PyMC3 Model theano tensor switch error

Questions
1

I have the following model

with pm.Model() as model:

    lam = pm.Normal(name="lam", mu=1, sigma=1)
    alpha = pm.Beta(name="alpha", alpha=1.5, beta=1.5)
    beta = pm.Beta(name="beta", alpha=1.5, beta=1.5)

    # Adjust sho depending on sign with either alpha or lambda and beta
    sho_w = tt.switch(tt.ge(sho, 0), sho ** alpha, (-1 * lam) * ((-1 * sho) ** beta))

   # Standardize
    sho_w_s = (sho_w - sho_w.mean()) / sho_w.std()
  
   # State specific 
    B = pm.Normal(name="B", mu=0, sigma=0.5, shape=(n_states, n_sho_vars))  # Sho
    B_s = B[state, np.arange(0, n_sho_vars)]

    # Model error
    eps = pm.InverseGamma(name="eps", alpha=9.0, beta=4.0)  

    # Model mean
    y_hat = pm.Deterministic(name="y_hat", var=pm.math.sum(sho_w_s * B_s, axis=1) )

    # Model likelihood
    y_like = pm.Normal(name="y_like", mu=y_hat, sd=eps, observed=y)

I am particularly interested in the values of lam, alpha and beta.
I use tt.switch to elementwise multiply the 2500x4 variable sho depending on whether its value is positive or negative with lam and raise it to the power of either alpha or beta.

However, the results for the “else” part in tt.switch (i.e. for the case that sho is negative) yield very strange results for lam and beta (lam is roughly zero and the posterior for beta is roughly equal to its prior), so it seems for me that the algorithm fully ignores the “else” part.
The negative and positive values in the sho array are roughly equally distributed.

Moreover, I am not sure whether the standardization statement standardizes the array column-wise.

I use PyMC 3.11.4

Thanks in advance

2

Moreover, I am not sure whether the standardization statement standardizes the array column-wise.

It’s not column standardized. You need to use the axis keyword argument for mean and std if sho_w is a matrix. To standardize column-wise, that means taking the mean over the row index, so axis=0.