td>17.263</td>\n",
       "      <td>8.962</td>\n",
       "      <td>0.173</td>\n",
       "      <td>...</td>\n",
       "      <td>19.216</td>\n",
       "      <td>17.751</td>\n",
       "      <td>10.915</td>\n",
       "      <td>2.126</td>\n",
       "      <td>-4.222</td>\n",
       "      <td>-4.710</td>\n",
       "      <td>0.661</td>\n",
       "      <td>7.009</td>\n",
       "      <td>11.403</td>\n",
       "      <td>12.868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 258 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sensor position subject identifier  sample_0  sample_1  sample_2  sample_3  \\\n",
       "0                0                  a    -8.921    -8.433    -2.574     5.239   \n",
       "1                1                  a     0.834     3.276     5.717     7.670   \n",
       "2                2                  a   -19.847   -12.522     1.149    14.821   \n",
       "\n",
       "   sample_4  sample_5  sample_6  sample_7  ...  sample_246  sample_247  \\\n",
       "0    11.587    14.028    11.587     6.704  ...      10.610       8.169   \n",
       "1     9.623     9.623     8.647     5.229  ...      12.065      15.483   \n",
       "2    20.681    17.263     8.962     0.173  ...      19.216      17.751   \n",
       "\n",
       "   sample_248  sample_249  sample_250  sample_251  sample_252  sample_253  \\\n",
       "0       4.751       2.797       2.797       3.286       3.774       4.262   \n",
       "1      12.553       5.229      -2.584      -6.490      -4.049       2.787   \n",
       "2      10.915       2.126      -4.222      -4.710       0.661       7.009   \n",
       "\n",
       "   sample_254  sample_255  \n",
       "0       5.727       8.169  \n",
       "1       9.623      13.041  \n",
       "2      11.403      12.868  \n",
       "\n",
       "[3 rows x 258 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Org.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>sensor position</th>\n",
       "      <th>sample_0</th>\n",
       "      <th>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>sample_3</th>\n",
       "      <th>sample_4</th>\n",
       "      <th>sample_5</th>\n",
       "      <th>sample_6</th>\n",
       "      <th>sample_7</th>\n",
       "      <th>sample_8</th>\n",
       "      <th>...</th>\n",
       "      <th>sample_246</th>\n",
       "      <th>sample_247</th>\n",
       "      <th>sample_248</th>\n",
       "      <th>sample_249</th>\n",
       "      <th>sample_250</th>\n",
       "      <th>sample_251</th>\n",
       "      <th>sample_252</th>\n",
       "      <th>sample_253</th>\n",
       "      <th>sample_254</th>\n",
       "      <th>sample_255</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15040</th>\n",
       "      <td>0</td>\n",
       "      <td>3.082</td>\n",
       "      <td>2.594</td>\n",
       "      <td>2.106</td>\n",
       "      <td>2.106</td>\n",
       "      <td>3.571</td>\n",
       "      <td>4.547</td>\n",
       "      <td>4.547</td>\n",
       "      <td>3.082</td>\n",
       "      <td>0.641</td>\n",
       "      <td>...</td>\n",
       "      <td>6.012</td>\n",
       "      <td>4.547</td>\n",
       "      <td>4.547</td>\n",
       "      <td>5.524</td>\n",
       "      <td>6.500</td>\n",
       "      <td>6.989</td>\n",
       "      <td>6.012</td>\n",
       "      <td>5.524</td>\n",
       "      <td>4.059</td>\n",
       "      <td>4.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15041</th>\n",
       "      <td>1</td>\n",
       "      <td>-5.056</td>\n",
       "      <td>-6.521</td>\n",
       "      <td>-6.032</td>\n",
       "      <td>-4.079</td>\n",
       "      <td>-0.661</td>\n",
       "      <td>1.292</td>\n",
       "      <td>1.780</td>\n",
       "      <td>-0.173</td>\n",
       "      <td>-1.638</td>\n",
       "      <td>...</td>\n",
       "      <td>4.710</td>\n",
       "      <td>2.757</td>\n",
       "      <td>3.245</td>\n",
       "      <td>5.198</td>\n",
       "      <td>7.151</td>\n",
       "      <td>8.128</td>\n",
       "      <td>7.151</td>\n",
       "      <td>6.175</td>\n",
       "      <td>4.222</td>\n",
