original_docstring: Input validation for standard estimators.
      Checks X and y for consistent length, enforces X to be 2D and y 1D. By
      default, X is checked to be non-empty and containing only finite values.
      Standard input checks are also applied to y, such as checking that y
      does not have np.nan or np.inf targets. For multi-label y, set
      multi_output=True to allow 2D and sparse y. If the dtype of X is
      object, attempt converting to float, raising on failure.
      Parameters
      ----------
      X : nd-array, list or sparse matrix
          Input data.
      y : nd-array, list or sparse matrix
          Labels.
      accept_sparse : string, boolean or list of string (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 will cause it to be accepted only
          if its indices are stored with a 32-bit dtype.
          .. versionadded:: 0.20
      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 an array will be forced to be fortran or c-style.
      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 X. This parameter
          does not influence whether y can have np.inf or np.nan values.
          The possibilities are:
          - True: Force all values of X to be finite.
          - False: accept both np.inf and np.nan in X.
          - 'allow-nan': accept only np.nan values in X. Values cannot be
            infinite.
          .. versionadded:: 0.20
             ``force_all_finite`` accepts the string ``'allow-nan'``.
      ensure_2d : boolean (default=True)
          Whether to raise a value error if X is not 2D.
      allow_nd : boolean (default=False)
          Whether to allow X.ndim > 2.
      multi_output : boolean (default=False)
          Whether to allow 2D y (array or sparse matrix). If false, y will be
          validated as a vector. y cannot have np.nan or np.inf values if
          multi_output=True.
      ensure_min_samples : int (default=1)
          Make sure that X has a minimum number of samples in its first
          axis (rows for a 2D array).
      ensure_min_features : int (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 X has effectively 2 dimensions or
          is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
          this check.
      y_numeric : boolean (default=False)
          Whether to ensure that y has a numeric type. If dtype of y is object,
          it is converted to float64. Should only be used for regression
          algorithms.
      estimator : str or estimator instance (default=None)
          If passed, include the name of the estimator in warning messages.
      Returns
      -------
      X_converted : object
          The converted and validated X.
      y_converted : object
          The converted and validated y.
      
docstring: Input validation for standard estimators.
  Checks X and y for consistent length, enforces X to be 2D and y 1D. By
  default, X is checked to be non-empty and containing only finite values.
  Standard input checks are also applied to y, such as checking that y
  does not have np.nan or np.inf targets. For multi-label y, set
  multi_output=True to allow 2D and sparse y. If the dtype of X is
  object, attempt converting to float, raising on failure.
  Parameters
  
  X : nd-array, list or sparse matrix
  Input data.
  y : nd-array, list or sparse matrix
  Labels.
  accept_sparse : string, boolean or list of string (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 will cause it to be accepted only
  if its indices are stored with a 32-bit dtype.
  versionadded:: 0.20
  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 an array will be forced to be fortran or c-style.
  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 X. This parameter
  does not influence whether y can have np.inf or np.nan values.
  The possibilities are:
  True: Force all values of X to be finite.
  
  X_converted : object
  The converted and validated X.
  y_converted : object
  The converted and validated y.
docstring_tokens: Input validation for standard estimators . Checks X and y for consistent length enforces X to be 2D and y 1D . By default X is checked to be non - empty and containing only finite values . Standard input checks are also applied to y such as checking that y does not have np . nan or np . inf targets . For multi - label y set multi_output = True to allow 2D and sparse y . If the dtype of X is object attempt converting to float raising on failure . Parameters X : nd - array list or sparse matrix Input data . y : nd - array list or sparse matrix Labels . accept_sparse : string boolean or list of string ( 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 will cause it to be accepted only if its indices are stored with a 32 - bit dtype . versionadded :: 0 . 20 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 an array will be forced to be fortran or c - style . 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 X . This parameter does not influence whether y can have np . inf or np . nan values . The possibilities are : True : Force all values of X to be finite . X_converted : object The converted and validated X . y_converted : object The converted and validated y .
code: def check_X_y(X, y, accept_sparse=False, accept_large_sparse=True,
                dtype="numeric", order=None, copy=False, force_all_finite=True,
                ensure_2d=True, allow_nd=False, multi_output=False,
                ensure_min_samples=1, ensure_min_features=1, y_numeric=False,
                estimator=None):
      """Input validation for standard estimators.
      Checks X and y for consistent length, enforces X to be 2D and y 1D. By
      default, X is checked to be non-empty and containing only finite values.
      Standard input checks are also applied to y, such as checking that y
      does not have np.nan or np.inf targets. For multi-label y, set
      multi_output=True to allow 2D and sparse y. If the dtype of X is
      object, attempt converting to float, raising on failure.
      Parameters
      ----------
      X : nd-array, list or sparse matrix
          Input data.
      y : nd-array, list or sparse matrix
          Labels.
      accept_sparse : string, boolean or list of string (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 will cause it to be accepted only
          if its indices are stored with a 32-bit dtype.
          .. versionadded:: 0.20
      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 an array will be forced to be fortran or c-style.
      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 X. This parameter
          does not influence whether y can have np.inf or np.nan values.
          The possibilities are:
          - True: Force all values of X to be finite.
          - False: accept both np.inf and np.nan in X.
          - 'allow-nan': accept only np.nan values in X. Values cannot be
            infinite.
          .. versionadded:: 0.20
             ``force_all_finite`` accepts the string ``'allow-nan'``.
      ensure_2d : boolean (default=True)
          Whether to raise a value error if X is not 2D.
      allow_nd : boolean (default=False)
          Whether to allow X.ndim > 2.
      multi_output : boolean (default=False)
          Whether to allow 2D y (array or sparse matrix). If false, y will be
          validated as a vector. y cannot have np.nan or np.inf values if
          multi_output=True.
      ensure_min_samples : int (default=1)
          Make sure that X has a minimum number of samples in its first
          axis (rows for a 2D array).
      ensure_min_features : int (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 X has effectively 2 dimensions or
          is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
          this check.
      y_numeric : boolean (default=False)
          Whether to ensure that y has a numeric type. If dtype of y is object,
          it is converted to float64. Should only be used for regression
          algorithms.
      estimator : str or estimator instance (default=None)
          If passed, include the name of the estimator in warning messages.
      Returns
      -------
      X_converted : object
          The converted and validated X.
      y_converted : object
          The converted and validated y.
      """
      if y is None:
          raise ValueError("y cannot be None")
  
