Configuration class#

class Config(polydfe_spectra_config: str = None, polydfe_init_file: str = None, polydfe_init_file_id: int = 1, sfs_neut: Spectra | Spectrum = None, sfs_sel: Spectra | Spectrum = None, intervals_del: Tuple[float, float, int] = (-100000000.0, -1e-05, 1000), intervals_ben: Tuple[float, float, int] = (1e-05, 10000.0, 1000), integration_mode: Literal['midpoint', 'quad'] = 'midpoint', linearized: bool = True, model: Parametrization | str = 'GammaExpParametrization', seed: int = 0, x0: Dict[str, Dict[str, float]] = {}, bounds: Dict[str, Tuple[float, float]] = {}, scales: Dict[str, Literal['lin', 'log', 'symlog']] = {}, loss_type: Literal['likelihood', 'L2'] = 'likelihood', opts_mle: dict = {}, n_runs: int = 10, fixed_params: Dict[str, Dict[str, float]] = {}, shared_params: List[SharedParams] = [], covariates: List[Covariate] = [], do_bootstrap: bool = False, n_bootstraps: int = 100, n_bootstrap_retries: int = 2, parallelize: bool = True, **kwargs)[source]#

Bases: object

Configuration class to be used for BaseInference and JointInference.

__init__(polydfe_spectra_config: str = None, polydfe_init_file: str = None, polydfe_init_file_id: int = 1, sfs_neut: Spectra | Spectrum = None, sfs_sel: Spectra | Spectrum = None, intervals_del: Tuple[float, float, int] = (-100000000.0, -1e-05, 1000), intervals_ben: Tuple[float, float, int] = (1e-05, 10000.0, 1000), integration_mode: Literal['midpoint', 'quad'] = 'midpoint', linearized: bool = True, model: Parametrization | str = 'GammaExpParametrization', seed: int = 0, x0: Dict[str, Dict[str, float]] = {}, bounds: Dict[str, Tuple[float, float]] = {}, scales: Dict[str, Literal['lin', 'log', 'symlog']] = {}, loss_type: Literal['likelihood', 'L2'] = 'likelihood', opts_mle: dict = {}, n_runs: int = 10, fixed_params: Dict[str, Dict[str, float]] = {}, shared_params: List[SharedParams] = [], covariates: List[Covariate] = [], do_bootstrap: bool = False, n_bootstraps: int = 100, n_bootstrap_retries: int = 2, parallelize: bool = True, **kwargs)[source]#

Create config object.

Parameters:

polydfe_spectra_config (str) – Path to polyDFE SFS config file.
polydfe_init_file (str) – Path to polyDFE init file.
polydfe_init_file_id (int) – ID of polyDFE init file.
sfs_neut (Spectra | Spectrum) – Neutral SFS. Note that we require monomorphic counts to be specified in order to infer the mutation rate.
sfs_sel (Spectra | Spectrum) – Selected SFS. Note that we require monomorphic counts to be specified in order to infer the mutation rate.
intervals_del (Tuple[float, float, int]) – Integration intervals for deleterious mutations in log space.
intervals_ben (Tuple[float, float, int]) – Integration intervals for beneficial mutations in log space.
integration_mode (Literal['midpoint', 'quad']) – Integration mode, quad not recommended
linearized (bool) – Whether to use the linearized version of the DFE, False not recommended.
model (Parametrization | str) – Parametrization of the DFE.
seed (int) – Seed for the random number generator. Use None for no seed.
x0 (Dict[str, Dict[str, float]]) – Dictionary of initial values in the form {type: {param: value}}
bounds (Dict[str, Tuple[float, float]]) – Bounds for the optimization in the form {param: (lower, upper)}
scales (Dict[str, Literal['lin', 'log', 'symlog']]) – Scales for the optimization in the form {param: scale}
loss_type (Literal['likelihood', 'L2']) – Loss function to use.
opts_mle (dict) – Options for the optimization.
n_runs (int) – Number of independent optimization runs out of which the best one is chosen. The first run will use the initial values if specified. Consider increasing this number if the optimization does not produce good results.
fixed_params (Dict[str, Dict[str, float]]) – Fixed parameters for the optimization.
shared_params (List[SharedParams]) – Shared parameters for the optimization.
covariates (List[Covariate]) – Covariates for the optimization.
do_bootstrap (bool) – Whether to do bootstrapping automatically.
n_bootstraps (int) – Number of bootstraps.
n_bootstrap_retries (int) – Number of retries for bootstraps that did not terminate normally.
parallelize (bool) – Whether to parallelize the optimization.
kwargs – Additional keyword arguments which are ignored.

update(**kwargs)[source]#

Update config with given data.

Parameters:: kwargs – Data to update.
Return type:: Config
Returns:: Updated config.

parse_polydfe_init_file(file: str, id: int = 1, type='all')[source]#

Parse polyDFE init file. This will define the initial parameters and which ones will be held fixed during the optimization.

Parameters:

type – Type of parameters to parse for.
id (int) – ID of the init file.
file (str) – Path to the init file.

create_polydfe_init_file(file: str, n: int, type: str = 'all')[source]#

Create an init file for polyDFE.

Parameters:

type (str) – Type to use for the init file.
n (int) – SFS samples size.
file (str) – Path to the init file to be created.

parse_polydfe_sfs_config(file: str)[source]#

Parse frequency spectra and mutational target site from polyDFE configuration file.

Parameters:: file (str) – Path to the polyDFE config file.

create_polydfe_sfs_config(file: str)[source]#

Create a sfs config file for polyDFE.

Parameters:: file (str) – Path to the sfs config file to be created.

to_dict()[source]#

Represent config as dictionary.

Return type:: dict
Returns:: Dictionary representation of config.

to_json()[source]#

Create JSON representation of object.

Return type:: str
Returns:: JSON string

to_yaml()[source]#

Create YAML representation of object.

Return type:: str
Returns:: YAML string

to_file(file: str)[source]#

Save object to file.

Parameters:: file (str) – Path to file.

static from_dict(data: dict)[source]#

Load config from dictionary.

Return type:: Config
Returns:: Config object.

static from_json(data: str)[source]#

Load config from JSON str.

Parameters:: data (str) – JSON string.
Return type:: Config
Returns:: Config object.

static from_yaml(data: str)[source]#

Load config from YAML str.

Parameters:: data (str) – YAML string.
Return type:: Config
Returns:: Config object.

classmethod from_file(file: str, cache: bool = True)[source]#

Load object from file.

Parameters:

file (str) – Path to file, possibly gzipped.
cache (bool) – Whether to use the cache if available.

Return type:

Config

Returns:

Config object.

get_polydfe_model()[source]#

Get the model name in polyDFE that corresponds to the configured DFE parametrization.

Return type:: str
Returns:: polyDFE model name.

Configuration class

Contents

Configuration class#