randomforest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c7eb3670-671e-4709-bc95-bc6301474bda",
   "metadata": {},
   "source": [
    "# Import Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d0bf2fa1-eeaf-425c-b843-0b25bd96dc9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75cec094-bde7-4ba4-a6e1-e6be278aea5c",
   "metadata": {},
   "source": [
    "# Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a7786fd3-4aef-481b-9cb7-8f33848b3141",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.read_csv(\"./data/1_X_train.csv\").values\n",
    "y = pd.read_csv(\"./data/1_Y_train.csv\").values.reshape(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0b59df-be09-45e1-8483-6416194613e4",
   "metadata": {},
   "source": [
    "# Set Free Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d9f1416d-222c-472c-b244-f16beecec3ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_depth = 10\n",
    "n_estimators = 50\n",
    "min_samples_split = 5\n",
    "\n",
    "num_splits = 10\n",
    "num_repeats = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed01196-81be-42a8-9bc7-3c64f3199762",
   "metadata": {},
   "source": [
    "# Instantiate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c18ae62-0c7d-432c-bd16-88809d26301e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(max_depth=max_depth, n_estimators=n_estimators,\n",
    "                            min_samples_split=min_samples_split)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0fab1a-1768-49e2-be45-6e5abfa62296",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8a360b16-6c54-448a-abb9-bca7156dbfa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-ratio:0.8092647058823529(0.00474478406026519)\n",
      "+ratio:0.839485294117647(0.0030325868569560637)\n"
     ]
    }
   ],
   "source": [
    "ratio_accs = []\n",
    "accs = []\n",
    "\n",
    "for repeat in range(num_repeats):\n",
    "    kf = KFold(n_splits=num_splits, random_state=repeat, shuffle=True)\n",
    "    running_acc = 0\n",
    "    ratio_running_acc = 0\n",
    "\n",
    "    for train_idx, test_idx in kf.split(x):\n",
    "        # split data into train, test\n",
    "        train_x, test_x = x[train_idx], x[test_idx]\n",
    "        train_y, test_y = y[train_idx], y[test_idx]\n",
    "\n",
    "        # training and test\n",
    "\n",
    "        # -ratio\n",
    "        # columns 0,7,8 of train_x and test_x\n",
    "        # corresponds to energy data ratio feature\n",
    "        rf.fit(train_x[:, [1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13]], train_y)\n",
    "        pred_y = rf.predict(test_x[:, [1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13]])\n",
    "        running_acc += sum(pred_y == test_y) / int(test_y.shape[0])\n",
    "\n",
    "        # +ratio\n",
    "        rf.fit(train_x, train_y)\n",
    "        pred_y = rf.predict(test_x)\n",
    "        ratio_running_acc += sum(pred_y == test_y) / int(test_y.shape[0])\n",
    "\n",
    "    mean_acc = running_acc / num_splits\n",
    "    accs.append(mean_acc)\n",
    "\n",
    "    ratio_mean_acc = ratio_running_acc / num_splits\n",
    "    ratio_accs.append(ratio_mean_acc)\n",
    "\n",
    "print(f\"-ratio:{np.mean(accs)}({np.std(accs)})\")\n",
    "print(f\"+ratio:{np.mean(ratio_accs)}({np.std(ratio_accs)})\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}