FuseKernel

Fused tree + spectral kernel model.

class modeva.models.MoFuseKernelRegressor(name=None, use_xgb=True, use_rbf=False, use_spectral=True, tree_depths=None, spectral_params=None, fit_method='grid', solver='nystrom', backend='xgboost', gbdt_params=None, residual_nw=False, max_interaction_features=6, max_interactions=10, interpret_n_grid=30, interpret_ref_size=1000, random_state=0)

fuseKernel fused kernel-ridge regressor for MoDeVa.

Parameters:

namestr, optional

use_xgb, use_rbf, use_spectralbool

Which kernel channels to fuse (defaults: tree + spectral). use_xgb turns on the tree co-membership channel regardless of backend.

tree_depthstuple of int, optional

One co-membership kernel per depth (multi-depth tree fusion).

spectral_paramsdict, optional

MS-SKM kwargs (H / K / kernel / …). None -> the fuseKernel defaults (kernel="laplace", H=4, K=8, solver="nystrom").

fit_method{“grid”, “adam”, “nlml”, “oof”, “gcv”, “sure”}, default=”grid”

Fusion-weight selection (grid/adam are leakage-free, query-scored).

solver{“nystrom”, “auto”, “lanczos”, “matfree”}, default=”nystrom”

How the spectral channel decodes its kernel (only used when use_spectral=True). "nystrom" is the linear-in-n low-rank decode and the fastest/most scalable default; "lanczos" is the exact dense decode (best for small data); "auto" uses dense below ~20k rows and switches to Nystrom above; "matfree" is a matrix-free CG solve. An explicit "solver" key in spectral_params overrides this.

backend{“xgboost”, “lightgbm”, “catboost”}, default=”xgboost”

Gradient-boosted ensemble that defines the leaf co-membership partition.

gbdt_paramsdict, optional

Native params for the chosen backend. None -> that backend’s defaults.

residual_nwpassthrough fuseKernel option.

max_interaction_features, max_interactionsinteraction screening for interpret.

interpret_n_grid, interpret_ref_sizefANOVA grid / reference-sample size.

random_stateint

calibrate_interval(X, y, alpha=0.1, max_depth=5)

fuseKernel’s intervals are the GP posterior – no fitting needed; just record alpha.

channel_contributions(X)

Exact additive per-channel decomposition of the fused prediction (regression).

Returns a ValidationResult: value holds the per-channel contribution arrays and the intercept; table is the mean absolute contribution per channel; plot() shows the bar.

diagnose_weak_clusters(dataset, n_clusters: int = 5, **kw)

Per-cluster train/test performance breakdown of the fitted fused kernel.

Nyström spectral clustering of the model’s own kernel partitions the data into n_clusters regions; the model metric is reported per region on train and test, so regions where the model underperforms (large train/test gap or low headline metric) are exposed. Works for both the classic two-channel and the general (spectral / multi-depth) paths.

Returns a ValidationResult: table is the per-cluster metric breakdown (with an ALL aggregate row); value holds the cluster labels, spectral embeddings and the weakest-cluster ranking; plot() shows the per-cluster test metric as a bar.

fit(X, y, sample_weight=None, feature_names=None)

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.

interpret(dataset)

Build the inherent FANOVA interpreter. Precomputes fuseKernel’s main-effect curves and pairwise-interaction surfaces over a reference sample, then exposes them through the standard InterpretFANOVA so ts.interpret_* work. Needs a spectral channel.

load(file_name: str)

Load the model into memory from file system.

Parameters:

file_name: str

The path and name of the file.

Returns:

estimator object

predict(X)

Model predictions, calling the child class’s ‘_predict’ method.

Parameters:

Xnp.ndarray of shape (n_samples, n_features)

Feature matrix for prediction.

Returns:

np.ndarray: The (calibrated) final prediction

predict_dist(X)

GP predictive (mean, variance) in target units.

predict_effect(fidx, X)

Raw prediction of one main effect (len-1 fidx) or pairwise interaction (len-2 fidx).

predict_interaction(X)

Pairwise-interaction raw predictions, shape (n, n_interactions).

predict_interval(X)

Closed-form GP posterior prediction interval at level 1 - alpha.

predict_main_effect(X)

Per-feature main-effect raw predictions, shape (n, n_features).

reset_calibrate_interval()

reset_calibrate_proba()

save(file_name: str)

Save the model into file system.

Parameters:

file_name: str

The path and name of the file.

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(*, feature_names: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → MoFuseKernelRegressor

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.

Added 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:

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

Metadata routing for feature_names parameter in fit.

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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MoFuseKernelRegressor

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.

Added 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.

property name

property version

class modeva.models.MoFuseKernelClassifier(name=None, use_xgb=True, use_rbf=False, use_spectral=True, tree_depths=None, spectral_params=None, fit_method='grid', solver='nystrom', backend='xgboost', gbdt_params=None, max_interaction_features=6, max_interactions=10, interpret_n_grid=30, interpret_ref_size=1000, random_state=0)

fuseKernel fused kernel-ridge classifier for MoDeVa (binary and multiclass).

The fused KRR decodes one-hot targets to per-class scores; predict_proba is a temperature-calibrated softmax. Same parameters as MoFuseKernelRegressor.

calibrate_interval(X, y, alpha=0.1)

Fit a conformal prediction model to the given data.

This method computes the model’s prediction interval calibrated to the given data.

