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(v1.0.1.9005) as.mo() improvements

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@ -186,7 +185,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">14 March 2020</h4>
<h4 class="date">17 March 2020</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -220,21 +219,36 @@
<span id="cb2-16"><a href="#cb2-16"></a> <span class="dt">times =</span> <span class="dv">10</span>)</span>
<span id="cb2-17"><a href="#cb2-17"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</span>
<span id="cb2-18"><a href="#cb2-18"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb2-19"><a href="#cb2-19"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb2-20"><a href="#cb2-20"></a><span class="co"># as.mo("sau") 8.9 9.3 9.6 9.6 9.9 10 10</span></span>
<span id="cb2-21"><a href="#cb2-21"></a><span class="co"># as.mo("stau") 41.0 41.0 51.0 43.0 67.0 74 10</span></span>
<span id="cb2-22"><a href="#cb2-22"></a><span class="co"># as.mo("STAU") 39.0 41.0 49.0 42.0 56.0 72 10</span></span>
<span id="cb2-23"><a href="#cb2-23"></a><span class="co"># as.mo("staaur") 9.0 9.2 9.7 9.5 9.9 11 10</span></span>
<span id="cb2-24"><a href="#cb2-24"></a><span class="co"># as.mo("STAAUR") 9.5 9.8 24.0 21.0 38.0 45 10</span></span>
<span id="cb2-25"><a href="#cb2-25"></a><span class="co"># as.mo("S. aureus") 15.0 16.0 26.0 18.0 38.0 61 10</span></span>
<span id="cb2-26"><a href="#cb2-26"></a><span class="co"># as.mo("S aureus") 15.0 15.0 17.0 16.0 17.0 21 10</span></span>
<span id="cb2-27"><a href="#cb2-27"></a><span class="co"># as.mo("Staphylococcus aureus") 5.2 5.6 8.4 6.0 6.5 30 10</span></span>
<span id="cb2-28"><a href="#cb2-28"></a><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 640.0 690.0 710.0 710.0 720.0 760 10</span></span>
<span id="cb2-29"><a href="#cb2-29"></a><span class="co"># as.mo("Sthafilokkockus aaureuz") 350.0 360.0 420.0 400.0 490.0 510 10</span></span>
<span id="cb2-30"><a href="#cb2-30"></a><span class="co"># as.mo("MRSA") 9.2 9.3 16.0 10.0 10.0 49 10</span></span>
<span id="cb2-31"><a href="#cb2-31"></a><span class="co"># as.mo("VISA") 25.0 27.0 46.0 56.0 57.0 60 10</span></span>
<span id="cb2-32"><a href="#cb2-32"></a><span class="co"># as.mo("VRSA") 26.0 27.0 39.0 28.0 32.0 120 10</span></span>
<span id="cb2-33"><a href="#cb2-33"></a><span class="co"># as.mo(22242419) 120.0 140.0 170.0 140.0 150.0 410 10</span></span></code></pre></div>
<span id="cb2-19"><a href="#cb2-19"></a><span class="co"># expr min lq mean median uq max</span></span>
<span id="cb2-20"><a href="#cb2-20"></a><span class="co"># as.mo("sau") 9.1 9.5 12.0 9.8 10.0 34.0</span></span>
<span id="cb2-21"><a href="#cb2-21"></a><span class="co"># as.mo("stau") 38.0 41.0 55.0 41.0 42.0 160.0</span></span>
<span id="cb2-22"><a href="#cb2-22"></a><span class="co"># as.mo("STAU") 41.0 41.0 50.0 44.0 63.0 67.0</span></span>
<span id="cb2-23"><a href="#cb2-23"></a><span class="co"># as.mo("staaur") 8.6 8.8 9.4 9.4 9.6 10.0</span></span>
<span id="cb2-24"><a href="#cb2-24"></a><span class="co"># as.mo("STAAUR") 8.6 8.8 12.0 9.3 9.8 37.0</span></span>
<span id="cb2-25"><a href="#cb2-25"></a><span class="co"># as.mo("S. aureus") 14.0 15.0 51.0 27.0 43.0 250.0</span></span>
<span id="cb2-26"><a href="#cb2-26"></a><span class="co"># as.mo("S aureus") 14.0 15.0 16.0 15.0 16.0 18.0</span></span>
<span id="cb2-27"><a href="#cb2-27"></a><span class="co"># as.mo("Staphylococcus aureus") 5.1 5.3 5.7 5.5 6.0 6.6</span></span>
<span id="cb2-28"><a href="#cb2-28"></a><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 640.0 670.0 700.0 690.0 730.0 760.0</span></span>
<span id="cb2-29"><a href="#cb2-29"></a><span class="co"># as.mo("Sthafilokkockus aaureuz") 370.0 380.0 420.0 400.0 440.0 510.0</span></span>
