def type_of_target(y):
    """Determine the type of data indicated by target `y`

    Parameters
    ----------
    y : array-like

    Returns
    -------
    target_type : string
        One of:
        * 'continuous': `y` is an array-like of floats that are not all
          integers, and is 1d or a column vector.
        * 'continuous-multioutput': `y` is a 2d array of floats that are
          not all integers, and both dimensions are of size > 1.
        * 'binary': `y` contains <= 2 discrete values and is 1d or a column
          vector.
        * 'multiclass': `y` contains more than two discrete values, is not a
          sequence of sequences, and is 1d or a column vector.
        * 'multiclass-multioutput': `y` is a 2d array that contains more
          than two discrete values, is not a sequence of sequences, and both
          dimensions are of size > 1.
        * 'multilabel-indicator': `y` is a label indicator matrix, an array
          of two dimensions with at least two columns, and at most 2 unique
          values.
        * 'unknown': `y` is array-like but none of the above, such as a 3d
          array, sequence of sequences, or an array of non-sequence objects.

    Examples
    --------
    >>> import numpy as np
    >>> type_of_target([0.1, 0.6])
    'continuous'
    >>> type_of_target([1, -1, -1, 1])
    'binary'
    >>> type_of_target(['a', 'b', 'a'])
    'binary'
    >>> type_of_target([1.0, 2.0])
    'binary'
    >>> type_of_target([1, 0, 2])
    'multiclass'
    >>> type_of_target([1.0, 0.0, 3.0])
    'multiclass'
    >>> type_of_target(['a', 'b', 'c'])
    'multiclass'
    >>> type_of_target(np.array([[1, 2], [3, 1]]))
    'multiclass-multioutput'
    >>> type_of_target([[1, 2]])
    'multiclass-multioutput'
    >>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]]))
    'continuous-multioutput'
    >>> type_of_target(np.array([[0, 1], [1, 1]]))
    'multilabel-indicator'
    """
    valid = ((isinstance(y, (Sequence, spmatrix)) or hasattr(y, '__array__'))
             and not isinstance(y, string_types))

    if not valid:
        raise ValueError('Expected array-like (array or non-string sequence), '
                         'got %r' % y)

    if is_multilabel(y):
        return 'multilabel-indicator'

    try:
        y = np.asarray(y)
    except __HOLE__:
        # Known to fail in numpy 1.3 for array of arrays
        return 'unknown'

    # The old sequence of sequences format
    try:
        if (not hasattr(y[0], '__array__') and isinstance(y[0], Sequence)
                and not isinstance(y[0], string_types)):
            raise ValueError('You appear to be using a legacy multi-label data'
                             ' representation. Sequence of sequences are no'
                             ' longer supported; use a binary array or sparse'
                             ' matrix instead.')
    except IndexError:
        pass

    # Invalid inputs
    if y.ndim > 2 or (y.dtype == object and len(y) and
                      not isinstance(y.flat[0], string_types)):
        return 'unknown'  # [[[1, 2]]] or [obj_1] and not ["label_1"]

    if y.ndim == 2 and y.shape[1] == 0:
        return 'unknown'  # [[]]

    if y.ndim == 2 and y.shape[1] > 1:
        suffix = "-multioutput"  # [[1, 2], [1, 2]]
    else:
        suffix = ""  # [1, 2, 3] or [[1], [2], [3]]

    # check float and contains non-integer float values
    if y.dtype.kind == 'f' and np.any(y != y.astype(int)):
        # [.1, .2, 3] or [[.1, .2, 3]] or [[1., .2]] and not [1., 2., 3.]
        return 'continuous' + suffix

    if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1):
        return 'multiclass' + suffix  # [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]
    else:
        return 'binary'  # [1, 2] or [["a"], ["b"]]
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/utils/multiclass.py/type_of_target