gemseo.mlearning.regression.algos.base_regressor module#

The base class for regression algorithms.

class BaseRegressor(data, settings_model=None, **settings)[source]#

Bases: BaseMLSupervisedAlgo

The base class for regression algorithms.

Parameters:

data (Dataset) -- The learning dataset.
settings_model (BaseMLAlgoSettings | None) -- The machine learning algorithm settings as a Pydantic model. If None, use **settings.
**settings (Any) -- The machine learning algorithm settings. These arguments are ignored when settings_model is not None.

Raises:

ValueError -- When both the variable and the group it belongs to have a transformer.

DataFormatters#: alias of RegressionDataFormatters

Settings#: alias of BaseRegressorSettings

predict_jacobian(input_data)[source]#

Predict the Jacobians of the regression model at input_data.

The user can specify these input data either as a NumPy array, e.g. array([1., 2., 3.]) or as a dictionary, e.g. {'a': array([1.]), 'b': array([2., 3.])}.

If the NumPy arrays are of dimension 2, their i-th rows represent the input data of the i-th sample; while if the NumPy arrays are of dimension 1, there is a single sample.

The type of the output data and the dimension of the output arrays will be consistent with the type of the input data and the size of the input arrays.

Parameters:: input_data (DataType) -- The input data.
Returns:: The predicted Jacobian data.
Return type:: DataType

predict_raw(input_data)[source]#

Predict output data from input data.

Parameters:: input_data (RealArray) -- The input data with shape (n_samples, n_inputs).
Returns:: The predicted output data with shape (n_samples, n_outputs).
Return type:: RealArray

DEFAULT_TRANSFORMER: DefaultTransformerType = mappingproxy({'inputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object>, 'outputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object>})#: The default transformer for the input and output data, if any.