env-llmeval/lib/python3.10/site-packages/nltk/test/metrics.doctest

.. Copyright (C) 2001-2023 NLTK Project
.. For license information, see LICENSE.TXT

=======
Metrics
=======

-----
Setup
-----

    >>> import pytest
    >>> _ = pytest.importorskip("numpy")


The `nltk.metrics` package provides a variety of *evaluation measures*
which can be used for a wide variety of NLP tasks.

   >>> from nltk.metrics import *

------------------
Standard IR Scores
------------------

We can use standard scores from information retrieval to test the
performance of taggers, chunkers, etc.

    >>> 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(accuracy(reference, test))
    0.8


The following measures apply to sets:

    >>> reference_set = set(reference)
    >>> test_set = set(test)
    >>> precision(reference_set, test_set)
    1.0
    >>> print(recall(reference_set, test_set))
    0.8
    >>> print(f_measure(reference_set, test_set))
    0.88888888888...

Measuring the likelihood of the data, given probability distributions:

    >>> from nltk import FreqDist, MLEProbDist
    >>> pdist1 = MLEProbDist(FreqDist("aldjfalskfjaldsf"))
    >>> pdist2 = MLEProbDist(FreqDist("aldjfalssjjlldss"))
    >>> print(log_likelihood(['a', 'd'], [pdist1, pdist2]))
    -2.7075187496...


----------------
Distance Metrics
----------------

String edit distance (Levenshtein):

    >>> edit_distance("rain", "shine")
    3
    >>> edit_distance_align("shine", "shine")
    [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5)]
    >>> edit_distance_align("rain", "brainy")
    [(0, 0), (0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (4, 6)]
    >>> edit_distance_align("", "brainy")
    [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6)]
    >>> edit_distance_align("", "")
    [(0, 0)]

Other distance measures:

    >>> s1 = set([1,2,3,4])
    >>> s2 = set([3,4,5])
    >>> binary_distance(s1, s2)
    1.0
    >>> print(jaccard_distance(s1, s2))
    0.6
    >>> print(masi_distance(s1, s2))
    0.868

----------------------
Miscellaneous Measures
----------------------

Rank Correlation works with two dictionaries mapping keys to ranks.
The dictionaries should have the same set of keys.

    >>> spearman_correlation({'e':1, 't':2, 'a':3}, {'e':1, 'a':2, 't':3})
    0.5

Windowdiff uses a sliding window in comparing two segmentations of the same input (e.g. tokenizations, chunkings).
Segmentations are represented using strings of zeros and ones.

    >>> s1 = "000100000010"
    >>> s2 = "000010000100"
    >>> s3 = "100000010000"
    >>> s4 = "000000000000"
    >>> s5 = "111111111111"
    >>> windowdiff(s1, s1, 3)
    0.0
    >>> abs(windowdiff(s1, s2, 3) - 0.3)  < 1e-6  # windowdiff(s1, s2, 3) == 0.3
    True
    >>> abs(windowdiff(s2, s3, 3) - 0.8)  < 1e-6  # windowdiff(s2, s3, 3) == 0.8
    True
    >>> windowdiff(s1, s4, 3)
    0.5
    >>> windowdiff(s1, s5, 3)
    1.0

----------------
Confusion Matrix
----------------

    >>> reference = 'This is the reference data.  Testing 123.  aoaeoeoe'
    >>> test =      'Thos iz_the rifirenci data.  Testeng 123.  aoaeoeoe'
    >>> print(ConfusionMatrix(reference, test))
      |   . 1 2 3 T _ a c d e f g h i n o r s t z |
    --+-------------------------------------------+
      |<8>. . . . . 1 . . . . . . . . . . . . . . |
    . | .<2>. . . . . . . . . . . . . . . . . . . |
    1 | . .<1>. . . . . . . . . . . . . . . . . . |
    2 | . . .<1>. . . . . . . . . . . . . . . . . |
    3 | . . . .<1>. . . . . . . . . . . . . . . . |
    T | . . . . .<2>. . . . . . . . . . . . . . . |
    _ | . . . . . .<.>. . . . . . . . . . . . . . |
    a | . . . . . . .<4>. . . . . . . . . . . . . |
    c | . . . . . . . .<1>. . . . . . . . . . . . |
    d | . . . . . . . . .<1>. . . . . . . . . . . |
    e | . . . . . . . . . .<6>. . . 3 . . . . . . |
    f | . . . . . . . . . . .<1>. . . . . . . . . |
    g | . . . . . . . . . . . .<1>. . . . . . . . |
    h | . . . . . . . . . . . . .<2>. . . . . . . |
    i | . . . . . . . . . . 1 . . .<1>. 1 . . . . |
    n | . . . . . . . . . . . . . . .<2>. . . . . |
    o | . . . . . . . . . . . . . . . .<3>. . . . |
    r | . . . . . . . . . . . . . . . . .<2>. . . |
    s | . . . . . . . . . . . . . . . . . .<2>. 1 |
    t | . . . . . . . . . . . . . . . . . . .<3>. |
    z | . . . . . . . . . . . . . . . . . . . .<.>|
    --+-------------------------------------------+
    (row = reference; col = test)
    <BLANKLINE>

