coco_pipe.decoding.configs

Comprehensive Pydantic models for strict validation of Decoding/ML experiments.

Key Components: - ModelConfigs: extensive hyperparameters for each estimator. - ExperimentConfig: Top-level configuration for the entire analysis workflow.

Attributes

AtomicEstimator
EstimatorConfigType

Classes

BaseEstimatorConfig	Base configuration for any estimator.
LinearMixin	Common parameters for linear models.
RegularizedLinearMixin	Parameters for regularized linear models.
TreeMixin	Common parameters for Tree-based models.
SupportVectorMixin	Common parameters for Support Vector Machines.
SGDMixin	!!! abstract "Usage Documentation"
LogisticRegressionConfig	Base configuration for any estimator.
RandomForestClassifierConfig	Base configuration for any estimator.
SVCConfig	Base configuration for any estimator.
KNeighborsClassifierConfig	Base configuration for any estimator.
GradientBoostingClassifierConfig	Base configuration for any estimator.
SGDClassifierConfig	Base configuration for any estimator.
MLPClassifierConfig	Base configuration for any estimator.
GaussianNBConfig	Base configuration for any estimator.
LDAConfig	Base configuration for any estimator.
AdaBoostClassifierConfig	Base configuration for any estimator.
DummyClassifierConfig	Base configuration for any estimator.
LPFTConfig	Configuration for Linear-Probe Fine-Tuning (LP-FT).
SkorchClassifierConfig	Configuration for generic PyTorch wrappers via Skorch.
SlidingEstimatorConfig	Configuration for MNE's SlidingEstimator.
GeneralizingEstimatorConfig	Configuration for MNE's GeneralizingEstimator.
LinearRegressionConfig	Base configuration for any estimator.
RidgeConfig	Base configuration for any estimator.
LassoConfig	Base configuration for any estimator.
ElasticNetConfig	Base configuration for any estimator.
RandomForestRegressorConfig	Base configuration for any estimator.
SVRConfig	Base configuration for any estimator.
GradientBoostingRegressorConfig	Base configuration for any estimator.
SGDRegressorConfig	Base configuration for any estimator.
MLPRegressorConfig	Base configuration for any estimator.
DummyRegressorConfig	Base configuration for any estimator.
DecisionTreeRegressorConfig	Base configuration for any estimator.
KNeighborsRegressorConfig	Base configuration for any estimator.
ExtraTreesRegressorConfig	Base configuration for any estimator.
HistGradientBoostingRegressorConfig	Base configuration for any estimator.
AdaBoostRegressorConfig	Base configuration for any estimator.
BayesianRidgeConfig	Base configuration for any estimator.
ARDRegressionConfig	Base configuration for any estimator.
TemporalConfig	Configuration for temporal decoding (Sliding/Generalizing).
CVConfig	Cross-validation settings.
TuningConfig	Hyperparameter Tuning Configuration.
FeatureSelectionConfig	Configuration for Sequential Feature Selection.
ExperimentConfig	Master configuration for a Decoding Experiment.

Module Contents

class coco_pipe.decoding.configs.BaseEstimatorConfig(/, **data: Any)

Bases: pydantic.BaseModel

Base configuration for any estimator.

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

random_state: int | None = None

class coco_pipe.decoding.configs.LinearMixin(/, **data: Any)

Bases: pydantic.BaseModel

Common parameters for linear models.

fit_intercept: bool = True

copy_X: bool = True

n_jobs: int | None = None

class coco_pipe.decoding.configs.RegularizedLinearMixin(/, **data: Any)

Bases: LinearMixin

Parameters for regularized linear models.

tol: float = 0.001

max_iter: int | None = None

solver: str = 'auto'

warm_start: bool = False

positive: bool = False

class coco_pipe.decoding.configs.TreeMixin(/, **data: Any)

Bases: pydantic.BaseModel

Common parameters for Tree-based models.

n_estimators: int = None

max_depth: int | None = None

min_samples_split: int | float = 2

min_samples_leaf: int | float = 1

min_weight_fraction_leaf: float = 0.0

max_features: str | int | float | None = 'sqrt'

max_leaf_nodes: int | None = None

min_impurity_decrease: float = 0.0

ccp_alpha: float = 0.0

n_jobs: int | None = None

verbose: int = 0

warm_start: bool = False

class coco_pipe.decoding.configs.SupportVectorMixin(/, **data: Any)

Bases: pydantic.BaseModel

Common parameters for Support Vector Machines.

