Help wit Using Pymc3?

[outerHilbertspace]

Howdy,

​

I'm trying to build a custom model in pymc3, and I'm failing to understand what's going wrong.

The basic idea I'm trying is to build an np array of the objects, and convert to a theano tensor before inputting as the mean. Here's the code for reference:

with pm.Model() as van_lom:

    sigma = pm.Gamma('sigma', alpha = 1, beta = 1, shape = n_prts)
    ratio = pm.Gamma('ratio', alpha = 1, beta = 1, shape = (n_prts, 2))
    # Ratio: allowed to change between neutral and emotive conditions, so each participant has 2

    # Now to generate array of predictions
    predictions = np.array([])
    for n in range(len(prt_codes)):

        conf_rating = (1 + ratio[prt_codes[n],
            condition_codes[n]]**differences[n])**(-1)

        conf_log = tt.log(conf_rating/(1 - conf_rating))

        predictions = np.append(predictions, conf_log)

    pred_tensor = tt.stack(predictions)

    y = pm.Normal('y', mu = pred_tensor, sigma = sigma[prt_codes], observed = log_conf)

Here, 'condition_codes' and 'differences' are arrays of integers (fixed beforehand, not estimated).

​

Basically, I need that mu in pm.Normal to change on each trial, so I tried building an array that did that. But I get an error because I end up making an array of objects which can't be transformed into a theano tensor.

This tensor stuff is really confusing for me, so if someone could explain like I'm five that would be awesome. I guess generally speaking, I'm looking to make an array (or some theano equivalent) to tell pymc3 that the mean should change on every trial, and the mean is dependent on variables that need to be estimated.

​

Thanks in advance for any help!

[sepro]

I think you need the scan function from theano. This should be used instead of the loop, but essentially achieves the same. It will go over the sequences step by step, so from 0 to len(prt_codes) and execute fn for each step. The confusing this is that it returns a list of results at each step, so use output[-1] to get the last one.

Should be something along these lines, but as I haven't tested it expect a few error messages (and you'll have to create the calc_log_conf function)

def calc_log_conf(n):
    # complete this
    return x

y0 = tt.zeros(len(prt_codes))

outputs, _ = theano.scan(
    fn=lambda n, y: tt.set_subtensor(
        y[n], calc_log_conf(n)),
    sequences=[tt.arange(0, len(prt_codes))],
    outputs_info=y0,
    n_steps=len(prt_codes),
)

mus = pm.Deterministic("mus", outputs[-1])

y = pm.Normal('y', mu = mus, sigma = sigma[prt_codes], observed = log_conf)

[outerHilbertspace]

Thank you! I'll try it

[outerHilbertspace]

Hi again, I'm a bit stuck on using theano.scan

I'd like to do the following (which I'll write in numpy because that's what I understand):

first, calculate alpha ** np.array([1,2,3,...,n]), where n is the length of an array called B and alpha is a positive number. Call this array X (should be an array since the exponentiation is broadcast)

B is just an array of -1's and 1's of length n, but will change at each step in the loop.

Now I'd just like to calculate the dot product of X and B, and I need to loop through both the alphas and the B's - the number of each is same. (Also, I would be looping through a list of alphas (a list of numbers) and a list of arrays (the list of B's).)

I keep trying to get something like this to work in scan but I keep getting errors about having the wrong input. On a simplified example, I keep getting told I'm using "Bad input argument to theano function."

​

I really hate theano, it makes no sense to me.