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
References
----------
[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
Methods
init
init(self, X, y, train_idx, **kwargs)
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Parameters:
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X: 2D array, optional (default=None)
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Feature matrix of the whole dataset. It is a reference which will not use additional memory.
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y: array-like, optional (default=None)
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Label matrix of the whole dataset. It is a reference which will not use additional memory.
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train_idx: array-like
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the index of training data.
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lambda: float, optional (default=1.0)
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A regularization parameter used in the regularization learning
framework.
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kernel : {'linear', 'poly', 'rbf', callable}, optional (default='rbf')
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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)``.
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degree : int, optional (default=3)
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Degree of the polynomial kernel function ('poly').
Ignored by all other kernels.
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gamma : float, optional (default=1.)
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Kernel coefficient for 'rbf', 'poly'.
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coef0 : float, optional (default=1.)
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Independent term in kernel function.
It is only significant in 'poly'.
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select
select(self, label_index, unlabel_index, batch_size=1)
Select indexes from the unlabel_index for querying.
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Parameters:
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label_index: {list, np.ndarray, IndexCollection}
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The indexes of labeled samples.
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unlabel_index: {list, np.ndarray, IndexCollection}
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The indexes of unlabeled samples.
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Returns:
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selected_idx: list
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The selected indexes which is a subset of unlabel_index.
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