adapt.parameter_based.TransferTreeClassifier

class adapt.parameter_based.TransferTreeClassifier(estimator=None, Xt=None, yt=None, algo='', copy=True, verbose=1, random_state=None, **params)[source]

TransferTreeClassifier: Modify a source Decision tree on a target dataset.

Parameters

estimatorsklearn DecsionTreeClassifier (default=None): Source decision tree 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
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.

Examples

>>> from adapt.utils import make_classification_da
>>> from sklearn.tree import DecisionTreeClassifier
>>> from adapt.parameter_based import TransferTreeClassifier
>>> Xs, ys, Xt, yt = make_classification_da()
>>> src_model = DecisionTreeClassifier().fit(Xs, ys)
>>> src_model.score(Xt, yt)
0.62
>>> tgt_model = TransferTreeClassifier(src_model)
>>> tgt_model.fit(Xt[[1, -1]], yt[[1, -1]])
>>> tgt_model.score(Xt, yt)
0.92

Attributes

estimator_sklearn DecsionTreeClassifier: Transferred decision tree classifier using target data.
parentsnumpy array of int.
bool_parents_lrnumpy array of {-1,0,1} values.
pathsnumpy array of int arrays.
rulesnumpy array of 3-tuple arrays.
depthsnumpy array of int.

Methods

`__init__`([estimator, Xt, yt, algo, copy, ...])
`extend`(node, subtree)
`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.
`prune`(node[, include_node, lr, leaf_value])
`score`(X, y[, sample_weight, domain])	Return the estimator score.
`set_params`(**params)	Set the parameters of this estimator.
`swap_subtrees`(node1, node2)
`unsupervised_score`(Xs, Xt)	Return unsupervised score.
`updateSplit`(node, feature, threshold)
`updateValue`(node, values)

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

extend(node, subtree)[source]

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

Fit TransferTreeClassifier.

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_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.

prune(node, include_node=False, lr=0, leaf_value=None)[source]

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.

swap_subtrees(node1, node2)[source]

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.

updateSplit(node, feature, threshold)[source]

updateValue(node, values)[source]