       "      <td>3.733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15042</th>\n",
       "      <td>2</td>\n",
       "      <td>16.449</td>\n",
       "      <td>14.496</td>\n",
       "      <td>10.101</td>\n",
       "      <td>6.195</td>\n",
       "      <td>6.195</td>\n",
       "      <td>8.148</td>\n",
       "      <td>10.101</td>\n",
       "      <td>9.125</td>\n",
       "      <td>5.707</td>\n",
       "      <td>...</td>\n",
       "      <td>5.707</td>\n",
       "      <td>3.754</td>\n",
       "      <td>3.265</td>\n",
       "      <td>3.265</td>\n",
       "      <td>4.242</td>\n",
       "      <td>5.219</td>\n",
       "      <td>6.195</td>\n",
       "      <td>7.172</td>\n",
       "      <td>7.172</td>\n",
       "      <td>6.195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 257 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sensor position  sample_0  sample_1  sample_2  sample_3  sample_4  \\\n",
       "15040                0     3.082     2.594     2.106     2.106     3.571   \n",
       "15041                1    -5.056    -6.521    -6.032    -4.079    -0.661   \n",
       "15042                2    16.449    14.496    10.101     6.195     6.195   \n",
       "\n",
       "       sample_5  sample_6  sample_7  sample_8  ...  sample_246  sample_247  \\\n",
       "15040     4.547     4.547     3.082     0.641  ...       6.012       4.547   \n",
       "15041     1.292     1.780    -0.173    -1.638  ...       4.710       2.757   \n",
       "15042     8.148    10.101     9.125     5.707  ...       5.707       3.754   \n",
       "\n",
       "       sample_248  sample_249  sample_250  sample_251  sample_252  sample_253  \\\n",
       "15040       4.547       5.524       6.500       6.989       6.012       5.524   \n",
       "15041       3.245       5.198       7.151       8.128       7.151       6.175   \n",
       "15042       3.265       3.265       4.242       5.219       6.195       7.172   \n",
       "\n",
       "       sample_254  sample_255  \n",
       "15040       4.059       4.059  \n",
       "15041       4.222       3.733  \n",
       "15042       7.172       6.195  \n",
       "\n",
       "[3 rows x 257 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alcoholic = (df_Org[df_Org['subject identifier'] == 'a'])\n",
    "alcoholic = alcoholic.drop(['subject identifier'], axis=1)\n",
    "control = (df_Org[df_Org['subject identifier'] == 'c'])\n",
    "control = control.drop(['subject identifier'], axis=1)\n",
    "control.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the mean amplitude for allcoholics and controls per channel\n",
    "plt.figure(figsize=(20, 10))\n",
    "for j in range(64):\n",
    "  ax = plt.subplot(7, 10, j+1)\n",
    "  plt.plot(samp_range, (control[control['sensor position']==j].mean(axis=0))[1:258])\n",
    "  plt.plot(samp_range, (alcoholic[alcoholic['sensor position']==j].mean(axis=0))[1:258])\n",
    "  plt.title('ch%d'%j)\n",
    "  plt.xticks([0, 300])\n",
    "  plt.ylim([0, 8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# according to the plot above, we are choosing only one channel for simplicity\n",
    "con_1ch= (control[control['sensor position']==33])[1:258].mean(axis=0)\n",
    "alc_1ch= (alcoholic[alcoholic['sensor position']==33])[1:258].mean(axis=0)\n",
    "con_1ch= con_1ch.drop(['sensor position'], axis=0)\n",
    "con_1ch = con_1ch.T\n",
    "alc_1ch= alc_1ch.drop(['sensor position'], axis=0)\n",
    "alc_1ch = alc_1ch.T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d866eb85e0>]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.plot(con_1ch)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(alc_1ch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dinak\\AppData\\Local\\Temp\\ipykernel_16996\\72328723.py:3: UserWarning: Only one segment is calculated since parameter NFFT (=256) >= signal length (=256).\n",
      "  Pxx, freqs, bins, im = plt.specgram(con_1ch,NFFT=2**8,noverlap=2**8-10,Fs=fs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dinak\\AppData\\Local\\Temp\\ipykernel_16996\\72328723.py:9: UserWarning: Only one segment is calculated since parameter NFFT (=256) >= signal length (=256).\n",