C: float = None

kernel: Literal['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'] = 'rbf'

degree: int = 3

gamma: str | float = 'scale'

coef0: float = 0.0

tol: float = 0.001

verbose: bool = False

max_iter: int = -1

shrinking: bool = True

cache_size: float = 200

class coco_pipe.decoding.configs.SGDMixin(/, **data: Any)

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”

[Models](../concepts/models.md)

A base class for creating Pydantic models.

__class_vars__

The names of the class variables defined on the model.

__private_attributes__

Metadata about the private attributes of the model.

__signature__

The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__

The core schema of the model.

__pydantic_custom_init__

Whether the model has a custom __init__ function.

__pydantic_decorators__

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__

A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__

The name of the post-init method for the model, if defined.

__pydantic_root_model__

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__

The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__

The names of fields explicitly set during instantiation.

__pydantic_private__

Values of private attributes set on the model instance.

loss: str = 'hinge'

penalty: Literal['l2', 'l1', 'elasticnet', 'null'] = 'l2'

alpha: float = 0.0001

l1_ratio: float = 0.15

fit_intercept: bool = True

max_iter: int = 1000

tol: float = 0.001

shuffle: bool = True

verbose: int = 0

epsilon: float = 0.1

n_jobs: int | None = None

learning_rate: str = 'optimal'

eta0: float = 0.0

power_t: float = 0.5

early_stopping: bool = False

validation_fraction: float = 0.1

n_iter_no_change: int = 5

warm_start: bool = False

average: bool = False

class coco_pipe.decoding.configs.LogisticRegressionConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['LogisticRegression'] = 'LogisticRegression'

penalty: Literal['l1', 'l2', 'elasticnet', 'none', None] = 'l2'

dual: bool = False

tol: float = 0.0001

C: float = None

fit_intercept: bool = True

intercept_scaling: float = 1.0

class_weight: Dict | str | None = None

solver: Literal['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'] = 'lbfgs'

max_iter: int = 100

multiclass: Literal['auto', 'ovr', 'multinomial'] = 'auto'

verbose: int = 0

warm_start: bool = False

n_jobs: int | None = None

l1_ratio: float | None = None

class coco_pipe.decoding.configs.RandomForestClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig, TreeMixin

Base configuration for any estimator.

method: Literal['RandomForestClassifier'] = 'RandomForestClassifier'

criterion: Literal['gini', 'entropy', 'log_loss'] = 'gini'

bootstrap: bool = True

oob_score: bool = False

class_weight: str | Dict | List | None = None

max_samples: int | float | None = None

class coco_pipe.decoding.configs.SVCConfig(/, **data: Any)

Bases: BaseEstimatorConfig, SupportVectorMixin

Base configuration for any estimator.

method: Literal['SVC'] = 'SVC'

probability: bool = True

class_weight: Dict | str | None = None

decision_function_shape: Literal['ovo', 'ovr'] = 'ovr'

break_ties: bool = False

class coco_pipe.decoding.configs.KNeighborsClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['KNeighborsClassifier'] = 'KNeighborsClassifier'

n_neighbors: int = None

weights: Literal['uniform', 'distance'] = 'uniform'

algorithm: Literal['auto', 'ball_tree', 'kd_tree', 'brute'] = 'auto'

leaf_size: int = 30

p: int = 2

metric: str = 'minkowski'

metric_params: Dict | None = None

n_jobs: int | None = None

class coco_pipe.decoding.configs.GradientBoostingClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['GradientBoostingClassifier'] = 'GradientBoostingClassifier'

loss: Literal['log_loss', 'exponential'] = 'log_loss'

learning_rate: float = 0.1

n_estimators: int = 100

subsample: float = 1.0

criterion: Literal['friedman_mse', 'squared_error'] = 'friedman_mse'

min_samples_split: int | float = 2

min_samples_leaf: int | float = 1

min_weight_fraction_leaf: float = 0.0

max_depth: int = 3

min_impurity_decrease: float = 0.0

init: str | None = None

max_features: str | int | float | None = None

verbose: int = 0

max_leaf_nodes: int | None = None

warm_start: bool = False

validation_fraction: float = 0.1

n_iter_no_change: int | None = None

tol: float = 0.0001

ccp_alpha: float = 0.0

class coco_pipe.decoding.configs.SGDClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig, SGDMixin

Base configuration for any estimator.

