P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-08 20:41:58 +02:00
Code Releases Activity

(v0.7.1.9063) septic_patients -> example_isolates

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2019-08-27 16:45:42 +02:00
parent 7a6fce4eb8
commit 93be16484b
92 changed files with 1143 additions and 818 deletions
Download Patch File Download Diff File Expand all files Collapse all files

78
docs/articles/benchmarks.html
View File

@ -40,7 +40,7 @@
</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">0.7.1.9055</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9063</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">13 August 2019</h4>
<h4 class="date">27 August 2019</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -211,13 +211,13 @@
<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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>)</a>
<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb2-11" data-line-number="11"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-12" data-line-number="12"><span class="co"># as.mo("sau") 8.6 8.9 11.0 9.4 9.7 26.0 10</span></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"><span class="co"># as.mo("stau") 32.0 33.0 43.0 42.0 52.0 56.0 10</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14"><span class="co"># as.mo("staaur") 8.5 8.8 11.0 9.3 11.0 26.0 10</span></a>
<a class="sourceLine" id="cb2-15" data-line-number="15"><span class="co"># as.mo("STAAUR") 8.7 8.8 9.3 9.3 9.7 10.0 10</span></a>
<a class="sourceLine" id="cb2-16" data-line-number="16"><span class="co"># as.mo("S. aureus") 23.0 24.0 28.0 25.0 26.0 45.0 10</span></a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="co"># as.mo("S. aureus") 23.0 24.0 34.0 25.0 25.0 120.0 10</span></a>
<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># as.mo("Staphylococcus aureus") 4.1 4.2 4.3 4.3 4.5 4.7 10</span></a></code></pre></div>
<a class="sourceLine" id="cb2-12" data-line-number="12"><span class="co"># as.mo("sau") 8.1 8.1 8.3 8.3 8.4 8.6 10</span></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"><span class="co"># as.mo("stau") 31.0 31.0 48.0 40.0 48.0 130.0 10</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14"><span class="co"># as.mo("staaur") 8.1 8.3 10.0 8.4 8.6 24.0 10</span></a>
<a class="sourceLine" id="cb2-15" data-line-number="15"><span class="co"># as.mo("STAAUR") 8.1 8.1 12.0 8.4 9.2 28.0 10</span></a>
<a class="sourceLine" id="cb2-16" data-line-number="16"><span class="co"># as.mo("S. aureus") 22.0 23.0 26.0 23.0 24.0 40.0 10</span></a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="co"># as.mo("S. aureus") 22.0 23.0 23.0 23.0 23.0 24.0 10</span></a>
<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># as.mo("Staphylococcus aureus") 3.6 3.8 4.0 3.9 4.0 5.2 10</span></a></code></pre></div>
<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. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Thermus islandicus</em> (<code>B_THERMS_ISL</code>), a bug probably never found before in humans:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1">T.islandicus &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"theisl"</span>),</a>
@ -229,12 +229,12 @@
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(T.islandicus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("theisl") 280 290 300 310 310 320 10</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("THEISL") 280 300 310 310 320 330 10</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("T. islandicus") 140 150 160 160 170 180 10</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("T. islandicus") 140 150 160 160 160 170 10</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Thermus islandicus") 50 53 69 57 74 140 10</span></a></code></pre></div>
<p>That takes 9.8 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. 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>Thermus islandicus</em>) are almost fast - these are the most probable input from most data sets.</p>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("theisl") 260 270 280 280 290 300 10</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("THEISL") 260 280 300 290 300 370 10</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("T. islandicus") 130 140 140 140 150 150 10</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("T. islandicus") 130 130 130 130 140 150 10</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Thermus islandicus") 47 48 53 50 52 71 10</span></a></code></pre></div>
<p>That takes 9.6 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. 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>Thermus islandicus</em>) are almost fast - these 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>Thermus islandicus</em> (which is very uncommon):</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/par">par</a></span>(<span class="dt">mar =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">5</span>, <span class="dv">16</span>, <span class="dv">4</span>, <span class="dv">2</span>)) <span class="co"># set more space for left margin text (16)</span></a>
<a class="sourceLine" id="cb4-2" data-line-number="2"></a>
@ -255,8 +255,8 @@
<a href="#repetitive-results" class="anchor"></a>Repetitive results</h3>
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. We will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr)</a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co"># take all MO codes from the septic_patients data set</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3">x &lt;-<span class="st"> </span>septic_patients<span class="op">$</span>mo <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co"># take all MO codes from the example_isolates data set</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3">x &lt;-<span class="st"> </span>example_isolates<span class="op">$</span>mo <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="st"> </span><span class="co"># keep only the unique ones</span></a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/unique">unique</a></span>() <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="st"> </span><span class="co"># pick 50 of them at random</span></a>
@ -280,8 +280,8 @@
<a class="sourceLine" id="cb5-24" data-line-number="24"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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>)</a>
<a class="sourceLine" id="cb5-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb5-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-27" data-line-number="27"><span class="co"># mo_name(x) 641 673 690 684 701 775 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.68 seconds (684 ms). You only lose time on your unique input values.</p>
