mvpa.clfs.smlr.SMLR¶
- class mvpa.clfs.smlr.SMLR(**kwargs)¶
Sparse Multinomial Logistic Regression Classifier.
This is an implementation of the SMLR algorithm published in Krishnapuram et al., 2005 (2005, IEEE Transactions on Pattern Analysis and Machine Intelligence). Be sure to cite that article if you use this classifier for your work.
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- estimates+: Internal classifier estimates the most recent predictions are based on
- predicting_time+: Time (in seconds) which took classifier to predict
- predictions+: Most recent set of predictions
- trained_dataset: The dataset it has been trained on
- trained_nsamples+: Number of samples it has been trained on
- trained_targets+: Set of unique targets it has been trained on
- training_stats: Confusion matrix of learning performance
- training_time+: Time (in seconds) it took to train the learner
(Conditional attributes enabled by default suffixed with +)
Initialize an SMLR classifier.
Parameters : lm :
The penalty term lambda. Larger values will give rise to more sparsification. (Default: 0.10000000000000001)
convergence_tol :
When the weight change for each cycle drops below this value the regression is considered converged. Smaller values lead to tighter convergence. (Default: 0.001)
resamp_decay :
Decay rate in the probability of resampling a zero weight. 1.0 will immediately decrease to the min_resamp from 1.0, 0.0 will never decrease from 1.0. (Default: 0.5)
min_resamp :
Minimum resampling probability for zeroed weights. (Default: 0.001)
maxiter :
Maximum number of iterations before stopping if not converged. (Default: 10000)
has_bias :
Whether to add a bias term to allow fits to data not through zero. (Default: True)
fit_all_weights :
Whether to fit weights for all classes or to the number of classes minus one. Both should give nearly identical results, but if you set fit_all_weights to True it will take a little longer and yield weights that are fully analyzable for each class. Also, note that the convergence rate may be different, but convergence point is the same. (Default: True)
implementation :
Use C or Python as the implementation of stepwise_regression. C version brings significant speedup thus is the default one. (Default: ‘C’)
seed :
Seed to be used to initialize random generator, might be used to replicate the run. (Default: None)
unsparsify :
*EXPERIMENTAL* Whether to unsparsify the weights via regression. Note that it likely leads to worse classifier performance, but more interpretable weights. (Default: False)
std_to_keep :
Standard deviation threshold of weights to keep when unsparsifying. (Default: 2.0)
enable_ca : None or list of str
Names of the conditional attributes which should be enabled in addition to the default ones
disable_ca : None or list of str
Names of the conditional attributes which should be disabled
auto_train : bool
Flag whether the learner will automatically train itself on the input dataset when called untrained.
force_train : bool
Flag whether the learner will enforce training on the input dataset upon every call.
space: str, optional :
Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.
descr : str
Description of the instance
- biases¶
- get_sensitivity_analyzer(**kwargs)¶
Returns a sensitivity analyzer for SMLR.
- weights¶
