Repository Analysis

pyro-ppl/pyro

Deep universal probabilistic programming with Python and PyTorch

14.7 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of pyro-ppl/pyro, a Python project with 9,036 GitHub stars. SynthScan v2.0 examined 136,982 lines of code across 668 source files, recording 1658 pattern matches distributed across 16 syntactic categories. The overall adjusted score of 14.7 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

14.7
Adjusted Score
14.7
Raw Score
100%
Time Factor
2026-08-04
Last Push
9.0K
Stars
Python
Language
137.0K
Lines of Code
668
Files
1.7K
Pattern Hits
2026-08-09
Scan Date
0.14
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 95MEDIUM 69LOW 1494

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 1658 distinct pattern matches across 16 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers570 hits · 541 pts
SeverityFileLineSnippetContext
LOWtests/conftest.py58def _get_highest_specificity_marker(stage_marker):CODE
LOWtests/conftest.py86def pytest_collection_modifyitems(config, items):CODE
LOWtests/nn/conftest.py7def pytest_collection_modifyitems(items):CODE
LOWtests/nn/test_module.py76def test_svi_elbomodule_interface(CODE
LOWtests/nn/test_module.py139def test_local_param_global_behavior_fails(local_params):CODE
LOWtests/nn/test_module.py266def test_submodule_contains_torch_module():CODE
LOWtests/nn/test_module.py273def test_hierarchy_prior_cached():CODE
LOWtests/nn/test_module.py669def test_torch_serialize_attributes(local_params):CODE
LOWtests/nn/test_module.py692def test_torch_serialize_decorators(local_params):CODE
LOWtests/nn/test_module.py1016 def get_position_indexing_modulelist_bnn(self) -> PositionIndexingModuleListBNN:CODE
LOWtests/nn/test_module.py1024 def get_slice_indexing_modulelist_bnn(self) -> SliceIndexingModuleListBNN:CODE
LOWtests/nn/test_module.py1043 def test_with_position_indexing(self) -> None:CODE
LOWtests/nn/test_module.py1055 def test_with_position_indexing(self) -> None:CODE
LOWtests/nn/test_module.py1069def test_render_constrained_param(use_module_local_params):CODE
LOWtests/integration_tests/conftest.py7def pytest_collection_modifyitems(items):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py51 def test_elbo_reparameterized(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py55 def test_elbo_nonreparameterized(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py121 def test_elbo_reparameterized(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py124 def test_elbo_nonreparameterized_both_baselines(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py135 def test_elbo_nonreparameterized_decaying_baseline(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py146 def test_elbo_nonreparameterized_nn_baseline(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py296 def test_elbo_reparameterized(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py299 def test_elbo_nonreparameterized(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py359 def test_elbo_reparameterized(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py362 def test_elbo_nonreparameterized(self):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py437 def test_nested_iplate_in_elbo(self, n_steps=4000):CODE
LOWtests/integration_tests/test_tracegraph_elbo.py499 def test_plate_in_elbo_with_superfluous_rvs(self):CODE
LOW…ts/integration_tests/test_conjugate_gaussian_models.py310 def test_elbo_reparameterized_three_layers(self):CODE
LOW…ts/integration_tests/test_conjugate_gaussian_models.py317 def test_elbo_reparameterized_four_layers(self):CODE
LOW…ts/integration_tests/test_conjugate_gaussian_models.py324 def test_elbo_nonreparameterized_two_layers(self):CODE
LOW…ts/integration_tests/test_conjugate_gaussian_models.py330 def test_elbo_nonreparameterized_three_layers(self):CODE
LOW…ts/integration_tests/test_conjugate_gaussian_models.py336 def test_elbo_nonreparameterized_two_layers_model_permuted(self):CODE
LOW…ts/integration_tests/test_conjugate_gaussian_models.py346 def test_elbo_nonreparameterized_three_layers_model_permuted(self):CODE
LOW…ts/integration_tests/test_conjugate_gaussian_models.py352 def calculate_variational_targets(self):CODE
LOWtests/poutine/test_poutines.py183 def test_block_full_hide_expose(self):CODE
LOWtests/poutine/test_poutines.py209 def test_block_partial_expose(self):CODE
