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(v1.0.1.9000) first PCA implementation

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dr. M.S. (Matthijs) Berends
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@ -39,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.0.1.9000</span>
</span>
</div>
@ -75,6 +75,13 @@
Predict antimicrobial resistance
</a>
</li>
<li>
<a href="../articles/PCA.html">
<span class="fa fa-compress"></span>
Conduct principal component analysis for AMR
</a>
</li>
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
@ -179,7 +186,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 February 2020</h4>
<h4 class="date">07 March 2020</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -213,21 +220,36 @@
<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") 8.2 8.5 12.0 9.1 9.5 34 10</span></span>
<span id="cb2-21"><a href="#cb2-21"></a><span class="co"># as.mo("stau") 36.0 37.0 50.0 40.0 65.0 82 10</span></span>
<span id="cb2-22"><a href="#cb2-22"></a><span class="co"># as.mo("STAU") 38.0 41.0 49.0 41.0 64.0 72 10</span></span>
<span id="cb2-23"><a href="#cb2-23"></a><span class="co"># as.mo("staaur") 8.4 8.9 9.2 9.2 9.3 10 10</span></span>
<span id="cb2-24"><a href="#cb2-24"></a><span class="co"># as.mo("STAAUR") 8.4 8.7 13.0 9.4 9.5 43 10</span></span>
<span id="cb2-25"><a href="#cb2-25"></a><span class="co"># as.mo("S. aureus") 14.0 15.0 30.0 37.0 40.0 44 10</span></span>
<span id="cb2-26"><a href="#cb2-26"></a><span class="co"># as.mo("S aureus") 14.0 15.0 43.0 15.0 37.0 250 10</span></span>
<span id="cb2-27"><a href="#cb2-27"></a><span class="co"># as.mo("Staphylococcus aureus") 4.7 5.0 7.4 5.1 5.5 28 10</span></span>
<span id="cb2-28"><a href="#cb2-28"></a><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 660.0 690.0 720.0 730.0 730.0 790 10</span></span>
<span id="cb2-29"><a href="#cb2-29"></a><span class="co"># as.mo("Sthafilokkockus aaureuz") 350.0 380.0 420.0 400.0 440.0 520 10</span></span>
<span id="cb2-30"><a href="#cb2-30"></a><span class="co"># as.mo("MRSA") 8.1 8.5 12.0 9.1 9.3 38 10</span></span>
<span id="cb2-31"><a href="#cb2-31"></a><span class="co"># as.mo("VISA") 24.0 26.0 35.0 28.0 46.0 52 10</span></span>
<span id="cb2-32"><a href="#cb2-32"></a><span class="co"># as.mo("VRSA") 24.0 26.0 35.0 29.0 47.0 57 10</span></span>
<span id="cb2-33"><a href="#cb2-33"></a><span class="co"># as.mo(22242419) 130.0 130.0 150.0 140.0 150.0 240 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") 8.0 8.2 9.1 8.4 8.5 16</span></span>
<span id="cb2-21"><a href="#cb2-21"></a><span class="co"># as.mo("stau") 37.0 40.0 51.0 52.0 60.0 76</span></span>
<span id="cb2-22"><a href="#cb2-22"></a><span class="co"># as.mo("STAU") 36.0 38.0 58.0 60.0 68.0 100</span></span>
<span id="cb2-23"><a href="#cb2-23"></a><span class="co"># as.mo("staaur") 8.2 8.4 9.5 8.6 8.9 14</span></span>
<span id="cb2-24"><a href="#cb2-24"></a><span class="co"># as.mo("STAAUR") 8.2 8.3 15.0 9.2 14.0 53</span></span>
<span id="cb2-25"><a href="#cb2-25"></a><span class="co"># as.mo("S. aureus") 13.0 21.0 64.0 21.0 45.0 260</span></span>
<span id="cb2-26"><a href="#cb2-26"></a><span class="co"># as.mo("S aureus") 13.0 14.0 33.0 24.0 44.0 76</span></span>
<span id="cb2-27"><a href="#cb2-27"></a><span class="co"># as.mo("Staphylococcus aureus") 4.7 4.8 9.9 6.8 7.9 42</span></span>
<span id="cb2-28"><a href="#cb2-28"></a><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 620.0 640.0 770.0 700.0 860.0 1100</span></span>
<span id="cb2-29"><a href="#cb2-29"></a><span class="co"># as.mo("Sthafilokkockus aaureuz") 330.0 350.0 460.0 490.0 560.0 570</span></span>
<span id="cb2-30"><a href="#cb2-30"></a><span class="co"># as.mo("MRSA") 8.1 8.3 14.0 12.0 13.0 48</span></span>
