Boosting Machines

Boosting Machine Classifier

class snapml.BoostingMachineClassifier(n_jobs=1, num_round=100, max_depth=None, min_max_depth=1, max_max_depth=5, early_stopping_rounds=10, random_state=0, base_score=None, learning_rate=0.1, verbose=False, compress_trees=False, class_weight=None, use_histograms=True, hist_nbins=256, use_gpu=False, gpu_ids=[0], colsample_bytree=1.0, subsample=1.0, lambda_l2=0.0, tree_select_probability=1.0, regularizer=1.0, fit_intercept=False, gamma=1.0, n_components=10)

Boosting machine for binary and multi-class classification tasks.

A heterogeneous boosting machine that mixes binary decision trees (of stochastic max_depth) with linear models with random fourier features (approximation of kernel ridge regression).

Parameters:

num_roundint, default=100

Number of boosting iterations.

learning_ratefloat, default=0.1

Learning rate / shrinkage factor.

random_stateint, default=0

Random seed.

colsample_bytreefloat, default=1.0

Fraction of feature columns used at each boosting iteration.

subsamplefloat, default=1.0

Fraction of training examples used at each boosting iteration.

verbosebool, default=False

Print off information during training.

lambda_l2float, default=0.0

L2-reguralization penalty used during tree-building.

early_stopping_roundsint, default=10

When a validation set is provided, training will stop if the validation loss does not decrease after a fixed number of rounds.

compress_treesbool, default=False

Compress trees after training for fast inference.

base_scorefloat, default=None

Base score to initialize boosting algorithm. If None then the algorithm will initialize the base score to be the average target (regression) or the logit of the probability of the positive class (binary classification) or zero (multiclass classification).

class_weight{‘balanced’, None}, default=None

If set to ‘balanced’ samples weights will be applied to account for class imbalance, otherwise no sample weights will be used.

max_depthint, default=None

If set, will set min_max_depth = max_depth = max_max_depth

min_max_depthint, default=1

Minimum max_depth of trees in the ensemble.

max_max_depthint, default=5

Maximum max_depth of trees in the ensemble.

n_jobsint, default=1

Number of threads to use during training.

use_histogramsbool, default=True

Use histograms to accelerate tree-building.

hist_nbinsint, default=256

Number of histogram bins.

use_gpubool, default=False

Use GPU for tree-building.

gpu_idsarray-like of int, default: [0]

Device IDs of the GPUs which will be used when GPU acceleration is enabled.

tree_select_probabilityfloat, default=1.0

Probability of selecting a tree (rather than a kernel ridge regressor) at each boosting iteration.

regularizerfloat, default=1.0

L2-regularization penality for the kernel ridge regressor.

fit_interceptbool, default=False

Include intercept term in the kernel ridge regressor.

gammafloat, default=1.0

Guassian kernel parameter.

n_componentsint, default=10

Number of components in the random projection.

Attributes:

feature_importances_array-like, shape=(n_features,)

Feature importances computed across trees.

apply(X)

Map batch of examples to leaf indices and labels.

Parameters:

Xdense matrix (ndarray)

Batch of examples.

Returns:

indicesarray-like, shape = (n_samples, num_round) or (n_samples, num_round, num_classes)

The leaf indices. Output is 2-dim for binary classification. Output is 3-dim for multiclass classification.

labelsarray-like, shape = (n_samples, num_round) or (n_samples, num_round, num_classes)

The leaf labels. Output is 2-dim for binary classification. Output is 3-dim for multiclass classification.

export_model(output_file, output_type='pmml')

Export model trained in snapml to the given output file using a format of the given type.

Currently only PMML is supported as export format. The corresponding output file type to be provided to the export_model function is ‘pmml’.

Parameters:

output_filestr

Output filename

output_type{‘pmml’}

Output file type

fit(X, y, sample_weight=None, X_val=None, y_val=None, sample_weight_val=None, aggregate_importances=True)

Fit the model according to the given train data.

Parameters:

Xdense matrix (ndarray)

Train dataset

yarray-like, shape = (n_samples,)

The target vector corresponding to X.

sample_weightarray-like, shape = (n_samples,)

Training sample weights

X_valdense matrix (ndarray)

Validation dataset

y_valarray-like, shape = (n_samples,)

The target vector corresponding to X_val.

sample_weight_valarray-like, shape = (n_samples,)

Validation sample weights

aggregate_importancesbool, default=True

Aggregate feature importances over boosting rounds

Returns:

selfobject

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.

import_model(input_file, input_type, tree_format='auto', X=None)

Import a pre-trained boosted ensemble model and optimize the trees for fast inference.

