query_strategy.query_labels. QueryInstanceQUIRE

QueryInstanceQUIRE(X, y, train_idx, **kwargs)

Querying Informative and Representative Examples (QUIRE)

Query the most informative and representative examples where the metrics measuring and combining are done using min-max approach.

The implementation refers to the project: https://github.com/ntucllab/libact



[1] Yang, Y.-Y.; Lee, S.-C.; Chung, Y.-A.; Wu, T.-E.; Chen, S.- A.; and Lin, H.-T. 2017. libact: Pool-based active learning in python. Technical report, National Taiwan University. available as arXiv preprint https://arxiv.org/abs/ 1710.00379.

[2] Huang, S.; Jin, R.; and Zhou, Z. 2014. Active learning by querying informative and representative examples. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(10):1936\–1949



init(self, X, y, train_idx, **kwargs)
X: 2D array, optional (default=None)
Feature matrix of the whole dataset. It is a reference which will not use additional memory.
y: array-like, optional (default=None)
Label matrix of the whole dataset. It is a reference which will not use additional memory.
train_idx: array-like
the index of training data.
lambda: float, optional (default=1.0)
A regularization parameter used in the regularization learning
kernel : {'linear', 'poly', 'rbf', callable}, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', or a callable.
If a callable is given it is used to pre-compute the kernel matrix
from data matrices; that matrix should be an array of shape
``(n_samples, n_samples)``.
degree : int, optional (default=3)
Degree of the polynomial kernel function ('poly').
Ignored by all other kernels.
gamma : float, optional (default=1.)
Kernel coefficient for 'rbf', 'poly'.
coef0 : float, optional (default=1.)
Independent term in kernel function.
It is only significant in 'poly'.


select(self, label_index, unlabel_index, batch_size=1)

Select indexes from the unlabel_index for querying.

label_index: {list, np.ndarray, IndexCollection}
The indexes of labeled samples.
unlabel_index: {list, np.ndarray, IndexCollection}
The indexes of unlabeled samples.
selected_idx: list
The selected indexes which is a subset of unlabel_index.

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