pyrcn.extreme_learning_machine¶

The pyrcn.extreme_learning_machine.

It contains a simple object-oriented implementation of Extreme Learning Machines 1.

Separate implementations of Classifiers and Regressors as specified by scikit-learn.

References

1: Guang-Bin Huang et al., ‘Extreme learning machine: Theory and applications’, p. 489-501, 2006, doi: 10.1016/j.neucom.2005.12.126.

class pyrcn.extreme_learning_machine.ELMRegressor(*, input_to_node: pyrcn.base.blocks._input_to_node.InputToNode | None = None, regressor: pyrcn.linear_model._incremental_regression.IncrementalRegression | sklearn.base.RegressorMixin | None = None, chunk_size: int | None = None, verbose: bool = False, **kwargs: Any)¶

Bases: RegressorMixin, MultiOutputMixin, BaseEstimator

Extreme Learning Machine regressor.

This model optimizes the mean squared error loss function using linear regression.

Parameters

input_to_node (Optional[InputToNode], default=None) – Any `InputToNode` object that transforms the inputs. If `None`, a `pyrcn.base.blocks.InputToNode` object is instantiated.
regressor (Union[IncrementalRegression, RegressorMixin, None],) –
default=None – Regressor object such as derived from RegressorMixin. This regressor will automatically be cloned each time prior to fitting. If `None`, a `pyrcn.linear_model.IncrementalRegression` object is instantiated.
chunk_size (Optional[int], default=None) – if X.shape[0] > chunk_size, calculate results incrementally with partial_fit
verbose (bool = False) – Verbosity output
kwargs (Any, default = None) – keyword arguments passed to the subestimators if this is desired, default=None

property chunk_size: None | int | numpy.integer¶

Return the chunk_size, in which X will be chopped.

Returns: chunk_size
Return type: Union[int, np.integer]

fit(X: ndarray, y: ndarray, n_jobs: int | numpy.integer | None = None, transformer_weights: numpy.ndarray | None = None) → ELMRegressor¶

Fit the regressor.

Parameters

X (ndarray of shape (n_samples, n_features)) –
y (ndarray of shape (n_samples,) or (n_samples, n_targets)) – The targets to predict.
n_jobs (int, default=None) – The number of jobs to run in parallel. `-1` means using all processors. See Glossary for more details.
transformer_weights (Union[np.ndarray, None], default=None) – ignored

Returns

self

Return type

Returns a trained ELMRegressor model.

get_params(deep: bool = True) → dict¶: Get all parameters of the ESNRegressor.

hidden_layer_state(X: ndarray) → ndarray¶

Return the hidden_layer_state, e.g. the reservoir state over time.

Parameters: X (ndarray of shape (n_samples, n_features)) –
Returns: hidden_layer_state – The hidden_layer_state, e.g. the reservoir state over time.
Return type: ndarray of (n_samples,)

property input_to_node: InputToNode¶

Return the input_to_node Estimator.

Returns: input_to_node
Return type: InputToNode

partial_fit(X: ndarray, y: ndarray, transformer_weights: numpy.ndarray | None = None, postpone_inverse: bool = False) → ELMRegressor¶

Fit the regressor partially.

Parameters

X (ndarray of shape (n_samples, n_features)) –
y (ndarray of shape (n_samples,) or (n_samples, n_targets)) – The targets to predict.
transformer_weights (Union[np.ndarray, None], default=None) – ignored
postpone_inverse (bool, default=False) – If the output weights have not been fitted yet, regressor might be hinted at postponing inverse calculation. Refer to `IncrementalRegression` for details.

Returns

self

Return type

Returns a trained `ELMRegressor` model.

predict(X: ndarray) → ndarray¶

Predict the targets using the trained `ELMRegressor`.

Parameters: X (ndarray of shape (n_samples, n_features)) –
Returns: y – The predicted targets
Return type: ndarray of (n_samples,) or (n_samples, n_targets)

property regressor: sklearn.base.RegressorMixin | pyrcn.linear_model._incremental_regression.IncrementalRegression¶

Return the regressor.

Returns: regressor
Return type: RegressorMixin

set_fit_request(*, n_jobs: Union[bool, None, str] = '$UNCHANGED$', transformer_weights: Union[bool, None, str] = '$UNCHANGED$') → ELMRegressor¶

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters

n_jobs (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for n_jobs parameter in fit.
transformer_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for transformer_weights parameter in fit.

Returns

self – The updated object.

Return type

object

set_params(**parameters: dict) → ELMRegressor¶: Set all possible parameters of the ELMRegressor.

set_partial_fit_request(*, postpone_inverse: Union[bool, None, str] = '$UNCHANGED$', transformer_weights: Union[bool, None, str] = '$UNCHANGED$') → ELMRegressor¶

Configure whether metadata should be requested to be passed to the partial_fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to partial_fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters

postpone_inverse (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for postpone_inverse parameter in partial_fit.
transformer_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for transformer_weights parameter in partial_fit.

