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496 lines
16 KiB
Python
496 lines
16 KiB
Python
# Natural Language Toolkit: Agreement Metrics
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Tom Lippincott <tom@cs.columbia.edu>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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#
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"""
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Implementations of inter-annotator agreement coefficients surveyed by Artstein
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and Poesio (2007), Inter-Coder Agreement for Computational Linguistics.
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An agreement coefficient calculates the amount that annotators agreed on label
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assignments beyond what is expected by chance.
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In defining the AnnotationTask class, we use naming conventions similar to the
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paper's terminology. There are three types of objects in an annotation task:
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the coders (variables "c" and "C")
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the items to be annotated (variables "i" and "I")
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the potential categories to be assigned (variables "k" and "K")
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Additionally, it is often the case that we don't want to treat two different
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labels as complete disagreement, and so the AnnotationTask constructor can also
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take a distance metric as a final argument. Distance metrics are simply
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functions that take two arguments, and return a value between 0.0 and 1.0
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indicating the distance between them. If not supplied, the default is binary
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comparison between the arguments.
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The simplest way to initialize an AnnotationTask is with a list of triples,
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each containing a coder's assignment for one object in the task:
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task = AnnotationTask(data=[('c1', '1', 'v1'),('c2', '1', 'v1'),...])
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Note that the data list needs to contain the same number of triples for each
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individual coder, containing category values for the same set of items.
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Alpha (Krippendorff 1980)
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Kappa (Cohen 1960)
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S (Bennet, Albert and Goldstein 1954)
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Pi (Scott 1955)
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TODO: Describe handling of multiple coders and missing data
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Expected results from the Artstein and Poesio survey paper:
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>>> from nltk.metrics.agreement import AnnotationTask
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>>> import os.path
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>>> t = AnnotationTask(data=[x.split() for x in open(os.path.join(os.path.dirname(__file__), "artstein_poesio_example.txt"))])
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>>> t.avg_Ao()
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0.88
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>>> t.pi()
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0.7995322418977615...
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>>> t.S()
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0.8199999999999998...
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This would have returned a wrong value (0.0) in @785fb79 as coders are in
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the wrong order. Subsequently, all values for pi(), S(), and kappa() would
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have been wrong as they are computed with avg_Ao().
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>>> t2 = AnnotationTask(data=[('b','1','stat'),('a','1','stat')])
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>>> t2.avg_Ao()
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1.0
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The following, of course, also works.
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>>> t3 = AnnotationTask(data=[('a','1','othr'),('b','1','othr')])
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>>> t3.avg_Ao()
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1.0
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"""
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from __future__ import print_function, unicode_literals, division
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import logging
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from itertools import groupby
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from operator import itemgetter
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from six import iteritems
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from nltk.probability import FreqDist, ConditionalFreqDist
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from nltk.internals import deprecated
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from nltk.compat import python_2_unicode_compatible
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from nltk.metrics.distance import binary_distance
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log = logging.getLogger(__name__)
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@python_2_unicode_compatible
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class AnnotationTask(object):
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"""Represents an annotation task, i.e. people assign labels to items.
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Notation tries to match notation in Artstein and Poesio (2007).
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In general, coders and items can be represented as any hashable object.
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Integers, for example, are fine, though strings are more readable.
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Labels must support the distance functions applied to them, so e.g.
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a string-edit-distance makes no sense if your labels are integers,
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whereas interval distance needs numeric values. A notable case of this
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is the MASI metric, which requires Python sets.
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"""
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def __init__(self, data=None, distance=binary_distance):
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"""Initialize an annotation task.
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The data argument can be None (to create an empty annotation task) or a sequence of 3-tuples,
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each representing a coder's labeling of an item:
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(coder,item,label)
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The distance argument is a function taking two arguments (labels) and producing a numerical distance.
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The distance from a label to itself should be zero:
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distance(l,l) = 0
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"""
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self.distance = distance
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self.I = set()
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self.K = set()
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self.C = set()
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self.data = []
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if data is not None:
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self.load_array(data)
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def __str__(self):
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return "\r\n".join(
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map(
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lambda x: "%s\t%s\t%s"
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% (x['coder'], x['item'].replace('_', "\t"), ",".join(x['labels'])),
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self.data,
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)
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)
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def load_array(self, array):
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"""Load an sequence of annotation results, appending to any data already loaded.
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The argument is a sequence of 3-tuples, each representing a coder's labeling of an item:
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(coder,item,label)
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"""
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for coder, item, labels in array:
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self.C.add(coder)
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self.K.add(labels)
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self.I.add(item)
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self.data.append({'coder': coder, 'labels': labels, 'item': item})
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def agr(self, cA, cB, i, data=None):
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"""Agreement between two coders on a given item
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"""
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data = data or self.data
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# cfedermann: we don't know what combination of coder/item will come
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# first in x; to avoid StopIteration problems due to assuming an order
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# cA,cB, we allow either for k1 and then look up the missing as k2.