Supported import formats include PMML, ONNX, XGBoost json and lightGBM text. The corresponding input file types to be provided to the import_model function are ‘pmml’, ‘onnx’, ‘xgb_json’, and ‘lightgbm’ respectively.

Depending on how the tree_format argument is set, this function will return a different optimized model format. This format determines which inference engine is used for subsequent calls to ‘predict’ or ‘predict_proba’.

If tree_format is set to ‘compress_trees’, the model will be optimized for execution on the CPU, using our compressed decision trees approach. Note: if this option is selected, an optional dataset X can be provided, which will be used to predict node access characteristics during node clustering.

If tree_format is set to ‘zdnn_tensors’, the model will be optimized for execution on the IBM z16 AI accelerator, using a matrix-based inference algorithm leveraging the zDNN library.

By default tree_format is set to ‘auto’. A check is performed and if the IBM z16 AI accelerator is available the model will be optimized according to ‘zdnn_tensors’, otherwise it will be optimized according to ‘compress_trees’. The selected optimized tree format can be read by parameter self.booster_.optimized_tree_format_.

Note: If the input file contains features that are not supported by the import function, then an exception is thrown indicating the feature and the line number within the input file containing the feature.

Parameters:

input_filestr

Input filename

input_type{‘pmml’, ‘onnx’, ‘xgb_json’, ‘lightgbm’}

Input file type

tree_format{‘auto’, ‘compress_trees’, ‘zdnn_tensors’}

Tree format

Xdense matrix (ndarray)

Optional input dataset used for compressing trees

Returns:

selfobject

optimize_trees(tree_format='auto', X=None)

Optimize the trees in the ensemble for fast inference.

Depending on how the tree_format argument is set, this function will return a different optimized model format. This format determines which inference engine is used for subsequent calls to ‘predict’ or ‘predict_proba’.

If tree_format is set to ‘compress_trees’, the model will be optimized for execution on the CPU, using our compressed decision trees approach. Note: if this option is selected, an optional dataset X can be provided, which will be used to predict node access characteristics during node clustering.

If tree_format is set to ‘zdnn_tensors’, the model will be optimized for execution on the IBM z16 AI accelerator, using a matrix-based inference algorithm leveraging the zDNN library.

By default tree_format is set to ‘auto’. A check is performed and if the IBM z16 AI accelerator is available the model will be optimized according to ‘zdnn_tensors’, otherwise it will be optimized according to ‘compress_trees’. The selected optimized tree format can be read by parameter self.booster_.optimized_tree_format_.

Parameters:

tree_format{‘auto’, ‘compress_trees’, ‘zdnn_tensors’}

Tree format

Xdense matrix (ndarray)

Optional input dataset used for compressing trees

Returns:

selfobject

predict(X, n_jobs=None)

Predict class labels

Parameters:

Xdense matrix (ndarray)

Dataset used for predicting class estimates.

n_jobsint

Number of threads to use for prediction.

Returns:

pred: array-like, shape = (n_samples,)

Returns the predicted class labels

predict_proba(X, n_jobs=None)

Predict class label probabilities

Parameters:

Xdense matrix (ndarray)

Dataset used for predicting class estimates.

n_jobsint

Number of threads to use for prediction.

Returns:

proba: array-like, shape = (n_samples, 2)

Returns the 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(*, X_val: bool | None | str = '$UNCHANGED$', aggregate_importances: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$', sample_weight_val: bool | None | str = '$UNCHANGED$', y_val: bool | None | str = '$UNCHANGED$') → BoostingMachineClassifier

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:

X_valstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_val parameter in fit.

aggregate_importancesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for aggregate_importances parameter in fit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

sample_weight_valstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight_val parameter in fit.

y_valstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for y_val parameter in fit.

Returns:

selfobject

The updated object.

set_params(**params)

Set the parameters of this model.

Valid parameter keys can be listed with get_params().

Returns:

self

set_predict_proba_request(*, n_jobs: bool | None | str = '$UNCHANGED$') → BoostingMachineClassifier

Request metadata passed to the predict_proba 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_proba 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_proba.

