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{
"cells": [
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import pickle as pk\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"value = 4\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 1.00 0.97 0.99 39\n",
" 1 0.95 0.98 0.96 41\n",
" 2 0.97 1.00 0.99 39\n",
" 3 0.97 0.95 0.96 38\n",
"\n",
" accuracy 0.97 157\n",
" macro avg 0.98 0.97 0.97 157\n",
"weighted avg 0.97 0.97 0.97 157\n",
"\n"
]
}
],
"source": [
"data_pose = pd.read_csv(f\"./csv_files/{value}_poses_data_pose.csv\")\n",
"features = data_pose.drop([\"pose\"], axis=1)\n",
"target = data_pose[[\"pose\"]]\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2)\n",
"\n",
"data_all_pose_model = RandomForestClassifier()\n",
"data_all_pose_model.fit(X_train, y_train)\n",
"\n",
"print(classification_report(y_test, data_all_pose_model.predict(X_test)))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pk.dump(data_all_pose_model, open(f\"./models/{value}_poses.model\", \"wb\"))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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