Random_forest_size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "with open(\"../compilation-analysis/option_columns.json\",\"r\") as f:\n",
    "    option_columns = json.load(f)\n",
    "\n",
    "#Find the dataset\n",
    "df = pd.read_csv(\"../tuxml-datasets/dataset_encoded_size.csv\", dtype={k:\"int8\" for k in option_columns})\n",
    "\n",
    "df.query(\"cid >= 30000\", inplace=True)\n",
    "df.fillna(-1, inplace=True)\n",
    "df.query(\"kernel_size >= 0\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import ensemble\n",
    "\n",
    "def runML_RF(hyperparams, with_tree=False):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(df.drop(columns=\"cid\").drop(columns=\"kernel_size\"), df[\"kernel_size\"], test_size=0.1)  \n",
    "    reg = ensemble.RandomForestRegressor(**hyperparams)\n",
    "    reg.fit(X_train, y_train)\n",
    "    y_pred = reg.predict(X_test)\n",
    "    dfErrors = pd.DataFrame({'Actual':y_test, 'Predicted':y_pred, \"error\":(y_pred - y_test).abs(), \"% error\":((y_pred - y_test)/y_test).abs()*100})\n",
    "    if with_tree:\n",
    "        return dfErrors[\"% error\"].describe(), reg\n",
    "    return dfErrors[\"% error\"].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 2 of 50\n",
      "building tree 3 of 50\n",
      "building tree 4 of 50\n",
      "building tree 5 of 50\n",
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      "building tree 43 of 50\n",
      "building tree 44 of 50\n",
      "building tree 45 of 50\n",
      "building tree 46 of 50\n",
      "building tree 47 of 50\n",
      "building tree 48 of 50\n",
      "building tree 49 of 50\n",
      "building tree 50 of 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed: 90.9min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    9248.000000\n",
       "mean       10.268395\n",
       "std         9.714618\n",
       "min         0.001532\n",
       "25%         3.581829\n",
       "50%         7.868058\n",
       "75%        13.921638\n",
       "max       191.718545\n",
       "Name: % error, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "hyperparams = {\n",
    "    \"max_depth\":18,\n",
    "    \"n_estimators\":50,\n",
    "    \"verbose\":2\n",
    "}\n",
    "res = runML_RF(hyperparams)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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