Datasets
Models
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stress Detection Using ML"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reading the Dataset"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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>net_acc_mean</th>\n",
" <th>net_acc_std</th>\n",
" <th>net_acc_min</th>\n",
" <th>net_acc_max</th>\n",
" <th>ACC_x_mean</th>\n",
" <th>ACC_x_std</th>\n",
" <th>ACC_x_min</th>\n",
" <th>ACC_x_max</th>\n",
" <th>ACC_y_mean</th>\n",
" <th>ACC_y_std</th>\n",
" <th>...</th>\n",
" <th>age</th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>gender_ female</th>\n",
" <th>gender_ male</th>\n",
" <th>coffee_today_YES</th>\n",
" <th>sport_today_YES</th>\n",
" <th>smoker_NO</th>\n",
" <th>smoker_YES</th>\n",
" <th>feel_ill_today_YES</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.029937</td>\n",
" <td>0.009942</td>\n",
" <td>0.000000</td>\n",
" <td>0.087383</td>\n",
" <td>0.029510</td>\n",
" <td>0.011145</td>\n",
" <td>-0.024082</td>\n",
" <td>0.087383</td>\n",
" <td>0.000020</td>\n",
" <td>0.000008</td>\n",
" <td>...</td>\n",
" <td>27</td>\n",
" <td>175</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.021986</td>\n",
" <td>0.015845</td>\n",
" <td>0.000000</td>\n",
" <td>0.071558</td>\n",
" <td>0.017352</td>\n",
" <td>0.020817</td>\n",
" <td>-0.037843</td>\n",
" <td>0.071558</td>\n",
" <td>0.000012</td>\n",
" <td>0.000014</td>\n",
" <td>...</td>\n",
" <td>27</td>\n",
" <td>175</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.020839</td>\n",
" <td>0.011034</td>\n",
" <td>0.002752</td>\n",
" <td>0.054356</td>\n",
" <td>0.020839</td>\n",
" <td>0.011034</td>\n",
" <td>0.002752</td>\n",
" <td>0.054356</td>\n",
" <td>0.000014</td>\n",
" <td>0.000008</td>\n",
" <td>...</td>\n",
" <td>27</td>\n",
" <td>175</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.034449</td>\n",
" <td>0.003185</td>\n",
" <td>0.013761</td>\n",
" <td>0.040595</td>\n",
" <td>0.034449</td>\n",
" <td>0.003185</td>\n",
" <td>0.013761</td>\n",
" <td>0.040595</td>\n",
" <td>0.000024</td>\n",
" <td>0.000002</td>\n",
" <td>...</td>\n",
" <td>27</td>\n",
" <td>175</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 58 columns</p>\n",
"</div>"
],
"text/plain": [
" net_acc_mean net_acc_std net_acc_min net_acc_max ACC_x_mean ACC_x_std \\\n",
"0 0.029937 0.009942 0.000000 0.087383 0.029510 0.011145 \n",
"1 0.021986 0.015845 0.000000 0.071558 0.017352 0.020817 \n",
"2 0.020839 0.011034 0.002752 0.054356 0.020839 0.011034 \n",
"3 0.034449 0.003185 0.013761 0.040595 0.034449 0.003185 \n",
"\n",
" ACC_x_min ACC_x_max ACC_y_mean ACC_y_std ... age height weight \\\n",
"0 -0.024082 0.087383 0.000020 0.000008 ... 27 175 80 \n",
"1 -0.037843 0.071558 0.000012 0.000014 ... 27 175 80 \n",
"2 0.002752 0.054356 0.000014 0.000008 ... 27 175 80 \n",
"3 0.013761 0.040595 0.000024 0.000002 ... 27 175 80 \n",
"\n",
" gender_ female gender_ male coffee_today_YES sport_today_YES smoker_NO \\\n",
"0 0 1 0 0 1 \n",
"1 0 1 0 0 1 \n",
"2 0 1 0 0 1 \n",
"3 0 1 0 0 1 \n",
"\n",
" smoker_YES feel_ill_today_YES \n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"\n",
"[4 rows x 58 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('data/merged.csv', index_col=0)\n",
"df.head(4)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['net_acc_mean', 'net_acc_std', 'net_acc_min', 'net_acc_max',\n",
" 'ACC_x_mean', 'ACC_x_std', 'ACC_x_min', 'ACC_x_max', 'ACC_y_mean',\n",
" 'ACC_y_std', 'ACC_y_min', 'ACC_y_max', 'ACC_z_mean', 'ACC_z_std',\n",