<span id="cb2-30"><a href="#cb2-30"></a><span class="co"># as.mo("MRSA") 8.6 8.7 11.0 9.1 9.6 32.0</span></span>
<span id="cb2-31"><a href="#cb2-31"></a><span class="co"># as.mo("VISA") 24.0 25.0 38.0 38.0 50.0 53.0</span></span>
<span id="cb2-32"><a href="#cb2-32"></a><span class="co"># as.mo("VRSA") 24.0 25.0 49.0 37.0 52.0 150.0</span></span>
<span id="cb2-33"><a href="#cb2-33"></a><span class="co"># as.mo(22242419) 130.0 140.0 150.0 140.0 160.0 160.0</span></span>
<span id="cb2-34"><a href="#cb2-34"></a><span class="co"># neval</span></span>
<span id="cb2-35"><a href="#cb2-35"></a><span class="co"># 10</span></span>
<span id="cb2-36"><a href="#cb2-36"></a><span class="co"># 10</span></span>
<span id="cb2-37"><a href="#cb2-37"></a><span class="co"># 10</span></span>
<span id="cb2-38"><a href="#cb2-38"></a><span class="co"># 10</span></span>
<span id="cb2-39"><a href="#cb2-39"></a><span class="co"># 10</span></span>
<span id="cb2-40"><a href="#cb2-40"></a><span class="co"># 10</span></span>
<span id="cb2-41"><a href="#cb2-41"></a><span class="co"># 10</span></span>
<span id="cb2-42"><a href="#cb2-42"></a><span class="co"># 10</span></span>
<span id="cb2-43"><a href="#cb2-43"></a><span class="co"># 10</span></span>
<span id="cb2-44"><a href="#cb2-44"></a><span class="co"># 10</span></span>
<span id="cb2-45"><a href="#cb2-45"></a><span class="co"># 10</span></span>
<span id="cb2-46"><a href="#cb2-46"></a><span class="co"># 10</span></span>
<span id="cb2-47"><a href="#cb2-47"></a><span class="co"># 10</span></span>
<span id="cb2-48"><a href="#cb2-48"></a><span class="co"># 10</span></span></code></pre></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="562.5"></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
@ -246,19 +260,19 @@
<span id="cb3-6"><a href="#cb3-6"></a> <span class="dt">times =</span> <span class="dv">10</span>)</span>
<span id="cb3-7"><a href="#cb3-7"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(M.semesiae, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</span>
<span id="cb3-8"><a href="#cb3-8"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb3-9"><a href="#cb3-9"></a><span class="co"># expr min lq mean median uq</span></span>
<span id="cb3-10"><a href="#cb3-10"></a><span class="co"># as.mo("metsem") 1485.000 1507.000 1524.00 1519.000 1538.000</span></span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="co"># as.mo("METSEM") 1371.000 1495.000 1557.00 1567.000 1633.000</span></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="co"># as.mo("M. semesiae") 16.010 16.310 25.38 16.480 42.840</span></span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co"># as.mo("M. semesiae") 15.700 15.900 16.74 16.370 17.480</span></span>
<span id="cb3-14"><a href="#cb3-14"></a><span class="co"># as.mo("Methanosarcina semesiae") 5.885 6.116 11.79 6.347 8.155</span></span>
<span id="cb3-9"><a href="#cb3-9"></a><span class="co"># expr min lq mean median uq</span></span>
<span id="cb3-10"><a href="#cb3-10"></a><span class="co"># as.mo("metsem") 1530.00 1561.000 1618.00 1620.000 1665.000</span></span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="co"># as.mo("METSEM") 1482.00 1523.000 1544.00 1555.000 1567.000</span></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="co"># as.mo("M. semesiae") 15.01 16.140 27.85 17.080 43.620</span></span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co"># as.mo("M. semesiae") 15.54 16.200 19.30 16.570 17.040</span></span>
<span id="cb3-14"><a href="#cb3-14"></a><span class="co"># as.mo("Methanosarcina semesiae") 6.05 6.307 9.35 6.437 7.649</span></span>
<span id="cb3-15"><a href="#cb3-15"></a><span class="co"># max neval</span></span>
<span id="cb3-16"><a href="#cb3-16"></a><span class="co"># 1577.00 10</span></span>