      "  Pxx2, freqs2, bins2, im2 = plt.specgram(alc_1ch,NFFT=2**8,noverlap=2**8-10,Fs=fs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = 256\n",
    "plt.title('spectrogram for mean of control group')\n",
    "Pxx, freqs, bins, im = plt.specgram(con_1ch,NFFT=2**8,noverlap=2**8-10,Fs=fs)\n",
    "plt.pcolormesh(bins, freqs, 10*np.log10(Pxx))\n",
    "plt.ylabel('Frequency (Hz)')\n",
    "plt.show()\n",
    "\n",
    "plt.title('spectrogram for mean of alcoholic group')\n",
    "Pxx2, freqs2, bins2, im2 = plt.specgram(alc_1ch,NFFT=2**8,noverlap=2**8-10,Fs=fs)\n",
    "plt.pcolormesh(bins2, freqs2, 10*np.log10(Pxx2))\n",
    "plt.ylabel('Frequency (Hz)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CNN training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import os #operation system\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from tqdm import tnrange, tqdm_notebook\n",
    "from IPython import display\n",
    "import time\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import models, layers, activations\n",
    "from keras.models import Model\n",
    "from PIL import Image, ImageFilter\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Org2 = df_Org.drop(['sensor position'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dinak\\AppData\\Local\\Temp\\ipykernel_16996\\607470813.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  label.loc[label==\"a\"]=1.0\n",
      "C:\\Users\\dinak\\AppData\\Local\\Temp\\ipykernel_16996\\607470813.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  label.loc[label==\"c\"]=0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7488, 256)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "label = df_Org2.iloc[:,0]\n",
    "df_Org3 = df_Org2.iloc[:, 1:]\n",
    "label.loc[label==\"a\"]=1.0\n",
    "label.loc[label==\"c\"]=0.0\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_Org3, label)\n",
    "n_classes = 2\n",
    "y_train = y_train.astype(\"float32\")\n",
    "y_test = y_test.astype(\"float32\")\n",
    "\n",
    "\n",
    "#df_Org3_see = df_Org3.to_numpy\n",
    "\n",
    "#print(type(df_Org3_see))\n",
    "df_Org3_see = df_Org3\n",
    "#df_Org3_see.iloc[:,-1] = np.nan \n",
    "#df_Org3_see = df_Org3_see.add (df_Org3_see, axis= 2, level=None, fill_value=None)\n",
    "x_train = (X_train.values.reshape(-1, 256, X_train.shape[0]).transpose(2, 1, 0) / 255.)\n",
    "x_test = (X_test.values.reshape(-1, 256, X_test.shape[0]).transpose(2, 1, 0) / 255.)\n",
    "df_Org3_see = (df_Org3_see.values.reshape(-1, 256, 29952).transpose(2, 1, 0) / 255.)\n",
    "\n",
    "\n",
    "X_test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7488"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_size_in = df_Org3_see.shape\n",
    "data_size_in = (256, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your network builder function. You can assume the (32, 32, 3) input size.\n",
    "def build_neural_network(data_size_in, n_classes):\n",
    "\n",
    "    inputs = layers.Input(shape=data_size_in)\n",
    "    network_1 = layers.Conv1D(32, kernel_size=(3), activation='relu')(inputs)\n",
    "    network_2 = layers.MaxPool1D(pool_size=(2))(network_1)\n",
    "    network_3 = layers.Conv1D(64, kernel_size=(3), activation='relu')(network_2)\n",
    "    network_4 = layers.MaxPool1D(pool_size=(2))(network_3)\n",
    "    network_5 = layers.Conv1D(128, kernel_size=(3), activation='relu')(network_4)\n",
    "    network_6 = layers.MaxPool1D(pool_size=(2))(network_5)\n",
    "    network_7 = layers.Flatten()(network_6)\n",
    "    network_8 = layers.Dense(128, activation='relu')(network_7)\n",
    "    network_9 = layers.Flatten()(network_8)\n",
    "    prediction = layers.Dense(1, activation='sigmoid')(network_9)\n",
    "\n",
    "    model = Model(inputs=inputs, outputs=prediction)\n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_27 (InputLayer)       [(None, 256, 1)]          0         \n",
      "                                                                 \n",
      " conv1d_68 (Conv1D)          (None, 254, 32)           128       \n",
