Intel® oneAPI Data Analytics Library (oneDAL) is a highly optimized data analytics and machine learning (ML) library. It provides C++, Java*, and Python* interfaces, as well as newly introduced Data Parallel C++ (DPC++) interface.
The library with Python interface is also called DAAL4py. It is available along with Intel® Distribution for Python* (IDP), which is part of Intel® oneAPI Base Toolkit and Intel® AI Analytics Toolkit, as well as available from open-source repository: DAAL4py GitHub* repository.
Intel® oneDAL DAAL4py Public Samples
In order to help users to understand the Intel® AI Analytics Toolkit product, we have officially published oneAPI DAAL4py samples at AIkit code-sample. For example:
- Intel oneAPI Data Analytics Library Hello World (daal4py)
- Intel oneAPI Data Analytics Library for Distributed Linear Regression Training and Prediction (daal4py)
- Intel oneAPI Data Analytics Library for Distributed K-Means Training and Prediction (daal4py)
Intel® DAAL4py Self-Owned Examples
In order to help users to understand the Intel DAAL4py product, we also provide a suit of examples to demonstrate the product functionality. For example:
- Decision Forest for Classification and Regression
- Gradient Boosted Trees (GBT) for Classification and Regression
- K-Means Clustering
- K-Nearest Neighbor (KNN)
- Logistic Regression
- Multinomial Naïve Bayes Classifier
- Support Vector Machines (SVM) with Linear and Radial Basis Function (RBF) Kernels
- Multiclass Classification Using a One-Against-One Strategy
- Principal Components Analysis (PCA)
- And more
Intel DAAL4py-related Samples and Products
Thus, we have three DAAL4py-related samples and products at hand. See the table:
|AIKit Code Samples||https://github.com/intel/AiKit-code-samples|
|DAAL4py Self-owned Examples||<oneAPI INSTALLATION PATH> /Intelpyton/latest/pkgs/daal4py-xxx/info/test/examples|
DAAL4py Self-owned Examples in GitHub*
In order to avoid confusion, we encourage you to use the matching product samples for a product.
If you try the samples in a different product other than the intended one, errors may arise like below,
Unexpected Error: TypeError: __cinit__() got an unexpected keyword argument 'votingMethod'
To run the DAAL4py Self-owned Example in GitHub* with Intel® oneAPI Base Toolkit product
#Set oneAPI environment: source /opt/intel/inteloneapi/setvars.sh #But using the DAAL4py Example from the DAAL4py public GitHub* repository: $git clone https://github.com/IntelPython/daal4py.git $cd daal4py/examples $ python decision_forest_classification_batch.py Error Message Traceback (most recent call last): File "decision_forest_classification_batch.py", line 76, in <module> (train_result, predict_result, plabels) = main() File "decision_forest_classification_batch.py", line 62, in main resultsToEvaluate="computeClassLabels|computeClassProbabilities", votingMethod="unweighted") File "build/daal4py_cy.pyx", line 12663, in _daal4py.decision_forest_classification_prediction.__cinit__ TypeError: __cinit__() got an unexpected keyword argument 'votingMethod'
We keep developing new algorithms and committing the latest changes to DAAL4py GitHub* repository. Not all of the latest changed are applicable to other products. Hence, it is wrong to check the DAAL4py GitHub* examples with the oneAPI product.
Use matched sample with its intended product. For example, to use the self-owned DAAL4py examples with the Intel oneAPI Base Toolkit(beta):
$ source /opt/intel/inteloneapi/setvars.sh $ cd /opt/intel/inteloneapi/intelpython/latest/pkgs/daal4py-2021.1b4-py37ha68da19_4/info/test/examples/ $ python decision_forest_classification_batch.py Variable importance results: [[78.0208408 79.09469066 0.27575422]] ...... OOB error: [[0.03299533]] PASSED