Note

Go to the end to download the full example code

Getting started with Visual Bayesic

Check out the tutorial.xircuit file to see the visual representation of the code below. from argparse import ArgumentParser

  • tutorial
  • Likelihood mean , Likelihood mean , Likelihood std, Likelihood std
  • Likelihood mean , Likelihood mean , Likelihood std, Likelihood std
  • Likelihood mean , Likelihood mean , Likelihood std, Likelihood std
  • Likelihood mean , Likelihood std
  • tutorial
Executing: NormalSampler

Executing: GammaSampler

Executing: NormalSampler

Executing: PyroModel

Executing: VisualizeModelGraph

Executing: FullInference

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Executing: ArvizObject
C:\Users\MigueldelaVarga\PycharmProjects\VisualBayesic\venv\lib\site-packages\arviz\data\io_pyro.py:157: UserWarning: Could not get vectorized trace, log_likelihood group will be omitted. Check your model vectorization or set log_likelihood=False
  warnings.warn(

Executing: PlotPrior

Executing: PlotTrace

Executing: PlotDensity

Executing: PlotNormalLikelihoodJoy
Setting Backend To: AvailableBackends.numpy

Finished Executing


from argparse import ArgumentParser
from xai_components.base import SubGraphExecutor
from xai_components.xai_plotting.probabilistic_plot import PlotDensity, PlotNormalLikelihoodJoy, PlotPrior, ArvizObject, PlotTrace, VisualizeModelGraph
from xai_components.xai_probabilistic_models.probabilistic_models_I import PyroModel
from xai_components.xai_probability_distributions.probabilistic_distributions import GammaSampler, NormalSampler
from xai_components.xai_pyro.probabilistic_node import FullInference

def main(args):
    ctx = {}
    ctx['args'] = args
    c_0 = VisualizeModelGraph()
    c_1 = NormalSampler()
    c_2 = NormalSampler()
    c_3 = FullInference()
    c_4 = ArvizObject()
    c_5 = PlotNormalLikelihoodJoy()
    c_6 = GammaSampler()
    c_7 = PlotDensity()
    c_8 = PlotTrace()
    c_9 = PlotPrior()
    c_10 = PyroModel()
    c_0.model_function = c_10.model
    c_0.model_function = c_10.model
    c_0.model_function = c_10.model
    c_1.name.value = 'Likelihood\n'
    c_1.mean = c_2.sample
    c_1.std = c_6.sample
    c_1.obs.value = [2.12, 2.06, 2.08, 2.05]
    c_2.name.value = 'Likelihood mean\n'
    c_2.mean.value = 2.07
    c_2.std.value = 0.08
    c_3.model = c_10.model
    c_3.num_samples.value = 1000
    c_4.mcmc = c_3.mcmc
    c_4.prior_predictive_values = c_3.prior_predictive
    c_4.posterior_predictive_values = c_3.posterior_predictive
    c_5.az_data = c_4.az_data
    c_5.mean_sample_name.value = 'Likelihood mean\n'
    c_5.std_sample_name.value = 'Likelihood std'
    c_5.y_sample_name.value = 'Likelihood\n'
    c_5.n_samples.value = 19
    c_6.name.value = 'Likelihood std'
    c_6.concentration.value = 3.3
    c_6.rate.value = 1.2
    c_7.az_data = c_4.az_data
    c_8.az_data = c_4.az_data
    c_9.az_data = c_4.az_data
    c_10.arg1 = c_1.sample
    c_0.next = c_3
    c_1.next = c_10
    c_2.next = c_6
    c_3.next = c_4
    c_4.next = c_9
    c_5.next = None
    c_6.next = c_1
    c_7.next = c_5
    c_8.next = c_7
    c_9.next = c_8
    c_10.next = c_0
    next_component = c_2
    while next_component:
        next_component = next_component.do(ctx)
if __name__ == '__main__':
    parser = ArgumentParser()
    main(parser.parse_args())
    print('\nFinished Executing')

Total running time of the script: (0 minutes 12.333 seconds)

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