method: Literal['SGDClassifier'] = 'SGDClassifier'

class_weight: Dict | str | None = None

class coco_pipe.decoding.configs.MLPClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['MLPClassifier'] = 'MLPClassifier'

hidden_layer_sizes: tuple = (100,)

activation: Literal['identity', 'logistic', 'tanh', 'relu'] = 'relu'

solver: Literal['lbfgs', 'sgd', 'adam'] = 'adam'

alpha: float = 0.0001

batch_size: int | str = 'auto'

learning_rate: Literal['constant', 'invscaling', 'adaptive'] = 'constant'

learning_rate_init: float = 0.001

power_t: float = 0.5

max_iter: int = 200

shuffle: bool = True

tol: float = 0.0001

verbose: bool = False

warm_start: bool = False

momentum: float = 0.9

nesterovs_momentum: bool = True

early_stopping: bool = False

validation_fraction: float = 0.1

beta_1: float = 0.9

beta_2: float = 0.999

epsilon: float = 1e-08

n_iter_no_change: int = 10

max_fun: int = 15000

class coco_pipe.decoding.configs.GaussianNBConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['GaussianNB'] = 'GaussianNB'

priors: List[float] | None = None

var_smoothing: float = 1e-09

class coco_pipe.decoding.configs.LDAConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['LinearDiscriminantAnalysis'] = 'LinearDiscriminantAnalysis'

solver: Literal['svd', 'lsqr', 'eigen'] = 'svd'

shrinkage: str | float | None = None

priors: List[float] | None = None

n_components: int | None = None

store_covariance: bool = False

tol: float = 0.0001

class coco_pipe.decoding.configs.AdaBoostClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['AdaBoostClassifier'] = 'AdaBoostClassifier'

n_estimators: int = 50

learning_rate: float = 1.0

algorithm: Literal['SAMME', 'SAMME.R'] = 'SAMME.R'

class coco_pipe.decoding.configs.DummyClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['DummyClassifier'] = 'DummyClassifier'

strategy: Literal['stratified', 'most_frequent', 'prior', 'uniform'] = 'prior'

constant: Any | None = None

class coco_pipe.decoding.configs.LPFTConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Configuration for Linear-Probe Fine-Tuning (LP-FT). Reference: Kumar et al. (2022).

method: Literal['LPFTClassifier'] = 'LPFTClassifier'

backbone_name: str = 'gpt2'

lp_lr: float = 0.001

lp_epochs: int = 10

ft_lr: float = 1e-05

ft_epochs: int = 5

batch_size: int = 32

max_length: int = 128

device: str = 'cpu'

class coco_pipe.decoding.configs.SkorchClassifierConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Configuration for generic PyTorch wrappers via Skorch.

method: Literal['SkorchClassifier'] = 'SkorchClassifier'

module_name: str

max_epochs: int = 10

lr: float = 0.01

batch_size: int = 64

optimizer: str = 'Adam'

device: str = 'cpu'

class coco_pipe.decoding.configs.SlidingEstimatorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Configuration for MNE’s SlidingEstimator. Fits a separate estimator for each time point.

method: Literal['SlidingEstimator'] = 'SlidingEstimator'

base_estimator: EstimatorConfigType

scoring: str | Callable | None = None

n_jobs: int | None = 1

position: float | None = 0

allow_2d: bool = False

verbose: bool | str | int | None = None

class coco_pipe.decoding.configs.GeneralizingEstimatorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Configuration for MNE’s GeneralizingEstimator. Fits an estimator on each time point and tests on all other time points.

method: Literal['GeneralizingEstimator'] = 'GeneralizingEstimator'

base_estimator: EstimatorConfigType

scoring: str | Callable | None = None

n_jobs: int | None = 1

position: float | None = 0

allow_2d: bool = False

verbose: bool | str | int | None = None

class coco_pipe.decoding.configs.LinearRegressionConfig(/, **data: Any)

Bases: BaseEstimatorConfig, LinearMixin

Base configuration for any estimator.

method: Literal['LinearRegression'] = 'LinearRegression'

positive: bool = False

class coco_pipe.decoding.configs.RidgeConfig(/, **data: Any)

Bases: BaseEstimatorConfig, RegularizedLinearMixin

Base configuration for any estimator.

method: Literal['Ridge'] = 'Ridge'

alpha: float = 1.0

fit_intercept: bool = True

copy_X: bool = True

class coco_pipe.decoding.configs.LassoConfig(/, **data: Any)

Bases: BaseEstimatorConfig, RegularizedLinearMixin

Base configuration for any estimator.

method: Literal['Lasso'] = 'Lasso'

alpha: float = 1.0

precompute: bool | List = False

fit_intercept: bool = True

copy_X: bool = True

selection: Literal['cyclic', 'random'] = 'cyclic'

class coco_pipe.decoding.configs.ElasticNetConfig(/, **data: Any)

