mars.tensor.unique#

mars.tensor.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None, aggregate_size=None)[source]#

Find the unique elements of a tensor.

Returns the sorted unique elements of a tensor. There are three optional outputs in addition to the unique elements:

  • the indices of the input tensor that give the unique values

  • the indices of the unique tensor that reconstruct the input tensor

  • the number of times each unique value comes up in the input tensor

Parameters

  • ar (array_like) – Input tensor. Unless axis is specified, this will be flattened if it is not already 1-D.

  • return_index (bool, optional) – If True, also return the indices of ar (along the specified axis, if provided, or in the flattened tensor) that result in the unique tensor.

  • return_inverse (bool, optional) – If True, also return the indices of the unique tensor (for the specified axis, if provided) that can be used to reconstruct ar.

  • return_counts (bool, optional) – If True, also return the number of times each unique item appears in ar.

  • axis (int or None, optional) – The axis to operate on. If None, ar will be flattened. If an integer, the subarrays indexed by the given axis will be flattened and treated as the elements of a 1-D tensor with the dimension of the given axis, see the notes for more details. Object tensors or structured tensors that contain objects are not supported if the axis kwarg is used. The default is None.

  • aggregate_size (int or None, optional) – How many chunks will be after unique, default as #input.chunks / options.combine_size

Returns

  • unique (Tensor) – The sorted unique values.

  • unique_indices (Tensor, optional) – The indices of the first occurrences of the unique values in the original tensor. Only provided if return_index is True.

  • unique_inverse (Tensor, optional) – The indices to reconstruct the original tensor from the unique tensor. Only provided if return_inverse is True.

  • unique_counts (Tensor, optional) – The number of times each of the unique values comes up in the original tensor. Only provided if return_counts is True.

Examples

>>> import mars.tensor as mt

>>> mt.unique([1, 1, 2, 2, 3, 3]).execute()
array([1, 2, 3])
>>> a = mt.array([[1, 1], [2, 3]])
>>> mt.unique(a).execute()
array([1, 2, 3])

Return the unique rows of a 2D tensor

>>> a = mt.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
>>> mt.unique(a, axis=0).execute()
array([[1, 0, 0], [2, 3, 4]])

Return the indices of the original tensor that give the unique values:

>>> a = mt.array(['a', 'b', 'b', 'c', 'a'])
>>> u, indices = mt.unique(a, return_index=True)
>>> u.execute()
array(['a', 'b', 'c'],
       dtype='|S1')
>>> indices.execute()
array([0, 1, 3])
>>> a[indices].execute()
array(['a', 'b', 'c'],
       dtype='|S1')

Reconstruct the input array from the unique values:

>>> a = mt.array([1, 2, 6, 4, 2, 3, 2])
>>> u, indices = mt.unique(a, return_inverse=True)
>>> u.execute()
array([1, 2, 3, 4, 6])
>>> indices.execute()
array([0, 1, 4, 3, 1, 2, 1])
>>> u[indices].execute()
array([1, 2, 6, 4, 2, 3, 2])