• 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:

n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for n_jobs parameter in predict_proba.

Returns:

selfobject

The updated object.

set_predict_request(*, n_jobs: bool | None | str = '$UNCHANGED$') → BoostingMachineClassifier

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:

n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for n_jobs parameter in predict.

Returns:

selfobject

The updated object.

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

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.

Boosting Machine Regressor

class snapml.BoostingMachineRegressor(n_jobs=1, num_round=100, objective='mse', max_depth=None, min_max_depth=1, max_max_depth=5, early_stopping_rounds=10, random_state=0, base_score=None, learning_rate=0.1, verbose=False, compress_trees=False, use_histograms=True, hist_nbins=256, use_gpu=False, gpu_id=0, colsample_bytree=1.0, subsample=1.0, lambda_l2=0.0, max_delta_step=0.0, alpha=0.5, min_h_quantile=0.0, tree_select_probability=1.0, regularizer=1.0, fit_intercept=False, gamma=1.0, n_components=10)

Boosting machine for regression tasks.

A heterogeneous boosting machine that mixes binary decision trees (of stochastic max_depth) with linear models with random fourier features (approximation of kernel ridge regression).

Parameters:

num_roundint, default=100

Number of boosting iterations.

objective{‘mse’, ‘cross_entropy’, ‘poisson’, ‘quantile’}, default=’mse’

Training objective.

learning_ratefloat, default=0.1

Learning rate / shrinkage factor.

random_stateint, default=0

Random seed.

colsample_bytreefloat, default=1.0

Fraction of feature columns used at each boosting iteration.

subsamplefloat, default=1.0

Fraction of training examples used at each boosting iteration.

verbosebool, default=False

Print off information during training.

lambda_l2float, default=0.0

L2-reguralization penalty used during tree-building.

max_delta_stepfloat, default=0.0

Reguralization term to ensure numerical stability when “objective = poisson”.

alphafloat, default=0.5

Quantile used when “objective = quantile”.

min_h_quantilefloat, default=0.0

Regularization term for quantile regression

early_stopping_roundsint, default=10

When a validation set is provided, training will stop if the validation loss does not decrease after a fixed number of rounds.

compress_treesbool, default=False

Compress trees after training for fast inference.

base_scorefloat, default=None

Base score to initialize boosting algorithm. If None then the algorithm will initialize the base score to be the average target (regression) or the logit of the probability of the positive class (binary classification).

max_depthint, default=None

If set, will set min_max_depth = max_depth = max_max_depth

min_max_depthint, default=1

Minimum max_depth of trees in the ensemble.

max_max_depthint, default=5

Maximum max_depth of trees in the ensemble.

n_jobsint, default=1

Number of threads to use during training.

use_histogramsbool, default=True

Use histograms to accelerate tree-building.

hist_nbinsint, default=256

Number of histogram bins.

use_gpubool, default=False

Use GPU for tree-building.

gpu_idint, default=0

Device ID for GPU to use during training.

tree_select_probabilityfloat, default=1.0

Probability of selecting a tree (rather than a kernel ridge regressor) at each boosting iteration.

regularizerfloat, default=1.0

L2-regularization penality for the kernel ridge regressor.

fit_interceptbool, default=False

Include intercept term in the kernel ridge regressor.

gammafloat, default=1.0

Guassian kernel parameter.

n_componentsint, default=10

Number of components in the random projection.

Attributes:

feature_importances_array-like, shape=(n_features,)

Feature importances computed across trees.

apply(X)

Map batch of examples to leaf indices and labels.

Parameters:

Xdense matrix (ndarray)

Batch of examples.

Returns:

indicesarray-like, shape = (n_samples, num_round)

The leaf indices.

labelsarray-like, shape = (n_samples, num_round)

The leaf labels.

export_model(output_file, output_type='pmml')

Export model trained in snapml to the given output file using a format of the given type.

Currently only PMML is supported as export format. The corresponding output file type to be provided to the export_model function is ‘pmml’.

Parameters:

output_filestr

Output filename

output_type{‘pmml’}

Output file type

fit(X, y, sample_weight=None, X_val=None, y_val=None, sample_weight_val=None, aggregate_importances=True)

Fit the model according to the given train data.

