error_functions.residual_base.ResidualFunction
error_functions.residual_base.ResidualFunction(self, database, datasets, phase_models, symbols_to_fit=None, weight=None)
Protocol class for computing the error (residual) between data and a model prediction given a set of parameters.
Classes that implement this protocol typically will concerned with implementing a residual/likelihood function for a certain type of data. The protocol is intentitionally left to be simple to implement while enabling flexibility to implement additional methods and attributes for performance and debugging purposes.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
database |
Database | required | |
datasets |
PickleableTinyDB | The candidate datasets for the a contribution. Usually the datasets are a superset (in components, phases, data types, etc.) of the actual data used to compute the residual for any particular contribution. | required |
phase_models |
PhaseModels | Defines the active set of components that should be fit and any user-provided overrides to the PyCalphad Model class for each phase. | required |
symbols_to_fit |
Optional[List[SymbolName]] | User-provided symbols to fit. By default, the symbols to fit should be set by espei.utils.database_symbols_to_fit. |
None |
weight |
Optional[float] | When computing the likelihood, this should be used to modify the probability distribution. Higher weights should correspond to narrower probability distributions, but it’s exact use will depend on the particular probability distribution. | None |
Attributes
| Name | Type | Description |
|---|---|---|
| weight | Optional[Union[float, Dict[str, float]]] |
Methods
| Name | Description |
|---|---|
| get_likelihood | Return log-likelihood for the set of parameters. |
| get_residuals | Return the residual comparing the selected data to the set of parameters. |
get_likelihood
error_functions.residual_base.ResidualFunction.get_likelihood(parameters)
Return log-likelihood for the set of parameters.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
parameters |
ArrayLike | 1D parameter vector. The size of the parameters array should match the number of fitting symbols used to build the models. This is not checked. | required |
Returns
| Type | Description |
|---|---|
| float | Value of log-likelihood for the given set of parameters |
get_residuals
error_functions.residual_base.ResidualFunction.get_residuals(parameters)
Return the residual comparing the selected data to the set of parameters.
The residual is zero if the database predictions under the given parameters agrees with the data exactly.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
parameters |
ArrayLike | 1D parameter vector. The size of the parameters array should match the number of fitting symbols used to build the models. This is not checked. | required |
Returns
| Type | Description |
|---|---|
| Tuple[List[float], List[float]] | Tuple of (residuals, weights), which must obey len(residuals) == len(weights) |