LOWtests/poutine/test_poutines.py454 def test_random_module_prior_dict(self):CODE
LOWtests/poutine/test_poutines.py583 def test_stack_overwrite_behavior(self):CODE
LOWtests/poutine/test_poutines.py627 def test_stack_overwrite_behavior(self):CODE
LOWtests/poutine/test_poutines.py774 def test_sample_site_equalization(self):CODE
LOWtests/poutine/test_poutines.py812def test_condition_by_equalize(loc_x, scale_x, loc_y, scale_y, keep_dist):CODE
LOWtests/poutine/test_poutines.py880def test_replay_enumerate_poutine(depth, first_available_dim):CODE
LOWtests/poutine/test_poutines.py921def test_plate_preserves_has_rsample(has_rsample, depth):CODE
LOWtests/poutine/test_poutines.py932def test_plate_error_on_enter():CODE
LOWtests/poutine/test_poutines.py957def test_decorator_interface_primitives():CODE
LOWtests/poutine/test_poutines.py987def test_decorator_interface_queue():CODE
LOWtests/poutine/test_poutines.py1008def test_method_decorator_interface_condition():CODE
LOWtests/poutine/test_poutines.py1024def test_trace_log_prob_err_msg():CODE
LOWtests/poutine/test_poutines.py1034def test_trace_log_prob_sum_err_msg():CODE
LOWtests/poutine/test_poutines.py1044def test_trace_score_parts_err_msg():CODE
LOWtests/poutine/conftest.py7def pytest_collection_modifyitems(items):CODE
LOWtests/poutine/test_trace_struct.py56def test_connectivity_on_removal(edges):CODE
LOWtests/poutine/test_counterfactual.py28def test_counterfactual_query(intervene, observe, flip):CODE
LOWtests/poutine/test_counterfactual.py81def test_plate_duplication_smoke():CODE
LOWtests/poutine/test_mapdata.py177def test_model_guide_mismatch(behavior, model_size, guide_size, model):CODE
LOWtests/optim/conftest.py7def pytest_collection_modifyitems(items):CODE
LOWtests/optim/test_optim.py245def test_dctadam_param_subsample():CODE
LOWtests/optim/test_optim.py281def test_name_preserved_by_to_pyro_module():CODE
LOWtests/optim/test_optim.py440def test_centered_clipped_adam(plot):CODE
LOWtests/optim/test_multi.py75def test_multi_optimizer_disjoint_ok():CODE
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Cross-File Repetition93 hits · 465 pts
SeverityFileLineSnippetContext
HIGHtutorial/source/search_inference.py0exact inference by enumerating over all possible executionsSTRING
HIGHexamples/rsa/search_inference.py0exact inference by enumerating over all possible executionsSTRING
HIGHpyro/contrib/oed/search.py0exact inference by enumerating over all possible executionsSTRING
HIGHtests/contrib/test_minipyro.py0assert that inference works without warnings or errors.STRING
HIGHtests/infer/test_valid_models.py0assert that inference works without warnings or errors.STRING
HIGHtests/infer/mcmc/test_valid_models.py0assert that inference works without warnings or errors.STRING
HIGHpyro/contrib/timeseries/lgssm.py0get the :class:`~pyro.distributions.gaussianhmm` distribution that corresponds to :class:`genericlgssmwithgpnoisemodel`.STRING
HIGHpyro/contrib/timeseries/gp.py0get the :class:`~pyro.distributions.gaussianhmm` distribution that corresponds to :class:`genericlgssmwithgpnoisemodel`.STRING
HIGHpyro/contrib/timeseries/lgssmgp.py0get the :class:`~pyro.distributions.gaussianhmm` distribution that corresponds to :class:`genericlgssmwithgpnoisemodel`.STRING
HIGHpyro/contrib/timeseries/lgssm.py0:param torch.tensor targets: a 2-dimensional tensor of real-valued targets of shape ``(t, obs_dim)``, where ``t`` is theSTRING
HIGHpyro/contrib/timeseries/lgssmgp.py0:param torch.tensor targets: a 2-dimensional tensor of real-valued targets of shape ``(t, obs_dim)``, where ``t`` is theSTRING
HIGHpyro/contrib/timeseries/gp.py0:param torch.tensor targets: a 2-dimensional tensor of real-valued targets of shape ``(t, obs_dim)``, where ``t`` is theSTRING
HIGHpyro/contrib/timeseries/lgssm.py0return the filtering state for the associated state space model.STRING
HIGHpyro/contrib/timeseries/gp.py0return the filtering state for the associated state space model.STRING
HIGHpyro/contrib/timeseries/lgssmgp.py0return the filtering state for the associated state space model.STRING
HIGHpyro/contrib/gp/likelihoods/binary.py0samples :math:`y` given :math:`f_{loc}`, :math:`f_{var}` according to .. math:: f & \sim \mathbb{normal}(f_{loc}, f_{varSTRING
HIGHpyro/contrib/gp/likelihoods/poisson.py0samples :math:`y` given :math:`f_{loc}`, :math:`f_{var}` according to .. math:: f & \sim \mathbb{normal}(f_{loc}, f_{varSTRING