" 'ACC_z_min', 'ACC_z_max', 'BVP_mean', 'BVP_std', 'BVP_min', 'BVP_max',\n",
" 'EDA_mean', 'EDA_std', 'EDA_min', 'EDA_max', 'EDA_phasic_mean',\n",
" 'EDA_phasic_std', 'EDA_phasic_min', 'EDA_phasic_max', 'EDA_smna_mean',\n",
" 'EDA_smna_std', 'EDA_smna_min', 'EDA_smna_max', 'EDA_tonic_mean',\n",
" 'EDA_tonic_std', 'EDA_tonic_min', 'EDA_tonic_max', 'Resp_mean',\n",
" 'Resp_std', 'Resp_min', 'Resp_max', 'TEMP_mean', 'TEMP_std', 'TEMP_min',\n",
" 'TEMP_max', 'BVP_peak_freq', 'TEMP_slope', 'subject', 'label', 'age',\n",
" 'height', 'weight', 'gender_ female', 'gender_ male',\n",
" 'coffee_today_YES', 'sport_today_YES', 'smoker_NO', 'smoker_YES',\n",
" 'feel_ill_today_YES'],\n",
" dtype='object')"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(df['label'])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"labels = {\n",
" 0: \"Amused\",\n",
" 1: \"Neutral\",\n",
" 2: \"Stressed\"\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feature Selection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"nbconvert_remove"
]
},
"outputs": [],
"source": [
"# nbconvert_remove\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"plt.figure(figsize=(2,100))\n",
"cor = df.corr()\n",
"n_targets = len(df.columns)\n",
"cor_target = cor['label'].values.reshape(n_targets, 1)\n",
"cor_features = cor['label'].keys()\n",
"ax = sns.heatmap(cor_target, annot=True, cmap=plt.cm.Accent_r)\n",
"ax.set_yticklabels(cor_features)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1178, 15), (1178,))"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selected_feats = [\n",
" 'BVP_mean', 'BVP_std', 'EDA_phasic_mean', 'EDA_phasic_min', 'EDA_smna_min', \n",
" 'EDA_tonic_mean', 'Resp_mean', 'Resp_std', 'TEMP_mean', 'TEMP_std', 'TEMP_slope',\n",
" 'BVP_peak_freq', 'age', 'height', 'weight'\n",
" ]\n",
"\n",
"X = df[selected_feats]\n",
"y = df['label']\n",
"\n",
"X.shape, y.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ML Model"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1060, 15), (118, 15))"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train, X_test = train_test_split(X, test_size=0.1, random_state=0)\n",
"y_train, y_test = train_test_split(y, test_size=0.1, random_state=0)\n",
"\n",
"X_train.shape, X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = RandomForestClassifier()\n",
"model.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"def accuracy(predicted, actual):\n",
" n = 0\n",
" for p, a in zip(predicted, actual):\n",
" if p == a:\n",
" n += 1\n",
" return n/len(predicted) * 100"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"def predict(arr):\n",
" arr = np.array(arr)\n",
"\n",
" global model\n",
" result = model.predict(arr.reshape(1,-1)).flatten()\n",
" # _prob = model.predict_proba(arr.reshape(1,-1)).flatten()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"tags": [
"nbconvert_remove"
]
},
"outputs": [
{
"data": {
"text/plain": [
"95.76271186440678"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted = []\n",
"for data in X_test.values:\n",
" predicted.append(predict(data))\n",
"predicted\n",
"\n",
"accuracy(predicted, y_test.values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Saving the trained model in a pickle file to be later used by the API function to predict"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"filename = 'trained_model.sav'\n",
"pickle.dump(model, open(filename, 'wb'))"
]
}
],
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