Class LARS
source code
Least angle regression (LARS) Classifier.
LARS is the model selection algorithm from:
Bradley Efron, Trevor Hastie, Iain Johnstone and Robert
Tibshirani, Least Angle Regression Annals of Statistics (with
discussion) (2004) 32(2), 407-499. A new method for variable
subset selection, with the lasso and 'epsilon' forward stagewise
methods as special cases.
Similar to SMLR, it performs a feature selection while performing
classification, but instead of starting with all features, it
starts with none and adds them in, which is similar to boosting.
This classifier behaves more like a ridge regression in that it
returns prediction values and it treats the training labels as
continuous.
In the true nature of the PyMVPA framework, this algorithm is
actually implemented in R by Trevor Hastie and wrapped via RPy.
To make use of LARS, you must have R and RPy installed as well as
the LARS contributed package. You can install the R and RPy with
the following command on Debian-based machines:
sudo aptitude install python-rpy python-rpy-doc r-base-dev
You can then install the LARS package by running R as root and
calling:
install.packages()
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__init__(self,
model_type="lasso",
trace=False,
normalize=True,
intercept=True,
max_steps=None,
use_Gram=False,
**kwargs)
Initialize LARS. |
source code
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Inherited from base.Classifier:
__str__,
clone,
isTrained,
predict,
repredict,
retrain,
summary,
train,
trained,
untrain
Inherited from misc.state.ClassWithCollections:
__getattribute__,
__new__,
__setattr__,
reset
Inherited from object:
__delattr__,
__format__,
__hash__,
__reduce__,
__reduce_ex__,
__sizeof__,
__subclasshook__
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_clf_internals = ['lars', 'regression', 'linear', 'has_sensiti...
Describes some specifics about the classifier -- is that it is
doing regression for instance....
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weights = property(lambda self: self.__weights)
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Inherited from base.Classifier:
_DEV__doc__,
feature_ids,
predicting_time,
predictions,
regression,
retrainable,
trained_dataset,
trained_labels,
trained_nsamples,
training_confusion,
training_time,
values
Inherited from misc.state.ClassWithCollections:
descr
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__weights
The beta weights for each feature.
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__trained_model
The model object after training that will be used for
predictions.
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Inherited from object:
__class__
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__init__(self,
model_type="lasso",
trace=False,
normalize=True,
intercept=True,
max_steps=None,
use_Gram=False,
**kwargs)
(Constructor)
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Initialize LARS.
See the help in R for further details on the following parameters:
- Parameters:
model_type (string) - Type of LARS to run. Can be one of ('lasso', 'lar',
'forward.stagewise', 'stepwise').trace (boolean) - Whether to print progress in R as it works.normalize (boolean) - Whether to normalize the L2 Norm.intercept (boolean) - Whether to add a non-penalized intercept to the model.max_steps (None or int) - If not None, specify the total number of iterations to run. Each
iteration adds a feature, but leaving it none will add until
convergence.use_Gram (boolean) - Whether to compute the Gram matrix (this should be false if you
have more features than samples.) - Overrides:
object.__init__
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String summary of the object
- Parameters:
fullname - Either to include full name of the moduleprefixes - What other prefixes to prepend to list of arguments - Overrides:
object.__repr__
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_clf_internals
Describes some specifics about the classifier -- is that it is
doing regression for instance....
- Value:
['lars', 'regression', 'linear', 'has_sensitivity', 'does_feature_sele
ction',]
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