.. _array_api:

================================ Array API support (experimental)

.. currentmodule:: sklearn

The Array API <https://data-apis.org/array-api/latest/>_ specification defines a standard API for all array manipulation libraries with a NumPy-like API. Scikit-learn vendors pinned copies of array-api-compat <https://github.com/data-apis/array-api-compat>__ and array-api-extra <https://github.com/data-apis/array-api-extra>__.

Some scikit-learn estimators that primarily rely on NumPy (as opposed to using Cython) to implement the algorithmic logic of their fit, predict or transform methods can be configured to accept any Array API compatible input data structures and automatically dispatch operations to the underlying namespace instead of relying on NumPy.

At this stage, this support is considered experimental and must be enabled explicitly by the array_api_dispatch configuration. See below for details.

.. note:: Currently, only array-api-strict, cupy, and PyTorch are known to work with scikit-learn's estimators.

The following video provides an overview of the standard's design principles and how it facilitates interoperability between array libraries:

• Scikit-learn on GPUs with Array API <https://www.youtube.com/watch?v=c_s8tr1AizA>_ by :user:Thomas Fan <thomasjpfan> at PyData NYC 2023.

Enabling array API support

The configuration array_api_dispatch=True needs to be set to True to enable array API support. We recommend setting this configuration globally to ensure consistent behaviour and prevent accidental mixing of array namespaces. Note that in the examples below, we use a context manager (:func:config_context) to avoid having to reset it to False at the end of every code snippet, so as to not affect the rest of the documentation.

Scikit-learn's support for the array API standard requires the environment variable SCIPY_ARRAY_API to be set to 1 before importing scipy and scikit-learn:

.. prompt:: bash $

export SCIPY_ARRAY_API=1

Please note that this environment variable is intended for temporary use. For more details, refer to SciPy's Array API documentation <https://docs.scipy.org/doc/scipy/dev/api-dev/array_api.html#using-array-api-standard-support>_.

The array API functionality assumes that the latest versions of scikit-learn's dependencies are installed. Older versions might work, but we make no promises. While array API support is marked as experimental, backwards compatibility is not guaranteed. In particular, when a newer version of a dependency fixes a bug we will not introduce additional code to backport the fix or maintain compatibility with older versions.

Scikit-learn accepts :term:array-like inputs for all :mod:metrics and some estimators. When array_api_dispatch=False, these inputs are converted into NumPy arrays using :func:numpy.asarray (or :func:numpy.array). While this will successfully convert some array API inputs (e.g., JAX array), we generally recommend setting array_api_dispatch=True when using array API inputs. This is because NumPy conversion can often fail, e.g., torch tensor allocated on GPU.

Example usage

The example code snippet below demonstrates how to use CuPy <https://cupy.dev/>_ to run :class:~discriminant_analysis.LinearDiscriminantAnalysis on a GPU::

>>> from sklearn.datasets import make_classification
>>> from sklearn import config_context
>>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
>>> import cupy

>>> X_np, y_np = make_classification(random_state=0)
>>> X_cu = cupy.asarray(X_np)
>>> y_cu = cupy.asarray(y_np)
>>> X_cu.device
<CUDA Device 0>

>>> with config_context(array_api_dispatch=True):
...     lda = LinearDiscriminantAnalysis()
...     X_trans = lda.fit_transform(X_cu, y_cu)
>>> X_trans.device
<CUDA Device 0>

After the model is trained, fitted attributes that are arrays will also be from the same Array API namespace as the training data. For example, if CuPy's Array API namespace was used for training, then fitted attributes will be on the GPU. We provide an experimental _estimator_with_converted_arrays utility that transfers an estimator attributes from Array API to an ndarray::

>>> from sklearn.utils._array_api import _estimator_with_converted_arrays
>>> cupy_to_ndarray = lambda array : array.get()
>>> lda_np = _estimator_with_converted_arrays(lda, cupy_to_ndarray)
>>> X_trans = lda_np.transform(X_np)
>>> type(X_trans)
<class 'numpy.ndarray'>