      "                                                                 \n",
      " max_pooling1d_50 (MaxPoolin  (None, 127, 32)          0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " conv1d_69 (Conv1D)          (None, 125, 64)           6208      \n",
      "                                                                 \n",
      " max_pooling1d_51 (MaxPoolin  (None, 62, 64)           0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " conv1d_70 (Conv1D)          (None, 60, 128)           24704     \n",
      "                                                                 \n",
      " max_pooling1d_52 (MaxPoolin  (None, 30, 128)          0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " flatten_19 (Flatten)        (None, 3840)              0         \n",
      "                                                                 \n",
      " dense_38 (Dense)            (None, 128)               491648    \n",
      "                                                                 \n",
      " flatten_20 (Flatten)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_39 (Dense)            (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 522,817\n",
      "Trainable params: 522,817\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# experimental setup\n",
    "ls = 'BinaryCrossentropy' # define the (string) loss function\n",
    "lr = 0.1 # pick a value for your learning rate\n",
    "sgd = keras.optimizers.SGD(learning_rate=lr) # define Stochastic Gradient Descent as the keras optimizer, which takes the learning rate as input parameter\n",
    "metric = ['acc'] # define (Python) list of metrics\n",
    "\n",
    "network = build_neural_network(data_size_in, n_classes)\n",
    "network.compile(loss=ls, optimizer=sgd, metrics=metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training phase\n",
    "def train_network(network, x_training, y_training, x_validation, y_validation, n_epoch, batch_size, network_filepath):\n",
    "\n",
    "    results = network.fit(x_training, y_training,validation_data=(x_validation, y_validation), batch_size=batch_size, epochs= n_epoch)\n",
    "    pd.DataFrame(results.history).plot(figsize=(8,5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "351/351 [==============================] - 5s 14ms/step - loss: 0.6302 - acc: 0.6544 - val_loss: 0.5648 - val_acc: 0.7099\n",
      "Epoch 2/10\n",
      "351/351 [==============================] - 4s 12ms/step - loss: 0.5195 - acc: 0.7467 - val_loss: 0.4719 - val_acc: 0.7751\n",
      "Epoch 3/10\n",
      "351/351 [==============================] - 8s 22ms/step - loss: 0.4005 - acc: 0.8264 - val_loss: 0.4287 - val_acc: 0.8084\n",
      "Epoch 4/10\n",
      "351/351 [==============================] - 9s 24ms/step - loss: 0.2838 - acc: 0.8870 - val_loss: 0.2762 - val_acc: 0.8897\n",
      "Epoch 5/10\n",
      "351/351 [==============================] - 10s 28ms/step - loss: 0.3538 - acc: 0.8608 - val_loss: 0.3763 - val_acc: 0.8431\n",
      "Epoch 6/10\n",
      "351/351 [==============================] - 10s 29ms/step - loss: 0.2251 - acc: 0.9099 - val_loss: 0.2145 - val_acc: 0.9204\n",
      "Epoch 7/10\n",
      "351/351 [==============================] - 8s 24ms/step - loss: 0.1949 - acc: 0.9320 - val_loss: 0.3901 - val_acc: 0.8567\n",
      "Epoch 8/10\n",
      "351/351 [==============================] - 11s 30ms/step - loss: 0.1451 - acc: 0.9473 - val_loss: 0.2097 - val_acc: 0.9276\n",
      "Epoch 9/10\n",
      "351/351 [==============================] - 9s 26ms/step - loss: 0.0840 - acc: 0.9702 - val_loss: 0.2446 - val_acc: 0.9217\n",
      "Epoch 10/10\n",
      "351/351 [==============================] - 10s 28ms/step - loss: 0.0650 - acc: 0.9778 - val_loss: 0.2177 - val_acc: 0.9314\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_size = 64\n",
    "n_epoch = 10\n",
    "file_dir = './'\n",
    "network_filepath = os.path.join(file_dir, 'best_model_net_task_2.h5')\n",
    "\n",
    "train_network(network, X_train, y_train,\n",
    "              X_test, y_test, n_epoch, batch_size, network_filepath)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}