Thank you.
That array is t_test. test_time_idx are integers. If it helps, my model is:
with pm.Model(coords=coords) as constant_model:
#Data that does not change
cat_to_bl_map = pm.Data('cat_to_bl_map', cat_to_bl_idx, mutable=False)
subcat_to_cat_map = pm.Data('subcat_to_cat_map', subcat_to_cat_idx, mutable=False)
ic_to_subcat_map = pm.Data('ic_to_subcat_map', ic_to_subcat_idx, mutable=False)
ic_to_item_map = pm.Data('ic_to_item_map', ic_to_item_idx, mutable = False)
#Data that does change
pm_loc_idx = pm.Data('loc_idx', location_idx, mutable = True)
pm_item_idx = pm.Data('item_idx', item_idx, mutable=True)
pm_time_idx = pm.Data('time_idx', time_idx, mutable=True)
observed_eaches = pm.Data('observed_eaches', df1.residual, mutable=True)
t_ = pm.Data('t', time_idx, mutable = True)
promo_ = pm.Data('promotion', promo_idx, mutable = True)
cann_ = pm.Data('cannibalization', cann_idx, mutable = True)
dc_discount_ = pm.Data('dc_discount', dc_idx, mutable = True)
free_fin_ = pm.Data('free_fin', free_fin_idx, mutable = True)
pvbv_ = pm.Data('pvbv', promo_pvbv_idx, mutable = True)
giftset_ = pm.Data('giftset', giftset_idx, mutable = True)
month_ = pm.Data('month', month_idx, mutable = True)
#Random Variables
mu_intercept = pm.Normal('mu_intercept', mu = 0, sigma = .5)
bl_intercept = utility_functions.make_next_level_hierarchy_variable(name='bl_intercept', mu=mu_intercept, alpha=2, beta=1, dims=['business_line'])
cat_intercept = utility_functions.make_next_level_hierarchy_variable(name='cat_intercept', mu=bl_intercept[cat_to_bl_map], alpha=2, beta=1, dims=['category'])
subcat_intercept = utility_functions.make_next_level_hierarchy_variable(name='subcat_intercept', mu=cat_intercept[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory'])
ic_intercept = utility_functions.make_next_level_hierarchy_variable(name='ic_intercept', mu=subcat_intercept[ic_to_subcat_map], alpha=2, beta=1, dims=['ic'])
item_intercept = utility_functions.make_next_level_hierarchy_variable(name='item_intercept', mu=ic_intercept[ic_to_item_map], alpha=2, beta=1, dims=['item'])
loc_intercept = pm.Normal('loc_intercept', mu = 0, sigma = .5, dims = ['location'])
loc_bl = utility_functions.make_next_level_hierarchy_variable(name='loc_bl', mu=loc_intercept, alpha=2, beta=1, dims=['business_line', 'location'])
loc_cat = utility_functions.make_next_level_hierarchy_variable(name='loc_cat', mu=loc_bl[cat_to_bl_map], alpha=2, beta=1, dims=['category', 'location'])
loc_subcat = utility_functions.make_next_level_hierarchy_variable(name='loc_subcat', mu=loc_cat[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory', 'location'])
loc_ic = utility_functions.make_next_level_hierarchy_variable(name='loc_ic', mu=loc_subcat[ic_to_subcat_map], alpha=2, beta=1, dims=['ic', 'location'])
loc_item = utility_functions.make_next_level_hierarchy_variable(name='loc_item', mu=loc_ic[ic_to_item_map], alpha=2, beta=1, dims=['item', 'location'])
promo_intercept = pm.Normal('promo_intercept', mu =0, sigma = .5)
bl_promo = utility_functions.make_next_level_hierarchy_variable(name='bl_promo', mu=promo_intercept, alpha=2, beta=1, dims=['business_line'])
cat_promo = utility_functions.make_next_level_hierarchy_variable(name='cat_promo', mu=bl_promo[cat_to_bl_map], alpha=2, beta=1, dims=['category'])
subcat_promo = utility_functions.make_next_level_hierarchy_variable(name='subcat_promo', mu=cat_promo[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory'])
ic_promo = utility_functions.make_next_level_hierarchy_variable(name='ic_promo', mu=subcat_promo[ic_to_subcat_map], alpha=2, beta=1, dims=['ic'])
item_promo = utility_functions.make_next_level_hierarchy_variable(name='item_promo', mu=ic_promo[ic_to_item_map], alpha=2, beta=1, dims=['item'])
mu_cann = pm.Normal('mu_cann', mu = 0, sigma = .5)
bl_cann = utility_functions.make_next_level_hierarchy_variable(name='bl_cann', mu=mu_cann, alpha=2, beta=1, dims=['business_line'])
cat_cann = utility_functions.make_next_level_hierarchy_variable(name='cat_cann', mu=bl_cann[cat_to_bl_map], alpha=2, beta=1, dims=['category'])
subcat_cann = utility_functions.make_next_level_hierarchy_variable(name='subcat_cann', mu=cat_cann[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory'])
