Remove NormalMixture comp_shape kwarg

[ricardoV94]

Description

This has no effect besides what shape already does

CC @lucianopaz do you remember why this existed? I imagine it had something to do with the old way that weight used to work.

[mohammed052]

@ricardoV94 Can you elaborate this issue more,I wish to contribute to this issue.

[lucianopaz]

@ricardoV94, in version 3.x it used to be necessary to supply the component distribution shape. This could be used to do weird things like making multivariate mixture components out of scalar distributions (what I mean is that the shape of the support dimensions could effectively be changed). As far as I understand, this is no longer the case since version 4.x. Mixtures reshape their component batch dimensions so that they broadcast correctly with the mixture shape, and there is no way of changing the sport dimensions.

[ricardoV94]

Thanks for the context Luciano!

We may reintroduce something like that later down the road but for now seems safe to remove the useless kwarg

[ricardoV94]

@mohammed052 sorry for the delay. If you are still interested, it's as simple as removing any mentions of comp_shape in

pymc/pymc/distributions/mixture.py

Lines 496 to 560 in 2051d0b

	class NormalMixture:
	R"""
	Normal mixture log-likelihood
	.. math::
	f(x \mid w, \mu, \sigma^2) = \sum_{i = 1}^n w_i N(x \mid \mu_i, \sigma^2_i)
	======== =======================================
	Support :math:`x \in \mathbb{R}`
	Mean :math:`\sum_{i = 1}^n w_i \mu_i`
	Variance :math:`\sum_{i = 1}^n w_i (\sigma^2_i + \mu_i^2) - \left(\sum_{i = 1}^n w_i \mu_i\right)^2`
	======== =======================================
	Parameters
	----------
	w : tensor_like of float
	w >= 0 and w <= 1
	the mixture weights
	mu : tensor_like of float
	the component means
	sigma : tensor_like of float
	the component standard deviations
	tau : tensor_like of float
	the component precisions
	comp_shape : shape of the Normal component
	notice that it should be different than the shape
	of the mixture distribution, with the last axis representing
	the number of components.
	Notes
	-----
	You only have to pass in sigma or tau, but not both.
	Examples
	--------
	.. code-block:: python
	n_components = 3
	with pm.Model() as gauss_mix:
	μ = pm.Normal(
	"μ",
	mu=data.mean(),
	sigma=10,
	shape=n_components,
	transform=pm.distributions.transforms.ordered,
	initval=[1, 2, 3],
	)
	σ = pm.HalfNormal("σ", sigma=10, shape=n_components)
	weights = pm.Dirichlet("w", np.ones(n_components))
	y = pm.NormalMixture("y", w=weights, mu=μ, sigma=σ, observed=data)
	"""
	def __new__(cls, name, w, mu, sigma=None, tau=None, comp_shape=(), **kwargs):
	_, sigma = get_tau_sigma(tau=tau, sigma=sigma)
	return Mixture(name, w, Normal.dist(mu, sigma=sigma, size=comp_shape), **kwargs)
	@classmethod
	def dist(cls, w, mu, sigma=None, tau=None, comp_shape=(), **kwargs):
	_, sigma = get_tau_sigma(tau=tau, sigma=sigma)
	return Mixture.dist(w, Normal.dist(mu, sigma=sigma, size=comp_shape), **kwargs)

[mohammed052]

Thank you @ricardoV94 and @lucianopaz for explanation of the issue and its solution
As I see, comp_shape is passed as parameter in new and dist functions
So removing comp_shape from the argument list of these functions and removing size passed to Normal.dist parameter list must solve this issue
Correct me if I am wrong,I am new to open source community

[ricardoV94]

Yes, that's about it

[ricardoV94]

If it's your first time, you may find this guide useful: https://www.pymc.io/projects/docs/en/latest/contributing/pr_tutorial.html

[mohammed052]

@ricardoV94 can you review the pull request I sent on 11th jan for closing this issue