Batched Tree Ensembles

Batched Tree Ensemble Classifier

class snapml.BatchedTreeEnsembleClassifier(base_ensemble=SnapBoostingMachineClassifier(), max_sub_ensembles=10, inner_lr_scaling=0.5, outer_lr_scaling=0.5)

Batched Tree Ensemble Classifier

This class enables batched training of a tree ensemble classifier on large datasets. Given a tree ensemble classifier, provided as a base ensemble, the algorithm will split the trees into a number of sub-ensembles. Each sub-ensemble is trained on a different batch of data, and the boosting mechanism is applied across batches to improve accuracy.

Parameters:

base_ensemble{sklearn.ensemble.RandomForestClassifier, sklearn.ensemble.ExtraTreesClassifier, snapml.SnapRandomForestClassifier, snapml.SnapBoostingMachineClassifier, xgboost.XGBClassifier, lightgbm.LGBMClassifier}, default=snapml.SnapBoostingMachineClassifier: The base ensemble that will be split into sub-ensembles and used for batched training.
max_sub_ensembles: int, default=10: The maximum number of sub-ensembles to use. It is recommended to set this parameter roughly equal to the expected number of batches. If more batches are provided than the number of sub-ensembles, the last sub-ensemble will be replaced.
outer_lr_scaling: float, default=0.5: The boosting mechanism across batches will use learning rate 1.0/(max_sub_ensembles ** outer_lr_scaling)
inner_lr_scaling: float, defualt=0.5: If the base ensemble has a learning rate (e.g. it is a boosting machine), the learning rate will be scaled by a factor (max_sub_ensembles ** inner_lr_scaling)

Attributes:

n_classes_int: The number of classes
classes_ndarary, shape (n_classes, ): Set of unique classes
ensembles_list: Trained sub-ensembles

build_ensemble(X, target, weights)

Build a new sub-ensemble and insert it into model

Parameters:

Xndarray, shape (n_samples, n_features): Batch of training data.
targetndarray, shape (n_samples,): Boosting target.
weightsndarray, shape (n_samples,): Boosting weights.
first_batch: bool: Is this the first batch?

fit(X, y, sample_weight=None)

Fit the base ensemble on a batch of data.

Parameters:

Xndarray, shape (n_samples, n_features): Training data.
yndarray, shape (n_samples,): Training labels.
sample_weightndarray, shape (n_samples,), default=None: Sample weights to be applied during training.

Returns:

selfobject: Returns an instance of self.

get_metadata_routing()

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)

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.

partial_fit(X, y, sample_weight=None, classes=None)

Continue training the model with a new batch of data.

Parameters:

Xndarray, shape (n_samples, n_features): Batch of training data.
yndarray, shape (n_samples,): Batch of training labels.
sample_weightndarray, shape (n_samples,), default=None: Sample weights to be applied during training.
classesndarray, shape (n_classes,), default=None: Set of unique classes across the entire dataset. This argument is only required for first call to partial fit.

Returns:

selfobject: Returns an instance of self.

predict(X)

Predict class labels

Parameters:

Xndarray, shape=(n_samples, n_features): Samples to be used for prediction

Returns:

predndarray, shape = (n_samples,): Predicted class labels

predict_proba(X)

Predict class probabilities

Parameters:

Xndarray, shape=(n_samples, n_features): Samples to be used for prediction

Returns:

predndarray, shape = (n_samples, n_classes): Predicted class probabilities

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_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BatchedTreeEnsembleClassifier

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:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

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.

set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → BatchedTreeEnsembleClassifier

Request metadata passed to the partial_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 partial_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 partial_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:

classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for classes parameter in partial_fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BatchedTreeEnsembleClassifier

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:

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.

train_on_batch(X, y, sample_weight=None)

Train on a new batch of data

Parameters:

Xndarray, shape (n_samples, n_features): Batch of training data.
yndarray, shape (n_samples,): Batch of training labels.
sample_weightndarray, shape (n_samples,), default=None: Sample weights to be applied during training.

Batched Tree Ensemble Regressor

class snapml.BatchedTreeEnsembleRegressor(base_ensemble=SnapBoostingMachineRegressor(), max_sub_ensembles=10, inner_lr_scaling=0.5, outer_lr_scaling=0.5)

Batched Tree Ensemble Regressor

This class enables batched training of a tree ensemble regressor on large datasets. Given a tree ensemble regressor, provided as a base ensemble, the algorithm will split the trees into a number of sub-ensembles. Each sub-ensemble is trained on a different batch of data, and the boosting mechanism is applied across batches to improve accuracy.

Parameters:

base_ensemble{sklearn.ensemble.RandomForestRegressor, sklearn.ensemble.ExtraTreesRegressor, snapml.SnapRandomForestRegressor, snapml.SnapBoostingMachineRegressor, xgboost.XGBRegressor, lightgbm.LGBMRegressor}, default=snapml.SnapBoostingMachineRegressor: The base ensemble that will be split into sub-ensembles and used for batched training.
max_sub_ensembles: int, default=10: The maximum number of sub-ensembles to use. It is recommended to set this parameter roughly equal to the expected number of batches. If more batches are provided than the number of sub-ensembles, the last sub-ensemble will be replaced.
outer_lr_scaling: float, default=0.5: The boosting mechanism across batches will use learning rate 1.0/(max_sub_ensembles ** outer_lr_scaling)
inner_lr_scaling: float, defualt=0.5: If the base ensemble has a learning rate (e.g. it is a boosting machine), the learning rate will be scaled by a factor (max_sub_ensembles ** inner_lr_scaling)

Attributes:

ensembles_list: Trained sub-ensembles

build_ensemble(X, target, weights)

Build a new sub-ensemble and insert it into model

Parameters:

Xndarray, shape (n_samples, n_features): Batch of training data.
targetndarray, shape (n_samples,): Boosting target.
weightsndarray, shape (n_samples,): Boosting weights.
first_batch: bool: Is this the first batch?

fit(X, y, sample_weight=None)

Fit the base ensemble on a batch of data.

Parameters:

Xndarray, shape (n_samples, n_features): Training data.
yndarray, shape (n_samples,): Training labels.
sample_weightndarray, shape (n_samples,), default=None: Sample weights to be applied during training.

Returns:

selfobject: Returns an instance of self.

get_metadata_routing()

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)

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.

partial_fit(X, y, sample_weight=None)

Continue training the model with a new batch of data.

Parameters:

Xndarray, shape (n_samples, n_features): Batch of training data.
yndarray, shape (n_samples,): Batch of training regression targets.
sample_weightndarray, shape (n_samples,), default=None: Sample weights to be applied during training.

Returns:

selfobject: Returns an instance of self.

predict(X)

Predict target values

Parameters:

Xndarray, shape=(n_samples, n_features): Samples to be used for prediction

Returns:

predndarray, shape = (n_samples,): Predicted target values

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BatchedTreeEnsembleRegressor

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:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

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.

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BatchedTreeEnsembleRegressor

Request metadata passed to the partial_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 partial_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 partial_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:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BatchedTreeEnsembleRegressor

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:

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.

train_on_batch(X, y, sample_weight=None)

Train on a new batch of data

Parameters:

Xndarray, shape (n_samples, n_features): Batch of training data.
yndarray, shape (n_samples,): Batch of training labels.
sample_weightndarray, shape (n_samples,), default=None: Sample weights to be applied during training.