[Numpy-discussion] Indexing NEP draft
Sebastian Berg
sebastian at sipsolutions.net
Wed Nov 11 05:02:50 EST 2015
Hi all,
at scipy discussing with Nathaniel and others, we thought that maybe we
can push for orthogonal type indexing into numpy. Now with the new
version out and some other discussions done, I thought it is time to
pick it up :).
The basic ideas are twofold. First make indexing easier and less
confusing for starters (and advanced users also), and second improve
interoperability with projects such as xray for whom orthogonal/outer
type indexing makes more sense.
I have started working on:
1. A preliminary draft of an NEP you can view at
https://github.com/numpy/numpy/pull/6256/files?short_path=01e4dd9#diff-01e4dd9d2ecf994b24e5883f98f789e6
or at the end of this mail.
2. A preliminary implementation of `oindex` attribute with
orthogonal/outer style indexing in
https://github.com/numpy/numpy/pull/6075 which you can try out by
cloning numpy and then running from the source dir:
git fetch upstream pull/6075/head:pr-6075 && git checkout pr-6075;
python runtests.py --ipython
This will fetch my PR, switch to the branch and open an interactive
ipython shell where you will be able to do arr.oindex[].
Note that I consider the NEP quite preliminary in many parts, and it may
still be very confusing unless you are well versed with current advanced
indexing. There are some longer examples comparing the different styles
and another "example" which tries to show a "use case" example going
from simpler to more complex indexing operations.
Any comments are very welcome, and if it is "I don't understand a
word" :). I know it is probably too short and, at least without
examples, not easy to understand.
Best,
Sebastian
==================================================================================
The current NEP draft:
==========================================================
Implementing intuitive and full featured advanced indexing
==========================================================
:Author: Sebastian Berg
:Date: 2015-08-27
:Status: draft
Executive summary
=================
Advanced indexing with multiple array indices is typically confusing to
both new, and in many cases even old, users of NumPy. To avoid this
problem
and allow for more and clearer features, we propose to:
1. Introduce ``arr.oindex[indices]`` which allows advanced indices, but
uses outer indexing logic.
2. Introduce ``arr.vindex[indices]`` which use the current
"vectorized"/broadcasted logic but with two differences from
fancy indexing:
1. Boolean indices always use the outer indexing logic.
(Multi dimensional booleans should be allowed).
2. The integer index result dimensions are always the first axes
of the result array. No transpose is done, even for a single
integer array index.
3. Vanilla indexing on the array will only give warnings and eventually
errors either:
* when there is ambiguity between legacy fancy and outer indexing
(note that ``arr[[1, 2], :, 0]`` is such a case, an integer
can be the "second" integer index array),
* when any integer index array is present (possibly additional for
more then one boolean index array).
These constraints are sufficient for making indexing generally
consistent
with expectations and providing a less surprising learning curve with
``oindex``.
Note that all things mentioned here apply both for assignment as well as
subscription.
Understanding these details is *not* easy. The `Examples` section gives
code
examples. And the hopefully easier `Motivational Example` provides some
motivational use-cases for the general ideas and is likely a good start
for
anyone not intimately familiar with advanced indexing.
Motivation
==========
Old style advanced indexing with multiple array (boolean or integer)
indices,
also called "fancy indexing", tends to be very confusing for new users.
While fancy (or legacy) indexing is useful in many cases one would
naively
assume that the result of multiple 1-d ranges is analogous to multiple
slices along each dimension (also called "outer indexing").
However, legacy fancy indexing with multiple arrays broadcasts these
arrays
into a single index over multiple dimensions. There are three main
points
of confusion when multiple array indices are involved:
1. Most new users will usually expect outer indexing (consistent with
slicing). This is also the most common way of handling this in other
packages or languages.
2. The axes introduced by the array indices are at the front, unless
all array indices are consecutive, in which case one can deduce where
the user "expects" them to be:
* `arr[:, [0, 1], :, [0, 1]]` will have the first dimension shaped 2.
* `arr[:, [0, 1], [0, 1]]` will have the second dimension shaped 2.
3. When a boolean array index is mixed with another boolean or integer
array, the result is very hard to understand (the boolean array is
converted to integer array indices and then broadcast), and hardly
useful.
There is no well defined broadcast for booleans, so that boolean
indices are logically always "``outer``" type indices.
