Deciding my favorite artist

This section was labeled under, or is related to Programming and Art

I enjoy art and paintings, someone dear to me gifted me recently I wrote this “recently” around 7 months ago when I got the idea of writing this post, and it was left as a draft. I was gifted that book in Jan 2024, almost a year. an encyclopedia of art, it has the names of “every” More like the ones we get to know artist, starting from roughly where civilization striated, or to be more accurate it starts 600 B.C. and attributes everything before that to “unknown”, which is not a big deal for me.

I started reading from it, checking artworks on daily-basis, and document the ones I like in my PKMS. I do skip so many modern artists (for reasons I can explain somewhere else, and I should) but I believe I can cover them later. As of today, I kept my habit for exploring an artwork everyday, for 253 day, I’m still on the “D” letter.

I didn’t encounter yet many artists that I love. When I was asked before about my favorite artist, I usually responded with Caspar David Friedrich, I loved the man’s journals before his art. Sometimes I would answer with Karl Friedrich Schinkel or Francois-Auguste Ravier. Sometimes just Leonadrdo da Vinci so the other person can relate and contribute to the conversation about the artist.

For me, choosing a favorite artist is much more difficult that choosing a favorite sonata. I still think that both are not possible, I usually answer such questions only to keep the conversation warm. But what if we can define “favorite”? Because it’s really hard to tell, is it asking about the most enjoyable? The most noble? The one I endorse the most?

I had that discussion regarding the word favorite with a colleague, and she had a nice suggestion, what if we could decide a formula (thus, a definition) of the most “favored” artists? her approach was; the most favorable is the one that occurs the most, as simple as that. But I wanted more sophisticated approach. Here’s how her approach might be represented formally:

\[\text{Favor Score} = \frac{L}{T} \times \log(L + 1)\]

where \(L\) represents the number of loved artworks and \(T\) represents the total artworks in your library by that artist.

It simply tries to care more about the density of appreciation within exposure rather than occurrence. The ratio \(r/T\) captures what we might call “hit rate”: how consistently an artist moves you relative to how much of their work you’ve encountered. An artist who captivates you with 30 out of 50 works demonstrates a more reliable connection than one who captivates you with 63 out of 700. The logarithmic term \(\log(L + 1)\) is a “volume bonus” that prevents the formula from over-penalizing prolific artists. The logarithm grows slowly enough to reward breadth of appreciation without letting it dominate the hit rate. I will call this algorithm 1. Let’s see how we can implement it.

Implementing naive occurrence counting (algorithm 1)

We will need first to start with data collection. The book didn’t include a number for how many each artist has of works, and I didn’t like all of them so I can’t depend on my PKMS, nor did I count them. Lucky enough, wikiart.org provides a number of artworks for each artist entity. A quick trick, that I learnt from the Data Engineering 101 class, got me the following list:

Ivan Aivazovsky :nworks:   700
Benjamin West :nworks:   156
Emile Claus :nworks:   26
John Constable :nworks:   190
William Ashford :nworks:   87
Carl Aagaard :nworks:   220

nworks here stands for number of works. I also needed to populate this into my PKMS, which is just Org mode file, I loaded the above result into an EMACS buffer, and evaluated the following:

(defun salih/org-set-nworks-from-buffer (data-buffer org-buffer)
  (interactive
   (list (read-buffer "Data buffer: " (current-buffer))
         (read-buffer "Org buffer: ")))
  (let (alist)
    (with-current-buffer data-buffer
      (goto-char (point-min))
      (while (re-search-forward
              "^\\([^:]+?\\)[[:space:]]*:nworks:[[:space:]]*\\([0-9]+\\)" nil t)
        (let ((name   (string-trim (match-string 1)))
              (count  (match-string 2)))
          (message "Parsed: %s -> %s" name count)
          (push (cons name count) alist))))
    (with-current-buffer org-buffer
      (org-map-entries
       (lambda ()
         (let* ((heading (string-trim (nth 4 (org-heading-components))))
                (entry   (assoc-string heading alist t)))
           (if entry
               (progn
                 (message "Setting %s NWORKS=%s" heading (cdr entry))
                 (org-set-property "NWORKS" (cdr entry)))
             (message "No data for: %s" heading))))
       t 'file))))

This means that each artist entity in the file will look like the following:

Nicolai Abildgaard
:PROPERTIES:
:ID:       i0jf8l21fkk0
:CUSTOM_ID: i0jf8l21fkk0
:NWORKS:   30
:END:
Jacques-Laurent Agasse
:PROPERTIES:
:ID:       dcjcb680hkk0
:CUSTOM_ID: dcjcb680hkk0
:NWORKS:   45
:END:
Mariotto Albertinelli
:PROPERTIES:
:ID:       2um0gc80hkk0
:CUSTOM_ID: 2um0gc80hkk0
:NWORKS:   19
:END:
Domenico Ghirlandaio
:PROPERTIES:
:ID:       1dq2tj80hkk0
:CUSTOM_ID: 1dq2tj80hkk0
:NWORKS:   108

Now we can implement \(\frac{L}{T} \times \log(L + 1)\\) easily in Elisp;

(simple-score (loved total)
              "Simple approach: (L/T) * log(L+1)"
              (if (and (> total 0) (> loved 0))
                  (* (/ (float loved) total)
                     (log (1+ loved)))
                0.0))

Here’s the simple score result:

Note I’m running this here as of the date of writing it [2025-12-07 Sun 01:02], as I mentioned above, I only browsed the artists of the D letter. I will add here “Last run” property, so reader can relate to the last update date

Last run [2025-12-07 Sun 01:28]

Artist                            Total  Simple Eval. 

Thomas Cole                         130       0.5943      
Alexey Bogolyubov                    46       0.5213      
Mariotto Albertinelli                19       0.2189      
Pellizza da Volpedo                  54       0.2162      
Charles Courtney Curran              32       0.2012      
Frank Dicksee                        27       0.1540      
Giuseppe Abbati                      28       0.1485      
Jasper Francis Cropsey               31       0.1342      
Frank Cadogan Cowper                 20       0.1099      
William Ashford                      87       0.1030      
Pierre-Henri de Valenciennes        100       0.0896      
Gustav Pope                           9       0.0770      
Pietro da Cortona                    29       0.0758      
Ivan Aivazovsky                     700       0.0757      
Jacques Clement Wagrez               10       0.0693      
Thomas Francis Dicksee               12       0.0578      
Gustave-Claude-Etienne Courtois      13       0.0533      
Jacques-Laurent Agasse               45       0.0488      
Frederic William Burton              50       0.0439      
Jean-Joseph-Xavier Bidauld           16       0.0433      
Edouard Debat-Ponsan                 17       0.0408      
Denis van Alsloot                    61       0.0360      
Jean-Baptiste Camille Corot         600       0.0345      
Arnold Böcklin                      124       0.0335      
Jacques-Louis David                 128       0.0325      
Reza Abbasi                          23       0.0301      
Carl Aagaard                        220       0.0293      
William-Adolphe Bouguereau          822       0.0292      
Emile Claus                          26       0.0267      
Sir Lawrence Alma-Tadema            444       0.0263      
Thomas Couture                       28       0.0248      
Hermann David Salomon Corrodi        28       0.0248      
Alexandre Cabanel                    90       0.0244      
Nicolai Abildgaard                   30       0.0231      
Joachim Wtewael                      30       0.0231      
Charles-Francois Daubigny           102       0.0215      
Mattia Preti                         33       0.0210      
Eduard Quitton                       37       0.0187      
Domingos Sequeira                    40       0.0173      
Jacques Stella                       41       0.0169      
Walter Crane                         50       0.0139      
Battistello Caracciolo               52       0.0133      
Joseph DeCamp                        54       0.0128      
John Constable                      190       0.0116      
Jurriaan Andriessen                  60       0.0116      
Edwin Austin Abbey                   61       0.0114      
Oswald Achenbach                     65       0.0107      
Anselm Feuerbach                     66       0.0105      
Alexandre Antigna                    67       0.0103      
Frederic Edwin Church                76       0.0091      
Giulio Cesare Procaccini             76       0.0091      
Knud Baade                           79       0.0088      
Francesco Albani                     80       0.0087      
Johan Christian Dahl                100       0.0069      
Domenico Ghirlandaio                108       0.0064      
Guido Reni                          148       0.0047      
Benjamin West                       156       0.0044      
Sandro Botticelli                   207       0.0033

However, the issue with depending on occurrence is that, it’s very likely that the artists who made significantly more artworks than others, to unfairly be on top of the list, e.g. I love around 63 artworks of Ivan Aivazovsky, but my library of his consists of around 700 artworks (he reportedly made much more than that), it’s unfair in my opinion to evaluate this number to another artist, like Thomas Cole for example, who only had around 200 artworks.

