/usr/share/pyshared/mvpa2/clfs/lars.py is in python-mvpa2 2.1.0-1.
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Least angle regression (LARS)."""
__docformat__ = 'restructuredtext'
# system imports
import numpy as np
import mvpa2.base.externals as externals
# do conditional to be able to build module reference
if externals.exists('lars', raise_=True):
import rpy2.robjects
import rpy2.robjects.numpy2ri
if hasattr(rpy2.robjects.numpy2ri,'activate'):
rpy2.robjects.numpy2ri.activate()
RRuntimeError = rpy2.robjects.rinterface.RRuntimeError
r = rpy2.robjects.r
r.library('lars')
from mvpa2.support.rpy2_addons import Rrx2
# local imports
from mvpa2.clfs.base import Classifier, accepts_dataset_as_samples, \
FailedToPredictError
from mvpa2.base.learner import FailedToTrainError
from mvpa2.measures.base import Sensitivity
from mvpa2.datasets.base import Dataset
from mvpa2.base import warning
if __debug__:
from mvpa2.base import debug
known_models = ('lasso', 'stepwise', 'lar', 'forward.stagewise')
class LARS(Classifier):
"""Least angle regression (LARS).
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 learner 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()
"""
# XXX from yoh: it is linear, isn't it?
__tags__ = [ 'lars', 'regression', 'linear', 'has_sensitivity',
'does_feature_selection', 'rpy2' ]
def __init__(self, model_type="lasso", trace=False, normalize=True,
intercept=True, max_steps=None, use_Gram=False, **kwargs):
"""
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.)
"""
# init base class first
Classifier.__init__(self, **kwargs)
if not model_type in known_models:
raise ValueError('Unknown model %s for LARS is specified. Known' %
model_type + 'are %s' % `known_models`)
# set up the params
self.__type = model_type
self.__normalize = normalize
self.__intercept = intercept
self.__trace = trace
self.__max_steps = max_steps
self.__use_Gram = use_Gram
# pylint friendly initializations
self.__lowest_Cp_step = None
self.__weights = None
"""The beta weights for each feature."""
self.__trained_model = None
"""The model object after training that will be used for
predictions."""
def __repr__(self):
"""String summary of the object
"""
return "LARS(type='%s', normalize=%s, intercept=%s, trace=%s, " \
"max_steps=%s, use_Gram=%s, " \
"enable_ca=%s)" % \
(self.__type,
self.__normalize,
self.__intercept,
self.__trace,
self.__max_steps,
self.__use_Gram,
str(self.ca.enabled))
def _train(self, data):
"""Train the classifier using `data` (`Dataset`).
"""
targets = data.sa[self.get_space()].value[:, np.newaxis]
# some non-Python friendly R-lars arguments
lars_kwargs = {'use.Gram': self.__use_Gram}
if self.__max_steps is not None:
lars_kwargs['max.steps'] = self.__max_steps
trained_model = r.lars(data.samples,
targets,
type=self.__type,
normalize=self.__normalize,
intercept=self.__intercept,
trace=self.__trace,
**lars_kwargs
)
#import pydb
#pydb.debugger()
# find the step with the lowest Cp (risk)
# it is often the last step if you set a max_steps
# must first convert dictionary to array
Cp_vals = None
try:
Cp_vals = np.asanyarray(Rrx2(trained_model, 'Cp'))
except TypeError, e:
raise FailedToTrainError, \
"Failed to train %s on %s. Got '%s' while trying to access " \
"trained model %s" % (self, data, e, trained_model)
if Cp_vals is None:
# if there were no any -- just choose 0th
lowest_Cp_step = 0
elif np.isnan(Cp_vals[0]):
# sometimes may come back nan, so just pick the last one
lowest_Cp_step = len(Cp_vals)-1
else:
# determine the lowest
lowest_Cp_step = Cp_vals.argmin()
self.__lowest_Cp_step = lowest_Cp_step
# set the weights to the lowest Cp step
self.__weights = np.asanyarray(
Rrx2(trained_model, 'beta'))[lowest_Cp_step]
self.__trained_model = trained_model # bind to an instance
# # set the weights to the final state
# self.__weights = self.__trained_model['beta'][-1,:]
@accepts_dataset_as_samples
def _predict(self, data):
"""
Predict the output for the provided data.
"""
# predict with the final state (i.e., the last step)
# predict with the lowest Cp step
try:
res = r.predict(self.__trained_model,
data,
mode='step',
s=self.__lowest_Cp_step)
#s=self.__trained_model['beta'].shape[0])
fit = np.atleast_1d(Rrx2(res, 'fit'))
except RRuntimeError, e:
raise FailedToPredictError, \
"Failed to predict on %s using %s. Exceptions was: %s" \
% (data, self, e)
self.ca.estimates = fit
return fit
def _init_internals(self):
"""Reinitialize all internals
"""
self.__lowest_Cp_step = None
self.__weights = None
"""The beta weights for each feature."""
self.__trained_model = None
"""The model object after training that will be used for
predictions."""
def _untrain(self):
super(LARS, self)._untrain()
self._init_internals()
##REF: Name was automagically refactored
def _get_feature_ids(self):
"""Return ids of the used features
"""
return np.where(np.abs(self.__weights)>0)[0]
##REF: Name was automagically refactored
def get_sensitivity_analyzer(self, **kwargs):
"""Returns a sensitivity analyzer for LARS."""
return LARSWeights(self, **kwargs)
weights = property(lambda self: self.__weights)
class LARSWeights(Sensitivity):
"""`SensitivityAnalyzer` that reports the weights LARS trained
on a given `Dataset`.
"""
_LEGAL_CLFS = [ LARS ]
def _call(self, dataset=None):
"""Extract weights from LARS classifier.
LARS always has weights available, so nothing has to be computed here.
"""
clf = self.clf
weights = clf.weights
if __debug__:
debug('LARS',
"Extracting weights for LARS - "+
"Result: min=%f max=%f" %\
(np.min(weights), np.max(weights)))
return Dataset(np.atleast_2d(weights))
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