examples/notebooks/ridge_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3768ec43",
   "metadata": {},
   "source": [
    "# Extension for Scikit-learn Ridge Regression for Airlines DepDelay dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1b922d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeit import default_timer as timer\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from IPython.display import HTML\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1a9234",
   "metadata": {},
   "source": [
    "### Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e73dc65",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f852cad8",
   "metadata": {},
   "source": [
    "### Preprocessing\n",
    "Let's encode categorical features with LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27ebb377",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in [\"UniqueCarrier\", \"Origin\", \"Dest\"]:\n",
    "    le = LabelEncoder().fit(x[col])\n",
    "    x[col] = le.transform(x[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "147b3e82",
   "metadata": {},
   "source": [
    "Split the data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d332789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((9000000, 9), (1000000, 9), (9000000,), (1000000,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246f819f",
   "metadata": {},
   "source": [
    "Normalize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "454a341c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "scaler_x = MinMaxScaler()\n",
    "scaler_y = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df400504",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.to_numpy().reshape(-1, 1)\n",
    "y_test = y_test.to_numpy().reshape(-1, 1)\n",
    "\n",
    "scaler_x.fit(x_train)\n",
    "x_train = scaler_x.transform(x_train)\n",
    "x_test = scaler_x.transform(x_test)\n",
    "\n",
    "scaler_y.fit(y_train)\n",
    "y_train = scaler_y.transform(y_train).ravel()\n",
    "y_test = scaler_y.transform(y_test).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1d4fac",
   "metadata": {},
   "source": [
    "### Patch original Scikit-learn with Extension for Scikit-learn\n",
    "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 Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef6938df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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": "20c5ab48",
   "metadata": {},
   "source": [
    "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": "f80273e7",
   "metadata": {},
   "source": [
    "Training of the Ridge Regression algorithm with Extension for Scikit-learn for Airlines DepDelay dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4dd1c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Extension for Scikit-learn time: 0.06 s'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "params = {\n",
    "    \"alpha\": 0.3,\n",
    "    \"fit_intercept\": False,\n",
    "    \"random_state\": 0,\n",
    "    \"copy_X\": False,\n",
    "}\n",
    "start = timer()\n",
    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
    "train_patched = timer() - start\n",
    "f\"Extension for Scikit-learn time: {train_patched:.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10b51fc",
   "metadata": {},
   "source": [
    "Predict and get a result of the Ridge Regression algorithm with Extension for Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d4295a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Patched Scikit-learn MSE: 1.0014288520708046'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model.predict(x_test)\n",
    "mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
    "f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe6db0d",
   "metadata": {},
   "source": [
    "### Train the same algorithm with original Scikit-learn\n",
    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f64ba97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearnex import unpatch_sklearn\n",
    "\n",
    "unpatch_sklearn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f242c6da",
   "metadata": {},
   "source": [
    "Training of the Ridge Regression algorithm with original Scikit-learn library for Airlines DepDelay dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67243849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Original Scikit-learn time: 0.70 s'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "start = timer()\n",
    "model = Ridge(random_state=0).fit(x_train, y_train)\n",
    "train_unpatched = timer() - start\n",
    "f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85a125c",
   "metadata": {},
   "source": [
    "Predict and get a result of the Ridge Regression algorithm with original Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd9e726c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Original Scikit-learn MSE: 1.0014288520708057'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model.predict(x_test)\n",
    "mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
    "f\"Original Scikit-learn MSE: {mse_metric_original}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1bde360d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0014288520708046 <br>MSE metric of unpatched Scikit-learn: 1.0014288520708057 <br>Metrics ratio: 0.9999999999999989 <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>Get comparable model quality</li><li>Get a <strong>10.9x</strong> speedup.</li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "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_metric_opt} <br>\"\n",
    "    f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
    "    f\"Metrics ratio: {mse_metric_opt/mse_metric_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>Get comparable model quality</li>\"\n",
    "    f\"<li>Get a <strong>{(train_unpatched/train_patched):.1f}x</strong> speedup.</li>\"\n",
    "    f\"</ul>\"\n",
    ")"
   ]
  }
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