<a class="sourceLine" id="cb5-27" data-line-number="27"><span class="co"># mo_name(x) 611 618 646 642 656 720 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.64 seconds (641 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -294,10 +294,10 @@
<a class="sourceLine" id="cb6-5" data-line-number="5"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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>)</a>
<a class="sourceLine" id="cb6-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb6-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co"># A 6.760 7.320 7.630 7.54 8.070 8.500 10</span></a>
<a class="sourceLine" id="cb6-9" data-line-number="9"><span class="co"># B 23.800 25.300 28.500 25.50 26.100 50.100 10</span></a>
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="co"># C 0.906 0.911 0.941 0.93 0.969 0.991 10</span></a></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>
<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co"># A 6.440 6.690 9.070 7.040 7.500 27.800 10</span></a>
<a class="sourceLine" id="cb6-9" data-line-number="9"><span class="co"># B 21.800 22.200 23.600 23.400 23.800 30.200 10</span></a>
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="co"># C 0.661 0.826 0.844 0.836 0.928 0.936 10</span></a></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="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1">run_it &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">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>),</a>
<a class="sourceLine" id="cb7-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
<a class="sourceLine" id="cb7-3" data-line-number="3"> <span class="dt">C =</span> <span class="kw"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
@ -310,14 +310,14 @@
<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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>)</a>
<a class="sourceLine" id="cb7-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb7-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-13" data-line-number="13"><span class="co"># A 0.464 0.465 0.484 0.482 0.485 0.528 10</span></a>
<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co"># B 0.621 0.632 0.659 0.636 0.650 0.784 10</span></a>
<a class="sourceLine" id="cb7-15" data-line-number="15"><span class="co"># C 0.673 0.749 0.785 0.788 0.826 0.918 10</span></a>
<a class="sourceLine" id="cb7-16" data-line-number="16"><span class="co"># D 0.446 0.451 0.477 0.467 0.474 0.586 10</span></a>
<a class="sourceLine" id="cb7-17" data-line-number="17"><span class="co"># E 0.442 0.456 0.481 0.464 0.469 0.648 10</span></a>
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="co"># F 0.442 0.459 0.470 0.466 0.490 0.494 10</span></a>
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># G 0.446 0.449 0.469 0.471 0.481 0.499 10</span></a>
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># H 0.445 0.452 0.466 0.458 0.463 0.522 10</span></a></code></pre></div>
<a class="sourceLine" id="cb7-13" data-line-number="13"><span class="co"># A 0.468 0.472 0.483 0.483 0.487 0.520 10</span></a>
<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co"># B 0.619 0.621 0.649 0.648 0.656 0.698 10</span></a>
<a class="sourceLine" id="cb7-15" data-line-number="15"><span class="co"># C 0.702 0.732 0.770 0.788 0.799 0.809 10</span></a>
<a class="sourceLine" id="cb7-16" data-line-number="16"><span class="co"># D 0.456 0.457 0.487 0.466 0.475 0.684 10</span></a>
<a class="sourceLine" id="cb7-17" data-line-number="17"><span class="co"># E 0.451 0.463 0.466 0.466 0.471 0.479 10</span></a>
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="co"># F 0.437 0.457 0.464 0.463 0.469 0.497 10</span></a>
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># G 0.450 0.454 0.468 0.466 0.477 0.495 10</span></a>
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># H 0.454 0.458 0.471 0.464 0.471 0.510 10</span></a></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> too, 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">
@ -344,13 +344,13 @@
<a class="sourceLine" id="cb8-18" data-line-number="18"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">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>)</a>
<a class="sourceLine" id="cb8-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb8-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb8-21" data-line-number="21"><span class="co"># en 18.92 19.31 19.46 19.45 19.65 20.00 10</span></a>
<a class="sourceLine" id="cb8-22" data-line-number="22"><span class="co"># de 20.65 20.85 21.77 21.28 22.33 25.34 10</span></a>
<a class="sourceLine" id="cb8-23" data-line-number="23"><span class="co"># nl 26.17 26.78 32.00 28.27 31.87 49.90 10</span></a>
<a class="sourceLine" id="cb8-24" data-line-number="24"><span class="co"># es 19.94 20.45 23.13 20.95 22.09 40.02 10</span></a>
<a class="sourceLine" id="cb8-25" data-line-number="25"><span class="co"># it 20.43 20.65 23.48 21.71 22.62 40.72 10</span></a>
<a class="sourceLine" id="cb8-26" data-line-number="26"><span class="co"># fr 20.28 20.69 22.35 21.09 21.32 31.54 10</span></a>
<a class="sourceLine" id="cb8-27" data-line-number="27"><span class="co"># pt 19.92 20.28 23.24 21.67 22.46 41.19 10</span></a></code></pre></div>
<a class="sourceLine" id="cb8-21" data-line-number="21"><span class="co"># en 17.37 18.13 21.04 18.34 20.65 35.24 10</span></a>
<a class="sourceLine" id="cb8-22" data-line-number="22"><span class="co"># de 18.69 19.32 19.49 19.44 19.77 20.06 10</span></a>
<a class="sourceLine" id="cb8-23" data-line-number="23"><span class="co"># nl 24.85 25.07 27.07 25.37 25.58 42.68 10</span></a>
<a class="sourceLine" id="cb8-24" data-line-number="24"><span class="co"># es 18.96 19.55 23.91 19.88 21.02 41.15 10</span></a>
<a class="sourceLine" id="cb8-25" data-line-number="25"><span class="co"># it 18.85 19.34 21.08 19.52 20.22 35.18 10</span></a>
<a class="sourceLine" id="cb8-26" data-line-number="26"><span class="co"># fr 18.80 19.32 21.40 19.56 20.29 37.00 10</span></a>
<a class="sourceLine" id="cb8-27" data-line-number="27"><span class="co"># pt 18.81 19.45 19.57 19.59 19.64 20.20 10</span></a></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>
@ -362,7 +362,7 @@
<footer><div class="copyright">
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alex W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alex W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
</div>
<div class="pkgdown">