more descriptive name?

There are many different standards for what to name these matrices. Zero agreement between sources that use NMF. I'm inclined to eventually use what sklearn.decomposition.non_negative_factorization uses, which would mean MM->X, X->W, Y->H. But I'd like to leave this as is for the moment until there's a consensus about what would be the most clear or standard. If people will be finding this tool from the sNMF paper, there's also an argument for using the X, Y, and A names because that was used there.

OK, sounds good. It has to be very good reason to break PEP8. The only good enough reason I can think of is to be consistent with scikit-learn. Another way of saying it is that we can "adopt the scikit-learn standard"

I'm fine with adopting the scikit-learn standard, but I also like the idea of giving them descriptive (and lowercase) names. The argument against that is that some lines of code use A/X/Y 10+ times in quick succession, so it would make the code very verbose.

more readable code is always better, so lower-case descriptive is preferred by me. I don't actually like that scikit-learn breaks this. Shall we go with lower-case? Names can be short if they are defined in a function in the docstring and docs too. Just hte code benefits from being readable, so I would say use your judgement on that.

I've started the conversion to lower case, but it's a large enough process (involving many poorly labeled sub-variables of the uppercase ones) that it feels like it should be its own separate PR. Does that make sense?

can we have a more descriptive variable name? Is this a range? What is the range?

ping on this one.

self.rng is not a numerical range. It's an instance of NumPy's default_rng() (introduced in NumPy 1.17), which is used to generate reproducible pseudo-random numbers when seeded with an integer from random_state. The actual range is chosen when you use the rng object in the code. We could change it, but both rng and random_state are standard names for these, including in scikit-learn.

OK, let's keep the name and we can say how it is used in the docstring. Something like "The value used to initialize the random state in ..." where .... differentiates it from the docstring for random_state which is presumably the same but used somewhere different?

Ah, looking at the code, I see tha tit is generated from random_state. If this is never accessed by the user, just passed around internally, then we could make it private. We do that by giving it an underscore in front, so self._rng and then we don't have to make a docstring for it. The users never access it, but it is available to internal functions.

K and M are probably good names if the matrix decomposition equation is in hte docstring, so they get defined there.

I think you addressed this with your comment to MM, but as a general rule, please respond to each comment so the reviewer knows you have seen it. It wouldn't work here, but just thumbs up works if you have seen a comment and agree, but it saves time in the long run as I don't have to write this long comment...... :)

Got it. I'd like to put the matrix decomposition in the docstring, but I'm having trouble formatting it. Might have to ask about this in one of the meetings.

yes, I am not 100% sure but I think there is a way.

This is fantastic! Thanks for this. Please see here for our docstring standards, I am not sure if you looked at it:
https://scikit-package.github.io/scikit-package/programming-guides/billinge-group-standards.html#docstrings

For classes it is a bit tricky because what info do we put in the "Class" docstring and what info do we put in the "constructor" (i.e., the __init__()) docstring. After some googling we came up with the breakout that is shown in the DiffractionObjects class that is shown there. We would be after something similar here.

By way of example, I would probably do like this in this case

def SNMFOptimizer:
       '''Configuration and methods to run the stretched NMF algorithm, sNMF

        Instantiating the SNMFOptimizer class runs all the analysis
        immediately. The results can then be accessed as instance attributes
        of the class (X, Y, and A). 

        Please see <reference to paper> for more details about the algorithm.

        Attributes
        -----------
         mm : ndarray
            The array containing the data to be decomposed. Shape is (length_of_signal, number_of_conditions)
        y0 : ndarray
            The array containing initial guesses for the component weights
            at each stretching condition.  Shape is (number_of_components, number_of_conditions
            ...
'''

put future development plans into issues, not in the docstring. Just describe the current behavior. Try and keep it brief but highly informational.

To conform to PEP8 standards I lower-cased the variables. I know they correspond to matrices but we should decide which standard to break. The tie-breaker should probably be scikit-learn. Whatever they do, let's do that. Let's also add a small comment (not in the docstring) to remind ourselves in the future if it breaks PEP8 or it will annoy me every time we revisit it and I will try and change it back......

Conditions on instantiation will go in the constructor docstring.

That one describes the init method so should look more like a function docstring. It would look something like....

    def __init__(mm....)
        '''Initialize a SNMFOptimizer instance and run the optimization
 
       Parameters
       ------------
            mm : ndarray
                The array containing the data to be decomposed. Shape is (length_of_signal, number_of_conditions)
           y0 : ndarray Optional.  Defaults to None
                The array containing initial guesses for the component weights
                at each stretching condition.  Shape is (number_of_components, number_of_conditions
        ...