It computes the calibration quantile based on predicted probabilities for the positive class.

Parameters:

XXnp.ndarray of shape (n_samples, n_features)

Feature matrix for prediction.

yarray-like of shape (n_samples, )

Target values.

alphafloat, default=0.1

Expected miscoverage for the conformal prediction.

Raises:

ValueError: If the model is neither a regressor nor a classifier.

calibrate_proba(X, y, sample_weight=None, method='sigmoid')

Fit the calibration method on the model’s predictions.

Parameters:

Xnp.ndarray of shape (n_samples, n_features)

Feature matrix for prediction.

ynp.ndarray of shape (n_samples, )

Ground truth labels.

sample_weightarray-like, shape (n_samples,), default=None

Sample weights.

method{‘sigmoid’, ‘isotonic’}, default=’sigmoid’

The calibration method.

• ‘sigmoid’: Platt’s method, i.e., fit a logistic regression on predicted probabilities and y

• ‘isotonic’: Fit an isotonic regression on predicted probabilities and y.

Returns:

self: Calibrated estimator

channel_contributions(X)

Exact additive per-channel decomposition of the fused prediction (regression).

Returns a ValidationResult: value holds the per-channel contribution arrays and the intercept; table is the mean absolute contribution per channel; plot() shows the bar.

decision_function(X)

Computes the decision function for the given input data.

Parameters:

Xnp.ndarray of shape (n_samples, n_features)

Feature matrix for prediction.

calibrationbool, default=True

If True, will use calibrated probability if calibration is done. Otherwise, will use raw probability.

Returns:

logit_predictionarray, shape (n_samples,) or (n_samples, n_classes)

Array of (calibrated) logit predictions.

diagnose_weak_clusters(dataset, n_clusters: int = 5, **kw)

Per-cluster train/test performance breakdown of the fitted fused kernel.

Nyström spectral clustering of the model’s own kernel partitions the data into n_clusters regions; the model metric is reported per region on train and test, so regions where the model underperforms (large train/test gap or low headline metric) are exposed. Works for both the classic two-channel and the general (spectral / multi-depth) paths.

Returns a ValidationResult: table is the per-cluster metric breakdown (with an ALL aggregate row); value holds the cluster labels, spectral embeddings and the weakest-cluster ranking; plot() shows the per-cluster test metric as a bar.

fit(X, y, sample_weight=None, feature_names=None)

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.

interpret(dataset)

Build the inherent FANOVA interpreter. Precomputes fuseKernel’s main-effect curves and pairwise-interaction surfaces over a reference sample, then exposes them through the standard InterpretFANOVA so ts.interpret_* work. Needs a spectral channel.

load(file_name: str)

Load the model into memory from file system.

Parameters:

file_name: str

The path and name of the file.

Returns:

estimator object

predict(X)

Model predictions, calling the child class’s ‘_predict’ method.

Parameters:

Xnp.ndarray of shape (n_samples, n_features)

Feature matrix for prediction.

calibrationbool, default=True

If True, will use calibrated probability if calibration is done. Otherwise, will use raw probability.

Returns:

np.ndarray: The (calibrated) final prediction

predict_effect(fidx, X)

Raw prediction of one main effect (len-1 fidx) or pairwise interaction (len-2 fidx).

predict_interaction(X)

Pairwise-interaction raw predictions, shape (n, n_interactions).

predict_interval(X)

Predict the prediction set for the given data based on the conformal prediction model.

This method computes the model prediction interval (regression) or prediction sets (classification) using conformal prediction.

Parameters:

Xnp.ndarray of shape (n_samples, n_features)

Feature matrix for prediction.

Returns:

np.ndarray: The lower and upper bounds of the prediction intervals for each sample

in the format [n_samples, 2] for regressors or a flattened array for classifiers.

Raises:

ValueError: If fit_conformal has not been called to fit the conformal prediction model

before calling this method.

predict_main_effect(X)

Per-feature main-effect raw predictions, shape (n, n_features).

predict_proba(X)

Predict (calibrated) probabilities for X.

Parameters:

Xnp.ndarray of shape (n_samples, n_features)

Feature matrix for prediction.

calibrationbool, default=True

If True, will return calibrated probability if calibration is done. Otherwise, will return raw probability.

Returns:

np.ndarray: The (calibrated) predicted probabilities

reset_calibrate_interval()

reset_calibrate_proba()

save(file_name: str)

Save the model into file system.

Parameters:

file_name: str

The path and name of the file.

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_decision_function_request(*, calibration: bool | None | str = '$UNCHANGED$') → MoFuseKernelClassifier

Request metadata passed to the decision_function 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 decision_function 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 decision_function.

• 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.

Added 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:

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

Metadata routing for calibration parameter in decision_function.

Returns:

selfobject

The updated object.

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

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.

Added 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:

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

Metadata routing for feature_names parameter in fit.

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_predict_proba_request(*, calibration: bool | None | str = '$UNCHANGED$') → MoFuseKernelClassifier

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.

Added 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:

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

Metadata routing for calibration parameter in predict_proba.

Returns:

selfobject

The updated object.

set_predict_request(*, calibration: bool | None | str = '$UNCHANGED$') → MoFuseKernelClassifier

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.

Added 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:

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

Metadata routing for calibration parameter in predict.

Returns:

selfobject

The updated object.

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

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.

Added 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.

property name

property version