<span id="cb3-17"><a href="#cb3-17"></a><span class="co"># 1663.00 10</span></span>
<span id="cb3-18"><a href="#cb3-18"></a><span class="co"># 48.53 10</span></span>
<span id="cb3-19"><a href="#cb3-19"></a><span class="co"># 18.55 10</span></span>
<span id="cb3-20"><a href="#cb3-20"></a><span class="co"># 32.92 10</span></span></code></pre></div>
<p>That takes 5.5 times as much time on average. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<span id="cb3-16"><a href="#cb3-16"></a><span class="co"># 1710.00 10</span></span>
<span id="cb3-17"><a href="#cb3-17"></a><span class="co"># 1586.00 10</span></span>
<span id="cb3-18"><a href="#cb3-18"></a><span class="co"># 50.39 10</span></span>
<span id="cb3-19"><a href="#cb3-19"></a><span class="co"># 42.95 10</span></span>
<span id="cb3-20"><a href="#cb3-20"></a><span class="co"># 33.42 10</span></span></code></pre></div>
<p>That takes 5.7 times as much time on average. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-6-1.png" width="900"></p>
<p>Uncommon microorganisms take a lot more time than common microorganisms. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
@ -292,8 +306,8 @@
<span id="cb4-24"><a href="#cb4-24"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</span>
<span id="cb4-25"><a href="#cb4-25"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb4-26"><a href="#cb4-26"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb4-27"><a href="#cb4-27"></a><span class="co"># mo_name(x) 542 585 605 601 614 738 100</span></span></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.6 seconds (600 ms). You only lose time on your unique input values.</p>
<span id="cb4-27"><a href="#cb4-27"></a><span class="co"># mo_name(x) 572 626 648 645 666 792 100</span></span></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.64 seconds (644 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -305,10 +319,10 @@
<span id="cb5-4"><a href="#cb5-4"></a> <span class="dt">times =</span> <span class="dv">10</span>)</span>
<span id="cb5-5"><a href="#cb5-5"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</span>
<span id="cb5-6"><a href="#cb5-6"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb5-7"><a href="#cb5-7"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb5-8"><a href="#cb5-8"></a><span class="co"># A 6.760 6.900 7.43 7.070 7.540 9.290 10</span></span>
<span id="cb5-9"><a href="#cb5-9"></a><span class="co"># B 14.200 14.400 18.80 14.900 16.000 51.500 10</span></span>
<span id="cb5-10"><a href="#cb5-10"></a><span class="co"># C 0.586 0.726 0.74 0.757 0.763 0.804 10</span></span></code></pre></div>
<span id="cb5-7"><a href="#cb5-7"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb5-8"><a href="#cb5-8"></a><span class="co"># A 6.730 6.860 7.110 6.950 7.170 8.47 10</span></span>
<span id="cb5-9"><a href="#cb5-9"></a><span class="co"># B 14.500 14.800 18.100 15.500 15.900 43.10 10</span></span>
<span id="cb5-10"><a href="#cb5-10"></a><span class="co"># C 0.726 0.753 0.821 0.791 0.882 1.04 10</span></span></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0008 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>run_it &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</span>
<span id="cb6-2"><a href="#cb6-2"></a> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</span>
@ -322,14 +336,14 @@
<span id="cb6-10"><a href="#cb6-10"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</span>
<span id="cb6-11"><a href="#cb6-11"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb6-12"><a href="#cb6-12"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb6-13"><a href="#cb6-13"></a><span class="co"># A 0.374 0.381 0.389 0.389 0.395 0.416 10</span></span>
<span id="cb6-14"><a href="#cb6-14"></a><span class="co"># B 0.404 0.411 0.422 0.421 0.425 0.452 10</span></span>
