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Hi, I'm having trouble when creating the coef_extra_features priors for 2 geos. I'm using a normal distribution as a probability function, however I'm attributing different loc and scale parameters depending on the extra feature I'm considering. This is a sample code:
extra_features_prior_scale = {"extra_feat_1": 0, "extra_feat_2": -1, "extra_feat_3": 1, ..., "extra_feat_12": 1}
extra_features_prior_loc = {"extra_feat_1": 1, "extra_feat_2": 1, "extra_feat_3": 1, ..., "extra_feat_12": 1}
extra_features_loc_priors = jnp.array(list(extra_features_prior_loc.values()) + [0])
extra_features_scale_priors = jnp.array(list(extra_features_prior_scale.values()) + [1])
custom_priors = {
"half_max_effective_concentration": numpyro.distributions.Gamma(
concentration=media_concentration_weights_priors,
rate=media_concentration_weights_priors,
),
"slope": numpyro.distributions.Gamma(
concentration=slope_concentration_priors, rate=slope_rate_priors
),
"lag_weight": numpyro.distributions.Beta(
concentration1=lag_weight_high_priors, concentration0=lag_weight_low_priors
),
"intercept": numpyro.distributions.Normal(
loc=intercept_loc_priors, scale=intercept_scale_priors
),
"coef_extra_features": numpyro.distributions.Normal(
loc=extra_features_loc_priors,
scale=extra_features_scale_priors,
)
}
However, when training my algorithm, I'm getting the following error:
ValueError: Incompatible shapes for broadcasting: shapes=[(12, 2), (12, 12)]
I'm pretty sure this error comes from the coef_extra_features
prior since I didn't have this until I decided to customize my priors on these features.
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