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Braun-Blanquet similarity #444

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Aug 10, 2022
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Braun-Blanquet similarity #444

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5328cd5
feat : Braun-Blanquet-similarity added #349
sepandhaghighi Aug 10, 2022
2bd805e
fix : BB verified test added #349
sepandhaghighi Aug 10, 2022
bb5a1b0
fix : tests updated #349
sepandhaghighi Aug 10, 2022
52e8f29
fix : minor bug in BB verified tests fixed
sepandhaghighi Aug 10, 2022
2137b44
doc : Document updated #349
sepandhaghighi Aug 10, 2022
fb38966
doc : minor edit in Document #349
sepandhaghighi Aug 10, 2022
f568f28
fix : autopep8
sepandhaghighi Aug 10, 2022
b28451d
doc : CHANGELOG updated
sepandhaghighi Aug 10, 2022
8169ff3
doc : new reference added
sepandhaghighi Aug 10, 2022
36a4460
doc : references format updated
sepandhaghighi Aug 10, 2022
26298b8
doc : minor edit in BB reference #444
sepandhaghighi Aug 10, 2022
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
## [Unreleased]
### Added
- Hamming distance
- Braun-Blanquet similarity
### Changed
- `classes` parameter added to `matrix_params_from_table` function
- Matrices with `numpy.integer` elements are now accepted
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46 changes: 45 additions & 1 deletion Document/Document.ipynb
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Expand Up @@ -125,6 +125,7 @@
" <li><a href=\"#AGM-(Adjusted-G-mean)\">Adjusted G-Mean</a></li> \n",
" <li><a href=\"#AGF-(Adjusted-F-score)\">Adjusted F-Score</a></li>\n",
" <li><a href=\"#OC-(Overlap-coefficient)\">Overlap Coefficient</a></li>\n",
" <li><a href=\"#BB-(Braun-Blanquet-similarity)\">Braun-Blanquet Similarity</a></li>\n",
" <li><a href=\"#OOC-(Otsuka-Ochiai-coefficient)\">Otsuka Ochiai Coefficient</a></li>\n",
" <li><a href=\"#TI-(Tversky-index)\">Tversky Index</a></li> \n",
" <li><a href=\"#AUPR-(Area-under-the-PR-curve)\">Area Under The PR Curve</a></li> \n",
Expand Down Expand Up @@ -6152,6 +6153,45 @@
"</ul>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BB (Braun-Blanquet similarity)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Braun-Blanquet coefficient is a similarity measure that is mostly used in botany. It is defined as the size of the intersection divided by the larger of the size of the two sets [[82]](#ref82) [[83]](#ref83)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$BB=\\frac{TP}{max(TOP,P)}=min(PPV,TPR)$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cm.BB"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<ul>\n",
" <li><span style=\"color:red;\">Notice </span> : new in <span style=\"color:red;\">version 3.6</span> </li>\n",
"</ul>"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -14029,7 +14069,11 @@
"\n",
"<blockquote id=\"ref80\">80- R. W. Hamming, \"Error detecting and error correcting codes,\" The Bell system technical journal, vol. 29, no. 2, pp. 147-160, 1950.</blockquote>\n",
"\n",
"<blockquote id=\"ref81\">81- S. S. Choi, S. H. Cha, and C. C. Tappert, \"A survey of binary similarity and distance measures,\" Journal of systemics, cybernetics and informatics, vol. 8, no. 1, pp. 43-48, 2010.</blockquote>\n"
"<blockquote id=\"ref81\">81- S. S. Choi, S. H. Cha, and C. C. Tappert, \"A survey of binary similarity and distance measures,\" Journal of systemics, cybernetics and informatics, vol. 8, no. 1, pp. 43-48, 2010.</blockquote>\n",
"\n",
"<blockquote id=\"ref82\">82- Josias Braun-Blanquet. Plant Sociology: The Study of Plant Communities. McGraw-Hill Book Company, New York, 1932.</blockquote>\n",
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@alirezazolanvari Would you please check the format of the references? (82, 83)
Then I will update README.md

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@sepandhaghighi the IEEE style bibliography of them are as follows. I couldn't find any other version of 83 for referencing.

"<blockquote id="ref82">82- J. Braun-Blanquet, "Plant sociology. The study of plant communities," Plant sociology. The study of plant communities. First ed., 1932.\n",
"\n",
"<blockquote id="ref83">83- C. C. Little, "Abydos Documentation," 2018.\n"

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@alirezazolanvari
Take a look at this link
Zenodo gives you the option of changing the citation format.

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I took a look but the provided citation in the IEEE style does not look correct. I suggest to use the google scholar format. I found the citation for its 2020 version.

