adapt.feature_based.fMMD

class adapt.feature_based.fMMD(estimator=None, Xt=None, threshold='auto', kernel='linear', gamma=1.0, degree=2, coef=1.0, copy=True, verbose=1, random_state=None, **params)[source]

fMMD : feature Selection with MMD

LDM selects input features in order to minimize the maximum mean discrepancy (MMD) between the source and the target data.

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.

thresholdfloat or ‘auto’ (default=’auto’)

Threshold on features_scores_ all feature with score above threshold will be removed. If ‘auto’ the threshold is chosen to maximize the difference between scores of selected features and removed ones.

kernelstr (default=’linear’)

Choose the kernel between [‘linear’, ‘rbf’, ‘poly’]. The kernels are computed as follows:

kernel = linear:
```
k(X, Y) = <X, Y>
```
kernel = rbf:
```
k(X, Y) = exp(gamma * ||X-Y||^2)
```

kernel = poly:

poly(X, Y) = (gamma * <X, Y> + coef)^degree

gammafloat (default=1.)

Gamma multiplier for the ‘rbf’ and ‘poly’ kernels.

degreeint (default=2)

Degree of the ‘poly’ kernel

coeffloat (default=1.)

Coef of the ‘poly’ kernel

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

CORAL
FE

References

1: [1] S. Uguroglu and J. Carbonell. “Feature selection for transfer learning.” In ECML PKDD. 2011.

Examples

>>> from sklearn.linear_model import RidgeClassifier
>>> from adapt.utils import make_classification_da
>>> from adapt.feature_based import fMMD
>>> Xs, ys, Xt, yt = make_classification_da()
>>> model = fMMD(RidgeClassifier(), Xt=Xt, kernel="linear", random_state=0, verbose=0)
>>> model.fit(Xs, ys)
>>> model.score(Xt, yt)
0.45

Attributes

estimator_object: Estimator.
selected_features_numpy array: The selected features
features_scores_numpy array: The score attributed to each feature

Methods

`__init__`([estimator, Xt, threshold, kernel, ...])
`fit`(X, y[, Xt, yt, domains])	Fit Adapt Model.
`fit_estimator`(X, y[, sample_weight, ...])	Fit estimator on X, y.
`fit_transform`(Xs, Xt, **kwargs)	Fit embeddings.
`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_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Return the projection of X on the selected featues.
`unsupervised_score`(Xs, Xt)	Return unsupervised score.

__init__(estimator=None, Xt=None, threshold='auto', kernel='linear', gamma=1.0, degree=2, coef=1.0, copy=True, verbose=1, random_state=None, **params)[source]

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

Fit Adapt Model.

For feature-based models, the transformation of the input features Xs and Xt is first fitted. In a second stage, the estimator_ is fitted on the transformed features.

For instance-based models, source importance weights are first learned based on Xs, ys and Xt. In a second stage, the estimator_ is fitted on Xs, ys with the learned importance weights.

Parameters

Xnumpy array: Source input data.
ynumpy array: Source output data.
Xtarray (default=None): Target input data. If None, the Xt argument given in init is used.
ytarray (default=None): Target input data. Only needed for supervised and semi-supervised Adapt model. If None, the yt argument given in init is used.
domainsarray (default=None): Vector giving the domain for each source data. Can be used for multisource purpose.
fit_paramskey, value arguments: Arguments given to the fit method of the estimator.

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

fit_transform(Xs, Xt, **kwargs)[source]

Fit embeddings.

Parameters

Xsarray: Input source data.
Xtarray: Input target data.
kwargskey, value argument: Not used, present here for adapt consistency.

Returns

Xs_embembedded source data

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_params(**params)[source]

Set the parameters of this estimator.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

transform(X)[source]

Return the projection of X on the selected featues.

Parameters

Xarray: Input data.

Returns

X_embarray: Embeddings of X.

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.