231 lines
7.8 KiB
Python
231 lines
7.8 KiB
Python
# Natural Language Toolkit: Evaluation
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#
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# Copyright (C) 2001-2019 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 math import fabs
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import operator
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from random import shuffle
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from functools import reduce
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from six.moves import range, zip
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try:
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from scipy.stats.stats import betai
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except ImportError:
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betai = None
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from nltk.util import LazyConcatenation, LazyMap
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def accuracy(reference, test):
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"""
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Given a list of reference values and a corresponding list of test
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values, return the fraction of corresponding values that are
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equal. In particular, return the fraction of indices
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``0<i<=len(test)`` such that ``test[i] == reference[i]``.
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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 corresponding
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reference values.
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:raise ValueError: If ``reference`` and ``length`` do not have the
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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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return sum(x == y for x, y in zip(reference, test)) / len(test)
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def precision(reference, test):
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"""
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Given a set of reference values and a set of test values, return
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the fraction of test values that appear in the reference set.
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In particular, return card(``reference`` intersection ``test``)/card(``test``).
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If ``test`` is empty, then return None.
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:type reference: set
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:param reference: A set of reference values.
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:type test: set
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:param test: A set of values to compare against the reference set.
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:rtype: float or None
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"""
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if not hasattr(reference, "intersection") or not hasattr(test, "intersection"):
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raise TypeError("reference and test should be sets")
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if len(test) == 0:
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return None
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else:
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return len(reference.intersection(test)) / len(test)
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def recall(reference, test):
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"""
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Given a set of reference values and a set of test values, return
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the fraction of reference values that appear in the test set.
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In particular, return card(``reference`` intersection ``test``)/card(``reference``).
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If ``reference`` is empty, then return None.
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:type reference: set
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:param reference: A set of reference values.
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:type test: set
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:param test: A set of values to compare against the reference set.
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:rtype: float or None
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"""
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if not hasattr(reference, "intersection") or not hasattr(test, "intersection"):
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raise TypeError("reference and test should be sets")
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if len(reference) == 0:
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return None
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else:
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return len(reference.intersection(test)) / len(reference)
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def f_measure(reference, test, alpha=0.5):
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"""
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Given a set of reference values and a set of test values, return
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the f-measure of the test values, when compared against the
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reference values. The f-measure is the harmonic mean of the
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``precision`` and ``recall``, weighted by ``alpha``. In particular,
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given the precision *p* and recall *r* defined by:
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- *p* = card(``reference`` intersection ``test``)/card(``test``)
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- *r* = card(``reference`` intersection ``test``)/card(``reference``)
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The f-measure is:
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- *1/(alpha/p + (1-alpha)/r)*
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If either ``reference`` or ``test`` is empty, then ``f_measure``
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returns None.
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:type reference: set
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:param reference: A set of reference values.
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:type test: set
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:param test: A set of values to compare against the reference set.
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:rtype: float or None
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"""
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p = precision(reference, test)
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r = recall(reference, test)
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if p is None or r is None:
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return None
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if p == 0 or r == 0:
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return 0
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return 1.0 / (alpha / p + (1 - alpha) / r)
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def log_likelihood(reference, test):
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"""
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Given a list of reference values and a corresponding list of test
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probability distributions, return the average log likelihood of
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the reference values, given the probability distributions.
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:param reference: A list of reference values
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:type reference: list
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:param test: A list of probability distributions over values to
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compare against the corresponding reference values.
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:type test: list(ProbDistI)
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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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# Return the average value of dist.logprob(val).
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total_likelihood = sum(dist.logprob(val) for (val, dist) in zip(reference, test))
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return total_likelihood / len(reference)
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def approxrand(a, b, **kwargs):
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"""
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Returns an approximate significance level between two lists of
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independently generated test values.
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Approximate randomization calculates significance by randomly drawing
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from a sample of the possible permutations. At the limit of the number
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of possible permutations, the significance level is exact. The
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approximate significance level is the sample mean number of times the
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statistic of the permutated lists varies from the actual statistic of
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the unpermuted argument lists.
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:return: a tuple containing an approximate significance level, the count
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of the number of times the pseudo-statistic varied from the
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actual statistic, and the number of shuffles
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:rtype: tuple
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:param a: a list of test values
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:type a: list
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:param b: another list of independently generated test values
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:type b: list
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"""
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shuffles = kwargs.get("shuffles", 999)
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# there's no point in trying to shuffle beyond all possible permutations
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shuffles = min(shuffles, reduce(operator.mul, range(1, len(a) + len(b) + 1)))
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stat = kwargs.get("statistic", lambda lst: sum(lst) / len(lst))
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verbose = kwargs.get("verbose", False)
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if verbose:
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print("shuffles: %d" % shuffles)
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actual_stat = fabs(stat(a) - stat(b))
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if verbose:
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print("actual statistic: %f" % actual_stat)
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print("-" * 60)
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c = 1e-100
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lst = LazyConcatenation([a, b])
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indices = list(range(len(a) + len(b)))
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for i in range(shuffles):
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if verbose and i % 10 == 0:
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print("shuffle: %d" % i)
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shuffle(indices)
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pseudo_stat_a = stat(LazyMap(lambda i: lst[i], indices[: len(a)]))
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pseudo_stat_b = stat(LazyMap(lambda i: lst[i], indices[len(a) :]))
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pseudo_stat = fabs(pseudo_stat_a - pseudo_stat_b)
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if pseudo_stat >= actual_stat:
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c += 1
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if verbose and i % 10 == 0:
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print("pseudo-statistic: %f" % pseudo_stat)
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print("significance: %f" % ((c + 1) / (i + 1)))
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print("-" * 60)
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significance = (c + 1) / (shuffles + 1)
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if verbose:
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print("significance: %f" % significance)
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if betai:
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for phi in [0.01, 0.05, 0.10, 0.15, 0.25, 0.50]:
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print("prob(phi<=%f): %f" % (phi, betai(c, shuffles, phi)))
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return (significance, c, shuffles)
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def demo():
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print("-" * 75)
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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("Accuracy:", accuracy(reference, test))
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print("-" * 75)
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reference_set = set(reference)
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test_set = set(test)
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print("Reference =", reference_set)
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print("Test = ", test_set)
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print("Precision:", precision(reference_set, test_set))
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print(" Recall:", recall(reference_set, test_set))
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print("F-Measure:", f_measure(reference_set, test_set))
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print("-" * 75)
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if __name__ == "__main__":
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demo()
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