CompoConf

CompoConf is a Python library for compositional configuration management. It provides a type-safe way to define, parse, and instantiate configurations for complex, modular systems.

Features

• Type-safe configuration parsing with dataclass support
• Registry-based class instantiation
• Inheritance-based interface registration
• Support for nested configurations
• Optional OmegaConf integration
• Strict type checking and validation

Installation

pip install compoconf

Quick Start

Here's a simple example of how to use CompoConf:

from dataclasses import dataclass
from compoconf import (
    RegistrableConfigInterface,
    ConfigInterface,
    register_interface,
    register,
)

# Define an interface
@register_interface
class ModelInterface(RegistrableConfigInterface):
    pass

# Define a configuration
@dataclass
class MLPConfig(ConfigInterface):
    hidden_size: int = 128
    num_layers: int = 2

# Register a class with its configuration
@register
class MLPModel(ModelInterface):
    config_class = MLPConfig

    def __init__(self, config):
        self.config = config
        # Initialize model with config...

# Create and use configurations
config = MLPConfig(hidden_size=256)
model = config.instantiate(ModelInterface)

Advanced Usage

Nested Configurations

CompoConf supports nested configurations through type annotations:

@dataclass
class TrainerConfig(ConfigInterface):
    model: ModelInterface.cfgtype  # References the interface type
    learning_rate: float = 0.001

# Parse nested configuration
config = {
    "model": {
        "class_name": "MLPModel",
        "hidden_size": 256
    },
    "learning_rate": 0.01
}

trainer_config = parse_config(TrainerConfig, config)

Type Safety

The library provides comprehensive type checking:

• Validates configuration values against their type annotations
• Ensures registered classes match their interfaces
• Checks for missing required fields
• Supports strict mode for catching unknown configuration keys

Loading config files

parse_file reads a JSON or YAML file (format inferred from the extension) and parses it into a typed config in one call:

from compoconf import parse_file

config = parse_file(ModelConfig, "config.yaml")   # or "config.json"

YAML support requires PyYAML (pip install pyyaml); JSON works out of the box.

Strict scalar parsing

By default, scalar values are coerced to the annotated type (e.g. the string "5" becomes 5 for an int field, and a float is truncated to an int). Pass strict_types=True to parse_config (or parse_file) to reject mismatched scalars instead of silently converting them — useful for catching config typos. The only widening allowed is int → float.

parse_config(MyConfig, {"n": "5"})                     # -> n == 5   (coerced)
parse_config(MyConfig, {"n": "5"}, strict_types=True)  # -> raises ValueError

Enums

enum.Enum fields are supported. A value parses from an existing member, a member name, or a member value, and serializes back to its value (so configs round-trip and stay JSON/YAML-safe):

from enum import Enum
from dataclasses import dataclass
from compoconf import ConfigInterface, parse_config, dump_config

class Color(Enum):
    RED = "red"
    GREEN = "green"

@dataclass
class StyleConfig(ConfigInterface):
    color: Color = Color.RED

parse_config(StyleConfig, {"color": "RED"})    # by name  -> Color.RED
parse_config(StyleConfig, {"color": "green"})  # by value -> Color.GREEN
dump_config(StyleConfig(color=Color.GREEN))    # -> {"class_name": "", "color": "green"}

Built-in scalar types

Beyond the JSON primitives, a few common stdlib scalar types are supported and round-trip through strings (JSON/YAML have no native form for them): pathlib.Path, datetime.datetime / date / time (ISO-8601), decimal.Decimal, and uuid.UUID. They parse from their string form, serialize back to a string via dump_config/asdict, and map to {"type": "string"} (with a format where applicable) in to_json_schema.

from datetime import datetime
from pathlib import Path
from dataclasses import dataclass
from compoconf import ConfigInterface, parse_config, dump_config

@dataclass
class RunConfig(ConfigInterface):
    out_dir: Path = Path(".")
    started: datetime = datetime(2020, 1, 1)

cfg = parse_config(RunConfig, {"out_dir": "runs/exp1", "started": "2020-01-02T03:04:05"})
dump_config(cfg)  # {"class_name": "", "out_dir": "runs/exp1", "started": "2020-01-02T03:04:05"}

JSON Schema export

to_json_schema converts a config class (or any supported annotation) into a JSON Schema (draft 2020-12) document — handy for editor validation/autocomplete and external tooling. The type mapping mirrors parse_config; dataclasses are placed in $defs and referenced via $ref (so shared and recursive configs work), and registered configs pin their class_name for union discrimination.

import json
from compoconf import to_json_schema

schema = to_json_schema(ModelConfig, title="ModelConfig")
json.dumps(schema)   # ready for a JSON Schema validator / editor

OmegaConf Integration

CompoConf optionally integrates with OmegaConf for enhanced configuration handling:

from omegaconf import OmegaConf

# Load configuration from YAML
conf = OmegaConf.load('config.yaml')
config = parse_config(ModelConfig, conf)

Registry System

The registry system allows for dynamic class instantiation based on configuration:

# Register multiple implementations
@dataclass
class CNNConfig(ConfigInterface):
    kernel_size: int = 4

@register
class CNNModel(ModelInterface):
    config_class = CNNConfig

@dataclass
class TransformerConfig(ConfigInterface):
    hidden_size: int = 128
    num_heads: int = 4

@register
class TransformerModel(ModelInterface):
    config_class = TransformerConfig

# Configuration automatically creates correct instance
config = {
    "model": {
        "class_name": "TransformerModel",
        "num_heads": 8,
        "hidden_size": 512
    }
}

Discovering and inspecting registrations

Registration happens as a side effect of importing the module that defines a class. If the module is never imported, the implementation is never registered — which previously surfaced as a confusing downstream error. CompoConf makes this explicit and debuggable:

import compoconf

# Import a module/package so its @register decorators run; returns what got registered.
compoconf.load("mypackage.models")               # single module
compoconf.load("mypackage", recurse=True)        # whole package (walks submodules)
# -> [<class 'mypackage.models.MLPModel'>, <class 'mypackage.models.CNNModel'>, ...]

