/usr/share/pyshared/mvpa2/clfs/plr.py is in python-mvpa2 2.1.0-1.
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Penalized logistic regression classifier."""
__docformat__ = 'restructuredtext'
import numpy as np
from mvpa2.misc.exceptions import ConvergenceError
from mvpa2.base.learner import FailedToTrainError
from mvpa2.clfs.base import Classifier, accepts_dataset_as_samples
if __debug__:
from mvpa2.base import debug
class PLR(Classifier):
"""Penalized logistic regression `Classifier`.
"""
__tags__ = [ 'plr', 'binary', 'linear', 'has_sensitivity' ]
def __init__(self, lm=1, criterion=1, reduced=0.0, maxiter=20, **kwargs):
"""
Initialize a penalized logistic regression analysis
Parameters
----------
lm : int
the penalty term lambda.
criterion : int
the criterion applied to judge convergence.
reduced : float
if not 0, the rank of the data is reduced before
performing the calculations. In that case, reduce is taken
as the fraction of the first singular value, at which a
dimension is not considered significant anymore. A
reasonable criterion is reduced=0.01
maxiter : int
maximum number of iterations. If no convergence occurs
after this number of iterations, an exception is raised.
"""
# init base class first
Classifier.__init__(self, **kwargs)
self.__lm = lm
self.__criterion = criterion
self.__reduced = reduced
self.__maxiter = maxiter
def __repr__(self):
"""String summary over the object
"""
return """PLR(lm=%f, criterion=%d, reduced=%s, maxiter=%d, enable_ca=%s)""" % \
(self.__lm, self.__criterion, self.__reduced, self.__maxiter,
str(self.ca.enabled))
def _train(self, data):
"""Train the classifier using `data` (`Dataset`).
"""
# Set up the environment for fitting the data
X = data.samples.T
d = self._attrmap.to_numeric(data.sa[self.get_space()].value)
if set(d) != set([0, 1]):
raise ValueError, \
"Regressors for logistic regression should be [0,1]. Got %s" \
%(set(d),)
if self.__reduced != 0 :
# Data have reduced rank
from scipy.linalg import svd
# Compensate for reduced rank:
# Select only the n largest eigenvectors
U, S, V = svd(X.T)
if S[0] == 0:
raise FailedToTrainError(
"Data provided to PLR seems to be degenerate -- "
"0-th singular value is 0")
S /= S[0]
V = np.matrix(V[:, :np.max(np.where(S > self.__reduced)) + 1])
# Map Data to the subspace spanned by the eigenvectors
X = (X.T * V).T
nfeatures, npatterns = X.shape
# Weighting vector
w = np.matrix(np.zeros( (nfeatures + 1, 1), 'd'))
# Error for convergence criterion
dw = np.matrix(np.ones( (nfeatures + 1, 1), 'd'))
# Patterns of interest in the columns
X = np.matrix( \
np.concatenate((X, np.ones((1, npatterns), 'd')), 0) \
)
p = np.matrix(np.zeros((1, npatterns), 'd'))
# Matrix implementation of penalty term
Lambda = self.__lm * np.identity(nfeatures + 1, 'd')
Lambda[nfeatures, nfeatures] = 0
# Gradient
g = np.matrix(np.zeros((nfeatures + 1, 1), 'd'))
# Fisher information matrix
H = np.matrix(np.identity(nfeatures + 1, 'd'))
# Optimize
k = 0
while np.sum(np.ravel(dw.A ** 2)) > self.__criterion:
p[:, :] = self.__f(w.T * X)
g[:, :] = X * (d - p).T - Lambda * w
H[:, :] = X * np.diag(p.A1 * (1 - p.A1)) * X.T + Lambda
dw[:, :] = H.I * g
w += dw
k += 1
if k > self.__maxiter:
raise ConvergenceError, \
"More than %d Iterations without convergence" % \
(self.__maxiter)
if __debug__:
debug("PLR", \
"PLR converged after %d steps. Error: %g" % \
(k, np.sum(np.ravel(dw.A ** 2))))
if self.__reduced:
# We have computed in rank reduced space ->
# Project to original space
self.w = V * w[:-1]
self.bias = w[-1]
else:
self.w = w[:-1]
self.bias = w[-1]
def __f(self, y):
"""This is the logistic function f, that is used for determination of
the vector w"""
return 1. / (1 + np.exp(-y))
@accepts_dataset_as_samples
def _predict(self, data):
"""
Predict the class labels for the provided data
Returns a list of class labels
"""
# make sure the data are in matrix form
data = np.matrix(np.asarray(data))
# get the values and then predictions
values = np.ravel(self.__f(self.bias + data * self.w))
predictions = values > 0.5
# save the state if desired, relying on State._setitem_ to
# decide if we will actually save the values
self.ca.predictions = predictions
self.ca.estimates = values
return predictions
def get_sensitivity_analyzer(self, **kwargs):
"""Returns a sensitivity analyzer for PLR."""
return PLRWeights(self, **kwargs)
from mvpa2.base.state import ConditionalAttribute
from mvpa2.base.types import asobjarray
from mvpa2.measures.base import Sensitivity
from mvpa2.datasets.base import Dataset
class PLRWeights(Sensitivity):
"""`Sensitivity` reporting linear weights of PLR"""
_LEGAL_CLFS = [ PLR ]
def _call(self, dataset=None):
"""Extract weights from PLR classifier.
PLR always has weights available, so nothing has to be computed here.
"""
clf = self.clf
attrmap = clf._attrmap
if attrmap:
# labels (values of the corresponding space) which were used
# for mapping Here we rely on the fact that they are sorted
# originally (just an arange())
labels_num = attrmap.values()
labels = attrmap.to_literal(asobjarray([tuple(sorted(labels_num))]),
recurse=True)
else:
labels = [(0, 1)] # we just had our good old numeric ones
ds = Dataset(clf.w.T, sa={clf.get_space(): labels,
'biases' : [clf.bias]})
return ds
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