Measures: Add effect size - ΔP

BLKSerene · Nov 3, 2024 · a65698c · a65698c
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -20,7 +20,7 @@
 
 ## [3.6.0](https://github.com/BLKSerene/Wordless/releases/tag/3.6.0) - ??/??/2024
 ### 🎉 New Features
-- Measures: Add effect size - conditional probability / squared association ratio
+- Measures: Add effect size - conditional probability / ΔP / squared association ratio
 - Utils: Add Stanza's Sindhi dependency parser
 
 ### 📌 Bugfixes

diff --git a/doc/doc.md b/doc/doc.md
@@ -1471,15 +1471,15 @@ Test of Statistical Significance|Measure of Bayes Factor|Formula|Collocation Ext
 <span id="ref-z-test-berry-rogghes"></span>Z-test (Berry-Rogghe)<br>([Berry-Rogghe, 1973](#ref-berry-rogghe-1973))||![Formula](/doc/measures/statistical_significance/z_test_berry_rogghe.svg)<br>where **S** is the average span size on both sides of the node word.|✔|✖️
 
 <!--
-%DIFF:
-    \text{%DIFF} = \frac{\left(\frac{O_{11}}{O_{x1}} - \frac{O_{12}}{O_{x2}}\right) \times 100}{\frac{O_{12}}{O_{x2}}}
-
 Conditional probability:
     \text{P} = \frac{O_{11}}{O_{x1}} \times 100
 
 Cubic association ratio:
     \text{IM}^3 = \log_{2} \frac{{O_{11}}^3}{E_{11}}
 
+ΔP:
+    \Delta\text{P} = \frac{O_{11}}{O_{x1}} - \frac{O_{12}}{O_{x2}}
+
 Dice-Sørensen coefficient:
     \text{DSC} = \frac{2 \times O_{11}}{O_{1x} + O_{x1}}
 
@@ -1519,6 +1519,9 @@ Mutual information:
 Odds ratio:
     \text{Odds ratio} = \frac{O_{11} \times O_{22}}{O_{12} \times O_{21}}
 
+%DIFF:
+    \text{%DIFF} = \frac{\left(\frac{O_{11}}{O_{x1}} - \frac{O_{12}}{O_{x2}}\right) \times 100}{\frac{O_{12}}{O_{x2}}}
+
 Pointwise mutual information:
     \text{PMI} = \log_{2} \frac{O_{11}}{E_{11}}
 
@@ -1534,9 +1537,9 @@ Squared phi coefficient:
 