<span id="cb2-31"><a href="#cb2-31"></a><span class="co"># as.mo("VISA") 24.0 25.0 34.0 26.0 38.0 59</span></span>
<span id="cb2-32"><a href="#cb2-32"></a><span class="co"># as.mo("VRSA") 23.0 24.0 37.0 27.0 39.0 78</span></span>
<span id="cb2-33"><a href="#cb2-33"></a><span class="co"># as.mo(22242419) 120.0 130.0 150.0 140.0 160.0 240</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>
@ -239,19 +261,19 @@
<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") 1497.000 1536.000 1604.00 1575.00 1693.000</span></span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="co"># as.mo("METSEM") 1472.000 1510.000 1563.00 1563.00 1615.000</span></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="co"># as.mo("M. semesiae") 14.520 14.760 22.18 15.39 36.430</span></span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co"># as.mo("M. semesiae") 14.310 14.630 19.94 15.18 16.080</span></span>
<span id="cb3-14"><a href="#cb3-14"></a><span class="co"># as.mo("Methanosarcina semesiae") 5.376 5.482 8.41 5.81 5.911</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"># 1709.00 10</span></span>
<span id="cb3-17"><a href="#cb3-17"></a><span class="co"># 1641.00 10</span></span>
<span id="cb3-18"><a href="#cb3-18"></a><span class="co"># 40.57 10</span></span>
<span id="cb3-19"><a href="#cb3-19"></a><span class="co"># 40.27 10</span></span>
<span id="cb3-20"><a href="#cb3-20"></a><span class="co"># 32.27 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>
<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") 1349.000 1352.000 1597.000 1411.000 1983.000</span></span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="co"># as.mo("METSEM") 1316.000 2146.000 2069.000 2226.000 2245.000</span></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="co"># as.mo("M. semesiae") 13.330 14.110 32.960 21.840 53.090</span></span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co"># as.mo("M. semesiae") 13.730 20.960 29.720 21.430 40.000</span></span>
<span id="cb3-14"><a href="#cb3-14"></a><span class="co"># as.mo("Methanosarcina semesiae") 4.802 5.171 6.667 6.551 8.036</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"># 2184.000 10</span></span>
<span id="cb3-17"><a href="#cb3-17"></a><span class="co"># 2337.000 10</span></span>
<span id="cb3-18"><a href="#cb3-18"></a><span class="co"># 62.780 10</span></span>
<span id="cb3-19"><a href="#cb3-19"></a><span class="co"># 64.510 10</span></span>
<span id="cb3-20"><a href="#cb3-20"></a><span class="co"># 8.735 10</span></span></code></pre></div>
<p>That takes 6.1 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>
@ -285,8 +307,8 @@
<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) 568 614 651 634 657 1170 100</span></span></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.63 seconds (634 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) 564 605 673 630 657 1100 100</span></span></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.63 seconds (630 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -298,10 +320,10 @@
<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 6.630 6.790 7.330 7.230 7.690 8.39 10</span></span>
<span id="cb5-9"><a href="#cb5-9"></a><span class="co"># B 13.900 14.300 19.000 14.700 17.000 53.00 10</span></span>
<span id="cb5-10"><a href="#cb5-10"></a><span class="co"># C 0.847 0.875 0.947 0.901 0.977 1.16 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 6.58 6.590 7.340 6.630 6.780 13.00 10</span></span>
<span id="cb5-9"><a href="#cb5-9"></a><span class="co"># B 13.50 13.700 18.700 13.900 14.600 60.80 10</span></span>
<span id="cb5-10"><a href="#cb5-10"></a><span class="co"># C 0.72 0.863 0.917 0.898 0.935 1.26 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 0.0009 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>
