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# Licensed under the Apache License, Version 2.0 (the "License");
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from typing import Union
import numpy as np
from maxframe import opcodes
from maxframe.serialization.serializables import BoolField
from maxframe.tensor.core import TensorOrder
from maxframe.tensor.datasource import tensor as astensor
from maxframe.tensor.operators import TensorOperator, TensorOperatorMixin
from maxframe.typing_ import TileableType
class TensorIsIn(TensorOperator, TensorOperatorMixin):
_op_type_ = opcodes.ISIN
assume_unique = BoolField("assume_unique")
invert = BoolField("invert")
def __call__(self, element, test_elements):
self.dtype = np.dtype(bool)
return self.new_tensor(
[element, test_elements], shape=element.shape, order=TensorOrder.C_ORDER
)
[docs]
def isin(
element: Union[TileableType, np.ndarray],
test_elements: Union[TileableType, np.ndarray, list],
assume_unique: bool = False,
invert: bool = False,
):
"""
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 : Tensor, bool
Has the same shape as `element`. The values `element[isin]`
are in `test_elements`.
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 maxframe.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]])
"""
element, test_elements = astensor(element), astensor(test_elements).ravel()
op = TensorIsIn(assume_unique=assume_unique, invert=invert)
return op(element, test_elements)