FunctionParamFitter

class FunctionParamFitter(param, func, kw_args=None, X_type=None)[source]

Constructs a parameter fitter from an arbitrary callable.

A FunctionParamFitter forwards its X argument to a user-defined function (or callable object) and sets the result of this function to the param attribute. This can be useful for stateless estimators such as simple conditional parameter selectors.

Note: If a lambda function is used as the func, then the resulting estimator will not be pickleable.

Parameters:

paramstr: The name of the parameter to set.
funccallable (X: X_type, **kwargs) -> Any: The callable to use for the parameter estimation. This will be passed the same arguments as estimator, with args and kwargs forwarded.
kw_argsdict, default=None: Dictionary of additional keyword arguments to pass to func.
X_typestr, one of “pd.DataFrame, pd.Series, np.ndarray”, or list thereof: default = [“pd.DataFrame”, “pd.Series”, “np.ndarray”] list of types that func is assumed to allow for X (see signature above) if X passed to transform/inverse_transform is not on the list,

it will be converted to the first list element before passed to funcs

Attributes:

is_fitted: Whether fit has been called.

Examples

This class could be used to construct a parameter estimator that selects a forecaster based on the input data’s length. The selected forecaster can be stored in the selected_forecaster_ attribute, which can be then passed down to a MultiplexForecaster via a PluginParamsForecaster.

>>> import numpy as np
>>> from sktime.param_est.compose import FunctionParamFitter
>>> param_est = FunctionParamFitter(
...     param="selected_forecaster",
...     func=(
...         lambda X, threshold: "naive-seasonal"
...         if len(X) >= threshold
...         else "naive-last"
...     ),
...     kw_args={"threshold": 7},
... )
>>> param_est.fit(np.asarray([1, 2, 3, 4]))
FunctionParamFitter(...)
>>> param_est.get_fitted_params()
{'selected_forecaster': 'naive-last'}
>>> param_est.fit(np.asarray([1, 2, 3, 4, 5, 6, 7]))
FunctionParamFitter(...)
>>> param_est.get_fitted_params()
{'selected_forecaster': 'naive-seasonal'}

The full conditional forecaster selection pipeline could look like this:

>>> from sktime.forecasting.compose import MultiplexForecaster
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.param_est.plugin import PluginParamsForecaster
>>> forecaster = PluginParamsForecaster(
...     param_est=param_est,
...     forecaster=MultiplexForecaster(
...         forecasters=[
...             ("naive-last", NaiveForecaster()),
...             ("naive-seasonal", NaiveForecaster(sp=7)),
...         ]
...     ),
... )
>>> forecaster.fit(np.asarray([1, 2, 3, 4]))
PluginParamsForecaster(...)
>>> forecaster.predict(fh=[1,2,3])
array([[4.],
       [4.],
       [4.]])
>>> forecaster.fit(np.asarray([1, 2, 3, 4, 5, 6, 7]))
PluginParamsForecaster(...)
>>> forecaster.predict(fh=[1,2,3])
array([[1.],
       [2.],
       [3.]])

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit estimator and estimate parameters.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(X[, y])	Update fitted parameters on more data.