gemseo / mlearning / regression

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base_random_process_regressor module

A base class for regressors based on a random process.

A class implementing a Gaussian process regressor must derive from it.

class gemseo.mlearning.regression.base_random_process_regressor.BaseRandomProcessRegressor(data, transformer=mappingproxy({}), input_names=None, output_names=None, **parameters)

Bases: BaseMLRegressionAlgo

A base class for regressors base on a random process.

Parameters:

• data (IODataset) – The learning dataset.

• transformer (TransformerType) –

  The strategies to transform the variables. The values are instances of BaseTransformer while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the BaseTransformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

  By default it is set to {}.

• input_names (Iterable[str] | None) – The names of the input variables. If None, consider all the input variables of the learning dataset.

• output_names (Iterable[str] | None) – The names of the output variables. If None, consider all the output variables of the learning dataset.

• **parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.

Raises:

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

abstract compute_samples(input_data, n_samples, seed=0)

Sample a random vector from the conditioned Gaussian process.

Parameters:

• input_data (ndarray[Any, dtype[floating]]) – The \(N\) input points of dimension \(d\) at which to observe the conditioned Gaussian process; shaped as (N, d).

• n_samples (int) – The number of samples M.

• seed (int) –

  The seed for reproducible results.

  By default it is set to 0.

Returns:

The output samples per output dimension shaped as (N, M).

Return type:

list[ndarray[Any, dtype[floating]]]

abstract predict_std(input_data)

Predict the standard deviation from input data.

The user can specify these input data either as a NumPy array, e.g. array([1., 2., 3.]) or as a dictionary of NumPy arrays, 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.

Parameters:

input_data (ndarray | Iterable[Any] | Any) – The input data.

Returns:

The standard deviation at the query points.

Return type:

ndarray[Any, dtype[floating]]

Warning

This statistic is expressed in relation to the transformed output space. You can sample the predict() method to estimate it in relation to the original output space if it is different from the transformed output space.

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the input variables.

input_space_center: dict[str, ndarray]

The center of the input space.

learning_set: Dataset

The learning dataset.

output_names: list[str]

The names of the output variables.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

resampling_results: dict[str, tuple[BaseResampler, list[BaseMLAlgo], list[ndarray] | ndarray]]

The resampler class names bound to the resampling results.

A resampling result is formatted as (resampler, ml_algos, predictions) where resampler is a BaseResampler, ml_algos is the list of the associated machine learning algorithms built during the resampling stage and predictions are the predictions obtained with the latter.

resampling_results stores only one resampling result per resampler type (e.g., "CrossValidation", "LeaveOneOut" and "Boostrap").

transformer: dict[str, BaseTransformer]

The strategies to transform the variables, if any.

The values are instances of BaseTransformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the BaseTransformer will be applied to all the variables of this group.

Examples using BaseRandomProcessRegressor

Scaling

Scaling