ic_cann = utility_functions.make_next_level_hierarchy_variable(name='ic_cann', mu=subcat_cann[ic_to_subcat_map], alpha=2, beta=1, dims=['ic'])
item_cann = utility_functions.make_next_level_hierarchy_variable(name='item_cann', mu=ic_cann[ic_to_item_map], alpha=2, beta=1, dims=['item'])
mu_dc_discount = pm.Normal('mu_dc_discount', mu = 0, sigma = .5)
bl_dc_discount = utility_functions.make_next_level_hierarchy_variable(name='bl_dc_discount', mu=mu_dc_discount, alpha=2, beta=1, dims=['business_line'])
cat_dc_discount = utility_functions.make_next_level_hierarchy_variable(name='cat_dc_discount', mu=bl_dc_discount[cat_to_bl_map], alpha=2, beta=1, dims=['category'])
subcat_dc_discount = utility_functions.make_next_level_hierarchy_variable(name='subcat_dc_discount', mu=cat_dc_discount[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory'])
ic_dc_discount = utility_functions.make_next_level_hierarchy_variable(name='ic_dc_discount', mu=subcat_dc_discount[ic_to_subcat_map], alpha=2, beta=1, dims=['ic'])
item_dc_discount = utility_functions.make_next_level_hierarchy_variable(name='item_dc_discount', mu=ic_dc_discount[ic_to_item_map], alpha=2, beta=1, dims=['item'])
mu_free_fin = pm.Normal('mu_free_fin', mu = 0, sigma = .5)
bl_free_fin = utility_functions.make_next_level_hierarchy_variable(name='bl_free_fin', mu=mu_free_fin, alpha=2, beta=1, dims=['business_line'])
cat_free_fin = utility_functions.make_next_level_hierarchy_variable(name='cat_free_fin', mu=bl_free_fin[cat_to_bl_map], alpha=2, beta=1, dims=['category'])
subcat_free_fin = utility_functions.make_next_level_hierarchy_variable(name='subcat_free_fin', mu=cat_free_fin[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory'])
ic_free_fin = utility_functions.make_next_level_hierarchy_variable(name='ic_free_fin', mu=subcat_free_fin[ic_to_subcat_map], alpha=2, beta=1, dims=['ic'])
item_free_fin = utility_functions.make_next_level_hierarchy_variable(name='item_free_fin', mu=ic_free_fin[ic_to_item_map], alpha=2, beta=1, dims=['item'])
mu_pvbv = pm.Normal('mu_pvbv', mu = 0, sigma = .5)
bl_pvbv = utility_functions.make_next_level_hierarchy_variable(name='bl_pvbv', mu=mu_pvbv, alpha=2, beta=1, dims=['business_line'])
cat_pvbv = utility_functions.make_next_level_hierarchy_variable(name='cat_pvbv', mu=bl_pvbv[cat_to_bl_map], alpha=2, beta=1, dims=['category'])
subcat_pvbv = utility_functions.make_next_level_hierarchy_variable(name='subcat_pvbv', mu=cat_pvbv[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory'])
ic_pvbv = utility_functions.make_next_level_hierarchy_variable(name='ic_pvbv', mu=subcat_pvbv[ic_to_subcat_map], alpha=2, beta=1, dims=['ic'])
item_pvbv = utility_functions.make_next_level_hierarchy_variable(name='item_pvbv', mu=ic_pvbv[ic_to_item_map], alpha=2, beta=1, dims=['item'])
mu_giftset = pm.Normal('mu_giftset', mu = 0, sigma = .5)
bl_giftset = utility_functions.make_next_level_hierarchy_variable(name='bl_giftset', mu=mu_giftset, alpha=2, beta=1, dims=['business_line'])
cat_giftset = utility_functions.make_next_level_hierarchy_variable(name='cat_giftset', mu=bl_giftset[cat_to_bl_map], alpha=2, beta=1, dims=['category'])
subcat_giftset = utility_functions.make_next_level_hierarchy_variable(name='subcat_giftset', mu=cat_giftset[subcat_to_cat_map], alpha=2, beta=1, dims=['subcategory'])
ic_giftset = utility_functions.make_next_level_hierarchy_variable(name='ic_giftset', mu=subcat_giftset[ic_to_subcat_map], alpha=2, beta=1, dims=['ic'])
item_giftset = utility_functions.make_next_level_hierarchy_variable(name='item_giftset', mu=ic_giftset[ic_to_item_map], alpha=2, beta=1, dims=['item'])
month_coeff = pm.Normal('month_coeff', mu = 0, sigma = .5)
mu = (item_intercept[pm_item_idx] * t_[pm_time_idx] + loc_item[pm_item_idx, pm_loc_idx] + item_promo[item_idx]*promo_ +
item_cann[pm_item_idx]*cann_ + item_dc_discount[pm_item_idx]*dc_discount_ + item_free_fin[pm_item_idx]*free_fin_ + item_pvbv[pm_item_idx]*pvbv_ +
item_giftset[pm_item_idx]*giftset_ + month_coeff*month_)
sigma = pm.HalfNormal('sigma', sigma=10)
eaches = pm.Normal('predicted_eaches',
mu=mu,
sigma=sigma,
# lower = 0,
observed=observed_eaches)
I’m not sure why it would work when I fit to when I switch it out with the OOS data. It’s just a lopped off data set from the original used to fit.