GaussianRandomWalk error 4.0.0b5

Questions v5
1

I have tried upgradinging to 4.0.0b5. But I get an error with the GaussianRandomWalk distribution. I have recoded to the new API. The error message is a bit cryptic: “.dist() missing 1 required positional argument: ‘dist_params’”.

My code …

def define_model(n_brands, n_days, poll_brand,
zero_centered_y,
measurement_error_sd):
“”“PyMC3 model for pooling/aggregating voter opinion polls”“”

model = pm.Model()
with  model:
    # priors 
    unanchored_house_bias = pm.Cauchy("unanchored_house_bias", 
                                      alpha=0, beta=10, shape=n_brands)
    zero_sum_house_bias = pm.Deterministic('zero_sum_house_bias', 
        var=(unanchored_house_bias - unanchored_house_bias.mean()))
    
    # temporal model
    EARLY_DATA_ITEMS = 5 
    educated_guess = zero_centered_y[:min(EARLY_DATA_ITEMS, len(zero_centered_y))].mean()
    SPAN = 2.0
    starting_distro = pm.Normal('starting_distro', mu=educated_guess, sigma=SPAN) 
    DRIFT = 0.0
    innovation = 0.15 # from experience ... day-to-day change distribution sigma
    grw = pm.GaussianRandomWalk('grw', mu=DRIFT, sigma=innovation, 
                                init=starting_distro, steps=n_days) ### FAILS HERE

    # the observational model
    observed = pm.Normal("observed", 
                         mu=grw[poll_day] 
                            + zero_sum_house_bias[poll_brand.to_list()],
                         sigma=measurement_error_sd, observed=zero_centered_y)
return model
1 Like
2

@RavinKumar, you have been working on this, no?

3

init has to be an “unnamed” distribution, created with the .dist API. In your code you should use this:

starting_distro = pm.Normal.dist(mu=educated_guess, sigma=SPAN)

You don’t want this distribution registered in the model directly, because the logp is accounted for by the GaussianRandomWalk. Otherwise it would double count it.

4

Ricardo, thanks for your quick reply.

I have made the change you suggest, but still get the same error message.

The updated code follows.

def define_model(n_brands, n_days, poll_brand, 
             zero_centered_y, 
             measurement_error_sd):
    """PyMC model for pooling/aggregating voter opinion polls"""

    model = pm.Model()
    with  model:
        # priors 
        unanchored_house_bias = pm.Cauchy("unanchored_house_bias", 
                                          alpha=0, beta=10, shape=n_brands)
        zero_sum_house_bias = pm.Deterministic('zero_sum_house_bias', 
            var=(unanchored_house_bias - unanchored_house_bias.mean()))
    
        # temporal model
        DRIFT = 0.0
        INNOVATION = 0.15 # from experience ... day-to-day change distribution sigma
        EARLY_DATA_ITEMS = 5 
        SIGMA = 2.0
        educated_guess = zero_centered_y[:min(EARLY_DATA_ITEMS, 
                         len(zero_centered_y))].mean()
        start_dist = pm.Normal.dist(mu=educated_guess, sigma=SIGMA)
        print(f'DRIFT: {DRIFT}, INNOVATION: {INNOVATION}, '
              f'educated_guess: {educated_guess}, SIGMA: {SIGMA}'
              f'\ninit: {type(start_dist)}')
        grw = pm.GaussianRandomWalk('grw', mu=DRIFT, sigma=INNOVATION, 
                                    init=start_dist, steps=n_days) ### FAILS HERE

        # the observational model
        observed = pm.Normal("observed", 
                             mu=grw[poll_day] 
                                + zero_sum_house_bias[poll_brand.to_list()],
                             sigma=measurement_error_sd, observed=zero_centered_y)
    return model

You can see that I have added a diagnostic print statement, which tells me the type of the starting_dist is <class ‘aesara.tensor.var.TensorVariable’>

Any further thoughts or suggestions would be welcomed.

5

Can you share the error traceback?

6

Apologies, I just realized the GaussianRandomWalk is not yet refactored/ready in the latest beta release. It should be available on the next release, or if you install directly from github main branch

1 Like
7

TypeError Traceback (most recent call last)
Input In [22], in <cell line: 1>()
2 print(series[‘title’])
4 (zero_centered_y, centre_offset, n_polls, n_days, day_zero, poll_day,
5 poll_brand, brand_map, n_brands, measurement_error_sd) = (
6 prepare_data_for_analysis(tpp, series[‘column’]))
----> 8 model = define_model(n_brands, n_days, poll_brand, zero_centered_y,
9 measurement_error_sd)
11 trace, summary = draw_samples(model)
13 plot_aggregation(trace, tpp, series[‘column’], day_zero, n_days,
14 centre_offset, series[‘point_color’],
15 series[‘line_color’], series[‘title’])

Input In [16], in define_model(n_brands, n_days, poll_brand, zero_centered_y, measurement_error_sd)
20 start_dist = pm.Normal.dist(mu=educated_guess, sigma=SIGMA)
21 print(f’DRIFT: {DRIFT}, INNOVATION: {INNOVATION}, ’
22 f’educated_guess: {educated_guess}, SIGMA: {SIGMA}’
23 f’\ninit: {type(start_dist)}’)
—> 24 grw = pm.GaussianRandomWalk(‘grw’, mu=DRIFT, sigma=INNOVATION,
25 init=start_dist, steps=n_days) ### FAILS HERE
27 # the observational model
28 observed = pm.Normal(“observed”,
29 mu=grw[poll_day]
30 + zero_sum_house_bias[poll_brand.to_list()],
31 sigma=measurement_error_sd, observed=zero_centered_y)

File /opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/pymc/distributions/distribution.py:267, in Distribution.new(cls, name, rng, dims, initval, observed, total_size, transform, *args, **kwargs)
263 rng = model.next_rng()
265 # Create the RV and process dims and observed to determine
266 # a shape by which the created RV may need to be resized.
→ 267 rv_out, dims, observed, resize_shape = _make_rv_and_resize_shape(
268 cls=cls, dims=dims, model=model, observed=observed, args=args, rng=rng, **kwargs
269 )
271 if resize_shape:
272 # A batch size was specified through dims, or implied by observed.
273 rv_out = change_rv_size(rv_var=rv_out, new_size=resize_shape, expand=True)

File /opt/homebrew/Caskroom/miniforge/base/lib/python3.9/site-packages/pymc/distributions/distribution.py:166, in _make_rv_and_resize_shape(cls, dims, model, observed, args, **kwargs)
163 “”“Creates the RV and processes dims or observed to determine a resize shape.”""
164 # Create the RV without dims information, because that’s not something tracked at the Aesara level.
165 # If necessary we’ll later replicate to a different size implied by already known dims.
→ 166 rv_out = cls.dist(*args, **kwargs)
167 ndim_actual = rv_out.ndim
168 resize_shape = None

TypeError: dist() missing 1 required positional argument: ‘dist_params’

8

How do I install directly from github?

9

You have instructions in here: Installation Guide (Linux) · pymc-devs/pymc Wiki · GitHub

That’s for Linux, you can check the respective wiki pages for Windows or MacOS, if you are using one of those

1 Like
10

Thanks, that worked a treat.

1 Like