Parameters:

Xdense matrix (ndarray)

Train dataset

yarray-like, shape = (n_samples,)

The target vector corresponding to X

sample_weightarray-like, shape = (n_samples,)

Training sample weights

X_valdense matrix (ndarray)

Validation dataset

y_valarray-like, shape = (n_samples,)

The target vector corresponding to X_val.

sample_weight_valarray-like, shape = (n_samples,)

Validation sample weights

aggregate_importancesbool, default=True

Aggregate feature importances over boosting rounds

Returns:

selfobject

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.

import_model(input_file, input_type, tree_format='auto', X=None)

Import a pre-trained boosted ensemble model and optimize the trees for fast inference.

Supported import formats include PMML, ONNX, XGBoost json and lightGBM text. The corresponding input file types to be provided to the import_model function are ‘pmml’, ‘onnx’, ‘xgb_json’, and ‘lightgbm’ respectively.

Depending on how the tree_format argument is set, this function will return a different optimized model format. This format determines which inference engine is used for subsequent calls to ‘predict’ or ‘predict_proba’.

If tree_format is set to ‘compress_trees’, the model will be optimized for execution on the CPU, using our compressed decision trees approach. Note: if this option is selected, an optional dataset X can be provided, which will be used to predict node access characteristics during node clustering.

If tree_format is set to ‘zdnn_tensors’, the model will be optimized for execution on the IBM z16 AI accelerator, using a matrix-based inference algorithm leveraging the zDNN library.

By default tree_format is set to ‘auto’. A check is performed and if the IBM z16 AI accelerator is available the model will be optimized according to ‘zdnn_tensors’, otherwise it will be optimized according to ‘compress_trees’. The selected optimized tree format can be read by parameter self.booster_.optimized_tree_format_.

Note: If the input file contains features that are not supported by the import function, then an exception is thrown indicating the feature and the line number within the input file containing the feature.

Parameters:

input_filestr

Input filename

input_type{‘pmml’, ‘onnx’, ‘xgb_json’, ‘lightgbm’}

Input file type

tree_format{‘auto’, ‘compress_trees’, ‘zdnn_tensors’}

Tree format

Xdense matrix (ndarray)

Optional input dataset used for compressing trees

Returns:

selfobject

optimize_trees(tree_format='auto', X=None)

Optimize the trees in the ensemble for fast inference.

Depending on how the tree_format argument is set, this function will return a different optimized model format. This format determines which inference engine is used for subsequent calls to ‘predict’ or ‘predict_proba’.

If tree_format is set to ‘compress_trees’, the model will be optimized for execution on the CPU, using our compressed decision trees approach. Note: if this option is selected, an optional dataset X can be provided, which will be used to predict node access characteristics during node clustering.

If tree_format is set to ‘zdnn_tensors’, the model will be optimized for execution on the IBM z16 AI accelerator, using a matrix-based inference algorithm leveraging the zDNN library.

By default tree_format is set to ‘auto’. A check is performed and if the IBM z16 AI accelerator is available the model will be optimized according to ‘zdnn_tensors’, otherwise it will be optimized according to ‘compress_trees’. The selected optimized tree format can be read by parameter self.booster_.optimized_tree_format_.

Parameters:

tree_format{‘auto’, ‘compress_trees’, ‘zdnn_tensors’}

Tree format

Xdense matrix (ndarray)

Optional input dataset used for compressing trees

Returns:

selfobject

predict(X, n_jobs=None)

Predict estimates

Parameters:

Xdense matrix (ndarray)

Dataset used for prediction

n_jobsint

Number of threads to use for prediction.

Returns:

pred: array-like, shape = (n_samples,)

Returns the predictions

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(*, X_val: bool | None | str = '$UNCHANGED$', aggregate_importances: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$', sample_weight_val: bool | None | str = '$UNCHANGED$', y_val: bool | None | str = '$UNCHANGED$') → BoostingMachineRegressor

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:

X_valstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_val parameter in fit.

aggregate_importancesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for aggregate_importances parameter in fit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

sample_weight_valstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight_val parameter in fit.

y_valstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for y_val parameter in fit.

Returns:

selfobject

The updated object.

set_params(**params)

Set the parameters of this model.

Valid parameter keys can be listed with get_params().

Returns:

self

set_predict_request(*, n_jobs: bool | None | str = '$UNCHANGED$') → BoostingMachineRegressor

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:

n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for n_jobs parameter in predict.

Returns:

selfobject

The updated object.

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

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.