Source code for nltk.metrics.confusionmatrix
# Natural Language Toolkit: Confusion Matrices
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
# Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from __future__ import print_function, unicode_literals
from nltk.probability import FreqDist
from nltk.compat import python_2_unicode_compatible
@python_2_unicode_compatible
[docs]class ConfusionMatrix(object):
"""
The confusion matrix between a list of reference values and a
corresponding list of test values. Entry *[r,t]* of this
matrix is a count of the number of times that the reference value
*r* corresponds to the test value *t*. E.g.:
>>> from nltk.metrics import ConfusionMatrix
>>> ref = 'DET NN VB DET JJ NN NN IN DET NN'.split()
>>> test = 'DET VB VB DET NN NN NN IN DET NN'.split()
>>> cm = ConfusionMatrix(ref, test)
>>> print(cm['NN', 'NN'])
3
Note that the diagonal entries *Ri=Tj* of this matrix
corresponds to correct values; and the off-diagonal entries
correspond to incorrect values.
"""
[docs] def __init__(self, reference, test, sort_by_count=False):
"""
Construct a new confusion matrix from a list of reference
values and a corresponding list of test values.
:type reference: list
:param reference: An ordered list of reference values.
:type test: list
:param test: A list of values to compare against the
corresponding reference values.
:raise ValueError: If ``reference`` and ``length`` do not have
the same length.
"""
if len(reference) != len(test):
raise ValueError('Lists must have the same length.')
# Get a list of all values.
if sort_by_count:
ref_fdist = FreqDist(reference)
test_fdist = FreqDist(test)
def key(v): return -(ref_fdist[v]+test_fdist[v])
values = sorted(set(reference+test), key=key)
else:
values = sorted(set(reference+test))
# Construct a value->index dictionary
indices = dict((val,i) for (i,val) in enumerate(values))
# Make a confusion matrix table.
confusion = [[0 for val in values] for val in values]
max_conf = 0 # Maximum confusion
for w,g in zip(reference, test):
confusion[indices[w]][indices[g]] += 1
max_conf = max(max_conf, confusion[indices[w]][indices[g]])
#: A list of all values in ``reference`` or ``test``.
self._values = values
#: A dictionary mapping values in ``self._values`` to their indices.
self._indices = indices
#: The confusion matrix itself (as a list of lists of counts).
self._confusion = confusion
#: The greatest count in ``self._confusion`` (used for printing).
self._max_conf = max_conf
#: The total number of values in the confusion matrix.
self._total = len(reference)
#: The number of correct (on-diagonal) values in the matrix.
self._correct = sum(confusion[i][i] for i in range(len(values)))
def __getitem__(self, li_lj_tuple):
"""
:return: The number of times that value ``li`` was expected and
value ``lj`` was given.
:rtype: int
"""
(li, lj) = li_lj_tuple
i = self._indices[li]
j = self._indices[lj]
return self._confusion[i][j]
def __repr__(self):
return '<ConfusionMatrix: %s/%s correct>' % (self._correct,
self._total)
def __str__(self):
return self.pretty_format()
[docs] def pretty_format(self, show_percents=False, values_in_chart=True,
truncate=None, sort_by_count=False):
"""
:return: A multi-line string representation of this confusion matrix.
:type truncate: int
:param truncate: If specified, then only show the specified
number of values. Any sorting (e.g., sort_by_count)
will be performed before truncation.
:param sort_by_count: If true, then sort by the count of each
label in the reference data. I.e., labels that occur more
frequently in the reference label will be towards the left
edge of the matrix, and labels that occur less frequently
will be towards the right edge.
@todo: add marginals?
"""
confusion = self._confusion
values = self._values
if sort_by_count:
values = sorted(values, key=lambda v:
-sum(self._confusion[self._indices[v]]))
if truncate:
values = values[:truncate]
if values_in_chart:
value_strings = ["%s" % val for val in values]
else:
value_strings = [str(n+1) for n in range(len(values))]
# Construct a format string for row values
valuelen = max(len(val) for val in value_strings)
value_format = '%' + repr(valuelen) + 's | '
# Construct a format string for matrix entries
if show_percents:
entrylen = 6
entry_format = '%5.1f%%'
zerostr = ' .'
else:
entrylen = len(repr(self._max_conf))
entry_format = '%' + repr(entrylen) + 'd'
zerostr = ' '*(entrylen-1) + '.'
# Write the column values.
s = ''
for i in range(valuelen):
s += (' '*valuelen)+' |'
for val in value_strings:
if i >= valuelen-len(val):
s += val[i-valuelen+len(val)].rjust(entrylen+1)
else:
s += ' '*(entrylen+1)
s += ' |\n'
# Write a dividing line
s += '%s-+-%s+\n' % ('-'*valuelen, '-'*((entrylen+1)*len(values)))
# Write the entries.
for val, li in zip(value_strings, values):
i = self._indices[li]
s += value_format % val
for lj in values:
j = self._indices[lj]
if confusion[i][j] == 0:
s += zerostr
elif show_percents:
s += entry_format % (100.0*confusion[i][j]/self._total)
else:
s += entry_format % confusion[i][j]
if i == j:
prevspace = s.rfind(' ')
s = s[:prevspace] + '<' + s[prevspace+1:] + '>'
else: s += ' '
s += '|\n'
# Write a dividing line
s += '%s-+-%s+\n' % ('-'*valuelen, '-'*((entrylen+1)*len(values)))
# Write a key
s += '(row = reference; col = test)\n'
if not values_in_chart:
s += 'Value key:\n'
for i, value in enumerate(values):
s += '%6d: %s\n' % (i+1, value)
return s
[docs] def key(self):
values = self._values
str = 'Value key:\n'
indexlen = len(repr(len(values)-1))
key_format = ' %'+repr(indexlen)+'d: %s\n'
for i in range(len(values)):
str += key_format % (i, values[i])
return str
def demo():
reference = 'DET NN VB DET JJ NN NN IN DET NN'.split()
test = 'DET VB VB DET NN NN NN IN DET NN'.split()
print('Reference =', reference)
print('Test =', test)
print('Confusion matrix:')
print(ConfusionMatrix(reference, test))
print(ConfusionMatrix(reference, test).pretty_format(sort_by_count=True))
if __name__ == '__main__':
demo()