adapt.parameter_based.TransferForestClassifier

class adapt.parameter_based.TransferForestClassifier(estimator=None, Xt=None, yt=None, algo='', bootstrap=False, cpy=True, verbose=1, random_state=None, **params)

TransferForestClassifier: Modify a source Random Forest on a target dataset.

Parameters

estimatorsklearn RandomForestClassifier (default=None)

Source random forest classifier.

Xtnumpy array (default=None)

Target input data.

ytnumpy array (default=None)

Target output data.

algostr or callable (default=””)

Leaves relabeling if “” or “relab”. “ser” and “strut” for SER and STRUT algorithms

bootstrapboolean (default=True).

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.

References

1

[1] Segev, Noam and Harel, Maayan Mannor, Shie and Crammer, Koby and El-Yaniv, Ran “Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests”. In IEEE TPAMI, 2017.

2

[2] Minvielle, Ludovic and Atiq, Mounir Peignier, Sergio and Mougeot, Mathilde “Transfer Learning on Decision Tree with Class Imbalance”. In IEEE ICTAI, 2019.

Attributes

estimator_sklearn RandomForestClassifier

Transferred random forest classifier using target data.

rf_sizeint.

estimators_numpy array of TransferTreeClassifier.

Methods

__init__([estimator, Xt, yt, algo, ...])
fit([Xt, yt])	Fit TransferTreeClassifier.
fit_estimator(X, y[, sample_weight, ...])	Fit estimator on X, y.
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.
unsupervised_score(Xs, Xt)	Return unsupervised score.

__init__(estimator=None, Xt=None, yt=None, algo='', bootstrap=False, cpy=True, verbose=1, random_state=None, **params)

fit(Xt=None, yt=None, **fit_params)

Fit TransferTreeClassifier.

Parameters

Xtnumpy array (default=None)

Target input data.

ytnumpy array (default=None)

Target output data.

fit_paramskey, value arguments

Arguments for the estimator.

Returns

selfreturns an instance of self

fit_estimator(X, y, sample_weight=None, random_state=None, warm_start=True, **fit_params)

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_params(deep=True)

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)

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)

Return estimator predictions for X.

Parameters

Xarray

input data

Returns

y_predarray

prediction of estimator.

score(X, y, sample_weight=None, domain=None)

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)

Set the parameters of this estimator.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

unsupervised_score(Xs, Xt)

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