diff --git a/.ci/pipeline/ci.yml b/.ci/pipeline/ci.yml index 504a608cfe..1fb30af41c 100644 --- a/.ci/pipeline/ci.yml +++ b/.ci/pipeline/ci.yml @@ -17,7 +17,7 @@ trigger: branches: include: - - master + - main - rls/* paths: exclude: @@ -28,7 +28,7 @@ trigger: pr: branches: include: - - master + - main - rls/* paths: exclude: diff --git a/.ci/pipeline/docs.yml b/.ci/pipeline/docs.yml index bbf6cb73c4..d2d6ab3bc8 100644 --- a/.ci/pipeline/docs.yml +++ b/.ci/pipeline/docs.yml @@ -17,7 +17,7 @@ trigger: branches: include: - - master + - main - rls/* paths: include: @@ -28,7 +28,7 @@ trigger: pr: branches: include: - - master + - main - rls/* paths: include: diff --git a/.github/workflows/renovate-validation.yml b/.github/workflows/renovate-validation.yml index c4f40e0385..0dbc19e454 100644 --- a/.github/workflows/renovate-validation.yml +++ b/.github/workflows/renovate-validation.yml @@ -3,13 +3,13 @@ name: renovate-validation on: pull_request: branches: - - master + - main paths: - .github/workflows/renovate-validation.yml - .github/renovate.json push: branches: - - master + - main paths: - .github/workflows/renovate-validation.yml - .github/renovate.json diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 63cfb6825c..bab6fcc8a9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -22,7 +22,7 @@ This document explains how to participate in project conversations, log bugs and ## Licensing -Intel(R) Extension for Scikit-learn uses the [Apache 2.0 License](https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE). +Intel(R) Extension for Scikit-learn uses the [Apache 2.0 License](https://github.com/intel/scikit-learn-intelex/blob/main/LICENSE). By contributing to the project, you agree to the license and copyright terms and release your own contributions under these terms. ## Pull Requests @@ -41,13 +41,13 @@ Make sure your ``.gitconfig`` is set up correctly so you can use `git commit -s` * For a larger feature, provide a relevant test. * Document your code. Our project uses reStructuredText for documentation. * For new file(s), specify the appropriate copyright year in the first line. -* Submit a pull request into the master branch. +* Submit a pull request into the main branch. Continuous Integration (CI) testing is enabled for the repository. Your pull request must pass all checks before it can be merged. We will review your contribution and may provide feedback to guide you if any additional fixes or modifications are necessary. When reviewed and accepted, your pull request will be merged into our GitHub repository. ## Code Style -We use [black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/) formatters for Python* code. The line length is 90 characters; use default options otherwise. You can find the linter configuration in [.pyproject.toml](https://github.com/intel/scikit-learn-intelex/blob/master/pyproject.toml). +We use [black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/) formatters for Python* code. The line length is 90 characters; use default options otherwise. You can find the linter configuration in [.pyproject.toml](https://github.com/intel/scikit-learn-intelex/blob/main/pyproject.toml). A GitHub* Action verifies if your changes comply with the output of the auto-formatting tools. diff --git a/INSTALL.md b/INSTALL.md index e57eb38c9c..1281b3802a 100755 --- a/INSTALL.md +++ b/INSTALL.md @@ -173,7 +173,7 @@ Intel(R) Extension for Scikit-learn is easily built from sources with the majori * Python version >= 3.8, <= 3.11 * daal4py >= 2024.0 -**NOTE:** You can [build daal4py from sources](https://github.com/intel/scikit-learn-intelex/blob/master/daal4py/INSTALL.md) or get it from [distribution channels](https://intelpython.github.io/daal4py/#getting-daal4py). +**NOTE:** You can [build daal4py from sources](https://github.com/intel/scikit-learn-intelex/blob/main/daal4py/INSTALL.md) or get it from [distribution channels](https://intelpython.github.io/daal4py/#getting-daal4py). ### Configure the build with environment variables * SKLEARNEX_VERSION: sets package version diff --git a/README.md b/README.md index 7fe02f2e79..14b14dba7b 100755 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # Intel(R) Extension for Scikit-learn* -[Installation](INSTALL.md)   |   [Documentation](https://intel.github.io/scikit-learn-intelex/)   |   [Examples](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks)   |   [Support](#-support)   |   [FAQ](#-faq)    +[Installation](INSTALL.md)   |   [Documentation](https://intel.github.io/scikit-learn-intelex/)   |   [Examples](https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks)   |   [Support](#-support)   |   [FAQ](#-faq)    -[![Build Status](https://dev.azure.com/daal/daal4py/_apis/build/status/CI?branchName=master)](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=master) +[![Build Status](https://dev.azure.com/daal/daal4py/_apis/build/status/CI?branchName=main)](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=main) [![Coverity Scan Build Status](https://scan.coverity.com/projects/21716/badge.svg)](https://scan.coverity.com/projects/daal4py) [![Join the community on GitHub Discussions](https://badgen.net/badge/join%20the%20discussion/on%20github/black?icon=github)](https://github.com/intel/scikit-learn-intelex/discussions) [![PyPI Version](https://img.shields.io/pypi/v/scikit-learn-intelex)](https://pypi.org/project/scikit-learn-intelex/) @@ -50,7 +50,7 @@ One of the ways to patch scikit-learn is by modifying the code. First, you impor ``` 馃憖 Read about [other ways to patch scikit-learn](https://intel.github.io/scikit-learn-intelex/index.html#usage) and [other methods for offloading to GPU devices](https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html). -Check out available [notebooks](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks) for more examples. +Check out available [notebooks](https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks) for more examples. This software acceleration is achieved through the use of vector instructions, IA hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel platforms at launch time. @@ -62,7 +62,7 @@ You may still use algorithms and parameters not supported by Intel(R) Extension ## 馃殌 Acceleration -![](https://raw.githubusercontent.com/intel/scikit-learn-intelex/master/doc/sources/_static/scikit-learn-acceleration-2021.2.3.PNG) +![](https://raw.githubusercontent.com/intel/scikit-learn-intelex/main/doc/sources/_static/scikit-learn-acceleration-2021.2.3.PNG) Configurations: - HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket - SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8 @@ -71,7 +71,7 @@ Configurations: ## 馃洜 Installation -[System Requirements](https://intel.github.io/scikit-learn-intelex/latest/system-requirements.html)   |    [Install via pip or conda](https://github.com/intel/scikit-learn-intelex/blob/master/INSTALL.md)   |   [Build from sources](INSTALL.md#build-from-sources) +[System Requirements](https://intel.github.io/scikit-learn-intelex/latest/system-requirements.html)   |    [Install via pip or conda](https://github.com/intel/scikit-learn-intelex/blob/main/INSTALL.md)   |   [Build from sources](INSTALL.md#build-from-sources) Intel(R) Extension for Scikit-learn is available at the [Python Package Index](https://pypi.org/project/scikit-learn-intelex/), on Anaconda Cloud in [Conda-Forge channel](https://anaconda.org/conda-forge/scikit-learn-intelex) and in [Intel channel](https://anaconda.org/intel/scikit-learn-intelex). You can also build the extension from [sources](INSTALL.md#build-from-sources). @@ -85,7 +85,7 @@ pip install scikit-learn-intelex ``` ## 馃敆 Important Links -- [Notebook examples](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks) +- [Notebook examples](https://github.com/intel/scikit-learn-intelex/tree/main/examples/notebooks) - [Documentation](https://intel.github.io/scikit-learn-intelex/) - [Supported algorithms and parameters](https://intel.github.io/scikit-learn-intelex/latest/algorithms.html) - [Machine Learning Benchmarks](https://github.com/IntelPython/scikit-learn_bench) @@ -154,11 +154,10 @@ Intel(R) Extension for Scikit-learn is part of [oneAPI](https://oneapi.io) and [ The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library (oneDAL). Learn more: - [About Intel(R) oneAPI Data Analytics Library](https://github.com/oneapi-src/oneDAL) -- [About daal4py](https://github.com/intel/scikit-learn-intelex/tree/master/daal4py) +- [About daal4py](https://github.com/intel/scikit-learn-intelex/tree/main/daal4py) --- -鈿狅笍Intel(R) Extension for Scikit-learn contains scikit-learn patching functionality that was originally available in [**daal4py**](https://github.com/intel/scikit-learn-intelex/tree/master/daal4py) package. All future updates for the patches will be available only in Intel(R) Extension for Scikit-learn. We recommend you to use scikit-learn-intelex package instead of daal4py. +鈿狅笍Intel(R) Extension for Scikit-learn contains scikit-learn patching functionality that was originally available in [**daal4py**](https://github.com/intel/scikit-learn-intelex/tree/main/daal4py) package. All future updates for the patches will be available only in Intel(R) Extension for Scikit-learn. We recommend you to use scikit-learn-intelex package instead of daal4py. You can learn more about daal4py in [daal4py documentation](https://intelpython.github.io/daal4py). --- - diff --git a/daal4py/README.md b/daal4py/README.md index 4736e172e3..4c4a64bd15 100755 --- a/daal4py/README.md +++ b/daal4py/README.md @@ -1,5 +1,5 @@ # daal4py - A Convenient Python API to the Intel(R) oneAPI Data Analytics Library -[![Build Status](https://dev.azure.com/daal/daal4py/_apis/build/status/CI?branchName=master)](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=master) +[![Build Status](https://dev.azure.com/daal/daal4py/_apis/build/status/CI?branchName=main)](https://dev.azure.com/daal/daal4py/_build/latest?definitionId=9&branchName=main) [![Coverity Scan Build Status](https://scan.coverity.com/projects/21716/badge.svg)](https://scan.coverity.com/projects/daal4py) [![Join the community on GitHub Discussions](https://badgen.net/badge/join%20the%20discussion/on%20github/black?icon=github)](https://github.com/IntelPython/daal4py/discussions) [![PyPI Version](https://img.shields.io/pypi/v/daal4py)](https://pypi.org/project/daal4py/) @@ -23,7 +23,7 @@ We publish blogs on Medium, so [follow us](https://medium.com/intel-analytics-so ## 馃敆 Important links - [Documentation](https://intelpython.github.io/daal4py/) - [scikit-learn API and patching](https://intelpython.github.io/daal4py/sklearn.html#sklearn) -- [Building from Sources](https://github.com/intel/scikit-learn-intelex/blob/master/daal4py/INSTALL.md) +- [Building