I think there was some text before about how Y0 was required. But if it is required it may be better to make it a required (positional) variable in the constructor and not have it optional. we can discuss design decisions too if you like.

Either Y0 or n_components needs to be provided. Currently, Y0.shape overrides n_components if both are provided, and throws an error if neither are provided. The way scikit-learn is a little more flexible and also allows for an n_components which is different from Y0.shape, although I'm not clear on why you'd want that. But I'm not matching their behavior exactly because the current code doesn't allow that.

scikit-learn actually does break PEP8 to upper-case the matrices

This is too long. Needs to be < 80 characters, followed by a blank line.

Done.

we would normally do a list of Class attributes here. Everything that is self.something. This is obviously strongly overlapped with the arguments of the constructor, as many of the attributes get defined in the constructor, but logically they are different. Here we list and dsecribe the class attributes, there we describe the init function arguments.

I'm not clear on how I'd distinguish the arguments from the attributes. I understand how they are different semantically, but what part of that is necessary to make clear here? Can you give an example? Those have been helpful.

everything that is self.something (except for methods which are self.functions() which are not considered attributes) is an attribute. So MM, Y0, X0 are attributes, but also M, N, rng, num_updates etc.

Inside a function or method the parameters are the arguments of the function. so for the __init__() function they will be MM, Y0, X0, A, rho, eta and so on). Some of the descriptions will overlap but for the function argument the user needs to know if it is optional or not, what the default is, and anything else they need to know to successfully instantiate the class. People will generally not see the two docstrings at the same time, so there can be some repetition, but try and keep it short but informative.

these need a space before the colon (not sure why we adopted that standard, but we did). So mm : ndarray

normally we would raise an exception if two conflicting things are provided (we don't want to guess which is the right one) unless there is a good functional reason to do it another way. We like to avoid "magic" and the current behavior of the code could be "magic". Please raise an exception unless there is a strong reason to do otherwise.

attempt? So sometimes it extracs fewer than n_components when it attempts but doesn't manage?

It should never find less. "Attempt" means that sometimes the optimization may not work. But if this is unclear I can change it.

yes, delete "attempt" to make it clearer.

ping on this one?

"The random seed used to initialize". I think the second sentence is useful information, but I think everyone will know what this is. btw, let's cross-check if you didn't already so we are using the names for common variables as scikit-learn.

I removed the second sentence, which I think is what you mean here. And yes, random_state is the name in scikit-learn.

Can we change also "The interger...." to "The random seed used to initialize...."

ping on this one.

I think you addressed this with your comment to MM, but as a general rule, please respond to each comment so the reviewer knows you have seen it. It wouldn't work here, but just thumbs up works if you have seen a comment and agree, but it saves time in the long run as I don't have to write this long comment...... :)

typo than -> that

Also it might help to know a good value or range of values to choose when not setting it to zero?

Added the suggested adjustment factor, but the good range is honestly not clear yet across a broad set of data. Will add once it is.

everything that is self.something (except for methods which are self.functions() which are not considered attributes) is an attribute. So MM, Y0, X0 are attributes, but also M, N, rng, num_updates etc.

Inside a function or method the parameters are the arguments of the function. so for the __init__() function they will be MM, Y0, X0, A, rho, eta and so on). Some of the descriptions will overlap but for the function argument the user needs to know if it is optional or not, what the default is, and anything else they need to know to successfully instantiate the class. People will generally not see the two docstrings at the same time, so there can be some repetition, but try and keep it short but informative.

more readable code is always better, so lower-case descriptive is preferred by me. I don't actually like that scikit-learn breaks this. Shall we go with lower-case? Names can be short if they are defined in a function in the docstring and docs too. Just hte code benefits from being readable, so I would say use your judgement on that.

yes, I am not 100% sure but I think there is a way.

ping on this one?

this is a little unclear. What matrices?

Ah, looking at the code, I see tha tit is generated from random_state. If this is never accessed by the user, just passed around internally, then we could make it private. We do that by giving it an underscore in front, so self._rng and then we don't have to make a docstring for it. The users never access it, but it is available to internal functions.

Looking at this, it seems that we have already instantiated some kind of SNMF class, then this is doing the optimization. Is there a particular reason why we make this a class and not a function? It feels much more like a function to me. Could you think what the downsides are of making it a function? Is scikit-learn doing some thing like this too?