/usr/share/pyshared/mvpa/clfs/knn.py is in python-mvpa 0.4.8-3.
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""k-Nearest-Neighbour classifier."""
__docformat__ = 'restructuredtext'
import sys
# not worthy of externals checking
_dict_has_key = sys.version_info >= (2, 5)
import numpy as N
from mvpa.base import warning
from mvpa.misc.support import indentDoc
from mvpa.clfs.base import Classifier
from mvpa.base.dochelpers import enhancedDocString
from mvpa.clfs.distance import squared_euclidean_distance
if __debug__:
from mvpa.base import debug
class kNN(Classifier):
"""
k-Nearest-Neighbour classifier.
This is a simple classifier that bases its decision on the distances
between the training dataset samples and the test sample(s). Distances
are computed using a customizable distance function. A certain number
(`k`)of nearest neighbors is selected based on the smallest distances
and the labels of this neighboring samples are fed into a voting
function to determine the labels of the test sample.
Training a kNN classifier is extremely quick, as no actuall training
is performed as the training dataset is simply stored in the
classifier. All computations are done during classifier prediction.
.. note::
If enabled, kNN stores the votes per class in the 'values' state after
calling predict().
"""
_clf_internals = ['knn', 'non-linear', 'binary', 'multiclass',
'notrain2predict' ]
def __init__(self, k=2, dfx=squared_euclidean_distance,
voting='weighted', **kwargs):
"""
:Parameters:
k: unsigned integer
Number of nearest neighbours to be used for voting.
dfx: functor
Function to compute the distances between training and test samples.
Default: squared euclidean distance
voting: str
Voting method used to derive predictions from the nearest neighbors.
Possible values are 'majority' (simple majority of classes
determines vote) and 'weighted' (votes are weighted according to the
relative frequencies of each class in the training data).
**kwargs:
Additonal arguments are passed to the base class.
"""
# init base class first
Classifier.__init__(self, **kwargs)
self.__k = k
self.__dfx = dfx
self.__voting = voting
self.__data = None
def __repr__(self, prefixes=[]):
"""Representation of the object
"""
return super(kNN, self).__repr__(
["k=%d" % self.__k, "dfx=%s" % self.__dfx,
"voting=%s" % repr(self.__voting)]
+ prefixes)
def __str__(self):
return "%s\n data: %s" % \
(Classifier.__str__(self), indentDoc(self.__data))
def _train(self, data):
"""Train the classifier.
For kNN it is degenerate -- just stores the data.
"""
self.__data = data
if __debug__:
if str(data.samples.dtype).startswith('uint') \
or str(data.samples.dtype).startswith('int'):
warning("kNN: input data is in integers. " + \
"Overflow on arithmetic operations might result in"+\
" errors. Please convert dataset's samples into" +\
" floating datatype if any error is reported.")
self.__weights = None
# create dictionary with an item for each condition
uniquelabels = data.uniquelabels
self.__votes_init = dict(zip(uniquelabels,
[0] * len(uniquelabels)))
def _predict(self, data):
"""Predict the class labels for the provided data.
Returns a list of class labels (one for each data sample).
"""
# make sure we're talking about arrays
data = N.asarray(data)
# checks only in debug mode
if __debug__:
if not data.ndim == 2:
raise ValueError, "Data array must be two-dimensional."
if not data.shape[1] == self.__data.nfeatures:
raise ValueError, "Length of data samples (features) does " \
"not match the classifier."
# compute the distance matrix between training and test data with
# distances stored row-wise, ie. distances between test sample [0]
# and all training samples will end up in row 0
dists = self.__dfx(self.__data.samples, data).T
# determine the k nearest neighbors per test sample
knns = dists.argsort(axis=1)[:, :self.__k]
# predicted class labels will go here
predicted = []
if self.__voting == 'majority':
vfx = self.getMajorityVote
elif self.__voting == 'weighted':
vfx = self.getWeightedVote
else:
raise ValueError, "kNN told to perform unknown voting '%s'." \
% self.__voting
# perform voting
results = [vfx(knn) for knn in knns]
# extract predictions
predicted = [r[0] for r in results]
# store the predictions in the state. Relies on State._setitem to do
# nothing if the relevant state member is not enabled
self.predictions = predicted
self.values = [r[1] for r in results]
return predicted
def getMajorityVote(self, knn_ids):
"""Simple voting by choosing the majority of class neighbors.
"""
# local bindings
_data = self.__data
labels = _data.labels
# number of occerences for each unique class in kNNs
votes = self.__votes_init.copy()
for nn in knn_ids:
votes[labels[nn]] += 1
# find the class with most votes
# return votes as well to store them in the state
if _dict_has_key:
# approx 5% faster implementation than below
maxvotes = max(votes.iteritems(), key=lambda x:x[1])[0]
else:
# no key keyword for max in elderly versions
maxvotes = max([(v, k) for k, v in votes.iteritems()])[1]
return maxvotes, \
[votes[ul] for ul in _data.uniquelabels] # transform into lists
def getWeightedVote(self, knn_ids):
"""Vote with classes weighted by the number of samples per class.
"""
# local bindings
_data = self.__data
uniquelabels = _data.uniquelabels
# Lazy evaluation
if self.__weights is None:
#
# It seemed to Yarik that this has to be evaluated just once per
# training dataset.
#
self.__labels = labels = self.__data.labels
Nlabels = len(labels)
Nuniquelabels = len(uniquelabels)
# TODO: To get proper speed up for the next line only,
# histogram should be computed
# via sorting + counting "same" elements while reducing.
# Guaranteed complexity is NlogN whenever now it is N^2
# compute the relative proportion of samples belonging to each
# class (do it in one loop to improve speed and reduce readability
self.__weights = \
[ 1.0 - ((labels == label).sum() / Nlabels) \
for label in uniquelabels ]
self.__weights = dict(zip(uniquelabels, self.__weights))
labels = self.__labels
# number of occerences for each unique class in kNNs
votes = self.__votes_init.copy()
for nn in knn_ids:
votes[labels[nn]] += 1
# weight votes
votes = [ self.__weights[ul] * votes[ul] for ul in uniquelabels]
# find the class with most votes
# return votes as well to store them in the state
return uniquelabels[N.asarray(votes).argmax()], \
votes
def untrain(self):
"""Reset trained state"""
self.__data = None
super(kNN, self).untrain()
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