Add notes for 2023-04-27

2024-11-21 22:25:02 +01:00 · 2023-04-27 13:10:13 -07:00 · 2023-04-27 13:10:13 -07:00 · ad8516bbb3
commit ad8516bbb3
parent 0ca3cadbef
33 changed files with 129 additions and 41 deletions
--- a/content/posts/2022-06.md
+++ b/content/posts/2022-06.md
@ -202,7 +202,7 @@ $ xsv join --full alpha2 /tmp/clarisa-un-cgspace-xsv-full.csv alpha2 /tmp/mel-co

 ## 2022-06-28

- Start working on the CGSpace subject export for FAO
+- Start working on the CGSpace subject export for FAO / AGROVOC
 - First I exported a list of all metadata in our `dcterms.subject` and other center-specific subject fields with their counts:

 ```console
@ -220,7 +220,7 @@ $ ./ilri/agrovoc-lookup.py -i /tmp/2022-06-28-cgspace-subjects.txt -o /tmp/2022-
 - I keep getting timeouts after every five or ten requests, so this will not be feasible for 27,000 subjects!
 - I think I will have to write some custom script to use the AGROVOC RDF file
  - Using rdflib to open the 1.2GB `agrovoc_lod.rdf` file takes several minutes and doesn't seem very efficient
- I tried using [lightrdf](https://github.com/ozekik/lightrdf) and it's much quicker, but the documentation is limiting and I'm not sure how to search yet
+- I tried using [lightrdf](https://github.com/ozekik/lightrdf) and it's much quicker, but the documentation is limited and I'm not sure how to search yet
  - I had to try in different Python versions because 3.10.x is apparently too new
 - For future reference I was able to search with lightrdf:

--- a/content/posts/2023-04.md
+++ b/content/posts/2023-04.md
@ -481,6 +481,10 @@ $ psql -d dspace -c "update bundle set primary_bitstream_id=NULL where primary_b
  - As the quality settings are not comparable between formats, we need to compare the formats at matching perceptual scores (ssimulacra2 in this case)
  - I used a ssimulacra2 score of 80 because that's the about the highest score I see with WebP using my samples, though JPEG and AVIF do go higher
  - Also, according to current ssimulacra2 (v2.1), a score of 70 is "high quality" and a score of 90 is "very high quality", so 80 should be reasonably high enough...
+- Here is a plot of the qualities and ssimulacra2 scores:
+
+![Quality vs Score](/cgspace-notes/2023/04/quality-vs-score-ssimulacra-v2.1.png)
+
 - Export CGSpace to check for missing Initiatives mappings

 ## 2023-04-22
@ -491,4 +495,43 @@ $ psql -d dspace -c "update bundle set primary_bitstream_id=NULL where primary_b
  - Also, I found a few items submitted by MEL that had dates in DD/MM/YYYY format, so I sent them to Salem for him to investigate
 - Start a harvest on AReS

+## 2023-04-26
+
+- Begin working on the list of non-AGROVOC CGSpace subjects for FAO
+  - The last time I did this was in 2022-06
+  - I used the following SQL query to dump values from all subject fields, lower case them, and group by counts:
+
+```console
+localhost/dspacetest= ☘ \COPY (SELECT DISTINCT(lower(text_value)) AS "subject", count(*) FROM metadatavalue WHERE dspace_object_id in (SELECT dspace_object_id FROM item) AND metadata_field_id IN (187, 120, 210, 122, 215, 127, 208, 124, 128, 123, 125, 135, 203, 236, 238, 119) GROUP BY "subject" ORDER BY count DESC) to /tmp/2023-04-26-cgspace-subjects.csv WITH CSV HEADER;
+COPY 26315
+Time: 2761.981 ms (00:02.762)
+```
+
+- Then I extracted the subjects and looked them up against AGROVOC:
+
+```console
+$ csvcut -c subject /tmp/2023-04-26-cgspace-subjects.csv | sed '1d' > /tmp/2023-04-26-cgspace-subjects.txt
+$ ./ilri/agrovoc_lookup.py -i /tmp/2023-04-26-cgspace-subjects.txt -o /tmp/2023-04-26-cgspace-subjects-results.csv
+```
+
+## 2023-04-27
+
+- The AGROVOC lookup from yesterday finished, so I extracted all terms that did not match and joined them with the original CSV so I can see the counts:
+  - (I also note that the `agrovoc_lookup.py` script didn't seem to be caching properly, as it had to look up everything again the next time I ran it despite the requests cache being 174MB!)