HIGHpyro/contrib/gp/likelihoods/multi_class.py0samples :math:`y` given :math:`f_{loc}`, :math:`f_{var}` according to .. math:: f & \sim \mathbb{normal}(f_{loc}, f_{varSTRING
HIGHpyro/infer/predictive.py0guesses max_plate_nesting by running the (model,guide) pair once without enumeration. this optimistically assumes staticSTRING
HIGHpyro/infer/mcmc/util.py0guesses max_plate_nesting by running the (model,guide) pair once without enumeration. this optimistically assumes staticSTRING
HIGHpyro/infer/elbo.py0guesses max_plate_nesting by running the (model,guide) pair once without enumeration. this optimistically assumes staticSTRING
HIGHpyro/infer/predictive.py0method that calls :meth:`forward` and returns parameter values of the guide as a `tuple` instead of a `dict`, which is aSTRING
HIGHpyro/infer/autoguide/guides.py0method that calls :meth:`forward` and returns parameter values of the guide as a `tuple` instead of a `dict`, which is aSTRING
HIGHpyro/infer/autoguide/effect.py0method that calls :meth:`forward` and returns parameter values of the guide as a `tuple` instead of a `dict`, which is aSTRING
HIGHpyro/infer/rws.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/traceenum_elbo.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/tracetmc_elbo.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/tracegraph_elbo.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/trace_elbo.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/elbo.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/renyi_elbo.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/trace_mmd.py0returns a single trace from the guide, and the model that is run against it.STRING
HIGHpyro/infer/traceenum_elbo.py0runs the guide and runs the model against the guide with the result packaged as a trace generator.STRING
HIGHpyro/infer/tracetmc_elbo.py0runs the guide and runs the model against the guide with the result packaged as a trace generator.STRING
HIGHpyro/infer/elbo.py0runs the guide and runs the model against the guide with the result packaged as a trace generator.STRING
HIGHpyro/infer/traceenum_elbo.py0like :class:`tracemeanfield_elbo` but uses :func:`pyro.ops.jit.trace` to compile :meth:`loss_and_grads`. this works onlySTRING
HIGHpyro/infer/tracegraph_elbo.py0like :class:`tracemeanfield_elbo` but uses :func:`pyro.ops.jit.trace` to compile :meth:`loss_and_grads`. this works onlySTRING
HIGHpyro/infer/trace_elbo.py0like :class:`tracemeanfield_elbo` but uses :func:`pyro.ops.jit.trace` to compile :meth:`loss_and_grads`. this works onlySTRING
HIGHpyro/infer/trace_mean_field_elbo.py0like :class:`tracemeanfield_elbo` but uses :func:`pyro.ops.jit.trace` to compile :meth:`loss_and_grads`. this works onlySTRING
HIGHpyro/infer/tracegraph_elbo.py0:returns: returns an estimate of the elbo :rtype: float evaluates the elbo with an estimator that uses num_particles manSTRING
HIGHpyro/infer/trace_elbo.py0:returns: returns an estimate of the elbo :rtype: float evaluates the elbo with an estimator that uses num_particles manSTRING
HIGHpyro/infer/trace_mean_field_elbo.py0:returns: returns an estimate of the elbo :rtype: float evaluates the elbo with an estimator that uses num_particles manSTRING
HIGHpyro/infer/renyi_elbo.py0:returns: returns an estimate of the elbo :rtype: float evaluates the elbo with an estimator that uses num_particles manSTRING
HIGHpyro/distributions/transforms/spline.py0:param y: the output of the bijection :type y: torch.tensor inverts y => x. uses a previously cached inverse if availablSTRING
HIGHpyro/distributions/transforms/spline_autoregressive.py0:param y: the output of the bijection :type y: torch.tensor inverts y => x. uses a previously cached inverse if availablSTRING
HIGHpyro/distributions/transforms/spline_coupling.py0:param y: the output of the bijection :type y: torch.tensor inverts y => x. uses a previously cached inverse if availablSTRING
HIGHpyro/distributions/transforms/affine_autoregressive.py0:param y: the output of the bijection :type y: torch.tensor inverts y => x. uses a previously cached inverse if availablSTRING
HIGHpyro/distributions/transforms/affine_coupling.py0:param y: the output of the bijection :type y: torch.tensor inverts y => x. uses a previously cached inverse if availablSTRING