Bases: BaseEstimatorConfig, RegularizedLinearMixin

Base configuration for any estimator.

method: Literal['ElasticNet'] = 'ElasticNet'

alpha: float = 1.0

l1_ratio: float = 0.5

precompute: bool | List = False

fit_intercept: bool = True

copy_X: bool = True

selection: Literal['cyclic', 'random'] = 'cyclic'

class coco_pipe.decoding.configs.RandomForestRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig, TreeMixin

Base configuration for any estimator.

method: Literal['RandomForestRegressor'] = 'RandomForestRegressor'

criterion: Literal['squared_error', 'absolute_error', 'friedman_mse', 'poisson'] = 'squared_error'

bootstrap: bool = True

oob_score: bool = False

max_samples: int | float | None = None

class coco_pipe.decoding.configs.SVRConfig(/, **data: Any)

Bases: BaseEstimatorConfig, SupportVectorMixin

Base configuration for any estimator.

method: Literal['SVR'] = 'SVR'

epsilon: float = 0.1

class coco_pipe.decoding.configs.GradientBoostingRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['GradientBoostingRegressor'] = 'GradientBoostingRegressor'

loss: Literal['squared_error', 'absolute_error', 'huber', 'quantile'] = 'squared_error'

learning_rate: float = 0.1

n_estimators: int = 100

subsample: float = 1.0

criterion: Literal['friedman_mse', 'squared_error'] = 'friedman_mse'

min_samples_split: int | float = 2

min_samples_leaf: int | float = 1

min_weight_fraction_leaf: float = 0.0

max_depth: int = 3

min_impurity_decrease: float = 0.0

init: str | None = None

max_features: str | int | float | None = None

alpha: float = 0.9

verbose: int = 0

max_leaf_nodes: int | None = None

warm_start: bool = False

validation_fraction: float = 0.1

n_iter_no_change: int | None = None

tol: float = 0.0001

ccp_alpha: float = 0.0

class coco_pipe.decoding.configs.SGDRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig, SGDMixin

Base configuration for any estimator.

method: Literal['SGDRegressor'] = 'SGDRegressor'

loss: str = 'squared_error'

class coco_pipe.decoding.configs.MLPRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['MLPRegressor'] = 'MLPRegressor'

hidden_layer_sizes: tuple = (100,)

activation: Literal['identity', 'logistic', 'tanh', 'relu'] = 'relu'

alpha: float = 0.0001

batch_size: int | str = 'auto'

learning_rate: Literal['constant', 'invscaling', 'adaptive'] = 'constant'

learning_rate_init: float = 0.001

power_t: float = 0.5

max_iter: int = 200

shuffle: bool = True

tol: float = 0.0001

verbose: bool = False

warm_start: bool = False

momentum: float = 0.9

nesterovs_momentum: bool = True

early_stopping: bool = False

validation_fraction: float = 0.1

beta_1: float = 0.9

beta_2: float = 0.999

epsilon: float = 1e-08

n_iter_no_change: int = 10

max_fun: int = 15000

class coco_pipe.decoding.configs.DummyRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['DummyRegressor'] = 'DummyRegressor'

strategy: Literal['mean', 'median', 'quantile', 'constant'] = 'mean'

constant: int | float | List | None = None

quantile: float | None = None

class coco_pipe.decoding.configs.DecisionTreeRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['DecisionTreeRegressor'] = 'DecisionTreeRegressor'

criterion: Literal['squared_error', 'friedman_mse', 'absolute_error', 'poisson'] = 'squared_error'

splitter: Literal['best', 'random'] = 'best'

max_depth: int | None = None

min_samples_split: int | float = 2

min_samples_leaf: int | float = 1

min_weight_fraction_leaf: float = 0.0

max_features: str | int | float | None = None

random_state: int | None = None

max_leaf_nodes: int | None = None

min_impurity_decrease: float = 0.0

ccp_alpha: float = 0.0

class coco_pipe.decoding.configs.KNeighborsRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['KNeighborsRegressor'] = 'KNeighborsRegressor'

n_neighbors: int = None

weights: Literal['uniform', 'distance'] = 'uniform'

algorithm: Literal['auto', 'ball_tree', 'kd_tree', 'brute'] = 'auto'

leaf_size: int = 30

p: int = 2

metric: str = 'minkowski'

metric_params: Dict | None = None

n_jobs: int | None = None

class coco_pipe.decoding.configs.ExtraTreesRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig, TreeMixin

Base configuration for any estimator.

