mars.tensor.isin#

mars.tensor.isin(element: Union[TileableType, ndarray], test_elements: Union[TileableType, ndarray, list], assume_unique: bool = False, invert: bool = False)[source]#

Calculates element in test_elements, broadcasting over element only. Returns a boolean array of the same shape as element that is True where an element of element is in test_elements and False otherwise.

Parameters

  • element (array_like) – Input tensor.

  • test_elements (array_like) – The values against which to test each value of element. This argument is flattened if it is a tensor or array_like. See notes for behavior with non-array-like parameters.

  • assume_unique (bool, optional) – If True, the input tensors are both assumed to be unique, which can speed up the calculation. Default is False.

  • invert (bool, optional) – If True, the values in the returned tensor are inverted, as if calculating element not in test_elements. Default is False. mt.isin(a, b, invert=True) is equivalent to (but faster than) mt.invert(mt.isin(a, b)).

Returns

isin – Has the same shape as element. The values element[isin] are in test_elements.

Return type

Tensor, bool

See also

in1d

Flattened version of this function.

Notes

isin is an element-wise function version of the python keyword in. isin(a, b) is roughly equivalent to mt.array([item in b for item in a]) if a and b are 1-D sequences.

element and test_elements are converted to tensors if they are not already. If test_elements is a set (or other non-sequence collection) it will be converted to an object tensor with one element, rather than a tensor of the values contained in test_elements. This is a consequence of the tensor constructor’s way of handling non-sequence collections. Converting the set to a list usually gives the desired behavior.

Examples

>>> import mars.tensor as mt

>>> element = 2*mt.arange(4).reshape((2, 2))
>>> element.execute()
array([[0, 2],
       [4, 6]])
>>> test_elements = [1, 2, 4, 8]
>>> mask = mt.isin(element, test_elements)
>>> mask.execute()
array([[ False,  True],
       [ True,  False]])
>>> element[mask].execute()
array([2, 4])
>>> mask = mt.isin(element, test_elements, invert=True)
>>> mask.execute()
array([[ True, False],
       [ False, True]])
>>> element[mask]
array([0, 6])

Because of how array handles sets, the following does not work as expected:

>>> test_set = {1, 2, 4, 8}
>>> mt.isin(element, test_set).execute()
array([[ False, False],
       [ False, False]])

Casting the set to a list gives the expected result:

>>> mt.isin(element, list(test_set)).execute()
array([[ False,  True],
       [ True,  False]])