Multivariate Pattern Analysis in Python

# mvpa2.clfs.glmnet.GLMNET_R

class `mvpa2.clfs.glmnet.``GLMNET_R`(**kwargs)

  GLM-NET Gaussian Regression Classifier.

  This is the GLM-NET algorithm from

  Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf

  parameterized to be a regression.

  See GLMNET_C for the multinomial classifier version.

  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.
  `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 GLMNET.
  `Initialize` |
  `See` |

  Parameters:

  family : {gaussian, multinomial}, optional

  > Response type of your targets (either ‘gaussian’ for regression or ‘multinomial’ for classification). Constraints: value must be one of (‘gaussian’, ‘multinomial’). [Default: ‘gaussian’]

  alpha : float, optional

  > The elastic net mixing parameter. Larger values will give rise to less L2 regularization, with alpha=1.0 as a true LASSO penalty. Constraints: value must be convertible to type ‘float’, and value must be in range [0.01, 1.0]. [Default: 1.0]

  nlambda : int, optional

  > Maximum number of lambdas to calculate before stopping if not converged. Constraints: value must be convertible to type ‘int’, and value must be in range [1, inf]. [Default: 100]

  standardize : bool, optional

  > Whether to standardize the variables prior to fitting. Constraints: value must be convertible to type bool. [Default: True]

  thresh : float, optional

  > Convergence threshold for coordinate descent. Constraints: value must be convertible to type ‘float’, and value must be in range [1e-10, 1.0]. [Default: 0.0001]

  pmax : int or None, optional

  > Limit the maximum number of variables ever to be nonzero. Constraints: (value must be convertible to type ‘int’, and value must be in range [1, inf]), or value must be `None`. [Default: None]

  maxit : int, optional

  > Maximum number of outer-loop iterations for ‘multinomial’ families. Constraints: value must be convertible to type ‘int’, and value must be in range [10, inf]. [Default: 100]

  model_type : {covariance, naive}, optional

  > ‘covariance’ saves all inner-products ever computed and can be much faster than ‘naive’. The latter can be more efficient for nfeatures>>nsamples situations. Constraints: value must be one of (‘covariance’, ‘naive’). [Default: ‘covariance’]

  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.
  `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 GLMNET.