Proposed rules
==============
From the three problems noted above some expectations for NumPy can
be deduced:
1. There should be a prominent outer/orthogonal indexing method such as
``arr.oindex[indices]``.
2. Considering how confusing fancy indexing can be, it should only
occur explicitly (e.g. ``arr.vindex[indices]``)
3. A new ``arr.vindex[indices]`` method, would not be tied to the
confusing transpose rules of fancy indexing (which is for example
needed for the simple case of a single advanced index). Thus, it
no transposing should be done. The axes of the advanced indices are
always inserted at the front, even for a single index.
4. Boolean indexing is conceptionally outer indexing. A broadcasting
together with other advanced indices in the manner of legacy
"fancy indexing" is generally not helpful or well defined.
A user who wishes the "``nonzero``" plus broadcast behaviour can thus
be expected to do this manually.
Using this rule, a single boolean index can index into multiple
dimensions at once.
5. An ``arr.lindex`` or ``arr.findex`` should likely be implemented to
allow
legacy fancy indexing indefinetly. This also gives a simple way to
update fancy indexing code making deprecations to vanilla indexing
easier.
6. Vanilla indexing ``arr[...]`` could return an error for ambiguous
cases.
For the beginning, this probably means cases where ``arr[ind]`` and
``arr.oindex[ind]`` return different results gives deprecation
warnings.
However, the exact rules for this (especially the final behaviour)
are not
quite clear in cases such as ``arr[0, :, index_arr]``.
All other rules for indexing are identical.
Open Questions
==============
1. Especially for the new indexing attributes ``oindex`` and ``vindex``,
a case could be made to not implicitly add an ``Ellipsis`` index if
necessary.
This helps finding bugs since a too high dimensional array can be
caught.
(I am in favor for this, but doubt we should think about this for
vanilla
indexing.)
2. The names ``oindex`` and ``vindex`` are just suggestions at the time
of
writing this, another name NumPy has used for something like
``oindex``
is ``np.ix_``. See also below.
3. It would be possible to limit the use of boolean indices in
``vindex``,
assuming that they are rare and to some degree special.
(This would make implementation simpler, but I do not see a big
reason.)
4. ``oindex`` and ``vindex`` could always return copies, even when no
array
operation occurs. One argument for using the same rules is that this
way
``oindex`` can be used as a general index replacement.
(There is likely no big reason for this, however, there is one
reason:
``arr.vindex[array_scalar, ...]`` can occur, where ``arr_scalar``
should be a 0-D array. Copying always "fixes" the possible
inconsistency.)
5. The final state to morph indexing in is not fixed in this PEP. It is
for
example possible that `arr[index]`` will be equivalent to
``arr.oindex``
at some point in the future. Since such a change will take years, it
seems unnecessary to make specific decisions now.
6. Proposed changes to vanilla indexing could be postponed indefinetly
or
not taken in order to not break or force fixing of existing code
bases.
7. Possible the ``vindex`` combination with boolean indexing could be
rethought or not allowed at all for simplicity.
Necessary changes to NumPy
==========================
Implement ``arr.oindex`` and ``arr.vindex`` objects to allow these
indexing
operations and create warnings (and eventually deprecate) ambiguous
direct
indexing operations on arrays.
Alternative Names
=================
Possible names suggested (more suggestions will be added).
============== ======== =======
**Orthogonal** oindex oix
**Vectorized** vindex fix
**Legacy** l/findex
============== ======== =======
Examples
========
Since the various kinds of indexing is hard to grasp in many cases,
these
examples hopefully give some more insights. Note that they are all in
terms
of shape. All original dimensions start with 5, advanced indexing
inserts less long dimensions. (Note that ``...`` or ``Ellipsis`` mostly
inserts as many slices as needed to index the full array). These
examples
may be hard to grasp without working knowledge of advanced indexing as
of
NumPy 1.9.
