Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d6c9aa3-7550-4255-84a9-48cccedaa1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444cbac3-32d5-4e4d-aa7c-3561338c2489",
   "metadata": {},
   "source": [
    "# Data\n",
    "Loading the data and defining the feature names of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10785dc3-f7a4-4d72-b247-d0a541819752",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"test/Data/AAPL.csv\"\n",
    "data = pd.read_csv(file_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9996dff6-a661-4f6e-9f5b-3f598381babc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,0:6]\n",
    "y = data.iloc[:,7]\n",
    "\n",
    "# feature names should not have empty spaces in them\n",
    "features = ['Adj_Close', 'Close', 'High', 'Low', 'Open', 'Volume']\n",
    "\n",
    "x.columns = features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09823dea-9b80-4994-81fb-7948af3c9d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test = x.iloc[:5000,:], x.iloc[5000:,:]\n",
    "y_train,y_test = y[:5000],y[5000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a032266e-77f2-4091-855a-849979ea2b52",
   "metadata": {},
   "source": [
    "# XGB model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1304be1c-624f-4e96-8589-5c1cff264595",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing and training the xgb model\n",
    "import xgboost as xgb\n",
    "model_xgb = xgb.XGBRegressor()\n",
    "model_xgb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c446ff6-eb47-4868-b541-d41ae02a9679",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dumping the model detils to a file\n",
    "model_xgb._Booster.dump_model(\"./test/Models/xgb_model.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e1c700-580e-46ff-9c82-6d1c6c82275c",
   "metadata": {},
   "source": [
    "### Normal XGB model Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "716d0cfe-ae4d-4cc0-b379-84f28d415a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_prediction = model_xgb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bda4769-9956-4143-a789-232dbb080897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03996581, -0.28144288,  0.49001595, ...,  0.7893067 ,\n",
       "        0.71400946,  0.75283796], dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ffc9fe5-2bb1-4b4a-9fc6-46b1dc3b70c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.1 ms ± 13 ms per loop (mean ± std. dev. of 3 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 2 -r 3\n",
    "\n",
    "model_xgb.predict(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "329dff98-02f5-49e0-bcc9-9e9e5ad6a55d",
   "metadata": {},
   "source": [
    "### Volta_XGB model Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f092aae-0fac-40b1-8bd9-2a0bda0ac28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import voltatrees.volta_XGBM as vxgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "818478cc-94eb-46f3-9339-2af60a90d00b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = vxgb.Model(\"./test/Models/xgb_model.txt\", features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8dcbb47-7f35-47b3-82e7-3614c03c61e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "57025f22-833d-44a3-b37c-f421a4f857ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "our_predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2bf93391-fa34-4cb4-834d-14876a7f6d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03996586, -0.28144285,  0.49001607, ...,  0.7893066 ,\n",
       "        0.71400942,  0.75283789])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "our_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7fe5c734-2913-409c-8869-d5d881609bbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.6 ms ± 1.71 ms per loop (mean ± std. dev. of 3 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 2 -r 3\n",
    "\n",
    "model.predict(x, n_jobs=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d784228b-b2d1-453d-aadf-f595bdbc3aa1",
   "metadata": {},
   "source": [
    "# LGB Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0df31656-9278-4817-a2c5-c38fc4be7e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "model_lgb = lgb.LGBMRegressor()\n",
    "model_lgb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a64e1a9-9335-4221-ac61-40de37f242ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x7f20dc0770a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# saving the model\n",
    "model_lgb.booster_.save_model(\"test/Models/lgb_model.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb8b3be-c113-4413-a70b-fd962d6947ff",
   "metadata": {},
   "source": [
    "### Normal LGB model Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "554b77f9-8fb8-4b78-ab95-a20c6bb37d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_prediction = model_lgb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1d989478-4cfc-40f0-9f32-c8b4ea912af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05097371,  0.05097371, -0.04177585, ...,  0.05097371,\n",
       "        0.05097371,  0.05097371])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "111001d0-92d7-474b-b48d-2f713682198f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.9 ms ± 4.34 ms per loop (mean ± std. dev. of 3 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 2 -r 3\n",
    "\n",
    "model_lgb.predict(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2fe66da-e14e-477a-8253-9b3ebc89c09e",
   "metadata": {},
   "source": [
    "### Volta_LGB model Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0bd7a1b4-4cf1-43c9-abae-4d5e4308b997",
   "metadata": {},
   "outputs": [],
   "source": [
    "import voltatrees.volta_LGBM as vlgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9c1e48fe-49fc-4fb1-b540-2dc61a43a50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = vlgb.Model(model_file=\"test/Models/lgb_model.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e1e35efa-f7b3-430e-a385-a2012a206014",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b42cdf2e-4a31-4379-a5cf-b25a7f6528a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "our_predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cbe54c15-0b8d-4f19-9be4-9b266cd214bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05097371,  0.05097371, -0.04177585, ...,  0.05097371,\n",
       "        0.05097371,  0.05097371])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "our_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ce334e3-b9f7-40c9-b845-ba7afac81885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.4 ms ± 543 µs per loop (mean ± std. dev. of 3 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 2 -r 3\n",
    "\n",
    "model.predict(x, n_jobs=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a41c19d-293e-40bb-adc3-664aa0e57a5f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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