spras.config package

Submodules

spras.config.config module

This config file is being used as a singleton. Because python creates a single instance of modules when they’re imported, we rely on the Snakefile instantiating the module. In particular, when the Snakefile calls init_config, it will reassign config to take the value of the actual config provided by Snakemake. After that point, any module that imports this module can access a config option by checking the object’s value. For example

import spras.config.config as config container_framework = config.config.container_settings.framework

will grab the top level registry configuration option as it appears in the config file

class spras.config.config.Config(raw_config: dict[str, Any])

Bases: object

process_algorithms(raw_config: RawConfig): Parse algorithm information Each algorithm’s parameters are provided as a list of dictionaries Defaults are handled in the Python function or class that wraps running that algorithm Keys in the parameter dictionary are strings

process_analysis(raw_config: RawConfig)

process_config(raw_config: RawConfig)

process_datasets(raw_config: RawConfig): Parse dataset information Datasets is initially a list, where each list entry has a dataset label and lists of input files Convert the dataset list into a dict where the label is the key and update the config data structure

spras.config.config.attach_spras_revision(label: str) → str

spras.config.config.init_from_file(filepath)

spras.config.config.init_global(config_dict)

spras.config.config.spras_revision() → str: Gets the revision of the current SPRAS repository. If this file is inside a .git repository, this uses the revision hash. Otherwise, this uses the package version.

spras.config.schema module

Contains the raw pydantic schema for the configuration file.

Using Pydantic as our backing config parser allows us to declaratively type our config, giving us more robust user errors with guarantees that parts of the config exist after parsing it through Pydantic.

We declare models using two classes here: - BaseModel (docs: https://docs.pydantic.dev/latest/concepts/models/) - CaseInsensitiveEnum (see ./util.py)

class spras.config.schema.Algorithm(*, name: str, params: AlgorithmParams)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

params: AlgorithmParams

class spras.config.schema.AlgorithmParams(*, include: bool, directed: bool | None = None, **extra_data: Any)

Bases: BaseModel

directed: bool | None

include: bool

model_config: ClassVar[ConfigDict] = {'extra': 'allow'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class spras.config.schema.Analysis(*, summary: ~spras.config.schema.SummaryAnalysis = SummaryAnalysis(include=False), cytoscape: ~spras.config.schema.CytoscapeAnalysis = CytoscapeAnalysis(include=False), ml: ~spras.config.schema.MlAnalysis = MlAnalysis(include=False, aggregate_per_algorithm=False, components=2, labels=True, kde=False, remove_empty_pathways=False, linkage=<MlLinkage.ward: 'ward'>, metric=<MlMetric.euclidean: 'euclidean'>), evaluation: ~spras.config.schema.EvaluationAnalysis = EvaluationAnalysis(include=False, aggregate_per_algorithm=False))

Bases: BaseModel

cytoscape: CytoscapeAnalysis

evaluation: EvaluationAnalysis

ml: MlAnalysis

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

summary: SummaryAnalysis

class spras.config.schema.CytoscapeAnalysis(*, include: bool)

Bases: BaseModel

include: bool

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class spras.config.schema.Dataset(*, label: ~typing.Annotated[str, ~pydantic.functional_validators.AfterValidator(func=~spras.config.schema.label_validator.<locals>.validate)], node_files: list[str], edge_files: list[str], other_files: list[str], data_dir: str)

Bases: BaseModel

data_dir: str

edge_files: list[str]

label: validate)]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_files: list[str]

other_files: list[str]

class spras.config.schema.EvaluationAnalysis(*, include: bool, aggregate_per_algorithm: bool = False)

Bases: BaseModel

aggregate_per_algorithm: bool

include: bool

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class spras.config.schema.GoldStandard(*, label: ~typing.Annotated[str, ~pydantic.functional_validators.AfterValidator(func=~spras.config.schema.label_validator.<locals>.validate)], node_files: list[str] = [], edge_files: list[str] = [], data_dir: str, dataset_labels: list[str])

Bases: BaseModel

data_dir: str

dataset_labels: list[str]

edge_files: list[str]

label: validate)]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

node_files: list[str]

class spras.config.schema.Locations(*, reconstruction_dir: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

reconstruction_dir: str

class spras.config.schema.MlAnalysis(*, include: bool, aggregate_per_algorithm: bool = False, components: int = 2, labels: bool = True, kde: bool = False, remove_empty_pathways: bool = False, linkage: MlLinkage = MlLinkage.ward, metric: MlMetric = MlMetric.euclidean)

Bases: BaseModel

aggregate_per_algorithm: bool

components: int

include: bool

kde: bool

labels: bool

linkage: MlLinkage

metric: MlMetric

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

remove_empty_pathways: bool

class spras.config.schema.MlLinkage(value)

Bases: CaseInsensitiveEnum

average = 'average'

complete = 'complete'

single = 'single'

ward = 'ward'

class spras.config.schema.MlMetric(value)

Bases: CaseInsensitiveEnum

cosine = 'cosine'

euclidean = 'euclidean'

manhattan = 'manhattan'

class spras.config.schema.RawConfig(*, containers: ~spras.config.container_schema.ContainerSettings, enable_profiling: bool = False, hash_length: int = 7, algorithms: list[~spras.config.schema.Algorithm], datasets: list[~spras.config.schema.Dataset], gold_standards: list[~spras.config.schema.GoldStandard] = [], analysis: ~spras.config.schema.Analysis = Analysis(summary=SummaryAnalysis(include=False), cytoscape=CytoscapeAnalysis(include=False), ml=MlAnalysis(include=False, aggregate_per_algorithm=False, components=2, labels=True, kde=False, remove_empty_pathways=False, linkage=<MlLinkage.ward: 'ward'>, metric=<MlMetric.euclidean: 'euclidean'>), evaluation=EvaluationAnalysis(include=False, aggregate_per_algorithm=False)), reconstruction_settings: ~spras.config.schema.ReconstructionSettings)

Bases: BaseModel

algorithms: list[Algorithm]

analysis: Analysis

containers: ContainerSettings

datasets: list[Dataset]

enable_profiling: bool

gold_standards: list[GoldStandard]

hash_length: int: The length of the hash used to identify a parameter combination

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'use_attribute_docstrings': True}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

reconstruction_settings: ReconstructionSettings

class spras.config.schema.ReconstructionSettings(*, locations: Locations)

Bases: BaseModel

locations: Locations

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class spras.config.schema.SummaryAnalysis(*, include: bool)

Bases: BaseModel

include: bool

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

spras.config.schema.label_validator(name: str): A validator takes in a label and ensures that it contains only letters, numbers, or underscores.

spras.config.util module

class spras.config.util.CaseInsensitiveEnum(value)

Bases: str, Enum

We prefer this over Enum to make sure the config parsing is more relaxed when it comes to string enum values.

spras.config package

Submodules

spras.config.config module

spras.config.schema module

spras.config.util module

Module contents