    >>> cm = ConfusionMatrix(reference, test)
    >>> print(cm.pretty_format(sort_by_count=True))
      |   e a i o s t . T h n r 1 2 3 c d f g _ z |
    --+-------------------------------------------+
      |<8>. . . . . . . . . . . . . . . . . . 1 . |
    e | .<6>. 3 . . . . . . . . . . . . . . . . . |
    a | . .<4>. . . . . . . . . . . . . . . . . . |
    i | . 1 .<1>1 . . . . . . . . . . . . . . . . |
    o | . . . .<3>. . . . . . . . . . . . . . . . |
    s | . . . . .<2>. . . . . . . . . . . . . . 1 |
    t | . . . . . .<3>. . . . . . . . . . . . . . |
    . | . . . . . . .<2>. . . . . . . . . . . . . |
    T | . . . . . . . .<2>. . . . . . . . . . . . |
    h | . . . . . . . . .<2>. . . . . . . . . . . |
    n | . . . . . . . . . .<2>. . . . . . . . . . |
    r | . . . . . . . . . . .<2>. . . . . . . . . |
    1 | . . . . . . . . . . . .<1>. . . . . . . . |
    2 | . . . . . . . . . . . . .<1>. . . . . . . |
    3 | . . . . . . . . . . . . . .<1>. . . . . . |
    c | . . . . . . . . . . . . . . .<1>. . . . . |
    d | . . . . . . . . . . . . . . . .<1>. . . . |
    f | . . . . . . . . . . . . . . . . .<1>. . . |
    g | . . . . . . . . . . . . . . . . . .<1>. . |
    _ | . . . . . . . . . . . . . . . . . . .<.>. |
    z | . . . . . . . . . . . . . . . . . . . .<.>|
    --+-------------------------------------------+
    (row = reference; col = test)
    <BLANKLINE>

    >>> print(cm.pretty_format(sort_by_count=True, truncate=10))
      |   e a i o s t . T h |
    --+---------------------+
      |<8>. . . . . . . . . |
    e | .<6>. 3 . . . . . . |
    a | . .<4>. . . . . . . |
    i | . 1 .<1>1 . . . . . |
    o | . . . .<3>. . . . . |
    s | . . . . .<2>. . . . |
    t | . . . . . .<3>. . . |
    . | . . . . . . .<2>. . |
    T | . . . . . . . .<2>. |
    h | . . . . . . . . .<2>|
    --+---------------------+
    (row = reference; col = test)
    <BLANKLINE>

    >>> print(cm.pretty_format(sort_by_count=True, truncate=10, values_in_chart=False))
       |                   1 |
       | 1 2 3 4 5 6 7 8 9 0 |
    ---+---------------------+
     1 |<8>. . . . . . . . . |
     2 | .<6>. 3 . . . . . . |
     3 | . .<4>. . . . . . . |
     4 | . 1 .<1>1 . . . . . |
     5 | . . . .<3>. . . . . |
     6 | . . . . .<2>. . . . |
     7 | . . . . . .<3>. . . |
     8 | . . . . . . .<2>. . |
     9 | . . . . . . . .<2>. |
    10 | . . . . . . . . .<2>|
    ---+---------------------+
    (row = reference; col = test)
    Value key:
         1:
         2: e
         3: a
         4: i
         5: o
         6: s
         7: t
         8: .
         9: T
        10: h
    <BLANKLINE>

For "e", the number of true positives should be 6, while the number of false negatives is 3.
So, the recall ought to be 6 / (6 + 3):

    >>> cm.recall("e") # doctest: +ELLIPSIS
    0.666666...

For "e", the false positive is just 1, so the precision should be 6 / (6 + 1):

    >>> cm.precision("e") # doctest: +ELLIPSIS
    0.857142...