Example array::
>>> arr = np.ones((5, 6, 7, 8))
Legacy fancy indexing
---------------------
Single index is transposed (this is the same for all indexing types)::
>>> arr[[0], ...].shape
(1, 6, 7, 8)
>>> arr[:, [0], ...].shape
(5, 1, 7, 8)
Multiple indices are transposed *if* consecutive::
>>> arr[:, [0], [0], :].shape # future error
(5, 1, 7)
>>> arr[:, [0], :, [0]].shape # future error
(1, 5, 6)
It is important to note that a scalar *is* integer array index in this
sense
(and gets broadcasted with the other advanced index)::
>>> arr[:, [0], 0, :].shape # future error (scalar is "fancy")
(5, 1, 7)
>>> arr[:, [0], :, 0].shape # future error (scalar is "fancy")
(1, 5, 6)
Single boolean index can act on multiple dimensions (especially the
whole
array). It has to match (as of 1.10. a deprecation warning) the
dimensions.
The boolean index is otherwise identical to (multiple consecutive)
integer
array indices::
>>> # Create boolean index with one True value for the last two
dimensions:
>>> bindx = np.zeros((7, 8), dtype=np.bool_)
>>> bindx[[0, 0]] = True
>>> arr[:, 0, bindx].shape
(5, 1)
>>> arr[0, :, bindx].shape
(1, 6)
The combination with anything that is not a scalar is confusing, e.g.::
>>> arr[[0], :, bindx].shape # bindx result broadcasts with [0]
(1, 6)
>>> arr[:, [0, 1], bindx] # IndexError
Outer indexing
--------------
Multiple indices are "orthogonal" and their result axes are inserted
at the same place (they are not broadcasted)::
>>> arr.oindex[:, [0], [0, 1], :].shape
(5, 1, 2, 8)
>>> arr.oindex[:, [0], :, [0, 1]].shape
(5, 1, 7, 2)
>>> arr.oindex[:, [0], 0, :].shape
(5, 1, 8)
>>> arr.oindex[:, [0], :, 0].shape
(5, 1, 7)
Boolean indices results are always inserted where the index is::
>>> # Create boolean index with one True value for the last two
dimensions:
>>> bindx = np.zeros((7, 8), dtype=np.bool_)
>>> bindx[[0, 0]] = True
>>> arr.oindex[:, 0, bindx].shape
(5, 1)
>>> arr.oindex[0, :, bindx].shape
(6, 1)
Nothing changed in the presence of other advanced indices since::
>>> arr.oindex[[0], :, bindx].shape
(1, 6, 1)
>>> arr.oindex[:, [0, 1], bindx]
(5, 2, 1)
Vectorized/inner indexing
-------------------------
Multiple indices are broadcasted and iterated as one like fancy
indexing,
but the new axes area always inserted at the front::
>>> arr.vindex[:, [0], [0, 1], :].shape
(2, 5, 8)
>>> arr.vindex[:, [0], :, [0, 1]].shape
(2, 5, 7)
>>> arr.vindex[:, [0], 0, :].shape
(1, 5, 8)
>>> arr.vindex[:, [0], :, 0].shape
(1, 5, 7)
Boolean indices results are always inserted where the index is, exactly
as in ``oindex`` given how specific they are to the axes they operate
on::
>>> # Create boolean index with one True value for the last two
dimensions:
>>> bindx = np.zeros((7, 8), dtype=np.bool_)
>>> bindx[[0, 0]] = True
>>> arr.vindex[:, 0, bindx].shape
(5, 1)
>>> arr.vindex[0, :, bindx].shape
(6, 1)
But other advanced indices are again transposed to the front::
>>> arr.vindex[[0], :, bindx].shape
(1, 6, 1)
>>> arr.vindex[:, [0, 1], bindx]
(2, 5, 1)
Related Questions
=================
There exist a further indexing or indexing like method. That is the
inverse of a command such as ``np.argmin(arr, axis=axis)``, to pick
the specific elements *along* an axis given an array of (at least
typically) the same size.
Doing such a thing with the indexing notation is not quite straight
forward
since the axis on which to pick elements has to be supplied. One
plausible
solution would be to create a function (calling it pick here for
simplicity)::
np.pick(arr, index_arr, axis=axis)
where ``index_arr`` has to be the same shape as ``arr`` except along
``axis``.
One could imagine that this can be useful together with other indexing
types,
but such a function may be sufficient and extra information needed seems
easier
to pass using a function convention. Another option would be to allow an
argument
such as ``compress_axes=None`` (just to have some name) which maps the
axes from
the index array to the new array with ``None`` signaling a new axis.
Also keepdims could be added as a simple default. (Note that the use of
axis is not
compatible to ``np.take`` for an ``index_arr`` which is not zero or one
dimensional.)
