rbf module¶
The RBF network for regression.
The radial basis function surrogate discipline expresses the model output as a weighted sum of kernel functions centered on the learning input data:
and the coefficients \((w_1, w_2, \ldots, w_n)\) are estimated by least squares minimization.
Dependence¶
The RBF model relies on the Rbf class of the scipy library.
- class gemseo.mlearning.regression.rbf.RBFRegressor(data, transformer=mappingproxy({}), input_names=None, output_names=None, function='multiquadric', der_function=None, epsilon=None, smooth=0.0, norm='euclidean')[source]¶
Bases:
MLRegressionAlgoRegression based on radial basis functions (RBFs).
This model relies on the SciPy class
scipy.interpolate.Rbf.- Parameters:
data (Dataset) – The learning dataset.
transformer (TransformerType) –
The strategies to transform the variables. The values are instances of
Transformerwhile 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, theTransformerwill be applied to all the variables of this group. IfIDENTITY, 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.function (str | Callable[[float, float], float]) –
The radial basis function taking a radius \(r\) as input, representing a distance between two points. If it is a string, then it must be one of the following:
"multiquadric"for \(\sqrt{(r/\epsilon)^2 + 1}\),"inverse"for \(1/\sqrt{(r/\epsilon)^2 + 1}\),"gaussian"for \(\exp(-(r/\epsilon)^2)\),"linear"for \(r\),"cubic"for \(r^3\),"quintic"for \(r^5\),"thin_plate"for \(r^2\log(r)\).
If it is a callable, then it must take the two arguments
selfandras inputs, e.g.lambda self, r: sqrt((r/self.epsilon)**2 + 1)for the multiquadric function. The epsilon parameter will be available asself.epsilon. Other keyword arguments passed in will be available as well.By default it is set to “multiquadric”.
der_function (Callable[[ndarray], ndarray] | None) – The derivative of the radial basis function, only to be provided if
functionis a callable and if the use of the model with its derivative is required. IfNoneand iffunctionis a callable, an error will be raised. IfNoneand iffunctionis a string, the class will look for its internal implementation and will raise an error if it is missing. Theder_functionshall take three arguments (input_data,norm_input_data,eps). For an RBF of the form function(\(r\)), der_function(\(x\), \(|x|\), \(\epsilon\)) shall return \(\epsilon^{-1} x/|x| f'(|x|/\epsilon)\).epsilon (float | None) – An adjustable constant for Gaussian or multiquadric functions. If
None, use the average distance between input data.smooth (float) –
The degree of smoothness,
0involving an interpolation of the learning points.By default it is set to 0.0.
norm (str | Callable[[ndarray, ndarray], float]) –
The distance metric to be used, either a distance function name known by SciPy or a function that computes the distance between two points.
By default it is set to “euclidean”.
- Raises:
ValueError – When both the variable and the group it belongs to have a transformer.
- class DataFormatters¶
Bases:
DataFormattersMachine learning regression model decorators.
- classmethod format_dict(predict)¶
Make an array-based function be called with a dictionary of NumPy arrays.
- Parameters:
predict (Callable[[ndarray], ndarray]) – The function to be called; it takes a NumPy array in input and returns a NumPy array.
- Returns:
A function making the function ‘predict’ work with either a NumPy data array or a dictionary of NumPy data arrays indexed by variables names. The evaluation will have the same type as the input data.
- Return type:
Callable[[Union[ndarray, Mapping[str, ndarray]]], Union[ndarray, Mapping[str, ndarray]]]
- classmethod format_dict_jacobian(predict_jac)¶
Wrap an array-based function to make it callable with a dictionary of NumPy arrays.
- Parameters:
predict_jac (Callable[[ndarray], ndarray]) – The function to be called; it takes a NumPy array in input and returns a NumPy array.
- Returns:
The wrapped ‘predict_jac’ function, callable with either a NumPy data array or a dictionary of numpy data arrays indexed by variables names. The return value will have the same type as the input data.
- Return type:
Callable[[Union[ndarray, Mapping[str, ndarray]]], Union[ndarray, Mapping[str, ndarray]]]
- classmethod format_input_output(predict)¶
Make a function robust to type, array shape and data transformation.
- Parameters:
predict (Callable[[ndarray], ndarray]) – The function of interest to be called.
- Returns:
A function calling the function of interest ‘predict’, while guaranteeing consistency in terms of data type and array shape, and applying input and/or output data transformation if required.
- Return type:
Callable[[Union[ndarray, Mapping[str, ndarray]]], Union[ndarray, Mapping[str, ndarray]]]
- classmethod format_samples(predict)¶
Make a 2D NumPy array-based function work with 1D NumPy array.
- Parameters:
predict (Callable[[ndarray], ndarray]) – The function to be called; it takes a 2D NumPy array in input and returns a 2D NumPy array. The first dimension represents the samples while the second one represents the components of the variables.
- Returns:
A function making the function ‘predict’ work with either a 1D NumPy array or a 2D NumPy array. The evaluation will have the same dimension as the input data.
- Return type:
- classmethod format_transform(transform_inputs=True, transform_outputs=True)¶
Force a function to transform its input and/or output variables.
- Parameters:
- Returns:
A function evaluating a function of interest, after transforming its input data and/or before transforming its output data.
- Return type:
- classmethod transform_jacobian(predict_jac)¶
Apply transformation to inputs and inverse transformation to outputs.