The f-measure with default value of ``alpha = 0.5`` should then be:

* *1/(alpha/p + (1-alpha)/r) =*
* *1/(0.5/p + 0.5/r) =*
* *2pr / (p + r) =*
* *2 * 0.857142... * 0.666666... / (0.857142... + 0.666666...) =*
* *0.749999...*

    >>> cm.f_measure("e") # doctest: +ELLIPSIS
    0.749999...

--------------------
Association measures
--------------------

These measures are useful to determine whether the coocurrence of two random
events is meaningful. They are used, for instance, to distinguish collocations
from other pairs of adjacent words.

We bring some examples of bigram association calculations from Manning and
Schutze's SNLP, 2nd Ed. chapter 5.

    >>> n_new_companies, n_new, n_companies, N = 8, 15828, 4675, 14307668
    >>> bam = BigramAssocMeasures
    >>> bam.raw_freq(20, (42, 20), N) == 20. / N
    True
    >>> bam.student_t(n_new_companies, (n_new, n_companies), N)
    0.999...
    >>> bam.chi_sq(n_new_companies, (n_new, n_companies), N)
    1.54...
    >>> bam.likelihood_ratio(150, (12593, 932), N)
    1291...

For other associations, we ensure the ordering of the measures:

    >>> bam.mi_like(20, (42, 20), N) > bam.mi_like(20, (41, 27), N)
    True
    >>> bam.pmi(20, (42, 20), N) > bam.pmi(20, (41, 27), N)
    True
    >>> bam.phi_sq(20, (42, 20), N) > bam.phi_sq(20, (41, 27), N)
    True
    >>> bam.poisson_stirling(20, (42, 20), N) > bam.poisson_stirling(20, (41, 27), N)
    True
    >>> bam.jaccard(20, (42, 20), N) > bam.jaccard(20, (41, 27), N)
    True
    >>> bam.dice(20, (42, 20), N) > bam.dice(20, (41, 27), N)
    True
    >>> bam.fisher(20, (42, 20), N) > bam.fisher(20, (41, 27), N) # doctest: +SKIP
    False

For trigrams, we have to provide more count information:

    >>> n_w1_w2_w3 = 20
    >>> n_w1_w2, n_w1_w3, n_w2_w3 = 35, 60, 40
    >>> pair_counts = (n_w1_w2, n_w1_w3, n_w2_w3)
    >>> n_w1, n_w2, n_w3 = 100, 200, 300
    >>> uni_counts = (n_w1, n_w2, n_w3)
    >>> N = 14307668
    >>> tam = TrigramAssocMeasures
    >>> tam.raw_freq(n_w1_w2_w3, pair_counts, uni_counts, N) == 1. * n_w1_w2_w3 / N
    True
    >>> uni_counts2 = (n_w1, n_w2, 100)
    >>> tam.student_t(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.student_t(n_w1_w2_w3, pair_counts, uni_counts, N)
    True
    >>> tam.chi_sq(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.chi_sq(n_w1_w2_w3, pair_counts, uni_counts, N)
    True
    >>> tam.mi_like(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.mi_like(n_w1_w2_w3, pair_counts, uni_counts, N)
    True
    >>> tam.pmi(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.pmi(n_w1_w2_w3, pair_counts, uni_counts, N)
    True
    >>> tam.likelihood_ratio(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.likelihood_ratio(n_w1_w2_w3, pair_counts, uni_counts, N)
    True
    >>> tam.poisson_stirling(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.poisson_stirling(n_w1_w2_w3, pair_counts, uni_counts, N)
    True
    >>> tam.jaccard(n_w1_w2_w3, pair_counts, uni_counts2, N) > tam.jaccard(n_w1_w2_w3, pair_counts, uni_counts, N)
    True


For fourgrams, we have to provide more count information:

    >>> n_w1_w2_w3_w4 = 5
    >>> n_w1_w2, n_w1_w3, n_w2_w3 = 35, 60, 40
    >>> n_w1_w2_w3, n_w2_w3_w4 = 20, 10
    >>> pair_counts = (n_w1_w2, n_w1_w3, n_w2_w3)
    >>> triplet_counts = (n_w1_w2_w3, n_w2_w3_w4)
    >>> n_w1, n_w2, n_w3, n_w4 = 100, 200, 300, 400
    >>> uni_counts = (n_w1, n_w2, n_w3, n_w4)
    >>> N = 14307668
    >>> qam = QuadgramAssocMeasures
    >>> qam.raw_freq(n_w1_w2_w3_w4, pair_counts, triplet_counts, uni_counts, N) == 1. * n_w1_w2_w3_w4 / N
    True