from Sources](https://github.com/intel/scikit-learn-intelex/blob/main/daal4py/INSTALL.md) - [About Intel(R) oneAPI Data Analytics Library](https://github.com/oneapi-src/oneDAL) ## 馃挰 Support @@ -110,7 +110,7 @@ conda install impi_rt -c intel -You can [build daal4py from sources](https://github.com/intel/scikit-learn-intelex/blob/master/daal4py/INSTALL.md) as well. +You can [build daal4py from sources](https://github.com/intel/scikit-learn-intelex/blob/main/daal4py/INSTALL.md) as well. # 鈿狅笍 Scikit-learn patching diff --git a/doc/daal4py/algorithms.rst b/doc/daal4py/algorithms.rst index e443e5e31e..f0d8e7ef3c 100755 --- a/doc/daal4py/algorithms.rst +++ b/doc/daal4py/algorithms.rst @@ -31,9 +31,9 @@ Parameters and semantics are described in |onedal-dg-classification-decision-for .. rubric:: Examples: - `Single-Process Decision Forest Classification Default Dense method - `__ + `__ - `Single-Process Decision Forest Classification Histogram method - `__ + `__ .. autoclass:: daal4py.decision_forest_classification_training :members: compute @@ -53,7 +53,7 @@ Parameters and semantics are described in |onedal-dg-classification-decision-tre .. rubric:: Examples: - `Single-Process Decision Tree Classification - `__ + `__ .. autoclass:: daal4py.decision_tree_classification_training :members: compute @@ -73,7 +73,7 @@ Parameters and semantics are described in |onedal-dg-classification-gradient-boo .. rubric:: Examples: - `Single-Process Gradient Boosted Classification - `__ + `__ .. autoclass:: daal4py.gbt_classification_training :members: compute @@ -93,7 +93,7 @@ Parameters and semantics are described in |onedal-dg-k-nearest-neighbors-knn|_. .. rubric:: Examples: - `Single-Process kNN - `__ + `__ .. autoclass:: daal4py.kdtree_knn_classification_training :members: compute @@ -128,7 +128,7 @@ Parameters and semantics are described in |onedal-dg-classification-adaboost|_. .. rubric:: Examples: - `Single-Process AdaBoost Classification - `__ + `__ .. autoclass:: daal4py.adaboost_training :members: compute @@ -148,7 +148,7 @@ Parameters and semantics are described in |onedal-dg-classification-brownboost|_ .. rubric:: Examples: - `Single-Process BrownBoost Classification - `__ + `__ .. autoclass:: daal4py.brownboost_training :members: compute @@ -168,7 +168,7 @@ Parameters and semantics are described in |onedal-dg-classification-logitboost|_ .. rubric:: Examples: - `Single-Process LogitBoost Classification - `__ + `__ .. autoclass:: daal4py.logitboost_training :members: compute @@ -188,7 +188,7 @@ Parameters and semantics are described in |onedal-dg-classification-weak-learner .. rubric:: Examples: - `Single-Process Stump Weak Learner Classification - `__ + `__ .. autoclass:: daal4py.stump_classification_training :members: compute @@ -207,9 +207,9 @@ Parameters and semantics are described in |onedal-dg-naive-bayes|_. .. rubric:: Examples: -- `Single-Process Naive Bayes `__ -- `Streaming Naive Bayes `__ -- `Multi-Process Naive Bayes `__ +- `Single-Process Naive Bayes `__ +- `Streaming Naive Bayes `__ +- `Multi-Process Naive Bayes `__ .. autoclass:: daal4py.multinomial_naive_bayes_training :members: compute @@ -231,7 +231,7 @@ Note: For the labels parameter, data is formatted as -1s and 1s .. rubric:: Examples: - `Single-Process SVM - `__ + `__ .. autoclass:: daal4py.svm_training :members: compute @@ -251,9 +251,9 @@ Parameters and semantics are described in |onedal-dg-logistic-regression|_. .. rubric:: Examples: - `Single-Process Binary Class Logistic Regression - `__ + `__ - `Single-Process Logistic Regression - `__ + `__ .. autoclass:: daal4py.logistic_regression_training :members: compute @@ -277,9 +277,9 @@ Parameters and semantics are described in |onedal-dg-regression-decision-forest| .. rubric:: Examples: - `Single-Process Decision Forest Regression Default Dense method - `__ + `__ - `Single-Process Decision Forest Regression Histogram method - `__ + `__ .. autoclass:: daal4py.decision_forest_regression_training :members: compute @@ -299,7 +299,7 @@ Parameters and semantics are described in |onedal-dg-regression-decision-tree|_. .. rubric:: Examples: - `Single-Process Decision Tree Regression - `__ + `__ .. autoclass:: daal4py.decision_tree_regression_training :members: compute @@ -319,7 +319,7 @@ Parameters and semantics are described in |onedal-dg-regression-gradient-boosted .. rubric:: Examples: - `Single-Process Boosted Regression Regression - `__ + `__ .. autoclass:: daal4py.gbt_regression_training :members: compute @@ -338,9 +338,9 @@ Parameters and semantics are described in |onedal-dg-linear-regression|_. .. rubric:: Examples: -- `Single-Process Linear Regression `__ -- `Streaming Linear Regression `__ -- `Multi-Process Linear Regression `__ +- `Single-Process Linear Regression `__ +- `Streaming Linear Regression `__ +- `Multi-Process Linear Regression `__ .. autoclass:: daal4py.linear_regression_training :members: compute @@ -359,7 +359,7 @@ Parameters and semantics are described in |onedal-dg-least-absolute-shrinkage-an .. rubric:: Examples: -- `Single-Process LASSO Regression `__ +- `Single-Process LASSO Regression `__ .. autoclass:: daal4py.lasso_regression_training :members: compute @@ -378,9 +378,9 @@ Parameters and semantics are described in |onedal-dg-ridge-regression|_. .. rubric:: Examples: -- `Single-Process Ridge Regression `__ -- `Streaming