 Measure of Effect Size|Formula|Collocation Extraction|Keyword Extraction
 ----------------------|-------|:--------------------:|:----------------:
-<span id="ref-pct-diff"></span>%DIFF<br>([Gabrielatos & Marchi, 2011](#ref-gabrielatos-marchi-2011))|![Formula](/doc/measures/effect_size/pct_diff.svg)|✖️|✔
 <span id="ref-conditional-probability"></span>Conditional probability<br>([Durrant, 2008, p. 84](#ref-durrant-2008))|![Formula](/doc/measures/effect_size/conditional_probability.svg)|✔|✖️
 <span id="ref-im3"></span>Cubic association ratio<br>([Daille, 1994, p. 139](#ref-daille-1994); [Kilgarriff, 2001, p, 99](#ref-kilgarriff-2001))|![Formula](/doc/measures/effect_size/im3.svg)|✔|✔
+<span id="ref-delta-p"></span><br>ΔP ([Gries, 2013](#ref-gries-2013))|![Formula](/doc/measures/effect_size/delta_p.svg)|✔|✖️
 <span id="ref-dice-sorensen-coeff"></span>Dice-Sørensen coefficient<br>([Smadja et al., 1996, p. 8](#ref-smadja-et-al-1996))|![Formula](/doc/measures/effect_size/dice_sorensen_coeff.svg)|✔|✖️
 <span id="ref-diff-coeff"></span>Difference coefficient<br>([Hofland & Johansson, 1982, p. 14](#ref-hofland-johansson-1982); [Gabrielatos, 2018, p. 236](#ref-gabrielatos-2018))|![Formula](/doc/measures/effect_size/diff_coeff.svg)|✖️|✔
 <span id="ref-jaccard-index"></span>Jaccard index<br>([Dunning, 1998, p. 48](#ref-dunning-1998))|![Formula](/doc/measures/effect_size/jaccard_index.svg)|✔|✖️
@@ -1550,6 +1553,7 @@ Measure of Effect Size|Formula|Collocation Extraction|Keyword Extraction
 <span id="ref-me"></span>Mutual Expectation<br>([Dias et al., 1999](#ref-dias-et-al-1999))|![Formula](/doc/measures/effect_size/me.svg)|✔|✖️
 <span id="ref-mi"></span>Mutual information<br>([Dunning, 1998, pp. 49–52](#ref-dunning-1998))|![Formula](/doc/measures/effect_size/mi.svg)|✔|✖️
 <span id="ref-odds-ratio"></span>Odds ratio<br>([Pecina, 2005, p. 15](#ref-pecina-2005), [Pojanapunya & Todd, 2016](#ref-pojanapunya-todd-2016))|![Formula](/doc/measures/effect_size/odds_ratio.svg)|✔|✔
+<span id="ref-pct-diff"></span>%DIFF<br>([Gabrielatos & Marchi, 2011](#ref-gabrielatos-marchi-2011))|![Formula](/doc/measures/effect_size/pct_diff.svg)|✖️|✔
 <span id="ref-pmi"></span>Pointwise mutual information<br>([Church & Hanks, 1990](#ref-church-hanks-1990); [Kilgarriff, 2001, pp. 104–105](#ref-kilgarriff-2001))|![Formula](/doc/measures/effect_size/pmi.svg)|✔|✔
 <span id="ref-poisson-collocation-measure"></span>Poisson collocation measure<br>([Quasthoff & Wolff, 2002](#ref-quasthoff-wolff-2002))|![Formula](/doc/measures/effect_size/poisson_collocation_measure.svg)|✔|✖️
 <span id="ref-im2"></span>Squared association ratio<br>([Daille, 1995, p. 21](#ref-daille-1995); [Kilgarriff, 2001, p, 99](#ref-kilgarriff-2001))|![Formula](/doc/measures/effect_size/im2.svg)|✔|✔
@@ -1655,6 +1659,8 @@ Measure of Effect Size|Formula|Collocation Extraction|Keyword Extraction
 1. [**^**](#ref-pct-diff) Gabrielatos, C., & Marchi, A. (2011, November 5). *Keyness: Matching metrics to definitions* [Conference session]. Corpus Linguistics in the South 1, University of Portsmouth, United Kingdom. https://eprints.lancs.ac.uk/id/eprint/51449/4/Gabrielatos_Marchi_Keyness.pdf
 <span id="ref-gries-2008"></span>
 1. [**^**](#ref-griess-dp) Gries, S. T. (2008). Dispersions and adjusted frequencies in corpora. *International Journal of Corpus Linguistics*, *13*(4), 403–437. https://doi.org/10.1075/ijcl.13.4.02gri
+<span id="ref-gries-2013"></span>
+1. [**^**](#ref-delta-p) Gries, S. T. (2013). 50-something years of work on collocations: What is or should be next …. *International Journal of Corpus Linguistics*, *18*(1), 137–165. https://doi.org/10.1075/ijcl.18.1.09gri
 <span id="ref-guiraud-1954"></span>
 1. [**^**](#ref-rttr) Guiraud, P. (1954). *Les caractères statistiques du vocabulaire: Essai de méthodologie*. Presses Universitaires de France.
 <span id="ref-gunning-1968"></span>

diff --git a/doc/measures/effect_size/delta_p.svg b/doc/measures/effect_size/delta_p.svg
diff --git a/tests/tests_measures/test_measures_effect_size.py b/tests/tests_measures/test_measures_effect_size.py
@@ -35,30 +35,6 @@ def assert_zeros(func, result = 0):
         numpy.array([result] * 10)
     )
 
