# Source code for mars.tensor.indexing.choose

```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import FieldTypes, KeyField, ListField, StringField
from ..utils import broadcast_shape, check_out_param
from ..operands import TensorOperand, TensorOperandMixin
from ..datasource import tensor as astensor
from ..core import Tensor, TensorOrder
from ..array_utils import as_same_device, device
class TensorChoose(TensorOperand, TensorOperandMixin):
_op_type_ = OperandDef.CHOOSE
_a = KeyField("a")
_choices = ListField("choices", FieldTypes.key)
_mode = StringField("mode")
def __init__(self, mode=None, **kw):
super().__init__(_mode=mode, **kw)
def __setattr__(self, key, value):
if key == "_mode" and value not in ("raise", "wrap", "clip"):
raise ValueError(f"mode should be raise, wrap or clip, not {value}")
super().__setattr__(key, value)
@property
def a(self):
return self._a
@property
def choices(self):
return self._choices
@property
def mode(self):
return self._mode
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self._a = self._inputs[0]
self._choices = self._inputs[1:]
def __call__(self, a, choices, out=None):
if out is not None and not isinstance(out, Tensor):
raise TypeError(f"out should be Tensor object, got {type(out)} instead")
inputs = [a] + choices
shape = broadcast_shape(a.shape, *[c.shape for c in choices])
order = TensorOrder.C_ORDER if out is None else out.order
t = self.new_tensor(inputs, shape, order=order)
if out is None:
return t
check_out_param(out, t, "unsafe")
out_shape, out_dtype = out.shape, out.dtype
# if `out` is specified, use out's dtype and shape
if out_shape != t.shape:
raise ValueError(f"output shape should be {t.shape}, got {out_shape}")
setattr(self, "dtype", out_dtype)
out.data = t.data
return out
@classmethod
def tile(cls, op):
from ..arithmetic.core import TensorElementWise
return (yield from TensorElementWise.tile(op))
@classmethod
def execute(cls, ctx, op):
inputs, device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True
)
a, choices = inputs[0], inputs[1:]
out = op.outputs[0]
with device(device_id):
ctx[out.key] = xp.choose(a, choices, mode=op.mode).astype(
op.dtype, order=out.order.value, copy=False
)
[docs]def choose(a, choices, out=None, mode="raise"):
"""
Construct a tensor from an index tensor and a set of tensors to choose from.
First of all, if confused or uncertain, definitely look at the Examples -
in its full generality, this function is less simple than it might
seem from the following code description (below ndi =
`mt.lib.index_tricks`):
``mt.choose(a,c) == mt.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.
But this omits some subtleties. Here is a fully general summary:
Given an "index" tensor (`a`) of integers and a sequence of `n` tensors
(`choices`), `a` and each choice tensor are first broadcast, as necessary,
to tensors of a common shape; calling these *Ba* and *Bchoices[i], i =
0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
for each `i`. Then, a new array with shape ``Ba.shape`` is created as
follows:
* if ``mode=raise`` (the default), then, first of all, each element of
`a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that
`i` (in that range) is the value at the `(j0, j1, ..., jm)` position
in `Ba` - then the value at the same position in the new array is the
value in `Bchoices[i]` at that same position;
* if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)
integer; modular arithmetic is used to map integers outside the range
`[0, n-1]` back into that range; and then the new array is constructed
as above;
* if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)
integer; negative integers are mapped to 0; values greater than `n-1`
are mapped to `n-1`; and then the new tensor is constructed as above.
Parameters
----------
a : int tensor
This tensor must contain integers in `[0, n-1]`, where `n` is the number
of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any
integers are permissible.
choices : sequence of tensors
Choice tensors. `a` and all of the choices must be broadcastable to the
same shape. If `choices` is itself a tensor (not recommended), then
its outermost dimension (i.e., the one corresponding to
``choices.shape[0]``) is taken as defining the "sequence".
out : tensor, optional
If provided, the result will be inserted into this tensor. It should
be of the appropriate shape and dtype.
mode : {'raise' (default), 'wrap', 'clip'}, optional
Specifies how indices outside `[0, n-1]` will be treated:
* 'raise' : an exception is raised
* 'wrap' : value becomes value mod `n`
* 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1
Returns
-------
merged_array : Tensor
The merged result.
Raises
------
ValueError: shape mismatch
If `a` and each choice tensor are not all broadcastable to the same
shape.
See Also
--------
Tensor.choose : equivalent method
Notes
-----
To reduce the chance of misinterpretation, even though the following
"abuse" is nominally supported, `choices` should neither be, nor be
thought of as, a single tensor, i.e., the outermost sequence-like container
should be either a list or a tuple.
Examples
--------
>>> import mars.tensor as mt
>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
... [20, 21, 22, 23], [30, 31, 32, 33]]
>>> mt.choose([2, 3, 1, 0], choices
... # the first element of the result will be the first element of the
... # third (2+1) "array" in choices, namely, 20; the second element
... # will be the second element of the fourth (3+1) choice array, i.e.,
... # 31, etc.
... ).execute()
array([20, 31, 12, 3])
>>> mt.choose([2, 4, 1, 0], choices, mode='clip').execute() # 4 goes to 3 (4-1)
array([20, 31, 12, 3])
>>> # because there are 4 choice arrays
>>> mt.choose([2, 4, 1, 0], choices, mode='wrap').execute() # 4 goes to (4 mod 4)
array([20, 1, 12, 3])
>>> # i.e., 0
A couple examples illustrating how choose broadcasts:
>>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
>>> choices = [-10, 10]
>>> mt.choose(a, choices).execute()
array([[ 10, -10, 10],
[-10, 10, -10],
[ 10, -10, 10]])
>>> # With thanks to Anne Archibald
>>> a = mt.array([0, 1]).reshape((2,1,1))
>>> c1 = mt.array([1, 2, 3]).reshape((1,3,1))
>>> c2 = mt.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
>>> mt.choose(a, (c1, c2)).execute() # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
array([[[ 1, 1, 1, 1, 1],
[ 2, 2, 2, 2, 2],
[ 3, 3, 3, 3, 3]],
[[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5]]])
"""
a = astensor(a, dtype="i8")
choices = [astensor(c) for c in choices]
dtype = np.result_type(*[c.dtype for c in choices])
op = TensorChoose(mode=mode, dtype=dtype)
return op(a, choices, out=out)
```