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Intel Extension for Scikit-learn: SVC for Adult dataset readme update (#1480)
* Fixes for 2023.1 AI Kit (#1409) * Intel Python Numpy Numba_dpes kNN sample (#1292) * *.py and *.ipynb files with implementation * README.md and sample.json files with documentation * License and thir party programs * Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample (#1293) * add IntelPytorch Quantization code samples (#1301) * add IntelPytorch Quantization code samples * fix the spelling error in the README file * use john's README with grammar fix and title change * Rename third-party-grograms.txt to third-party-programs.txt Co-authored-by: Jimmy Wei <[email protected]> * AMX bfloat16 mixed precision learning TensorFlow Transformer sample (#1317) * [New Sample] Intel Extension for TensorFlow Getting Started (#1313) * first draft * Update README.md * remove redunant file * [New Sample] [oneDNN] Benchdnn tutorial (#1315) * New Sample: benchDNN tutorial * Update readme: new sample * Rename sample to benchdnn_tutorial * Name fix * Add files via upload (#1320) * [New Sample] oneCCL Bindings for PyTorch Getting Started (#1316) * Update README.md * [New Sample] oneCCL Bindings for PyTorch Getting Started * Update README.md * add torch-ccl version check * [New Sample] Intel Extension for PyTorch Getting Started (#1314) * add new ipex GSG notebook for dGPU * Update sample.json for expertise field * Update requirements.txt Update package versions to comply with Snyk tool * Updated title field in sample.json in TF Transformer AMX bfloat16 Mixed Precision sample to fit within character length range (#1327) * add arch checker class (#1332) * change gpu.patch to convert the code samples from cpu to gpu correctly (#1334) * Fixes for spelling in AMX bfloat16 transformer sample and printing error in python code in numpy vs numba sample (#1335) * 2023.1 ai kit itex get started example fix (#1338) * Fix the typo * Update ResNet50_Inference.ipynb * fix resnet inference demo link (#1339) * Fix printing issue in numpy vs numba AI sample (#1356) * Fix Invalid Kmeans parameters on oneAPI 2023 (#1345) * Update README to add new samples into the list (#1366) * PyTorch AMX BF16 Training sample: remove graphs and performance numbers (#1408) * Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample * remove performance graphs, update README * remove graphs from README and folder * update top README in Features and Functionality --------- Co-authored-by: krzeszew <[email protected]> Co-authored-by: alexsin368 <[email protected]> Co-authored-by: ZhaoqiongZ <[email protected]> Co-authored-by: Louie Tsai <[email protected]> Co-authored-by: Orel Yehuda <[email protected]> Co-authored-by: yuning <[email protected]> Co-authored-by: Wang, Kai Lawrence <[email protected]> Co-authored-by: xiguiw <[email protected]> * Update readme dlkafsdkl --------- Co-authored-by: Jimmy Wei <[email protected]> Co-authored-by: krzeszew <[email protected]> Co-authored-by: alexsin368 <[email protected]> Co-authored-by: ZhaoqiongZ <[email protected]> Co-authored-by: Louie Tsai <[email protected]> Co-authored-by: Orel Yehuda <[email protected]> Co-authored-by: yuning <[email protected]> Co-authored-by: Wang, Kai Lawrence <[email protected]> Co-authored-by: xiguiw <[email protected]>
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‎AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult/README.md

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# `Intel® Extension for Scikit-learn: SVC for Adult dataset` Sample
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This sample code uses the [Adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) to show how to train and predict with a SVC algorithm using Intel® Extension for Scikit-learn. It demonstrates how to use software products that can be found in the [Intel® oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html), [Intel(R) Extension for Scikit-learn](https://intel.github.io/scikit-learn-intelex/), and [Intel® AI Analytics Toolkit (AI Kit)](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html).
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# `Intel® Extension for Scikit-learn*: SVC for Adult Data Set` Sample
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| Optimized for | Description
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| :--- | :---
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| OS | 64-bit Linux: Ubuntu 18.04 or higher, 64-bit Windows 10, macOS 10.14 or higher
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| Hardware | Intel Atom® Processors; Intel® Core™ Processor Family; Intel® Xeon® Processor Family; Intel® Xeon® Scalable processor family
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| Software | Intel® AI Analytics Toolkit
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| What you will learn | How to get started with Intel® Extension for Scikit-learn
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| Time to complete | 25 minutes
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The `Intel® Extension for Scikit-learn*: SVC for Adult Data Set` sample uses the [Adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) to show how to train and predict with an SVC algorithm using Intel® Extension for Scikit-learn*.
