skactiveml.pool.MonteCarloEER#
- class skactiveml.pool.MonteCarloEER(method='misclassification_loss', cost_matrix=None, subtract_current=False, missing_label=nan, random_state=None)[source]#
Bases:
ExpectedErrorReductionThis class implements the expected error method from [1] that uses a Monte-Carlo approach to estimate the error.
- Therefore, it implements the following two steps:
determining ever candidates x label pair and simulate its outcome in the classifier by simulating it
determining some kind of risk for the new classifier
- Parameters
- methodstring, optional (default=’misclassification_loss’)
The optimization method. Possible values are ‘misclassification_loss’ and ‘log_loss’.
- cost_matrix: array-like, shape (n_classes, n_classes), optional
- (default=None)
Cost matrix with cost_matrix[i,j] defining the cost of predicting class j for a sample with the actual class i. Used for misclassification loss and ignored for log loss.
- subtract_currentbool, optional (default=False)
If True, the current error estimate is subtracted from the simulated score. This might be helpful to define a stopping criterion.
- missing_labelscalar or string or np.nan or None, default=np.nan
Value to represent a missing label.
- random_stateint or np.random.RandomState
The random state to use.
References
- [1] Roy, N., & McCallum, A. (2001). Toward optimal active learning through
monte carlo estimation of error reduction. ICML, (pp. 441-448).
Methods
Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
query(X, y, clf[, fit_clf, ...])Determines for which candidate samples labels are to be queried.
set_params(**params)Set the parameters of this estimator.
- get_metadata_routing()#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
- routingMetadataRequest
A
MetadataRequestencapsulating 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.
- query(X, y, clf, fit_clf=True, ignore_partial_fit=True, sample_weight=None, candidates=None, sample_weight_candidates=None, X_eval=None, sample_weight_eval=None, batch_size=1, return_utilities=False)#
Determines for which candidate samples labels are to be queried.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Training data set, usually complete, i.e. including the labeled and unlabeled samples.
- yarray-like of shape (n_samples)
Labels of the training data set (possibly including unlabeled ones indicated by self.MISSING_LABEL.
- clfskactiveml.base.SkactivemlClassifier
Model implementing the methods fit and predict_proba.
- fit_clfbool, optional (default=True)
Defines whether the classifier should be fitted on X, y, and sample_weight.
- ignore_partial_fitbool, optional (default=True)
Relevant in cases where clf implements partial_fit. If True, the partial_fit function is ignored and fit is used instead.
- sample_weightarray-like of shape (n_samples), optional
- (default=None)
Weights of training samples in X.
- candidatesNone or array-like of shape (n_candidates), dtype=int or
array-like of shape (n_candidates, n_features), optional (default=None) If candidates is None, the unlabeled samples from (X,y) are considered as candidates. If candidates is of shape (n_candidates) and of type int, candidates is considered as the indices of the samples in (X,y). If candidates is of shape (n_candidates, n_features), the candidates are directly given in candidates (not necessarily contained in X). This is not supported by all query strategies.
- sample_weight_candidatesarray-like of shape (n_candidates),
optional (default=None) Weights of candidates samples in candidates if candidates are directly given (i.e., candidates.ndim > 1). Otherwise weights for candidates are given in sample_weight.
- X_evalarray-like of shape (n_eval_samples, n_features),
optional (default=None). Unlabeled evaluation data set that is used for estimating the risk. Not applicable for all EER methods.
- sample_weight_evalarray-like of shape (n_eval_samples),
optional (default=None) Weights of evaluation samples in X_eval if given. Used to weight the importance of samples when estimating the risk.
- batch_sizeint, optional (default=1)
The number of samples to be selected in one AL cycle.
- return_utilitiesbool, optional (default=False)
If true, also return the utilities based on the query strategy.
- Returns
- query_indicesnumpy.ndarray of shape (batch_size)
The query_indices indicate for which candidate sample a label is to queried, e.g., query_indices[0] indicates the first selected sample. If candidates is None or of shape (n_candidates), the indexing refers to samples in X. If candidates is of shape (n_candidates, n_features), the indexing refers to samples in candidates.
- utilitiesnumpy.ndarray of shape (batch_size, n_samples) or
numpy.ndarray of shape (batch_size, n_candidates) The utilities of samples after each selected sample of the batch, e.g., utilities[0] indicates the utilities used for selecting the first sample (with index query_indices[0]) of the batch. Utilities for labeled samples will be set to np.nan. If candidates is None or of shape (n_candidates), the indexing refers to samples in X. If candidates is of shape (n_candidates, n_features), the indexing refers to samples in candidates.
- 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.