@ -315,14 +337,14 @@
<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 0.476 0.485 0.501 0.498 0.504 0.554 10</span></span>
<span id="cb6-14"><a href="#cb6-14"></a><span class="co"># B 0.515 0.521 0.548 0.545 0.553 0.614 10</span></span>
<span id="cb6-15"><a href="#cb6-15"></a><span class="co"># C 0.710 0.791 0.870 0.842 0.855 1.330 10</span></span>
<span id="cb6-16"><a href="#cb6-16"></a><span class="co"># D 0.491 0.524 0.539 0.535 0.546 0.613 10</span></span>
<span id="cb6-17"><a href="#cb6-17"></a><span class="co"># E 0.488 0.500 0.583 0.541 0.635 0.830 10</span></span>
<span id="cb6-18"><a href="#cb6-18"></a><span class="co"># F 0.477 0.488 0.509 0.495 0.519 0.569 10</span></span>
<span id="cb6-19"><a href="#cb6-19"></a><span class="co"># G 0.473 0.490 0.507 0.498 0.534 0.547 10</span></span>
<span id="cb6-20"><a href="#cb6-20"></a><span class="co"># H 0.477 0.486 0.500 0.494 0.509 0.561 10</span></span></code></pre></div>
<span id="cb6-13"><a href="#cb6-13"></a><span class="co"># A 0.499 0.511 0.516 0.517 0.522 0.544 10</span></span>
<span id="cb6-14"><a href="#cb6-14"></a><span class="co"># B 0.532 0.539 0.550 0.542 0.563 0.592 10</span></span>
<span id="cb6-15"><a href="#cb6-15"></a><span class="co"># C 0.718 0.787 0.832 0.843 0.889 0.904 10</span></span>
<span id="cb6-16"><a href="#cb6-16"></a><span class="co"># D 0.538 0.548 0.566 0.567 0.571 0.607 10</span></span>
<span id="cb6-17"><a href="#cb6-17"></a><span class="co"># E 0.503 0.509 0.515 0.513 0.516 0.549 10</span></span>
<span id="cb6-18"><a href="#cb6-18"></a><span class="co"># F 0.502 0.504 0.514 0.511 0.519 0.539 10</span></span>
<span id="cb6-19"><a href="#cb6-19"></a><span class="co"># G 0.493 0.513 0.538 0.514 0.536 0.684 10</span></span>
<span id="cb6-20"><a href="#cb6-20"></a><span class="co"># H 0.499 0.501 0.509 0.505 0.516 0.531 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">
@ -349,13 +371,13 @@
<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 24.69 25.88 32.93 26.49 28.24 165.60 100</span></span>
<span id="cb7-22"><a href="#cb7-22"></a><span class="co"># de 26.07 27.22 32.82 28.08 29.90 75.78 100</span></span>
<span id="cb7-23"><a href="#cb7-23"></a><span class="co"># nl 32.01 33.49 39.63 34.78 36.68 78.48 100</span></span>
<span id="cb7-24"><a href="#cb7-24"></a><span class="co"># es 25.66 27.31 32.25 28.04 29.53 68.15 100</span></span>
<span id="cb7-25"><a href="#cb7-25"></a><span class="co"># it 25.69 27.13 32.40 28.22 30.01 62.64 100</span></span>
<span id="cb7-26"><a href="#cb7-26"></a><span class="co"># fr 25.77 27.22 33.72 28.03 30.46 71.35 100</span></span>
<span id="cb7-27"><a href="#cb7-27"></a><span class="co"># pt 25.65 27.12 31.94 27.78 29.08 60.06 100</span></span></code></pre></div>
<span id="cb7-21"><a href="#cb7-21"></a><span class="co"># en 23.72 25.30 30.59 25.77 26.99 76.03 100</span></span>
<span id="cb7-22"><a href="#cb7-22"></a><span class="co"># de 24.88 26.81 31.11 27.47 28.93 69.86 100</span></span>
<span id="cb7-23"><a href="#cb7-23"></a><span class="co"># nl 30.65 32.77 38.07 33.70 35.23 74.79 100</span></span>
<span id="cb7-24"><a href="#cb7-24"></a><span class="co"># es 24.89 26.33 32.10 27.13 28.87 68.79 100</span></span>
<span id="cb7-25"><a href="#cb7-25"></a><span class="co"># it 24.78 26.72 33.51 27.53 28.91 166.60 100</span></span>
<span id="cb7-26"><a href="#cb7-26"></a><span class="co"># fr 24.84 26.58 31.50 27.13 28.29 67.38 100</span></span>
<span id="cb7-27"><a href="#cb7-27"></a><span class="co"># pt 24.88 26.58 32.38 27.50 29.20 79.30 100</span></span></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
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