anml.parameter¶
anml.parameter.main¶
- class anml.parameter.main.Parameter(variables, transform=None, offset=None, priors=None)[source]¶
Bases:
object
Parameter class contains information from a list of variables that are used to parametrize the distribution parameter. Parameter class also include optional transformation function, offset and list of priors.
- Parameters
variables (List[Variable]) – A list of variables that parametrized the linear predictor of the parameter.
transform (Optional[SmoothMapping]) – A
SmoothMapping
instance that transforms the linear prediction from the variables to the parameter space. Default is None, which will be converted into identity mapping.offset (Optional[Union[str, Component]]) – A data component contains the offset information for the parameter. Default is None which indicates no offset. Offset will be applied onto the linear predictor.
priors (Optional[List[Prior]]) – A list of additional priors that directly apply to the parameter linear predictor. Default is None, where no additional priors will be added.
- property variables¶
A list of variables that parametrized the linear predictor of the parameter.
- Raises
TypeError – Raised if the input variables are not all instances of
Variable
.
- property transform¶
A function that transforms the linear prediction to the parameter space.
- Raises
TypeError – Raised when the input transform is not an instance of
SmoothMapping
.
- property offset¶
Offset for the linear predictor.
- Raises
TypeError – Raised when the input offset is not None, or a string, or an instance of
DataComponent
.
- property priors¶
A list of additional priors that apply to the linear predictor.
- Raises
TypeError – Raised when the input priors are not None or a list of instances of
Prior
.
- property size: int¶
Size of the parameter coefficients. It is the sum of all sizes for variables.
- attach(df)[source]¶
Attach data frame to offset and cache the design matrix and gather the prior information.
- Parameters
df (DataFrame) – Given data frame.
- get_params(x, df=None, order=0)[source]¶
Compute and return the parameter. Denote \(x\) as the coefficients, \(A\) as the design matrix, \(z\) as the offset, \(f\) as the transformation function, the parameter \(p\) can be represented as
\[p = f(z + Ax)\]Here we call \(Ax\) as the linear predictor.
- Parameters
x (NDArray) – Coefficients for the design matrix.
df (Optional[DataFrame]) – Given data frame used for compute the design matrix. Default is None.
order (int) – Order of the derivative. Default is 0.
- Returns
When order=0, it will return the parameter value. When order=1, it will return the Jacobian matrix. And when order=2, it will return the second order Jacobian tensor.
- Return type
NDArray
- Raises
ValueError – Raised when there is not cache of the design matrix and no data frame is provided.
- prior_objective(x)[source]¶
Objective function from the prior.
- Parameters
x (NDArray) – Coefficients for the design matrix.
- Returns
Objective value from the prior.
- Return type
float
anml.parameter.smoothmapping¶
- class anml.parameter.smoothmapping.SmoothMapping[source]¶
Bases:
abc.ABC
Smooth mapping that contains function, first and second derivative information.
- property inverse: anml.parameter.smoothmapping.SmoothMapping¶
Inverse function of the current instance. The inverse function is also an instance of
SmoothMapping
.
- class anml.parameter.smoothmapping.Identity[source]¶
Bases:
anml.parameter.smoothmapping.SmoothMapping
Identity smooth mapping.
- property inverse: anml.parameter.smoothmapping.SmoothMapping¶
- class anml.parameter.smoothmapping.Exp[source]¶
Bases:
anml.parameter.smoothmapping.SmoothMapping
Exponential smooth mapping.
- property inverse: anml.parameter.smoothmapping.SmoothMapping¶
- class anml.parameter.smoothmapping.Log[source]¶
Bases:
anml.parameter.smoothmapping.SmoothMapping
Logarithm smooth mapping.
- Raises
ValueError – Raised when the argument is not all positive.
- property inverse: anml.parameter.smoothmapping.SmoothMapping¶
- class anml.parameter.smoothmapping.Expit[source]¶
Bases:
anml.parameter.smoothmapping.SmoothMapping
Expit smooth mapping.
\[\mathrm{expit}(x) = \frac{1}{1 + \exp(-x)}\]- property inverse: anml.parameter.smoothmapping.SmoothMapping¶
- class anml.parameter.smoothmapping.Logit[source]¶
Bases:
anml.parameter.smoothmapping.SmoothMapping
Logit smooth mapping.
\[\mathrm{logit}(x) = \log\left(\frac{x}{1 - x}\right)\]- Raises
ValueError – Raised when the argument is not all strictly between 0 and 1.
- property inverse: anml.parameter.smoothmapping.SmoothMapping¶