.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples\2-examples\1-visual_scripting.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_2-examples_1-visual_scripting.py: Visual Scripting compiled model =============================== .. GENERATED FROM PYTHON SOURCE LINES 7-146 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /examples/2-examples/images/sphx_glr_1-visual_scripting_001.png :alt: 1 visual scripting :srcset: /examples/2-examples/images/sphx_glr_1-visual_scripting_001.png :class: sphx-glr-multi-img * .. image-sg:: /examples/2-examples/images/sphx_glr_1-visual_scripting_002.png :alt: $\mu$, $\mu$, $\sigma$, $\sigma$ :srcset: /examples/2-examples/images/sphx_glr_1-visual_scripting_002.png :class: sphx-glr-multi-img * .. image-sg:: /examples/2-examples/images/sphx_glr_1-visual_scripting_003.png :alt: Likelihood :srcset: /examples/2-examples/images/sphx_glr_1-visual_scripting_003.png :class: sphx-glr-multi-img * .. image-sg:: /examples/2-examples/images/sphx_glr_1-visual_scripting_004.png :alt: 1 visual scripting :srcset: /examples/2-examples/images/sphx_glr_1-visual_scripting_004.png :class: sphx-glr-multi-img * .. image-sg:: /examples/2-examples/images/sphx_glr_1-visual_scripting_005.png :alt: 1 visual scripting :srcset: /examples/2-examples/images/sphx_glr_1-visual_scripting_005.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Executing: Normal Executing: Sample Executing: Gamma Executing: Sample Executing: Normal Executing: Sample Executing: PyroModel Executing: VisualizeModelGraph Executing: PriorPredictive Executing: NUTS Executing: MCMC Executing: RunMCMC Warmup: 0%| | 0/100 [00:00, ?it/s] Warmup: 31%|████ | 31/100 [00:00, 286.49it/s, step size=2.48e-02, acc. prob=0.753] Sample: 60%|███████▊ | 60/100 [00:00, 270.15it/s, step size=3.71e-01, acc. prob=0.917] Sample: 100%|████████████| 100/100 [00:00, 314.77it/s, step size=3.71e-01, acc. prob=0.925] Executing: PosteriorPredictive 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( posterior predictive shape not compatible with number of chains and draws.This can mean that some draws or even whole chains are not represented. Executing: PlotPrior Executing: PlotNormalLikelihood Setting Backend To: AvailableBackends.numpy Executing: PlotNormalLikelihoodJoy Executing: PlotMarginals Finished Executing | .. code-block:: default from argparse import ArgumentParser from xai_components.base import SubGraphExecutor from xai_components.xai_controlflow.branches import BranchComponent from xai_components.xai_obsolete.prob_models import VisualizeModelGraph from xai_components.xai_plotting.probabilistic_plot import ArvizObject, PlotPrior, PlotNormalLikelihood, PlotMarginals, PlotNormalLikelihoodJoy from xai_components.xai_probabilistic_models.probabilistic_models_I import PyroModel from xai_components.xai_probability_distributions.probabilistic_distributions import Gamma, Uniform, Normal from xai_components.xai_pyro.probabilistic_node import MCMC, RunMCMC, NUTS, PriorPredictive, PosteriorPredictive, Sample def main(args): ctx = {} ctx['args'] = args c_0 = Normal() c_1 = Sample() c_2 = PyroModel() c_3 = VisualizeModelGraph() c_4 = PriorPredictive() c_5 = ArvizObject() c_6 = PlotPrior() c_7 = PlotNormalLikelihood() c_8 = PlotNormalLikelihoodJoy() c_9 = PosteriorPredictive() c_10 = Sample() c_11 = Gamma() c_12 = PlotMarginals() c_13 = Uniform() c_14 = BranchComponent() c_15 = Sample() c_16 = Normal() c_17 = Sample() c_18 = MCMC() c_19 = RunMCMC() c_20 = NUTS() c_0.mean = c_17.sample c_0.std = c_10.sample c_1.name.value = '$y$' c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.fn = c_0.fn c_1.obs.value = [2.12, 2.06, 2.08, 2.05, 2.08, 2.09, 2.19, 2.07, 2.16, 2.11, 2.13, 1.92] c_2.arg1 = c_1.sample c_3.model_function = c_2.model c_4.model = c_2.model c_4.num_samples.value = 20 c_5.mcmc = c_18.mcmc c_5.prior_predictive_values = c_4.prior c_5.posterior_predictive_values = c_9.posterior_predictive c_6.az_data = c_5.az_data c_6.az_data = c_5.az_data c_7.az_data = c_5.az_data c_7.mean_sample_name.value = '$\\mu$' c_7.std_sample_name.value = '$\\sigma$' c_7.y_sample_name.value = '$y$' c_8.az_data = c_5.az_data c_8.mean_sample_name.value = '$\\mu$' c_8.std_sample_name.value = '$\\sigma$' c_8.y_sample_name.value = '$y$' c_9.model = c_2.model c_9.MCMC = c_18.mcmc c_9.num_samples.value = 20 c_10.name.value = '$\\sigma$' c_10.fn = c_11.fn c_10.fn = c_11.fn c_11.concentration.value = 0.3 c_11.rate.value = 3.01 c_12.az_data = c_5.az_data c_12.sample_1_name.value = '$\\mu$' c_12.sample_2_name.value = '$\\sigma$' c_13.low.value = 0.0 c_13.high.value = 10.0 c_14.condition.value = True c_15.name.value = '$\\mu$' c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_15.fn = c_13.fn c_16.mean.value = 2.07 c_16.std.value = 0.07 c_17.name.value = '$\\mu$' c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_17.fn = c_16.fn c_18.NUTS = c_20.NUTS c_18.NUTS = c_20.NUTS c_18.num_samples.value = 50 c_18.num_chains.value = 1 c_19.mcmc = c_18.mcmc c_20.model = c_2.model c_0.next = c_1 c_1.next = c_2 c_2.next = c_3 c_3.next = c_4 c_4.next = c_20 c_5.next = c_6 c_6.next = c_7 c_7.next = c_8 c_8.next = c_12 c_9.next = c_5 c_10.next = c_0 c_11.next = c_10 c_12.next = None c_13.next = c_15 c_14.next = c_11 c_14.when_true = SubGraphExecutor(c_16) c_14.when_false = SubGraphExecutor(c_13) c_15.next = None c_16.next = c_17 c_17.next = None c_18.next = c_19 c_19.next = c_9 c_20.next = c_18 next_component = c_14 while next_component: next_component = next_component.do(ctx) if __name__ == '__main__': parser = ArgumentParser() main(parser.parse_args()) print('\nFinished Executing') .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 9.945 seconds) .. _sphx_glr_download_examples_2-examples_1-visual_scripting.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 1-visual_scripting.py <1-visual_scripting.py>` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 1-visual_scripting.ipynb <1-visual_scripting.ipynb>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_