HIGHpyro/distributions/transforms/spline.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/radial.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/planar.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/block_autoregressive.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/spline_autoregressive.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/spline_coupling.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/affine_autoregressive.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/sylvester.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/neural_autoregressive.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/polynomial.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/affine_coupling.py0calculates the elementwise determinant of the log jacobianSTRING
HIGHpyro/distributions/transforms/spline.py0a helper function to create a :class:`~pyro.distributions.transforms.splinecoupling` object for consistency with other hSTRING
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Unused Imports554 hits · 450 pts
SeverityFileLineSnippetContext
LOWtests/contrib/funsor/test_pyroapi_funsor.py17CODE
LOWtests/contrib/funsor/test_pyroapi_funsor.py10CODE
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LOWpyro/util.py32CODE
LOWpyro/util.py33CODE
LOWpyro/util.py34CODE
LOWpyro/generic.py6CODE
LOWpyro/generic.py7CODE
LOWpyro/__init__.py4CODE
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LOWpyro/__init__.py8CODE
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LOWpyro/__init__.py26CODE
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LOWpyro/nn/__init__.py4CODE
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Deep Nesting152 hits · 144 pts
SeverityFileLineSnippetContext
LOWtests/common.py198CODE
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LOWtests/test_examples.py327CODE
LOWtests/nn/test_autoregressive.py19CODE
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LOWtests/poutine/test_nesting.py14CODE
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LOWtests/contrib/forecast/test_forecaster.py177CODE
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LOWtests/contrib/mue/test_missingdatahmm.py257CODE
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LOWtests/ops/test_gamma_gaussian.py98CODE
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LOWtests/distributions/test_distributions.py362CODE
LOWtests/distributions/test_spanning_tree.py181CODE
LOWexamples/svi_horovod.py64CODE
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LOWexamples/hmm.py317CODE
LOWexamples/hmm.py372CODE
LOWexamples/hmm.py460CODE
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Self-Referential Comments35 hits · 108 pts
SeverityFileLineSnippetContext
MEDIUMsetup.py12# This file is auto-generated with the version information during setup.py installation.COMMENT
MEDIUMtutorial/source/conf.py13# This file is execfile()d with the current directory set to itsCOMMENT
MEDIUMtests/infer/reparam/test_split.py111 # Create a model conditioned on the observationCOMMENT
MEDIUMdocs/source/conf.py14# This file is execfile()d with the current directory set to itsCOMMENT
MEDIUMexamples/svi_horovod.py65 # Create a model, synthetic data, and a guide.COMMENT
MEDIUMexamples/svi_horovod.py90 # Create an ELBO loss and a Pyro optimizer.COMMENT
MEDIUMexamples/svi_horovod.py98 # Create a dataloader.COMMENT
MEDIUMexamples/sparse_regression.py216# Create an artifical dataset with N datapoints and P feature dimensions. Of the PCOMMENT
MEDIUMexamples/svi_torch.py57 # Create a synthetic dataset from a randomly initialized model.COMMENT
MEDIUMexamples/svi_torch.py64 # Create a model and a guide, both as (Pyro)Modules.COMMENT
MEDIUMexamples/svi_torch.py68 # Create a loss function as a Module that includes model and guide parameters.COMMENT
MEDIUMexamples/svi_torch.py76 # Create a dataloader.COMMENT
MEDIUMexamples/svi_torch.py86 # Create a PyTorch optimizer for the parameters of the model and guide in loss_fn.COMMENT
MEDIUMexamples/minipyro.py20 # Define a basic model with a single Normal latent random variable `loc`COMMENT
MEDIUMexamples/minipyro.py27 # Define a guide (i.e. variational distribution) with a NormalCOMMENT
MEDIUMexamples/svi_lightning.py80 # Create a model, synthetic data, a guide, and a lightning module.COMMENT
MEDIUMexamples/svi_lightning.py90 # Create a dataloader.COMMENT
MEDIUMexamples/sparse_gamma_def.py168# Define a guide using the EasyGuide class.COMMENT