-# Reference: Gabrielatos, C., & Marchi, A. (2011, November 5). Keyness: Matching metrics to definitions [Conference session]. Corpus Linguistics in the South 1, University of Portsmouth, United Kingdom. https://eprints.lancs.ac.uk/id/eprint/51449/4/Gabrielatos_Marchi_Keyness.pdf | p. 18
-def test_pct_diff():
-    numpy.testing.assert_array_equal(
-        numpy.round(wl_measures_effect_size.pct_diff(
-            main,
-            numpy.array([206523] * 2),
-            numpy.array([178174] * 2),
-            numpy.array([959641 - 206523] * 2),
-            numpy.array([1562358 - 178174] * 2)
-        ), 1),
-        numpy.array([88.7] * 2)
-    )
-
-    numpy.testing.assert_array_equal(
-        wl_measures_effect_size.pct_diff(
-            main,
-            numpy.array([0, 1, 0]),
-            numpy.array([1, 0, 0]),
-            numpy.array([0, 0, 0]),
-            numpy.array([1, 1, 0])
-        ),
-        numpy.array([float('-inf'), float('inf'), 0])
-    )
-
 # Reference: Durrant, P. (2008). High frequency collocations and second language learning [Doctoral dissertation, University of Nottingham]. Nottingham eTheses. https://eprints.nottingham.ac.uk/10622/1/final_thesis.pdf | pp. 80, 84
 def test_conditional_probability():
     numpy.testing.assert_array_equal(
@@ -72,9 +48,26 @@ def test_conditional_probability():
         numpy.array([0.178, 0.349])
     )
 
+    assert_zeros(wl_measures_effect_size.conditional_probability)
+
 def test_im3():
     assert_zeros(wl_measures_effect_size.im3)
 
+# Reference: Gries, S. T. (2013). 50-something years of work on collocations: What is or should be next …. International Journal of Corpus Linguistics, 18(1), 137–165. https://doi.org/10.1075/ijcl.18.1.09gri | p. 144
+def test_delta_p():
+    numpy.testing.assert_array_equal(
+        numpy.round(wl_measures_effect_size.delta_p(
+            main,
+            numpy.array([5610, 5610]),
+            numpy.array([2257, 168938]),
+            numpy.array([168938, 2257]),
+            numpy.array([10233063, 10233063])
+        ), 3),
+        numpy.array([0.032, 0.697])
+    )
+
+    assert_zeros(wl_measures_effect_size.delta_p)
+
 # Reference: Smadja, F., McKeown, K. R., & Hatzivassiloglou, V. (1996). Translating collocations for bilingual lexicons: A statistical approach. Computational Linguistics, 22(1), pp. 1–38. | p. 13
 def test_dice_sorensen_coeff():
     numpy.testing.assert_array_equal(
@@ -216,6 +209,30 @@ def test_odds_ratio():
         numpy.array([float('-inf'), float('inf'), 0])
     )
 
+# Reference: Gabrielatos, C., & Marchi, A. (2011, November 5). Keyness: Matching metrics to definitions [Conference session]. Corpus Linguistics in the South 1, University of Portsmouth, United Kingdom. https://eprints.lancs.ac.uk/id/eprint/51449/4/Gabrielatos_Marchi_Keyness.pdf | p. 18
+def test_pct_diff():
+    numpy.testing.assert_array_equal(
+        numpy.round(wl_measures_effect_size.pct_diff(
+            main,
+            numpy.array([206523] * 2),
+            numpy.array([178174] * 2),
+            numpy.array([959641 - 206523] * 2),
+            numpy.array([1562358 - 178174] * 2)
+        ), 1),
+        numpy.array([88.7] * 2)
+    )
+
+    numpy.testing.assert_array_equal(
+        wl_measures_effect_size.pct_diff(
+            main,
+            numpy.array([0, 1, 0]),
+            numpy.array([1, 0, 0]),
+            numpy.array([0, 0, 0]),
+            numpy.array([1, 1, 0])
+        ),
+        numpy.array([float('-inf'), float('inf'), 0])
+    )
+
 # Reference: Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29. | p. 24
 def test_pmi():
     numpy.testing.assert_array_equal(
@@ -256,6 +273,7 @@ def test_squared_phi_coeff():
     test_pct_diff()
     test_conditional_probability()
     test_im3()
+    test_delta_p()
     test_dice_sorensen_coeff()
     test_diff_coeff()
     test_jaccard_index()

diff --git a/wordless/wl_measures/wl_measures_effect_size.py b/wordless/wl_measures/wl_measures_effect_size.py
@@ -22,24 +22,6 @@
 
 from wordless.wl_measures import wl_measures_statistical_significance, wl_measure_utils
 
