check_array#

sklearn.utils.check_array(array,accept_sparse=False,*,accept_large_sparse=True,dtype='numeric',order=None,copy=False,force_writeable=False,force_all_finite='deprecated',ensure_all_finite=None,ensure_non_negative=False,ensure_2d=True,allow_nd=False,ensure_min_samples=1,ensure_min_features=1,estimator=None,input_name='')[source]#

Input validation on an array, list, sparse matrix or similar.

By default, the input is checked to be a non-empty 2D array containingonly finite values. If the dtype of the array is object, attemptconverting to float, raising on failure.

Parameters:
arrayobject

Input object to check / convert.

accept_sparsestr, bool or list/tuple of str, default=False

String[s] representing allowed sparse matrix formats, such as ‘csc’,‘csr’, etc. If the input is sparse but not in the allowed format,it will be converted to the first listed format. True allows the inputto be any format. False means that a sparse matrix input willraise an error.

accept_large_sparsebool, default=True

If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted byaccept_sparse, accept_large_sparse=False will cause it to be acceptedonly if its indices are stored with a 32-bit dtype.

Added in version 0.20.

dtype‘numeric’, type, list of type or None, default=’numeric’

Data type of result. If None, the dtype of the input is preserved.If “numeric”, dtype is preserved unless array.dtype is object.If dtype is a list of types, conversion on the first type is onlyperformed if the dtype of the input is not in the list.

order{‘F’, ‘C’} or None, default=None

Whether an array will be forced to be fortran or c-style.When order is None (default), then if copy=False, nothing is ensuredabout the memory layout of the output array; otherwise (copy=True)the memory layout of the returned array is kept as close as possibleto the original array.

copybool, default=False

Whether a forced copy will be triggered. If copy=False, a copy mightbe triggered by a conversion.

force_writeablebool, default=False

Whether to force the output array to be writeable. If True, the returned arrayis guaranteed to be writeable, which may require a copy. Otherwise thewriteability of the input array is preserved.

Added in version 1.6.

force_all_finitebool or ‘allow-nan’, default=True

Whether to raise an error on np.inf, np.nan, pd.NA in array. Thepossibilities are:

  • True: Force all values of array to be finite.

  • False: accepts np.inf, np.nan, pd.NA in array.

  • ‘allow-nan’: accepts only np.nan and pd.NA values in array. Valuescannot be infinite.

Added in version 0.20:force_all_finite accepts the string'allow-nan'.

Changed in version 0.23:Acceptspd.NA and converts it intonp.nan

Deprecated since version 1.6:force_all_finite was renamed toensure_all_finite and will be removedin 1.8.

ensure_all_finitebool or ‘allow-nan’, default=True

Whether to raise an error on np.inf, np.nan, pd.NA in array. Thepossibilities are:

  • True: Force all values of array to be finite.

  • False: accepts np.inf, np.nan, pd.NA in array.

  • ‘allow-nan’: accepts only np.nan and pd.NA values in array. Valuescannot be infinite.

Added in version 1.6:force_all_finite was renamed toensure_all_finite.

ensure_non_negativebool, default=False

Make sure the array has only non-negative values. If True, an array thatcontains negative values will raise a ValueError.

Added in version 1.6.

ensure_2dbool, default=True

Whether to raise a value error if array is not 2D.

allow_ndbool, default=False

Whether to allow array.ndim > 2.

ensure_min_samplesint, default=1

Make sure that the array has a minimum number of samples in its firstaxis (rows for a 2D array). Setting to 0 disables this check.

ensure_min_featuresint, default=1

Make sure that the 2D array has some minimum number of features(columns). The default value of 1 rejects empty datasets.This check is only enforced when the input data has effectively 2dimensions or is originally 1D andensure_2d is True. Setting to 0disables this check.

estimatorstr or estimator instance, default=None

If passed, include the name of the estimator in warning messages.

input_namestr, default=””

The data name used to construct the error message. In particularifinput_name is “X” and the data has NaN values andallow_nan is False, the error message will link to the imputerdocumentation.

Added in version 1.1.0.

Returns:
array_convertedobject

The converted and validated array.

Examples

>>>fromsklearn.utils.validationimportcheck_array>>>X=[[1,2,3],[4,5,6]]>>>X_checked=check_array(X)>>>X_checkedarray([[1, 2, 3], [4, 5, 6]])

Gallery examples#

Release Highlights for scikit-learn 1.6

Release Highlights for scikit-learn 1.6