# Inspect the current registry.
compoconf.registered()                # {ModelInterface: ["CNNModel", "TransformerModel"], ...}
compoconf.registered(ModelInterface)  # ["CNNModel", "TransformerModel"]
print(Registry)                       # full human-readable dump

If a class_name is requested that isn't registered, the error now lists the available options and reminds you to import (or compoconf.load(...)) the module that defines it.

API Reference

Core Classes

• RegistrableConfigInterface: Base class for interfaces that can be configured
• ConfigInterface: Base class for configuration dataclasses
• Registry: Singleton managing registration of interfaces and implementations
• NonStrictDataclass: Base class for dataclasses that accept extra (undeclared) keyword arguments
• FrozenNonStrictDataclass: Immutable (hashable) counterpart of NonStrictDataclass

Decorators

• @register_interface: Register a new interface
• @register: Register an implementation class

Functions

• parse_config(config_class, data, strict=True, strict_types=False): Parse configuration data into typed objects
• parse_file(config_class, path, *, strict=True, strict_types=False, file_format=None): Load a JSON/YAML file and parse it into a typed config
• dump_config(obj): Convert a config (tree of dataclasses) into a pure Python structure (JSON/YAML-ready)
• asdict(obj): Convert a dataclass (including NonStrictDataclass, with extras flattened) to a dictionary
• to_json_schema(config_class, *, title=None): Generate a JSON Schema (draft 2020-12) for a config type
• load(module, *, recurse=True): Import a module/package to run its registrations; returns the classes registered
• registered(interface=None): Introspect the registry (names per interface, or a full mapping)

Enhanced Functionality

Parsing Module

The parsing module has been enhanced to provide more robust and flexible configuration parsing capabilities. Key improvements include:

• Improved handling of nested configurations and unions.
• Enhanced type validation and error reporting.
• Support for parsing configurations from various data sources (e.g., JSON, YAML).

Non-Strict Dataclasses

NonStrictDataclass is a dataclass base that may be extended at runtime with extra keyword arguments beyond its declared fields. Inheriting classes must use @dataclass(init=False) so the custom initializer is preserved:

from dataclasses import dataclass, replace
from compoconf import NonStrictDataclass, asdict

@dataclass(init=False)
class MyConfig(NonStrictDataclass):
    a: int
    b: str = "default_b"

cfg = MyConfig(a=1, c="extra", d=3.14)   # c, d are "extras"
cfg.c                                     # -> "extra"
asdict(cfg)                               # -> {"a": 1, "b": "default_b", "c": "extra", "d": 3.14}
replace(cfg, a=2)                         # extras are preserved across dataclasses.replace

It works with the standard dataclasses helpers (replace, asdict, astuple, fields), as well as copy/deepcopy and pickle.

Extras are untyped. Extra attributes are stored as-is and are never type-checked or re-typed on parsing. Because of this, extras must be plain data (scalars, and arbitrarily nested dict/list/tuple of plain data). Storing a dataclass/config as an extra is not supported — it cannot be serialized or round-tripped through the parser, since there is no type information to reconstruct it.

If you need a nested, typed config that round-trips, declare it as a real field instead of relying on extras. Make it optional by giving it a Type | None = None annotation so it is an explicit, parseable option:

@dataclass(init=False)
class Parent(NonStrictDataclass):
    name: str = "p"
    child: MyConfig | None = None        # typed, optional, round-trips through parse_config

For an immutable variant, inherit from FrozenNonStrictDataclass and decorate subclasses with @dataclass(init=False, frozen=True). Frozen instances are read-only (declared fields and extras) and hashable. Note that a frozen non-strict dataclass must inherit from FrozenNonStrictDataclass — Python forbids a frozen dataclass inheriting from the non-frozen NonStrictDataclass.

Limitations and notes:

• Subclasses must use @dataclass(init=False) (or @dataclass(init=False, frozen=True) for the frozen variant) so the shared custom initializer is inherited rather than regenerated.
• InitVar fields are supported: they are forwarded to __post_init__ (in declaration order) and are not stored. On the frozen variant, a __post_init__ that derives fields must assign via object.__setattr__, as with any frozen dataclass.
• A frozen non-strict dataclass must inherit from FrozenNonStrictDataclass; Python forbids a frozen subclass of the non-frozen NonStrictDataclass.
• Extras are untyped plain data only (scalars / nested dict/list/tuple); see above.

Util Module

The util module includes utilities for dynamic configuration and validation:

• partial_call: Turn a plain function into a registered, config-driven implementation of an interface — the config supplies the function's arguments. See the API docs for the full signature and an example.
• from_annotations: Build and register a config-driven implementation from an existing class, deriving the configuration fields from that class's constructor annotations. See the API docs.
• validate_literal_field / assert_check_literals: Validate that Literal-typed fields hold an allowed value.

from dataclasses import dataclass
from typing import Literal

from compoconf import ConfigInterface, assert_check_literals, validate_literal_field

@dataclass
class OptimizerConfig(ConfigInterface):
    mode: Literal["adam", "sgd"] = "adam"

cfg = OptimizerConfig(mode="adam")
validate_literal_field(cfg, "mode")   # -> True
assert_check_literals(cfg)            # raises compoconf.LiteralError if any Literal field is invalid

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License

Author

Korbinian Pöppel (korbip@korbip.de)