Refactor FunctionalCPD to use dedicated Tabular and LinearGaussian adapters

[daehyun99]

Motivation

• Reduce complexity of FunctionalCPD_Refactor by extracting tabular and linear fitting/representation logic into small adapter classes following the existing SkproAdapter pattern.
• Improve modularity and make it easier to extend or mock individual fitting strategies (tabular, linear, external ML) independently.

Description

• Added TabularAdapter (pgmpy/factors/hybrid/TabularAdapter.py) to encapsulate tabular counting, normalization and TabularCPD construction and representation.
• Added LinearGaussianAdapter (pgmpy/factors/hybrid/LinearGaussianAdapter.py) to encapsulate OLS/MLE coefficient estimation, variance/std computation and LinearGaussianCPD construction and representation.
• Updated FunctionalCPD_Refactor to delegate _fit_tabular and _fit_linear to the new adapters and to simplify fitted __repr__ to include the adapter's representation (keeps SkproAdapter usage unchanged for external models).
• Updated tests (pgmpy/tests/test_models/test_FunctionalBayesianNetwork_Refactor.py) to assert that fitted objects are adapter instances and to validate numeric results via adapter.fitted_cpd_ to preserve existing coverage.

Testing

• Ran pytest -v pgmpy/tests/test_models/test_FunctionalBayesianNetwork_Refactor.py, which completed with 2 passed and 1 skipped tests.
• Attempted to run pre-commit run --all-files but the pre-commit command was not available in the execution environment, so hooks were not run here.

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Codex Task

[gemini-code-assist]

Code Review

This pull request refactors the FunctionalCPD class by delegating its tabular and linear fitting logic to new TabularAdapter and LinearGaussianAdapter classes, which improves modularity and simplifies the main class. The review feedback identifies a potential division-by-zero issue in TabularAdapter when processing empty or NaN data and suggests adding a missing numpy import to support the fix. Additionally, a formatting improvement is recommended for the LinearGaussianAdapter string representation to handle cases without evidence variables more cleanly.

[gemini-code-assist]

If counts.values.sum() is zero (e.g., when the input data for the variable is empty or all NaN), this division will result in NaN values and a RuntimeWarning. The case with parents handles division by zero by using .replace(0, 1). A similar safeguard should be added here. Using np.nan_to_num will safely convert NaN to 0.0.

Suggested change

	probs = counts.values / counts.values.sum()
	probs = np.nan_to_num(counts.values / counts.values.sum())

[gemini-code-assist]

The current __repr__ format for a fitted model without evidence variables results in P(variable | ), which is slightly awkward. It would be cleaner to omit the | when there are no evidence variables, similar to how it's handled in TabularAdapter.

Suggested change

	return (
	f"<LinearGaussianAdapter representing P({cpd.variable} | {', '.join(cpd.evidence)}) "
	f"~ N({beta_str}, std={cpd.std:.3f}) at {hex(id(self))}>"
	)
	evidence_str = f" | {', '.join(cpd.evidence)}" if cpd.evidence else ""
	return (
	f"<LinearGaussianAdapter representing P({cpd.variable}{evidence_str}) "
	f"~ N({beta_str}, std={cpd.std:.3f}) at {hex(id(self))}>"
	)

[gemini-code-assist]

The file uses numpy features (e.g., counts.values.sum()), and a suggested fix below requires an explicit np reference. It's best practice to add import numpy as np at the top of the file for clarity and to support the fix.

Suggested change

	from pgmpy.estimators import MaximumLikelihoodEstimator as MLE
	import numpy as np
	from pgmpy.estimators import MaximumLikelihoodEstimator as MLE