Multivariate Pattern Analysis in Python

# mvpa2.clfs.smlr.SMLR

class `mvpa2.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+`: None
  - `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
  - `raw_results`: None
  - `trained_dataset`: None
  - `trained_nsamples+`: None
  - `trained_targets+`: None
  - `training_stats`: Confusion matrix of learning performance
  - `training_time+`: None

  (Conditional attributes enabled by default suffixed with `+`)

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `biases` |
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon everycalled.
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `space` | Processing space name of this node
  `trained` | Either classifier was already trained
  `weights` |

  Methods

  `get_sensitivity_analyzer`(**kwargs) | Returns a sensitivity analyzer for SMLR.

  Initialize an SMLR classifier.

  Parameters:

  lm : float, optional

  > The penalty term lambda. Larger values will give rise to more sparsification. Constraints: value must be convertible to type ‘float’, and value must be in range [1e-10, inf]. [Default: 0.1]

  convergence_tol : float, optional

  > When the weight change for each cycle drops below this value the regression is considered converged. Smaller values lead to tighter convergence. Constraints: value must be convertible to type ‘float’, and value must be in range [1e-10, 1.0]. [Default: 0.001]

  resamp_decay : float, optional

  > 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. Constraints: value must be convertible to type ‘float’, and value must be in range [0.0, 1.0]. [Default: 0.5]

  min_resamp : float, optional

  > Minimum resampling probability for zeroed weights. Constraints: value must be convertible to type ‘float’, and value must be in range [1e-10, 1.0]. [Default: 0.001]

  maxiter : int, optional

  > Maximum number of iterations before stopping if not converged. Constraints: value must be convertible to type ‘int’, and value must be in range [1, inf]. [Default: 10000]

  has_bias : bool, optional

  > Whether to add a bias term to allow fits to data not through zero. Constraints: value must be convertible to type bool. [Default: True]

  fit_all_weights : bool, optional

  > 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. Constraints: value must be convertible to type bool. [Default: True]

  implementation : {C, Python}, optional

  > Use C or Python as the implementation of stepwise_regression. C version brings significant speedup thus is the default one. Constraints: value must be one of (‘C’, ‘Python’). [Default: ‘C’]

  ties : str, optional

  > Resolve ties which could occur. At the moment only obvious ties resulting in identical weights per two classes are detected and resolved randomly by injecting small amount of noise into the estimates of tied categories. Set to False to avoid this behavior. Constraints: value must be a string. [Default: ‘random’]

  seed : None or int, optional

  > Seed to be used to initialize random generator, might be used to replicate the run. Constraints: value must be `None`, or value must be convertible to type ‘int’. [Default: 1629309216]

  unsparsify : bool, optional

  > *EXPERIMENTAL* Whether to unsparsify the weights via regression. Note that it likely leads to worse classifier performance, but more interpretable weights. Constraints: value must be convertible to type bool. [Default: False]

  std_to_keep : float, optional

  > Standard deviation threshold of weights to keep when unsparsifying. Constraints: value must be convertible to type ‘float’. [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.

  pass_attr : str, list of str|tuple, optional

  > Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see `Dataset.get_attr()`), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

  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

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `biases` |
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon everycalled.
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `space` | Processing space name of this node
  `trained` | Either classifier was already trained
  `weights` |

  Methods

  `get_sensitivity_analyzer`(**kwargs) | Returns a sensitivity analyzer for SMLR.

  `biases`
  `get_sensitivity_analyzer`(**kwargs)

    Returns a sensitivity analyzer for SMLR.

  `weights`