Returns

self – The updated object.

Return type

object

set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → ELMRegressor¶

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns: self – The updated object.
Return type: object

class pyrcn.extreme_learning_machine.ELMClassifier(*, input_to_node: pyrcn.base.blocks._input_to_node.InputToNode | None = None, regressor: pyrcn.linear_model._incremental_regression.IncrementalRegression | sklearn.base.RegressorMixin | None = None, chunk_size: int | None = None, verbose: bool = False, **kwargs: Any)¶

Bases: ClassifierMixin, ELMRegressor

Extreme Learning Machine classifier.

This model optimizes the mean squared error loss function using linear regression.

Parameters

input_to_node (Optional[InputToNode], default=None) – Any `InputToNode` object that transforms the inputs. If `None`, a `pyrcn.base.blocks.InputToNode` object is instantiated.
regressor (Union[IncrementalRegression, RegressorMixin, None],) –
default=None – Regressor object such as derived from RegressorMixin. This regressor will automatically be cloned each time prior to fitting. If `None`, a `pyrcn.linear_model.IncrementalRegression` object is instantiated.
chunk_size (Optional[int], default=None) – if X.shape[0] > chunk_size, calculate results incrementally with partial_fit
verbose (bool = False) – Verbosity output
kwargs (Any, default = None) – keyword arguments passed to the subestimators if this is desired, default=None

fit(X: ndarray, y: ndarray, n_jobs: int | numpy.integer | None = None, transformer_weights: numpy.ndarray | None = None) → ELMClassifier¶

Fit the classifier.

Parameters

X (ndarray of shape (n_samples, n_features)) –
y (ndarray of shape (n_samples,) or (n_samples, n_classes)) – The targets to predict.
n_jobs (Union[int, np.integer, None], default=None) – The number of jobs to run in parallel. `-1` means using all processors. See Glossary for more details.
transformer_weights (Optional[np.ndarray], default=None) – ignored

Returns

self

Return type

Returns a trained ELMClassifier model.

partial_fit(X: ndarray, y: ndarray, transformer_weights: numpy.ndarray | None = None, postpone_inverse: bool = False, classes: numpy.ndarray | None = None) → ELMClassifier¶

Fit the classifier partially.

Parameters

X (ndarray of shape (n_samples, n_features)) –
y (ndarray of shape (n_samples,) or (n_samples, n_classes)) – The targets to predict.
classes (Optional[ndarray] of shape (n_classes,), default=None) – Classes across all calls to partial_fit. Can be obtained via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
transformer_weights (Optional[ndarray], default=None) – ignored
postpone_inverse (bool, default=False) – If the output weights have not been fitted yet, regressor might be hinted at postponing inverse calculation. Refer to `IncrementalRegression` for details.

Returns

self

Return type

returns a trained ELMClassifier model

predict(X: ndarray) → ndarray¶

Predict the classes using the trained `ELMClassifier`.

Parameters: X (ndarray of shape (n_samples, n_features)) – The input data.
Returns: y_pred – The predicted classes.
Return type: ndarray of shape (n_samples,) or (n_samples, n_classes)

predict_log_proba(X: ndarray) → ndarray¶

Predict the log probability estimated using a trained `ELMClassifier`.

Parameters: X (ndarray of shape (n_samples, n_features)) – The input data.
Returns: y_pred – The predicted logarithmic probability estimated.
Return type: ndarray of shape (n_samples,) or (n_samples, n_classes)

predict_proba(X: ndarray) → ndarray¶

Predict the probability estimated using a trained `ELMClassifier`.

Parameters: X (ndarray of shape (n_samples, n_features)) – The input data.
Returns: y_pred – The predicted probability estimates.
Return type: ndarray of shape (n_samples,) or (n_samples, n_classes)

set_fit_request(*, n_jobs: Union[bool, None, str] = '$UNCHANGED$', transformer_weights: Union[bool, None, str] = '$UNCHANGED$') → ELMClassifier¶

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters

n_jobs (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for n_jobs parameter in fit.
transformer_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for transformer_weights parameter in fit.

Returns

self – The updated object.

Return type

object

set_partial_fit_request(*, classes: Union[bool, None, str] = '$UNCHANGED$', postpone_inverse: Union[bool, None, str] = '$UNCHANGED$', transformer_weights: Union[bool, None, str] = '$UNCHANGED$') → ELMClassifier¶

Configure whether metadata should be requested to be passed to the partial_fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to partial_fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters

classes (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for classes parameter in partial_fit.
postpone_inverse (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for postpone_inverse parameter in partial_fit.
transformer_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for transformer_weights parameter in partial_fit.

Returns

self – The updated object.

Return type

object

set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → ELMClassifier¶

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns: self – The updated object.
Return type: object