PyTorch Support

PyTorch Tensors can also be passed directly::

>>> import torch
>>> X_torch = torch.asarray(X_np, device="cuda", dtype=torch.float32)
>>> y_torch = torch.asarray(y_np, device="cuda", dtype=torch.float32)

>>> with config_context(array_api_dispatch=True):
...     lda = LinearDiscriminantAnalysis()
...     X_trans = lda.fit_transform(X_torch, y_torch)
>>> type(X_trans)
<class 'torch.Tensor'>
>>> X_trans.device.type
'cuda'

.. _array_api_supported:

Support for Array API-compatible inputs

Estimators and other tools in scikit-learn that support Array API compatible inputs.

Estimators

• :class:decomposition.PCA (with svd_solver="full", svd_solver="covariance_eigh", or svd_solver="randomized" (svd_solver="randomized" only if power_iteration_normalizer="QR"))
• :class:kernel_approximation.Nystroem
• :class:linear_model.LogisticRegression (with solver="lbfgs")
• :class:linear_model.PoissonRegressor (with solver="lbfgs")
• :class:linear_model.Ridge (with solver="svd")
• :class:linear_model.RidgeCV (see :ref:device_support_for_float64)
• :class:linear_model.RidgeClassifier (with solver="svd")
• :class:linear_model.RidgeClassifierCV (see :ref:device_support_for_float64)
• :class:discriminant_analysis.LinearDiscriminantAnalysis (with solver="svd")
• :class:naive_bayes.GaussianNB
• :class:preprocessing.Binarizer
• :class:preprocessing.KernelCenterer
• :class:preprocessing.LabelBinarizer (with sparse_output=False)
• :class:preprocessing.LabelEncoder
• :class:preprocessing.MaxAbsScaler
• :class:preprocessing.MinMaxScaler
• :class:preprocessing.Normalizer
• :class:preprocessing.PolynomialFeatures
• :class:preprocessing.StandardScaler (see :ref:device_support_for_float64)
• :class:mixture.GaussianMixture (with init_params="random" or init_params="random_from_data" and warm_start=False)

Meta-estimators

Meta-estimators that accept Array API inputs conditioned on the fact that the base estimator also does:

• :class:calibration.CalibratedClassifierCV (with method="temperature")
• :class:pipeline.FeatureUnion
• :class:model_selection.GridSearchCV
• :class:model_selection.RandomizedSearchCV
• :class:model_selection.HalvingGridSearchCV
• :class:model_selection.HalvingRandomSearchCV

