autoqild.mi_estimators.auto_gluon_estimator

AutoGluon-based MI estimator that leverages automated machine learning (AutoML) to estimate MI with optimized hyperparameters.

Classes

AutoMIGluonEstimator(n_features, n_classes)

AutoMIGluonEstimator integrates the AutoGluon framework into the Mutual Information (MI) estimation process for classification tasks.

class autoqild.mi_estimators.auto_gluon_estimator.AutoMIGluonEstimator(n_features, n_classes, time_limit=1800, output_folder=None, eval_metric='accuracy', use_hyperparameters=True, delete_tmp_folder_after_terminate=True, auto_stack=True, remove_boosting_models=True, verbosity=6, random_state=None, **kwargs)

Bases: ClassificationMIEstimator

AutoMIGluonEstimator integrates the AutoGluon framework into the Mutual Information (MI) estimation process for classification tasks. This class extends the ClassficationMIEstimator by using AutoGluon as the base estimator to perform MI estimation. It allows for various configurations and model tuning, making it flexible for different classification scenarios.

Parameters:

• n_features (int) – The number of features in the input data.

• n_classes (int) – The number of classes in the classification task.

• time_limit (int, optional, default=1800) – Time limit for training the model, in seconds.

• output_folder (str, optional) – Directory where the trained model and related files will be saved. Default is None.

• eval_metric (str, optional, default=`accuracy`) – Evaluation metric used to assess the performance of the model.

• use_hyperparameters (bool, optional, default=True) – Whether to use predefined hyperparameters for model training.

• delete_tmp_folder_after_terminate (bool, optional, default=True) – Whether to delete the temporary folder after model training is complete.

• auto_stack (bool, optional, default=True) – Whether to use automatic stacking of models in AutoGluon.

• remove_boosting_models (bool, optional, default=True) – Whether to exclude boosting models (like GBM, CAT, XGB) from the hyperparameters.

• verbosity (int, optional, default=6) – Level of verbosity for logging and output.

• random_state (int or None, optional, default=None) – Seed for random number generation to ensure reproducibility.

• **kwargs (dict, optional) – Additional keyword arguments passed to the AutoGluonClassifier constructor.

base_estimator

The base AutoML estimator used for classification.

Type:

AutoGluonClassifier

learner_params

Parameters used to configure the base learner.

Type:

dict

base_learner

Instance of the AutoGluon classifier used for learning.

Type:

AutoGluonClassifier

decision_function(X, verbose=0)

Predict confidence scores for samples, which may coincide with the probability scores in X.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input samples.

• verbose (int, optional, default=0) – Verbosity level.

Returns:

scores – Predicted confidence scores.

Return type:

array-like of shape (n_samples, n_classes)

estimate_mi(X, y, method='LogLossMI', **kwargs)

Estimate Mutual Information using the specified method.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input data.

• y (array-like of shape (n_samples,)) – Target labels.

• method (str, optional, default=`LogLossMI`) –

  The method to use for mutual information estimation. Options include:

  • ’MidPointMI’: Estimate MI using Mid-point method.

  • LogLossMI: Estimate MI using Log-Loss method.

  • LogLossMIIsotonicRegression: Estimate MI using Log-Loss method with Isotonic Regression.

  • LogLossMIPlattScaling: Estimate MI using Log-Loss method with Platt Scaling.

  • LogLossMIBetaCalibration: Estimate MI using Log-Loss method with Beta Calibration.

  • LogLossMITemperatureScaling: Estimate MI using Log-Loss method with Temperature Scaling.

  • LogLossMIHistogramBinning: Estimate MI using Log-Loss method with Histogram Binning.

  • PCSoftmaxMI: Estimate MI using Softmax probabilities.

• **kwargs (dict, optional) – Additional keyword arguments passed to the estimation methods.

Returns:

mutual_information – A mean of estimated MI values from cross-validation splits.

Return type:

float

fit(X, y, **kwd)

Fit the classification model to the data.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data.

• y (array-like of shape (n_samples,)) – Target labels.

• **kwd (dict, optional) – Additional keyword arguments passed to the fit method of the base learner.

Returns:

self – Fitted estimator.

Return type:

object

predict(X, verbose=0)

Predict class labels for samples in X.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input samples.

• verbose (int, optional, default=0) – Verbosity level.

Returns:

y_pred – Predicted class labels.

Return type:

array-like of shape (n_samples,)

predict_proba(X, verbose=0)

Predict class probabilities for samples in X.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input samples.

• verbose (int, optional, default=0) – Verbosity level.

Returns:

p_pred – Predicted class probabilities.

Return type:

array-like of shape (n_samples, n_classes)

score(X, y, sample_weight=None, verbose=0)

Return the accuracy score of the model on the given test data and labels.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Test samples.

• y (array-like of shape (n_samples,)) – True labels for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

• verbose (int, optional, default=0) – Verbosity level.

Returns:

score – Mean accuracy of self.predict(X) w.r.t. y.

Return type:

float