"<blockquote id="ref83">83- C. C. Little, "Abydos Documentation," 2020.\n"

"\n",
"<blockquote id=\"ref83\">83- Little, Christopher C. (2020). Abydos NLP/IR library for Python (v0.4.1). Zenodo. https://doi.org/10.5281/zenodo.3603514</blockquote>\n"
]
}
],
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229 changes: 116 additions & 113 deletions Test/function_test.py
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Expand Up @@ -56,119 +56,120 @@
10-AUNP
11-AUNU
12-AUPR
13-BCD
14-BM
15-Bangdiwala B
16-Bennett S
17-CBA
18-CEN
19-CSI
20-Chi-Squared
21-Chi-Squared DF
22-Conditional Entropy
23-Cramer V
24-Cross Entropy
25-DOR
26-DP
27-DPI
28-ERR
29-F0.5
30-F1
31-F1 Macro
32-F1 Micro
33-F2
34-FDR
35-FN
36-FNR
37-FNR Macro
38-FNR Micro
39-FOR
40-FP
41-FPR
42-FPR Macro
43-FPR Micro
44-G
45-GI
46-GM
47-Gwet AC1
48-HD
49-Hamming Loss
50-IBA
51-ICSI
52-IS
53-J
54-Joint Entropy
55-KL Divergence
56-Kappa
57-Kappa 95% CI
58-Kappa No Prevalence
59-Kappa Standard Error
60-Kappa Unbiased
61-Krippendorff Alpha
62-LS
63-Lambda A
64-Lambda B
65-MCC
66-MCCI
67-MCEN
68-MK
69-Mutual Information
70-N
71-NIR
72-NLR
73-NLRI
74-NPV
75-OC
76-OOC
77-OP
78-Overall ACC
79-Overall CEN
80-Overall J
81-Overall MCC
82-Overall MCEN
83-Overall RACC
84-Overall RACCU
85-P
86-P-Value
87-PLR
88-PLRI
89-POP
90-PPV
91-PPV Macro
92-PPV Micro
93-PRE
94-Pearson C
95-Phi-Squared
96-Q
97-QI
98-RACC
99-RACCU
100-RCI
101-RR
102-Reference Entropy
103-Response Entropy
104-SOA1(Landis & Koch)
105-SOA2(Fleiss)
106-SOA3(Altman)
107-SOA4(Cicchetti)
108-SOA5(Cramer)
109-SOA6(Matthews)
110-Scott PI
111-Standard Error
112-TN
113-TNR
114-TNR Macro
115-TNR Micro
116-TON
117-TOP
118-TP
119-TPR
120-TPR Macro
121-TPR Micro
122-Y
123-Zero-one Loss
124-dInd
125-sInd
13-BB
14-BCD
15-BM
16-Bangdiwala B
17-Bennett S
18-CBA
19-CEN
20-CSI
21-Chi-Squared
22-Chi-Squared DF
23-Conditional Entropy
24-Cramer V
25-Cross Entropy
26-DOR
27-DP
28-DPI
29-ERR
30-F0.5
31-F1
32-F1 Macro
33-F1 Micro
34-F2
35-FDR
36-FN
37-FNR
38-FNR Macro
39-FNR Micro
40-FOR
41-FP
42-FPR
43-FPR Macro
44-FPR Micro
45-G
46-GI
47-GM
48-Gwet AC1
49-HD
50-Hamming Loss
51-IBA
52-ICSI
53-IS
54-J
55-Joint Entropy
56-KL Divergence
57-Kappa
58-Kappa 95% CI
59-Kappa No Prevalence
60-Kappa Standard Error
61-Kappa Unbiased
62-Krippendorff Alpha
63-LS
64-Lambda A
65-Lambda B
66-MCC
67-MCCI
68-MCEN
69-MK
70-Mutual Information
71-N
72-NIR
73-NLR
74-NLRI
75-NPV
76-OC
77-OOC
78-OP
79-Overall ACC
80-Overall CEN
81-Overall J
82-Overall MCC
83-Overall MCEN
84-Overall RACC
85-Overall RACCU
86-P
87-P-Value
88-PLR
89-PLRI
90-POP
91-PPV
92-PPV Macro
93-PPV Micro
94-PRE
95-Pearson C
96-Phi-Squared
97-Q
98-QI
99-RACC
100-RACCU
101-RCI
102-RR
103-Reference Entropy
104-Response Entropy
105-SOA1(Landis & Koch)
106-SOA2(Fleiss)
107-SOA3(Altman)
108-SOA4(Cicchetti)
109-SOA5(Cramer)
110-SOA6(Matthews)
111-Scott PI
112-Standard Error
113-TN
114-TNR
115-TNR Macro
116-TNR Micro
117-TON
118-TOP
119-TP
120-TPR
121-TPR Macro
122-TPR Micro
123-Y
124-Zero-one Loss
125-dInd
126-sInd
>>> online_help("J")
...
>>> online_help("J",alt_link=True)
Expand Down Expand Up @@ -220,6 +221,8 @@
'None'
>>> RCI_calc(24,0)
'None'
>>> BB_calc(0,0,0)
'None'
>>> CEN_calc([1,2,3], {1:{1:0,2:0},2:{1:0,2:0}}, {1:2,2:3}, {1:2,2:3}, 2, modified=False)
'None'
>>> convex_combination([1,2,3], {1:{1:0,2:0},2:{1:0,2:0}}, {1:2,2:3}, {1:2,2:3}, 2, modified=False)
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2 changes: 2 additions & 0 deletions Test/output_test.py
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Expand Up @@ -197,6 +197,7 @@
AUC(Area under the ROC curve) None 0.5625 0.63725 0.5
AUCI(AUC value interpretation) None Poor Fair Poor
AUPR(Area under the PR curve) None 0.61607 0.41667 None