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k1 = next((x for x in data if x['coder'] in (cA, cB) and x['item'] == i))
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if k1['coder'] == cA:
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k2 = next((x for x in data if x['coder'] == cB and x['item'] == i))
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else:
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k2 = next((x for x in data if x['coder'] == cA and x['item'] == i))
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ret = 1.0 - float(self.distance(k1['labels'], k2['labels']))
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log.debug("Observed agreement between %s and %s on %s: %f", cA, cB, i, ret)
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log.debug(
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"Distance between \"%r\" and \"%r\": %f",
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k1['labels'],
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k2['labels'],
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1.0 - ret,
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)
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return ret
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def Nk(self, k):
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return float(sum(1 for x in self.data if x['labels'] == k))
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def Nik(self, i, k):
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return float(sum(1 for x in self.data if x['item'] == i and x['labels'] == k))
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def Nck(self, c, k):
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return float(sum(1 for x in self.data if x['coder'] == c and x['labels'] == k))
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@deprecated('Use Nk, Nik or Nck instead')
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def N(self, k=None, i=None, c=None):
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"""Implements the "n-notation" used in Artstein and Poesio (2007)
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"""
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if k is not None and i is None and c is None:
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ret = self.Nk(k)
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elif k is not None and i is not None and c is None:
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ret = self.Nik(i, k)
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elif k is not None and c is not None and i is None:
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ret = self.Nck(c, k)
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else:
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raise ValueError(
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"You must pass either i or c, not both! (k=%r,i=%r,c=%r)" % (k, i, c)
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)
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log.debug("Count on N[%s,%s,%s]: %d", k, i, c, ret)
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return ret
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def _grouped_data(self, field, data=None):
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data = data or self.data
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return groupby(sorted(data, key=itemgetter(field)), itemgetter(field))
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def Ao(self, cA, cB):
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"""Observed agreement between two coders on all items.
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"""
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data = self._grouped_data(
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'item', (x for x in self.data if x['coder'] in (cA, cB))
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)
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ret = sum(self.agr(cA, cB, item, item_data) for item, item_data in data) / len(
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self.I
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)
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log.debug("Observed agreement between %s and %s: %f", cA, cB, ret)
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return ret
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def _pairwise_average(self, function):
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"""
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Calculates the average of function results for each coder pair
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"""
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total = 0
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n = 0
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s = self.C.copy()
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for cA in self.C:
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s.remove(cA)
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for cB in s:
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total += function(cA, cB)
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n += 1
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ret = total / n
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return ret
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def avg_Ao(self):
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"""Average observed agreement across all coders and items.
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"""
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ret = self._pairwise_average(self.Ao)
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log.debug("Average observed agreement: %f", ret)
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return ret
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def Do_Kw_pairwise(self, cA, cB, max_distance=1.0):
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"""The observed disagreement for the weighted kappa coefficient.
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"""
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total = 0.0
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data = (x for x in self.data if x['coder'] in (cA, cB))
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for i, itemdata in self._grouped_data('item', data):
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# we should have two items; distance doesn't care which comes first
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total += self.distance(next(itemdata)['labels'], next(itemdata)['labels'])
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ret = total / (len(self.I) * max_distance)
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log.debug("Observed disagreement between %s and %s: %f", cA, cB, ret)
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return ret
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def Do_Kw(self, max_distance=1.0):
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"""Averaged over all labelers
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"""
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ret = self._pairwise_average(
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lambda cA, cB: self.Do_Kw_pairwise(cA, cB, max_distance)
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)
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log.debug("Observed disagreement: %f", ret)
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return ret
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# Agreement Coefficients
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def S(self):
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"""Bennett, Albert and Goldstein 1954
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"""
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Ae = 1.0 / len(self.K)
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ret = (self.avg_Ao() - Ae) / (1.0 - Ae)
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return ret
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def pi(self):
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"""Scott 1955; here, multi-pi.
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Equivalent to K from Siegel and Castellan (1988).
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"""
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total = 0.0
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label_freqs = FreqDist(x['labels'] for x in self.data)
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for k, f in iteritems(label_freqs):
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total += f ** 2
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Ae = total / ((len(self.I) * len(self.C)) ** 2)
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return (self.avg_Ao() - Ae) / (1 - Ae)
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def Ae_kappa(self, cA, cB):
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Ae = 0.0
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nitems = float(len(self.I))
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label_freqs = ConditionalFreqDist((x['labels'], x['coder']) for x in self.data)
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for k in label_freqs.conditions():
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Ae += (label_freqs[k][cA] / nitems) * (label_freqs[k][cB] / nitems)
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return Ae
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def kappa_pairwise(self, cA, cB):
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"""
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"""
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Ae = self.Ae_kappa(cA, cB)
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ret = (self.Ao(cA, cB) - Ae) / (1.0 - Ae)
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log.debug("Expected agreement between %s and %s: %f", cA, cB, Ae)
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return ret
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def kappa(self):
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"""Cohen 1960
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Averages naively over kappas for each coder pair.
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"""
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return self._pairwise_average(self.kappa_pairwise)
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def multi_kappa(self):
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"""Davies and Fleiss 1982
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Averages over observed and expected agreements for each coder pair.
