API Reference¶

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Modules¶

Module	Summary
`pygx.symbolic`	Symbolic classes, containers and functions, plus the operations on them.
`pygx.typing`	Runtime typing for symbolic objects: schemas, fields, and value specs.
`pygx.geno`	Program genome and genotypes.
`pygx.hyper`	Hyper objects: representing template-based object space.
`pygx.detouring`	Redirecting class instantiation to other classes or functions in a scope.
`pygx.patching`	Systematic patching on symbolic values.
`pygx.tuning`	Distributed tuning with pluggable backends.
`pygx.views`	Pluggable rendering of symbolic objects and other Python values.
`pygx.views.html`	HTML views for PyGX objects.
`pygx.views.html.controls`	Common HTML controls.
`pygx.io`	Pluggable IO with unified interfaces.
`pygx.coding`	Runtime compilation, execution and sandboxing of Python source code.
`pygx.concurrent`	Concurrent execution under PyGX's contextual overrides.
`pygx.formatting`	String formatting primitives shared across PyGX.
`pygx.topology`	Tree topology primitives: paths, missing-value sentinels, and traversal.
`pygx.wire`	Wire formats for serializing PyGX values.
`pygx.contextual`	Context-scoped value override for PyGX.
`pygx.error_handling`	Exception classification and contextual error capture for PyGX.
`pygx.instrument`	PyGX's instrumentation sub-packages.
`pygx.instrument.logging`	Logging for PyGX.
`pygx.instrument.monitoring`	Pluggable metric systems for monitoring.
`pygx.algo`	PyGX's algorithm sub-packages.
`pygx.algo.early_stopping`	Early stopping policies for tuning loops.
`pygx.algo.evolution`	Compositional evolutionary algorithms.
`pygx.algo.evolution.selectors`	Common selectors for evolutionary algorithms.
`pygx.algo.evolution.mutators`	Mutators for evolutionary algorithms.
`pygx.algo.evolution.recombinators`	Common recombinators for evolutionary algorithms.
`pygx.algo.evolution.where`	Interfaces and utilities for decision point selection.
`pygx.algo.mutfun`	General mutable functions for evolution and symbolic regression.
`pygx.algo.scalars`	Step-based scalars used as hyper-parameters for PyGX algorithms.