Two large tasks, discussed with <a class="user-mention notranslate" data-hovercard-typ

Niko's monster model with adaptive precision, <a href="https://github.com/nsiccha/mons

Putting prior, prior predictive, posterior into one inferencedata for hierarchical sbc about stanify HOT 4 OPEN

data4dm commented on July 23, 2024

Putting prior, prior predictive, posterior into one inferencedata for hierarchical sbc

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Comments (4)

hyunjimoon commented on July 23, 2024

Niko's monster model with adaptive precision, this line uses ode_bdf where we can learn the output format for integrated result.

I also wonder the reason for using ragged array from ragged.stan file which I questioned in discourse.

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hyunjimoon commented on July 23, 2024

Without hierarchy, integrated_result was able to be declared and initialized at the same time. With hierarchy as for loop for region is coming in which forces it to be pre-declared than value is assigned from ode function.

Before I made the change, I faced identifier integrate_result not in scope error which is not informative error message.

    array[n_t, r] vector[3] integrated_result;
    for (region in 1:r){
        integrated_result[:, region, :]  = ode_rk45(vensim_ode_func, initial_outcome, initial_time, times, prey_birth_frac, pred_birth_frac[region], time_step, process_noise_scale);
    }
    array[n_t] vector [r] prey = integrated_result[1];
    array[n_t] vector [r] process_noise = integrated_result[ 2];
    array[n_t] vector [r] predator = integrated_result[3];

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hyunjimoon commented on July 23, 2024

Treatment of hierarchical and SBC multi S should be the same.

Questions from sbc-cmdstanpy

cause of scale parameter 0; need for a zero avoiding prior?

Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/home/oriol/Public/arviz/doc/source/user_guide/wrappers/linreg_model.stan', line 27, column 2 to column 64)

benefit of write_stan_json? Faster? (I am using dict, and "Values for all data variables in the model, specified either as a dictionary with entries matching the data variables, or as the path of a data file in JSON or Rdump format." from cmdstanpy).
Rdump?

prior_fit = prior_model.sample(data="linreg_prior.data.json", chains=2, iter_sampling=100)

idata_kwargs = dict(
    prior_predictive=["slack_comments_hat","github_commits_hat"],
    posterior_predictive=["slack_comments_hat","github_commits_hat"],
    log_likelihood={
        "slack_comments": "log_likelihood_slack_comments",
        "github_commits": "log_likelihood_github_commits"
    },
    coords={"developer": names},
    dims={
        "slack_comments": ["developer"],
        "github_commits" : ["developer"],
        "slack_comments_hat": ["developer"],
        "github_commits_hat": ["developer"],
        "time_since_joined": ["developer"],
    }
)
idata_orig = az.from_cmdstanpy(
    prior=prior_fit, **idata_kwargs
).stack(prior_draw=["chain", "draw"], groups="prior_groups")

why MultiIndex? (multi means what and what?)

prior
Dimensions:      (prior_draw: 200)
Coordinates:
  * prior_draw   (prior_draw) object MultiIndex
  * chain        (prior_draw) int64 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1
  * draw         (prior_draw) int64 0 1 2 3 4 5 6 7 ... 92 93 94 95 96 97 98 99
Data variables:
    b0           (prior_draw) float64 104.4 -239.1 -249.9 ... 222.8 -46.17
    b1           (prior_draw) float64 116.8 -175.4 -71.92 ... -162.4 7.715 274.3
    log_b_sigma  (prior_draw) float64 6.163 6.122 5.205 ... 3.633 6.172 6.076
    b_sigma      (prior_draw) float64 474.6 455.6 182.3 ... 37.84 479.3 435.3
    c0           (prior_draw) float64 -2.87 -6.738 11.82 ... -10.68 -11.76
    c1           (prior_draw) float64 18.08 -7.793 4.934 ... -2.836 -14.52
    log_c_sigma  (prior_draw) float64 6.163 6.122 5.205 ... 3.633 6.172 6.076
    c_sigma      (prior_draw) float64 9.529 6.93 6.605 ... 1.642 4.487 3.449

prior_predictive
Dimensions:             (developer: 5, prior_draw: 200)
Coordinates:
  * developer           (developer) object 'Alice' 'Bob' ... 'Danielle' 'Erika'
  * prior_draw          (prior_draw) object MultiIndex
  * chain               (prior_draw) int64 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1
  * draw                (prior_draw) int64 0 1 2 3 4 5 6 ... 94 95 96 97 98 99
Data variables:
    slack_comments_hat  (developer, prior_draw) float64 701.6 ... 4.755e+03
    github_commits_hat  (developer, prior_draw) float64 82.57 -39.29 ... -270.4

sample_stats_prior
Dimensions:          (prior_draw: 200)
Coordinates:
  * prior_draw       (prior_draw) object MultiIndex
  * chain            (prior_draw) int64 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1
  * draw             (prior_draw) int64 0 1 2 3 4 5 6 7 ... 93 94 95 96 97 98 99
Data variables:
    lp               (prior_draw) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
    acceptance_rate  (prior_draw) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

for next (week, month) meeting, need help in

loglik-based plots
rvar support from posterior? Mike's comment

The nc-to-rvars conversion is done by the class_composition R6 class here, specifically the nc_to_rvar method here

plots for ode model (e.g. degeneracy diagnosis for prey-predator and inventory model)

funnel only from hierarchical model? (trying to introduce hierarchical prey-predator) + funnel degeneracy is included in..?
a. multimodality of hierarchical prey-predator from this issue

This adds 4 regions with variety in the alpha/beta/gamma/delta parameters, loosely structured after https://betanalpha.github.io/assets/case_studies/hierarchical_modeling.html that @hyunjimoon linked. Part of the intent was to create the opportunity for funnels. I haven't run it much yet, but I think it does show evidence of multimodality, or of pathologies that cause Powell to get stuck occasionally. I haven't tested MCMC either, but will get there soon.

b. how to set # of estimated parameters in ode model? inventory model from this issue

how often do you use diagnose() function, and does arviz provide plot which use the output of diagnose()?

02:49:23 - cmdstanpy - WARNING - Some chains may have failed to converge.
	Chain 1 had 48 divergent transitions (96.0%)
	Chain 2 had 47 divergent transitions (94.0%)
	Use function "diagnose()" to see further information.

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hyunjimoon commented on July 23, 2024

One example of prior predictive (synthetic data generated from s'th prior draw) are as follows, on which posterior is fit.

Dimensions:             (predator_obs_dim_0: 200, prey_obs_dim_0: 200)
Coordinates:
  * predator_obs_dim_0  (predator_obs_dim_0) int64 0 1 2 3 4 ... 196 197 198 199
  * prey_obs_dim_0      (prey_obs_dim_0) int64 0 1 2 3 4 ... 195 196 197 198 199
    prior_draw          object (0, 0)
    chain               int64 0
    draw                int64 0
Data variables:
    predator_obs        (predator_obs_dim_0) float64 4.027 4.049 ... 8.142 7.973
    prey_obs            (prey_obs_dim_0) float64 30.16 30.63 ... 6.133 6.205
Attributes: (4)

Error I am facing is:

ValueError: different number of dimensions on data and dims: 3 vs 4

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Putting prior, prior predictive, posterior into one inferencedata for hierarchical sbc about stanify HOT 4 OPEN

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