Supported input types

Just like scikit-learn estimators, estimators from the Extension for scikit-learn* are able to work with different classes of input data, including:

• Numpy arrays.

  • Note: masked arrays are also supported, but just like in stock scikit-learn, the underlying array values are used without the mask.

• Other array classes implementing the Array API protocol (see Array API support for details).

• SciPy sparse arrays and sparse matrices (depending on the estimator - see Supported Algorithms).

• Pandas DataFrame and Series classes.

• Other DataFrame classes recognize by data validators from scikit-learn, such as the ones from Polars.

In addition, Extension for scikit-learn* also supports dpnp.ndarray arrays in estimators with GPU support when array API mode is enabled (see also Array API support).

Extension for scikit-learn* currently does not offer accelerated routines for input types not listed here - when receiving an unsupported class, estimators will either convert to a supported class under some circumstances (e.g. PyArrow tables might get converted to NumPy arrays when passed to data validators from stock scikit-learn), throw an error (e.g. when passing some data format not that’s not recognized by scikit-learn), or fall back to stock scikit-learn to handle it (when array API is enabled but the input is unsupported).

Warning

In some cases, data passed to estimators might be copied/duplicated during calls to methods such as fit/predict. The affected cases are listed below:

• Non-contiguous NumPy array - i.e. where strides are wider than one element across both rows and columns.

• For SciPy CSR matrix / array, index arrays are always copied. Note that sparse matrices in formats other than CSR will be converted to CSR, which implies more than just data copying.

• Heterogeneous NumPy array.

• If a SYCL queue is used for Target offload option for a device without float64 support but data is float64, data will be converted to float32.

• If a dpnp.ndarray array on GPU is used as input without Array API support being enabled, then data will be transferred to CPU and the result returned as a NumPy array (see GPU support for details).

• If a dpnp.ndarray array on GPU is passed to an estimator with GPU support but the requested operation is not supported on GPU, and if array API is not enabled or scikit-learn does not support array API for the requested operation, then data might be transferred to CPU and the operation done there (see Configuration Contexts and Global Options).