-# %DIFF
-# Reference: Gabrielatos, C., & Marchi, A. (2011, November 5). Keyness: Matching metrics to definitions [Conference session]. Corpus Linguistics in the South 1, University of Portsmouth, United Kingdom. https://eprints.lancs.ac.uk/id/eprint/51449/4/Gabrielatos_Marchi_Keyness.pdf
-def pct_diff(main, o11s, o12s, o21s, o22s):
-    _, _, ox1s, ox2s = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
-
-    return numpy.where(
-        (o11s == 0) & (o12s > 0),
-        -numpy.inf,
-        numpy.where(
-            (o11s > 0) & (o12s == 0),
-            numpy.inf,
-            wl_measure_utils.numpy_divide(
-                (wl_measure_utils.numpy_divide(o11s, ox1s) - wl_measure_utils.numpy_divide(o12s, ox2s)) * 100,
-                wl_measure_utils.numpy_divide(o12s, ox2s)
-            )
-        )
-    )
-
 # Conditional probability
 # Reference: Durrant, P. (2008). High frequency collocations and second language learning [Doctoral dissertation, University of Nottingham]. Nottingham eTheses. https://eprints.nottingham.ac.uk/10622/1/final_thesis.pdf | p. 84
 def conditional_probability(main, o11s, o12s, o21s, o22s):
@@ -54,6 +36,13 @@ def im3(main, o11s, o12s, o21s, o22s):
 
     return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(o11s ** 3, e11s))
 
+# ΔP
+# Reference: Gries, S. T. (2013). 50-something years of work on collocations: What is or should be next …. International Journal of Corpus Linguistics, 18(1), 137–165. https://doi.org/10.1075/ijcl.18.1.09gri
+def delta_p(main, o11s, o12s, o21s, o22s):
+    _, _, ox1s, ox2s = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
+
+    return wl_measure_utils.numpy_divide(o11s, ox1s) - wl_measure_utils.numpy_divide(o12s, ox2s)
+
 # Dice-Sørensen coefficient
 # Reference: Smadja, F., McKeown, K. R., & Hatzivassiloglou, V. (1996). Translating collocations for bilingual lexicons: A statistical approach. Computational Linguistics, 22(1), 1–38. | p. 8
 def dice_sorensen_coeff(main, o11s, o12s, o21s, o22s):
@@ -188,6 +177,24 @@ def odds_ratio(main, o11s, o12s, o21s, o22s):
         )
     )
 
+# %DIFF
+# Reference: Gabrielatos, C., & Marchi, A. (2011, November 5). Keyness: Matching metrics to definitions [Conference session]. Corpus Linguistics in the South 1, University of Portsmouth, United Kingdom. https://eprints.lancs.ac.uk/id/eprint/51449/4/Gabrielatos_Marchi_Keyness.pdf
+def pct_diff(main, o11s, o12s, o21s, o22s):
+    _, _, ox1s, ox2s = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
+
+    return numpy.where(
+        (o11s == 0) & (o12s > 0),
+        -numpy.inf,
+        numpy.where(
+            (o11s > 0) & (o12s == 0),
+            numpy.inf,
+            wl_measure_utils.numpy_divide(
+                (wl_measure_utils.numpy_divide(o11s, ox1s) - wl_measure_utils.numpy_divide(o12s, ox2s)) * 100,
+                wl_measure_utils.numpy_divide(o12s, ox2s)
+            )
+        )
+    )
+
 # Pointwise mutual information
 # Reference: Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.
 def pmi(main, o11s, o12s, o21s, o22s):