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| Optimized for | Description
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| :--- | :---
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| What you will learn | How to get started with Intel® Extension for Scikit-learn*
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| Time to complete | 25 minutes
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| Category | Concepts and Functionality
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The sample demonstrates how to use software products that can be found in the [Intel® oneAPI Data Analytics Library (oneDAL)](https://github.com/oneapi-src/oneDAL), [Intel® Extension for Scikit-learn*](https://intel.github.io/scikit-learn-intelex/), and the [Intel® AI Analytics Toolkit (AI Kit)](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html).
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## Purpose
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Intel® Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application. The acceleration is achieved through the use of the Intel® oneAPI Data Analytics Library ([oneAPI Data Analytics Library (oneDAL)](https://github.com/oneapi-src/oneDAL)). Patching scikit-learn makes it a well-suited machine learning framework for dealing with real-life problems.
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In this sample, you will run an SVC algorithm with Intel® Extension for Scikit-learn* and compare its performance against the original stock version of scikit-learn. You will see that patching scikit-learn results in a significant increase in performance over the original scikit-learn while also maintaining the same precision.
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The acceleration is achieved through the use of the oneDAL. Patching scikit-learn makes it a well-suited machine learning framework for dealing with real-life problems.
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## Prerequisites
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| Optimized for | Description
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| :--- | :---
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| OS | Ubuntu 20.04 (or newer)
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| Hardware | Intel Atom® Processors <br> Intel® Core™ Processor Family <br> Intel® Xeon® Processor Family <br> Intel® Xeon® Scalable processor family
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| Software | Intel® AI Analytics Toolkit (AI Kit)
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### For Local Development Environments
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You will need to download and install the following toolkits, tools, and components to use the sample.
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In this sample, you will run a SVC algorithm with Intel® Extension for Scikit-learn and compare its performance against the original stock version of scikit-learn. You will see that patching scikit-learn results in a significant increase in performance over the original scikit-learn while also maintaining the same precision.
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- **Intel® AI Analytics Toolkit (AI Kit)**
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You can get the AI Kit from [Intel® oneAPI Toolkits](https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html#analytics-kit). <br> See [*Get Started with the Intel® AI Analytics Toolkit for Linux**](https://www.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux) for AI Kit installation information and post-installation steps and scripts.
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- **Jupyter Notebook**
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Install using PIP: `$pip install notebook`. <br> Alternatively, see [*Installing Jupyter*](https://jupyter.org/install) for detailed installation instructions.
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### For Intel® DevCloud
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The necessary tools and components are already installed in the environment. You do not need to install additional components. See [Intel® DevCloud for oneAPI](https://devcloud.intel.com/oneapi/get_started/) for information.
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## Key Implementation Details
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The sample code is written in Python and it targets CPU architecture. The example assumes you have Intel® Extension for Scikit-learn installed.
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## License
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Code samples are licensed under the MIT license. See
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
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The sample code is written in Python and it targets CPU architecture. The example assumes you have Intel® Extension for Scikit-learn* installed.
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## Set Environment Variables
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Third party program Licenses can be found here: [third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt)
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When working with the command-line interface (CLI), you should configure the oneAPI toolkits using environment variables. Set up your CLI environment by sourcing the `setvars` script every time you open a new terminal window. This practice ensures that your compiler, libraries, and tools are ready for development.
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## Build and Run the Sample
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## Run the Sample
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### Pre-requirement
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### On Linux*
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> NOTE: No action is required if you are using Intel DevCloud as your environment.
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Refer to [Intel® DevCloud for oneAPI](https://intelsoftwaresites.secure.force.com/devcloud/oneapi) for Intel DevCloud.
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> **Note**: If you have not already done so, set up your CLI
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> environment by sourcing the `setvars` script in the root of your oneAPI installation.