Ridge Regression `__ -- `Multi-Process Ridge Regression `__ +- `Single-Process Ridge Regression `__ +- `Streaming Ridge Regression `__ +- `Multi-Process Ridge Regression `__ .. autoclass:: daal4py.ridge_regression_training :members: compute @@ -400,7 +400,7 @@ Parameters and semantics are described in |onedal-dg-regression-stump|_. .. rubric:: Examples: - `Single-Process Stump Regression - `__ + `__ .. autoclass:: daal4py.stump_regression_training :members: compute @@ -419,8 +419,8 @@ Parameters and semantics are described in |onedal-dg-pca|_. .. rubric:: Examples: -- `Single-Process PCA `__ -- `Multi-Process PCA `__ +- `Single-Process PCA `__ +- `Multi-Process PCA `__ .. autoclass:: daal4py.pca :members: compute @@ -433,7 +433,7 @@ Parameters and semantics are described in |onedal-dg-pca-transform|_. .. rubric:: Examples: -- `Single-Process PCA Transform `__ +- `Single-Process PCA Transform `__ .. autoclass:: daal4py.pca_transform :members: compute @@ -446,8 +446,8 @@ Parameters and semantics are described in |onedal-dg-k-means-clustering|_. .. rubric:: Examples: -- `Single-Process K-Means `__ -- `Multi-Process K-Means `__ +- `Single-Process K-Means `__ +- `Multi-Process K-Means `__ K-Means Initialization ^^^^^^^^^^^^^^^^^^^^^^ @@ -473,7 +473,7 @@ Parameters and semantics are described in |onedal-dg-density-based-spatial-clust .. rubric:: Examples: -- `Single-Process DBSCAN `__ +- `Single-Process DBSCAN `__ .. autoclass:: daal4py.dbscan :members: compute @@ -488,7 +488,7 @@ Parameters and semantics are described in |onedal-dg-multivariate-outlier-detect .. rubric:: Examples: -- `Single-Process Multivariate Outlier Detection `__ +- `Single-Process Multivariate Outlier Detection `__ .. autoclass:: daal4py.multivariate_outlier_detection :members: compute @@ -501,7 +501,7 @@ Parameters and semantics are described in |onedal-dg-univariate-outlier-detectio .. rubric:: Examples: -- `Single-Process Univariate Outlier Detection `__ +- `Single-Process Univariate Outlier Detection `__ .. autoclass:: daal4py.univariate_outlier_detection :members: compute @@ -514,7 +514,7 @@ Parameters and semantics are described in |onedal-dg-multivariate-bacon-outlier- .. rubric:: Examples: -- `Single-Process Bacon Outlier Detection `__ +- `Single-Process Bacon Outlier Detection `__ .. autoclass:: daal4py.bacon_outlier_detection :members: compute @@ -530,9 +530,9 @@ Mean Squared Error Algorithm (MSE) Parameters and semantics are described in |onedal-dg-mse|_. .. rubric:: Examples: -- `In Adagrad `__ -- `In LBFGS `__ -- `In SGD `__ +- `In Adagrad `__ +- `In LBFGS `__ +- `In SGD `__ .. autoclass:: daal4py.optimization_solver_mse :members: compute, setup @@ -544,7 +544,7 @@ Logistic Loss Parameters and semantics are described in |onedal-dg-logistic-loss|_. .. rubric:: Examples: -- `In SGD `__ +- `In SGD `__ .. autoclass:: daal4py.optimization_solver_logistic_loss :members: compute, setup @@ -556,7 +556,7 @@ Cross-entropy Loss Parameters and semantics are described in |onedal-dg-cross-entropy-loss|_. .. rubric:: Examples: -- `In LBFGS `__ +- `In LBFGS `__ .. autoclass:: daal4py.optimization_solver_cross_entropy_loss :members: compute, setup @@ -570,8 +570,8 @@ Stochastic Gradient Descent Algorithm Parameters and semantics are described in |onedal-dg-sgd|_. .. rubric:: Examples: -- `Using Logistic Loss `__ -- `Using MSE `__ +- `Using Logistic Loss `__ +- `Using MSE `__ .. autoclass:: daal4py.optimization_solver_sgd :members: compute @@ -583,7 +583,7 @@ Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm Parameters and semantics are described in |onedal-dg-lbfgs|_. .. rubric:: Examples: -- `Using MSE `__ +- `Using MSE `__ .. autoclass:: daal4py.optimization_solver_lbfgs :members: compute @@ -595,7 +595,7 @@ Adaptive Subgradient Method Parameters and semantics are described in |onedal-dg-adagrad|_. .. rubric:: Examples: -- `Using MSE `__ +- `Using MSE `__ .. autoclass:: daal4py.optimization_solver_adagrad :members: compute @@ -607,7 +607,7 @@ Stochastic Average Gradient Descent Parameters and semantics are described in |onedal-dg-stochastic-average-gradient-descent-saga|_. .. rubric:: Examples: -- `Single Proces saga-logistc_loss `__ +- `Single Proces saga-logistc_loss `__ .. autoclass:: daal4py.optimization_solver_saga :members: compute @@ -622,7 +622,7 @@ Parameters and semantics are described in |onedal-dg-cosine-distance|_. .. rubric:: Examples: -- `Single-Process Cosine Distance `__ +- `Single-Process Cosine Distance `__ .. autoclass:: daal4py.cosine_distance :members: compute @@ -635,7 +635,7 @@ Parameters and semantics are described in |onedal-dg-correlation-distance|_. .. rubric:: Examples: -- `Single-Process Correlation Distance `__ +- `Single-Process Correlation Distance `__ .. autoclass:: daal4py.correlation_distance :members: compute @@ -652,7 +652,7 @@ Parameters and semantics are described in |onedal-dg-expectation-maximization-in .. rubric:: Examples: -- `Single-Process Expectation-Maximization `__ +- `Single-Process Expectation-Maximization `__ .. autoclass:: daal4py.em_gmm_init :members: compute @@ -665,7 +665,7 @@ Parameters and semantics are described in |onedal-dg-expectation-maximization-fo .. rubric:: Examples: -- `Single-Process Expectation-Maximization `__ +- `Single-Process Expectation-Maximization `__ .. autoclass:: daal4py.em_gmm :members: compute @@ -682,8 +682,8 @@ Parameters