pgmpy/pgmpy

[ENH] Update `preprocess_data` method 1 day ago

AI Summary: This issue proposes to refactor the `preprocess_data` method in `pgmpy/utils/utils.py`. The goal is to simplify the existing if-else structure used to determine if dataframe variables are numerical, categorical, or ordinal.

Complexity: 2/5

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Make `lru_cache` max size configurable by the user 5 days ago

AI Summary: This issue proposes making the maximum size of the `lru_cache` in the `structure_score` module configurable. Currently, the cache size is fixed at 1e5, which can be a memory concern on systems with limited RAM. Allowing users to set this size during score class initialization would provide greater flexibility and resource management.

Complexity: 2/5

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] `hungary_chickenpox` dataset failing to load 5 days ago

AI Summary: The `hungary_chickenpox` dataset is failing to load because the underlying ground truth data, when parsed as a Directed Acyclic Graph (DAG), contains cycles. This indicates an issue with the format or content of the ground truth definition for this specific dataset.

Complexity: 3/5

Good First Issue Datasets

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] Fixing the doctest errors in pgmpy/sampling/Sampling.py 5 days ago

AI Summary: This issue reports doctest failures in the `pgmpy/sampling/Sampling.py` file. Specifically, `forward_sample` produces unexpected output, and `likelihood_weighted_sample` and `rejection_sample` raise a `TypeError` due to an unexpected keyword argument 'return_type'. The goal is to fix these doctest errors.

Complexity: 2/5

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[Refactor] Change to use `pytest` from `unittest` 6 days ago

Tests Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add a variant `double` to SHD metric 6 days ago

AI Summary: This issue proposes adding a new variant to the Structural Hamming Distance (SHD) metric. The current SHD counts all edge differences as 1, but the enhancement would allow for reverse edge directions to be counted as 2, reflecting the cost of removing an edge and adding another.

Complexity: 2/5

Good First Issue Metrics

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Refactor the `ExhaustiveSearch` algorithm 12 days ago

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Incorrect contingency table computation in `power_divergence` test 15 days ago

AI Summary: A bug has been identified in the `power_divergence` test within the pgmpy library. The contingency table computation incorrectly handles edge cases where certain combinations of variable states are absent within a stratum. This leads to incomplete contingency tables, failing to represent all possible states of the variables.

Complexity: 2/5

Bug Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Incorrect tags for `tubingen/81`dataset 17 days ago

AI Summary: This bug report indicates that the `tubingen/81` dataset is incorrectly tagged as not having missing data, when in fact it does. The issue requires correcting the existing tag and implementing a new test case to automatically verify the presence of NaN values against the dataset's tag.

Complexity: 2/5

Bug Good First Issue Datasets

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Refactor the `MMHC` algorithm 18 days ago

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Improve search space handling in PC 19 days ago

Good First Issue status: stale

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Remove `state_names` arguemnt from ApproxInference.query 21 days ago

Good First Issue Probabilistic Inference status: stale

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Fix apply_meeks_rule example in PDAG class 21 days ago

Bug Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Fix doctests across the package 22 days ago

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Refactor `TreeSearch` algorithm 22 days ago

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Fix copy method in base graph classes 23 days ago

Bug Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add causal estimation + causal discovery datasets 23 days ago

Good First Issue Datasets

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] Fixing the doctest errors in pgmpy/prediction/DoubleMLRegressor.py 23 days ago

AI Summary: This issue addresses failing doctests in the `DoubleMLRegressor.py` file, specifically related to the `fit` method. The error indicates a `UserWarning` from scikit-learn about fitting `LinearRegression` without feature names, suggesting a potential mismatch in how feature names are handled or expected during the fitting process.

Complexity: 2/5

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Advanced filtering options for `list_models` and `list_datasets` methods 23 days ago

Good First Issue Datasets Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] Fix failing example notebooks 23 days ago

AI Summary: This issue addresses failing example notebooks within the `examples` directory. The goal is to fix the errors in the listed notebooks and ensure they pass tests by adding them to `notebook_test_list.txt`. Contributors are encouraged to pick a notebook and create a separate issue for their work.

Complexity: 2/5

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] Fixing the doctest errors in pgmpy/factors/hybrid/FunctionalCPD.py 24 days ago

AI Summary: This issue addresses a doctest failure in the `FunctionalCPD.sample` method within the pgmpy library. The error occurs because the `torch.as_tensor` function is being passed a string for the `dtype` argument instead of a `torch.dtype` object. The fix involves correcting the data type passed to `torch.as_tensor` to resolve the `TypeError`.

Complexity: 1/5

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add simulated datasets 24 days ago

Good First Issue Datasets

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add option to specify `root_nodes` in `ExpertKnowledge` 24 days ago

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add methods to read and write PDAGs from dagitty syntax 24 days ago

Good First Issue Model status: stale

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add an extension template for Conditional Independence tests 24 days ago

Good First Issue Maintenance

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] Fixing the doctest errors in pgmpy/utils/state_name.py 24 days ago

AI Summary: This issue addresses failing doctests in the `pgmpy/utils/state_name.py` file. The doctest for `state_name.StateNameMixin.store_state_names` is failing because it expects no output, but it's receiving a dictionary of state names. This indicates a discrepancy between the expected and actual behavior of the `store_state_names` method.

Complexity: 2/5

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] Fixing the doctest errors in pgmpy/factors/discrete/DiscreteFactor.py 24 days ago

AI Summary: This issue is a documentation fix for doctest errors in the `DiscreteFactor` class. The doctest is failing because the memory address of the `DiscreteFactor` object is being compared, which is not stable. This likely requires updating the expected output in the doctest to be more robust or to remove the address comparison.

Complexity: 1/5

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] Fixing the doctest error in pgmpy/factors/discrete/JointProbabilityDistribution.py 25 days ago

AI Summary: This issue addresses failing doctests in the `JointProbabilityDistribution` class within pgmpy. The failures occur in the `copy` and `minimal_imap` methods, specifically related to comparing boolean values and object representations. The goal is to fix these doctests to ensure the code behaves as expected.

Complexity: 2/5

Tests Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add a `__repr__` and `__str__` methods for `ExpertKnowledge` class 25 days ago

AI Summary: This issue proposes adding `__repr__` and `__str__` methods to the `ExpertKnowledge` class. These methods will improve the usability of the class by providing concise summaries (`__repr__`) and full details (`__str__`) when inspecting `ExpertKnowledge` objects.

Complexity: 1/5

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Add tests for TabularCPD.__str__ 26 days ago

Tests Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Port Instrumental Variables from `CausalInference` to `identification` 26 days ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add method to test whether causal discovery method is complaint 30 days ago

Tests Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add ACIC 2016 Dataset 30 days ago

Good First Issue Datasets

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add a `get_random_cpds` method to `LinearGaussianBayesianNetwork` 30 days ago

Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add `estimator` argument to GCM conditional independence test about 1 month ago

AI Summary: This enhancement proposes adding an `estimator` argument to the GCM conditional independence test in pgmpy. This will allow users to specify a scikit-learn compatible estimator for computing residuals, making the test more flexible than the current default least squares regression. The new argument will leverage the `fit` and `predict` methods of the provided estimator.

Complexity: 2/5

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add `estimator` argument to pillai trace conditional independence test about 1 month ago

AI Summary: This enhancement proposes adding an `estimator` argument to the Pillai trace conditional independence test, similar to a previous issue. The goal is to allow the use of any scikit-learn estimator, with a specific requirement for a `predict_proba` method if discrete variables are present.

Complexity: 3/5

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add a `predict_probability` method to `LinearGaussianBayesianNetwork` about 1 month ago

Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add `get_stats` method to `DAG` class about 1 month ago

Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Throw error for incorrect argument in `list_models` and `list_datasets` methods about 1 month ago

AI Summary: This enhancement request aims to improve the `list_models` and `list_datasets` methods in pgmpy. Currently, these methods fail silently when provided with an incorrect filtering argument (tag). The goal is to implement checks to validate these arguments and throw a meaningful error to the user when an unrecognized tag is used, providing better feedback and usability.

Complexity: 2/5

Good First Issue Datasets Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Refactor ExpertInLoop Algorithm about 1 month ago

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Causal Games notebook throws errors about 1 month ago

Bug Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Improve iteroperability with dagitty syntax about 1 month ago

Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Port identification methods from `inference.CausalInference` to `identification` about 1 month ago

Good First Issue Causal Identification status: stale

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Some structure scores fail for integer nodes about 1 month ago

Good First Issue Causal Discovery status: stale

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Fail to using ExpectationMaximization with missing data about 1 month ago

Enhancement Good First Issue Causal Discovery status: stale

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Add functions to compute Structural Hamming Distance and Structural Intervention Distance about 1 month ago

Good First Issue status: stale

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Improve error message for `load_model` method about 1 month ago

AI Summary: This issue proposes to improve the error handling for the `load_model` method in `pgmpy`. Currently, providing a non-existent model name results in a generic `IndexError`. The enhancement would involve raising a more informative `ValueError` that guides the user to use `list_models()` to find available models.

Complexity: 1/5

Good First Issue Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Cache conditional independence (CI) test results about 1 month ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Example model loader from dagbase about 1 month ago

Good First Issue Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Reduce dependencies for the package about 1 month ago

Enhancement Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

`DAG.get_independencies()` improvements about 1 month ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Separate direct effect and total effect Instrumental variable about 1 month ago

Enhancement Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Port Conditional Instrumental Variables from `CausalInference` to `identification` about 1 month ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Add type hinting in the codebase about 1 month ago

Enhancement Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Failing doctests in `DAG.py` about 1 month ago

AI Summary: This issue reports that multiple doctests within the `DAG.py` file are failing. The goal is to update these doctests so that all provided examples execute successfully when run with `python -m doctest pgmpy/base/DAG.py`.

Complexity: 2/5

Tests Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add generator for enumerating all DAGs represented by a PDAG/CPDAG about 1 month ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] The behavior of `DiscreteBayesianNetwork.do` is incorrect about 1 month ago

AI Summary: This bug report identifies an incorrect implementation in the `DiscreteBayesianNetwork.do` method. The current marginalization of do states from child states is wrong; instead, child node CPDs should be conditioned on the parent node's do state. This suggests a fundamental misunderstanding or misapplication of the do-calculus in the current code.

Complexity: 3/5

Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Fix docstring examples in `AncestralBase.py` about 2 months ago

Bug Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Fix docstring example in `structure_score.py` about 2 months ago

AI Summary: The `structure_score.py` docstring contains an example that uses an incorrect scoring method ('bic-g') for a discrete dataset. The error message indicates that 'bic-d' should be used instead. This issue requires updating the docstring to reflect the correct scoring method for discrete models.

Complexity: 1/5

Good First Issue Metrics Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Fix docstring example in `frontdoor.py` about 2 months ago

Bug Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Change from `exposure` <-> `exposures` in Adjustment.py about 2 months ago

Bug Good First Issue Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] The dataset template file doesn't ask for `base_url` about 2 months ago

Bug Good First Issue Datasets Maintenance

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Doctests for pgmpy about 2 months ago

Enhancement Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add casual chambers datasets about 2 months ago

Good First Issue Datasets

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Define a JSON schema for our file format for storing `LinearGaussianBayesianNetwork` 2 months ago

Good First Issue Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add example models from bnRep repository 2 months ago

Good First Issue Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] `load` and `save` methods for `LinearGaussianBayesianNetwork` 2 months ago

AI Summary: This enhancement request aims to add `load` and `save` methods to the `LinearGaussianBayesianNetwork` (LGBN) class, similar to those available for `DiscreteBayesianNetwork`. These methods will serialize and deserialize LGBN models using a previously defined JSON file format. The implementation should leverage existing JSON loading code and update related components to utilize the new methods.

Complexity: 2/5

Enhancement Good First Issue Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Refactor example models 2 months ago

AI Summary: This issue proposes refactoring remaining example models into the `pgmpy/example_models` module, similar to recently moved models. Developers need to create individual files for each listed discrete, continuous, and DAG model, defining their respective tags.

Complexity: 2/5

Good First Issue Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Create R script to convert bnRep models to BIF/JSON format 2 months ago

Good First Issue Example Models

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Refactor GES algorithm 2 months ago

AI Summary: This issue proposes refactoring the GES (Greedy Equivalence Search) algorithm to align with the scikit-learn API. The goal is to make the algorithm more integrated and user-friendly within the scikit-learn ecosystem, with further details available in a parent issue.

Complexity: 3/5

Good First Issue Causal Discovery

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add datasets 3 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Support node-specific priors in EM (Dirichlet/ BDeu) 5 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Failing Doctests in NaiveAdjustment Reg 5 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

Causal Structure for sklearn checks in DML 6 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[Refactor] Refactoring the way `outcomes` and `exposures` are handled among node roles 6 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[DOC] The example notebook on data simulation is missing "missing data" 6 months ago

Good First Issue category: Documentation

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Rename `DAG._get_ancestors_of` to `DAG.ancestors` 6 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Improve interoperability of the output of `DynamicBayesianNetwork.simulate` method 6 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add a method to import example datasets 7 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add `_GraphRolesMixin` to other causal graph classes 8 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Change default scoring method to `BIC-*` 8 months ago

Good First Issue category: Learning

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add variable roles to `ADMG` class 8 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Port Frontdoor adjustment from `CausalInference` to `identification` 8 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add variable roles to `PDAG` class 8 months ago

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add methods to convert DAGs to dagitty and lavaan representation 9 months ago

Good First Issue category: Base Model

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[ENH] Add a log_likelihood method for LinearGaussianBayesianNetwork 10 months ago

AI Summary: Implement a `log_likelihood` method for the `LinearGaussianBayesianNetwork` class in the pgmpy Python library, mirroring the functionality already added (or planned) for other Bayesian network types. This involves calculating the log-likelihood of a dataset given a fitted Linear Gaussian Bayesian Network model.

Complexity: 4/5

Good First Issue category: Inference

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

[BUG] Read-write methods fail when state names have commas in them 10 months ago

AI Summary: The task is to fix a bug in the pgmpy library where models with commas in state names cannot be saved and loaded correctly. The solution involves improving the handling of commas in state names during file I/O, potentially by adding warnings or using a more robust delimiter.

Complexity: 4/5

Good First Issue

pgmpy/pgmpy

3.2K

Python library for Causal AI

Python

#bayesian-networks#causal-discovery#causal-effect#causal-identification#causal-inference#causal-models#causal-prediction#causal-validation#graphical-models#hacktoberfest#mixed-data#probabilistic-inference#python#simulation#synthetic-data

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