- class RBFDerivatives[source]¶
Bases:
objectDerivatives of functions used in
RBFRegressor.For an RBF of the form \(f(r)\), \(r\) scalar, the derivative functions are defined by \(d(f(r))/dx\), with \(r=|x|/\epsilon\). The functions are thus defined by \(df/dx = \epsilon^{-1} x/|x| f'(|x|/\epsilon)\). This convention is chosen to avoid division by \(|x|\) when the terms may be cancelled out, as \(f'(r)\) often has a term in \(r\).
- classmethod der_cubic(input_data, norm_input_data, eps)[source]¶
Compute derivative w.r.t. \(x\) of the function \(f(r) = r^3\).
- classmethod der_gaussian(input_data, norm_input_data, eps)[source]¶
Compute derivative w.r.t. \(x\) of the function \(f(r) = \exp(-r^2)\).
- classmethod der_inverse_multiquadric(input_data, norm_input_data, eps)[source]¶
Compute derivative w.r.t. \(x\) of the function \(f(r) = 1/\sqrt{r^2 + 1}\).
- classmethod der_linear(input_data, norm_input_data, eps)[source]¶
Compute derivative w.r.t. \(x\) of the function \(f(r) = r\). If \(x=0\), return 0 (determined up to a tolerance).
- classmethod der_multiquadric(input_data, norm_input_data, eps)[source]¶
Compute derivative of \(f(r) = \sqrt{r^2 + 1}\) wrt \(x\).
- classmethod der_quintic(input_data, norm_input_data, eps)[source]¶
Compute derivative w.r.t. \(x\) of the function \(f(r) = r^5\).
- classmethod der_thin_plate(input_data, norm_input_data, eps)[source]¶
Compute derivative w.r.t. \(x\) of the function \(f(r) = r^2 \log(r)\). If \(x=0\), return 0 (determined up to a tolerance).
- TOL = 2.220446049250313e-16¶
- learn(samples=None, fit_transformers=True)¶
Train the machine learning algorithm from the learning dataset.
- load_algo(directory)¶
Load a machine learning algorithm from a directory.
- Parameters:
directory (str | Path) – The path to the directory where the machine learning algorithm is saved.
- Return type:
None
- predict(input_data, *args, **kwargs)¶
Evaluate ‘predict’ with either array or dictionary-based input data.
Firstly, the pre-processing stage converts the input data to a NumPy data array, if these data are expressed as a dictionary of NumPy data arrays.
Then, the processing evaluates the function ‘predict’ from this NumPy input data array.
Lastly, the post-processing transforms the output data to a dictionary of output NumPy data array if the input data were passed as a dictionary of NumPy data arrays.
- Parameters:
- Returns:
The output data with the same type as the input one.
- Return type:
- predict_jacobian(input_data, *args, **kwargs)¶
Evaluate ‘predict_jac’ with either array or dictionary-based data.
Firstly, the pre-processing stage converts the input data to a NumPy data array, if these data are expressed as a dictionary of NumPy data arrays.
Then, the processing evaluates the function ‘predict_jac’ from this NumPy input data array.
Lastly, the post-processing transforms the output data to a dictionary of output NumPy data array if the input data were passed as a dictionary of NumPy data arrays.
- Parameters:
input_data – The input data.
*args – The positional arguments of the function ‘predict_jac’.
**kwargs – The keyword arguments of the function ‘predict_jac’.
- Returns:
The output data with the same type as the input one.
- predict_raw(input_data)¶
Predict output data from input data.
- save(directory=None, path='.', save_learning_set=False)¶
Save the machine learning algorithm.
- Parameters:
directory (str | None) – The name of the directory to save the algorithm.
path (str | Path) –
The path to parent directory where to create the directory.
By default it is set to “.”.
save_learning_set (bool) –
Whether to save the learning set or get rid of it to lighten the saved files.
By default it is set to False.
- Returns:
The path to the directory where the algorithm is saved.
- Return type:
- AVAILABLE_FUNCTIONS: list[str] = ['multiquadric', 'inverse_multiquadric', 'gaussian', 'linear', 'cubic', 'quintic', 'thin_plate']¶
- DEFAULT_TRANSFORMER: DefaultTransformerType = mappingproxy({'inputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>, 'outputs': <gemseo.mlearning.transform.scaler.min_max_scaler.MinMaxScaler object>})¶
The default transformer for the input and output data, if any.
- IDENTITY: Final[DefaultTransformerType] = mappingproxy({})¶
A transformer leaving the input and output variables as they are.
- SHORT_ALGO_NAME: ClassVar[str] = 'RBF'¶
The short name of the machine learning algorithm, often an acronym.
Typically used for composite names, e.g.
f"{algo.SHORT_ALGO_NAME}_{dataset.name}"orf"{algo.SHORT_ALGO_NAME}_{discipline.name}".
- algo: Any¶
The interfaced machine learning algorithm.
- der_function: Callable[[ndarray], ndarray]¶
The derivative of the radial basis function.
- property function: str¶
The name of the kernel function.
The name is possibly different from self.parameters[‘function’], as it is mapped (scipy). Examples:
‘inverse’ -> ‘inverse_multiquadric’ ‘InverSE MULtiQuadRIC’ -> ‘inverse_multiquadric’
- property learning_samples_indices: Sequence[int]¶
The indices of the learning samples used for the training.
- transformer: dict[str, Transformer]¶
The strategies to transform the variables, if any.
The values are instances of
Transformerwhile 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, theTransformerwill be applied to all the variables of this group.
- y_average: ndarray¶
The mean of the learning output data.