<span id="cb6-15"><a href="#cb6-15"></a><span class="co"># C 0.615 0.711 0.726 0.730 0.751 0.861 10</span></span>
<span id="cb6-16"><a href="#cb6-16"></a><span class="co"># D 0.405 0.409 0.429 0.428 0.435 0.485 10</span></span>
<span id="cb6-17"><a href="#cb6-17"></a><span class="co"># E 0.381 0.384 0.392 0.390 0.394 0.429 10</span></span>
<span id="cb6-18"><a href="#cb6-18"></a><span class="co"># F 0.365 0.366 0.379 0.375 0.383 0.419 10</span></span>
<span id="cb6-19"><a href="#cb6-19"></a><span class="co"># G 0.362 0.372 0.378 0.380 0.388 0.391 10</span></span>
<span id="cb6-20"><a href="#cb6-20"></a><span class="co"># H 0.378 0.381 0.403 0.387 0.393 0.556 10</span></span></code></pre></div>
<span id="cb6-13"><a href="#cb6-13"></a><span class="co"># A 0.348 0.398 0.405 0.404 0.419 0.438 10</span></span>
<span id="cb6-14"><a href="#cb6-14"></a><span class="co"># B 0.407 0.416 0.461 0.426 0.490 0.667 10</span></span>
<span id="cb6-15"><a href="#cb6-15"></a><span class="co"># C 0.701 0.745 0.757 0.754 0.769 0.814 10</span></span>
<span id="cb6-16"><a href="#cb6-16"></a><span class="co"># D 0.435 0.440 0.497 0.464 0.569 0.588 10</span></span>
<span id="cb6-17"><a href="#cb6-17"></a><span class="co"># E 0.351 0.397 0.435 0.419 0.489 0.520 10</span></span>
<span id="cb6-18"><a href="#cb6-18"></a><span class="co"># F 0.358 0.385 0.396 0.393 0.420 0.430 10</span></span>
<span id="cb6-19"><a href="#cb6-19"></a><span class="co"># G 0.322 0.363 0.409 0.401 0.450 0.508 10</span></span>
<span id="cb6-20"><a href="#cb6-20"></a><span class="co"># H 0.340 0.372 0.384 0.385 0.403 0.432 10</span></span></code></pre></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
@ -356,18 +370,18 @@
<span id="cb7-18"><a href="#cb7-18"></a><span class="kw"><a href="https://rdrr.io/r/base/print.html">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</span>
<span id="cb7-19"><a href="#cb7-19"></a><span class="co"># Unit: milliseconds</span></span>
<span id="cb7-20"><a href="#cb7-20"></a><span class="co"># expr min lq mean median uq max neval</span></span>
<span id="cb7-21"><a href="#cb7-21"></a><span class="co"># en 24.76 26.92 35.44 27.70 31.93 143.10 100</span></span>
<span id="cb7-22"><a href="#cb7-22"></a><span class="co"># de 26.46 28.18 33.90 29.51 30.51 64.85 100</span></span>
<span id="cb7-23"><a href="#cb7-23"></a><span class="co"># nl 32.40 34.89 39.79 35.94 37.28 75.95 100</span></span>
<span id="cb7-24"><a href="#cb7-24"></a><span class="co"># es 26.41 28.80 34.46 29.56 31.58 67.56 100</span></span>
<span id="cb7-25"><a href="#cb7-25"></a><span class="co"># it 26.44 28.52 35.22 29.30 30.37 156.00 100</span></span>
<span id="cb7-26"><a href="#cb7-26"></a><span class="co"># fr 26.24 28.09 34.78 29.52 31.23 65.88 100</span></span>
<span id="cb7-27"><a href="#cb7-27"></a><span class="co"># pt 26.28 28.32 36.00 29.49 32.22 66.76 100</span></span></code></pre></div>
<span id="cb7-21"><a href="#cb7-21"></a><span class="co"># en 25.28 26.99 33.53 27.88 29.51 61.96 100</span></span>
<span id="cb7-22"><a href="#cb7-22"></a><span class="co"># de 26.99 29.27 36.92 30.02 33.09 160.30 100</span></span>
<span id="cb7-23"><a href="#cb7-23"></a><span class="co"># nl 32.37 34.58 40.59 35.88 37.10 67.43 100</span></span>
<span id="cb7-24"><a href="#cb7-24"></a><span class="co"># es 27.54 29.14 34.40 29.88 31.45 61.89 100</span></span>
<span id="cb7-25"><a href="#cb7-25"></a><span class="co"># it 26.77 28.79 34.28 29.73 31.30 69.41 100</span></span>
<span id="cb7-26"><a href="#cb7-26"></a><span class="co"># fr 27.01 28.81 34.96 29.39 30.79 161.10 100</span></span>
<span id="cb7-27"><a href="#cb7-27"></a><span class="co"># pt 26.76 28.85 34.13 29.69 31.74 63.08 100</span></span></code></pre></div>
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