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"""
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Ae = self._pairwise_average(self.Ae_kappa)
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return (self.avg_Ao() - Ae) / (1.0 - Ae)
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def Disagreement(self, label_freqs):
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total_labels = sum(label_freqs.values())
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pairs = 0.0
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for j, nj in iteritems(label_freqs):
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for l, nl in iteritems(label_freqs):
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pairs += float(nj * nl) * self.distance(l, j)
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return 1.0 * pairs / (total_labels * (total_labels - 1))
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def alpha(self):
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"""Krippendorff 1980
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"""
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# check for degenerate cases
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if len(self.K) == 0:
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raise ValueError("Cannot calculate alpha, no data present!")
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if len(self.K) == 1:
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log.debug("Only one annotation value, allpha returning 1.")
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return 1
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if len(self.C) == 1 and len(self.I) == 1:
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raise ValueError("Cannot calculate alpha, only one coder and item present!")
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total_disagreement = 0.0
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total_ratings = 0
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all_valid_labels_freq = FreqDist([])
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total_do = 0.0 # Total observed disagreement for all items.
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for i, itemdata in self._grouped_data('item'):
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label_freqs = FreqDist(x['labels'] for x in itemdata)
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labels_count = sum(label_freqs.values())
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if labels_count < 2:
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# Ignore the item.
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continue
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all_valid_labels_freq += label_freqs
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total_do += self.Disagreement(label_freqs) * labels_count
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do = total_do / sum(all_valid_labels_freq.values())
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de = self.Disagreement(all_valid_labels_freq) # Expected disagreement.
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k_alpha = 1.0 - do / de
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return k_alpha
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def weighted_kappa_pairwise(self, cA, cB, max_distance=1.0):
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"""Cohen 1968
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"""
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total = 0.0
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label_freqs = ConditionalFreqDist(
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(x['coder'], x['labels']) for x in self.data if x['coder'] in (cA, cB)
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)
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for j in self.K:
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for l in self.K:
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total += label_freqs[cA][j] * label_freqs[cB][l] * self.distance(j, l)
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De = total / (max_distance * pow(len(self.I), 2))
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log.debug("Expected disagreement between %s and %s: %f", cA, cB, De)
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Do = self.Do_Kw_pairwise(cA, cB)
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ret = 1.0 - (Do / De)
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return ret
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def weighted_kappa(self, max_distance=1.0):
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"""Cohen 1968
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"""
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return self._pairwise_average(
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lambda cA, cB: self.weighted_kappa_pairwise(cA, cB, max_distance)
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)
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if __name__ == '__main__':
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import re
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import optparse
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from nltk.metrics import distance
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# process command-line arguments
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parser = optparse.OptionParser()
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parser.add_option(
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"-d",
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"--distance",
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dest="distance",
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default="binary_distance",
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help="distance metric to use",
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)
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parser.add_option(
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"-a",
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"--agreement",
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dest="agreement",
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default="kappa",
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help="agreement coefficient to calculate",
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)
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parser.add_option(
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"-e",
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"--exclude",
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dest="exclude",
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action="append",
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default=[],
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help="coder names to exclude (may be specified multiple times)",
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)
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parser.add_option(
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"-i",
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"--include",
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dest="include",
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action="append",
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default=[],
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help="coder names to include, same format as exclude",
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)
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parser.add_option(
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"-f",
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"--file",
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dest="file",
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help="file to read labelings from, each line with three columns: 'labeler item labels'",
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)
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parser.add_option(
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"-v",
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"--verbose",
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dest="verbose",
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default='0',
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help="how much debugging to print on stderr (0-4)",
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)
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parser.add_option(
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"-c",
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"--columnsep",
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dest="columnsep",
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default="\t",
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help="char/string that separates the three columns in the file, defaults to tab",
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)
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parser.add_option(
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"-l",
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"--labelsep",
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dest="labelsep",
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default=",",
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help="char/string that separates labels (if labelers can assign more than one), defaults to comma",
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)
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parser.add_option(
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"-p",
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"--presence",
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dest="presence",
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default=None,
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help="convert each labeling into 1 or 0, based on presence of LABEL",
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)
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parser.add_option(
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"-T",
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"--thorough",
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dest="thorough",
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default=False,
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action="store_true",
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help="calculate agreement for every subset of the annotators",
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)
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(options, remainder) = parser.parse_args()
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if not options.file:
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parser.print_help()
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exit()
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logging.basicConfig(level=50 - 10 * int(options.verbose))
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# read in data from the specified file
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data = []
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with open(options.file, 'r') as infile:
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for l in infile:
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toks = l.split(options.columnsep)
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coder, object_, labels = (
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toks[0],
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str(toks[1:-1]),
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frozenset(toks[-1].strip().split(options.labelsep)),
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)
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if (
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(options.include == options.exclude)
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or (len(options.include) > 0 and coder in options.include)
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or (len(options.exclude) > 0 and coder not in options.exclude)
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):
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data.append((coder, object_, labels))
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if options.presence:
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task = AnnotationTask(
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data, getattr(distance, options.distance)(options.presence)
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)
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else:
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task = AnnotationTask(data, getattr(distance, options.distance))
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if options.thorough:
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pass
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else:
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print(getattr(task, options.agreement)())
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logging.shutdown()
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