      X = check_array(X, accept_sparse=accept_sparse,
                      accept_large_sparse=accept_large_sparse,
                      dtype=dtype, order=order, copy=copy,
                      force_all_finite=force_all_finite,
                      ensure_2d=ensure_2d, allow_nd=allow_nd,
                      ensure_min_samples=ensure_min_samples,
                      ensure_min_features=ensure_min_features,
                      estimator=estimator)
      if multi_output:
          y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
                          dtype=None)
      else:
          y = column_or_1d(y, warn=True)
          _assert_all_finite(y)
      if y_numeric and y.dtype.kind == 'O':
          y = y.astype(np.float64)
  
      check_consistent_length(X, y)
  
      return X, y
code_tokens: def check_X_y ( X , y , accept_sparse = False , accept_large_sparse = True , dtype = "numeric" , order = None , copy = False , force_all_finite = True , ensure_2d = True , allow_nd = False , multi_output = False , ensure_min_samples = 1 , ensure_min_features = 1 , y_numeric = False , estimator = None ) : if y is None : raise ValueError ( "y cannot be None" ) X = check_array ( X , accept_sparse = accept_sparse , accept_large_sparse = accept_large_sparse , dtype = dtype , order = order , copy = copy , force_all_finite = force_all_finite , ensure_2d = ensure_2d , allow_nd = allow_nd , ensure_min_samples = ensure_min_samples , ensure_min_features = ensure_min_features , estimator = estimator ) if multi_output : y = check_array ( y , 'csr' , force_all_finite = True , ensure_2d = False , dtype = None ) else : y = column_or_1d ( y , warn = True ) _assert_all_finite ( y ) if y_numeric and y . dtype . kind == 'O' : y = y . astype ( np . float64 ) check_consistent_length ( X , y ) return X , y
short_docstring: Input validation for standard estimators.
short_docstring_tokens: Input validation for standard estimators .
comment: """Input validation for standard estimators.
      Checks X and y for consistent length, enforces X to be 2D and y 1D. By
      default, X is checked to be non-empty and containing only finite values.
      Standard input checks are also applied to y, such as checking that y
      does not have np.nan or np.inf targets. For multi-label y, set
      multi_output=True to allow 2D and sparse y. If the dtype of X is
      object, attempt converting to float, raising on failure.
      Parameters
      ----------
      X : nd-array, list or sparse matrix
          Input data.
      y : nd-array, list or sparse matrix
          Labels.
      accept_sparse : string, boolean or list of string (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 will cause it to be accepted only
          if its indices are stored with a 32-bit dtype.
          .. versionadded:: 0.20
      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 an array will be forced to be fortran or c-style.
      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 X. This parameter
          does not influence whether y can have np.inf or np.nan values.
          The possibilities are:
          - True: Force all values of X to be finite.
          - False: accept both np.inf and np.nan in X.
          - 'allow-nan': accept only np.nan values in X. Values cannot be
            infinite.
          .. versionadded:: 0.20
             ``force_all_finite`` accepts the string ``'allow-nan'``.
      ensure_2d : boolean (default=True)
          Whether to raise a value error if X is not 2D.
      allow_nd : boolean (default=False)
          Whether to allow X.ndim > 2.
      multi_output : boolean (default=False)
          Whether to allow 2D y (array or sparse matrix). If false, y will be
          validated as a vector. y cannot have np.nan or np.inf values if
          multi_output=True.
      ensure_min_samples : int (default=1)
          Make sure that X has a minimum number of samples in its first
          axis (rows for a 2D array).
      ensure_min_features : int (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 X has effectively 2 dimensions or
          is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
          this check.
      y_numeric : boolean (default=False)
          Whether to ensure that y has a numeric type. If dtype of y is object,
          it is converted to float64. Should only be used for regression
          algorithms.
      estimator : str or estimator instance (default=None)
          If passed, include the name of the estimator in warning messages.
      Returns
      -------
      X_converted : object
          The converted and validated X.
      y_converted : object
          The converted and validated y.
      """