and semantics are described in |onedal-dg-qr-decomposition-without-pi .. rubric:: Examples: -- `Single-Process QR `__ -- `Streaming QR `__ +- `Single-Process QR `__ +- `Streaming QR `__ .. autoclass:: daal4py.qr :members: compute @@ -696,7 +696,7 @@ Parameters and semantics are described in |onedal-dg-pivoted-qr-decomposition|_. .. rubric:: Examples: -- `Single-Process Pivoted QR `__ +- `Single-Process Pivoted QR `__ .. autoclass:: daal4py.pivoted_qr :members: compute @@ -713,7 +713,7 @@ Parameters and semantics are described in |onedal-dg-z-score|_. .. rubric:: Examples: -- `Single-Process Z-Score Normalization `__ +- `Single-Process Z-Score Normalization `__ .. autoclass:: daal4py.normalization_zscore :members: compute @@ -726,7 +726,7 @@ Parameters and semantics are described in |onedal-dg-min-max|_. .. rubric:: Examples: -- `Single-Process Min-Max Normalization `__ +- `Single-Process Min-Max Normalization `__ .. autoclass:: daal4py.normalization_minmax :members: compute @@ -777,7 +777,7 @@ Parameters and semantics are described in |onedal-dg-bernoulli-distribution|_. .. rubric:: Examples: -- `Single-Process Bernoulli Distribution `__ +- `Single-Process Bernoulli Distribution `__ .. autoclass:: daal4py.distributions_bernoulli :members: compute @@ -790,7 +790,7 @@ Parameters and semantics are described in |onedal-dg-normal-distribution|_. .. rubric:: Examples: -- `Single-Process Normal Distribution `__ +- `Single-Process Normal Distribution `__ .. autoclass:: daal4py.distributions_normal :members: compute @@ -803,7 +803,7 @@ Parameters and semantics are described in |onedal-dg-uniform-distribution|_. .. rubric:: Examples: -- `Single-Process Uniform Distribution `__ +- `Single-Process Uniform Distribution `__ .. autoclass:: daal4py.distributions_uniform :members: compute @@ -816,7 +816,7 @@ Parameters and semantics are described in |onedal-dg-association-rules|_. .. rubric:: Examples: -- `Single-Process Association Rules `__ +- `Single-Process Association Rules `__ .. autoclass:: daal4py.association_rules :members: compute @@ -829,7 +829,7 @@ Parameters and semantics are described in |onedal-dg-cholesky-decomposition|_. .. rubric:: Examples: -- `Single-Process Cholesky `__ +- `Single-Process Cholesky `__ .. autoclass:: daal4py.cholesky :members: compute @@ -842,9 +842,9 @@ Parameters and semantics are described in |onedal-dg-correlation-and-variance-co .. rubric:: Examples: -- `Single-Process Covariance `__ -- `Streaming Covariance `__ -- `Multi-Process Covariance `__ +- `Single-Process Covariance `__ +- `Streaming Covariance `__ +- `Multi-Process Covariance `__ .. autoclass:: daal4py.covariance :members: compute @@ -857,7 +857,7 @@ Parameters and semantics are described in |onedal-dg-implicit-alternating-least- .. rubric:: Examples: -- `Single-Process implicit ALS `__ +- `Single-Process implicit ALS `__ .. autoclass:: daal4py.implicit_als_training :members: compute @@ -876,9 +876,9 @@ Parameters and semantics are described in |onedal-dg-moments-of-low-order|_. .. rubric:: Examples: -- `Single-Process Low Order Moments `__ -- `Streaming Low Order Moments `__ -- `Multi-Process Low Order Moments `__ +- `Single-Process Low Order Moments `__ +- `Streaming Low Order Moments `__ +- `Multi-Process Low Order Moments `__ .. autoclass:: daal4py.low_order_moments :members: compute @@ -891,7 +891,7 @@ Parameters and semantics are described in |onedal-dg-quantiles|_. .. rubric:: Examples: -- `Single-Process Quantiles `__ +- `Single-Process Quantiles `__ .. autoclass:: daal4py.quantiles :members: compute @@ -904,9 +904,9 @@ Parameters and semantics are described in |onedal-dg-svd|_. .. rubric:: Examples: -- `Single-Process SVD `__ -- `Streaming SVD `__ -- `Multi-Process SVD `__ +- `Single-Process SVD `__ +- `Streaming SVD `__ +- `Multi-Process SVD `__ .. autoclass:: daal4py.svd :members: compute @@ -919,7 +919,7 @@ Parameters and semantics are described in |onedal-dg-sorting|_. .. rubric:: Examples: -- `Single-Process Sorting `__ +- `Single-Process Sorting `__ .. autoclass:: daal4py.sorting :members: compute @@ -932,12 +932,12 @@ Trees .. rubric:: Examples: -- `Decision Forest Regression `__ -- `Decision Forest Classification `__ -- `Decision Tree Regression `__ -- `Decision Tree Classification `__ -- `Gradient Boosted Trees Regression `__ -- `Gradient Boosted Trees Classification `__ +- `Decision Forest Regression `__ +- `Decision Forest Classification `__ +- `Decision Tree Regression `__ +- `Decision Tree Classification `__ +- `Gradient Boosted Trees Regression `__ +- `Gradient Boosted Trees Classification `__ .. Link replacements @@ -1129,4 +1129,3 @@ Trees .. |onedal-dg-min-max| replace:: Intel(R) oneAPI Data Analytics Library Min-Max .. _onedal-dg-min-max: https://oneapi-src.github.io/oneDAL/daal/algorithms/normalization/min-max.html - diff --git a/doc/daal4py/examples.rst b/doc/daal4py/examples.rst index 58d965a541..181b2c50ab 100755 --- a/doc/daal4py/examples.rst +++ b/doc/daal4py/examples.rst @@ -27,155 +27,155 @@ General usage Building models from Gradient Boosting frameworks -- `XGBoost* model conversion `_ -- `LightGBM* model conversion `_ -- `CatBoost* model conversion `_ +- `XGBoost* model conversion `_ +- `LightGBM* model conversion `_ +- `CatBoost* model conversion `_ Principal Component Analysis (PCA) Transform -- `Single-Process PCA `_ -- `Multi-Process PCA `_ +- `Single-Process