Entropy approach (algorithm 2)

The problem with that approach is that, it does not calculate my preference entropy which causes favoritism. For example, I love Caspir’s works, I know his melody and life, I would probably relate to any of his works and catch the reference quickly, it’s unlikely for me to do the same for someone like John Constable, who I barely know his life. We can count for this like follows:

\[\text{Favor Score} = \frac{L}{T} \cdot \left(1 - \frac{1}{\sqrt{T}}\right) \cdot \left(1 + \frac{H(L,T)}{\log(T+1)}\right)\]

It looks a bit scary, but I promise it’s as simple as the previous one, let me explain it. Information theory tells us there’s something fundamentally missing from the simple ratio approach: the shape of your preference matters.

Imagine you’re at a museum. Artist A has 100 paintings on display, and you love exactly 50 of them. Artist B has 100 paintings, and you love 95 of them. The simple counting method says they’re both “50% favorable” and “95% favorable” respectively, okay, sure. But there’s something it misses: with Artist A, you’re constantly unsure. Will the next painting move you? It’s a coin flip. With Artist B, you know you’re going to love it, you know him/her, you know you will relate to it and you probably have a backstory already about the painting. That certainty, that decisive connection is what favoritism actually feels like. Information theory gives us a way to measure this decisiveness through something called “entropy” basically, how surprised you are by your own reactions. When you’re consistently in love or consistently unmoved, entropy is low (good!). When you’re all over the place, entropy is high (suggests a less meaningful relationship). The formula also builds in a confidence check: if you’ve only seen 4 works by an artist, you can’t be that sure yet, so it gently downweights your score until you’ve seen enough to really know.

So The \(L/T\) ratio treats all proportions as equal. Loving 50 out of 100 works scores identically to loving 5 out of 10. The key insight from information theory is that entropy captures the decisiveness of your taste.

Consider Shannon entropy: \(H(L,T) = -\frac{L}{T}\log\frac{L}{T} - \frac{T-L}{T}\log\frac{T-L}{T}\)

This measures the uncertainty in predicting whether you’ll love a random work by that artist. When \(L/T \approx 0.5\), entropy maximizes, you’re ambivalent, essentially coin-flipping. When \(L/T \to 0\) or \(L/T \to 1\), entropy minimizes, your preference is sharp and predictable. A favorite artist should provoke a strong signal, not statistical noise.

The full equation integrates three components: base affinity (\(L/T\)), confidence scaling (\((1 - 1/\sqrt{T})\) which asymptotically approaches 1 with more samples), and entropy bonus (rewarding low-entropy, decisive preferences).

Now let’s bring both together, but sort by algorithm 2:

(defun salih/calculate-artist-favor-scores (org-file)
  (cl-flet ((simple-score (loved total)
              "Simple approach: (L/T) * log(L+1)"
              (if (and (> total 0) (> loved 0))
                  (* (/ (float loved) total)
                     (log (1+ loved)))
                0.0))
            (entropy-score (loved total)
              "approach with entropy"
              (if (or (<= total 0) (< loved 0))
                  0.0
                (let* ((ratio (/ (float loved) total))
                       (confidence (- 1 (/ 1 (sqrt total))))
                       (p1 ratio)
                       (p2 (- 1 ratio))
                       (entropy (if (and (> p1 0) (> p2 0))
                                    (- (+ (* p1 (log p1))
                                          (* p2 (log p2))))
                                  0.0))
                       (entropy-bonus (if (> total 1)
                                          (/ entropy (log (1+ total)))
                                        0.0)))
                  (* ratio
                     confidence
                     (1+ entropy-bonus))))))
    
    (with-temp-buffer
      (insert-file-contents org-file)
      (org-mode)
      (goto-char (point-min))
      
      (let ((results '()))
        (while (re-search-forward "^\\*\\* " nil t)
          (let* ((heading (org-get-heading t t t t))
                 (id (org-entry-get (point) "ID"))
                 (nworks (org-entry-get (point) "NWORKS")))
            
            (when (and id nworks)
              (let* ((total (string-to-number nworks))
                     (node (org-roam-node-from-id id))
                     (backlinks (when node (org-roam-backlinks-get node)))
                     (loved (length backlinks))
                     (simple (simple-score loved total))
                     (entropy (entropy-score loved total)))
                
                (push (list :name heading
                            :id id
                            ;; :loved loved
                            ;; :total total
                            :simple-score simple
                            :entropy-score entropy)
                      results)))))
        
        (sort results (lambda (a b)
                        (> (plist-get a :entropy-score)
                           (plist-get b :entropy-score))))))))

This gives us:

Artist                          Total      Entropy    Simple
-------------------------------------------------------------------
Alexey Bogolyubov                  46       0.2105    0.5213
Thomas Cole                       130       0.1849    0.5943
Mariotto Albertinelli              19       0.1394    0.2189
Charles Courtney Curran            32       0.1140    0.2012
Pellizza da Volpedo                54       0.1043    0.2162
Frank Dicksee                      27       0.0991    0.1540
Giuseppe Abbati                    28       0.0957    0.1485
Jasper Francis Cropsey             31       0.0867    0.1342
Frank Cadogan Cowper               20       0.0859    0.1099
Gustav Pope                         9       0.0853    0.0770
Jacques Clement Wagrez             10       0.0776    0.0693
Thomas Francis Dicksee             12       0.0659    0.0578
Gustave-Claude-Etienne Courtois    13       0.0613    0.0533
Pietro da Cortona                  29       0.0603    0.0758
William Ashford                    87       0.0538    0.1030
Jean-Joseph-Xavier Bidauld         16       0.0507    0.0433
Edouard Debat-Ponsan               17       0.0480    0.0408
Pierre-Henri de Valenciennes      100       0.0469    0.0896
Jacques-Laurent Agasse             45       0.0396    0.0488
Reza Abbasi                        23       0.0363    0.0301
Frederic William Burton            50       0.0358    0.0439
Emile Claus                        26       0.0324    0.0267
Thomas Couture                     28       0.0303    0.0248
Hermann David Salomon Corrodi      28       0.0303    0.0248
Denis van Alsloot                  61       0.0296    0.0360
Nicolai Abildgaard                 30       0.0284    0.0231
Joachim Wtewael                    30       0.0284    0.0231
Mattia Preti                       33       0.0260    0.0210
Ivan Aivazovsky                   700       0.0252    0.0757
Eduard Quitton                     37       0.0234    0.0187
Arnold Böcklin                    124       0.0225    0.0335
Jacques-Louis David               128       0.0219    0.0325
Domingos Sequeira                  40       0.0217    0.0173
Jacques Stella                     41       0.0212    0.0169
Alexandre Cabanel                  90       0.0203    0.0244
Charles-Francois Daubigny         102       0.0180    0.0215
Walter Crane                       50       0.0176    0.0139
Carl Aagaard                      220       0.0172    0.0293
Battistello Caracciolo             52       0.0170    0.0133
Joseph DeCamp                      54       0.0164    0.0128
Jurriaan Andriessen                60       0.0148    0.0116
Edwin Austin Abbey                 61       0.0146    0.0114
Jean-Baptiste Camille Corot       600       0.0146    0.0345
Oswald Achenbach                   65       0.0137    0.0107
Anselm Feuerbach                   66       0.0135    0.0105
Alexandre Antigna                  67       0.0133    0.0103
Sir Lawrence Alma-Tadema          444       0.0130    0.0263
William-Adolphe Bouguereau        822       0.0119    0.0292
Frederic Edwin Church              76       0.0118    0.0091
Giulio Cesare Procaccini           76       0.0118    0.0091
Knud Baade                         79       0.0114    0.0088
Francesco Albani                   80       0.0113    0.0087
John Constable                    190       0.0099    0.0116
Johan Christian Dahl              100       0.0091    0.0069
Domenico Ghirlandaio              108       0.0085    0.0064
Guido Reni                        148       0.0063    0.0047
Benjamin West                     156       0.0059    0.0044
Sandro Botticelli                 207       0.0045    0.0033
Gustav Klimt                      245       0.0038    0.0028
Peter Paul Rubens                 684       0.0014    0.0010
Nicolas Poussin                   300       0.0000    0.0000

Some of my favorite ones were not affected by approach change, like Thomas Cole, Alexey Bogolyubov and Frank Dicksee, which says that I’m not a lot into favoritism after all :).

I will add an agenda TODO here to remember to update the evaluation every couple of months.

[2025-12-07 Sun 02:32] Honestly I was expecting much more surprises than that, but I guess once the data inflate more (having more artists in my PKMS, finishing more from the encyclopedia) will help.

TODO Update favorite artist evaluation @general

State “DONE” from “TODO” [2025-12-07 Sun 01:35]

Footnotes:

I wrote this “recently” around 7 months ago when I got the idea of writing this post, and it was left as a draft. I was gifted that book in Jan 2024, almost a year.

More like the ones we get to know

Hereby, all birds fly

Implementing naive occurrence counting (algorithm 1)

Entropy approach (algorithm 2)

TODO Update favorite artist evaluation @general

Footnotes: