base on Python Library for Causal and Probabilistic Modeling using Bayesian Networks <div> <a href="https://www.pgmpy.org"><img src="https://raw.githubusercontent.com/pgmpy/pgmpy/dev/logo/logo_color.png" width="175" align="left" /></a> pgmpy is a Python library for causal and probabilistic modeling using Bayesian Networks and related models. It provides a uniform API for building, learning, and analyzing models such as Bayesian Networks, Dynamic Bayesian Networks, Directed Acyclic Graphs (DAGs), and Structural Equation Models(SEMs). By integrating tools from both probabilistic inference and causal inference, pgmpy enables users to seamlessly transition between predictive and interventional analyses. </div> <br/> <br/> <div align="center">  [](https://codecov.io/gh/pgmpy/pgmpy) [](https://pypi.org/project/pgmpy/) [](https://anaconda.org/conda-forge/pgmpy) [](https://pypi.org/project/pgmpy/) [](https://github.com/pgmpy/pgmpy/blob/dev/LICENSE) [](https://pypistats.org/packages/pgmpy) [](http://pgmpy.org/pgmpy-benchmarks/) </div> <div align="center"> [](https://discord.gg/DRkdKaumBs) [](https://pgmpy.org) [](https://github.com/pgmpy/pgmpy/tree/dev/examples) [](https://github.com/pgmpy/pgmpy_notebook) </div> ### Key Features | Feature | Description | |--------|-------------| | [**Causal Discovery / Structure Learning**](https://pgmpy.org/examples/Structure%20Learning%20in%20Bayesian%20Networks.html) | Learn the model structure from data, with optional integration of **expert knowledge**. | | [**Causal Validation**](https://pgmpy.org/metrics/metrics.html) | Assess how compatible the causal structure is with the data. | | [**Parameter Learning**](https://pgmpy.org/examples/Learning%20Parameters%20in%20Discrete%20Bayesian%20Networks.html) | Estimate model parameters (e.g., conditional probability distributions) from observed data. | | [**Probabilistic Inference**](https://pgmpy.org/examples/Inference%20in%20Discrete%20Bayesian%20Networks.html) | Compute posterior distributions conditioned on observed evidence. | | [**Causal Inference**](https://pgmpy.org/examples/Causal%20Inference.html) | Compute interventional and counterfactual distributions using do-calculus. | | [**Simulations**](https://github.com/pgmpy/pgmpy/blob/dev/examples/Simulating_Data.ipynb) | Generate synthetic data under specified evidence or interventions. | ### Resources and Links - **Example Notebooks:** [Examples](https://github.com/pgmpy/pgmpy/tree/dev/examples) - **Tutorial Notebooks:** [Tutorials](https://github.com/pgmpy/pgmpy_notebook) - **Blog Posts:** [Medium](https://medium.com/@ankurankan_23083) - **Documentation:** [Website](https://pgmpy.org/) - **Bug Reports and Feature Requests:** [GitHub Issues](https://github.com/pgmpy/pgmpy/issues) - **Questions:** [discord](https://discord.gg/DRkdKaumBs) · [Stack Overflow](https://stackoverflow.com/questions/tagged/pgmpy) ## Quickstart ### Installation pgmpy is available on both [PyPI](https://pypi.org/project/pgmpy/) and [anaconda](https://anaconda.org/conda-forge/pgmpy). To install from PyPI, use: ```bash pip install pgmpy ``` To install from conda-forge, use: ```bash conda install conda-forge::pgmpy ``` ### Discrete Data ```python from pgmpy.utils import get_example_model # Load a Discrete Bayesian Network and simulate data. discrete_bn = get_example_model('alarm') alarm_df = discrete_bn.simulate(n_samples=100) # Learn a network from simulated data. from pgmpy.estimators import PC dag = PC(data=alarm_df).estimate(ci_test='chi_square', return_type='dag') # Learn the parameters from the data. dag_fitted = dag.fit(alarm_df) dag_fitted.get_cpds() # Drop a column and predict using the learned model. evidence_df = alarm_df.drop(columns=['FIO2'], axis=1) pred_FIO2 = dag_fitted.predict(evidence_df) ``` ### Linear Gaussian Data ```python # Load an example Gaussian Bayesian Network and simulate data gaussian_bn = get_example_model('ecoli70') ecoli_df = gaussian_bn.simulate(n_samples=100) # Learn the network from simulated data. from pgmpy.estimators import PC dag = PC(data=ecoli_df).estimate(ci_test='pearsonr', return_type='dag') # Learn the parameters from the data. from pgmpy.models import LinearGausianBayesianNetwork gaussian_bn = LinearGausianBayesianNetwork(dag.edges()) dag_fitted = gaussian_bn.fit(ecoli_df) dag_fitted.get_cpds() # Drop a column and predict using the learned model. evidence_df = ecoli_df.drop(columns=['ftsJ'], axis=1) pred_ftsJ = dag_fitted.predict(evidence_df) ``` ## Contributing We welcome all contributions --not just code-- to pgmpy. Please refer out [contributing guide](https://github.com/pgmpy/pgmpy/blob/dev/Contributing.md) for more details. We also offer mentorship for new contributors and maintain a list of potential [mentored projects](https://github.com/pgmpy/pgmpy/wiki/Mentored-Projects). If you are interested in contributing to pgmpy, please join our [discord](https://discord.gg/DRkdKaumBs) server and introduce yourself. We will be happy to help you get started. ", Assign "at most 3 tags" to the expected json: {"id":"13874","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"