{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5c4abc0",
   "metadata": {},
   "source": [
    "# Intel® Extension for Scikit-learn Random Forest for Yolanda dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23512089",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeit import default_timer as timer\n",
    "from sklearn import metrics\n",
    "from IPython.display import HTML\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d0b6bb9",
   "metadata": {},
   "source": [
    "### Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27b99b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = fetch_openml(name=\"Yolanda\", return_X_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3a2483",
   "metadata": {},
   "source": [
    "Split the data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96e14dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((280000, 100), (120000, 100), (280000,), (120000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=72)\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0341cac9",
   "metadata": {},
   "source": [
    "### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
    "Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "244c5bc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/uxlfoundation/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "from sklearnex import patch_sklearn\n",
    "\n",
    "patch_sklearn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb14ac8",
   "metadata": {},
   "source": [
    "Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://uxlfoundation.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/uxlfoundation/scikit-learn-intelex/issues)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693b4e26",
   "metadata": {},
   "source": [
    "Training Random Forest algorithm with Intel® Extension for Scikit-learn for Yolanda dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8fecbbb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Intel® extension for Scikit-learn time: 42.56 s'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "params = {\"n_estimators\": 150, \"random_state\": 44, \"n_jobs\": -1}\n",
    "start = timer()\n",
    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
    "train_patched = timer() - start\n",
    "f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9279181",
   "metadata": {},
   "source": [
    "Predict and get a result of the Random Forest algorithm with Intel® Extension for Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d05bc57b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Intel® extension for Scikit-learn Mean Squared Error: 83.62232345666878'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict(x_test)\n",
    "mse_opt = metrics.mean_squared_error(y_test, y_pred)\n",
    "f\"Intel® extension for Scikit-learn Mean Squared Error: {mse_opt}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8e7b0b",
   "metadata": {},
   "source": [
    "### Train the same algorithm with original Scikit-learn\n",
    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class RandomForestRegressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bb884d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearnex import unpatch_sklearn\n",
    "\n",
    "unpatch_sklearn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cfa0dba",
   "metadata": {},
   "source": [
    "Training Random Forest algorithm with original Scikit-learn library for Yolanda dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76a8d5f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Original Scikit-learn time: 123.34 s'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "start = timer()\n",
    "rf = RandomForestRegressor(**params).fit(x_train, y_train)\n",
    "train_unpatched = timer() - start\n",
    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f162fe6b",
   "metadata": {},
   "source": [
    "Predict and get a result of the Random Forest algorithm with original Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5b5e45c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Original Scikit-learn Mean Squared Error: 83.62232345666878'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict(x_test)\n",
    "mse_original = metrics.mean_squared_error(y_test, y_pred)\n",
    "f\"Original Scikit-learn Mean Squared Error: {mse_opt}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e255e563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 83.62232345666878 <br>MSE metric of unpatched Scikit-learn: 83.80131297814816 <br>Metrics ratio: 0.9978641203208111 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>2.9</strong> times.</li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\n",
    "    f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
    "    f\"MSE metric of patched Scikit-learn: {mse_opt} <br>\"\n",
    "    f\"MSE metric of unpatched Scikit-learn: {mse_original} <br>\"\n",
    "    f\"Metrics ratio: {mse_opt/mse_original} <br>\"\n",
    "    f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
    "    f\"<ul>\"\n",
    "    f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
    "    f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
    "    f\"<li>Get the similar quality</li>\"\n",
    "    f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
    "    f\"</ul>\"\n",
    ")"
   ]
  }
 ],
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