+
+```console
+csvgrep -c 'number of matches' -r '^0$' /tmp/2023-04-26-cgspace-subjects-results.csv \
+  | csvcut -c subject \
+  | csvjoin -c subject /tmp/2023-04-26-cgspace-subjects.csv - \
+  > /tmp/2023-04-26-cgspace-non-agrovoc.csv
+```
+
+- I filtered for only those terms that had counts larger than fifty
+  - I also removed terms like "forages", "policy", "pests and diseases" because those exist as singular or separate terms in AGROVOC
+  - I also removed ambiguous terms like "cocoa", "diversity", "resistance" etc because there are various other preferred terms for those in AGROVOC
+  - I also removed spelling mistakes like "modeling" and "savanas" because those exist in their correct form in AGROVOC
+  - I also removed internal CGIAR terms like "tac", "crp", "internal review" etc (note: these are mostly from CGIAR System Office's subjects... perhaps I exclude those next time?)
+- I note that many of *our* terms would match if they were singular, plural, or split up into separate terms, so perhaps we should pair this with an excercise to review our own terms
+- I couldn't finish the work locally yet so I uploaded my list to Google Docs to continue later
+
 <!-- vim: set sw=2 ts=2: -->
--- a/docs/2022-06/index.html
+++ b/docs/2022-06/index.html
@ -58,7 +58,7 @@ There seem to be many more of these:
  "@type": "BlogPosting",
  "headline": "June, 2022",
  "url": "https://alanorth.github.io/cgspace-notes/2022-06/",
-  "wordCount": "1786",
+  "wordCount": "1788",
  "datePublished": "2022-06-06T09:01:36+03:00",
  "dateModified": "2022-08-03T21:01:39+03:00",
  "author": {
@ -349,7 +349,7 @@ There seem to be many more of these:
 </ul>
 <h2 id="2022-06-28">2022-06-28</h2>
 <ul>
-<li>Start working on the CGSpace subject export for FAO</li>
+<li>Start working on the CGSpace subject export for FAO / AGROVOC</li>
 <li>First I exported a list of all metadata in our <code>dcterms.subject</code> and other center-specific subject fields with their counts:</li>
 </ul>
 <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-console" data-lang="console"><span style="display:flex;"><span>localhost/dspacetest= ☘ \COPY (SELECT DISTINCT text_value AS &#34;subject&#34;, count(*) FROM metadatavalue WHERE dspace_object_id in (SELECT dspace_object_id FROM item) AND metadata_field_id IN (187, 120, 210, 122, 215, 127, 208, 124, 128, 123, 125, 135, 203, 236, 238, 119) GROUP BY &#34;subject&#34; ORDER BY count DESC) to /tmp/2022-06-28-cgspace-subjects.csv WITH CSV HEADER;
@ -366,7 +366,7 @@ There seem to be many more of these:
 <li>Using rdflib to open the 1.2GB <code>agrovoc_lod.rdf</code> file takes several minutes and doesn&rsquo;t seem very efficient</li>
 </ul>
 </li>
-<li>I tried using <a href="https://github.com/ozekik/lightrdf">lightrdf</a> and it&rsquo;s much quicker, but the documentation is limiting and I&rsquo;m not sure how to search yet
+<li>I tried using <a href="https://github.com/ozekik/lightrdf">lightrdf</a> and it&rsquo;s much quicker, but the documentation is limited and I&rsquo;m not sure how to search yet
 <ul>
 <li>I had to try in different Python versions because 3.10.x is apparently too new</li>
 </ul>
--- a/docs/2023-04/index.html
+++ b/docs/2023-04/index.html
@ -20,7 +20,7 @@ Start a harvest on AReS
 <meta property="og:type" content="article" />
 <meta property="og:url" content="https://alanorth.github.io/cgspace-notes/2023-04/" />
 <meta property="article:published_time" content="2023-04-02T08:19:36+03:00" />
-<meta property="article:modified_time" content="2023-04-20T22:44:18-07:00" />
+<meta property="article:modified_time" content="2023-04-22T16:37:19-07:00" />



@ -46,9 +46,9 @@ Start a harvest on AReS
  "@type": "BlogPosting",
  "headline": "April, 2023",
  "url": "https://alanorth.github.io/cgspace-notes/2023-04/",
-  "wordCount": "2033",
+  "wordCount": "2400",
  "datePublished": "2023-04-02T08:19:36+03:00",
-  "dateModified": "2023-04-20T22:44:18-07:00",
+  "dateModified": "2023-04-22T16:37:19-07:00",
  "author": {
    "@type": "Person",
    "name": "Alan Orth"
@ -661,6 +661,10 @@ Start a harvest on AReS
 <li>Also, according to current ssimulacra2 (v2.1), a score of 70 is &ldquo;high quality&rdquo; and a score of 90 is &ldquo;very high quality&rdquo;, so 80 should be reasonably high enough&hellip;</li>
 </ul>