MEDIUMexamples/vae/utils/vae_plots.py55 # Create the FacetGrid with scatter plotCOMMENT
MEDIUMexamples/vae/utils/mnist_cached.py14# This file contains utilities for caching, transforming and splitting MNIST dataCOMMENT
MEDIUMpyro/nn/auto_reg_nn.py63 # Create the indices that are assigned to the neuronsCOMMENT
MEDIUMpyro/nn/module.py681 # Create a new sub PyroModule, overwriting any old value.COMMENT
MEDIUMpyro/nn/module.py690 # Create a new PyroParam, overwriting any old value.COMMENT
MEDIUMpyro/nn/module.py738 # Create a new nn.Parameter, overwriting any old value.COMMENT
MEDIUMpyro/nn/module.py775 # Create a new PyroSample, overwriting any old value.COMMENT
MEDIUMpyro/contrib/minipyro.py301 # This method handles running the model and guide, constructing the lossCOMMENT
MEDIUMpyro/contrib/forecast/forecaster.py146 # Create a sample site.COMMENT
MEDIUMpyro/contrib/autoname/named.py24 >>> state.xs = named.List() # Create a contained list.STRING
MEDIUMpyro/infer/discrete.py46 # Create an enumerated trace.COMMENT
MEDIUMpyro/infer/autoguide/structured.py347 # Create a reparametrized Delta distribution.COMMENT
MEDIUMpyro/infer/autoguide/gaussian.py425 # Create a dense joint Gaussian over flattened variables.COMMENT
MEDIUMpyro/infer/reparam/strategies.py207 # Create an elementwise-learnable reparametrizer.COMMENT
MEDIUMpyro/ops/einsum/util.py17 # This function is copied and adapted from:COMMENT
MEDIUMpyro/distributions/transforms/simplex_to_ordered.py11# This class is a port of https://num.pyro.ai/en/stable/_modules/numpyro/distributions/transforms.html#SimplexToOrderedTCOMMENT
MEDIUMpyro/distributions/transforms/neural_autoregressive.py71 # Create the intermediate transform usedSTRING
Modern Structural Boilerplate91 hits · 89 pts
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LOWtests/test_examples.py21logger = logging.getLogger(__name__)CODE
LOWtests/integration_tests/test_tracegraph_elbo.py20logger = logging.getLogger(__name__)CODE
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LOWtests/poutine/test_poutines.py24logger = logging.getLogger(__name__)CODE
LOWtests/poutine/test_nesting.py11logger = logging.getLogger(__name__)CODE
LOWtests/poutine/test_mapdata.py14logger = logging.getLogger(__name__)CODE
LOWtests/contrib/epidemiology/test_models.py28logger = logging.getLogger(__name__)CODE
LOWtests/contrib/funsor/test_valid_models_plate.py26logger = logging.getLogger(__name__)CODE
LOWtests/contrib/funsor/test_enum_funsor.py29logger = logging.getLogger(__name__)CODE
LOWtests/contrib/funsor/test_tmc.py26logger = logging.getLogger(__name__)CODE
LOW…s/contrib/funsor/test_valid_models_sequential_plate.py25logger = logging.getLogger(__name__)CODE
LOWtests/contrib/funsor/test_named_handlers.py24logger = logging.getLogger(__name__)CODE
LOWtests/contrib/funsor/test_valid_models_enum.py32logger = logging.getLogger(__name__)CODE
LOWtests/contrib/funsor/test_infer_discrete.py26logger = logging.getLogger(__name__)CODE
LOWtests/contrib/autoname/test_scoping.py13logger = logging.getLogger(__name__)CODE
LOWtests/contrib/gp/test_models.py26logger = logging.getLogger(__name__)CODE
LOWtests/contrib/tracking/test_em.py19logger = logging.getLogger(__name__)CODE
LOWtests/contrib/tracking/test_assignment.py20logger = logging.getLogger(__name__)CODE
LOWtests/contrib/tracking/test_hashing.py12logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_gradient.py31logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_inference.py44logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_discrete.py20logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_tmc.py22logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_elbo_mapdata.py15logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_jit.py42logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_valid_models.py34logger = logging.getLogger(__name__)CODE
LOWtests/infer/test_enum.py32logger = logging.getLogger(__name__)CODE
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LOWtests/infer/mcmc/test_nuts.py29logger = logging.getLogger(__name__)CODE
LOWtests/infer/mcmc/test_valid_models.py25logger = logging.getLogger(__name__)CODE
LOWtests/infer/mcmc/test_hmc.py18logger = logging.getLogger(__name__)CODE
LOWtests/ops/test_newton.py14logger = logging.getLogger(__name__)CODE
LOWtests/ops/test_integrator.py13logger = logging.getLogger(__name__)CODE
LOWtests/distributions/test_gaussian_mixtures.py17logger = logging.getLogger(__name__)CODE