Metrics

• :func:sklearn.metrics.accuracy_score
• :func:sklearn.metrics.average_precision_score
• :func:sklearn.metrics.balanced_accuracy_score
• :func:sklearn.metrics.brier_score_loss
• :func:sklearn.metrics.cluster.calinski_harabasz_score
• :func:sklearn.metrics.cohen_kappa_score
• :func:sklearn.metrics.confusion_matrix
• :func:sklearn.metrics.d2_absolute_error_score
• :func:sklearn.metrics.d2_brier_score
• :func:sklearn.metrics.d2_log_loss_score
• :func:sklearn.metrics.d2_pinball_score
• :func:sklearn.metrics.d2_tweedie_score
• :func:sklearn.metrics.det_curve
• :func:sklearn.metrics.explained_variance_score
• :func:sklearn.metrics.f1_score
• :func:sklearn.metrics.fbeta_score
• :func:sklearn.metrics.hamming_loss
• :func:sklearn.metrics.jaccard_score
• :func:sklearn.metrics.log_loss
• :func:sklearn.metrics.max_error
• :func:sklearn.metrics.mean_absolute_error
• :func:sklearn.metrics.mean_absolute_percentage_error
• :func:sklearn.metrics.mean_gamma_deviance
• :func:sklearn.metrics.mean_pinball_loss
• :func:sklearn.metrics.mean_poisson_deviance (requires enabling array API support for SciPy <https://docs.scipy.org/doc/scipy/dev/api-dev/array_api.html#using-array-api-standard-support>_)
• :func:sklearn.metrics.mean_squared_error
• :func:sklearn.metrics.mean_squared_log_error
• :func:sklearn.metrics.mean_tweedie_deviance
• :func:sklearn.metrics.median_absolute_error
• :func:sklearn.metrics.multilabel_confusion_matrix
• :func:sklearn.metrics.pairwise.additive_chi2_kernel
• :func:sklearn.metrics.pairwise.chi2_kernel
• :func:sklearn.metrics.pairwise.cosine_similarity
• :func:sklearn.metrics.pairwise.cosine_distances
• :func:sklearn.metrics.pairwise.pairwise_distances (only supports "cosine", "euclidean", "manhattan" and "l2" metrics)
• :func:sklearn.metrics.pairwise.pairwise_distances_argmin
• :func:sklearn.metrics.pairwise.euclidean_distances (see :ref:device_support_for_float64)
• :func:sklearn.metrics.pairwise.laplacian_kernel
• :func:sklearn.metrics.pairwise.linear_kernel
• :func:sklearn.metrics.pairwise.manhattan_distances
• :func:sklearn.metrics.pairwise.paired_cosine_distances
• :func:sklearn.metrics.pairwise.paired_euclidean_distances
• :func:sklearn.metrics.pairwise.paired_manhattan_distances
• :func:sklearn.metrics.pairwise.pairwise_kernels
• :func:sklearn.metrics.pairwise.polynomial_kernel
• :func:sklearn.metrics.pairwise.rbf_kernel (see :ref:device_support_for_float64)
• :func:sklearn.metrics.pairwise.sigmoid_kernel
• :func:sklearn.metrics.precision_score
• :func:sklearn.metrics.precision_recall_curve
• :func:sklearn.metrics.precision_recall_fscore_support
• :func:sklearn.metrics.r2_score
• :func:sklearn.metrics.recall_score
• :func:sklearn.metrics.roc_curve
• :func:sklearn.metrics.root_mean_squared_error
• :func:sklearn.metrics.root_mean_squared_log_error
• :func:sklearn.metrics.zero_one_loss

Tools

• :func:preprocessing.label_binarize (with sparse_output=False)
• :func:model_selection.cross_val_predict
• :func:model_selection.train_test_split
• :func:utils.check_consistent_length

Coverage is expected to grow over time. Please follow the dedicated meta-issue on GitHub <https://github.com/scikit-learn/scikit-learn/issues/22352>_ to track progress.

Input and output array type handling

Estimators and scoring functions are able to accept input arrays from different array libraries and/or devices. When a mixed set of input arrays is passed, scikit-learn converts arrays as needed to make them all consistent.

For estimators, the rule is "everything follows X " - mixed array inputs are converted so that they all match the array library and device of X. For scoring functions the rule is "everything follows y_pred " - mixed array inputs are converted so that they all match the array library and device of y_pred.

When a function or method has been called with array API compatible inputs, the convention is to return arrays from the same array library and on the same device as the input data.

Estimators

When an estimator is fitted with an array API compatible X, all other array inputs, including constructor arguments, (e.g., y, sample_weight) will be converted to match the array library and device of X, if they do not already. This behaviour enables switching from processing on the CPU to processing on the GPU at any point within a pipeline.

This allows estimators to accept mixed input types, enabling X to be moved to a different device within a pipeline, without explicitly moving y. Note that scikit-learn pipelines do not allow transformation of y (to avoid :ref:leakage <data_leakage>).

Take for example a pipeline where X and y both start on CPU, and go through the following three steps:

• :class:~sklearn.preprocessing.TargetEncoder, which will transform categorial X but also requires y, meaning both X and y need to be on CPU.
• :class:FunctionTransformer(func=partial(torch.asarray, device="cuda")) <sklearn.preprocessing.FunctionTransformer>, which moves X to GPU, to improve performance in the next step.
• :class:~sklearn.linear_model.Ridge, whose performance can be improved when passed arrays on a GPU, as they can handle large matrix operations very efficiently.