BB(Braun-Blanquet similarity) 0.0 0.375 0.33333 0.0
BCD(Bray-Curtis dissimilarity) 0.275 0.225 0.025 0.025
BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0
CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0
Expand Down Expand Up @@ -418,6 +419,7 @@
AUC(Area under the ROC curve) 0.86667 0.61111 0.63889
AUCI(AUC value interpretation) Very Good Fair Fair
AUPR(Area under the PR curve) 0.8 0.33333 0.625
BB(Braun-Blanquet similarity) 0.6 0.33333 0.5
BCD(Bray-Curtis dissimilarity) 0.09524 0.0 0.09524
BM(Informedness or bookmaker informedness) 0.73333 0.22222 0.27778
CEN(Confusion entropy) 0.25 0.52832 0.56439
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7 changes: 7 additions & 0 deletions Test/overall_test.py
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Expand Up @@ -102,6 +102,7 @@
AUC(Area under the ROC curve) 0.88889 0.61111 0.58333
AUCI(AUC value interpretation) Very Good Fair Poor
AUPR(Area under the PR curve) 0.8 0.41667 0.55
BB(Braun-Blanquet similarity) 0.6 0.33333 0.5
BCD(Bray-Curtis dissimilarity) 0.08333 0.04167 0.04167
BM(Informedness or bookmaker informedness) 0.77778 0.22222 0.16667
CEN(Confusion entropy) 0.25 0.49658 0.60442
Expand Down Expand Up @@ -254,6 +255,7 @@
AUC(Area under the ROC curve) 0.88889 0.61111 0.58333
AUCI(AUC value interpretation) Very Good Fair Poor
AUPR(Area under the PR curve) 0.8 0.41667 0.55
BB(Braun-Blanquet similarity) 0.6 0.33333 0.5
BCD(Bray-Curtis dissimilarity) 0.08333 0.04167 0.04167
BM(Informedness or bookmaker informedness) 0.77778 0.22222 0.16667
CEN(Confusion entropy) 0.25 0.49658 0.60442
Expand Down Expand Up @@ -421,6 +423,7 @@
AUC(Area under the ROC curve) None 0.5625 0.63725 0.5
AUCI(AUC value interpretation) None Poor Fair Poor
AUPR(Area under the PR curve) None 0.61607 0.41667 None
BB(Braun-Blanquet similarity) 0.0 0.375 0.33333 0.0
BCD(Bray-Curtis dissimilarity) 0.275 0.225 0.025 0.025
BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0
CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0
Expand Down Expand Up @@ -554,6 +557,7 @@
AUC(Area under the ROC curve) None 0.5625 0.63725 0.5
AUCI(AUC value interpretation) None Poor Fair Poor
AUPR(Area under the PR curve) None 0.61607 0.41667 None
BB(Braun-Blanquet similarity) 0.0 0.375 0.33333 0.0
BCD(Bray-Curtis dissimilarity) 0.275 0.225 0.025 0.025
BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0
CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0
Expand Down Expand Up @@ -845,6 +849,7 @@
AUC(Area under the ROC curve) 0.74167 0.66667 0.80952
AUCI(AUC value interpretation) Good Fair Very Good
AUPR(Area under the PR curve) 0.72115 0.55556 0.73333
BB(Braun-Blanquet similarity) 0.69231 0.55556 0.66667
BCD(Bray-Curtis dissimilarity) 0.01852 0.0 0.01852
BM(Informedness or bookmaker informedness) 0.48333 0.33333 0.61905
CEN(Confusion entropy) 0.45994 0.66249 0.47174
Expand Down Expand Up @@ -995,6 +1000,7 @@
AUC(Area under the ROC curve) 0.74167 0.66667 0.80952
AUCI(AUC value interpretation) Good Fair Very Good
AUPR(Area under the PR curve) 0.72115 0.55556 0.73333
BB(Braun-Blanquet similarity) 0.69231 0.55556 0.66667
BCD(Bray-Curtis dissimilarity) 0.01852 0.0 0.01852
BM(Informedness or bookmaker informedness) 0.48333 0.33333 0.61905
CEN(Confusion entropy) 0.45994 0.66249 0.47174
Expand Down Expand Up @@ -1147,6 +1153,7 @@
AUC(Area under the ROC curve) 0.86667 0.61111 0.63889
AUCI(AUC value interpretation) Very Good Fair Fair
AUPR(Area under the PR curve) 0.8 0.33333 0.625
BB(Braun-Blanquet similarity) 0.6 0.33333 0.5
BCD(Bray-Curtis dissimilarity) 0.09524 0.0 0.09524
BM(Informedness or bookmaker informedness) 0.73333 0.22222 0.27778
CEN(Confusion entropy) 0.25 0.52832 0.56439
Expand Down
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