bqlearn.ea.EasyADAPT

class bqlearn.ea.EasyADAPT(estimator)

A Frustratingly Easy approach to Domain Adaptation.

EasyADAPT [1] creates an augmented input space \(\tilde{\mathcal{X}} = \mathcal{X}^3\) with two different mapping for untrusted and trusted samples, \(\Psi_U:\mathcal{X}\mapsto \tilde{\mathcal{X}}\) and \(\Psi_T:\mathcal{X}\mapsto \tilde{\mathcal{X}}\).

• ..math::

  forall mathbf{x} in mathcal{X}, Psi_U(mathbf{x})=<mathbf{x}, mathbf{x}, mathbf{0}>

• ..math::

  forall mathbf{x} in mathcal{X}, Psi_T(mathbf{x})=<mathbf{x}, mathbf{0}, mathbf{x}>

This augmented domain \(\tilde{\mathcal{X}}\) allow for the classifier to learn different relation between the features and the target differently for the untrusted, trusted and general domain.

Parameters:

estimatorestimator object

An estimator object.

Attributes:

estimator_classifier

The fitted estimator.

classes_ndarray of shape (n_classes,)

The classes labels.

n_classes_int

The number of classes. Only defined if the underlying estimator exposes such an attribute when fit.

n_features_in_int

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

References

[1]

Daumé III, Hal. “Frustratingly Easy Domain Adaptation.” Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 2007.

Methods

augment(X[, sample_quality])	Augment the input dataset according to sample_quality.
decision_function(X)	Call decision function of the estimator on the augmented dataset.
fit(X, y[, sample_quality])	Fit the augmented model.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict the classes of X.
predict_log_proba(X)	Predict log probability for each possible outcome.
predict_proba(X)	Predict probability for each possible outcome.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.

augment(X, sample_quality=None)

Augment the input dataset according to sample_quality.

Parameters:

Xarray-like of shape (n_samples, n_features)

The samples.

sample_qualityarray-like, shape (n_samples,)

Per-sample qualities.

Returns:

X_augarray-like of shape (n_samples, 3 * n_features)

Returns the augmented samples.

decision_function(X)

Call decision function of the estimator on the augmented dataset.

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

yndarray, shape (n_samples,)

The predicted classes.

fit(X, y, sample_quality=None)

Fit the augmented model.

Parameters:

Xarray-like of shape (n_samples, n_features)

The samples.

yarray-like of shape (n_samples,)

The targets.

sample_qualityarray-like, shape (n_samples,)

Per-sample qualities.

Returns:

selfobject

Returns self.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

predict(X)

Predict the classes of X.

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

yndarray, shape (n_samples,)

The predicted classes.

predict_log_proba(X)

Predict log probability for each possible outcome.

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

log_parray, shape (n_samples, n_classes)

Array with log prediction probabilities.

predict_proba(X)

Predict probability for each possible outcome.

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

parray, shape (n_samples, n_classes)

The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.