adapt.parameter_based.LinInt

class adapt.parameter_based.LinInt(estimator=None, Xt=None, yt=None, prop=0.5, copy=True, verbose=1, random_state=None, **params)[source]

LinInt: Linear Interpolation between SrcOnly and TgtOnly.

LinInt linearly interpolates the predictions of the SrcOnly and TgtOnly models. The interpolation parameter is adjusted based on a small amount of target data removed from the training set of TgtOnly.

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.

propfloat (default=0.5)

Proportion between 0 and 1 of the data used to fit the TgtOnly model. The rest of the target data are used to estimate the interpolation parameter.

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

adapt.feature_based.FA
adapt.feature_based.PRED

References

1

[1] Daume III, H. “Frustratingly easy domain adaptation”. In ACL, 2007.

Examples

>>> from sklearn.linear_model import Ridge
>>> from adapt.utils import make_regression_da
>>> from adapt.parameter_based import LinInt
>>> Xs, ys, Xt, yt = make_regression_da()
>>> model = LinInt(Ridge(), Xt=Xt[:6], yt=yt[:6], prop=0.5,            
...              verbose=0, random_state=0)
>>> model.fit(Xs, ys)
>>> model.score(Xt, yt)
0.68...
Attributes
estimator_src_object

Fitted source estimator.

estimator_object

Fitted estimator.

Methods

__init__([estimator, Xt, yt, prop, copy, ...])

fit(Xs, ys[, Xt, yt])

Fit LinInt.

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)

Return LinInt predictions.

predict_estimator(X, **predict_params)

Return estimator predictions for X.

score(X, y)

Compute R2 score

set_fit_request(*[, Xs, ys])

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, prop=0.5, copy=True, verbose=1, random_state=None, **params)[source]
fit(Xs, ys, Xt=None, yt=None, **kwargs)[source]

Fit LinInt.

Parameters
Xsarray

Source input data.

ysarray

Source output data.

Xtarray

Target input data.

ytarray

Target output data.

kwargskey, value argument

Not used, present here for adapt consistency.

Returns
Xt_aug, ytaugmented input and output target data
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)[source]

Return LinInt predictions.

Parameters
Xarray

Input data.

Returns
yarray

Predictions

predict_estimator(X, **predict_params)[source]

Return estimator predictions for X.

Parameters
Xarray

input data

Returns
y_predarray

prediction of estimator.

score(X, y)[source]

Compute R2 score

Parameters
Xarray

input data

yarray

output data

Returns
scorefloat

estimator score.

set_fit_request(*, Xs: Union[bool, None, str] = '$UNCHANGED$', ys: Union[bool, None, str] = '$UNCHANGED$') → adapt.parameter_based._linint.LinInt[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
Xsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for Xs parameter in fit.

ysstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for ys 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._linint.LinInt[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._linint.LinInt[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.

Examples