LOWtests/distributions/test_hmm.py39logger = logging.getLogger(__name__)CODE
LOWpyro/util.py37def set_rng_seed(rng_seed: int) -> None:CODE
LOWpyro/util.py56def set_rng_state(state: Dict[str, Any]) -> None:CODE
LOWpyro/__init__.py39__all__ = [CODE
LOWpyro/nn/__init__.py20__all__ = [CODE
LOWpyro/poutine/__init__.py31__all__ = [CODE
LOWpyro/poutine/runtime.py255 def set_first_available_dim(self, first_available_dim: int) -> None:CODE
LOWpyro/optim/pytorch_optimizers.py9__all__ = []CODE
LOWpyro/optim/__init__.py11__all__ = [CODE
LOWpyro/optim/optim.py168 def set_state(self, state_dict: Dict) -> None:CODE
LOWpyro/contrib/__init__.py23__all__ = [STRING
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LOWpyro/contrib/forecast/forecaster.py30logger = logging.getLogger(__name__)CODE
LOWpyro/contrib/forecast/evaluate.py15logger = logging.getLogger(__name__)CODE
LOWpyro/contrib/easyguide/__init__.py6__all__ = [CODE
LOWpyro/contrib/timeseries/__init__.py18__all__ = [CODE
LOWpyro/contrib/bnn/__init__.py6__all__ = [CODE
LOWpyro/contrib/cevae/__init__.py40logger = logging.getLogger(__name__)CODE
LOWpyro/contrib/oed/__init__.py72__all__ = ["search", "eig"]CODE
LOWpyro/contrib/oed/eig.py17__all__ = [CODE
LOWpyro/contrib/epidemiology/models.py1271__all__ = []STRING
LOWpyro/contrib/epidemiology/compartmental.py54logger = logging.getLogger(__name__)CODE
LOWpyro/contrib/epidemiology/__init__.py9__all__ = [CODE
LOWpyro/contrib/funsor/__init__.py40__all__ = [CODE
LOWpyro/contrib/autoname/__init__.py13__all__ = [CODE
LOWpyro/contrib/gp/__init__.py7__all__ = [CODE
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Over-Commented Block63 hits · 61 pts
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LOWtutorial/source/conf.py1# Copyright (c) 2017-2019 Uber Technologies, Inc.COMMENT
LOWtutorial/source/conf.py21COMMENT
LOWtutorial/source/conf.py101# extend timeoutCOMMENT
LOWtutorial/source/conf.py141 # The font size ('10pt', '11pt' or '12pt').COMMENT
LOWtests/contrib/funsor/test_enum_funsor.py1101 elbo = infer.TraceEnum_ELBO(max_plate_nesting=0)COMMENT
LOWtests/contrib/funsor/test_enum_funsor.py1241 elbo.differentiable_loss(model_plate_plate, guide, data)COMMENT
LOWtests/contrib/funsor/test_valid_models_enum.py321 # z00 z10 z20COMMENT
LOWtests/infer/test_inspect.py261 "x": {"x": {"p"}, "y": {"p"}, "z": set()},COMMENT
LOWtests/infer/test_inspect.py301 },COMMENT
LOWtests/infer/test_inspect.py341 "y": {"y": _, "z": _},COMMENT
LOWtests/infer/test_valid_models.py1841 )COMMENT
LOWtests/infer/test_valid_models.py1861 # -------------------COMMENT
LOWtests/infer/test_enum.py2801def test_elbo_enumerate_plates_6(scale):COMMENT
LOWtests/infer/test_enum.py2941 # +-------------+COMMENT
LOWtests/ops/test_contract.py321 # a\ /aCOMMENT
LOWtests/ops/test_contract.py341 # |dCOMMENT
LOWtests/distributions/test_stable_log_prob.py141# # The below tests will be executed:COMMENT
LOWdocs/source/conf.py1# Copyright (c) 2017-2019 Uber Technologies, Inc.COMMENT
LOWdocs/source/conf.py21# serve to show the default.COMMENT
LOWdocs/source/conf.py141# Output file base name for HTML help builder.COMMENT
LOWexamples/svi_horovod.py1# Copyright Contributors to the Pyro project.COMMENT
LOWexamples/sir_hmc.py1# Copyright Contributors to the Pyro project.COMMENT
LOWexamples/sir_hmc.py41# This models the dynamics of three groups within a population:COMMENT
LOWexamples/sir_hmc.py141COMMENT
LOWexamples/svi_lightning.py1# Copyright Contributors to the Pyro project.COMMENT
LOWexamples/sparse_gamma_def.py1# Copyright (c) 2017-2019 Uber Technologies, Inc.COMMENT
LOWexamples/sparse_gamma_def.py161def clip_params():COMMENT
LOWexamples/dmm.py221 # we enclose all the sample statements in the model in a plate.COMMENT
LOWexamples/dmm.py281 # push the observed x's through the rnn;COMMENT
LOWexamples/hmm.py61log = logging.getLogger()COMMENT
LOWexamples/hmm.py121 dist.Bernoulli(probs_y[x.squeeze(-1)]),COMMENT
LOWexamples/hmm.py141# y_178_0 dist 16 88 |COMMENT
LOWexamples/hmm.py161# -2 and -3. If we had not used pyro.markov above, each enumerated variableCOMMENT
LOWexamples/hmm.py221# Let's see how batching changes the shapes of sample sites:COMMENT
LOWexamples/hmm.py241# x_2 dist 16 1 10 1 |COMMENT
LOWexamples/hmm.py301COMMENT
LOWexamples/hmm.py361# w[t-1] ----> w[t] ---> w[t+1]COMMENT
LOWexamples/hmm.py501# _______>______COMMENT
