alipy.query_strategy.query_labels.
QueryInstanceSPAL
Self-Paced Active Learning: Query the Right Thing at the Right Time (SPAL)
will query a batch of informative, representative and easy examples by minimizing a
well designed objective function.
The QP solver is cvxpy here which will not be installed by default because of the need of VC++ 14. Please install cvxpy manually before using this method.
The model used for instances selection is a linear regression model with the kernel form.
References
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[1] Tang, Y., and Huang, S. 2019. Self-Paced Active Learning:
Query the Right Thing at the Right Time. In The
Proceedings of the 33rd AAAI Conference on Artificial
Intelligence (AAAI'19).
Methods
init
__init__(self, X, y, mu=0.1, gamma=0.1, rho=1, lambda_init=0.1, lambda_pace=0.01, **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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mu: float, optional (default=0.1)
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The MMD parameter.
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gamma: float, optional (default=0.1)
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The l2-norm regularizer parameter.
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rho: float, optional (default=1)
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The parameter used in ADMM.
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lambda_init: float, optional (default=0.1)
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The initial value of lambda used in SP regularizer.
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lambda_pace: float, optional (default=0.01)
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The pace of lambda when updating.
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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_ker : 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=5, qp_solver='ECOS', **kwargs)
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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batch_size: int, optional (default=1)
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Selection batch size.
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qp_solver: str, optional (default='ECOS')
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The solver in cvxpy to solve QP, must be one of
['ECOS', 'OSQP']
ECOS: https://www.embotech.com/ECOS
OSQP: https://osqp.org/
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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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