PCA `_ +- `Multi-Process PCA `_ Singular Value Decomposition (SVD) -- `Single-Process PCA Transform `_ +- `Single-Process PCA Transform `_ -- `Single-Process SVD `_ -- `Streaming SVD `_ -- `Multi-Process SVD `_ +- `Single-Process SVD `_ +- `Streaming SVD `_ +- `Multi-Process SVD `_ Moments of Low Order -- `Single-Process Low Order Moments `_ -- `Streaming Low Order Moments `_ -- `Multi-Process Low Order Moments `_ +- `Single-Process Low Order Moments `_ +- `Streaming Low Order Moments `_ +- `Multi-Process Low Order Moments `_ Correlation and Variance-Covariance Matrices -- `Single-Process Covariance `_ -- `Streaming Covariance `_ -- `Multi-Process Covariance `_ +- `Single-Process Covariance `_ +- `Streaming Covariance `_ +- `Multi-Process Covariance `_ Decision Forest Classification - `Single-Process Decision Forest Classification Default Dense method - `_ + `_ - `Single-Process Decision Forest Classification Histogram method - `_ + `_ Decision Tree Classification - `Single-Process Decision Tree Classification - `_ + `_ Gradient Boosted Classification - `Single-Process Gradient Boosted Classification - `_ + `_ k-Nearest Neighbors (kNN) - `Single-Process kNN - `_ + `_ Multinomial Naive Bayes -- `Single-Process Naive Bayes `_ -- `Streaming Naive Bayes `_ -- `Multi-Process Naive Bayes `_ +- `Single-Process Naive Bayes `_ +- `Streaming Naive Bayes `_ +- `Multi-Process Naive Bayes `_ Support Vector Machine (SVM) - `Single-Process Binary SVM - `_ + `_ - `Single-Process Muticlass SVM - `_ + `_ Logistic Regression - `Single-Process Binary Class Logistic Regression - `_ + `_ - `Single-Process Logistic Regression - `_ + `_ Decision Forest Regression - `Single-Process Decision Forest Regression Default Dense method - `_ + `_ - `Single-Process Decision Forest Regression Histogram method - `_ + `_ - `Single-Process Decision Tree Regression - `_ + `_ Gradient Boosted Regression - `Single-Process Boosted Regression - `_ + `_ Linear Regression -- `Single-Process Linear Regression `_ -- `Streaming Linear Regression `_ -- `Multi-Process Linear Regression `_ +- `Single-Process Linear Regression `_ +- `Streaming Linear Regression `_ +- `Multi-Process Linear Regression `_ Ridge Regression -- `Single-Process Ridge Regression `_ -- `Streaming Ridge Regression `_ -- `Multi-Process Ridge Regression `_ +- `Single-Process Ridge Regression `_ +- `Streaming Ridge Regression `_ +- `Multi-Process Ridge Regression `_ K-Means Clustering -- `Single-Process K-Means `_ -- `Multi-Process K-Means `_ +- `Single-Process K-Means `_ +- `Multi-Process K-Means `_ Multivariate Outlier Detection -- `Single-Process Multivariate Outlier Detection `_ +- `Single-Process Multivariate Outlier Detection `_ Univariate Outlier Detection -- `Single-Process Univariate Outlier Detection `_ +- `Single-Process Univariate Outlier Detection `_ Optimization Solvers-Mean Squared Error Algorithm (MSE) -- `MSE In Adagrad `_ -- `MSE In LBFGS `_ -- `MSE In SGD `_ +- `MSE In Adagrad `_ +- `MSE In LBFGS `_ +- `MSE In SGD `_ Logistic Loss -- `Logistic Loss SGD `_ +- `Logistic Loss SGD `_ Stochastic Gradient Descent Algorithm -- `Stochastic Gradient Descent Algorithm Using Logistic Loss `_ -- `Stochastic Gradient Descent Algorithm Using MSE `_ +- `Stochastic Gradient Descent Algorithm Using Logistic Loss `_ +- `Stochastic Gradient Descent Algorithm Using MSE `_ Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm -- `Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm - Using MSE `_ +- `Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm - Using MSE `_ Adaptive Subgradient Method -- `Adaptive Subgradient Method Using MSE `_ +- `Adaptive Subgradient Method Using MSE `_ Cosine Distance Matrix -- `Single-Process Cosine Distance `_ +- `Single-Process Cosine Distance `_ Correlation Distance Matrix -- `Single-Process Correlation Distance `_ +- `Single-Process Correlation Distance `_ Trees -- `Decision Forest Regression `_ -- `Decision Forest Classification `_ -- `Decision Tree Regression `_ -- `Decision Tree Classification `_ -- `Gradient Boosted Trees Regression `_ -- `Gradient Boosted Trees Classification `_ +- `Decision Forest Regression `_ +- `Decision Forest Classification `_ +- `Decision Tree Regression `_ +- `Decision Tree Classification `_ +- `Gradient Boosted Trees Regression `_ +- `Gradient Boosted Trees Classification `_ diff --git a/doc/daal4py/index.rst b/doc/daal4py/index.rst index 2d44a0f7c4..e8f72fef8f 100644 --- a/doc/daal4py/index.rst +++ b/doc/daal4py/index.rst @@ -146,7 +146,7 @@ daal4py is available at the `Python Package Index `_ and in `Intel channel `_. Sources and build instructions are available in -`daal4py repository `_. +`daal4py repository `_. The daal4py package is available via same distribution channels and platforms as scikit-learn-intelex. See diff --git a/doc/daal4py/model-builders.rst b/doc/daal4py/model-builders.rst index 4e89b2a849..b9dde88e6f 100644 --- a/doc/daal4py/model-builders.rst +++ b/doc/daal4py/model-builders.rst @@ -124,10 +124,10 @@ Examples --------------------------------- Model Builders models conversion -- `XGBoost model conversion `_ -- `SHAP value prediction from an XGBoost model `_ -- `LightGBM model conversion `_ -- `CatBoost model conversion `_ +- `XGBoost model conversion `_ +- `SHAP value prediction from an XGBoost model `_ +- `LightGBM model conversion `_ +- `CatBoost model