logistictic_regression_cifar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5c4abc0",
   "metadata": {},
   "source": [
    "# Intel® Extension for Scikit-learn Logistic Regression for Cifar dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23512089",
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27b99b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "x, y = fetch_openml(name='CIFAR-100', return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96e14dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7e48a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((54000, 3072), (6000, 3072), (54000,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0341cac9",
   "metadata": {},
   "source": [
    "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": 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': 0.1,\n",
    "    'solver': 'lbfgs',\n",
    "    'multi_class': 'multinomial',\n",
    "    'n_jobs': -1,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693b4e26",
   "metadata": {},
   "source": [
    "Training of the Logistic Regression algorithm with Intel(R) Extension for Scikit-learn for CIFAR dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9b8f06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Intel(R) extension for Scikit-learn time: 24.75 s'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = time()\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
    "f\"Intel(R) extension for Scikit-learn time: {(time() - start):.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d01cdabc",
   "metadata": {},
   "source": [
    "Predict probability and get a result of the Logistic Regression algorithm with Intel(R) Extension for Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ead2a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.707353079001737"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = classifier.predict_proba(x_test)\n",
    "log_loss_metric = metrics.log_loss(y_test, y_predict)\n",
    "log_loss_metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8e7b0b",
   "metadata": {},
   "source": [
    "In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bb884d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearnex import unpatch_sklearn\n",
    "unpatch_sklearn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cfa0dba",
   "metadata": {},
   "source": [
    "Training of the Logistic Regression algorithm with original scikit-learn library for CIFAR dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ae421d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Original Scikit-learn time: 194.50 s'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = time()\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(**params).fit(x_train, y_train)\n",
    "f\"Original Scikit-learn time: {(time() - start):.2f} s\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d38dfb5",
   "metadata": {},
   "source": [
    "Predict probability and get a result of the Logistic Regression algorithm with original Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7644999d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7140869870173234"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = classifier.predict_proba(x_test)\n",
    "log_loss_metric = metrics.log_loss(y_test, y_predict)\n",
    "log_loss_metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb299d22",
   "metadata": {},
   "source": [
    "With scikit-learn-intelex patching you can:\n",
    "\n",
    "- Use your scikit-learn code for training and prediction with minimal changes (a couple of lines of code);\n",
    "- Fast execution training and prediction of scikit-learn models;\n",
    "- Get the same quality;\n",
    "- Get speedup more than **8** times."
   ]
  }
 ],
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