{
 "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"
   ]
  }
 ],
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