LOWexamples/air/main.py81# http://akosiorek.github.io/ml/2017/09/03/implementing-air.htmlCOMMENT
LOWexamples/contrib/funsor/hmm.py81# x[t-1] --> x[t] --> x[t+1]COMMENT
LOWexamples/contrib/funsor/hmm.py141# Sample Sites:COMMENT
LOWexamples/contrib/funsor/hmm.py161# value 88 |COMMENT
LOWexamples/contrib/funsor/hmm.py241# value 88 |COMMENT
LOWexamples/contrib/funsor/hmm.py261# value 16 1 1 |COMMENT
LOWexamples/contrib/funsor/hmm.py301 )COMMENT
LOWexamples/contrib/funsor/hmm.py361 with tones_plate as tones:COMMENT
LOWexamples/contrib/funsor/hmm.py501 with pyro.plate("tones_{}".format(t), data_dim, dim=-1):COMMENT
LOWexamples/contrib/funsor/hmm.py641# value | 16 51COMMENT
LOWpyro/nn/module.py941PyroModule[torch.nn.RNNBase]._flat_weights = _FlatWeightsDescriptor() # type: ignore[attr-defined]COMMENT
LOWpyro/poutine/reparam_messenger.py81 else:COMMENT
LOWpyro/contrib/minipyro.py321COMMENT
LOWpyro/contrib/forecast/forecaster.py121 )COMMENT
LOWpyro/contrib/funsor/handlers/plate_messenger.py241 # x_0 dist |COMMENT
LOWpyro/contrib/funsor/handlers/plate_messenger.py261 # value 3 1 1 1 1 |COMMENT
LOWpyro/contrib/gp/models/sgpr.py201 """COMMENT
LOWpyro/infer/reparam/conjugate.py81 infer={COMMENT
LOWpyro/infer/reparam/stable.py181 # 3. Decompose S = G * sqrt(Z) via the symmetric strategy, whereCOMMENT
LOWpyro/infer/mcmc/nuts.py381 r, r_unscaled = self._sample_r(name="r_t={}".format(self._t))COMMENT
LOWpyro/infer/mcmc/logger.py21LOG_MSG = "LOG"COMMENT
LOWpyro/distributions/hmm.py121 # 1 0 2COMMENT
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Decorative Section Separators18 hits · 44 pts
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MEDIUMtests/contrib/funsor/test_valid_models_enum.py328 # -------------------COMMENT
MEDIUMtests/infer/test_valid_models.py1861 # -------------------STRING
MEDIUMtests/ops/test_contract.py153 # ------------------------------------------------------COMMENT
MEDIUMtests/ops/test_contract.py197 # ------------------------------------------------------COMMENT
MEDIUMexamples/sir_hmc.py5# ============COMMENT
MEDIUMexamples/sir_hmc.py37# ====================COMMENT
MEDIUMexamples/sir_hmc.py512# ==========================COMMENT
MEDIUMexamples/sir_hmc.py602# ===========COMMENT
MEDIUMexamples/baseball.py58# ===================================COMMENT
MEDIUMexamples/baseball.py60# ===================================COMMENT
MEDIUMexamples/baseball.py136# ===================================STRING
MEDIUMexamples/baseball.py138# ===================================STRING
MEDIUMexamples/baseball.py196# ===================================STRING
MEDIUMexamples/baseball.py198# ===================================STRING
MEDIUMpyro/contrib/forecast/util.py175# ----------------------------------------------------------------------------COMMENT
MEDIUMpyro/params/param_store.py56 # -------------------------------------------------------------------------------COMMENT
MEDIUMpyro/params/param_store.py198 # -------------------------------------------------------------------------------COMMENT
MEDIUMpyro/params/param_store.py273 # -------------------------------------------------------------------------------COMMENT
AI Structural Patterns42 hits · 42 pts
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LOWtests/infer/mcmc/test_mcmc_api.py79CODE
LOWtests/infer/mcmc/test_mcmc_api.py106CODE
LOWtests/distributions/dist_fixture.py17CODE
LOWexamples/dmm.py153CODE
LOWexamples/air/air.py364CODE
LOWexamples/air/air.py379CODE
LOWexamples/air/air.py35CODE
LOWpyro/poutine/block_messenger.py145CODE
LOWpyro/poutine/handlers.py141CODE
LOWpyro/poutine/handlers.py155CODE
LOWpyro/poutine/handlers.py169CODE
LOWpyro/contrib/forecast/forecaster.py262CODE
LOWpyro/contrib/forecast/forecaster.py427CODE
LOWpyro/contrib/forecast/evaluate.py71CODE
LOWpyro/contrib/mue/models.py371CODE
LOWpyro/contrib/mue/missingdatahmm.py113CODE
LOWpyro/contrib/bnn/hidden_layer.py61CODE
LOWpyro/contrib/oed/glmm/glmm.py209CODE
LOWpyro/contrib/epidemiology/compartmental.py384CODE
LOWpyro/contrib/funsor/handlers/__init__.py38CODE
LOWpyro/infer/elbo.py110CODE
LOWpyro/infer/trace_mmd.py64CODE
LOWpyro/infer/autoguide/guides.py842CODE
LOWpyro/infer/mcmc/util.py370CODE
LOWpyro/infer/mcmc/nuts.py137CODE
LOWpyro/infer/mcmc/api.py454CODE
LOWpyro/infer/mcmc/api.py663CODE
LOWpyro/infer/mcmc/hmc.py96CODE
LOWpyro/ops/tensor_utils.py98CODE
LOWpyro/ops/tensor_utils.py179CODE
