Source code for nltk.metrics.confusionmatrix

# Natural Language Toolkit: Confusion Matrices
#
# Copyright (C) 2001-2022 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
#         Steven Bird <stevenbird1@gmail.com>
#         Tom Aarsen <>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

from nltk.probability import FreqDist


[docs]class ConfusionMatrix: """ 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 = {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 f"<ConfusionMatrix: {self._correct}/{self._total} correct>" 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 += "{}-+-{}+\n".format("-" * 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 += "{}-+-{}+\n".format("-" * 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
[docs] def recall(self, value): """Given a value in the confusion matrix, return the recall that corresponds to this value. The recall is defined as: - *r* = true positive / (true positive + false positive) and can loosely be considered the ratio of how often ``value`` was predicted correctly relative to how often ``value`` was the true result. :param value: value used in the ConfusionMatrix :return: the recall corresponding to ``value``. :rtype: float """ # Number of times `value` was correct, and also predicted TP = self[value, value] # Number of times `value` was correct TP_FN = sum(self[value, pred_value] for pred_value in self._values) if TP_FN == 0: return 0.0 return TP / TP_FN
[docs] def precision(self, value): """Given a value in the confusion matrix, return the precision that corresponds to this value. The precision is defined as: - *p* = true positive / (true positive + false negative) and can loosely be considered the ratio of how often ``value`` was predicted correctly relative to the number of predictions for ``value``. :param value: value used in the ConfusionMatrix :return: the precision corresponding to ``value``. :rtype: float """ # Number of times `value` was correct, and also predicted TP = self[value, value] # Number of times `value` was predicted TP_FP = sum(self[real_value, value] for real_value in self._values) if TP_FP == 0: return 0.0 return TP / TP_FP
[docs] def f_measure(self, value, alpha=0.5): """ Given a value used in the confusion matrix, return the f-measure that corresponds to this value. The f-measure is the harmonic mean of the ``precision`` and ``recall``, weighted by ``alpha``. In particular, given the precision *p* and recall *r* defined by: - *p* = true positive / (true positive + false negative) - *r* = true positive / (true positive + false positive) The f-measure is: - *1/(alpha/p + (1-alpha)/r)* With ``alpha = 0.5``, this reduces to: - *2pr / (p + r)* :param value: value used in the ConfusionMatrix :param alpha: Ratio of the cost of false negative compared to false positives. Defaults to 0.5, where the costs are equal. :type alpha: float :return: the F-measure corresponding to ``value``. :rtype: float """ p = self.precision(value) r = self.recall(value) if p == 0.0 or r == 0.0: return 0.0 return 1.0 / (alpha / p + (1 - alpha) / r)
[docs] def evaluate(self, alpha=0.5, truncate=None, sort_by_count=False): """ Tabulate the **recall**, **precision** and **f-measure** for each value in this confusion matrix. >>> reference = "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(reference, test) >>> print(cm.evaluate()) Tag | Prec. | Recall | F-measure ----+--------+--------+----------- DET | 1.0000 | 1.0000 | 1.0000 IN | 1.0000 | 1.0000 | 1.0000 JJ | 0.0000 | 0.0000 | 0.0000 NN | 0.7500 | 0.7500 | 0.7500 VB | 0.5000 | 1.0000 | 0.6667 <BLANKLINE> :param alpha: Ratio of the cost of false negative compared to false positives, as used in the f-measure computation. Defaults to 0.5, where the costs are equal. :type alpha: float :param truncate: If specified, then only show the specified number of values. Any sorting (e.g., sort_by_count) will be performed before truncation. Defaults to None :type truncate: int, optional :param sort_by_count: Whether to sort the outputs on frequency in the reference label. Defaults to False. :type sort_by_count: bool, optional :return: A tabulated recall, precision and f-measure string :rtype: str """ tags = self._values # Apply keyword parameters if sort_by_count: tags = sorted(tags, key=lambda v: -sum(self._confusion[self._indices[v]])) if truncate: tags = tags[:truncate] tag_column_len = max(max(len(tag) for tag in tags), 3) # Construct the header s = ( f"{' ' * (tag_column_len - 3)}Tag | Prec. | Recall | F-measure\n" f"{'-' * tag_column_len}-+--------+--------+-----------\n" ) # Construct the body for tag in tags: s += ( f"{tag:>{tag_column_len}} | " f"{self.precision(tag):<6.4f} | " f"{self.recall(tag):<6.4f} | " f"{self.f_measure(tag, alpha=alpha):.4f}\n" ) return s
[docs]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)) print(ConfusionMatrix(reference, test).recall("VB"))
if __name__ == "__main__": demo()