 </li>
+<li>Here is a plot of the qualities and ssimulacra2 scores:</li>
+</ul>
+<p><img src="/cgspace-notes/2023/04/quality-vs-score-ssimulacra-v2.1.png" alt="Quality vs Score"></p>
+<ul>
 <li>Export CGSpace to check for missing Initiatives mappings</li>
 </ul>
 <h2 id="2023-04-22">2023-04-22</h2>
@ -674,6 +678,47 @@ Start a harvest on AReS
 </li>
 <li>Start a harvest on AReS</li>
 </ul>
+<h2 id="2023-04-26">2023-04-26</h2>
+<ul>
+<li>Begin working on the list of non-AGROVOC CGSpace subjects for FAO
+<ul>
+<li>The last time I did this was in 2022-06</li>
+<li>I used the following SQL query to dump values from all subject fields, lower case them, and group by counts:</li>
+</ul>
+</li>
+</ul>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-console" data-lang="console"><span style="display:flex;"><span>localhost/dspacetest= ☘ \COPY (SELECT DISTINCT(lower(text_value)) AS &#34;subject&#34;, count(*) FROM metadatavalue WHERE dspace_object_id in (SELECT dspace_object_id FROM item) AND metadata_field_id IN (187, 120, 210, 122, 215, 127, 208, 124, 128, 123, 125, 135, 203, 236, 238, 119) GROUP BY &#34;subject&#34; ORDER BY count DESC) to /tmp/2023-04-26-cgspace-subjects.csv WITH CSV HEADER;
+</span></span><span style="display:flex;"><span>COPY 26315
+</span></span><span style="display:flex;"><span>Time: 2761.981 ms (00:02.762)
+</span></span></code></pre></div><ul>
+<li>Then I extracted the subjects and looked them up against AGROVOC:</li>
+</ul>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-console" data-lang="console"><span style="display:flex;"><span>$ csvcut -c subject /tmp/2023-04-26-cgspace-subjects.csv | sed <span style="color:#e6db74">&#39;1d&#39;</span> &gt; /tmp/2023-04-26-cgspace-subjects.txt
+</span></span><span style="display:flex;"><span>$ ./ilri/agrovoc_lookup.py -i /tmp/2023-04-26-cgspace-subjects.txt -o /tmp/2023-04-26-cgspace-subjects-results.csv
+</span></span></code></pre></div><h2 id="2023-04-27">2023-04-27</h2>
+<ul>
+<li>The AGROVOC lookup from yesterday finished, so I extracted all terms that did not match and joined them with the original CSV so I can see the counts:
+<ul>
+<li>(I also note that the <code>agrovoc_lookup.py</code> script didn&rsquo;t seem to be caching properly, as it had to look up everything again the next time I ran it despite the requests cache being 174MB!)</li>
+</ul>
+</li>
+</ul>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-console" data-lang="console"><span style="display:flex;"><span>csvgrep -c &#39;number of matches&#39; -r &#39;^0$&#39; /tmp/2023-04-26-cgspace-subjects-results.csv \
+</span></span><span style="display:flex;"><span>  | csvcut -c subject \
+</span></span><span style="display:flex;"><span>  | csvjoin -c subject /tmp/2023-04-26-cgspace-subjects.csv - \
+</span></span><span style="display:flex;"><span>  &gt; /tmp/2023-04-26-cgspace-non-agrovoc.csv
+</span></span></code></pre></div><ul>
+<li>I filtered for only those terms that had counts larger than fifty
+<ul>
+<li>I also removed terms like &ldquo;forages&rdquo;, &ldquo;policy&rdquo;, &ldquo;pests and diseases&rdquo; because those exist as singular or separate terms in AGROVOC</li>
+<li>I also removed ambiguous terms like &ldquo;cocoa&rdquo;, &ldquo;diversity&rdquo;, &ldquo;resistance&rdquo; etc because there are various other preferred terms for those in AGROVOC</li>
+<li>I also removed spelling mistakes like &ldquo;modeling&rdquo; and &ldquo;savanas&rdquo; because those exist in their correct form in AGROVOC</li>
+<li>I also removed internal CGIAR terms like &ldquo;tac&rdquo;, &ldquo;crp&rdquo;, &ldquo;internal review&rdquo; etc (note: these are mostly from CGIAR System Office&rsquo;s subjects&hellip; perhaps I exclude those next time?)</li>
+</ul>
+</li>
+<li>I note that many of <em>our</em> terms would match if they were singular, plural, or split up into separate terms, so perhaps we should pair this with an excercise to review our own terms</li>
+<li>I couldn&rsquo;t finish the work locally yet so I uploaded my list to Google Docs to continue later</li>
+</ul>
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 <meta property="og:description" content="Documenting day-to-day work on the [CGSpace](https://cgspace.cgiar.org) repository." />
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