LOWpyro/ops/gaussian.py474CODE
LOWpyro/distributions/diag_normal_mixture_shared_cov.py122CODE
LOWpyro/distributions/mixture.py126CODE
LOWpyro/distributions/mixture.py136CODE
LOWpyro/distributions/mixture.py151CODE
LOWpyro/distributions/diag_normal_mixture.py120CODE
LOWpyro/distributions/torch.py350CODE
LOWpyro/distributions/one_one_matching.py175CODE
LOWpyro/distributions/hmm.py369CODE
LOWpyro/distributions/hmm.py582CODE
LOWpyro/distributions/hmm.py899CODE
LOWpyro/distributions/transforms/spline.py83CODE
AI Slop Vocabulary11 hits · 20 pts
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LOWexamples/contrib/forecast/bart.py50 # simply pass in an empty tensor (see the preprocess() function above).COMMENT
MEDIUMexamples/scanvi/scanvi.py318 # Note we use TraceEnum_ELBO in order to leverage Pyro's machineryCOMMENT
MEDIUMscripts/perf_test.sh22# Use process time whenever possible to make timing more robustCOMMENT
MEDIUMpyro/optim/optim.py40 # provides no comprehensive class hierarchy, and (2) the base classCOMMENT
MEDIUMpyro/contrib/epidemiology/compartmental.py736 @set_approx_sample_thresh(100) # This is robust to gross approximation.STRING
MEDIUMpyro/contrib/funsor/handlers/named_messenger.py94 batch_shape = raw_value.batch_shape # TODO make make this more robustCODE
MEDIUMpyro/infer/inspect.py230 # u is henceforth downstream and d is henceforth upstream.STRING
MEDIUMpyro/infer/mcmc/logger.py35# not be used in advertising or publicity pertaining to distributionCOMMENT
MEDIUMpyro/infer/mcmc/adaptation.py521 # here, we will leverage mass_matrix_sqrt_inverse to reduce the cost toSTRING
MEDIUMpyro/distributions/one_two_matching.py110 # We find that Sinkhorn iteration is more robust and faster than theSTRING
MEDIUMpyro/distributions/one_one_matching.py109 # We find that Sinkhorn iteration is more robust and faster than theSTRING
Excessive Try-Catch Wrapping17 hits · 19 pts
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LOWsetup.py40except Exception:STRING
LOWsetup.py57except Exception as e:STRING
LOWtests/ops/test_gaussian.py382 except Exception:CODE
LOWtests/distributions/test_pickle.py84 except Exception:CODE
LOWprofiler/hmm.py43 except Exception:CODE
LOWpyro/infer/mcmc/util.py497 except Exception:CODE
MEDIUMpyro/infer/mcmc/util.py494def wrapped(sample, *args, **kwargs):CODE
LOWpyro/infer/mcmc/logger.py179 except Exception:CODE
LOWpyro/infer/mcmc/logger.py196 except Exception:CODE
LOWpyro/infer/mcmc/logger.py229 except Exception:CODE
MEDIUMpyro/infer/mcmc/logger.py189def emit(self, record):CODE
MEDIUMpyro/infer/mcmc/logger.py217def emit(self, record):CODE
LOWpyro/infer/mcmc/api.py141 except Exception as e:CODE
LOWpyro/infer/mcmc/hmc.py179 except Exception as e:STRING
LOWpyro/ops/integrator.py85 except Exception as e:STRING
LOWpyro/distributions/torch_patch.py27 except Exception:CODE
MEDIUMpyro/distributions/torch_patch.py24def decorator(new_fn):CODE
Cross-Language Confusion2 hits · 8 pts
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HIGHprofiler/distributions.py124 out.push(result_row)CODE
HIGHpyro/infer/csis.py19 program to minimise KL(model posterior || guide), and inference withSTRING
Redundant / Tautological Comments4 hits · 6 pts
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LOWexamples/cvae/util.py138 # Iterate over data.COMMENT
LOWexamples/cvae/cvae.py150 # Iterate over data.COMMENT
LOWpyro/primitives.py157 # Check if stack is empty.COMMENT
LOWpyro/contrib/examples/multi_mnist.py81 # Set RNG to known state.COMMENT
Slop Phrases2 hits · 5 pts
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MEDIUMpyro/contrib/minipyro.py323# This is a basic implementation of the Evidence Lower Bound, which is theCOMMENT
MEDIUMpyro/infer/inspect.py115 Here is a simple example with no plates. We see every node depends onCODE
Fake / Example Data3 hits · 4 pts
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LOWtests/infer/mcmc/test_mcmc_api.py47 return {"dummy_key": "dummy_value"}CODE
LOWtests/infer/mcmc/test_mcmc_api.py321 assert diagnostics["dummy_key"] == {CODE
LOWtests/infer/mcmc/test_mcmc_api.py322 "chain {}".format(i): "dummy_value" for i in range(num_chains)CODE
Overly Generic Function Names1 hit · 1 pts
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LOWpyro/contrib/examples/polyphonic_data_loader.py58def process_data(base_path, dataset, min_note=21, note_range=88):CODE