mars.learn.utils.check_array#

mars.learn.utils.check_array(array, accept_sparse=False, accept_large_sparse=True, dtype='numeric', order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, estimator=None) → Tensor[source]#

Input validation on a tensor, list, sparse matrix or similar.

By default, the input is checked to be a non-empty 2D array containing only finite values. If the dtype of the tensor is object, attempt converting to float, raising on failure.

Parameters

  • array (object) – Input object to check / convert.

  • accept_sparse (string, boolean or list/tuple of strings (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 input to be any format. False means that a sparse matrix input will raise an error.

  • accept_large_sparse (bool (default=True)) – If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype.

  • dtype (string, type, list of types 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 only performed if the dtype of the input is not in the list.

  • order ('F', 'C' or None (default=None)) – Whether a tenor will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output tensor; otherwise (copy=True) the memory layout of the returned tensor is kept as close as possible to the original tensor.

  • copy (boolean (default=False)) – Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

  • force_all_finite (boolean or 'allow-nan', (default=True)) –

    Whether to raise an error on np.inf and np.nan in tensor. The possibilities are:

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

    • False: accept both np.inf and np.nan in tensor.

    • ’allow-nan’: accept only np.nan values in tensor. Values cannot be infinite.

    For object dtyped data, only np.nan is checked and not np.inf.

  • ensure_2d (boolean (default=True)) – Whether to raise a value error if tensor is not 2D.

  • allow_nd (boolean (default=False)) – Whether to allow tensor.ndim > 2.

  • ensure_min_samples (int (default=1)) – Make sure that the tensor has a minimum number of samples in its first axis (rows for a 2D tensor). Setting to 0 disables this check.

  • ensure_min_features (int (default=1)) – Make sure that the 2D tensor 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 2 dimensions or is originally 1D and ensure_2d is True. Setting to 0 disables this check.

  • estimator (str or estimator instance (default=None)) – If passed, include the name of the estimator in warning messages.

Returns

array_converted – The converted and validated tensor.

Return type

object