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)	Extend the underlying decision tree estimator by a sub-tree at a given node.
`fit`([Xt, yt])	Fit TransferTreeClassifier.
`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[, domain])	Return estimator predictions after adaptation.
`predict_estimator`(X, **predict_params)	Return estimator predictions for X.
`prune`(node[, include_node, lr, leaf_value])	Pruning the corresponding sub-tree at a given node.
`score`(X, y[, sample_weight, domain])	Return the estimator score.
`set_fit_request`(*[, domains])	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.
`swap_subtrees`(node1, node2)	Swap respective sub-trees between two given nodes.
`unsupervised_score`(Xs, Xt)	Return unsupervised score.
`updateSplit`(node, feature, threshold)	Update the (feature,threshold) split for a given node.
`updateValue`(node, values)	Update class values for a given node.

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

extend(node, subtree)[source]

Extend the underlying decision tree estimator by a sub-tree at a given node.

Parameters

nodeint: Node to update.
subtreeDecisionTreeClassifier.

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_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, 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]

Pruning the corresponding sub-tree at a given node.

If include_node is False, replaces the node by a leaf (with values leaf_values if provided). If include_node is True, prunes the left (lr=-1) or (lr=1) child sub-tree and replaces the given node by the other sub-tree.

Parameters

nodeint: Node to prune.
include_nodeboolean (default=False): Type of pruning to apply.
lrfloat: Direction of pruning if include_node is True. Must be either -1 (left) or 1 (right) in this case.
leaf_valuenumpy array of float (default=None): If include_node is False, affects these values to the created leaf.

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_fit_request(*, domains: Union[bool, None, str] = '$UNCHANGED$') → adapt.parameter_based._transfer_tree.TransferTreeClassifier[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

domainsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for domains 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._transfer_tree.TransferTreeClassifier[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._transfer_tree.TransferTreeClassifier[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.

swap_subtrees(node1, node2)[source]

Swap respective sub-trees between two given nodes.

Each node must not be a sub-node of the other. Update the (feature,threshold) split for a given node.

Parameters

node1int: Node to swap.
node2int: Node to swap.

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]

Update the (feature,threshold) split for a given node.

Parameters

nodeint: Node to update.
featureint: New node feature.
thresholdfloat: New node threshold.

updateValue(node, values)[source]

Update class values for a given node.

Parameters

nodeint: Node to update.
valuesnumpy array of float: Class values to affect at node.