ml_grid.model_classes.svc_class

Classes

SVCClass

Initializes the SVCClass.

Module Contents

class ml_grid.model_classes.svc_class.SVCClass(X: pandas.DataFrame | None = None, y: pandas.Series | None = None, parameter_space_size: str | None = None)

Initializes the SVCClass.

This class requires scaled data. If the input data X is not detected as scaled, it will be automatically scaled using StandardScaler.

Parameters:

• X (Optional[pd.DataFrame]) – Feature matrix for training. Defaults to None.

• y (Optional[pd.Series]) – Target vector for training. Defaults to None.

• parameter_space_size (Optional[str]) – Size of the parameter space for optimization. Defaults to None.

Raises:

• ValueError – If parameter_space_size is not a valid key.

• RuntimeError – If data scaling fails.

X: pandas.DataFrame | None = None

y: pandas.Series | None = None

scaler: sklearn.preprocessing.StandardScaler | None = None

algorithm_implementation: sklearn.svm.SVC

method_name: str = 'SVC'

parameter_vector_space: ml_grid.util.param_space.ParamSpace

parameter_space: List[Dict[str, Any]]

is_data_scaled() → bool

Checks if the feature matrix X is scaled.

This method determines if the data appears to be scaled by checking if all feature values fall within the [0, 1] or [-1, 1] range.

Returns:

True if data appears to be scaled, False otherwise.

Return type:

bool

scale_data() → None

Scales the feature matrix X using MinMaxScaler.