MMC - unsupervised RLS; maximum margin clustering type, stochastic search¶
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class
rlscore.learner.mmc.MMC(X, regparam=1.0, number_of_clusters=2, kernel='LinearKernel', basis_vectors=None, Y=None, fixed_indices=None, callback=None, **kwargs)¶ Bases:
rlscore.predictor.predictor.PredictorInterfaceRLS-based maximum-margin clustering.
Performs stochastic search, that aims to find a labeling of the data such that would minimize the RLS-objective function.
Parameters: - X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Data matrix
- regparam : float, optional
regularization parameter, regparam > 0 (default=1.0)
- number_of_clusters : int, optional
number of clusters (default = 2)
- kernel : {‘LinearKernel’, ‘GaussianKernel’, ‘PolynomialKernel’, ‘PrecomputedKernel’, …}
kernel function name, imported dynamically from rlscore.kernel
- basis_vectors : {array-like, sparse matrix}, shape = [n_bvectors, n_features], optional
basis vectors (typically a randomly chosen subset of the training data)
- Y : {array-like}, shape = [n_samples] or [n_samples, n_clusters], optional
Initial clustering (binary or one-versus-all encoding)
- fixed_indixes : list of indices, optional
Instances whose clusters are prefixed (i.e. not allowed to change)
- callback : callback function, optional
called after each pass through data
Other Parameters: - bias : float, optional
LinearKernel: the model is w*x + bias*w0, (default=1.0)
- gamma : float, optional
GaussianKernel: k(xi,xj) = e^(-gamma*<xi-xj,xi-xj>) (default=1.0) PolynomialKernel: k(xi,xj) = (gamma * <xi, xj> + coef0)**degree (default=1.0)
- coef0 : float, optional
PolynomialKernel: k(xi,xj) = (gamma * <xi, xj> + coef0)**degree (default=0.)
- degree : int, optional
PolynomialKernel: k(xi,xj) = (gamma * <xi, xj> + coef0)**degree (default=2)
Notes
The MMC algorithm is described in [1].
References
[1] Fabian Gieseke, Tapio Pahikkala, and Oliver Kramer. Fast Evolutionary Maximum Margin Clustering. Proceedings of the 26th International Conference on Machine Learning, 361-368, ACM, 2009.
Attributes: - predictor : {LinearPredictor, KernelPredictor}
trained predictor
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predict(X)¶ Predicts outputs for new inputs
Parameters: - X : {array-like, sparse matrix}, shape = [n_samples, n_features]
input data matrix
Returns: - P : array, shape = [n_samples, n_tasks]
predictions
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solve(regparam)¶ Trains the learning algorithm, using the given regularization parameter.
Parameters: - regparam : float (regparam > 0)
regularization parameter
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step(flipindex)¶ Perform one iteration step by checking whether flipping the label indexed by flipindex improves the current state. If it does, then the label is flipped. Otherwise, the the current label vector is left unchanged. flipindex: the index of the label to be flipped. returns: True, if the label is flipped and False otherwise.
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stepLOO(flipindex)¶ Perform one iteration step by checking whether flipping the label indexed by flipindex improves the current state. If it does, then the label is flipped. Otherwise, the the current label vector is left unchanged. flipindex : the index of the label to be flipped. returns : True, if the label is flipped and False otherwise.