method: Literal['ExtraTreesRegressor'] = 'ExtraTreesRegressor'

bootstrap: bool = False

oob_score: bool = False

max_samples: int | float | None = None

class coco_pipe.decoding.configs.HistGradientBoostingRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['HistGradientBoostingRegressor'] = 'HistGradientBoostingRegressor'

loss: Literal['squared_error', 'absolute_error', 'poisson', 'quantile'] = 'squared_error'

learning_rate: float = 0.1

max_iter: int = 100

max_leaf_nodes: int = 31

max_depth: int | None = None

min_samples_leaf: int = 20

l2_regularization: float = 0.0

max_bins: int = 255

categorical_features: List[int] | List[str] | List[bool] | None = None

monotonic_cst: Any | None = None

interaction_cst: Any | None = None

warm_start: bool = False

early_stopping: str = 'auto'

scoring: str | None = 'loss'

validation_fraction: float = 0.1

n_iter_no_change: int = 10

tol: float = 1e-07

verbose: int = 0

random_state: int | None = None

class coco_pipe.decoding.configs.AdaBoostRegressorConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['AdaBoostRegressor'] = 'AdaBoostRegressor'

n_estimators: int = 50

learning_rate: float = 1.0

loss: Literal['linear', 'square', 'exponential'] = 'linear'

class coco_pipe.decoding.configs.BayesianRidgeConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['BayesianRidge'] = 'BayesianRidge'

n_iter: int = 300

tol: float = 0.001

alpha_1: float = 1e-06

alpha_2: float = 1e-06

lambda_1: float = 1e-06

lambda_2: float = 1e-06

alpha_init: float | None = None

lambda_init: float | None = None

compute_score: bool = False

fit_intercept: bool = True

copy_X: bool = True

verbose: bool = False

class coco_pipe.decoding.configs.ARDRegressionConfig(/, **data: Any)

Bases: BaseEstimatorConfig

Base configuration for any estimator.

method: Literal['ARDRegression'] = 'ARDRegression'

n_iter: int = 300

tol: float = 0.001

alpha_1: float = 1e-06

alpha_2: float = 1e-06

lambda_1: float = 1e-06

lambda_2: float = 1e-06

compute_score: bool = False

threshold_lambda: float = 10000.0

fit_intercept: bool = True

copy_X: bool = True

verbose: bool = False

coco_pipe.decoding.configs.AtomicEstimator

coco_pipe.decoding.configs.EstimatorConfigType

class coco_pipe.decoding.configs.TemporalConfig(/, **data: Any)

Bases: pydantic.BaseModel

Configuration for temporal decoding (Sliding/Generalizing).

enabled: bool = False

window_interaction: Literal['sliding', 'generalizing'] = 'sliding'

class coco_pipe.decoding.configs.CVConfig(/, **data: Any)

Bases: pydantic.BaseModel

Cross-validation settings.

strategy: Literal['stratified', 'kfold', 'group_kfold', 'stratified_group_kfold', 'leave_p_out', 'leave_one_out', 'timeseries', 'split'] = 'stratified'

n_splits: int = None

shuffle: bool = True

random_state: int = 42

class coco_pipe.decoding.configs.TuningConfig(/, **data: Any)

Bases: pydantic.BaseModel

Hyperparameter Tuning Configuration. Use this to define HOW to search (random vs grid). The WHAT (the grid itself) is passed in ExperimentConfig.grids.

enabled: bool = False

search_type: Literal['grid', 'random'] = 'grid'

n_iter: int = None

scoring: str | None = None

n_jobs: int = -1

class coco_pipe.decoding.configs.FeatureSelectionConfig(/, **data: Any)

Bases: pydantic.BaseModel

Configuration for Sequential Feature Selection.

enabled: bool = False

method: Literal['k_best', 'sfs'] = 'sfs'

n_features: int | None = None

direction: Literal['forward', 'backward'] = 'forward'

cv: int | None = None

scoring: str | None = None

class coco_pipe.decoding.configs.ExperimentConfig(/, **data: Any)

Bases: pydantic.BaseModel

Master configuration for a Decoding Experiment.

task: Literal['classification', 'regression'] = 'classification'

output_dir: pathlib.Path | None = None

tag: str = 'experiment'

models: Dict[str, EstimatorConfigType]

grids: Dict[str, Dict[str, List[Any]]] | None = None

cv: CVConfig = None

tuning: TuningConfig = None

feature_selection: FeatureSelectionConfig = None

metrics: List[str] = None

temporal: TemporalConfig = None

use_scaler: bool = None

n_jobs: int = -1

verbose: bool = True