Intel_Extension_for_SKLearn_Performance_SVC_Adult.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5c4abc0",
   "metadata": {},
   "source": [
    "# Intel® Extension for Scikit-learn Performance Sample: SVC for [Adult dataset](https://www.openml.org/d/1430)\n",
    "\n",
    "## Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23512089",
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27b99b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "x, y = fetch_openml(name='a9a', return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96e14dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0341cac9",
   "metadata": {},
   "source": [
    "## Using Intel(R) Extension for Scikit-learn\n",
    "\n",
    "Intel(R) 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(R) Extension for Scikit-learn by adding just two lines of code before the usual scikit-learn imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "244c5bc9",
   "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",
    "patch_sklearn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb14ac8",
   "metadata": {},
   "source": [
    "Intel(R) 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/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": "code",
   "execution_count": 6,
   "id": "41c13dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'C': 100.0,\n",
    "    'kernel': 'rbf',\n",
    "    'gamma': 'scale'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693b4e26",
   "metadata": {},
   "source": [
    "Train SVC algorithm with Intel(R) Extension for Scikit-learn on Adult dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9b8f06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Execution time with Intel(R) Extension for Scikit-learn: 59.44 s'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_opt = time()\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(**params).fit(x_train, y_train)\n",
    "finish_opt = time()\n",
    "f\"Execution time with Intel(R) Extension for Scikit-learn: {(finish_opt - start_opt):.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d01cdabc",
   "metadata": {},
   "source": [
    "Make predictions with SVC classifier and print a report of the main classification metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ead2a44",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for SVC trained with Intel(R) extension for Scikit-learn:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.87      0.90      0.88      7414\n",
      "         1.0       0.64      0.58      0.61      2355\n",
      "\n",
      "    accuracy                           0.82      9769\n",
      "   macro avg       0.76      0.74      0.75      9769\n",
      "weighted avg       0.82      0.82      0.82      9769\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predicted = classifier.predict(x_test)\n",
    "acc_opt = metrics.accuracy_score(y_test, predicted)\n",
    "report_opt = metrics.classification_report(y_test, predicted)\n",
    "print(f\"Classification report for SVC trained with Intel(R) extension for Scikit-learn:\\n{report_opt}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8e7b0b",
   "metadata": {},
   "source": [
    "*The first column of the classification report above contains class labels.*  \n",
    "\n",
    "## Using original Scikit-learn\n",
    "\n",
    "To cancel optimizations, use `unpatch_sklearn` and reimport the SVC class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5bb884d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearnex import unpatch_sklearn\n",
    "unpatch_sklearn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cfa0dba",
   "metadata": {},
   "source": [
    "Train SVC algorithm with original scikit-learn library on Adult dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae421d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Execution time with the original Scikit-learn: 721.62 s'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_unopt = time()\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(**params).fit(x_train, y_train)\n",
    "finish_unopt = time()\n",
    "f\"Execution time with the original Scikit-learn: {(finish_unopt - start_unopt):.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a7a747",
   "metadata": {},
   "source": [
    "Predict and get a result of the SVC algorithm with original Scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7644999d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for SVC trained with the original scikit-learn:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.87      0.90      0.88      7414\n",
      "         1.0       0.64      0.58      0.61      2355\n",
      "\n",
      "    accuracy                           0.82      9769\n",
      "   macro avg       0.76      0.74      0.75      9769\n",
      "weighted avg       0.82      0.82      0.82      9769\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predicted = classifier.predict(x_test)\n",
    "acc_unopt = metrics.accuracy_score(y_test, predicted)\n",
    "report_unopt = metrics.classification_report(y_test, predicted)\n",
    "print(f\"Classification report for SVC trained with the original scikit-learn:\\n{report_unopt}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8efca43d-e50e-478e-ac85-151380e3951b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be9ec76-8041-4f29-8dd0-e4c396161f57",
   "metadata": {},
   "source": [
    "### Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9f952e7a-93c7-432d-9809-9240d47bae66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance Speedup:  12.139416650865776\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "left = [1,2]\n",
    "pred_times = [finish_opt - start_opt, finish_unopt - start_unopt]\n",
    "tick_label = ['Intel Extension for SKLearn', 'Unoptimized SKLearn']\n",
    "plt.bar(left, pred_times, tick_label = tick_label, width = 0.5, color = ['blue', 'red'])\n",
    "plt.xlabel('Training Method'); plt.ylabel('time,s'); plt.title('SVC Training Time,s')\n",
    "plt.show()\n",
    "print(\"Performance Speedup: \",(finish_unopt - start_unopt)/(finish_opt - start_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddaa91e3-ed8f-4042-9d5c-f25cd7e74aaa",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "625b6fd5-c252-4971-95d1-36835fb44053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "left = [1,2]\n",
    "pred_acc = [acc_opt, acc_unopt]\n",
    "tick_label = ['Intel Extension for SKLearn', 'Unoptimized SKLearn']\n",
    "plt.bar(left, pred_acc, tick_label = tick_label, width = 0.5, color = ['blue', 'red'])\n",
    "plt.xlabel('Training Method'); plt.ylabel('accuracy, %'); plt.title('SVC Training Time, %')\n",
    "plt.show()\n",
    "print(\"Accuracy Difference: \",acc_opt - acc_unopt)"
   ]
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    "## Conclusions \n",
    "\n",
    "With Intel(R) Extension for Scikit-learn you can:\n",
    "\n",
    "- Use your existing scikit-learn code for training and prediction;\n",
    "- Add a couple of lines to execute your code up to be significantly faster than stock scikit-learn;\n",
    "- Get models of the same quality."
   ]
  }
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