Another solution is to provide functions or features to the
``arg*``functions
to map this to the equivalent ``vindex`` indexing operation.
Motivational Example
====================
Imagine having a data acquisition software storing ``D`` channels and
``N`` datapoints along the time. She stores this into an ``(N, D)``
shaped
array. During data analysis, we needs to fetch a pool of channels, for
example
to calculate a mean over them.
This data can be faked using::
>>> arr = np.random.random((100, 10))
Now one may remember indexing with an integer array and find the correct
code::
>>> group = arr[:, [2, 5]]
>>> mean_value = arr.mean()
However, assume that there were some specific time points (first
dimension
of the data) that need to be specially considered. These time points are
already known and given by::
>>> interesting_times = np.array([1, 5, 8, 10], dtype=np.intp)
Now to fetch them, we may try to modify the previous code::
>>> group_at_it = arr[interesting_times, [2, 5]]
IndexError: Ambiguous index, use `.oindex` or `.vindex`
An error such as this will point to read up the indexing documentation.
This should make it clear, that ``oindex`` behaves more like slicing.
So, out of the different methods it is the obvious choice
(for now, this is a shape mismatch, but that could possibly also mention
``oindex``)::
>>> group_at_it = arr.oindex[interesting_times, [2, 5]]
Now of course one could also have used ``vindex``, but it is much less
obvious how to achieve the right thing!::
>>> reshaped_times = interesting_times[:, np.newaxis]
>>> group_at_it = arr.vindex[reshaped_times, [2, 5]]
One may find, that for example our data is corrupt in some places.
So, we need to replace these values by zero (or anything else) for these
times. The first column may for example give the necessary information,
so that changing the values becomes easy remembering boolean indexing::
>>> bad_data = arr[0] > 0.5
>>> arr[bad_data, :] = 0
Again, however, the columns may need to be handled more individually
(but in
groups), and the ``oindex`` attribute works well::
>>> arr.oindex[bad_data, [2, 5]] = 0
Note that it would be very hard to do this using legacy fancy indexing.
The only way would be to create an integer array first::
>>> bad_data_indx = np.nonzero(bad_data)[0]
>>> bad_data_indx_reshaped = bad_data_indx[:, np.newaxis]
>>> arr[bad_data_indx_reshaped, [2, 5]]
In any case we can use only ``oindex`` to do all of this without getting
into any trouble or confused by the whole complexity of advanced
indexing.
But, some new features are added to the data acquisition. Different
sensors
have to be used depending on the times. Let us assume we already have
created an array of indices::
>>> correct_sensors = np.random.randint(10, size=(100, 2))
Which lists for each time the two correct sensors in an ``(N, 2)``
array.
A first try to achieve this may be ``arr[:, correct_sensors]`` and this
does
not work. It should be clear quickly that slicing cannot achieve the
desired
thing. But hopefully users will remember that there is ``vindex`` as a
more
powerful and flexible approach to advanced indexing.
One may, if trying ``vindex`` randomly, be confused about::
>>> new_arr = arr.vindex[:, correct_sensors]
which is neither the same, nor the correct result (see transposing
rules)!
This is because slicing works still the same in ``vindex``. However,
reading
the documentation and examples, one can hopefully quickly find the
desired
solution::
>>> rows = np.arange(len(arr))
>>> rows = rows[:, np.newaxis] # make shape fit with
correct_sensors
>>> new_arr = arr.vindex[rows, correct_sensors]
At this point we have left the straight forward world of ``oindex`` but
can
do random picking of any element from the array. Note that in the last
example
a method such as mentioned in the ``Related Questions`` section could be
more
straight forward. But this approach is even more flexible, since
``rows``
does not have to be a simple ``arange``, but could be
``intersting_times``::
>>> correct_sensors_at_it = correct_sensors[interesting_times, :]
>>> interesting_times_reshaped = interesting_times[:, np.newaxis]
>>> new_arr_it = arr[interesting_times_reshaped,
correct_sensors_at_it]
Truly complex situation would arise now if you would for example pool
``L``
experiments into an array shaped ``(L, N, D)``. But for ``oindex`` this
should
not result into surprises. ``vindex``, being more powerful, will quite
certainly create some confusion in this case but also cover pretty much
all
eventualities.
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