adapt.parameter_based.RegularTransferGP

class adapt.parameter_based.RegularTransferGP(estimator=None, Xt=None, yt=None, lambda_=1.0, copy=True, verbose=1, random_state=None, **params)[source]

Regular Transfer with Gaussian Process

RegularTransferGP is a parameter-based domain adaptation method.

The method is based on the assumption that a good target estimator can be obtained by adapting the parameters of a pre-trained source estimator using a few labeled target data.

The approach consist in fitting the alpha coeficients of a Gaussian Process estimator on target data according to an objective function regularized by the euclidean distance between the source and target alpha:

\[\alpha_T = \underset{\alpha \in \mathbb{R}^n}{\text{argmin}} \, ||K_{TS} \alpha - y_T||^2 + \lambda ||\alpha - \alpha_S||^2\]

Where:

$\alpha_T$ are the target model coeficients.
$\alpha_S = \underset{\alpha \in \mathbb{R}^n}{\text{argmin}} \, ||K_{SS} \alpha - y_S||^2$ are the source model coeficients.
$y_S, y_T$ are respectively the source and the target labels.
$K_{SS}$ is the pariwise kernel distance matrix between source input data.
$K_{TS}$ is the pariwise kernel distance matrix between target and source input data.
$n$ is the number of source data in $X_S$
$\lambda$ is a trade-off parameter. The larger $\lambda$ the closer the target model will be from the source model.

The estimator given to RegularTransferGP should be from classes sklearn.gaussian_process.GaussianProcessRegressor or sklearn.gaussian_process.GaussianProcessClassifier

Parameters

estimatorsklearn estimator or tensorflow Model (default=None): Estimator used to learn the task. If estimator is None, a LinearRegression instance is used as estimator.
Xtnumpy array (default=None): Target input data.
ytnumpy array (default=None): Target output data.
lambda_float (default=1.0): Trade-Off parameter. For large lambda_, the target model will be similar to the source model.
copyboolean (default=True): Whether to make a copy of estimator or not.
verboseint (default=1): Verbosity level.
random_stateint (default=None): Seed of random generator.
paramskey, value arguments: Arguments given at the different level of the adapt object. It can be, for instance, compile or fit parameters of the estimator or kernel parameters etc… Accepted parameters can be found by calling the method _get_legal_params(params).

See also

RegularTransferLR, RegularTransferNN

References

1: [1] C. Chelba and A. Acero. “Adaptation of maximum entropy classifier: Little data can help a lot”. In EMNLP, 2004.

Examples

>>> from sklearn.gaussian_process import GaussianProcessRegressor
>>> from sklearn.gaussian_process.kernels import Matern, WhiteKernel
>>> from adapt.utils import make_regression_da
>>> from adapt.parameter_based import RegularTransferGP
>>> Xs, ys, Xt, yt = make_regression_da()
>>> kernel = Matern() + WhiteKernel()
>>> src_model = GaussianProcessRegressor(kernel)
>>> src_model.fit(Xs, ys)
>>> print(src_model.score(Xt, yt))
-2.3409379221035382
>>> tgt_model = RegularTransferGP(src_model, lambda_=1.)
>>> tgt_model.fit(Xt[:3], yt[:3])
>>> tgt_model.score(Xt, yt)
-0.21947435769240653

Attributes

estimator_Same class as estimator: Fitted Estimator.

Methods

`__init__`([estimator, Xt, yt, lambda_, copy, ...])
`fit`([Xt, yt])	Fit RegularTransferGP.
`fit_estimator`(X, y[, sample_weight, ...])	Fit estimator on X, y.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X[, domain])	Return estimator predictions after adaptation.
`predict_estimator`(X, **predict_params)	Return estimator predictions for X.
`score`(X, y[, sample_weight, domain])	Return the estimator score.
`set_fit_request`(*[, domains])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, domain])	Request metadata passed to the `predict` method.
`set_score_request`(*[, domain, sample_weight])	Request metadata passed to the `score` method.
`unsupervised_score`(Xs, Xt)	Return unsupervised score.

__init__(estimator=None, Xt=None, yt=None, lambda_=1.0, copy=True, verbose=1, random_state=None, **params)[source]

fit(Xt=None, yt=None, **fit_params)[source]

Fit RegularTransferGP.

Parameters

Xtnumpy array (default=None): Target input data.
ytnumpy array (default=None): Target output data.
fit_paramskey, value arguments: Not used. Here for sklearn compatibility.

Returns

selfreturns an instance of self

fit_estimator(X, y, sample_weight=None, random_state=None, warm_start=True, **fit_params)[source]

Fit estimator on X, y.

Parameters

Xarray: Input data.
yarray: Output data.
sample_weightarray: Importance weighting.
random_stateint (default=None): Seed of the random generator
warm_startbool (default=True): If True, continue to fit estimator_, else, a new estimator is fitted based on a copy of estimator. (Be sure to set copy=True to use warm_start=False)
fit_paramskey, value arguments: Arguments given to the fit method of the estimator and to the compile method for tensorflow estimator.

Returns

estimator_fitted estimator

get_metadata_routing()[source]

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deepbool, default=True: Not used, here for scikit-learn compatibility.

Returns

paramsdict: Parameter names mapped to their values.

predict(X, domain=None, **predict_params)[source]

Return estimator predictions after adaptation.

For feature-based method (object which implements a transform method), the input feature X are first transformed. Then the predict method of the fitted estimator estimator_ is applied on the transformed X.

Parameters

Xarray: input data
domainstr (default=None): For antisymetric feature-based method, different transformation of the input X are applied for different domains. The domain should then be specified between “src” and “tgt”. If None the default transformation is the target one.

Returns

y_predarray: prediction of the Adapt Model.

predict_estimator(X, **predict_params)[source]

Return estimator predictions for X.

Parameters

Xarray: input data

Returns

y_predarray: prediction of estimator.

score(X, y, sample_weight=None, domain=None)[source]

Return the estimator score.

If the object has a transform method, the estimator is applied on the transformed features X. For antisymetric transformation, a parameter domain can be set to specified between source and target transformation.

Call score on sklearn estimator and evaluate on tensorflow Model.

Parameters

Xarray: input data
yarray: output data
sample_weightarray (default=None): Sample weights
domainstr (default=None): This parameter specifies for antisymetric feature-based method which transformation will be applied between “source” and “target”. If None the transformation by default is the target one.

Returns

scorefloat: estimator score.

set_fit_request(*, domains: Union[bool, None, str] = '$UNCHANGED$') → adapt.parameter_based._regular.RegularTransferGP[source]

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

domainsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for domains parameter in fit.

Returns

selfobject: The updated object.

set_params(**params)[source]

Set the parameters of this estimator.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

set_predict_request(*, domain: Union[bool, None, str] = '$UNCHANGED$') → adapt.parameter_based._regular.RegularTransferGP[source]

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict 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 predict.
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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for domain parameter in predict.

Returns

selfobject: The updated object.

set_score_request(*, domain: Union[bool, None, str] = '$UNCHANGED$', sample_weight: Union[bool, None, str] = '$UNCHANGED$') → adapt.parameter_based._regular.RegularTransferGP[source]

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for domain parameter in score.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns

selfobject: The updated object.

unsupervised_score(Xs, Xt)[source]

Return unsupervised score.

The normalized discrepancy distance is computed between the reweighted/transformed source input data and the target input data.

Parameters

Xsarray: Source input data.
Xtarray: Source input data.

Returns

scorefloat: Unsupervised score.