NominalRegressor

class sklearn_nominal.sklearn.nominal_model.NominalRegressor(backend='pandas', *args, **kwargs)

Base class for nominal regressors.

This class coordinates the regression workflow for models that handle nominal features natively.

Examples

>>> from sklearn_nominal.sklearn.nominal_model import NominalRegressor
>>> class MyRegressor(NominalRegressor):
...     def make_model(self, d):
...         # Return a backend-specific trainer
...         pass

Methods

build_error(criterion)	Builds the regression error function for the given criterion.
check_is_fitted()	Checks if the model has been fitted.
complexity()	Returns the complexity of the fitted model.
fit(x, y)	Fits the nominal regressor.
get_dtypes(x)	Extracts and maps data types from the input.
get_feature_names()	Returns the names of the features seen during fit.
make_model(d)	Abstract method to create the model trainer.
predict(x)	Predicts target values for input samples.
pretty_print([class_names])	Returns a string representation of the fitted model.
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_dtypes(x)	Sets and persists the data types based on the input.
set_model(model)	Sets the underlying backend model and marks it as fitted.
set_sklearn_tags(tags)	Sets scikit-learn tags for the supervised nominal model.
validate_data_fit_regression(x, y)	Validates and prepares data for regression fitting.
validate_data_predict(x)	Validates and prepares input data for prediction.

build_error(criterion)

Builds the regression error function for the given criterion.

Parameters:

criterionstr

The error criterion to use (e.g., “std” for standard deviation).

Returns:

TargetError

An instance of the requested error function.

Raises:

ValueError

If the criterion is not recognized.

check_is_fitted()

Checks if the model has been fitted.

Raises:

NotFittedError

If the is_fitted_ attribute is not set or is False.

complexity()

Returns the complexity of the fitted model.

The definition of complexity is backend and model dependent. For trees, it typically represents the number of nodes.

Returns:

int or float

The complexity metric of the model.

Raises:

NotFittedError

If the model has not been fitted yet.

fit(x, y)

Fits the nominal regressor.

Parameters:

x{array-like, sparse matrix} of shape (n_samples, n_features)

The training input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

The target values.

Returns:

selfobject

Returns the instance itself.

get_dtypes(x)

Extracts and maps data types from the input.

This method identifies the data types of the input features to ensure they are correctly handled by the backend.

Parameters:

x{array-like, sparse matrix} of shape (n_samples, n_features)

The input data.

Returns:

dict or None

A dictionary mapping column names to data types if x is a DataFrame, otherwise None.

get_feature_names()

Returns the names of the features seen during fit.

Returns:

ndarray of str or None

The feature names, or None if they were not available during fit (e.g., if input was a numpy array).

abstractmethod make_model(d)

Abstract method to create the model trainer.

Parameters:

dDataset

The training dataset prepared by validate_data_fit_regression.

Returns:

trainerobject

A trainer instance capable of fitting the provided dataset.

predict(x)

Predicts target values for input samples.

Parameters:

x{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples.

Returns:

ndarray of shape (n_samples,) or (n_samples, n_outputs)

The predicted target values.

pretty_print(class_names=None)

Returns a string representation of the fitted model.

Delegates the visualization logic to the underlying backend model.

Parameters:

class_nameslist of str, optional

Names of the classes to use in the output. If None, default identifiers are used.

Returns:

str

A human-readable representation of the model.

Raises:

NotFittedError

If the model has not been fitted yet.

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

\(R^2\) of self.predict(X) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_dtypes(x)

Sets and persists the data types based on the input.

This is called during fit to ensure that subsequent calls to predict can cast the input data to the same types, preserving nominal/numeric distinctions.

Parameters:

x{pd.DataFrame, np.ndarray, sparse matrix}

The input data to extract types from.

Raises:

ValueError

If the input type is not supported or if the input is not 2D.

set_model(model)

Sets the underlying backend model and marks it as fitted.

Parameters:

modelsklearn_nominal.backend.core.Model

The trained model instance from the backend.

set_sklearn_tags(tags)

Sets scikit-learn tags for the supervised nominal model.

Parameters:

tagsTags

The scikit-learn tags object to be modified.

validate_data_fit_regression(x, y)

Validates and prepares data for regression fitting.

This method ensures x and y are compatible, extracts data types, and packages them into a backend Dataset. It also ensures the target y is at least 2D for backend consistency.

Parameters:

xarray-like of shape (n_samples, n_features)

The input features.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

The target values.

Returns:

Dataset

The backend-specific dataset object.

Return type:

Dataset

validate_data_predict(x)

Validates and prepares input data for prediction.

This method ensures the input features match the structure seen during training, handles feature name alignment, and restores data types.

Parameters:

xarray-like of shape (n_samples, n_features)

The input data to validate.

Returns:

pd.DataFrame

The validated data as a pandas DataFrame, with dtypes restored to match those observed during training.

Raises:

NotFittedError

If the model has not been fitted yet.

ValueError

If the input contains no samples or has inconsistent features.