On Tue, Dec 11, 2018 at 10:32 AM Ralf Gommers <ralf.gomm...@gmail.com>
wrote:
>
>
> On Mon, Dec 10, 2018 at 10:27 AM Warren Weckesser <
> warren.weckes...@gmail.com> wrote:
>
>>
>>
>> On 12/10/18, Ralf Gommers <ralf.gomm...@gmail.com> wrote:
>> > On Sun, Dec 9, 2018 at 2:00 PM Alan Isaac <alan.is...@gmail.com> wrote:
>> >
>> >> I believe this was proposed in the past to little enthusiasm,
>> >> with the response, "you're using a library; learn its functions".
>> >>
>> >
>> > Not only that, NumPy and the core libraries around it are the standard
>> for
>> > numerical/statistical computing. If core Python devs want to replicate a
>> > small subset of that functionality in a new Python version like 3.6, it
>> > would be sensible for them to choose compatible names. I don't think
>> > there's any justification for us to bother our users based on new things
>> > that get added to the stdlib.
>> >
>> >
>> >> Nevertheless, given the addition of `choices` to the Python
>> >> random module in 3.6, it would be nice to have the *same name*
>> >> for parallel functionality in numpy.random.
>> >>
>> >> And given the redundancy of numpy.random.sample, it would be
>> >> nice to deprecate it with the intent to reintroduce
>> >> the name later, better aligned with Python's usage.
>> >>
>> >
>> > No, there is nothing wrong with the current API, so I'm -10 on
>> deprecating
>> > it.
>>
>> Actually, the `numpy.random.choice` API has one major weakness. When
>> `replace` is False and `size` is greater than 1, the function is actually
>> drawing a *one* sample from a multivariate distribution. For the other
>> multivariate distributions (multinomial, multivariate_normal and
>> dirichlet), `size` sets the number of samples to draw from the
>> distribution. With `replace=False` in `choice`, size becomes a *parameter*
>> of the distribution, and it is only possible to draw one (multivariate)
>> sample.
>>
>
> I'm not sure I follow. `choice` draws samples from a given 1-D array, more
> than 1:
>
> In [12]: np.random.choice(np.arange(5), size=2, replace=True)
> Out[12]: array([2, 2])
>
> In [13]: np.random.choice(np.arange(5), size=2, replace=False)
> Out[13]: array([3, 0])
>
> The multivariate distribution you're talking about is for generating the
> indices I assume. Does the current implementation actually give a result
> for size>1 that has different statistic properties from calling the
> function N times with size=1? If so, that's definitely worth a bug report
> at least (I don't think there is one for this).
>
>
There is no bug, just a limitation in the API.
When I draw without replacement, say, three values from a collection of
length five, the three values that I get are not independent. So really,
this is *one* sample from a three-dimensional (discrete-valued)
distribution. The problem with the current API is that I can't get
multiple samples from this three-dimensional distribution in one call. If
I need to repeat the process six times, I have to use a loop, e.g.:
>>> samples = [np.random.choice([10, 20, 30, 40, 50], replace=False,
size=3) for _ in range(6)]
With the `select` function I described in my previous email, which I'll
call `random_select` here, the parameter that determines the number of
items per sample, `nsample`, is separate from the parameter that determines
the number of samples, `size`:
>>> samples = random_select([10, 20, 30, 40, 50], nsample=3, size=6)
>>> samples
array([[30, 40, 50],
[40, 50, 30],
[10, 20, 40],
[20, 30, 50],
[40, 20, 50],
[20, 10, 30]])
(`select` is a really bad name, since `numpy.select` already exists and is
something completely different. I had the longer name `random.select` in
mind when I started using it. "There are only two hard problems..." etc.)
Warren
> Cheers,
> Ralf
>
>
>
>> I thought about this some time ago, and came up with an API that
>> eliminates the boolean flag, and separates the `size` argument from the
>> number of items drawn in one sample, which I'll call `nsample`. To avoid
>> creating a "false friend" with the standard library and with numpy's
>> `choice`, I'll call this function `select`.
>>
>> Here's the proposed signature and docstring. (A prototype implementation
>> is in a gist at
>> https://gist.github.com/WarrenWeckesser/2e5905d116e710914af383ee47adc2bf.)
>> The key feature is the `nsample` argument, which sets how many items to
>> select from the given collection. Also note that this function is *always*
>> drawing *without replacement*. It covers the `replace=True` case because
>> drawing one item without replacement is the same as drawing one item with
>> replacement.
>>
>> Whether or not an API like the following is used, it would be nice if
>> there was some way to get multiple samples in the `replace=False` case in
>> one function call.
>>
>> def select(items, nsample=None, p=None, size=None):
>> """
>> Select random samples from `items`.
>>
>> The function randomly selects `nsample` items from `items` without
>> replacement.
>>
>> Parameters
>> ----------
>> items : sequence
>> The collection of items from which the selection is made.
>> nsample : int, optional
>> Number of items to select without replacement in each draw.
>> It must be between 0 and len(items), inclusize.
>> p : array-like of floats, same length as `items, optional
>> Probabilities of the items. If this argument is not given,
>> the elements in `items` are assumed to have equal probability.
>> size : int, optional
>> Number of variates to draw.
>>
>> Notes
>> -----
>> `size=None` means "generate a single selection".
>>
>> If `size` is None, the result is equivalent to
>> numpy.random.choice(items, size=nsample, replace=False)
>>
>> `nsample=None` means draw one (scalar) sample.
>> If `nsample` is None, the functon acts (almost) like nsample=1 (see
>> below for more information), and the result is equivalent to
>> numpy.random.choice(items, size=size)
>> In effect, it does choice with replacement. The case `nsample=None`
>> can be interpreted as each sample is a scalar, and `nsample=k`
>> means each sample is a sequence with length k.
>>
>> If `nsample` is not None, it must be a nonnegative integer with
>> 0 <= nsample <= len(items).
>>
>> If `size` is not None, it must be an integer or a tuple of integers.
>> When `size` is an integer, it is treated as the tuple ``(size,)``.
>>
>> When both `nsample` and `size` are not None, the result
>> has shape ``size + (nsample,)``.
>>
>> Examples
>> --------
>> Make 6 choices with replacement from [10, 20, 30, 40]. (This is
>> equivalent to "Make 1 choice without replacement from [10, 20, 30,
>> 40];
>> do it six times.")
>>
>> >>> select([10, 20, 30, 40], size=6)
>> array([20, 20, 40, 10, 40, 30])
>>
>> Choose two items from [10, 20, 30, 40] without replacement. Do it six
>> times.
>>
>> >>> select([10, 20, 30, 40], nsample=2, size=6)
>> array([[40, 10],
>> [20, 30],
>> [10, 40],
>> [30, 10],
>> [10, 30],
>> [10, 20]])
>>
>> When `nsample` is an integer, there is always an axis at the end of
>> the
>> result with length `nsample`, even when `nsample=1`. For example, the
>> shape of the array returned in the following call is (2, 3, 1)
>>
>> >>> select([10, 20, 30, 40], nsample=1, size=(2, 3))
>> array([[[10],
>> [30],
>> [20]],
>>
>> [[10],
>> [40],
>> [20]]])
>>
>> When `nsample` is None, it acts like `nsample=1`, but the trivial
>> dimension is not included. The shape of the array returned in the
>> following call is (2, 3).
>>
>> >>> select([10, 20, 30, 40], size=(2, 3))
>> array([[20, 40, 30],
>> [30, 20, 40]])
>>
>> """
>>
>>
>> Warren
>>
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