Settle on the API for hierarchical models

# 1) class option
model = LinearTrend() + FourierSeasonality()
model = HierarchicalLinearTrend(group='subject') + FourierSeasonality()
model = GroupedLinearTrend(group='subject') + FourierSeasonality()

# 2) metaclass option
model = LinearTrend() + FourierSeasonality()
model = Hierarchical(LinearTrend(), on='subject') + FourierSeasonality()
model = Distinct(LinearTrend(), on='subject') + FourierSeasonality()

# 3) seperate hierarchy object 1
partial_subject = Share(type='partially_shared', on='subject')
distinct_subject = Share(type='distinct', on='subject')

model = LinearTrend() + FourierSeasonality()
model = LinearTrend().share(partial_subject) + FourierSeasonality()
model = LinearTrend().share(distinct_subject) + FourierSeasonality()

# 4) seperate hierarchy object 2
shared_subject = Share(type='shared')  # default
partial_subject = Share(type='partially_shared', on='subject')
distinct_subject = Share(type='distinct', on='subject')

model = LinearTrend(share=shared_subject) + FourierSeasonality(share=shared_subject)
model = LinearTrend(share=partial_subject) + FourierSeasonality()
model = LinearTrend(share=distinct_subject) + FourierSeasonality()

# 5) keyword option
model = LinearTrend(shared='full') + FourierSeasonality()  #  shared='full' is default
model = LinearTrend(group='subject', shared='partial') + FourierSeasonality()
model = LinearTrend(group='subject', shared='none') + FourierSeasonality()

# Cannibalism in this option
model = Constant(range=(0, None), shared='none') + LinearTrend(shared='full') + ((FourierSeasonality(perios=365) + FourierSeasonality(period=7)) * Constant(range=[-1, 1], group='subject', shared='none'))
model = LinearTrend() + (FourierSeasonality(perios=365) * Constant(range=[-1, 1], group='subject', shared='none')) + FourierSeasonality(period=7)

# 6) keyword option
model = LinearTrend() + FourierSeasonality()
model = LinearTrend(partially_shared_on='subject') + FourierSeasonality()
model = LinearTrend(distinct_on='subject') + FourierSeasonality()

# 7) bitwise or Operator option
model = LinearTrend()                                         # single estimation for all subjects
model = (LinearTrend() | 'subject') + FourierSeasonality()    # hierarchical estimation
model = (LinearTrend() || ('subject') + FourierSeasonality()  # independent estimation

#return day well spent

[MBrouns]

Now here’s a fun grammar problem. What does it now mean if we do model.groupby("product_id")

I agree that that probably shouldn’t be allowed. I wonder if it’s even worth setting this on the entire model, I expect in most cases that you shrink on some parameters but not on others.

That’s mainly because the groups can get verbose in practice (check out the three categories and the id) so re-use is appreciated.

True and that’s indeed one of the major pros of option 4. That said though, you can also get that behaviour with **kwargs like this:

product_categories = {
    "type": "shrinkage",
    "groups": ["prod_category_i", "prod_category_ii"]
}

product_group = {
    "type": "distinct",
    "groups": "product_id"
}

model = (
    Constant(**product_group)
    LinearTrend(**product_categories) + 
    FourierSeasonality(**product_categories)
)

What do you mean with the combination with switchpoints? Variance is a good point. didn’t really think about how that fits in the API yet at all.

[koaning]

What does it mean if pooling is partial, complete or None?