DOC: Add docs for 2025 releases

2023.2/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 6800a6ffac59c178770a0c0cc83712a8
+tags: 645f666f9bcd5a90fca523b33c5a78b7

2023.2/.doctrees/nbsphinx/samples/ElasticNet.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3768ec43",
+   "metadata": {},
+   "source": [
+    "# Intel® Extension for Scikit-learn ElasticNet 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": "34e460a7",
+   "metadata": {},
+   "source": [
+    "### Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00c2277b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = fetch_openml(name=\"Airlines_DepDelay_10M\", return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d309c0",
+   "metadata": {},
+   "source": [
+    "### Preprocessing\n",
+    "Let's encode categorical features with LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ff35bc2",
+   "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": "38637349",
+   "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 StandardScaler\n",
+    "\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_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 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": 7,
+   "id": "ef6938df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearnex import patch_sklearn\n",
+    "\n",
+    "patch_sklearn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c5ab48",
+   "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://intel.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/intel/scikit-learn-intelex/issues)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f80273e7",
+   "metadata": {},
+   "source": [
+    "Training of the ElasticNet algorithm with Intel® Extension for Scikit-learn for Airlines DepDelay dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4dd1c7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Intel® extension for Scikit-learn time: 0.28 s'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "params = {\n",
+    "    \"alpha\": 0.3,\n",
+    "    \"fit_intercept\": False,\n",
+    "    \"l1_ratio\": 0.7,\n",
+    "    \"random_state\": 0,\n",
+    "    \"copy_X\": False,\n",
+    "}\n",
+    "start = timer()\n",
+    "model = ElasticNet(**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": "f10b51fc",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with Intel® Extension for Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4295a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Patched Scikit-learn MSE: 1.0109113399224974'"
+      ]
+     },
+     "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 ElasticNet"
+   ]
+  },
+  {
+   "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 ElasticNet 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: 3.96 s'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "start = timer()\n",
+    "model = ElasticNet(**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": "c85a125c",
+   "metadata": {},
+   "source": [
+    "Predict and get a result of the ElasticNet algorithm with original Scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd9e726c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Original Scikit-learn MSE: 1.0109113399545733'"
+      ]
+     },
+     "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": "a2edbb65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 1.0109113399224974 <br>MSE metric of unpatched Scikit-learn: 1.0109113399545733 <br>Metrics ratio: 0.9999999999682703 <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>14.2</strong> times.</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>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",
+    ")"
+   ]
+  }
+ ],
+ "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": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}