207 lines
7.4 KiB
Python
207 lines
7.4 KiB
Python
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# Natural Language Toolkit: Confusion Matrices
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# Steven Bird <stevenbird1@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from __future__ import print_function, unicode_literals
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from nltk.probability import FreqDist
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from nltk.compat import python_2_unicode_compatible
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@python_2_unicode_compatible
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class ConfusionMatrix(object):
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"""
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The confusion matrix between a list of reference values and a
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corresponding list of test values. Entry *[r,t]* of this
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matrix is a count of the number of times that the reference value
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*r* corresponds to the test value *t*. E.g.:
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>>> from nltk.metrics import ConfusionMatrix
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>>> ref = 'DET NN VB DET JJ NN NN IN DET NN'.split()
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>>> test = 'DET VB VB DET NN NN NN IN DET NN'.split()
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>>> cm = ConfusionMatrix(ref, test)
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>>> print(cm['NN', 'NN'])
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3
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Note that the diagonal entries *Ri=Tj* of this matrix
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corresponds to correct values; and the off-diagonal entries
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correspond to incorrect values.
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"""
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def __init__(self, reference, test, sort_by_count=False):
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"""
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Construct a new confusion matrix from a list of reference
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values and a corresponding list of test values.
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:type reference: list
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:param reference: An ordered list of reference values.
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:type test: list
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:param test: A list of values to compare against the
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corresponding reference values.
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:raise ValueError: If ``reference`` and ``length`` do not have
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the same length.
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"""
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if len(reference) != len(test):
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raise ValueError('Lists must have the same length.')
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# Get a list of all values.
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if sort_by_count:
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ref_fdist = FreqDist(reference)
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test_fdist = FreqDist(test)
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def key(v): return -(ref_fdist[v]+test_fdist[v])
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values = sorted(set(reference+test), key=key)
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else:
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values = sorted(set(reference+test))
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# Construct a value->index dictionary
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indices = dict((val,i) for (i,val) in enumerate(values))
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# Make a confusion matrix table.
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confusion = [[0 for val in values] for val in values]
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max_conf = 0 # Maximum confusion
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for w,g in zip(reference, test):
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confusion[indices[w]][indices[g]] += 1
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max_conf = max(max_conf, confusion[indices[w]][indices[g]])
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#: A list of all values in ``reference`` or ``test``.
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self._values = values
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#: A dictionary mapping values in ``self._values`` to their indices.
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self._indices = indices
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#: The confusion matrix itself (as a list of lists of counts).
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self._confusion = confusion
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#: The greatest count in ``self._confusion`` (used for printing).
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self._max_conf = max_conf
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#: The total number of values in the confusion matrix.
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self._total = len(reference)
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#: The number of correct (on-diagonal) values in the matrix.
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self._correct = sum(confusion[i][i] for i in range(len(values)))
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def __getitem__(self, li_lj_tuple):
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"""
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:return: The number of times that value ``li`` was expected and
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value ``lj`` was given.
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:rtype: int
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"""
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(li, lj) = li_lj_tuple
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i = self._indices[li]
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j = self._indices[lj]
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return self._confusion[i][j]
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def __repr__(self):
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return '<ConfusionMatrix: %s/%s correct>' % (self._correct,
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self._total)
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def __str__(self):
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return self.pretty_format()
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def pretty_format(self, show_percents=False, values_in_chart=True,
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truncate=None, sort_by_count=False):
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"""
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:return: A multi-line string representation of this confusion matrix.
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:type truncate: int
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:param truncate: If specified, then only show the specified
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number of values. Any sorting (e.g., sort_by_count)
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will be performed before truncation.
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:param sort_by_count: If true, then sort by the count of each
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label in the reference data. I.e., labels that occur more
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frequently in the reference label will be towards the left
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edge of the matrix, and labels that occur less frequently
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will be towards the right edge.
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@todo: add marginals?
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"""
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confusion = self._confusion
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values = self._values
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if sort_by_count:
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values = sorted(values, key=lambda v:
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-sum(self._confusion[self._indices[v]]))
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if truncate:
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values = values[:truncate]
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if values_in_chart:
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value_strings = ["%s" % val for val in values]
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else:
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value_strings = [str(n+1) for n in range(len(values))]
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# Construct a format string for row values
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valuelen = max(len(val) for val in value_strings)
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value_format = '%' + repr(valuelen) + 's | '
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# Construct a format string for matrix entries
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if show_percents:
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entrylen = 6
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entry_format = '%5.1f%%'
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zerostr = ' .'
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else:
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entrylen = len(repr(self._max_conf))
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entry_format = '%' + repr(entrylen) + 'd'
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zerostr = ' '*(entrylen-1) + '.'
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# Write the column values.
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s = ''
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for i in range(valuelen):
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s += (' '*valuelen)+' |'
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for val in value_strings:
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if i >= valuelen-len(val):
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s += val[i-valuelen+len(val)].rjust(entrylen+1)
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else:
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s += ' '*(entrylen+1)
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s += ' |\n'
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# Write a dividing line
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s += '%s-+-%s+\n' % ('-'*valuelen, '-'*((entrylen+1)*len(values)))
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# Write the entries.
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for val, li in zip(value_strings, values):
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i = self._indices[li]
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s += value_format % val
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for lj in values:
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j = self._indices[lj]
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if confusion[i][j] == 0:
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s += zerostr
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elif show_percents:
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s += entry_format % (100.0*confusion[i][j]/self._total)
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else:
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s += entry_format % confusion[i][j]
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if i == j:
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prevspace = s.rfind(' ')
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s = s[:prevspace] + '<' + s[prevspace+1:] + '>'
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else: s += ' '
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s += '|\n'
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# Write a dividing line
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s += '%s-+-%s+\n' % ('-'*valuelen, '-'*((entrylen+1)*len(values)))
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# Write a key
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s += '(row = reference; col = test)\n'
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if not values_in_chart:
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s += 'Value key:\n'
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for i, value in enumerate(values):
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s += '%6d: %s\n' % (i+1, value)
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return s
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def key(self):
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values = self._values
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str = 'Value key:\n'
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indexlen = len(repr(len(values)-1))
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key_format = ' %'+repr(indexlen)+'d: %s\n'
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for i in range(len(values)):
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str += key_format % (i, values[i])
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return str
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def demo():
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reference = 'DET NN VB DET JJ NN NN IN DET NN'.split()
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test = 'DET VB VB DET NN NN NN IN DET NN'.split()
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print('Reference =', reference)
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print('Test =', test)
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print('Confusion matrix:')
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print(ConfusionMatrix(reference, test))
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print(ConfusionMatrix(reference, test).pretty_format(sort_by_count=True))
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if __name__ == '__main__':
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demo()
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