conversion `_ Articles and Blog Posts --------------------------------- diff --git a/doc/daal4py/scaling.rst b/doc/daal4py/scaling.rst index 097c4d2d18..824b8c815a 100644 --- a/doc/daal4py/scaling.rst +++ b/doc/daal4py/scaling.rst @@ -76,36 +76,36 @@ The following algorithms support distribution: - PCA (pca) - - `PCA `_ + - `PCA `_ - SVD (svd) - - `SVD `_ + - `SVD `_ - Linear Regression Training (linear_regression_training) - - `Linear Regression `_ + - `Linear Regression `_ - Ridge Regression Training (ridge_regression_training) - - `Ridge Regression `_ + - `Ridge Regression `_ - Multinomial Naive Bayes Training (multinomial_naive_bayes_training) - - `Naive Bayes `_ + - `Naive Bayes `_ - K-Means (kmeans_init and kmeans) - - `K-Means `_ + - `K-Means `_ - Correlation and Variance-Covariance Matrices (covariance) - - `Covariance `_ + - `Covariance `_ - Moments of Low Order (low_order_moments) - - `Low Order Moments `_ + - `Low Order Moments `_ - QR Decomposition (qr) - - `QR `_ + - `QR `_ diff --git a/doc/daal4py/sklearn.rst b/doc/daal4py/sklearn.rst index dc03cee9eb..a6815b305c 100755 --- a/doc/daal4py/sklearn.rst +++ b/doc/daal4py/sklearn.rst @@ -159,7 +159,7 @@ algorithms: Monkey-patched scikit-learn classes and functions passes scikit-learn's own test suite, with few exceptions, specified in `deselected_tests.yaml -`__. +`__. In particular the tests execute `check_estimator `__ diff --git a/doc/daal4py/streaming.rst b/doc/daal4py/streaming.rst index 3687fcdb56..f29562453d 100644 --- a/doc/daal4py/streaming.rst +++ b/doc/daal4py/streaming.rst @@ -48,7 +48,7 @@ daal4py's streaming mode is as easy as follows: The streaming algorithms also accept arrays and DataFrames as input, e.g. the data can come from a stream rather than from multiple files. Here is an example which simulates a data stream using a generator which reads a file in chunks: -`SVD reading stream of data `_ +`SVD reading stream of data `_ Supported Algorithms and Examples --------------------------------- @@ -56,28 +56,28 @@ The following algorithms support streaming: - SVD (svd) - - `SVD `_ + - `SVD `_ - Linear Regression Training (linear_regression_training) - - `Linear Regression `_ + - `Linear Regression `_ - Ridge Regression Training (ridge_regression_training) - - `Ridge Regression `_ + - `Ridge Regression `_ - Multinomial Naive Bayes Training (multinomial_naive_bayes_training) - - `Naive Bayes `_ + - `Naive Bayes `_ - Moments of Low Order - - `Low Order Moments `_ + - `Low Order Moments `_ - Covariance - - `Covariance `_ + - `Covariance `_ - QR - - `QR `_ + - `QR `_ diff --git a/doc/sources/algorithms.rst b/doc/sources/algorithms.rst index 73397f31c3..67f9e3b240 100755 --- a/doc/sources/algorithms.rst +++ b/doc/sources/algorithms.rst @@ -367,7 +367,7 @@ Scikit-learn tests Monkey-patched scikit-learn classes and functions passes scikit-learn's own test suite, with few exceptions, specified in `deselected_tests.yaml -`__. +`__. The results of the entire latest scikit-learn test suite with |intelex|: `CircleCI `_. \ No newline at end of file diff --git a/doc/sources/contribute.rst b/doc/sources/contribute.rst index b72c515b06..72252ef35e 100644 --- a/doc/sources/contribute.rst +++ b/doc/sources/contribute.rst @@ -44,7 +44,7 @@ To contribute your changes directly to the repository, do the following: - Make sure you can build the product and run all the examples with your patch. - For a larger feature, provide a relevant example. - Document your code. -- `Submit `_ a pull request into the `master` branch. +- `Submit `_ a pull request into the `main` branch. Provide a brief description of the changes you are contributing. Public CI is enabled for the repository. Your PR should pass all of our checks. diff --git a/doc/sources/distributed-mode.rst b/doc/sources/distributed-mode.rst index 79dd663a3a..3b152b68a0 100644 --- a/doc/sources/distributed-mode.rst +++ b/doc/sources/distributed-mode.rst @@ -22,7 +22,7 @@ Distributed Mode .. note:: |intelex| contains scikit-learn patching functionality that was originally available in - `daal4py `_ package. + `daal4py `_ package. We recommend you to use scikit-learn-intelex package instead of daal4py. You can learn more about daal4py in `daal4py documentation `_. diff --git a/doc/sources/index.rst b/doc/sources/index.rst index 5422ba696e..a7840e8266 100755 --- a/doc/sources/index.rst +++ b/doc/sources/index.rst @@ -46,7 +46,7 @@ Important Links - |intelex_repo|_ - `Machine Learning Benchmarks `_ - `About Intel(R) oneAPI Data Analytics Library `_ -- `About daal4py `_ +- `About daal4py `_ .. toctree:: :caption: Get Started diff --git a/doc/sources/installation.rst b/doc/sources/installation.rst index be10e818bc..fe719c3855 100644 --- a/doc/sources/installation.rst +++ b/doc/sources/installation.rst @@ -136,7 +136,7 @@ Sources are available in |intelex_repo|_. daal4py >= 2021.4 .. note:: - You can `build daal4py from sources `_ or get it from `distribution channels + You can `build daal4py from sources `_ or get it from `distribution channels `_. Configure the build with environment variables diff --git a/doc/sources/license.rst b/doc/sources/license.rst index 945542761b..df68abaea6 100644 --- a/doc/sources/license.rst +++ b/doc/sources/license.rst @@ -20,4 +20,4 @@ License ############################################ -License: https://github.com/intel/scikit-learn-intelex/blob/master/LICENSE \ No newline at end of file +License: https://github.com/intel/scikit-learn-intelex/blob/main/LICENSE \ No newline at end of file diff --git a/doc/sources/samples.rst b/doc/sources/samples.rst index 1e14a7880c..f649329113 100644 --- a/doc/sources/samples.rst +++ b/doc/sources/samples.rst @@ -22,7 +22,7 @@ Samples The following samples are also provided as Jupyter notebooks in |intelex| repository. If you want to run them locally, refer to `these instructions -`_. +`_. .. rubric:: Classification Tasks diff --git a/examples/notebooks/README.md b/examples/notebooks/README.md index 37455223e8..26320057e8 100644 --- a/examples/notebooks/README.md +++ b/examples/notebooks/README.md @@ -21,14 +21,14 @@ jupyter notebook --notebook-dir=./ --ip=* --no-browser | Algorithm | Workload | Task | Notebook | Scikit-learn estimator| | :----------------------:| :------------: | :---------------:| :------------: | :-------------------:| -| LogisticRegression | CIFAR-100 | 小lassification | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/logistictic_regression_cifar.ipynb) | [sklearn.linear_model.LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) | -| SVC | Adult | 小lassification | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/svc_adult.ipynb) | [sklearn.svm.SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) | -| KNeighborsClassifier | MNIST | 小lassification | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/knn_mnist.ipynb) | [sklearn.neighbors.KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) | -| NuSVR | Medical charges | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/nusvr_medical_charges.ipynb) | [sklearn.svm.NuSVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html) | -| RandomForestRegressor | Yolanda | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/random_forest_yolanda.ipynb) | [sklearn.ensemble.RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) | -| Ridge | Airlines DepDelay | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/ridge_regression.ipynb) | [sklearn.linear_model.Ridge](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) | -| ElasticNet | Airlines DepDelay | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/ElasticNet.ipynb) | [sklearn.linear_model.ElasticNet](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html) | -| Lasso | YearPredictionMSD | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/lasso_regression.ipynb) | [sklearn.linear_model.Lasso](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html) | -| Linear Regression | YearPredictionMSD | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/linear_regression.ipynb) | [sklearn.linear_model.LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) | -| KMeans | Spoken arabic digit | Clustering | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/kmeans.ipynb) | [sklearn.cluster.KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) | -| DBSCAN | Spoken arabic digit | Clustering | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/master/examples/notebooks/dbscan.ipynb) | [sklearn.cluster.DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) | +| LogisticRegression | CIFAR-100 | 小lassification | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/logistictic_regression_cifar.ipynb) | [sklearn.linear_model.LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) | +| SVC | Adult | 小lassification | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/svc_adult.ipynb) | [sklearn.svm.SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) | +| KNeighborsClassifier | MNIST | 小lassification | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/knn_mnist.ipynb) | [sklearn.neighbors.KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) | +| NuSVR | Medical charges | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/nusvr_medical_charges.ipynb) | [sklearn.svm.NuSVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html) | +| RandomForestRegressor | Yolanda | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/random_forest_yolanda.ipynb) | [sklearn.ensemble.RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) | +| Ridge | Airlines DepDelay | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/ridge_regression.ipynb) | [sklearn.linear_model.Ridge](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) | +| ElasticNet | Airlines DepDelay | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/ElasticNet.ipynb) | [sklearn.linear_model.ElasticNet](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html) | +| Lasso | YearPredictionMSD | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/lasso_regression.ipynb) | [sklearn.linear_model.Lasso](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html) | +| Linear Regression | YearPredictionMSD | Regression | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/linear_regression.ipynb) | [sklearn.linear_model.LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) | +| KMeans | Spoken arabic digit | Clustering | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/kmeans.ipynb) | [sklearn.cluster.KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) | +| DBSCAN | Spoken arabic digit | Clustering | [View source on GitHub](https://github.com/intel/scikit-learn-intelex/blob/main/examples/notebooks/dbscan.ipynb) | [sklearn.cluster.DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) |