X initially contains categorical string data (thus needs to be on CPU), which is target encoded to numerical values in :class:~sklearn.preprocessing.TargetEncoder. X is then explicitly moved to GPU to improve the performance of :class:~sklearn.linear_model.Ridge. y cannot be transformed by the pipeline (recall scikit-learn pipelines do not allow transformation of y) but as :class:~sklearn.linear_model.Ridge is able to accept mixed input types, this is not a problem and the pipeline is able to be run.

The fitted attributes of an estimator fitted with an array API compatible X, will be arrays from the same library as the input and stored on the same device. The predict and transform method subsequently expect inputs from the same array library and device as the data passed to the fit method.

Scoring functions

When an array API compatible y_pred is passed to a scoring function, all other array inputs (e.g., y_true, sample_weight) will be converted to match the array library and device of y_pred, if they do not already. This allows scoring functions to accept mixed input types, enabling them to be used within a :term:meta-estimator (or function that accepts estimators), with a pipeline that moves input arrays between devices (e.g., CPU to GPU).

For example, to be able to use the pipeline described above within e.g., :func:~sklearn.model_selection.cross_validate or :class:~sklearn.model_selection.GridSearchCV, the scoring function internally called needs to be able to accept mixed input types.

The output type of scoring functions depends on the number of output values. When a scoring function returns a scalar value, it will return a Python scalar (typically a float instance) instead of an array scalar value. For scoring functions that support :term:multiclass or :term:multioutput, an array from the same array library and device as y_pred will be returned when multiple values need to be output.

Common estimator checks

Add the array_api_support tag to an estimator's set of tags to indicate that it supports the array API. This will enable dedicated checks as part of the common tests to verify that the estimators' results are the same when using vanilla NumPy and array API inputs.

To run these checks you need to install array-api-strict <https://data-apis.org/array-api-strict/>_ in your test environment. This allows you to run checks without having a GPU. To run the full set of checks you also need to install PyTorch <https://pytorch.org/>, CuPy <https://cupy.dev/> and have a GPU. Checks that can not be executed or have missing dependencies will be automatically skipped. Therefore it's important to run the tests with the -v flag to see which checks are skipped:

.. prompt:: bash $

pip install array-api-strict  # and other libraries as needed
pytest -k "array_api" -v

Running the scikit-learn tests against array-api-strict should help reveal most code problems related to handling multiple device inputs via the use of simulated non-CPU devices. This allows for fast iterative development and debugging of array API related code.

However, to ensure full handling of PyTorch or CuPy inputs allocated on actual GPU devices, it is necessary to run the tests against those libraries and hardware. This can either be achieved by using Google Colab <https://gist.github.com/EdAbati/ff3bdc06bafeb92452b3740686cc8d7c>_ or leveraging our CI infrastructure on pull requests (manually triggered by maintainers for cost reasons).

.. _mps_support:

Note on MPS device support

On macOS, PyTorch can use the Metal Performance Shaders (MPS) to access hardware accelerators (e.g. the internal GPU component of the M1 or M2 chips). However, the MPS device support for PyTorch is incomplete at the time of writing. See the following github issue for more details:

• https://github.com/pytorch/pytorch/issues/77764

To enable the MPS support in PyTorch, set the environment variable PYTORCH_ENABLE_MPS_FALLBACK=1 before running the tests:

.. prompt:: bash $

PYTORCH_ENABLE_MPS_FALLBACK=1 pytest -k "array_api" -v

At the time of writing all scikit-learn tests should pass, however, the computational speed is not necessarily better than with the CPU device.

.. _device_support_for_float64:

Note on device support for float64

Certain operations within scikit-learn will automatically perform operations on floating-point values with float64 precision to prevent overflows and ensure correctness (e.g., :func:metrics.pairwise.euclidean_distances, :class:preprocessing.StandardScaler). However, certain combinations of array namespaces and devices, such as PyTorch on MPS (see :ref:mps_support) do not support the float64 data type. In these cases, scikit-learn will revert to using the float32 data type instead. This can result in different behavior (typically numerically unstable results) compared to not using array API dispatching or using a device with float64 support.