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>
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> Linux*:
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> - For system wide installations: `. /opt/intel/oneapi/setvars.sh`
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> - For private installations: ` . ~/intel/oneapi/setvars.sh`
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> - For non-POSIX shells, like csh, use the following command: `bash -c 'source <install-dir>/setvars.sh ; exec csh'`
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>
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> For more information on configuring environment variables, see *[Use the setvars Script with Linux* or macOS*](https://www.intel.com/content/www/us/en/develop/documentation/oneapi-programming-guide/top/oneapi-development-environment-setup/use-the-setvars-script-with-linux-or-macos.html)*.
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1. **Intel® AI Analytics Toolkit**
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Install the toolkit from the [oneAPI main page](https://software.intel.com/en-us/oneapi)
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and refer to the [Toolkit Get Started Guide for Linux](https://software.intel.com/en-us/get-started-with-intel-oneapi-linux-get-started-with-the-intel-ai-analytics-toolkit) for post-installation steps and scripts.
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### Open Jupyter Notebook
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2. **Jupyter Notebook**
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Install Jupyter Notebook via pip: `pip install notebook`.
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Refer to [Installing the Jupyter Software](https://jupyter.org/install) for details.
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1. Change to the sample directory.
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2. Launch Jupyter Notebook.
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```
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jupyter notebook
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```
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3. Locate and select the Notebook.
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```
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Intel_Extension_for_SKLearn_Performance_SVC_Adult.ipynb
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```
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4. Click the **Run** button to move through the cells in sequence.
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### Run the Python File
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### Running the Sample as a Jupyter Notebook
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1. Run the script.
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```
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python Intel_Extension_for_SKLearn_Performance_SVC_Adult.py
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```
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#### Troubleshooting
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1. Launch Jupyter notebook: `jupyter notebook --ip=0.0.0.0`
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2. Follow the instructions to open the URL with the token in your browser.
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3. Click the `Intel_Extension_for_SKLearn_Performance_SVC_Adult.ipynb` file.
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4. Run each cell of the notebook one by one.
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If you receive an error message, troubleshoot the problem using the **Diagnostics Utility for Intel® oneAPI Toolkits**. The diagnostic utility provides configuration and system checks to help find missing dependencies, permissions errors, and other issues. See the *[Diagnostics Utility for Intel® oneAPI Toolkits User Guide](https://www.intel.com/content/www/us/en/develop/documentation/diagnostic-utility-user-guide/top.html)* for more information on using the utility.
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### Running the Sample as a Python File
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1. `python Intel_Extension_for_SKLearn_Performance_SVC_Adult.py`
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### On Intel® DevCloud (Optional)
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### Example of Output
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>**Note**: For more information on using Intel® DevCloud, see the Intel® oneAPI [Get Started](https://devcloud.intel.com/oneapi/get_started/) page.
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1. Open a terminal on a Linux* system.
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2. Log in to the Intel® DevCloud.
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```
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ssh devcloud
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```
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3. Change to the sample directory.
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4. Perform steps as you would on Linux.
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5. Run the sample.
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6. Review the output.
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7. Disconnect from Intel® DevCloud.
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```
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exit
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```
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## Example Output
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```
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Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
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weighted avg 0.82 0.82 0.82 9769
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```
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## License
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If an error occurs, troubleshoot the problem using the Diagnostics Utility for Intel® oneAPI Toolkits.
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[Learn more](https://software.intel.com/content/www/us/en/develop/documentation/diagnostic-utility-user-guide/top.html)
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### Using Visual Studio Code* (VS Code)
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You can use VS Code extensions to set your environment, create launch configurations,
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and browse and download samples.
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The basic steps to build and run a sample using VS Code include:
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- Download a sample using the extension **Code Sample Browser for Intel oneAPI Toolkits**.
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- Configure the oneAPI environment with the extension **Environment Configurator for Intel oneAPI Toolkits**.
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- Open a Terminal in VS Code (**Terminal>New Terminal**).
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- Run the sample in the VS Code terminal using the instructions below.
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To learn more about the extensions and how to configure the oneAPI environment, see
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[Using Visual Studio Code with Intel® oneAPI Toolkits](https://software.intel.com/content/www/us/en/develop/documentation/using-vs-code-with-intel-oneapi/top.html).
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Code samples are licensed under the MIT license. See
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
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After learning how to use the extensions for Intel oneAPI Toolkits, return to this readme for instructions on how to build and run a sample.
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Third party program Licenses can be found here: [third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt).

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